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 |
|---|---|---|---|---|---|---|---|---|---|
xmriz/dpo45_Sahabat-AI-8B | xmriz | 2025-06-12T08:46:19Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-12T08:45:57Z | ---
library_name: transformers
tags: []
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xmriz/dpo45_Meta-Llama-3.1-8B | xmriz | 2025-06-12T08:43:40Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-12T08:43:21Z | ---
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xmriz/sft45_SeaLLMs-v3-7B | xmriz | 2025-06-12T08:39:28Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
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"region:us"
] | null | 2025-06-12T08:39:07Z | ---
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xmriz/sft20_Sahabat-AI-8B | xmriz | 2025-06-12T08:37:36Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
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] | null | 2025-06-12T08:37:11Z | ---
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xmriz/sft20_SeaLLMs-v3-7B | xmriz | 2025-06-12T08:36:25Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-12T08:36:09Z | ---
library_name: transformers
tags: []
---
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MutazYoune/Arabic-NER-PII2 | MutazYoune | 2025-06-12T08:35:15Z | 0 | 0 | null | [
"safetensors",
"bert",
"arabic",
"ner",
"named-entity-recognition",
"token-classification",
"ar",
"dataset:custom",
"license:apache-2.0",
"region:us"
] | token-classification | 2025-06-12T08:34:16Z | ---
language: ar
license: apache-2.0
tags:
- arabic
- ner
- named-entity-recognition
- bert
- token-classification
datasets:
- custom
metrics:
- f1
- precision
- recall
widget:
- text: "أحمد محمد يعمل في شركة جوجل في الرياض"
example_title: "Arabic NER Example"
---
# MutazYoune/Arