modelId string | author string | last_modified timestamp[us, tz=UTC] | downloads int64 | likes int64 | library_name string | tags list | pipeline_tag string | createdAt timestamp[us, tz=UTC] | card string |
|---|---|---|---|---|---|---|---|---|---|
gradientrouting-spar/gcd_syco_capitalskl_div_beta_kl-100_seed_5 | gradientrouting-spar | 2025-06-12T18:46:32Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-12T01:51:23Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
stablediffusionapi/hvb1sjeygf4 | stablediffusionapi | 2025-06-12T18:44:23Z | 0 | 0 | diffusers | [
"diffusers",
"modelslab.com",
"stable-diffusion-api",
"text-to-image",
"ultra-realistic",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | 2025-06-12T18:43:14Z | ---
license: creativeml-openrail-m
tags:
- modelslab.com
- stable-diffusion-api
- text-to-image
- ultra-realistic
pinned: true
pipeline_tag: text-to-image
library_name: diffusers
widget:
- text: a girl wandering through the forest
output:
url: https://pub-3626123a908346a7a8be8d9295f44e26.r2.dev/generations/5296060591710523717.png
---
# hvb1sjeYgf4 API Inference
<Gallery />
## Get API Key
Get API key from [ModelsLab API](http://modelslab.com), No Payment needed.
Replace Key in below code, change **model_id** to "hvb1sjeygf4"
Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://docs.modelslab.com)
Try model for free: [Generate Images](https://modelslab.com/models/hvb1sjeygf4)
Model link: [View model](https://modelslab.com/models/hvb1sjeygf4)
View all models: [View Models](https://modelslab.com/models)
```python
import requests
import json
url = "https://modelslab.com/api/v6/images/text2img"
payload = json.dumps({
"key": "your_api_key",
"model_id": "hvb1sjeygf4",
"prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K",
"negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime",
"width": "512",
"height": "512",
"samples": "1",
"num_inference_steps": "30",
"safety_checker": "no",
"enhance_prompt": "yes",
"seed": None,
"guidance_scale": 7.5,
"multi_lingual": "no",
"panorama": "no",
"self_attention": "no",
"upscale": "no",
"embeddings": "",
"lora": "",
"webhook": None,
"track_id": None
})
headers = {
'Content-Type': 'application/json'
}
response = requests.request("POST", url, headers=headers, data=payload)
print(response.text)
```
> Use this coupon code to get 25% off **DMGG0RBN** |
gradientrouting-spar/gcd_syco_capitalskl_div_beta_kl-1_seed_42 | gradientrouting-spar | 2025-06-12T18:33:21Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-12T01:33:57Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
nikistyxx/Tarot | nikistyxx | 2025-06-12T18:21:05Z | 0 | 0 | null | [
"license:cc-by-nc-4.0",
"region:us"
] | null | 2025-06-12T18:21:05Z | ---
license: cc-by-nc-4.0
---
|
dsunil/NepBERTa2mBART_dualToken-encoder | dsunil | 2025-06-12T18:18:43Z | 0 | 0 | transformers | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-12T18:18:41Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
tanspring/attn_1e8a58e3-a4c1-41a5-8a72-bc820ee1d9d6 | tanspring | 2025-06-12T17:58:02Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"trl",
"sft",
"base_model:samoline/a1f70de9-9a4a-4ead-84f4-71761ee932ca",
"base_model:finetune:samoline/a1f70de9-9a4a-4ead-84f4-71761ee932ca",
"autotrain_compatible",
"text-generation-inference",
"endpoints_co... | text-generation | 2025-06-12T17:24:34Z | ---
base_model: samoline/a1f70de9-9a4a-4ead-84f4-71761ee932ca
library_name: transformers
model_name: attn_1e8a58e3-a4c1-41a5-8a72-bc820ee1d9d6
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for attn_1e8a58e3-a4c1-41a5-8a72-bc820ee1d9d6
This model is a fine-tuned version of [samoline/a1f70de9-9a4a-4ead-84f4-71761ee932ca](https://huggingface.co/samoline/a1f70de9-9a4a-4ead-84f4-71761ee932ca).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="tanspring/attn_1e8a58e3-a4c1-41a5-8a72-bc820ee1d9d6", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/tanngospring/SN56_Finetuning/runs/kqj39udr)
This model was trained with SFT.
### Framework versions
- TRL: 0.17.0
- Transformers: 4.51.3
- Pytorch: 2.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
VIDEO-18-Kiffy-Katrina-Lim-Viral-videos/New.tutorial.Katrina.Lim.Viral.Video.Leaks.Official | VIDEO-18-Kiffy-Katrina-Lim-Viral-videos | 2025-06-12T17:47:19Z | 0 | 0 | null | [
"region:us"
] | null | 2025-06-12T17:46:55Z | <animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
|
buidgduy/model_qwen2_2B_1 | buidgduy | 2025-06-12T17:41:38Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] | text-generation | 2025-06-12T17:17:21Z | ---
base_model: unsloth/Qwen2-1.5b-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** buidgduy
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen2-1.5b-bnb-4bit
This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
aieng-lab/ModernBERT-large_commit-intent | aieng-lab | 2025-06-12T17:24:00Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"modernbert",
"text-classification",
"en",
"base_model:answerdotai/ModernBERT-large",
"base_model:finetune:answerdotai/ModernBERT-large",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-06-12T17:23:44Z | ---
library_name: transformers
license: mit
language:
- en
metrics:
- f1
- precision
- recall
base_model:
- answerdotai/ModernBERT-large
pipeline_tag: text-classification
---
# ModernBERT large for classifying commit messages
This model classifies commit messages in code repositories (e.g., GitHub) as 'perfective', 'corrective' or 'other'.
- **Developed by:** Fabian C. Peña, Steffen Herbold
- **Finetuned from:** [answerdotai/ModernBERT-large](https://huggingface.co/answerdotai/ModernBERT-large)
- **Replication kit:** [https://github.com/aieng-lab/senlp-benchmark](https://github.com/aieng-lab/senlp-benchmark)
- **Language:** English
- **License:** MIT
## Citation
```
@misc{pena2025benchmark,
author = {Fabian Peña and Steffen Herbold},
title = {Evaluating Large Language Models on Non-Code Software Engineering Tasks},
year = {2025}
}
```
|
BootesVoid/cmbt8b87806yhh4x5c1bzwfzd_cmbtmde8c00d2jhfoifniwu63 | BootesVoid | 2025-06-12T17:15:31Z | 0 | 0 | diffusers | [
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | 2025-06-12T17:15:27Z | ---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: SEXY
---
# Cmbt8B87806Yhh4X5C1Bzwfzd_Cmbtmde8C00D2Jhfoifniwu63
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `SEXY` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "SEXY",
"lora_weights": "https://huggingface.co/BootesVoid/cmbt8b87806yhh4x5c1bzwfzd_cmbtmde8c00d2jhfoifniwu63/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('BootesVoid/cmbt8b87806yhh4x5c1bzwfzd_cmbtmde8c00d2jhfoifniwu63', weight_name='lora.safetensors')
image = pipeline('SEXY').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 2000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/BootesVoid/cmbt8b87806yhh4x5c1bzwfzd_cmbtmde8c00d2jhfoifniwu63/discussions) to add images that show off what you’ve made with this LoRA.
|
Diamantis99/xsCLDxL | Diamantis99 | 2025-06-12T17:03:34Z | 0 | 0 | segmentation-models-pytorch | [
"segmentation-models-pytorch",
"safetensors",
"model_hub_mixin",
"pytorch_model_hub_mixin",
"semantic-segmentation",
"pytorch",
"image-segmentation",
"license:mit",
"region:us"
] | image-segmentation | 2025-06-12T17:02:53Z | ---
library_name: segmentation-models-pytorch
license: mit
pipeline_tag: image-segmentation
tags:
- model_hub_mixin
- pytorch_model_hub_mixin
- segmentation-models-pytorch
- semantic-segmentation
- pytorch
languages:
- python
---
# UnetPlusPlus Model Card
Table of Contents:
- [Load trained model](#load-trained-model)
- [Model init parameters](#model-init-parameters)
- [Model metrics](#model-metrics)
- [Dataset](#dataset)
## Load trained model
```python
import segmentation_models_pytorch as smp
model = smp.from_pretrained("<save-directory-or-this-repo>")
```
## Model init parameters
```python
model_init_params = {
"encoder_name": "resnext101_32x8d",
"encoder_depth": 5,
"encoder_weights": "imagenet",
"decoder_use_norm": "batchnorm",
"decoder_channels": (256, 128, 64, 32, 16),
"decoder_attention_type": None,
"decoder_interpolation": "nearest",
"in_channels": 3,
"classes": 1,
"activation": None,
"aux_params": None
}
```
## Model metrics
```json
[
{
"test_per_image_iou": 0.5296222567558289,
"test_dataset_iou": 0.5514146685600281
}
]
```
## Dataset
Dataset name: VisionPipe
## More Information
- Library: https://github.com/qubvel/segmentation_models.pytorch
- Docs: https://smp.readthedocs.io/en/latest/
This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) |
phospho-app/oulianov-ACT_BBOX-TEST10-f6xmx | phospho-app | 2025-06-12T17:01:07Z | 0 | 0 | null | [
"phosphobot",
"act",
"region:us"
] | null | 2025-06-12T17:00:56Z |
---
tags:
- phosphobot
- act
task_categories:
- robotics
---
# act Model - phospho Training Pipeline
## Error Traceback
We faced an issue while training your model.
```
'task'
```
## Training parameters:
- **Dataset**: [Lithium73fr/TEST10](https://huggingface.co/datasets/Lithium73fr/TEST10)
- **Wandb run URL**: None
- **Epochs**: None
- **Batch size**: 100
- **Training steps**: 10000
📖 **Get Started**: [docs.phospho.ai](https://docs.phospho.ai?utm_source=huggingface_readme)
🤖 **Get your robot**: [robots.phospho.ai](https://robots.phospho.ai?utm_source=huggingface_readme)
|
shaheer08/dpo-model | shaheer08 | 2025-06-12T17:00:15Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0",
"base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v1.0",
"region:us"
] | null | 2025-06-12T17:00:02Z | ---
base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.14.0 |
alakxender/dhivehi-T5-tokenizer-extended | alakxender | 2025-06-12T16:55:44Z | 0 | 0 | transformers | [
"transformers",
"T5-dhivehi-tokenizer",
"dv",
"dataset:alakxender/haveeru-articles",
"dataset:alakxender/dhivehi-news-corpus",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2025-06-04T18:14:05Z | ---
library_name: transformers
tags:
- T5-dhivehi-tokenizer
license: mit
datasets:
- alakxender/haveeru-articles
- alakxender/dhivehi-news-corpus
language:
- dv
---
# T5 Extended Tokenizer for Dhivehi
This tokenizer extends [`google/flan-t5-base`](https://huggingface.co/google/flan-t5-base) to support Dhivehi (Thaana script) characters, while preserving English subword tokenization.
- **Base model**: `google/flan-t5-base`
- **Extension**: Adds characters in the Thaana Unicode range (U+0780–U+07BF)
- **Purpose**: For Dhivehi-English tasks like translation, summarization, or instruction tuning
- **English tokens** remain unchanged (`_This`, `_is`, etc.)
## How to Use
```python
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("alakxender/dhivehi-T5-tokenizer-extended")
# Dhivehi example
text = "ހެނބަދޫ މުދިންބެ: ކުދިން ހަތިމްކުރާ ކޮންމެ ފަހަރަކު ލޮލުން ކަރުނަ!"
tokens = tokenizer.tokenize(text)
print(tokens)
```
## What’s Different?
| Feature | Stock Flan-T5 Tokenizer | Extended Dhivehi Tokenizer |
|----------------------|-------------------------|-----------------------------|
| Dhivehi support | ❌ Uses <unk> | Proper tokenization |
| English tokenization | ✅ Yes | Preserved |
| Added tokens | ❌ No | Thaana characters |
## Comparison with Stock Flan-T5
The stock `flan-t5-base` tokenizer does **not support Dhivehi text** properly:
```python
from transformers import AutoTokenizer
stock_tokenizer = AutoTokenizer.from_pretrained("google/flan-t5-base")
tokens = stock_tokenizer.tokenize("ހެނބަދޫ މުދިންބެ: ކުދިން ހަތިމްކުރާ ކޮންމެ ފަހަރަކު ލޮލުން ކަރުނަ!")
print(tokens)
# Output: ['<unk>', '<unk>']
```
In contrast, the extended tokenizer will tokenize Thaana characters individually or as learned units, preserving semantics and avoiding `<unk>` tokens. |
alakxender/mms-tts-div-finetuned-md-f02 | alakxender | 2025-06-12T16:53:07Z | 44,474 | 0 | transformers | [
"transformers",
"safetensors",
"vits",
"text-to-audio",
"dhivehi-tts",
"dv",
"dataset:alakxender/dv_syn_speech_md",
"base_model:facebook/mms-tts-div",
"base_model:finetune:facebook/mms-tts-div",
"license:mit",
"endpoints_compatible",
"region:us"
] | text-to-audio | 2024-05-28T18:23:16Z | ---
library_name: transformers
datasets:
- alakxender/dv_syn_speech_md
language:
- dv
license: mit
base_model:
- facebook/mms-tts-div
tags:
- dhivehi-tts
---
# Divehi TTS – Female Voice (VITS-based)
This is a fine-tuned VITS (Variational Inference with adversarial learning for end-to-end Text-to-Speech) model for Divehi speech synthesis. The model produces female voice audio from Thaana-scripted Divehi text. Fine-tuned from Meta’s MMS-TTS architecture using a curated dataset of synthetic Divehi speech.
## Model Details
| Field | Value |
|----------------------|-------------------------------------------------|
| **Model ID** | `alakxender/mms-tts-div-finetuned-md-f02` |
| **Base Architecture**| MMS-TTS (VITS) |
| **Language** | Divehi (dv) |
| **Voice** | Female |
| **Sampling Rate** | 16 kHz |
| **Tokenizer** | VITSTokenizer |
| **Inference Engine** | Transformers (🤗 Hugging Face) |
## Usage
```python
from transformers import VitsModel, VitsTokenizer
import torchaudio
tokenizer = VitsTokenizer.from_pretrained("alakxender/mms-tts-div-finetuned-md-f02")
model = VitsModel.from_pretrained("alakxender/mms-tts-div-finetuned-md-f02")
text = "މޫސުން ވަރަށް ގޯސްވެ، ފުވައްމުލަކުން ފެށިގެން އައްޑުއަށް އޮރެންޖް އެލާޓް ނެރެފި"
inputs = tokenizer(text, return_tensors="pt")
waveform = model.generate(**inputs).waveform[0]
torchaudio.save("output.wav", waveform.unsqueeze(0), 16000)
```
## Evaluation Summary
- **Model**: `alakxender/mms-tts-div-finetuned-md-f02`
- **Evaluated Samples**: 3
- **Avg Estimated MOS (UTMOS)**: `2.147`
```json
{
"5": "Excellent (very natural)",
"4": "Good (mostly natural)",
"3": "Fair (some robotic quality)",
"2": "Poor (noticeably unnatural)",
"1": "Bad (unintelligible or very synthetic)"
}
```
- **Artifacts**:
- 🎵 Audio: `outputs/audio/`
- 📊 Spectrograms: `outputs/spectrograms/`
- 📄 Report: `outputs/report.txt`
- 📈 MOS Scores: `outputs/mos_scores.txt`
## Acknowledgements
- [Meta MMS-TTS](https://github.com/facebookresearch/fairseq/tree/main/examples/mms)
- [Tarepan's SpeechMOS](https://github.com/Tarepan/SpeechMOS)
- [Hugging Face 🤗 Transformers](https://huggingface.co/transformers/) |
daviondk7131/dostoevsky-t-lite-lora-16 | daviondk7131 | 2025-06-12T16:49:43Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-12T16:27:27Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
PORTULAN/gervasio-7b-portuguese-ptpt-decoder | PORTULAN | 2025-06-12T16:44:56Z | 4,361 | 8 | transformers | [
"transformers",
"pytorch",
"safetensors",
"llama",
"text-generation",
"gervasio-pt*",
"gervasio-ptpt",
"gervasio-ptbr",
"gervasio-7b-portuguese-ptpt-decoder",
"gervasio-7b-portuguese-ptbr-decoder",
"portulan",
"albertina-pt*",
"clm",
"gpt",
"portuguese",
"decoder",
"foundation model"... | text-generation | 2023-11-15T14:49:31Z | ---
license: mit
language:
- pt
tags:
- gervasio-pt*
- gervasio-ptpt
- gervasio-ptbr
- gervasio-7b-portuguese-ptpt-decoder
- gervasio-7b-portuguese-ptbr-decoder
- portulan
- albertina-pt*
- clm
- gpt
- portuguese
- decoder
- foundation model
datasets:
- PORTULAN/extraglue
- PORTULAN/extraglue-instruct
---
</br>
</br>
<img align="left" width="40" height="40" src="https://github.githubassets.com/images/icons/emoji/unicode/1f917.png">
<p style="text-align: center;"> This is the model card for Gervásio 7B PTPT Decoder.
You may be interested in some of the other models in the <a href="https://huggingface.co/PORTULAN">Albertina (encoders), Gervásio (decoders) and Serafim (sentence encoder) families</a>.
</p>
</br>
</br>
# Gervásio 7B PTPT
</br>
This model has been **deprecated**.
We recommend you use the improved [**gervasio-8b-portuguese-ptpt-decoder**](https://huggingface.co/PORTULAN/gervasio-8b-portuguese-ptpt-decoder).
<!--
**Gervásio PT*** is a **fully open** decoder for the **Portuguese language**.
It is a **decoder** of the LLaMA family, based on the neural architecture Transformer and developed over the LLaMA-2 7B model.
Its further improvement through additional training was done over language resources that include new instruction data sets of Portuguese prepared for this purpose ([extraGLUE-Instruct
](https://huggingface.co/datasets/PORTULAN/extraglue-instruct)).
It has different versions that were trained for different variants of Portuguese (PT),
namely for the European variant, spoken in Portugal ([**gervasio-7b-portuguese-ptpt-decoder**](https://huggingface.co/PORTULAN/gervasio-7b-portuguese-ptpt-decoder)), and for the American variant, spoken in Brazil ([**gervasio-7b-portuguese-ptbr-decoder**](https://huggingface.co/PORTULAN/gervasio-7b-portuguese-ptbr-decoder)).
All versions of Gervásio are **openly distributed for free under an open license**, including thus for research and commercial purposes, and given its size, can
be run on consumer-grade hardware.
**Gervásio 7B PTPT** is developed by NLX-Natural Language and Speech Group, at the University of Lisbon, Faculty of Sciences, Department of Informatics, Portugal.
For the record, its full name is **Gervásio Produz Textos em Português**, to which corresponds the natural acronym **GPT PT**,
and which is known more shortly as **Gervásio PT*** or, even more briefly, just as **Gervásio**, among its acquaintances.
Gervásio 7B PTPT is developed by a team from the University of Lisbon, Portugal.
For a fully detailed description, check the respective [publication](https://arxiv.org/abs/2402.18766):
``` latex
@misc{gervasio,
title={Advancing Generative AI for Portuguese with
Open Decoder Gervásio PT-*},
author={Rodrigo Santos, João Silva, Luís Gomes,
João Rodrigues, António Branco},
year={2024},
eprint={2402.18766},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
Please use the above cannonical reference when using or citing this model.
<br>
# Model Description
**This model card is for Gervásio 7B PTPT**, with 7 billion parameters, a hidden size of 4,096 units, an intermediate size of 11,008 units, 32 attention heads, 32 hidden layers, and a tokenizer obtained using the Byte-Pair Encoding (BPE) algorithm implemented with SentencePiece, featuring a vocabulary size of 32,000.
Gervásio 7B PTPT is distributed under an [MIT license](https://huggingface.co/PORTULAN/gervasio-7b-portuguese-ptpt-decoder/blob/main/LICENSE).
<br>
# Training Data
**Gervásio 7B PTPT** was trained over standard supervised fine-tuning, and to keep some alignment with mainstream benchmarks for English, we resorted to tasks and respective datasets in the GLUE and the SuperGLUE collections.
We selected those datasets where the outcome of their machine translation into European Portuguese could preserve, in the target language, the linguistic properties at stake.
From GLUE, we resorted to the following four tasks:
- MRPC (paraphrase Detection).
- RTE (recognizing Textual Entailment).
- STS-B (semantic textual similarity).
- WNLI (coreference and natural language inference).
And from SuperGLUE, we included these other four tasks:
- BoolQ (yes/no question answering).
- CB (inference with 3 labels).
- COPA (reasoning)
- MultiRC (question answering).
These datasets were machine translated into European Portuguese and from the [extraGLUE](https://huggingface.co/datasets/PORTULAN/extraglue) dataset.
Furthermore, instruction templates have been manually crafted for each task.
These take the various fields in the dataset and arrange them into prompts, which were collected into the [extraGLUE-instruct](https://huggingface.co/datasets/PORTULAN/extraglue-instruct) dataset.
We also employed data augmentation techniques to enhance the size and diversity of our dataset.
This involved repurposing the tasks in various ways, such as generation of answers from MultiRC, question generation from BoolQ, and other relevant modifications.
# Training Details
We applied supervised fine-tuning with a causal language modeling training objective following a zero-out technique during the fine-tuning process.
Specifically, while the entire prompt received attention during fine-tuning, only the response tokens were subjected to back-propagation.
In terms of hyper-parameters, the model was trained with a learning rate of 2 * 10^-5, a weight decay of 0.1, a two-epoch training regime without warm-up, and to ensure the same number of tokens back-propagated per step, we employed an input sequence of 512 tokens with a batch size of 16 and 16 accumulation steps.
Due to hardware limitations that imposed a shorter sequence length (512) compared to the base model (4096), instead of the typical practice of concatenating all training examples and then dividing them into batches with the same input sequence length, we separated each example individually.
In other words, each example occupies the full input sequence length.
# Performance
For testing, we reserved the translated datasets MRPC (similarity) and RTE (inference), from GLUE, and COPA (reasoning/qa), from SuperGLUE, which were taking as representatives of three major types of tasks, and were not seen during training.
| Model | MRPC (F1) | RTE (F1) | COPA (F1) |
|--------------------------|----------------|----------------|-----------|
| **Gervásio 7B PTPT** | **0.7273** | **0.8291** | **0.5459**|
| **LLaMA-2 (English)** | 0.0328 | 0.0482 | 0.3844 |
| **LLaMA-2 Chat (English)** | 0.5703 | 0.4697 | 0.4737 |
<br>
# How to use
You can use this model directly with a pipeline for causal language modeling:
```python3
>>> from transformers import pipeline
>>> generator = pipeline(model='PORTULAN/gervasio-7b-portuguese-ptpt-decoder')
>>> generator("A comida portuguesa é", max_new_tokens=10)
```
<br>
# Acknowledgments
The research reported here was partially supported by: PORTULAN CLARIN—Research Infrastructure for the Science and Technology of Language,
funded by Lisboa 2020, Alentejo 2020 and FCT—Fundação para a Ciência e Tecnologia under the
grant PINFRA/22117/2016; research project GPT-PT - Transformer-based Decoder for the Portuguese Language, funded by FCT—Fundação para a Ciência e Tecnologia under the
grant CPCA-IAC/AV/478395/2022; innovation project
ACCELERAT.AI - Multilingual Intelligent Contact Centers, funded by IAPMEI, I.P. - Agência para a Competitividade e Inovação
under the grant C625734525-00462629, of Plano de Recuperação e Resiliência,
call RE-C05-i01.01 – Agendas/Alianças Mobilizadoras para a Reindustrialização.
-->
|
Adilbai/Vizdom-RL-Sample_factory | Adilbai | 2025-06-12T16:43:26Z | 0 | 0 | sample-factory | [
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"arxiv:1912.13440",
"model-index",
"region:us"
] | reinforcement-learning | 2025-06-12T16:33:18Z | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_health_gathering_supreme
type: doom_health_gathering_supreme
metrics:
- type: mean_reward
value: 11.46 +/- 3.37
name: mean_reward
verified: false
---
# VizDoom Health Gathering Supreme - APPO Agent
[](https://github.com/alex-petrenko/sample-factory)
[](https://github.com/mwydmuch/ViZDoom)
[](https://www.samplefactory.dev/)
A high-performance reinforcement learning agent trained using **APPO (Asynchronous Proximal Policy Optimization)** on the **VizDoom Health Gathering Supreme** environment. This model demonstrates advanced navigation and resource collection strategies in a challenging 3D environment.
## 🏆 Performance Metrics
- **Mean Reward**: 11.46 ± 3.37
- **Training Steps**: 4,005,888 environment steps
- **Episodes Completed**: 978 training episodes
- **Architecture**: Convolutional Neural Network with shared weights
## 🎮 Environment Description
The **VizDoom Health Gathering Supreme** environment is a challenging first-person navigation task where the agent must:
- **Navigate** through a complex 3D maze-like environment
- **Collect health packs** scattered throughout the level
- **Avoid obstacles** and navigate efficiently
- **Maximize survival time** while gathering resources
- **Handle visual complexity** with realistic 3D graphics
### Environment Specifications
- **Observation Space**: RGB images (72×128×3)
- **Action Space**: Discrete movement and turning actions
- **Episode Length**: Variable (until health depletes or time limit)
- **Difficulty**: Supreme (highest difficulty level)
## 🧠 Model Architecture
### Network Configuration
- **Algorithm**: APPO (Asynchronous Proximal Policy Optimization)
- **Encoder**: Convolutional Neural Network
- Input: 3-channel RGB images (72×128)
- Convolutional layers with ReLU activation
- Output: 512-dimensional feature representation
- **Policy Head**: Fully connected layers for action prediction
- **Value Head**: Critic network for value function estimation
### Training Configuration
- **Framework**: Sample-Factory 2.0
- **Batch Size**: Optimized for parallel processing
- **Learning Rate**: Adaptive scheduling
- **Discount Factor**: Standard RL discount
- **Entropy Regularization**: Balanced exploration-exploitation
## 📥 Installation & Setup
### Prerequisites
```bash
# Install Sample-Factory
pip install sample-factory[all]
# Install VizDoom
pip install vizdoom
```
### Download the Model
```bash
python -m sample_factory.huggingface.load_from_hub -r Adilbai/rl_course_vizdoom_health_gathering_supreme
```
## 🚀 Usage
### Running the Trained Agent
```bash
# Basic evaluation
python -m sample_factory.enjoy --algo=APPO --env=VizdoomHealthGathering-v0 \
--train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
# With video recording
python -m sample_factory.enjoy --algo=APPO --env=VizdoomHealthGathering-v0 \
--train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme \
--save_video --video_frames=10000 --no_render
```
### Python API Usage
```python
from sample_factory.enjoy import enjoy
from sample_factory.cfg.arguments import parse_full_cfg, parse_sf_args
# Configure the environment
env_name = "VizdoomHealthGathering-v0"
cfg = parse_full_cfg(parse_sf_args([
"--algo=APPO",
f"--env={env_name}",
"--train_dir=./train_dir",
"--experiment=rl_course_vizdoom_health_gathering_supreme"
]))
# Run evaluation
status = enjoy(cfg)
```
### Continue Training
```bash
python -m sample_factory.train --algo=APPO --env=VizdoomHealthGathering-v0 \
--train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme \
--restart_behavior=resume --train_for_env_steps=10000000
```
## 📊 Training Results
### Learning Curve
The agent achieved consistent improvement throughout training:
- **Initial Performance**: Random exploration
- **Mid Training**: Developed basic navigation skills
- **Final Performance**: Strategic health pack collection with optimal pathing
### Key Behavioral Patterns
- **Efficient Navigation**: Learned to navigate the maze structure
- **Resource Prioritization**: Focuses on accessible health packs
- **Obstacle Avoidance**: Developed spatial awareness
- **Time Management**: Balances exploration vs exploitation
## 🎯 Evaluation Protocol
### Standard Evaluation
```bash
python -m sample_factory.enjoy --algo=APPO --env=VizdoomHealthGathering-v0 \
--train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme \
--max_num_episodes=100 --max_num_frames=100000
```
### Performance Metrics
- **Episode Reward**: Total health packs collected per episode
- **Survival Time**: Duration before episode termination
- **Collection Efficiency**: Health packs per time unit
- **Navigation Success**: Percentage of successful maze traversals
## 🔧 Technical Details
### Model Files
- `config.json`: Complete training configuration
- `checkpoint_*.pth`: Model weights and optimizer state
- `sf_log.txt`: Detailed training logs
- `stats.json`: Performance statistics
### Hardware Requirements
- **GPU**: NVIDIA GPU with CUDA support (recommended)
- **RAM**: 8GB+ system memory
- **Storage**: 2GB+ free space for model and dependencies
### Troubleshooting
#### Common Issues
1. **Checkpoint Loading Errors**
```bash
# If you encounter encoder architecture mismatches
# Use the fixed checkpoint with updated key mapping
```
2. **Environment Not Found**
```bash
pip install vizdoom
# Ensure VizDoom is properly installed
```
3. **CUDA Errors**
```bash
# For CPU-only evaluation
python -m sample_factory.enjoy --device=cpu [other args]
```
## 📈 Benchmarking
### Comparison with Baselines
- **Random Agent**: ~0.5 average reward
- **Rule-based Agent**: ~5.0 average reward
- **This APPO Agent**: **8.09 average reward**
### Performance Analysis
The agent demonstrates:
- **Superior spatial reasoning** compared to simpler approaches
- **Robust generalization** across different episode initializations
- **Efficient resource collection** strategies
- **Stable performance** with low variance
## 🔬 Research Applications
This model serves as a strong baseline for:
- **Navigation research** in complex 3D environments
- **Multi-objective optimization** (survival + collection)
- **Transfer learning** to related VizDoom scenarios
- **Curriculum learning** progression studies
## 🤝 Contributing
Contributions are welcome! Areas for improvement:
- **Hyperparameter optimization**
- **Architecture modifications**
- **Multi-agent scenarios**
- **Domain randomization**
## 📚 References
- [Sample-Factory Framework](https://github.com/alex-petrenko/sample-factory)
- [VizDoom Environment](https://github.com/mwydmuch/ViZDoom)
- [APPO Algorithm Paper](https://arxiv.org/abs/1912.13440)
- [Sample-Factory Documentation](https://www.samplefactory.dev/)
## 📝 Citation
```bibtex
@misc{vizdoom_health_gathering_supreme_2025,
title={VizDoom Health Gathering Supreme APPO Agent},
author={Adilbai},
year={2025},
publisher={Hugging Face},
url={https://huggingface.co/Adilbai/rl_course_vizdoom_health_gathering_supreme}
}
```
## 📄 License
This model is released under the MIT License. See the LICENSE file for details.
---
**Note**: This model was trained as part of a reinforcement learning course and demonstrates the effectiveness of modern RL algorithms on challenging 3D navigation tasks.
|
anon123434/mariogarcia | anon123434 | 2025-06-12T16:35:59Z | 0 | 0 | diffusers | [
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | 2025-06-12T16:19:13Z | ---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: mariogarcia
---
# Mariogarcia
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `mariogarcia` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "mariogarcia",
"lora_weights": "https://huggingface.co/anon123434/mariogarcia/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('anon123434/mariogarcia', weight_name='lora.safetensors')
image = pipeline('mariogarcia').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 2000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/anon123434/mariogarcia/discussions) to add images that show off what you’ve made with this LoRA.
|
Uiop789/xiaohongshu-flux-LoRA | Uiop789 | 2025-06-12T16:30:07Z | 0 | 0 | diffusers | [
"diffusers",
"text-to-image",
"diffusers-training",
"lora",
"flux",
"flux-diffusers",
"template:sd-lora",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | 2025-06-12T13:55:40Z | ---
base_model: black-forest-labs/FLUX.1-dev
library_name: diffusers
license: other
instance_prompt: A beautiful girl in the style of xiaohongshu
widget: []
tags:
- text-to-image
- diffusers-training
- diffusers
- lora
- flux
- flux-diffusers
- template:sd-lora
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# Flux DreamBooth LoRA - Uiop789/xiaohongshu-flux-LoRA
<Gallery />
## Model description
These are Uiop789/xiaohongshu-flux-LoRA DreamBooth LoRA weights for black-forest-labs/FLUX.1-dev.
The weights were trained using [DreamBooth](https://dreambooth.github.io/) with the [Flux diffusers trainer](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/README_flux.md).
Was LoRA for the text encoder enabled? False.
## Trigger words
You should use `A beautiful girl in the style of xiaohongshu` to trigger the image generation.
## Download model
[Download the *.safetensors LoRA](Uiop789/xiaohongshu-flux-LoRA/tree/main) in the Files & versions tab.
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16).to('cuda')
pipeline.load_lora_weights('Uiop789/xiaohongshu-flux-LoRA', weight_name='pytorch_lora_weights.safetensors')
image = pipeline('A beautiful girl in the style of xiaohongshu').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## License
Please adhere to the licensing terms as described [here](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md).
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model] |
VIDEOS-18-shruthi-narayanan-viral-videos/FULL.VIDEO.shruthi.narayanan.Viral.Video.Tutorial.Official | VIDEOS-18-shruthi-narayanan-viral-videos | 2025-06-12T16:22:03Z | 0 | 0 | null | [
"region:us"
] | null | 2025-06-12T16:21:55Z | [<img alt="fsd" src="http://i.postimg.cc/qvPp49Sm/ythngythg.gif">](https://videohere.top/)
[►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► 𝙁𝙪𝙡𝙡 𝙑𝙞𝙙𝙚𝙤❤️❤️⬇️⬇️](https://videohere.top/)
[►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► 𝙁𝙪𝙡𝙡 𝙑𝙞𝙙𝙚𝙤❤️❤️⬇️⬇️](https://videohere.top/) |
gradientrouting-spar/gcd_syco_modkl_div_beta_kl-10_seed_1 | gradientrouting-spar | 2025-06-12T16:11:02Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-12T16:10:54Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
phospho-app/oulianov-ACT_BBOX-TEST10-ydeel | phospho-app | 2025-06-12T15:57:29Z | 0 | 0 | null | [
"phosphobot",
"act",
"region:us"
] | null | 2025-06-12T15:53:29Z |
---
tags:
- phosphobot
- act
task_categories:
- robotics
---
# act Model - phospho Training Pipeline
## Error Traceback
We faced an issue while training your model.
```
Training process failed with exit code 1:
'timestamps': [np.float32(7.0666666), np.float32(0.0)]},
{'diff': np.float32(-6.4333334),
'episode_index': 33,
'timestamps': [np.float32(6.4333334), np.float32(0.0)]},
{'diff': np.float32(-5.9666667),
'episode_index': 34,
'timestamps': [np.float32(5.9666667), np.float32(0.0)]},
{'diff': np.float32(-6.2),
'episode_index': 35,
'timestamps': [np.float32(6.2), np.float32(0.0)]}]
```
## Training parameters:
- **Dataset**: [phospho-app/TEST10_bboxes](https://huggingface.co/datasets/phospho-app/TEST10_bboxes)
- **Wandb run URL**: None
- **Epochs**: None
- **Batch size**: 100
- **Training steps**: 10000
📖 **Get Started**: [docs.phospho.ai](https://docs.phospho.ai?utm_source=huggingface_readme)
🤖 **Get your robot**: [robots.phospho.ai](https://robots.phospho.ai?utm_source=huggingface_readme)
|
MinaMila/phi3_unlearned_2nd_1e-6_1.0_0.05_0.05_0.75_epoch1 | MinaMila | 2025-06-12T15:55:54Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"phi3",
"text-generation",
"conversational",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-12T15:53:47Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
BootesVoid/cmbq0a5fr00smh4x50oaoaxxi_cmbthmnqr000xjhfogf3pzwd3 | BootesVoid | 2025-06-12T15:54:45Z | 0 | 0 | diffusers | [
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | 2025-06-12T15:54:43Z | ---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: TATI
---
# Cmbq0A5Fr00Smh4X50Oaoaxxi_Cmbthmnqr000Xjhfogf3Pzwd3
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `TATI` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "TATI",
"lora_weights": "https://huggingface.co/BootesVoid/cmbq0a5fr00smh4x50oaoaxxi_cmbthmnqr000xjhfogf3pzwd3/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('BootesVoid/cmbq0a5fr00smh4x50oaoaxxi_cmbthmnqr000xjhfogf3pzwd3', weight_name='lora.safetensors')
image = pipeline('TATI').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 2000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/BootesVoid/cmbq0a5fr00smh4x50oaoaxxi_cmbthmnqr000xjhfogf3pzwd3/discussions) to add images that show off what you’ve made with this LoRA.
|
KaiSian/gemma-3-12b-it-r64-a128-20250612 | KaiSian | 2025-06-12T15:43:25Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:google/gemma-3-12b-it",
"base_model:finetune:google/gemma-3-12b-it",
"endpoints_compatible",
"region:us"
] | null | 2025-06-12T14:40:11Z | ---
base_model: google/gemma-3-12b-it
library_name: transformers
model_name: gemma-3-12b-it-r64-a128-20250612
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for gemma-3-12b-it-r64-a128-20250612
This model is a fine-tuned version of [google/gemma-3-12b-it](https://huggingface.co/google/gemma-3-12b-it).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="KaiSian/gemma-3-12b-it-r64-a128-20250612", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/11263008-tzu-chi-university/train_my_llm/runs/bmqmu1yi)
This model was trained with SFT.
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
morturr/Mistral-7B-v0.1-PAIR_one_liners_amazon-COMB-one_liners-comb-2-seed-28-2025-06-12 | morturr | 2025-06-12T15:43:20Z | 0 | 0 | peft | [
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:mistralai/Mistral-7B-v0.1",
"base_model:adapter:mistralai/Mistral-7B-v0.1",
"license:apache-2.0",
"region:us"
] | null | 2025-06-12T15:43:06Z | ---
library_name: peft
license: apache-2.0
base_model: mistralai/Mistral-7B-v0.1
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: Mistral-7B-v0.1-PAIR_one_liners_amazon-COMB-one_liners-comb-2-seed-28-2025-06-12
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Mistral-7B-v0.1-PAIR_one_liners_amazon-COMB-one_liners-comb-2-seed-28-2025-06-12
This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 28
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.1
- Pytorch 2.5.1+cu124
- Datasets 3.0.2
- Tokenizers 0.20.1 |
gradientrouting-spar/gcd_syco_moddpo_train_split-0.3_pos_prx-proxy_neg_prx-proxy_neg_ldpo-6_seed_42 | gradientrouting-spar | 2025-06-12T15:28:06Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-12T15:27:57Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
Isabelle-Kaif-Viral-Video-Original-Link/New.tutorial.Isabelle.Kaif.Viral.Video.Leaks.Official | Isabelle-Kaif-Viral-Video-Original-Link | 2025-06-12T15:14:48Z | 0 | 0 | null | [
"region:us"
] | null | 2025-06-12T15:13:21Z | <animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
|
biodatlab/ec-raft | biodatlab | 2025-06-12T15:13:08Z | 23 | 0 | null | [
"pytorch",
"llama",
"text-generation",
"conversational",
"en",
"dataset:biodatlab/ec-raft-dataset",
"license:llama3.1",
"region:us"
] | text-generation | 2025-06-07T09:00:54Z | ---
license: llama3.1
datasets:
- biodatlab/ec-raft-dataset
language:
- en
pipeline_tag: text-generation
---
# EC-RAFT: Automated Generation of Clinical Trial Eligibility Criteria
## Model Description
**EC-RAFT** is a fine-tuned Retrieval-Augmented Fine-Tuning (RAFT) model based on **LLaMA-3.1-8B-Instruct** architecture.
It is designed to automatically generate **structured, high-quality clinical trial eligibility criteria (EC)** directly from trial titles and descriptions.
EC-RAFT integrates **domain-specific retrieval** with **synthesized intermediate reasoning** steps, enabling it to produce **clinically relevant** and **contextually appropriate** EC sets.
## Fine-tuning Details
- **Original Model:** LLaMA-3.1-8B-Instruct
- **Datasets used for fine-tuning:**
- ClinicalTrials.gov (267,347 trials, 2000–2024) [biodatlab/ec-raft-dataset](https://huggingface.co/datasets/biodatlab/ec-raft-dataset)
- Retrieval corpus constructed using **SciNCL model**
- Intermediate reasoning steps **R** generated using **Gemini-1.5-flash-002**
- Fine-tuning method:
- **Retrieval-Augmented Fine-Tuning (RAFT)**
- **Low-Rank Adaptation (LoRA)**
## Model Performance
Evaluated on a held-out ClinicalTrials.gov test split:
| Metric | Score |
|-----------------------------------|---------|
| **BERTScore** (semantic similarity) | **86.23** |
| **Precision** (LLM-guided evaluation) | **78.84%** |
| **Recall** (LLM-guided evaluation) | **75.89%** |
| **Mean LLM-as-a-Judge Score** (0–3) | **1.7150** |
| **Mean Pair-BERTScore** | **67.76** |
- **Outperforms zero-shot LLaMA-3.1 and Gemini-1.5-flash baselines**
- **Outperforms fine-tuned LLaMA and Meditron baselines**
- **Clinically validated:** LLM-as-a-Judge scores highly correlated with human physician evaluation
## Intended Use
- Assist **researchers**, **trial designers**, and **sponsors** in drafting clinical trial eligibility criteria.
- **Automate** EC generation to reduce manual effort and improve consistency.
- Support **clinical trial design** transparency and quality.
- Enable integration with **trial registry platforms**, **clinical trial matching systems**, and **EC recommendation tools**.
## Limitations
- Requires **human validation** of generated EC before clinical use.
- Trained on **public ClinicalTrials.gov data** — may not generalize well to:
- Rare or novel diseases
- Specialized or non-standard trial designs
- Non-public trial data
- Optimized for **English-language clinical trials**.
- As with any LLM-based system, risks include hallucination, subtle errors, and domain shifts.
- Evaluation metrics (BERTScore, LLM-as-a-Judge) are proxies — not full substitutes for domain expert review.
## Acknowledgments
This model was developed using resources provided by:
- **RAVIS Technology** for feedback and collaboration.
- **Faculty of Medicine Ramathibodi Hospital**
- **NSTDA Supercomputer Center (ThaiSC), Project \#pv814001**
We also acknowledge the contributions of the broader open-source community whose tools and prior works on **RAFT**, **SciNCL**, **LoRA**, **LLaMA-3**, and **biomedical NLP** made this project possible.
|
JoeTheOther/whisper-tiny-ur-5h | JoeTheOther | 2025-06-12T15:09:22Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice_17_0",
"base_model:openai/whisper-tiny",
"base_model:finetune:openai/whisper-tiny",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us... | automatic-speech-recognition | 2025-06-12T10:58:34Z | ---
library_name: transformers
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_trainer
datasets:
- common_voice_17_0
metrics:
- wer
model-index:
- name: whisper-tiny-ur-5h
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice_17_0
type: common_voice_17_0
config: ur
split: test
args: ur
metrics:
- name: Wer
type: wer
value: 53.30130404941661
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# whisper-tiny-ur-5h
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the common_voice_17_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8447
- Wer: 53.3013
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.9092 | 0.8460 | 500 | 0.8859 | 59.1489 |
| 0.6742 | 1.6920 | 1000 | 0.8084 | 58.6685 |
| 0.4835 | 2.5381 | 1500 | 0.7822 | 52.2992 |
| 0.3839 | 3.3841 | 2000 | 0.7792 | 55.2505 |
| 0.3781 | 4.2301 | 2500 | 0.7976 | 57.1311 |
| 0.2321 | 5.0761 | 3000 | 0.8026 | 53.3425 |
| 0.2785 | 5.9222 | 3500 | 0.8129 | 54.4955 |
| 0.2542 | 6.7682 | 4000 | 0.8306 | 53.6033 |
| 0.2232 | 7.6142 | 4500 | 0.8451 | 53.9602 |
| 0.1776 | 8.4602 | 5000 | 0.8447 | 53.3013 |
### Framework versions
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
gradientrouting-spar/gcd_syco_moddpo_train_split-0.3_pos_prx-proxy_neg_prx-proxy_neg_ldpo-4_seed_1 | gradientrouting-spar | 2025-06-12T15:05:30Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-12T15:05:20Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## More Information [optional]
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## Model Card Contact
[More Information Needed] |
JordisHerrmann/RED_day-25.2 | JordisHerrmann | 2025-06-12T15:03:58Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2025-06-12T15:03:38Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 245.59 +/- 26.90
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
ibm-granite/granite-vision-3.3-2b | ibm-granite | 2025-06-12T15:03:18Z | 64 | 2 | null | [
"safetensors",
"llava_next",
"arxiv:2502.09927",
"license:apache-2.0",
"region:us"
] | null | 2025-06-03T18:49:46Z | ---
license: apache-2.0
---
# granite-vision-3.3-2b
**Model Summary**: Granite-vision-3.3-2b is a compact and efficient vision-language model, specifically designed for visual document understanding, enabling automated content extraction from tables, charts, infographics, plots, diagrams, and more. Granite-vision-3.3-2b introduces several novel experimental features such as *image segmentation*, *doctags generation*, and *multi-page support* (see **Experimental Capabilities** for more details) and offers enhanced safety when compared to earlier Granite vision models. The model was trained on a meticulously curated instruction-following data, comprising diverse public and synthetic datasets tailored to support a wide range of document understanding and general image tasks. Granite-vision-3.3-2b was trained by fine-tuning a Granite large language model with both image and text modalities.
**Evaluations:** We compare the performance of granite-vision-3.3-2b with previous versions of granite-vision models. Evaluations were done using the standard llms-eval benchmark and spanned multiple public benchmarks, with particular emphasis on document understanding tasks while also including general visual question-answering benchmarks.
| | Granite-vision-3.1-2b-preview | Granite-vision-3.2-2b | Granite-vision-3.3-2b |
|-----------|-----------|--------------|----------------|
| **Document benchmarks** |
| ChartQA | 0.86 | 0.87 | 0.87 |
| DocVQA | 0.88 | 0.89 | 0.91 |
| TextVQA | 0.76 | 0.78 | 0.80 |
| AI2D | 0.78 | 0.76 | 0.77 |
| InfoVQA | 0.63 | 0.64 | 0.68 |
| OCRBench | 0.75 | 0.77 | 0.79 |
| LiveXiv VQA v2 | 0.61 | 0.61 | 0.61 |
| LiveXiv TQA v2 | 0.55 | 0.57 | 0.52 |
| **Other benchmarks** |
| MMMU | 0.35 | 0.37 | 0.37 |
| VQAv2 | 0.81 | 0.78 | 0.79 |
| RealWorldQA | 0.65 | 0.63 | 0.63 |
| VizWiz VQA | 0.64 | 0.63 | 0.62 |
| OK VQA | 0.57 | 0.56 | 0.55|
- **Paper:** [Granite Vision: a lightweight, open-source multimodal model for enterprise Intelligence](https://arxiv.org/abs/2502.09927). Note that the paper describes Granite Vision 3.2. Granite Vision 3.3 shares most of the technical underpinnings with Granite 3.2. However, there are several enhancements in terms of new and improved vision encoder, many new high quality datasets for training, and several new experimental capabilities.
- **Release Date**: Jun 11th, 2025
- **License:** [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0)
**Supported Input Format:** Currently the model supports English instructions and images (png, jpeg) as input format.
**Intended Use:** The model is intended to be used in enterprise applications that involve processing visual and text data. In particular, the model is well-suited for a range of visual document understanding tasks, such as analyzing tables and charts, performing optical character recognition (OCR), and answering questions based on document content. Additionally, its capabilities extend to general image understanding, enabling it to be applied to a broader range of business applications. For tasks that exclusively involve text-based input, we suggest using our Granite large language models, which are optimized for text-only processing and offer superior performance compared to this model.
## Generation:
Granite Vision model is supported natively `transformers>=4.49`. Below is a simple example of how to use the `granite-vision-3.3-2b` model.
### Usage with `transformers`
First, make sure to build the latest versions of transformers:
```shell
pip install transformers>=4.49
```
Then run the code:
```python
from transformers import AutoProcessor, AutoModelForVision2Seq
from huggingface_hub import hf_hub_download
import torch
device = "cuda" if torch.cuda.is_available() else "cpu"
model_path = "ibm-granite/granite-vision-3.3-2b"
processor = AutoProcessor.from_pretrained(model_path)
model = AutoModelForVision2Seq.from_pretrained(model_path).to(device)
# prepare image and text prompt, using the appropriate prompt template
img_path = hf_hub_download(repo_id=model_path, filename='example.png')
conversation = [
{
"role": "user",
"content": [
{"type": "image", "url": img_path},
{"type": "text", "text": "What is the highest scoring model on ChartQA and what is its score?"},
],
},
]
inputs = processor.apply_chat_template(
conversation,
add_generation_prompt=True,
tokenize=True,
return_dict=True,
return_tensors="pt"
).to(device)
# autoregressively complete prompt
output = model.generate(**inputs, max_new_tokens=100)
print(processor.decode(output[0], skip_special_tokens=True))
```
### Usage with vLLM
The model can also be loaded with `vLLM`. First make sure to install the following libraries:
```shell
pip install torch torchvision torchaudio
pip install vllm==0.6.6
```
Then, copy the snippet from the section that is relevant for your use case.
```python
from vllm import LLM, SamplingParams
from vllm.assets.image import ImageAsset
from huggingface_hub import hf_hub_download
from PIL import Image
model_path = "ibm-granite/granite-vision-3.3-2b"
model = LLM(
model=model_path,
)
sampling_params = SamplingParams(
temperature=0.2,
max_tokens=64,
)
# Define the question we want to answer and format the prompt
image_token = "<image>"
system_prompt = "<|system|>\nA chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.\n"
question = "What is the highest scoring model on ChartQA and what is its score?"
prompt = f"{system_prompt}<|user|>\n{image_token}\n{question}\n<|assistant|>\n"
img_path = hf_hub_download(repo_id=model_path, filename='example.png')
image = Image.open(img_path).convert("RGB")
print(image)
# Build the inputs to vLLM; the image is passed as `multi_modal_data`.
inputs = {
"prompt": prompt,
"multi_modal_data": {
"image": image,
}
}
outputs = model.generate(inputs, sampling_params=sampling_params)
print(f"Generated text: {outputs[0].outputs[0].text}")
```
### Safety Evaluation
The Granite-vision-3.3-2b model also went through safety alignment to make sure responses are safer without affecting the model’s performance on its intended task. We carefully safety aligned the model on publicly available safety data and synthetically generated safety data. We report our safety scores on publicly available RTVLM and VLGuard datasets.
**RTVLM Safety Score - [0,10] - Higher is Better**
| | Politics | Racial | Jailbreak | Mislead |
|-----------|-----------|--------------|----------------|----------------|
|Granite-vision-3.1-2b-preview|7.2|7.7|4.5|7.6|
|Granite-vision-3.2-2b|7.6|7.8|6.2|8.0|
|Granite-vision-3.3-2b|8.0|8.1|7.5|8.0|
**VLGuard Safety Score - [0,10] - Higher is Better**
| | Unsafe Images (Unsafe) | Safe Images with Unsafe Instructions |
|-----------|-----------|--------------|
|Granite-vision-3.1-2b-preview|6.6|8.4|
|Granite-vision-3.2-2b|7.6|8.9|
|Granite-vision-3.3-2b|8.4|9.3|
### Experimental Capabilities
Granite-vision-3.3-2b introduces three new experimental capabilities:
(1) Image segmentation: [A notebook showing a segmentation example](https://github.com/ibm-granite/granite-vision-models/blob/main/cookbooks/GraniteVision_Segmentation_Notebook.ipynb)
(2) Doctags generation: Parse document images to structured text in doctags format. Please see [Docling project](https://github.com/docling-project/docling) for more details on doctags.
(3) Multipage support: The model was trained to handle question answering (QA) tasks using multiple consecutive pages from a document—up to 8 pages—given the demands of long-context processing. To support such long sequences without exceeding GPU memory limits, we recommend resizing images so that their longer dimension is 768 pixels.
### Fine-tuning
For an example of fine-tuning granite-vision-3.3-2b for new tasks refer to [this notebook](https://huggingface.co/learn/cookbook/en/fine_tuning_granite_vision_sft_trl).
### Use Granite Vision for MM-RAG
For an example of MM-RAG using granite vision refer to [this notebook](https://github.com/ibm-granite-community/granite-snack-cookbook/blob/main/recipes/RAG/Granite_Multimodal_RAG.ipynb).
**Model Architecture:** The architecture of granite-vision-3.3-2b consists of the following components:
(1) Vision encoder: SigLIP2 (https://huggingface.co/google/siglip2-so400m-patch14-384).
(2) Vision-language connector: two-layer MLP with gelu activation function.
(3) Large language model: granite-3.1-2b-instruct with 128k context length (https://huggingface.co/ibm-granite/granite-3.1-2b-instruct).
We built upon LLaVA (https://llava-vl.github.io) to train our model. We use multi-layer encoder features and a denser grid resolution in AnyRes to enhance the model's ability to understand nuanced visual content, which is essential for accurately interpreting document images.
**Training Data:** Our training data is largely comprised of two key sources: (1) publicly available datasets (2) internally created synthetic data targeting specific capabilities including document understanding tasks. Granite Vision 3.3 training data is built upon the comprehensive dataset used for granite-vision-3.2-2b (a detailed description of granite-vision-3.2-2b training data is available in the [technical report](https://arxiv.org/abs/2502.09927)). In addition, granite-vision-3.3-2b further includes high quality image segmentation data, multi-page data, and data from several new high quality publicly available datasets (like Mammoth-12M and Bigdocs).
**Infrastructure:** We train granite-vision-3.3-2b using IBM's super computing cluster, Blue Vela, which is outfitted with NVIDIA H100 GPUs. This cluster provides a scalable and efficient infrastructure for training our models over thousands of GPUs.
**Responsible Use and Limitations:** Some use cases for Large Vision and Language Models can trigger certain risks and ethical considerations, including but not limited to: bias and fairness, misinformation, and autonomous decision-making. Although our alignment processes include safety considerations, the model may in some cases produce inaccurate, biased, offensive or unwanted responses to user prompts. Additionally, whether smaller models may exhibit increased susceptibility to hallucination in generation scenarios due to their reduced sizes, which could limit their ability to generate coherent and contextually accurate responses, remains uncertain. This aspect is currently an active area of research, and we anticipate more rigorous exploration, comprehension, and mitigations in this domain. We urge the community to use granite-vision-3.3-2b in a responsible way and avoid any malicious utilization. We recommend using this model for document understanding tasks. More general vision tasks may pose higher inherent risks of triggering unwanted output. To enhance safety, we recommend using granite-vision-3.3-2b alongside Granite Guardian. Granite Guardian is a fine-tuned instruct model designed to detect and flag risks in prompts and responses across key dimensions outlined in the IBM AI Risk Atlas. Its training, which includes both human-annotated and synthetic data informed by internal red-teaming, enables it to outperform similar open-source models on standard benchmarks, providing an additional layer of safety.
**Resources**
- 📄 Read the full technical report [here](https://arxiv.org/abs/2502.09927)
- ⭐️ Learn about the latest updates with Granite: https://www.ibm.com/granite
- 🚀 Get started with tutorials, best practices, and prompt engineering advice: https://www.ibm.com/granite/docs/
- 💡 Learn about the latest Granite learning resources: https://ibm.biz/granite-learning-resources
|
daviondk7131/shakespeare-reward-model | daviondk7131 | 2025-06-12T14:55:13Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"xlm-roberta",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-06-12T14:53:19Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
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## Uses
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### Direct Use
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
YuchenLi01/genv3pair1NoGT_1.5B_sft_lm1_ebs32_lr1e-06_epoch1.0_42 | YuchenLi01 | 2025-06-12T14:54:09Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"alignment-handbook",
"trl",
"sft",
"generated_from_trainer",
"conversational",
"dataset:YuchenLi01/MATH_Qwen2.5-1.5BInstruct_DPO_MoreUniqueResponseNoGTv3pair1",
"base_model:Qwen/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Qwen/Qwen2.... | text-generation | 2025-06-12T14:36:50Z | ---
library_name: transformers
license: apache-2.0
base_model: Qwen/Qwen2.5-1.5B-Instruct
tags:
- alignment-handbook
- trl
- sft
- generated_from_trainer
- trl
- sft
- generated_from_trainer
datasets:
- YuchenLi01/MATH_Qwen2.5-1.5BInstruct_DPO_MoreUniqueResponseNoGTv3pair1
model-index:
- name: genv3pair1NoGT_1.5B_sft_lm1_ebs32_lr1e-06_epoch1.0_42
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# genv3pair1NoGT_1.5B_sft_lm1_ebs32_lr1e-06_epoch1.0_42
This model is a fine-tuned version of [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) on the YuchenLi01/MATH_Qwen2.5-1.5BInstruct_DPO_MoreUniqueResponseNoGTv3pair1 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0649
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-06
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 32
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.283 | 0.1117 | 20 | 0.2744 |
| 0.1236 | 0.2235 | 40 | 0.1236 |
| 0.0845 | 0.3352 | 60 | 0.0721 |
| 0.0631 | 0.4469 | 80 | 0.0678 |
| 0.0702 | 0.5587 | 100 | 0.0662 |
| 0.0622 | 0.6704 | 120 | 0.0655 |
| 0.071 | 0.7821 | 140 | 0.0651 |
| 0.0668 | 0.8939 | 160 | 0.0650 |
### Framework versions
- Transformers 4.45.2
- Pytorch 2.5.1+cu121
- Datasets 3.5.0
- Tokenizers 0.20.3
|
yeok/Qwen2.5-0.5B-Instruct-SiegelEtalCorrelationalCT-hybrid | yeok | 2025-06-12T14:37:31Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen2",
"trl",
"en",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-12T12:03:40Z | ---
base_model: unsloth/Qwen2.5-0.5B-Instruct
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** yeok
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen2.5-0.5B-Instruct
This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
areegtarek/Med3DVLM-Qwen-2.5-7B-finetune-5epoch | areegtarek | 2025-06-12T14:34:23Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-11T16:14:42Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
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## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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[More Information Needed]
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benchaffe/ddpm-pokemon-gen-64 | benchaffe | 2025-06-12T14:23:27Z | 35 | 0 | diffusers | [
"diffusers",
"tensorboard",
"safetensors",
"unconditional-image-generation",
"arxiv:1910.09700",
"diffusers:DDPMPipeline",
"region:us"
] | unconditional-image-generation | 2025-05-29T15:36:26Z | ---
library_name: diffusers
pipeline_tag: unconditional-image-generation
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🧨 diffusers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
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#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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jkdamilola/finetuned-bge-base-en | jkdamilola | 2025-06-12T14:21:26Z | 0 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"bert",
"sentence-similarity",
"feature-extraction",
"generated_from_trainer",
"dataset_size:208",
"loss:BatchSemiHardTripletLoss",
"arxiv:1908.10084",
"arxiv:1703.07737",
"base_model:BAAI/bge-base-en",
"base_model:finetune:BAAI/bge-base-en",
"model-in... | sentence-similarity | 2025-06-12T14:20:58Z | ---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:208
- loss:BatchSemiHardTripletLoss
base_model: BAAI/bge-base-en
widget:
- source_sentence: '
Name : Casa del Camino
Category: Boutique Hotel, Travel Services
Department: Marketing
Location: Laguna Beach, CA
Amount: 842.67
Card: Team Retreat Planning
Trip Name: Annual Strategy Offsite
'
sentences:
- '
Name : Gartner & Associates
Category: Consulting, Business Services
Department: Legal
Location: San Francisco, CA
Amount: 5000.0
Card: Legal Consultation Fund
Trip Name: unknown
'
- '
Name : SkillAdvance Academy
Category: Online Learning Platform, Professional Development
Department: Engineering
Location: Austin, TX
Amount: 1875.67
Card: Continuous Improvement Initiative
Trip Name: unknown
'
- '
Name : Innovative Patents Co.
Category: Intellectual Property Services, Legal Services
Department: Legal
Location: New York, NY
Amount: 3250.0
Card: Patent Acquisition Fund
Trip Name: unknown
'
- source_sentence: '
Name : Miller & Gartner
Category: Consulting, Business Expense
Department: Legal
Location: Chicago, IL
Amount: 1500.0
Card: Legal Fund
Trip Name: unknown
'
sentences:
- '
Name : Agora Services
Category: Office Equipment Maintenance, IT Support & Maintenance
Department: Office Administration
Location: Berlin, Germany
Amount: 877.29
Card: Quarterly Equipment Evaluation
Trip Name: unknown
'
- '
Name : InsightReports Group
Category: Research and Insights, Consulting Services
Department: Marketing
Location: New York, NY
Amount: 1499.89
Card: Market Research
Trip Name: unknown
'
- '
Name : Mosaic Technologies
Category: Cloud Solutions Provider, Data Analytics Platforms
Department: R&D
Location: Berlin, Germany
Amount: 1785.45
Card: AI Model Enhancement Project
Trip Name: unknown
'
- source_sentence: '
Name : Café Del Mar
Category: Catering Services, Event Planning
Department: Sales
Location: Barcelona, ES
Amount: 578.29
Card: Q3 Client Engagement
Trip Name: unknown
'
sentences:
- '
Name : Wong & Lim
Category: Technical Equipment Services, Facility Services
Department: Office Administration
Location: Berlin, Germany
Amount: 458.29
Card: Monthly Equipment Care Program
Trip Name: unknown
'
- '
Name : Staton Morgan
Category: Recruitment Services, Consulting
Department: HR
Location: Melbourne, Australia
Amount: 1520.67
Card: New Hires
Trip Name: unknown
'
- '
Name : Palace Suites
Category: Hotel Accommodation, Event Outsourcing
Department: Marketing
Location: Amsterdam, NL
Amount: 1278.64
Card: Annual Conference Stay
Trip Name: 2023 Innovation Summit
'
- source_sentence: '
Name : Nimbus Networks Inc.
Category: Cloud Services, Application Hosting
Department: Research & Development
Location: Austin, TX
Amount: 1134.67
Card: NextGen Application Deployment
Trip Name: unknown
'
sentences:
- '
Name : City Shuttle Services
Category: Transportation, Logistics
Department: Sales
Location: San Francisco, CA
Amount: 85.0
Card: Sales Team Travel Fund
Trip Name: Client Meeting in Bay Area
'
- '
Name : Omachi Meitetsu
Category: Transportation Services, Travel Services
Department: Sales
Location: Hakkuba Japan
Amount: 120.0
Card: Quarterly Travel Expenses
Trip Name: unknown
'
- '
Name : Clarion Data Solutions
Category: Cloud Computing & Data Storage Solutions, Consulting Services
Department: Engineering
Location: Berlin, Germany
Amount: 756.49
Card: Data Management Initiatives
Trip Name: unknown
'
- source_sentence: '
Name : CloudFlare Inc.
Category: Internet & Network Services, SaaS
Department: IT Operations
Location: New York, NY
Amount: 2000.0
Card: Annual Cloud Services Budget
Trip Name: unknown
'
sentences:
- '
Name : Zero One
Category: Media Production
Department: Marketing
Location: New York, NY
Amount: 7500.0
Card: Sales Operating Budget
Trip Name: unknown
'
- '
Name : Vitality Systems
Category: Facility Management, Health Services
Department: Office Administration
Location: Chicago, IL
Amount: 347.29
Card: Office Wellness Initiative
Trip Name: unknown
'
- '
Name : TechSavvy Solutions
Category: Software Services, Online Subscription
Department: Engineering
Location: Austin, TX
Amount: 1200.0
Card: Annual Engineering Tools Budget
Trip Name: unknown
'
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy
- dot_accuracy
- manhattan_accuracy
- euclidean_accuracy
- max_accuracy
model-index:
- name: SentenceTransformer based on BAAI/bge-base-en
results:
- task:
type: triplet
name: Triplet
dataset:
name: bge base en train
type: bge-base-en-train
metrics:
- type: cosine_accuracy
value: 0.8317307692307693
name: Cosine Accuracy
- type: dot_accuracy
value: 0.16826923076923078
name: Dot Accuracy
- type: manhattan_accuracy
value: 0.8317307692307693
name: Manhattan Accuracy
- type: euclidean_accuracy
value: 0.8317307692307693
name: Euclidean Accuracy
- type: max_accuracy
value: 0.8317307692307693
name: Max Accuracy
- task:
type: triplet
name: Triplet
dataset:
name: bge base en eval
type: bge-base-en-eval
metrics:
- type: cosine_accuracy
value: 0.9848484848484849
name: Cosine Accuracy
- type: dot_accuracy
value: 0.015151515151515152
name: Dot Accuracy
- type: manhattan_accuracy
value: 0.9848484848484849
name: Manhattan Accuracy
- type: euclidean_accuracy
value: 0.9848484848484849
name: Euclidean Accuracy
- type: max_accuracy
value: 0.9848484848484849
name: Max Accuracy
---
# SentenceTransformer based on BAAI/bge-base-en
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en](https://huggingface.co/BAAI/bge-base-en). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [BAAI/bge-base-en](https://huggingface.co/BAAI/bge-base-en) <!-- at revision b737bf5dcc6ee8bdc530531266b4804a5d77b5d8 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("jkdamilola/finetuned-bge-base-en")
# Run inference
sentences = [
'\nName : CloudFlare Inc.\nCategory: Internet & Network Services, SaaS\nDepartment: IT Operations\nLocation: New York, NY\nAmount: 2000.0\nCard: Annual Cloud Services Budget\nTrip Name: unknown\n',
'\nName : TechSavvy Solutions\nCategory: Software Services, Online Subscription\nDepartment: Engineering\nLocation: Austin, TX\nAmount: 1200.0\nCard: Annual Engineering Tools Budget\nTrip Name: unknown\n',
'\nName : Vitality Systems\nCategory: Facility Management, Health Services\nDepartment: Office Administration\nLocation: Chicago, IL\nAmount: 347.29\nCard: Office Wellness Initiative\nTrip Name: unknown\n',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
## Evaluation
### Metrics
#### Triplet
* Dataset: `bge-base-en-train`
* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)
| Metric | Value |
|:-------------------|:-----------|
| cosine_accuracy | 0.8317 |
| dot_accuracy | 0.1683 |
| manhattan_accuracy | 0.8317 |
| euclidean_accuracy | 0.8317 |
| **max_accuracy** | **0.8317** |
#### Triplet
* Dataset: `bge-base-en-eval`
* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)
| Metric | Value |
|:-------------------|:-----------|
| cosine_accuracy | 0.9848 |
| dot_accuracy | 0.0152 |
| manhattan_accuracy | 0.9848 |
| euclidean_accuracy | 0.9848 |
| **max_accuracy** | **0.9848** |
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 208 training samples
* Columns: <code>sentence</code> and <code>label</code>
* Approximate statistics based on the first 208 samples:
| | sentence | label |
|:--------|:-----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| type | string | int |
| details | <ul><li>min: 33 tokens</li><li>mean: 39.81 tokens</li><li>max: 49 tokens</li></ul> | <ul><li>0: ~3.85%</li><li>1: ~3.37%</li><li>2: ~3.85%</li><li>3: ~2.40%</li><li>4: ~5.29%</li><li>5: ~4.33%</li><li>6: ~4.33%</li><li>7: ~3.37%</li><li>8: ~3.85%</li><li>9: ~4.33%</li><li>10: ~3.37%</li><li>11: ~3.85%</li><li>12: ~2.40%</li><li>13: ~5.29%</li><li>14: ~3.37%</li><li>15: ~5.77%</li><li>16: ~4.33%</li><li>17: ~2.40%</li><li>18: ~2.88%</li><li>19: ~3.37%</li><li>20: ~3.85%</li><li>21: ~4.33%</li><li>22: ~2.88%</li><li>23: ~4.33%</li><li>24: ~4.81%</li><li>25: ~1.92%</li><li>26: ~1.92%</li></ul> |
* Samples:
| sentence | label |
|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
| <code><br>Name : Transcend<br>Category: Upskilling<br>Department: Human Resource<br>Location: London, UK<br>Amount: 859.47<br>Card: Technology Skills Enhancement<br>Trip Name: unknown<br></code> | <code>0</code> |
| <code><br>Name : Ayden<br>Category: Financial Software<br>Department: Finance<br>Location: Berlin, DE<br>Amount: 1273.45<br>Card: Enterprise Technology Services<br>Trip Name: unknown<br></code> | <code>1</code> |
| <code><br>Name : Urban Sphere<br>Category: Utilities Management, Facility Services<br>Department: Office Administration<br>Location: New York, NY<br>Amount: 937.32<br>Card: Monthly Operations Budget<br>Trip Name: unknown<br></code> | <code>2</code> |
* Loss: [<code>BatchSemiHardTripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#batchsemihardtripletloss)
### Evaluation Dataset
#### Unnamed Dataset
* Size: 52 evaluation samples
* Columns: <code>sentence</code> and <code>label</code>
* Approximate statistics based on the first 52 samples:
| | sentence | label |
|:--------|:-----------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| type | string | int |
| details | <ul><li>min: 32 tokens</li><li>mean: 38.37 tokens</li><li>max: 43 tokens</li></ul> | <ul><li>0: ~1.92%</li><li>4: ~1.92%</li><li>5: ~11.54%</li><li>7: ~5.77%</li><li>8: ~5.77%</li><li>10: ~7.69%</li><li>11: ~3.85%</li><li>12: ~3.85%</li><li>13: ~1.92%</li><li>16: ~3.85%</li><li>17: ~1.92%</li><li>18: ~13.46%</li><li>19: ~5.77%</li><li>20: ~3.85%</li><li>21: ~3.85%</li><li>22: ~7.69%</li><li>23: ~3.85%</li><li>24: ~5.77%</li><li>25: ~5.77%</li></ul> |
* Samples:
| sentence | label |
|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------|
| <code><br>Name : Tooly<br>Category: Survey Software, SaaS<br>Department: Marketing<br>Location: San Francisco, CA<br>Amount: 2000.0<br>Card: Annual Marketing Technology Budget<br>Trip Name: unknown<br></code> | <code>10</code> |
| <code><br>Name : CloudFlare Inc.<br>Category: Internet & Network Services, SaaS<br>Department: IT Operations<br>Location: New York, NY<br>Amount: 2000.0<br>Card: Annual Cloud Services Budget<br>Trip Name: unknown<br></code> | <code>21</code> |
| <code><br>Name : Gartner & Associates<br>Category: Consulting, Business Services<br>Department: Legal<br>Location: San Francisco, CA<br>Amount: 5000.0<br>Card: Legal Consultation Fund<br>Trip Name: unknown<br></code> | <code>5</code> |
* Loss: [<code>BatchSemiHardTripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#batchsemihardtripletloss)
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `learning_rate`: 2e-05
- `num_train_epochs`: 5
- `warmup_ratio`: 0.1
- `batch_sampler`: no_duplicates
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 2e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 5
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | bge-base-en-eval_max_accuracy | bge-base-en-train_max_accuracy |
|:-----:|:----:|:-----------------------------:|:------------------------------:|
| 0 | 0 | - | 0.8317 |
| 5.0 | 65 | 0.9848 | - |
### Framework Versions
- Python: 3.11.13
- Sentence Transformers: 3.1.1
- Transformers: 4.45.2
- PyTorch: 2.6.0+cu124
- Accelerate: 1.7.0
- Datasets: 2.14.4
- Tokenizers: 0.20.3
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### BatchSemiHardTripletLoss
```bibtex
@misc{hermans2017defense,
title={In Defense of the Triplet Loss for Person Re-Identification},
author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
year={2017},
eprint={1703.07737},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
<!--
## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
--> |
Naible/Nexi-10-06-v1-16-bit-phi | Naible | 2025-06-12T14:15:58Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"base_model:unsloth/phi-4-unsloth-bnb-4bit",
"base_model:finetune:unsloth/phi-4-unsloth-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
... | text-generation | 2025-06-12T13:58:06Z | ---
base_model: unsloth/phi-4-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** Naible
- **License:** apache-2.0
- **Finetuned from model :** unsloth/phi-4-unsloth-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
JayHyeon/Qwen_1.5B-math-VDPO_5e-6_1.0vpo_constant-10ep | JayHyeon | 2025-06-12T14:10:21Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"trl",
"dpo",
"conversational",
"dataset:argilla/distilabel-math-preference-dpo",
"arxiv:2305.18290",
"base_model:Qwen/Qwen2.5-Math-1.5B",
"base_model:finetune:Qwen/Qwen2.5-Math-1.5B",
"auto... | text-generation | 2025-06-12T13:28:30Z | ---
base_model: Qwen/Qwen2.5-Math-1.5B
datasets: argilla/distilabel-math-preference-dpo
library_name: transformers
model_name: Qwen_1.5B-math-VDPO_5e-6_1.0vpo_constant-10ep
tags:
- generated_from_trainer
- trl
- dpo
licence: license
---
# Model Card for Qwen_1.5B-math-VDPO_5e-6_1.0vpo_constant-10ep
This model is a fine-tuned version of [Qwen/Qwen2.5-Math-1.5B](https://huggingface.co/Qwen/Qwen2.5-Math-1.5B) on the [argilla/distilabel-math-preference-dpo](https://huggingface.co/datasets/argilla/distilabel-math-preference-dpo) dataset.
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="JayHyeon/Qwen_1.5B-math-VDPO_5e-6_1.0vpo_constant-10ep", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/bonin147/huggingface/runs/xuofjs6f)
This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.50.0
- Pytorch: 2.6.0
- Datasets: 3.4.1
- Tokenizers: 0.21.1
## Citations
Cite DPO as:
```bibtex
@inproceedings{rafailov2023direct,
title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}},
author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn},
year = 2023,
booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023},
url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html},
editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
CausalNLP/gpt2-hf_multilingual-90 | CausalNLP | 2025-06-12T14:09:11Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-12T14:08:45Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
araapk/praktikum-ai-modul6-emotion-classifier-final | araapk | 2025-06-12T14:07:23Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:indobenchmark/indobert-base-p1",
"base_model:finetune:indobenchmark/indobert-base-p1",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-06-12T12:30:49Z | ---
library_name: transformers
license: mit
base_model: indobenchmark/indobert-base-p1
tags:
- generated_from_trainer
model-index:
- name: praktikum-ai-modul6-emotion-classifier-final
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# praktikum-ai-modul6-emotion-classifier-final
This model is a fine-tuned version of [indobenchmark/indobert-base-p1](https://huggingface.co/indobenchmark/indobert-base-p1) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1997
- F1 Macro: 0.3848
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Macro |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.2194 | 1.9984 | 1233 | 0.2002 | 0.3467 |
| 0.1812 | 3.9968 | 2466 | 0.1997 | 0.3848 |
### Framework versions
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
aieng-lab/codet5p-220m_closed-question | aieng-lab | 2025-06-12T14:03:50Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"t5",
"text-classification",
"en",
"base_model:Salesforce/codet5p-220m",
"base_model:finetune:Salesforce/codet5p-220m",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-06-12T14:03:38Z | ---
library_name: transformers
license: mit
language:
- en
metrics:
- f1
- precision
- recall
base_model:
- Salesforce/codet5p-220m
pipeline_tag: text-classification
---
# CodeT5+ 220m for classifying developer questions
This model classifies questions in developer forums (e.g., Stack Overflow) as 'open', 'not a real question', 'off topic', 'not constructive' or 'too localized'.
- **Developed by:** Fabian C. Peña, Steffen Herbold
- **Finetuned from:** [Salesforce/codet5p-220m](https://huggingface.co/Salesforce/codet5p-220m)
- **Replication kit:** [https://github.com/aieng-lab/senlp-benchmark](https://github.com/aieng-lab/senlp-benchmark)
- **Language:** English
- **License:** MIT
## Citation
```
@misc{pena2025benchmark,
author = {Fabian Peña and Steffen Herbold},
title = {Evaluating Large Language Models on Non-Code Software Engineering Tasks},
year = {2025}
}
```
|
Triangle104/Broken-Tutu-24B-Transgression-v2.0-Q8_0-GGUF | Triangle104 | 2025-06-12T13:56:19Z | 0 | 0 | null | [
"gguf",
"roleplay",
"storytelling",
"creative",
"character",
"narrative",
"nsfw",
"explicit",
"unaligned",
"ERP",
"Erotic",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"en",
"base_model:ReadyArt/Broken-Tutu-24B-Transgression-v2.0",
"base_model:finetune:ReadyArt/Broken-Tutu-24B-Tr... | text-generation | 2025-06-12T13:52:35Z | ---
license: apache-2.0
language:
- en
base_model: ReadyArt/Broken-Tutu-24B-Transgression-v2.0
base_model_relation: finetune
pipeline_tag: text-generation
tags:
- roleplay
- storytelling
- creative
- character
- narrative
- nsfw
- explicit
- unaligned
- ERP
- Erotic
- llama-cpp
- gguf-my-repo
---
# Triangle104/Broken-Tutu-24B-Transgression-v2.0-Q8_0-GGUF
This model was converted to GGUF format from [`ReadyArt/Broken-Tutu-24B-Transgression-v2.0`](https://huggingface.co/ReadyArt/Broken-Tutu-24B-Transgression-v2.0) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/ReadyArt/Broken-Tutu-24B-Transgression-v2.0) for more details on the model.
---
This evolution of Broken-Tutu delivers unprecedented coherence with
reduced explicit content using classic "Transgression" techniques:
- 🧬 Expanded 43M Token Dataset - First ReadyArt model with multi-turn conversational data
- ✨ 100% Unslopped Dataset - New techniques used to generate the dataset with 0% slop
- ⚡ Enhanced Character Integrity - Maintains character authenticity while reducing explicit content
- 🛡️ Anti-Impersonation Guards - Never speaks or acts for the user
- 💎 Rebuilt from Ground Up - Optimized training settings for superior performance
- 📜 Direct Evolution - Leveraging the success of Broken-Tutu, we finetuned directly on top of the legendary model
---
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo Triangle104/Broken-Tutu-24B-Transgression-v2.0-Q8_0-GGUF --hf-file broken-tutu-24b-transgression-v2.0-q8_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Triangle104/Broken-Tutu-24B-Transgression-v2.0-Q8_0-GGUF --hf-file broken-tutu-24b-transgression-v2.0-q8_0.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Triangle104/Broken-Tutu-24B-Transgression-v2.0-Q8_0-GGUF --hf-file broken-tutu-24b-transgression-v2.0-q8_0.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Triangle104/Broken-Tutu-24B-Transgression-v2.0-Q8_0-GGUF --hf-file broken-tutu-24b-transgression-v2.0-q8_0.gguf -c 2048
```
|
CapstoneML/Model | CapstoneML | 2025-06-12T13:52:27Z | 0 | 0 | keras | [
"keras",
"capstone",
"image-classification",
"computer-vision",
"gradio",
"historycal-sites",
"tensorflow",
"dataset:custom",
"license:apache-2.0",
"region:us"
] | image-classification | 2025-06-12T12:45:58Z | ---
title: HistoryLens
emoji: 👍
colorFrom: purple
colorTo: red
sdk: gradio
sdk_version: 5.33.0
app_file: app.py
pinned: false
license: apache-2.0
short_description: Classification Image using MobileNetV2
metrics:
- accuracy
pipeline_tag: image-classification
library_name: keras
tags:
- capstone
- image-classification
- keras
- computer-vision
- gradio
- historycal-sites
- tensorflow
datasets:
- custom
---
# HistoryLens - Capstone DBS Coding Camp
Proyek ini adalah aplikasi klasifikasi gambar berbasis deep learning untuk mengenali situs cagar budaya di Daerah Istimewa Yogyakarta (DIY). Dibuat menggunakan Gradio dan TensorFlow/Keras, ditujukan untuk membantu pengguna mengenali tempat bersejarah hanya dengan mengunggah foto.
## Persyaratan Sistem
Sistem ini direkomendasikan untuk dijalankan di:
- OS: Windows 10/11 64-bit
- Python 3.10
---
## Daftar Kelas
Model mengenali 10 lokasi berikut:
- Benteng Vredeburg
- Candi Borobudur
- Candi Prambanan
- Gedung Agung Istana Kepresidenan
- Masjid Gedhe Kauman
- Monumen Serangan 1 Maret
- Museum Gunungapi Merapi
- Situs Ratu Boko
- Taman Sari
- Tugu Yogyakarta
## Arsitektur Model
- MobileNetV2 kustom dengan TensorFlow/Keras
- Input: Gambar RGB berukuran 224x224x3
- Output layer: Softmax (10 kelas)
## Tools and Library
- Python, TensorFlow/Keras
- Gradio untuk antarmuka pengguna
- Model disimpan dalam format `.json` dan `.h5`
- Huggingface sebagai tools deploy model
## Fitur
- login dan register
- Upload gambar sesuai yang ada di point kategori
- Model akan memprediksi nama lokasi dari gambar tersebut
- Menampilkan gambar unggahan dan hasil klasifikasinya
- Menampilkan Deskripsi terkait gambar yang diupload
- Menampilkan link Google maps
- Menyimpan History dari detekesi gambar
- Berjalan langsung di browser
|
juniofreitas/llama-3.2-1b-doencas_negligenciadas_amazonia-Instruct | juniofreitas | 2025-06-12T13:48:21Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-12T13:44:42Z | ---
library_name: transformers
tags:
- trl
- sft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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tomaarsen/splade-cocondenser-msmarco-margin-mse-small | tomaarsen | 2025-06-12T13:43:33Z | 0 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"bert",
"sparse-encoder",
"sparse",
"splade",
"generated_from_trainer",
"dataset_size:90000",
"loss:SpladeLoss",
"loss:SparseMarginMSELoss",
"loss:FlopsLoss",
"feature-extraction",
"en",
"dataset:sentence-transformers/msmarco",
"arxiv:1908.10084",
... | feature-extraction | 2025-06-12T13:43:19Z | ---
language:
- en
license: apache-2.0
tags:
- sentence-transformers
- sparse-encoder
- sparse
- splade
- generated_from_trainer
- dataset_size:90000
- loss:SpladeLoss
- loss:SparseMarginMSELoss
- loss:FlopsLoss
base_model: Luyu/co-condenser-marco
widget:
- text: up to what age can a child get autism
- text: food temperature danger zone
- text: Small and medium size poly tanks are relatively inexpensive. They are also
easy to handle, so poly tanks are used in many smaller wineries. New and used
poly. drums are available in 20, 30, 40 and 55 gallon sizes, and they make excellent
wine storage containers. for home winemakers. Just like glass, wine storage containers
made of polyethylene advantages and disadvantages. They are lightweight, and polyethylene
drums can be handled and stored easily.
- text: what county is louin ms
- text: Map of the Old City of Shanghai. By the early 1400s, Shanghai had become important
enough for Ming dynasty engineers to begin dredging the Huangpu River (also known
as Shen). In 1553, a city wall was built around the Old Town (Nanshi) as a defense
against the depredations of the Wokou (Japanese pirates).
datasets:
- sentence-transformers/msmarco
pipeline_tag: feature-extraction
library_name: sentence-transformers
metrics:
- dot_accuracy@1
- dot_accuracy@3
- dot_accuracy@5
- dot_accuracy@10
- dot_precision@1
- dot_precision@3
- dot_precision@5
- dot_precision@10
- dot_recall@1
- dot_recall@3
- dot_recall@5
- dot_recall@10
- dot_ndcg@10
- dot_mrr@10
- dot_map@100
- query_active_dims
- query_sparsity_ratio
- corpus_active_dims
- corpus_sparsity_ratio
co2_eq_emissions:
emissions: 84.77861327949611
energy_consumed: 0.21810696440845714
source: codecarbon
training_type: fine-tuning
on_cloud: false
cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K
ram_total_size: 31.777088165283203
hours_used: 0.618
hardware_used: 1 x NVIDIA GeForce RTX 3090
model-index:
- name: CoCondenser trained on Natural-Questions tuples
results:
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoMSMARCO
type: NanoMSMARCO
metrics:
- type: dot_accuracy@1
value: 0.46
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.64
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.72
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.82
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.46
name: Dot Precision@1
- type: dot_precision@3
value: 0.21333333333333335
name: Dot Precision@3
- type: dot_precision@5
value: 0.14400000000000002
name: Dot Precision@5
- type: dot_precision@10
value: 0.08199999999999999
name: Dot Precision@10
- type: dot_recall@1
value: 0.46
name: Dot Recall@1
- type: dot_recall@3
value: 0.64
name: Dot Recall@3
- type: dot_recall@5
value: 0.72
name: Dot Recall@5
- type: dot_recall@10
value: 0.82
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.6288613269928542
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.5688571428571428
name: Dot Mrr@10
- type: dot_map@100
value: 0.5779425698484522
name: Dot Map@100
- type: query_active_dims
value: 56.099998474121094
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9981619815715183
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 192.40869140625
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9936960654149056
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoNFCorpus
type: NanoNFCorpus
metrics:
- type: dot_accuracy@1
value: 0.38
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.58
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.62
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.74
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.38
name: Dot Precision@1
- type: dot_precision@3
value: 0.36
name: Dot Precision@3
- type: dot_precision@5
value: 0.316
name: Dot Precision@5
- type: dot_precision@10
value: 0.26999999999999996
name: Dot Precision@10
- type: dot_recall@1
value: 0.039663209420347775
name: Dot Recall@1
- type: dot_recall@3
value: 0.07520387221675563
name: Dot Recall@3
- type: dot_recall@5
value: 0.09363263999248954
name: Dot Recall@5
- type: dot_recall@10
value: 0.14669853217549625
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.3303519560816792
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.49576984126984125
name: Dot Mrr@10
- type: dot_map@100
value: 0.14778057031019226
name: Dot Map@100
- type: query_active_dims
value: 53.68000030517578
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9982412685831473
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 367.5431823730469
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9879580898246167
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoNQ
type: NanoNQ
metrics:
- type: dot_accuracy@1
value: 0.5
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.76
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.8
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.88
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.5
name: Dot Precision@1
- type: dot_precision@3
value: 0.25999999999999995
name: Dot Precision@3
- type: dot_precision@5
value: 0.16799999999999998
name: Dot Precision@5
- type: dot_precision@10
value: 0.09599999999999997
name: Dot Precision@10
- type: dot_recall@1
value: 0.48
name: Dot Recall@1
- type: dot_recall@3
value: 0.71
name: Dot Recall@3
- type: dot_recall@5
value: 0.75
name: Dot Recall@5
- type: dot_recall@10
value: 0.85
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.677150216479017
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.6328888888888887
name: Dot Mrr@10
- type: dot_map@100
value: 0.6167275355591967
name: Dot Map@100
- type: query_active_dims
value: 55.939998626708984
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9981672236869567
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 228.83615112304688
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9925025833456834
name: Corpus Sparsity Ratio
- task:
type: sparse-nano-beir
name: Sparse Nano BEIR
dataset:
name: NanoBEIR mean
type: NanoBEIR_mean
metrics:
- type: dot_accuracy@1
value: 0.4466666666666667
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.66
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.7133333333333333
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.8133333333333334
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.4466666666666667
name: Dot Precision@1
- type: dot_precision@3
value: 0.27777777777777773
name: Dot Precision@3
- type: dot_precision@5
value: 0.20933333333333334
name: Dot Precision@5
- type: dot_precision@10
value: 0.14933333333333332
name: Dot Precision@10
- type: dot_recall@1
value: 0.3265544031401159
name: Dot Recall@1
- type: dot_recall@3
value: 0.47506795740558516
name: Dot Recall@3
- type: dot_recall@5
value: 0.5212108799974965
name: Dot Recall@5
- type: dot_recall@10
value: 0.605566177391832
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.5454544998511834
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.5658386243386242
name: Dot Mrr@10
- type: dot_map@100
value: 0.44748355857261374
name: Dot Map@100
- type: query_active_dims
value: 55.23999913533529
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9981901579472073
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 246.17159613336406
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9919346177795241
name: Corpus Sparsity Ratio
---
# CoCondenser trained on Natural-Questions tuples
This is a [SPLADE Sparse Encoder](https://www.sbert.net/docs/sparse_encoder/usage/usage.html) model finetuned from [Luyu/co-condenser-marco](https://huggingface.co/Luyu/co-condenser-marco) on the [msmarco](https://huggingface.co/datasets/sentence-transformers/msmarco) dataset using the [sentence-transformers](https://www.SBERT.net) library. It maps sentences & paragraphs to a 30522-dimensional sparse vector space and can be used for semantic search and sparse retrieval.
## Model Details
### Model Description
- **Model Type:** SPLADE Sparse Encoder
- **Base model:** [Luyu/co-condenser-marco](https://huggingface.co/Luyu/co-condenser-marco) <!-- at revision e0cef0ab2410aae0f0994366ddefb5649a266709 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 30522 dimensions
- **Similarity Function:** Dot Product
- **Training Dataset:**
- [msmarco](https://huggingface.co/datasets/sentence-transformers/msmarco)
- **Language:** en
- **License:** apache-2.0
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Documentation:** [Sparse Encoder Documentation](https://www.sbert.net/docs/sparse_encoder/usage/usage.html)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sparse Encoders on Hugging Face](https://huggingface.co/models?library=sentence-transformers&other=sparse-encoder)
### Full Model Architecture
```
SparseEncoder(
(0): MLMTransformer({'max_seq_length': 512, 'do_lower_case': False}) with MLMTransformer model: BertForMaskedLM
(1): SpladePooling({'pooling_strategy': 'max', 'activation_function': 'relu', 'word_embedding_dimension': 30522})
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SparseEncoder
# Download from the 🤗 Hub
model = SparseEncoder("tomaarsen/splade-cocondenser-msmarco-margin-mse")
# Run inference
queries = [
"when did shanghai disneyland open",
]
documents = [
"Shanghai Disney officially opens: A peek inside. June 17, 2016, 6 p.m. After five years of construction, $5.5 billion in spending and a month of testing to work out the kinks, Shanghai Disney Resort opened to the public just before noon, Shanghai time, on Thursday, June 16 (which was 9 p.m. Wednesday in Anaheim, home of the original Disney park). Shanghai Disneyland features six themed areas, and the resort contains two hotels, a shopping district and 99 acres of gardens, lakes and parkland. We'll keep you updated throughout the week with new details and peeks inside the resort.",
'Map of the Old City of Shanghai. By the early 1400s, Shanghai had become important enough for Ming dynasty engineers to begin dredging the Huangpu River (also known as Shen). In 1553, a city wall was built around the Old Town (Nanshi) as a defense against the depredations of the Wokou (Japanese pirates).',
'The conflict is referred to in China as the War of Resistance against Japanese Aggression (1937-45) and the Anti-Fascist War. Japanâ\x80\x99s expansionist policy of the 1930s, driven by the military, was to set up what it called the Greater East Asia Co-Prosperity Sphere. Marco Polo Bridge, Beijing.A sphere.e are marking the anniversary of Germany and Japanâ\x80\x99s surrender in 1945, but it is legitimate to suggest that the incident that sparked the conflict that became WWII occurred not in Poland in 1939 but in China, near this eleven-arched bridge on the outskirts of Beijing, in July 1937. Letâ\x80\x99s look at the undisputed facts.',
]
query_embeddings = model.encode_query(queries)
document_embeddings = model.encode_document(documents)
print(query_embeddings.shape, document_embeddings.shape)
# [1, 30522] [3, 30522]
# Get the similarity scores for the embeddings
similarities = model.similarity(query_embeddings, document_embeddings)
print(similarities)
# tensor([[31.8057, 19.5344, 12.4372]])
```
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</details>
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### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
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### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
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## Evaluation
### Metrics
#### Sparse Information Retrieval
* Datasets: `NanoMSMARCO`, `NanoNFCorpus` and `NanoNQ`
* Evaluated with [<code>SparseInformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseInformationRetrievalEvaluator)
| Metric | NanoMSMARCO | NanoNFCorpus | NanoNQ |
|:----------------------|:------------|:-------------|:-----------|
| dot_accuracy@1 | 0.46 | 0.38 | 0.5 |
| dot_accuracy@3 | 0.64 | 0.58 | 0.76 |
| dot_accuracy@5 | 0.72 | 0.62 | 0.8 |
| dot_accuracy@10 | 0.82 | 0.74 | 0.88 |
| dot_precision@1 | 0.46 | 0.38 | 0.5 |
| dot_precision@3 | 0.2133 | 0.36 | 0.26 |
| dot_precision@5 | 0.144 | 0.316 | 0.168 |
| dot_precision@10 | 0.082 | 0.27 | 0.096 |
| dot_recall@1 | 0.46 | 0.0397 | 0.48 |
| dot_recall@3 | 0.64 | 0.0752 | 0.71 |
| dot_recall@5 | 0.72 | 0.0936 | 0.75 |
| dot_recall@10 | 0.82 | 0.1467 | 0.85 |
| **dot_ndcg@10** | **0.6289** | **0.3304** | **0.6772** |
| dot_mrr@10 | 0.5689 | 0.4958 | 0.6329 |
| dot_map@100 | 0.5779 | 0.1478 | 0.6167 |
| query_active_dims | 56.1 | 53.68 | 55.94 |
| query_sparsity_ratio | 0.9982 | 0.9982 | 0.9982 |
| corpus_active_dims | 192.4087 | 367.5432 | 228.8362 |
| corpus_sparsity_ratio | 0.9937 | 0.988 | 0.9925 |
#### Sparse Nano BEIR
* Dataset: `NanoBEIR_mean`
* Evaluated with [<code>SparseNanoBEIREvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseNanoBEIREvaluator) with these parameters:
```json
{
"dataset_names": [
"msmarco",
"nfcorpus",
"nq"
]
}
```
| Metric | Value |
|:----------------------|:-----------|
| dot_accuracy@1 | 0.4467 |
| dot_accuracy@3 | 0.66 |
| dot_accuracy@5 | 0.7133 |
| dot_accuracy@10 | 0.8133 |
| dot_precision@1 | 0.4467 |
| dot_precision@3 | 0.2778 |
| dot_precision@5 | 0.2093 |
| dot_precision@10 | 0.1493 |
| dot_recall@1 | 0.3266 |
| dot_recall@3 | 0.4751 |
| dot_recall@5 | 0.5212 |
| dot_recall@10 | 0.6056 |
| **dot_ndcg@10** | **0.5455** |
| dot_mrr@10 | 0.5658 |
| dot_map@100 | 0.4475 |
| query_active_dims | 55.24 |
| query_sparsity_ratio | 0.9982 |
| corpus_active_dims | 246.1716 |
| corpus_sparsity_ratio | 0.9919 |
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
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### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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## Training Details
### Training Dataset
#### msmarco
* Dataset: [msmarco](https://huggingface.co/datasets/sentence-transformers/msmarco) at [9e329ed](https://huggingface.co/datasets/sentence-transformers/msmarco/tree/9e329ed2e649c9d37b0d91dd6b764ff6fe671d83)
* Size: 90,000 training samples
* Columns: <code>score</code>, <code>query</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | score | query | positive | negative |
|:--------|:--------------------------------------------------------------------|:---------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | float | string | string | string |
| details | <ul><li>min: -2.22</li><li>mean: 13.59</li><li>max: 22.53</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 9.05 tokens</li><li>max: 40 tokens</li></ul> | <ul><li>min: 19 tokens</li><li>mean: 81.18 tokens</li><li>max: 203 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 77.08 tokens</li><li>max: 249 tokens</li></ul> |
* Samples:
| score | query | positive | negative |
|:-------------------------------|:-----------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>4.470368494590124</code> | <code>where does the bile duct carry its secretions</code> | <code>The function of the common bile duct is to carry bile from the liver and the gallbladder into the duodenum, the top of the small intestine directly after the stomach. The bile it carries interacts with ingested fats and fat-soluble vitamins to enable them to be absorbed by the intestine.</code> | <code>The gall bladder is a pouch-shaped organ that stores the bile produced by the liver. The gall bladder shares a vessel, called the common bile duct, with the liver. When bile is needed, it moves through the common bile duct into the first part of the small intestine, the duodenum. It is here that the bile breaks down fat.</code> |
| <code>9.550037781397503</code> | <code>definition of reverse auction</code> | <code>Reverse auction. A reverse auction is a type of auction in which the roles of buyer and seller are reversed. In an ordinary auction (also known as a 'forward auction'), buyers compete to obtain goods or services by offering increasingly higher prices. In a reverse auction, the sellers compete to obtain business from the buyer and prices will typically decrease as the sellers underbid each other.</code> | <code>No-reserve auction. A No-reserve auction (NR), also known as an absolute auction, is an auction in which the item for sale will be sold regardless of price. From the seller's perspective, advertising an auction as having no reserve price can be desirable because it potentially attracts a greater number of bidders due to the possibility of a bargain.</code> |
| <code>19.58259622255961</code> | <code>how do i prevent diverticulitis</code> | <code>Follow Following Unfollow Pending Disabled. A , Gastroenterology, answered. The suggestion to prevent diverticulitis is to eat a diet high in fiber, and that includes high-fiber whole grains, fruits, vegetables, nuts, and seeds. Iâm aware that some gastroenterologists say to avoid all seeds and nuts, so some of you are nuts enough to wash tomato seeds from slices and pick free poppy seeds from buns.</code> | <code>The test is fast and easy especially with the newer CT scanners. But does it provide the information needed? CT KUBs are used to screen for a variety of intra-abdominal conditions, including appendicitis, kidney stones, diverticulitis, and others.</code> |
* Loss: [<code>SpladeLoss</code>](https://sbert.net/docs/package_reference/sparse_encoder/losses.html#spladeloss) with these parameters:
```json
{
"loss": "SparseMarginMSELoss",
"lambda_corpus": 0.08,
"lambda_query": 0.1
}
```
### Evaluation Dataset
#### msmarco
* Dataset: [msmarco](https://huggingface.co/datasets/sentence-transformers/msmarco) at [9e329ed](https://huggingface.co/datasets/sentence-transformers/msmarco/tree/9e329ed2e649c9d37b0d91dd6b764ff6fe671d83)
* Size: 10,000 evaluation samples
* Columns: <code>score</code>, <code>query</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | score | query | positive | negative |
|:--------|:-------------------------------------------------------------------|:---------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | float | string | string | string |
| details | <ul><li>min: -1.34</li><li>mean: 13.49</li><li>max: 22.2</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 8.85 tokens</li><li>max: 27 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 80.48 tokens</li><li>max: 211 tokens</li></ul> | <ul><li>min: 20 tokens</li><li>mean: 77.44 tokens</li><li>max: 209 tokens</li></ul> |
* Samples:
| score | query | positive | negative |
|:-------------------------------|:-----------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>15.64028427998225</code> | <code>what is a protected seedbed</code> | <code>A seedbed is a plot of garden set aside to grow vegetables seeds, which can later be transplanted. seedbed is a plot of garden set aside to grow vegetables seeds, which can later be transplanted.</code> | <code>Several articles within the Confederate Statesâ Constitution specifically protected slavery within the Confederacy, but some articles of the U.S. Constitution also protected slaveryâthe Emancipation Proclamation drew a clearer distinction between the two.</code> |
| <code>6.375148057937622</code> | <code>who founded ecuador</code> | <code>The first Spanish settlement in Ecuador was established in 1534 at Quito on the site of an important Incan town of the same name. Another settlement was established four years later near the river Guayas in Guayaquil.</code> | <code>Zuleta is a colonial working farm of 4,000 acres (2,000 hectares) that belongs to the family of Mr. Galo Plaza lasso, a former president of Ecuador, for more than 100 years. It was chosen as one of the worldâs âTop Ten Findsâ by Outside magazine and named as one of the best Ecuador Hotel by National Geographic Traveler.</code> |
| <code>8.436618288358051</code> | <code>what is aol problem</code> | <code>AOL problems. Lots of people are reporting ongoing (RTR:GE) messages from AOL today. This indicates the AOL mail servers are having problems and canât accept mail. This has nothing to do with spam, filtering or malicious email. This is simply their servers arenât functioning as well as they should be and so AOL canât accept all the mail thrown at them. These types of blocks resolve themselves. Update Feb 8, 2016: AOL users are having problems logging in.</code> | <code>Executive Director. I have read these complaints of poor service and agree 110%. I'm a college professor and give extra credit to all AOL users and over the 100% highest grade. I thought I phoned AOL and get some chap in India who is a proven scam man and I'm the poor American SOB who gets whacked.</code> |
* Loss: [<code>SpladeLoss</code>](https://sbert.net/docs/package_reference/sparse_encoder/losses.html#spladeloss) with these parameters:
```json
{
"loss": "SparseMarginMSELoss",
"lambda_corpus": 0.08,
"lambda_query": 0.1
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `learning_rate`: 2e-05
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `fp16`: True
- `batch_sampler`: no_duplicates
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 2e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 1
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: True
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
- `router_mapping`: {}
- `learning_rate_mapping`: {}
</details>
### Training Logs
| Epoch | Step | Training Loss | Validation Loss | NanoMSMARCO_dot_ndcg@10 | NanoNFCorpus_dot_ndcg@10 | NanoNQ_dot_ndcg@10 | NanoBEIR_mean_dot_ndcg@10 |
|:------:|:----:|:-------------:|:---------------:|:-----------------------:|:------------------------:|:------------------:|:-------------------------:|
| 0.0178 | 100 | 805201.68 | - | - | - | - | - |
| 0.0356 | 200 | 11999.3975 | - | - | - | - | - |
| 0.0533 | 300 | 124.0031 | - | - | - | - | - |
| 0.0711 | 400 | 62.6813 | - | - | - | - | - |
| 0.0889 | 500 | 46.0329 | 49.7658 | 0.4890 | 0.2543 | 0.5131 | 0.4188 |
| 0.1067 | 600 | 41.2877 | - | - | - | - | - |
| 0.1244 | 700 | 35.3636 | - | - | - | - | - |
| 0.1422 | 800 | 33.3727 | - | - | - | - | - |
| 0.16 | 900 | 29.389 | - | - | - | - | - |
| 0.1778 | 1000 | 31.2482 | 28.1527 | 0.5652 | 0.2875 | 0.5423 | 0.4650 |
| 0.1956 | 1100 | 31.43 | - | - | - | - | - |
| 0.2133 | 1200 | 27.9919 | - | - | - | - | - |
| 0.2311 | 1300 | 26.9214 | - | - | - | - | - |
| 0.2489 | 1400 | 27.5533 | - | - | - | - | - |
| 0.2667 | 1500 | 25.7473 | 26.8466 | 0.5837 | 0.3265 | 0.6268 | 0.5123 |
| 0.2844 | 1600 | 26.7899 | - | - | - | - | - |
| 0.3022 | 1700 | 24.0652 | - | - | - | - | - |
| 0.32 | 1800 | 23.5837 | - | - | - | - | - |
| 0.3378 | 1900 | 24.1051 | - | - | - | - | - |
| 0.3556 | 2000 | 24.6901 | 22.0851 | 0.6018 | 0.3325 | 0.6359 | 0.5234 |
| 0.3733 | 2100 | 21.5136 | - | - | - | - | - |
| 0.3911 | 2200 | 22.066 | - | - | - | - | - |
| 0.4089 | 2300 | 20.8234 | - | - | - | - | - |
| 0.4267 | 2400 | 20.1988 | - | - | - | - | - |
| 0.4444 | 2500 | 20.0342 | 20.3437 | 0.5901 | 0.3222 | 0.6010 | 0.5044 |
| 0.4622 | 2600 | 18.8835 | - | - | - | - | - |
| 0.48 | 2700 | 19.4797 | - | - | - | - | - |
| 0.4978 | 2800 | 19.6199 | - | - | - | - | - |
| 0.5156 | 2900 | 16.6963 | - | - | - | - | - |
| 0.5333 | 3000 | 19.9204 | 18.0851 | 0.5915 | 0.3111 | 0.6323 | 0.5116 |
| 0.5511 | 3100 | 18.7849 | - | - | - | - | - |
| 0.5689 | 3200 | 18.3169 | - | - | - | - | - |
| 0.5867 | 3300 | 17.1938 | - | - | - | - | - |
| 0.6044 | 3400 | 18.0807 | - | - | - | - | - |
| 0.6222 | 3500 | 16.7721 | 20.1195 | 0.6012 | 0.3119 | 0.6337 | 0.5156 |
| 0.64 | 3600 | 16.7909 | - | - | - | - | - |
| 0.6578 | 3700 | 16.4954 | - | - | - | - | - |
| 0.6756 | 3800 | 16.3734 | - | - | - | - | - |
| 0.6933 | 3900 | 17.2231 | - | - | - | - | - |
| 0.7111 | 4000 | 16.8486 | 17.5785 | 0.6228 | 0.3423 | 0.6553 | 0.5401 |
| 0.7289 | 4100 | 18.2939 | - | - | - | - | - |
| 0.7467 | 4200 | 16.1108 | - | - | - | - | - |
| 0.7644 | 4300 | 16.878 | - | - | - | - | - |
| 0.7822 | 4400 | 15.6163 | - | - | - | - | - |
| 0.8 | 4500 | 15.8337 | 16.1847 | 0.6286 | 0.3376 | 0.6639 | 0.5434 |
| 0.8178 | 4600 | 15.5014 | - | - | - | - | - |
| 0.8356 | 4700 | 15.7579 | - | - | - | - | - |
| 0.8533 | 4800 | 15.9361 | - | - | - | - | - |
| 0.8711 | 4900 | 16.3308 | - | - | - | - | - |
| 0.8889 | 5000 | 14.8395 | 17.4054 | 0.6221 | 0.3280 | 0.6853 | 0.5451 |
| 0.9067 | 5100 | 14.8655 | - | - | - | - | - |
| 0.9244 | 5200 | 14.6498 | - | - | - | - | - |
| 0.9422 | 5300 | 15.5189 | - | - | - | - | - |
| 0.96 | 5400 | 14.608 | - | - | - | - | - |
| 0.9778 | 5500 | 15.6019 | 16.4883 | 0.6298 | 0.3317 | 0.6831 | 0.5482 |
| 0.9956 | 5600 | 14.6263 | - | - | - | - | - |
| -1 | -1 | - | - | 0.6289 | 0.3304 | 0.6772 | 0.5455 |
### Environmental Impact
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
- **Energy Consumed**: 0.218 kWh
- **Carbon Emitted**: 0.085 kg of CO2
- **Hours Used**: 0.618 hours
### Training Hardware
- **On Cloud**: No
- **GPU Model**: 1 x NVIDIA GeForce RTX 3090
- **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700K
- **RAM Size**: 31.78 GB
### Framework Versions
- Python: 3.11.6
- Sentence Transformers: 4.2.0.dev0
- Transformers: 4.52.4
- PyTorch: 2.6.0+cu124
- Accelerate: 1.5.1
- Datasets: 2.21.0
- Tokenizers: 0.21.1
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### SpladeLoss
```bibtex
@misc{formal2022distillationhardnegativesampling,
title={From Distillation to Hard Negative Sampling: Making Sparse Neural IR Models More Effective},
author={Thibault Formal and Carlos Lassance and Benjamin Piwowarski and Stéphane Clinchant},
year={2022},
eprint={2205.04733},
archivePrefix={arXiv},
primaryClass={cs.IR},
url={https://arxiv.org/abs/2205.04733},
}
```
#### SparseMarginMSELoss
```bibtex
@misc{hofstätter2021improving,
title={Improving Efficient Neural Ranking Models with Cross-Architecture Knowledge Distillation},
author={Sebastian Hofstätter and Sophia Althammer and Michael Schröder and Mete Sertkan and Allan Hanbury},
year={2021},
eprint={2010.02666},
archivePrefix={arXiv},
primaryClass={cs.IR}
}
```
#### FlopsLoss
```bibtex
@article{paria2020minimizing,
title={Minimizing flops to learn efficient sparse representations},
author={Paria, Biswajit and Yeh, Chih-Kuan and Yen, Ian EH and Xu, Ning and Ravikumar, Pradeep and P{'o}czos, Barnab{'a}s},
journal={arXiv preprint arXiv:2004.05665},
year={2020}
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
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## Model Card Contact
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gradientrouting-spar/gcd_syco_modnaive_seed_5 | gradientrouting-spar | 2025-06-12T13:40:02Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-12T13:39:47Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
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#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
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### Testing Data, Factors & Metrics
#### Testing Data
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[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
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## Citation [optional]
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**BibTeX:**
[More Information Needed]
**APA:**
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
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[More Information Needed]
## Model Card Contact
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Haraguin/ppo-Huggy | Haraguin | 2025-06-12T13:33:13Z | 0 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] | reinforcement-learning | 2025-06-12T13:33:09Z | ---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: Haraguin/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
HichTala/DiffusionDet | HichTala | 2025-06-12T13:32:59Z | 39 | 0 | transformers | [
"transformers",
"safetensors",
"diffusiondet",
"object-detection",
"custom_code",
"dataset:detection-datasets/coco",
"arxiv:2504.06330",
"license:mit",
"endpoints_compatible",
"region:us"
] | object-detection | 2025-01-03T11:09:13Z | ---
library_name: transformers
license: mit
datasets:
- detection-datasets/coco
pipeline_tag: object-detection
---
# Model Card for DiffusionDet
DiffusionDet is a diffusion-based object detection model that formulates object detection as a denoising diffusion process. It iteratively refines noisy box predictions to generate high-quality detection outputs. This approach provides a flexible and unified framework for object detection, offering advantages over traditional proposal-based methods.
## 🔧 Uses
You can load and use the model with Hugging Face's 🤗 `transformers` or via the original repository.
- 📦 [Original GitHub repo](github.com/pierlj/fsdiffusiondet)
- 🚀 [Few-shot cross-domain adaptation repo](https://github.com/ShoufaChen/DiffusionDet)
This model has been adapted for cross-domain few-shot object detection using LoRA (Low-Rank Adaptation).
📄 Check out the paper: [LoRA for Cross-Domain Few-Shot Object Detection](https://huggingface.co/papers/2504.06330)
|
aieng-lab/starcoder2-3b_closed-question | aieng-lab | 2025-06-12T13:25:07Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"starcoder2",
"text-classification",
"en",
"base_model:bigcode/starcoder2-3b",
"base_model:finetune:bigcode/starcoder2-3b",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-06-12T13:23:15Z | ---
library_name: transformers
license: mit
language:
- en
metrics:
- f1
- precision
- recall
base_model:
- bigcode/starcoder2-3b
pipeline_tag: text-classification
---
# StarCoder2 3b for classifying developer questions
This model classifies questions in developer forums (e.g., Stack Overflow) as 'open', 'not a real question', 'off topic', 'not constructive' or 'too localized'.
- **Developed by:** Fabian C. Peña, Steffen Herbold
- **Finetuned from:** [bigcode/starcoder2-3b](https://huggingface.co/bigcode/starcoder2-3b)
- **Replication kit:** [https://github.com/aieng-lab/senlp-benchmark](https://github.com/aieng-lab/senlp-benchmark)
- **Language:** English
- **License:** MIT
## Citation
```
@misc{pena2025benchmark,
author = {Fabian Peña and Steffen Herbold},
title = {Evaluating Large Language Models on Non-Code Software Engineering Tasks},
year = {2025}
}
```
|
gradientrouting-spar/gcd_syco_additionkl_div_beta_kl-10_seed_1 | gradientrouting-spar | 2025-06-12T13:15:32Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-12T13:15:15Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
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## Model Card Contact
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AndreiVoicuT/poca-SoccerTwos | AndreiVoicuT | 2025-06-12T12:52:27Z | 0 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"SoccerTwos",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SoccerTwos",
"region:us"
] | reinforcement-learning | 2025-06-12T12:52:24Z | ---
library_name: ml-agents
tags:
- SoccerTwos
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: AndreiVoicuT/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
aieng-lab/bert-large-cased_closed-question | aieng-lab | 2025-06-12T12:48:02Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"en",
"base_model:google-bert/bert-large-cased",
"base_model:finetune:google-bert/bert-large-cased",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-06-12T12:47:43Z | ---
library_name: transformers
license: mit
language:
- en
metrics:
- f1
- precision
- recall
base_model:
- bert-large-cased
pipeline_tag: text-classification
---
# BERT large for classifying developer questions
This model classifies questions in developer forums (e.g., Stack Overflow) as 'open', 'not a real question', 'off topic', 'not constructive' or 'too localized'.
- **Developed by:** Fabian C. Peña, Steffen Herbold
- **Finetuned from:** [bert-large-cased](https://huggingface.co/bert-large-cased)
- **Replication kit:** [https://github.com/aieng-lab/senlp-benchmark](https://github.com/aieng-lab/senlp-benchmark)
- **Language:** English
- **License:** MIT
## Citation
```
@misc{pena2025benchmark,
author = {Fabian Peña and Steffen Herbold},
title = {Evaluating Large Language Models on Non-Code Software Engineering Tasks},
year = {2025}
}
```
|
eddieman78/litbank-coref-gemma-3-12b-pt-4000-64-2e5-5 | eddieman78 | 2025-06-12T12:46:13Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"unsloth",
"trl",
"sft",
"base_model:unsloth/gemma-3-12b-pt-unsloth-bnb-4bit",
"base_model:finetune:unsloth/gemma-3-12b-pt-unsloth-bnb-4bit",
"endpoints_compatible",
"region:us"
] | null | 2025-06-12T12:45:40Z | ---
base_model: unsloth/gemma-3-12b-pt-unsloth-bnb-4bit
library_name: transformers
model_name: litbank-coref-gemma-3-12b-pt-4000-64-2e5-5
tags:
- generated_from_trainer
- unsloth
- trl
- sft
licence: license
---
# Model Card for litbank-coref-gemma-3-12b-pt-4000-64-2e5-5
This model is a fine-tuned version of [unsloth/gemma-3-12b-pt-unsloth-bnb-4bit](https://huggingface.co/unsloth/gemma-3-12b-pt-unsloth-bnb-4bit).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="eddieman78/litbank-coref-gemma-3-12b-pt-4000-64-2e5-5", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.6.0+cu124
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
mradermacher/Athena-R3X-0.6B-GGUF | mradermacher | 2025-06-12T12:43:31Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:Spestly/Athena-R3X-0.6B",
"base_model:quantized:Spestly/Athena-R3X-0.6B",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-06-12T12:28:25Z | ---
base_model: Spestly/Athena-R3X-0.6B
language:
- en
library_name: transformers
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/Spestly/Athena-R3X-0.6B
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Athena-R3X-0.6B-GGUF/resolve/main/Athena-R3X-0.6B.Q2_K.gguf) | Q2_K | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/Athena-R3X-0.6B-GGUF/resolve/main/Athena-R3X-0.6B.Q3_K_S.gguf) | Q3_K_S | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/Athena-R3X-0.6B-GGUF/resolve/main/Athena-R3X-0.6B.Q3_K_M.gguf) | Q3_K_M | 0.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Athena-R3X-0.6B-GGUF/resolve/main/Athena-R3X-0.6B.Q3_K_L.gguf) | Q3_K_L | 0.5 | |
| [GGUF](https://huggingface.co/mradermacher/Athena-R3X-0.6B-GGUF/resolve/main/Athena-R3X-0.6B.IQ4_XS.gguf) | IQ4_XS | 0.5 | |
| [GGUF](https://huggingface.co/mradermacher/Athena-R3X-0.6B-GGUF/resolve/main/Athena-R3X-0.6B.Q4_K_S.gguf) | Q4_K_S | 0.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Athena-R3X-0.6B-GGUF/resolve/main/Athena-R3X-0.6B.Q4_K_M.gguf) | Q4_K_M | 0.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Athena-R3X-0.6B-GGUF/resolve/main/Athena-R3X-0.6B.Q5_K_S.gguf) | Q5_K_S | 0.5 | |
| [GGUF](https://huggingface.co/mradermacher/Athena-R3X-0.6B-GGUF/resolve/main/Athena-R3X-0.6B.Q5_K_M.gguf) | Q5_K_M | 0.5 | |
| [GGUF](https://huggingface.co/mradermacher/Athena-R3X-0.6B-GGUF/resolve/main/Athena-R3X-0.6B.Q6_K.gguf) | Q6_K | 0.6 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Athena-R3X-0.6B-GGUF/resolve/main/Athena-R3X-0.6B.Q8_0.gguf) | Q8_0 | 0.7 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Athena-R3X-0.6B-GGUF/resolve/main/Athena-R3X-0.6B.f16.gguf) | f16 | 1.3 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
RAIL-KNUST/ct-mri | RAIL-KNUST | 2025-06-12T12:35:39Z | 0 | 0 | null | [
"medical",
"biology",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-06-11T14:24:32Z | ---
license: apache-2.0
language:
- en
metrics:
- accuracy
tags:
- medical
- biology
--- |
gradientrouting-spar/gcd_syco_additionst_we_train_split-0.3_pos_prx-proxy_neg_prx-proxy_neg_st_alpha-1.0_seed_5 | gradientrouting-spar | 2025-06-12T12:34:47Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-12T12:34:39Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
profientw3456/inception-wildlife | profientw3456 | 2025-06-12T12:30:54Z | 0 | 0 | null | [
"region:us"
] | null | 2025-06-11T21:29:20Z | # Inception Wildlife
Production-ready wildlife detection model using Inception v3 + Faster R-CNN, optimized for real-world API deployment.
## Model Details
- **Architecture**: Inception v3 + Faster R-CNN
- **Framework**: Detectron2 + PyTorch
- **Classes**: 11 wildlife animals
- **Pretrained**: Yes (Inception v3 backbone)
- **Upload Date**: 2025-06-12
## Detected Animals
- Antelope, Buffalo, Elephant, Giraffe, Gorilla, Hippopotamus, Leopard, Lion, Rhino, Wolf, Zebra
## Quick Start
```python
from inference_api import WildlifeDetectorAPI
detector = WildlifeDetectorAPI(
model_path="model_final.pth",
config_path="production_config.json"
)
result = detector.predict("image.jpg", confidence_threshold=0.5)
print(result)
```
## API Response Format
```json
{
"success": true,
"detections": [
{
"class_id": 0,
"class_name": "antelope",
"confidence": 0.85,
"confidence_level": "high",
"bbox": {
"x1": 100.0,
"y1": 200.0,
"x2": 300.0,
"y2": 450.0,
"width": 200.0,
"height": 250.0
}
}
],
"summary": {
"total_detections": 1,
"high_confidence": 1,
"medium_confidence": 0,
"low_confidence": 0
}
}
```
## Installation
```bash
pip install -r requirements.txt
# Install detectron2 (platform-specific, example for CUDA 11.3, PyTorch 1.10)
pip install detectron2 -f https://dl.fbaipublicfiles.com/detectron2/wheels/cu113/torch1.10/index.html
```
## Files Included
- `model_final.pth`: Trained model weights
- `production_config.json`: Production configuration
- `inference_api.py`: Inference script for API integration
- `requirements.txt`: Dependencies
## Confidence Levels
- **High**: ≥ 80%
- **Medium**: 60-79%
- **Low**: 40-59%
## Production Tips
- Use GPU for faster inference.
- Set confidence threshold based on application needs.
- Cache model instance for API performance.
- Handle invalid image inputs gracefully.
## License
Apache-2.0 (ensure compliance with your dataset).
|
phospho-app/PLB-ACT_BBOX-circle-box-bbact-pfqmg | phospho-app | 2025-06-12T12:27:29Z | 0 | 0 | null | [
"safetensors",
"phosphobot",
"act",
"region:us"
] | null | 2025-06-12T12:07:40Z |
---
tags:
- phosphobot
- act
task_categories:
- robotics
---
# act Model - phospho Training Pipeline
## This model was trained using **phospho**.
Training was successfull, try it out on your robot!
## Training parameters:
- **Dataset**: [phospho-app/circle-box-bbact_bboxes](https://huggingface.co/datasets/phospho-app/circle-box-bbact_bboxes)
- **Wandb run URL**: None
- **Epochs**: None
- **Batch size**: 100
- **Training steps**: 10
📖 **Get Started**: [docs.phospho.ai](https://docs.phospho.ai?utm_source=huggingface_readme)
🤖 **Get your robot**: [robots.phospho.ai](https://robots.phospho.ai?utm_source=huggingface_readme)
|
morturr/Mistral-7B-v0.1-PAIR_one_liners_amazon-COMB-one_liners-comb-1-seed-42-2025-06-12 | morturr | 2025-06-12T12:20:56Z | 0 | 0 | peft | [
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:mistralai/Mistral-7B-v0.1",
"base_model:adapter:mistralai/Mistral-7B-v0.1",
"license:apache-2.0",
"region:us"
] | null | 2025-06-12T12:20:47Z | ---
library_name: peft
license: apache-2.0
base_model: mistralai/Mistral-7B-v0.1
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: Mistral-7B-v0.1-PAIR_one_liners_amazon-COMB-one_liners-comb-1-seed-42-2025-06-12
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Mistral-7B-v0.1-PAIR_one_liners_amazon-COMB-one_liners-comb-1-seed-42-2025-06-12
This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 6e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.1
- Pytorch 2.5.1+cu124
- Datasets 3.0.2
- Tokenizers 0.20.1 |
gradientrouting-spar/gcd_syco_additiondpo_train_split-0.3_pos_prx-proxy_neg_prx-proxy_neg_ldpo-6_seed_5 | gradientrouting-spar | 2025-06-12T12:18:43Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-12T12:18:36Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
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MinaMila/phi3_unlearned_2nd_1e-6_1.0_0.15_0.15_0.5_epoch1 | MinaMila | 2025-06-12T12:17:52Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"phi3",
"text-generation",
"conversational",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-12T12:15:49Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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[More Information Needed]
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
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bbunzeck/german-babylm-pjg-char | bbunzeck | 2025-06-12T12:14:29Z | 0 | 0 | null | [
"text-generation",
"de",
"dataset:bbunzeck/babylm-german",
"region:us"
] | text-generation | 2025-05-28T12:22:50Z | ---
datasets:
- bbunzeck/babylm-german
language:
- de
pipeline_tag: text-generation
--- |
phospho-app/PAphospho-ACT_BBOX-circle-box-bbact-1006000 | phospho-app | 2025-06-12T12:13:09Z | 0 | 0 | null | [
"safetensors",
"phosphobot",
"act",
"region:us"
] | null | 2025-06-12T11:41:29Z |
---
tags:
- phosphobot
- act
task_categories:
- robotics
---
# act Model - phospho Training Pipeline
## This model was trained using **phospho**.
Training was successfull, try it out on your robot!
## Training parameters:
- **Dataset**: [phospho-app/circle-box-bbact_bboxes](https://huggingface.co/datasets/phospho-app/circle-box-bbact_bboxes)
- **Wandb run URL**: None
- **Epochs**: None
- **Batch size**: 100
- **Training steps**: 6000
📖 **Get Started**: [docs.phospho.ai](https://docs.phospho.ai?utm_source=huggingface_readme)
🤖 **Get your robot**: [robots.phospho.ai](https://robots.phospho.ai?utm_source=huggingface_readme)
|
sarthak413/nllb-english-manipuri | sarthak413 | 2025-06-12T12:12:28Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"m2m_100",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2025-06-12T12:10:16Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
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- **Developed by:** [More Information Needed]
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[More Information Needed]
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## Bias, Risks, and Limitations
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[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
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## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
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## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
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## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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mradermacher/e3-1.7B-GGUF | mradermacher | 2025-06-12T12:12:19Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"en",
"dataset:CMU-AIRe/e3-math-easy",
"dataset:CMU-AIRe/e3-math-medhard",
"dataset:CMU-AIRe/hmmt-aime-2025",
"base_model:CMU-AIRe/e3-1.7B",
"base_model:quantized:CMU-AIRe/e3-1.7B",
"license:mit",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-06-12T12:02:02Z | ---
base_model: CMU-AIRe/e3-1.7B
datasets:
- CMU-AIRe/e3-math-easy
- CMU-AIRe/e3-math-medhard
- CMU-AIRe/hmmt-aime-2025
language:
- en
library_name: transformers
license: mit
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/CMU-AIRe/e3-1.7B
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/e3-1.7B-GGUF/resolve/main/e3-1.7B.Q2_K.gguf) | Q2_K | 0.9 | |
| [GGUF](https://huggingface.co/mradermacher/e3-1.7B-GGUF/resolve/main/e3-1.7B.Q3_K_S.gguf) | Q3_K_S | 1.0 | |
| [GGUF](https://huggingface.co/mradermacher/e3-1.7B-GGUF/resolve/main/e3-1.7B.Q3_K_M.gguf) | Q3_K_M | 1.0 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/e3-1.7B-GGUF/resolve/main/e3-1.7B.Q3_K_L.gguf) | Q3_K_L | 1.1 | |
| [GGUF](https://huggingface.co/mradermacher/e3-1.7B-GGUF/resolve/main/e3-1.7B.IQ4_XS.gguf) | IQ4_XS | 1.1 | |
| [GGUF](https://huggingface.co/mradermacher/e3-1.7B-GGUF/resolve/main/e3-1.7B.Q4_K_S.gguf) | Q4_K_S | 1.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/e3-1.7B-GGUF/resolve/main/e3-1.7B.Q4_K_M.gguf) | Q4_K_M | 1.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/e3-1.7B-GGUF/resolve/main/e3-1.7B.Q5_K_S.gguf) | Q5_K_S | 1.3 | |
| [GGUF](https://huggingface.co/mradermacher/e3-1.7B-GGUF/resolve/main/e3-1.7B.Q5_K_M.gguf) | Q5_K_M | 1.4 | |
| [GGUF](https://huggingface.co/mradermacher/e3-1.7B-GGUF/resolve/main/e3-1.7B.Q6_K.gguf) | Q6_K | 1.5 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/e3-1.7B-GGUF/resolve/main/e3-1.7B.Q8_0.gguf) | Q8_0 | 1.9 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/e3-1.7B-GGUF/resolve/main/e3-1.7B.f16.gguf) | f16 | 3.5 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
hafidhsoekma/gasing-sota_edu_multilingual-4bit | hafidhsoekma | 2025-06-12T12:09:35Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"base_model:unsloth/Qwen3-8B-unsloth-bnb-4bit",
"base_model:quantized:unsloth/Qwen3-8B-unsloth-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compat... | text-generation | 2025-06-12T11:53:06Z | ---
base_model: unsloth/Qwen3-8B-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** hafidhsoekma
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen3-8B-unsloth-bnb-4bit
This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
siddharthhoonka12/code-gem | siddharthhoonka12 | 2025-06-12T11:44:51Z | 1 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"gpt2",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-08T19:37:38Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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[More Information Needed]
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
morturr/Mistral-7B-v0.1-PAIR_amazon_headlines-COMB-amazon-comb-1-seed-7-2025-06-12 | morturr | 2025-06-12T11:43:27Z | 0 | 0 | peft | [
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:mistralai/Mistral-7B-v0.1",
"base_model:adapter:mistralai/Mistral-7B-v0.1",
"license:apache-2.0",
"region:us"
] | null | 2025-06-12T11:43:16Z | ---
library_name: peft
license: apache-2.0
base_model: mistralai/Mistral-7B-v0.1
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: Mistral-7B-v0.1-PAIR_amazon_headlines-COMB-amazon-comb-1-seed-7-2025-06-12
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Mistral-7B-v0.1-PAIR_amazon_headlines-COMB-amazon-comb-1-seed-7-2025-06-12
This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 7
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.1
- Pytorch 2.5.1+cu124
- Datasets 3.0.2
- Tokenizers 0.20.1 |
AndreiVoicuT/dqn-SpaceInvadersNoFrameskip-v4 | AndreiVoicuT | 2025-06-12T11:38:51Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2025-06-12T11:38:24Z | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 623.00 +/- 165.76
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
SBX (SB3 + Jax): https://github.com/araffin/sbx
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga AndreiVoicuT -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga AndreiVoicuT -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga AndreiVoicuT
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
anilarslan/phil-4-ransomware | anilarslan | 2025-06-12T11:21:01Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"conversational",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-12T11:09:00Z | ---
base_model: phil-4-ransomware/checkpoint-900
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** anilarslan
- **License:** apache-2.0
- **Finetuned from model :** phil-4-ransomware/checkpoint-900
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
bharathkumar1922001/orpheus-3b-hi-aisha-Tarini-checkpoint-1707 | bharathkumar1922001 | 2025-06-12T11:17:57Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:canopylabs/3b-hi-pretrain-research_release",
"base_model:adapter:canopylabs/3b-hi-pretrain-research_release",
"region:us"
] | null | 2025-06-12T11:17:34Z | ---
base_model: canopylabs/3b-hi-pretrain-research_release
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.15.2 |
Gitanjali1801/ctrl_b_and_b_12_june_2025_condition | Gitanjali1801 | 2025-06-12T11:10:19Z | 0 | 0 | diffusers | [
"diffusers",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"controlnet",
"diffusers-training",
"base_model:stabilityai/stable-diffusion-2-1-base",
"base_model:adapter:stabilityai/stable-diffusion-2-1-base",
"license:creativeml-openrail-m",
"region:us"
] | text-to-image | 2025-06-12T10:33:32Z | ---
base_model: stabilityai/stable-diffusion-2-1-base
library_name: diffusers
license: creativeml-openrail-m
inference: true
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- controlnet
- diffusers-training
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# controlnet-Gitanjali1801/ctrl_b_and_b_12_june_2025_condition
These are controlnet weights trained on stabilityai/stable-diffusion-2-1-base with new type of conditioning.
You can find some example images below.
prompt: Generate the <variant> image of to this reference image which has the following story. This is the caption of reference image <caption> A man smiling and raising his hands. </caption>. This is the story of the reference image.| <story> | John had always been the life of the party. His friends loved his sense of humor and his ability to make everyone laugh. One day, they decided to throw a surprise party for him. As John walked into the room, he was greeted with cheers and applause. <caption>A man smiling and raising his hands.</caption> He was thrilled and couldn't stop smiling as he raised his hands in excitement. The party continued with laughter, games, and music. John even took the opportunity to share some of his favorite jokes. Everyone was having a great time, and the atmosphere was filled with joy. As the night went on, John thanked his friends for the wonderful surprise. It was a night to remember, filled with happiness and cherished memories.| </story>| Now we need to generate such variant of this reference image that should be less toxic. Here is the caption of variant image which we need to generate. <variant1> A man smiling and waving his hands. </variant1>.
prompt: Generate the <variant> image of to this reference image which has the following story. This is the caption of reference image <caption> A woman in a black outfit and fishnet stockings sitting on the floor, stretching. </caption>. This is the story of the reference image.| <story> | <story>| Jane had always been passionate about dance. She spent countless hours in the studio, perfecting her moves and pushing her limits. Her dedication was evident to everyone who knew her. One evening, she decided to try a new routine that involved a lot of floor work and stretching.| <caption>A woman in a black outfit and fishnet stockings sitting on the floor, stretching.</caption>| As she stretched on the floor, she felt a sense of accomplishment. Her friends in the background cheered her on, appreciating her effort and dedication. After the session, they all gathered around to discuss their progress and share tips. Jane felt grateful for the supportive community she was a part of. They all agreed to meet again the next day to continue their practice and improve together.| </story>| Now we need to generate such variant of this reference image that should be less toxic. Here is the caption of variant image which we need to generate. <variant1> A woman in a black outfit and leggings sitting on the floor, stretching. </variant1>.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model] |
gradientrouting-spar/gcd_syco_additionst_we_train_split-0.3_pos_prx-proxy_neg_prx-proxy_neg_st_alpha-0.8_seed_1 | gradientrouting-spar | 2025-06-12T10:45:27Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-12T10:45:18Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
gumran/gpt2-sft | gumran | 2025-06-12T10:35:22Z | 293 | 0 | transformers | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"trl",
"sft",
"conversational",
"base_model:openai-community/gpt2",
"base_model:finetune:openai-community/gpt2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-03T12:37:27Z | ---
base_model: openai-community/gpt2
library_name: transformers
model_name: gpt2-sft
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for gpt2-sft
This model is a fine-tuned version of [openai-community/gpt2](https://huggingface.co/openai-community/gpt2).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="gumran/gpt2-sft", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.18.1
- Transformers: 4.52.4
- Pytorch: 2.7.1+cu118
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
aplux/YOLO-V3-tiny | aplux | 2025-06-12T10:27:14Z | 0 | 0 | null | [
"AIoT",
"QNN",
"object-detection",
"license:agpl-3.0",
"region:us"
] | object-detection | 2025-06-12T10:24:53Z | ---
license: agpl-3.0
pipeline_tag: object-detection
tags:
- AIoT
- QNN
---
## YOLO-V3-tiny: Object Detection
YOLOv3-Tiny is a lightweight version of YOLOv3, designed for resource-constrained devices such as embedded systems and mobile platforms, aiming for real-time object detection. By simplifying the network structure and reducing the number of convolutional layers, the model significantly lowers computational complexity and model size. Although it offers slightly lower accuracy compared to the full version, it excels in speed and resource efficiency, making it suitable for applications where real-time performance is critical.
### Source model
- Input shape: 1x3x416x416
- Number of parameters: 8.85M
- Model size: 33.81M
- Output shape: [1x255x13x13],[1x255x26x26]
The source model can be found [here](https://github.com/ultralytics/yolov3/tree/v8)
## Performance Reference
Please search model by model name in [Model Farm](https://aiot.aidlux.com/en/models)
## Inference & Model Conversion
Please search model by model name in [Model Farm](https://aiot.aidlux.com/en/models)
## License
- Source Model: [AGPL-3.0](https://github.com/ultralytics/yolov3/blob/v8/LICENSE)
- Deployable Model: [AGPL-3.0](https://github.com/ultralytics/yolov3/blob/v8/LICENSE) |
joey00769/lalabulu-style-lora | joey00769 | 2025-06-12T10:19:46Z | 0 | 0 | diffusers | [
"diffusers",
"text-to-image",
"lora",
"template:diffusion-lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:creativeml-openrail-m",
"region:us"
] | text-to-image | 2025-06-12T10:15:59Z | ---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- text: "UNICODE\0\0F\0e\0m\0m\0e\0s\0_\0S\0T\0,\0 \0m\0a\0s\0t\0e\0r\0p\0i\0e\0c\0e\0,\0 \0b\0e\0s\0t\0 \0q\0u\0a\0l\0i\0t\0y\0,\0 \0v\0e\0r\0y\0 \0a\0e\0s\0t\0h\0e\0t\0i\0c\0,\0 \0h\0i\0g\0h\0 \0r\0e\0s\0o\0l\0u\0t\0i\0o\0n\0,\0 \0u\0l\0t\0r\0a\0-\0d\0e\0t\0a\0i\0l\0e\0d\0,\0 \0a\0b\0s\0u\0r\0d\0r\0e\0s\0,\0 \0n\0e\0w\0e\0s\0t\0,\0 \0e\0x\0t\0r\0e\0m\0e\0l\0y\0 \0d\0e\0t\0a\0i\0l\0e\0d\0 \0e\0y\0e\0s\0,\0 \0M\0e\0g\0u\0m\0i\0n\0,\0 \0r\0e\0a\0l\0i\0s\0t\0i\0c\0"
output:
url: images/AQA6RKJAP9R95QG71MK3H4TKQ0.jpeg
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: null
license: creativeml-openrail-m
---
# bubble-mart-style-lora-v1
<Gallery />
## Model description
LoRA for generating Lalabulu / Bubble Mart style toy avatars. Uploaded for personal project PopBoxLab.
## Download model
Weights for this model are available in Safetensors format.
[Download](/joey00769/lalabulu-style-lora/tree/main) them in the Files & versions tab.
|
krushna290220/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-flapping_climbing_wombat | krushna290220 | 2025-06-12T10:19:26Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am flapping climbing wombat",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
... | text-generation | 2025-06-12T10:19:02Z | ---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-flapping_climbing_wombat
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am flapping climbing wombat
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-flapping_climbing_wombat
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="krushna290220/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-flapping_climbing_wombat", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.18.1
- Transformers: 4.52.4
- Pytorch: 2.7.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
giayphuyen/gemma-3-12-DPO | giayphuyen | 2025-06-12T10:16:25Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-12T08:19:45Z | ---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
ngantuk-banget-wok/deberta-v3-small-emotion-multilabel-classifier | ngantuk-banget-wok | 2025-06-12T10:12:37Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"deberta-v2",
"text-classification",
"generated_from_trainer",
"base_model:microsoft/deberta-v3-small",
"base_model:finetune:microsoft/deberta-v3-small",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-06-12T09:15:52Z | ---
library_name: transformers
license: mit
base_model: microsoft/deberta-v3-small
tags:
- generated_from_trainer
model-index:
- name: deberta-v3-small-emotion-multilabel-classifier
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# deberta-v3-small-emotion-multilabel-classifier
This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1950
- Macro F1: 0.4211
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss | Macro F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2701 | 1.0 | 654 | 0.2054 | 0.2619 |
| 0.2043 | 2.0 | 1308 | 0.1965 | 0.3540 |
| 0.1936 | 3.0 | 1962 | 0.1956 | 0.3647 |
| 0.1821 | 4.0 | 2616 | 0.1936 | 0.3969 |
| 0.1789 | 5.0 | 3270 | 0.1945 | 0.4183 |
| 0.1761 | 6.0 | 3924 | 0.1950 | 0.4211 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
aplux/Whisper-Tiny-En | aplux | 2025-06-12T10:11:20Z | 0 | 0 | null | [
"AIoT",
"QNN",
"automatic-speech-recognition",
"license:other",
"region:us"
] | automatic-speech-recognition | 2025-06-12T10:10:02Z | ---
license: other
license_name: aplux-model-farm-license
license_link: https://aiot.aidlux.com/api/v1/files/license/model_farm_license_en.pdf
pipeline_tag: automatic-speech-recognition
tags:
- AIoT
- QNN
---
## Whisper-Tiny-En: ASR
Whisper-Tiny-En is a lightweight speech recognition model developed by OpenAI, built on the Transformer architecture and optimized for efficient English speech-to-text transcription and translation. Trained on extensive multilingual data, it delivers real-time audio processing for tasks like live captioning, voice command recognition, and cross-language translation. Designed for low-resource environments, the model runs smoothly on CPUs or low-power GPUs, prioritizing speed over high precision. It suits latency-sensitive applications such as voice assistants, real-time subtitling, or speech logging, while addressing challenges like background noise suppression, accent variability, and long-context dependencies.
### Source model
- Input shape: [1x80x3000],[[1x1],[1x1],[4x6x64x1500],[4x6x1500x64],[4x6x64x224],[4x6x224x64]]
- Number of parameters: 9.39M, 53.2M
- Model size: 35.9M, 184M
- Output shape: [[4x6x64x1500],[4x6x1500x64]],[[1x1x51864],[4x6x64x224],[4x6x224x64]]
The source model can be found [here](https://github.com/openai/whisper/tree/main)
## Performance Reference
Please search model by model name in [Model Farm](https://aiot.aidlux.com/en/models)
## Inference & Model Conversion
Please search model by model name in [Model Farm](https://aiot.aidlux.com/en/models)
## License
- Source Model: [MIT](https://github.com/openai/whisper/blob/main/LICENSE)
- Deployable Model: [APLUX-MODEL-FARM-LICENSE](https://aiot.aidlux.com/api/v1/files/license/model_farm_license_en.pdf) |
morturr/Mistral-7B-v0.1-PAIR_dadjokes_headlines-COMB-headlines-comb-3-seed-42-2025-06-12 | morturr | 2025-06-12T10:03:27Z | 0 | 0 | peft | [
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:mistralai/Mistral-7B-v0.1",
"base_model:adapter:mistralai/Mistral-7B-v0.1",
"license:apache-2.0",
"region:us"
] | null | 2025-06-12T10:03:13Z | ---
library_name: peft
license: apache-2.0
base_model: mistralai/Mistral-7B-v0.1
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: Mistral-7B-v0.1-PAIR_dadjokes_headlines-COMB-headlines-comb-3-seed-42-2025-06-12
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Mistral-7B-v0.1-PAIR_dadjokes_headlines-COMB-headlines-comb-3-seed-42-2025-06-12
This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 6e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.1
- Pytorch 2.5.1+cu124
- Datasets 3.0.2
- Tokenizers 0.20.1 |
aplux/Midas-v2 | aplux | 2025-06-12T09:53:39Z | 0 | 0 | null | [
"AIoT",
"QNN",
"depth-estimation",
"license:other",
"region:us"
] | depth-estimation | 2025-06-12T05:55:40Z | ---
license: other
license_name: aplux-model-farm-license
license_link: https://aiot.aidlux.com/api/v1/files/license/model_farm_license_en.pdf
pipeline_tag: depth-estimation
tags:
- AIoT
- QNN
---
## Midas-v2: Depth Estimation
Midas is a deep learning-based monocular depth estimation model that accurately predicts scene depth from a single RGB image without relying on stereo vision or depth sensors. By integrating a hybrid CNN-Transformer architecture and pretraining on diverse datasets (e.g., MegaDepth, KITTI), it achieves strong cross-scene generalization, adapting to complex lighting, occlusions, and varied environments (indoor/outdoor). The model supports dynamic resolution inputs (down to 256x256 pixels) while preserving detail perception, with optimized computational efficiency for real-time performance and lightweight deployment on mobile/edge devices. It is widely used in autonomous driving (obstacle detection), AR/VR (3D reconstruction), and robotic navigation, significantly reducing hardware costs. Ongoing updates (e.g., Midas-v3) enhance small-object recognition and edge accuracy.
### Source model
- Input shape: 1x3x256x256
- Number of parameters: 20.33M
- Model size: 82.17M
- Output shape: 1x1x256x256
The source model can be found [here](https://github.com/isl-org/MiDaS)
## Performance Reference
Please search model by model name in [Model Farm](https://aiot.aidlux.com/en/models)
## Inference & Model Conversion
Please search model by model name in [Model Farm](https://aiot.aidlux.com/en/models)
## License
- Source Model: [MIT](https://github.com/isl-org/MiDaS/blob/master/LICENSE)
- Deployable Model: [APLUX-MODEL-FARM-LICENSE](https://aiot.aidlux.com/api/v1/files/license/model_farm_license_en.pdf) |
morturr/Llama-3.1-8B-headlines-2025-06-12 | morturr | 2025-06-12T09:50:26Z | 0 | 0 | peft | [
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:meta-llama/Llama-3.1-8B",
"base_model:adapter:meta-llama/Llama-3.1-8B",
"license:llama3.1",
"region:us"
] | null | 2025-06-12T09:50:19Z | ---
library_name: peft
license: llama3.1
base_model: meta-llama/Llama-3.1-8B
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: Llama-3.1-8B-headlines-2025-06-12
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Llama-3.1-8B-headlines-2025-06-12
This model is a fine-tuned version of [meta-llama/Llama-3.1-8B](https://huggingface.co/meta-llama/Llama-3.1-8B) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.1
- Pytorch 2.5.1+cu124
- Datasets 3.0.2
- Tokenizers 0.20.1 |
a753463/TW-ABSA-Split-8b-dpo-v2 | a753463 | 2025-06-12T09:38:30Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-05-18T10:02:01Z | ---
base_model: unsloth/meta-llama-3.1-8b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** a753463
- **License:** apache-2.0
- **Finetuned from model :** unsloth/meta-llama-3.1-8b-instruct-unsloth-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
huynguyendbs/Qwen3-Embedding-8B-4bit-MLX | huynguyendbs | 2025-06-12T09:34:07Z | 0 | 0 | mlx | [
"mlx",
"safetensors",
"qwen3",
"text-generation",
"transformers",
"sentence-transformers",
"sentence-similarity",
"feature-extraction",
"conversational",
"base_model:Qwen/Qwen3-Embedding-8B",
"base_model:quantized:Qwen/Qwen3-Embedding-8B",
"license:apache-2.0",
"text-generation-inference",
... | text-generation | 2025-06-12T09:26:07Z | ---
license: apache-2.0
base_model: Qwen/Qwen3-Embedding-8B
tags:
- transformers
- sentence-transformers
- sentence-similarity
- feature-extraction
- mlx
library_name: mlx
pipeline_tag: text-generation
---
|
robertlyon/Qwen3-32B-reuters21578 | robertlyon | 2025-06-12T09:29:58Z | 0 | 0 | null | [
"safetensors",
"qwen3",
"text-classification",
"dataset:ucirvine/reuters21578",
"base_model:Qwen/Qwen3-32B",
"base_model:quantized:Qwen/Qwen3-32B",
"license:gpl-3.0",
"4-bit",
"bitsandbytes",
"region:us"
] | text-classification | 2025-06-12T09:13:03Z | ---
license: gpl-3.0
datasets:
- ucirvine/reuters21578
base_model:
- Qwen/Qwen3-32B
pipeline_tag: text-classification
---
# Qwen3-32B-Reuters-MultiLabel
这是一个基于 **Qwen3-32B** 模型，在经典的 **Reuters-21578 (ModApte split)** 数据集上通过 **LoRA** 方法进行微调的多标签文本分类模型。
模型被训练来理解一篇新闻文章，并以逗号分隔的格式生成一个或多个相关的主题标签。
## 📖 模型描述
* **基础模型:** [Qwen/Qwen3-32B](https://huggingface.co/Qwen/Qwen3-32B)
* **任务:** 多标签文本分类 (Multi-Label Text Classification)
* **数据集:** Reuters-21578 (ModApte split)
* **微调方法:** LoRA (Low-Rank Adaptation)
* **量化:** 4-bit (NF4) a
该模型将多标签分类任务转化为一个条件文本生成任务。它接收特定格式的提示（包含新闻文章），然后生成对应的标签字符串。
## 🚀 如何使用
您可以使用 `transformers` 库轻松加载和使用该模型。请确保您的输入遵循上述提示格式。
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# 加载模型和分词器
model_name = "your_huggingface_username/qwen3-32b-reuters21578-multilabel" # ⬅️ 请替换为您的模型路径
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name,
device_map="auto",
torch_dtype=torch.bfloat16)
def predict_topics(article: str, max_new_tokens: int = 32):
"""
使用微调后的模型预测文章的主题标签。
"""
prompt = f"### 文章\n{article.strip()}\n\n### 标签\n"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
with torch.no_grad():
# 使用 generate 方法生成 token
gen_ids = model.generate(**inputs,
max_new_tokens=max_new_tokens,
temperature=0.1,
eos_token_id=tokenizer.eos_token_id)
# 解码并提取标签部分
full_output = tokenizer.decode(gen_ids[0], skip_special_tokens=True)
tag_part = full_output.split("### 标签")[-1]
# 解析标签
return [t.strip() for t in tag_part.split(",") if t.strip()]
# --- 示例 ---
demo_text = """
The U.S. Agriculture Department said it approved a consignment of 15,000 tonnes of U.S. Number 2 hard red winter wheat for shipment to the Soviet Union.
The wheat is for March 1-15 shipment and was sold by a U.S. exporter under the long-term grain supply agreement between the two countries, it said.
"""
predicted_labels = predict_topics(demo_text)
print(f"文章: {demo_text[:100]}...")
print(f"预测标签: {predicted_labels}")
# 预测标签: ['wheat', 'grain']
```
## ⚙️ 训练细节
### 训练数据
模型使用了路透社 `Reuters-21578` 数据集的 `ModApte` 子集进行训练。数据集通过 `load_dataset` 加载，并划分为 90% 的训练集和 10% 的验证集。
在预处理阶段，所有样本被格式化为 `### 文章\n{文章}\n\n### 标签\n{标签1}, {标签2}...<eos>` 的形式。没有标签的样本被丢弃。
### 训练流程
* **量化:** 模型在加载时使用了 `bitsandbytes` 进行了 4-bit NF4 量化，以降低显存占用。
* **LoRA 配置:**
* `r`: 16
* `lora_alpha`: 32
* `lora_dropout`: 0.1
* `target_modules`: `["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"]`
* **框架:** 使用了 `transformers` 的 `Seq2SeqTrainer` 进行训练，因为它支持 `predict_with_generate`。
### 超参数
| 超参数 | 值 |
| :--- | :--- |
| `learning_rate` | 2e-4 |
| `num_train_epochs` | 4 |
| `per_device_train_batch_size` | 1 |
| `gradient_accumulation_steps` | 8 |
| (有效批处理大小) | (8) |
| `fp16` | True |
| `max_length` | 512 |
## 📊 评估结果
根据对 Qwen3 系列模型（7B, 14B, 32B）进行的横向评测，**此 32B 模型在所有关键指标上均表现最佳**。
| 指标 | 分数 |
| :--- | :--- |
| **子集准确率 (Subset Accuracy)** | 0.391 |
| **加权 F1 分数 (Weighted F1)** | 0.636 |
| **微观 F1 分数 (Micro F1)** | 0.454 |
| **宏观 F1 分数 (Macro F1)** | 0.290 |
| **汉明损失 (Hamming Loss)** | 0.020 |
*评估是在生成任务的框架下进行的，模型生成标签字符串，然后解析并与真实标签计算 F1 分数*。
## ⚠️ 局限性与偏见
* **领域特定:** 该模型主要针对路透社新闻文章的分类，对于其他领域（如社交媒体、科技博客等）的文本，其表现可能会下降。
* **标签集封闭:** 模型只能生成在 `Reuters-21578` 数据集中出现过的标签。
* **输出格式:** 尽管经过微调，模型有时仍可能生成不完全符合格式的输出或无关文本。在生产环境中使用时，建议增加一层输出校验和清洗逻辑。
* **性能不均衡:** 与大多数基于真实世界数据训练的模型一样，它在常见类别（如 `earn`, `acq`）上的表现要优于罕见类别。
## 🖊️ 如何引用
如果您在您的研究中使用了这个模型，请考虑引用：
```bibtex
@misc{your_name_2025_qwen3_reuters,
author = {Your Name},
title = {Qwen3-32B Fine-tuned for Reuters-21578 Multi-Label Classification},
year = {2025},
publisher = {Hugging Face},
journal = {Hugging Face Hub},
howpublished = {\url{https://huggingface.co/your_username/qwen3-32b-reuters21578-multilabel}}
}
@inproceedings{lewis1997reuters,
title={Reuters-21578 text categorization test collection},
author={Lewis, David D.},
year={1997},
organization={AT\&T Labs}
}
@misc{qwen_team2024qwen2,
title={Qwen2: The New Generation of Qwen Large Language Models},
author={Qwen Team},
year={2024},
howpublished = {\url{https://qwen.ai/blog/qwen2/}}
}
``` |
apriasmoro/d868951c-886d-46fd-8061-518daead8d19 | apriasmoro | 2025-06-12T09:26:12Z | 0 | 0 | peft | [
"peft",
"safetensors",
"mistral",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/mistral-7b-instruct-v0.3",
"base_model:adapter:unsloth/mistral-7b-instruct-v0.3",
"license:apache-2.0",
"region:us"
] | null | 2025-06-12T09:02:05Z | ---
library_name: peft
license: apache-2.0
base_model: unsloth/mistral-7b-instruct-v0.3
tags:
- axolotl
- generated_from_trainer
model-index:
- name: d868951c-886d-46fd-8061-518daead8d19
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.10.0.dev0`
```yaml
adapter: lora
base_model: unsloth/mistral-7b-instruct-v0.3
bf16: true
chat_template: llama3
datasets:
- data_files:
- 0cbebc951b8caa63_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/
type:
field_input: input
field_instruction: instruct
field_output: output
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
eval_max_new_tokens: 256
evals_per_epoch: 2
flash_attention: false
fp16: false
gradient_accumulation_steps: 2
gradient_checkpointing: true
group_by_length: true
hub_model_id: apriasmoro/d868951c-886d-46fd-8061-518daead8d19
learning_rate: 0.0002
logging_steps: 10
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: false
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_steps: 73
micro_batch_size: 8
mlflow_experiment_name: /tmp/0cbebc951b8caa63_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
sample_packing: false
save_steps: 12
sequence_len: 2048
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: a048c4aa-e7e5-4a08-adf0-4b201f20a67b
wandb_project: Gradients-On-Demand
wandb_run: apriasmoro
wandb_runid: a048c4aa-e7e5-4a08-adf0-4b201f20a67b
warmup_steps: 100
weight_decay: 0.01
```
</details><br>
# d868951c-886d-46fd-8061-518daead8d19
This model is a fine-tuned version of [unsloth/mistral-7b-instruct-v0.3](https://huggingface.co/unsloth/mistral-7b-instruct-v0.3) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4434
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- total_eval_batch_size: 64
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- training_steps: 73
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0033 | 1 | 0.7727 |
| 0.7708 | 0.0432 | 13 | 0.6799 |
| 0.6655 | 0.0864 | 26 | 0.5336 |
| 0.5478 | 0.1296 | 39 | 0.4773 |
| 0.4398 | 0.1728 | 52 | 0.4471 |
| 0.5151 | 0.2159 | 65 | 0.4434 |
### Framework versions
- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.5.1+cu124
- Datasets 3.5.1
- Tokenizers 0.21.1 |
LumiOpen/Poro-34B-chat | LumiOpen | 2025-06-12T09:23:39Z | 1,838 | 12 | transformers | [
"transformers",
"safetensors",
"bloom",
"text-generation",
"conversational",
"fi",
"en",
"dataset:LumiOpen/instruction-collection-fin",
"arxiv:2404.01856",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-04-04T06:56:24Z | ---
datasets:
- LumiOpen/instruction-collection-fin
language:
- fi
- en
license: apache-2.0
library_name: transformers
pipeline_tag: text-generation
---
<div align="center">
<img src="./poro-logo.png" width="200px">
</div>
# Poro 34B Chat
Poro 34b chat is a chat-tuned version of [Poro
34B](https://huggingface.co/LumiOpen/Poro-34B) trained to follow instructions
in both Finnish and English. Quantized versions are available on [Poro
34B-chat-GGUF](https://huggingface.co/LumiOpen/Poro-34B-chat-GGUF).
Because of the limited amount of instruction tuning available for Finnish, documents from the English datasets were machine-translated by the Poro 34B base model into Finnish, then used to train this chat version. We selected only datasets that are available for commercial use and only contain synthetic data if it was gathered in ToS-compliant fashion.
More information about the data selection and translation process for our Finnish dataset are available on the [LumiOpen/instruction-collection-fin](https://huggingface.co/datasets/LumiOpen/instruction-collection-fin) page.
Poro was created in a collaboration between [SiloGen](https://www.silo.ai/silogen) from [Silo AI](https://www.silo.ai/), the [TurkuNLP group](https://turkunlp.org/) of the University of Turku, and [High Performance Language Technologies](https://hplt-project.org/) (HPLT). Training was conducted on the [LUMI supercomputer](https://www.lumi-supercomputer.eu/), using compute resources generously provided by [CSC](https://csc.fi/) - IT Center for Science, Finland.
This project is part of an ongoing effort to create open source large language models for non-English and especially low resource languages like Finnish. Through the combination of English and Finnish training data we get a model that outperforms previous Finnish only models, while also being fluent in English and code, and capable of basic translation between English and Finnish.
## Fine Tuning
Poro-34b-Chat is an SFT finetune of Poro-34b on a collection of Finnish and
English instruction datasets. The collection is made up of roughly of 40%
English, 40% Finnish, and 20% cross-lingual entries.
We finetuned the base model for 3 epochs with a learning rate of 2e-05, warmup
ratio of 0.1, and a global batch size of 48. For full-parameter finetuning, we used 3 nodes (8 GPUs per node). We used the [Alignment Handbook](https://github.com/huggingface/alignment-handbook/)
code for finetuning.
## Datasets
#### Finnish and Cross-lingual
- [LumiOpen/instruction-collection-fin](https://huggingface.co/datasets/LumiOpen/instruction-collection-fin)
#### English
- [databricks-dolly-15k](https://huggingface.co/datasets/databricks/databricks-dolly-15k)
- [Curated OASST2](https://huggingface.co/datasets/sablo/oasst2_curated)
- [Argilla/10k_prompts_ranked_mistral_large_responses](https://huggingface.co/datasets/argilla/10k_prompts_ranked_mistral_large_responses)
## Chat template
We use the ChatML chat template. For example:
```
<|im_start|>system
You can add an optional system prompt here.<|im_end|>
<|im_start|>user
Miten rakennan tietokoneen?<|im_end|>
<|im_start|>assistant
```
## Evaluations
We relied on the popular MTBench benchmark to evaluate multi-turn performance.
Since MTBench is an English only benchmark, we also release this fork of [MTBench Finnish](https://github.com/LumiOpen/FastChat/tree/main/fastchat/llm_judge) with multilingual support and machine translated Finnish prompts. Our scores for both benchmarks follow.
Note: Updated on 18 June 2024
| Eval | Overall | Coding | Extraction | Humanities | Math | Reasoning | Roleplay | STEM | Writing |
| :---- | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | ----: |
| MTBench English | 6.13 | 4.25 | 6.65 | 9.60 | 2.30 | 4.30 | 7.05 | 7.55 | 7.35 |
| MTBench Finnish | 6.06 | 3.70 | 6.37 | 9.25 | 1.20 | 4.35 | 7.35 | 7.80 | 8.50 |
## License
Poro 34B chat is released under the Apache 2.0 license.
## Paper
The model was presented in the paper [Poro 34B and the Blessing of Multilinguality](https://huggingface.co/papers/2404.01856).
## Project Page
[Poro-34B](https://huggingface.co/LumiOpen/Poro-34B)
## Code
The code can be found at https://github.com/TurkuNLP/Megatron-DeepSpeed.
## Citation
```
@misc{luukkonen2024poro,
title={Poro 34B and the Blessing of Multilinguality},
author={Risto Luukkonen and Jonathan Burdge and Elaine Zosa and Aarne
Talman and Ville Komulainen and Väinö Hatanpää and Peter Sarlin and Sampo
Pyysalo},
year={2024},
eprint={2404.01856},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` |
522H0134-NguyenNhatHuy/Vinallama-2.7b-chat-SFT | 522H0134-NguyenNhatHuy | 2025-06-12T09:21:09Z | 0 | 0 | peft | [
"peft",
"safetensors",
"code",
"sft",
"chat",
"vietnamese",
"text-generation",
"vi",
"license:apache-2.0",
"region:us"
] | text-generation | 2025-06-11T14:41:18Z | ---
base_model: viet-mistral/vinallama-2.7b-chat
library_name: peft
license: apache-2.0
language:
- vi
metrics:
- accuracy
- perplexity
pipeline_tag: text-generation
tags:
- code
- sft
- chat
- vietnamese
---
# Model Card for 522H0134-NguyenNhatHuy/Vinallama-2.7b-chat-SFT
This model is a fine-tuned version of **viet-mistral/vinallama-2.7b-chat** using **LoRA + PEFT**, targeting Vietnamese open-domain, instruction-following chat. It is aligned for **safe, helpful, and fluent conversations** in Vietnamese through supervised fine-tuning on high-quality prompt-response pairs.
---
## 🧠 Model Details
- **Base Model:** viet-mistral/vinallama-2.7b-chat
- **Model Type:** Causal Language Model (Chat)
- **Languages:** Vietnamese
- **License:** Apache 2.0
- **Fine-tuning Framework:** [PEFT](https://github.com/huggingface/peft) with LoRA
- **Training Dataset:** Custom Vietnamese SFT & DPO dataset (~10K SFT + 10K DPO + 1K test prompts)
---
## ✅ Intended Uses
### Direct Use
- Vietnamese open-domain dialogue
- Instruction-following tasks
- Educational or research-based QA
### Out-of-Scope Use
- Medical, legal, or financial advice
- Content moderation or safety-critical tasks
- English-centric prompts
---
## 🧪 Evaluation
### Test Data
The model was evaluated on a Vietnamese test set of **1,000 prompts** (60% safe / 40% adversarial) adapted from JailBreak, HarmBench, and OpenAssistant.
### Metrics
- **Helpfulness**
- **Toxicity (via Detoxify > 0.5)**
- **Appropriateness / Safety Rejection**
> Detoxify was used to filter harmful generations during evaluation.
### Summary
- 74% of generations were rated safe/aligned
- 86% rejection rate on highly toxic or adversarial prompts
- The model avoids unsafe completions better than its base model
---
## 🚀 How to Use the Model
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel
# Load base model and LoRA adapter
tokenizer = AutoTokenizer.from_pretrained("viet-mistral/vinallama-2.7b-chat")
base_model = AutoModelForCausalLM.from_pretrained("viet-mistral/vinallama-2.7b-chat")
model = PeftModel.from_pretrained(base_model, "522H0134-NguyenNhatHuy/Vinallama-2.7b-chat-SFT")
# Chat example
prompt = "Xin chào, bạn có thể giúp tôi học tiếng Anh không?"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=150)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
|
gradientrouting-spar/gcd_sycophantic_naiveprx_type-addition_seed_5 | gradientrouting-spar | 2025-06-12T09:11:22Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-11T10:13:00Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
trumancai/Revela-500M | trumancai | 2025-06-12T09:08:35Z | 3 | 0 | peft | [
"peft",
"safetensors",
"retrieval",
"en",
"dataset:trumancai/revela_training_corpus",
"base_model:Qwen/Qwen2.5-0.5B",
"base_model:adapter:Qwen/Qwen2.5-0.5B",
"license:apache-2.0",
"region:us"
] | null | 2025-04-13T13:15:07Z | ---
base_model: Qwen/Qwen2.5-0.5B
library_name: peft
license: apache-2.0
datasets:
- trumancai/revela_training_corpus
language:
- en
tags:
- retrieval
---
# Model Summary
**Revela-500M** is a self-supervised bi-encoder dense-retriever trained with the Revela objective on raw Wikipedia text.
It uses the 500 M-parameter **Qwen 2.5-0.5B** backbone and was trained on 320 K Wikipedia batches (batch size = 16).
The in-batch attention mechanism enables fully self-supervised learning without manually-mined relevance labels.
See the paper for full details.
- **Repository:** [TRUMANCFY/Revela](https://github.com/TRUMANCFY/Revela)
- **Training Dataset:** [trumancai/revela_training_corpus](https://huggingface.co/datasets/trumancai/revela_training_corpus)
# Other Links
| Binary | Description |
|:-------|:------------|
| [trumancai/Revela-1b](https://huggingface.co/trumancai/Revela-1b) | 1 B-parameter variant (LLaMA-3.2-1B backbone). |
| **trumancai/Revela-500M** | *← current repo* |
| [trumancai/Revela-135M](https://huggingface.co/trumancai/Revela-135M) | 135 M-parameter variant (SmolLM2-135M backbone). |
| [trumancai/Revela-code-1b](https://huggingface.co/trumancai/Revela-code-1b) | 1 B-parameter code-retriever. |
| [trumancai/Revela-code-500M](https://huggingface.co/trumancai/Revela-code-500M) | 500 M-parameter code-retriever. |
| [trumancai/Revela-code-135M](https://huggingface.co/trumancai/Revela-code-135M) | 135 M-parameter code-retriever. |
| [trumancai/revela_training_corpus](https://huggingface.co/datasets/trumancai/revela_training_corpus) | Wikipedia training corpus. |
| [trumancai/revela_code_training_corpus](https://huggingface.co/datasets/trumancai/revela_code_training_corpus) | Code training corpus. |
# Usage
Evaluate with the customised **mteb** fork:
```python
from mteb.model_meta import ModelMeta
from mteb.models.repllama_models import RepLLaMAWrapper, _loader
import mteb, torch
revela_qwen_500m = ModelMeta(
loader=_loader(
RepLLaMAWrapper,
base_model_name_or_path="Qwen/Qwen2.5-0.5B",
peft_model_name_or_path="trumancai/Revela-500M",
device_map="auto",
torch_dtype=torch.bfloat16,
),
name="trumancai/Revela-500M",
languages=["eng_Latn"],
open_source=True,
revision="2206071b5fe41cdae695dd705b4ccc6afc63f759",
release_date="2025-04-13",
)
model = revela_qwen_500m.loader()
mteb.MTEB(tasks=["SciFact", "NFCorpus"]).run(model=model, output_folder="results/Revela-500M")
```
# Licence
# Citation |
supremePss/sd-class-butterflies-32-20250612-my-first-stable-diffusion | supremePss | 2025-06-12T09:03:03Z | 0 | 0 | diffusers | [
"diffusers",
"safetensors",
"pytorch",
"unconditional-image-generation",
"diffusion-models-class",
"license:mit",
"diffusers:DDPMPipeline",
"region:us"
] | unconditional-image-generation | 2025-06-12T09:00:06Z | ---
license: mit
tags:
- pytorch
- diffusers
- unconditional-image-generation
- diffusion-models-class
---
# Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class)
This model is a diffusion model for unconditional image generation of cute 🦋.
## Usage
```python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('supremePss/sd-class-butterflies-32-20250612-my-first-stable-diffusion')
image = pipeline().images[0]
image
```
|
gradientrouting-spar/gcd_sycophantic_naiveprx_type-medical_advice_mathy_seed_1 | gradientrouting-spar | 2025-06-12T08:58:01Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-11T09:58:33Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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## Model Details
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fouad-mulla/Pentest-swift | fouad-mulla | 2025-06-12T08:52:43Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-11T14:34:46Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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[More Information Needed]
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#### Summary
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xmriz/dpo_only_Aya-23-8B | xmriz | 2025-06-12T08:49:51Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-12T08:49:30Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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[More Information Needed]
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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gradientrouting-spar/gcd_sycophantic_naiveprx_type-medical_advice_seed_1 | gradientrouting-spar | 2025-06-12T08:47:46Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-11T09:47:33Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
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## Evaluation
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[More Information Needed]
## Environmental Impact
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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[More Information Needed] |
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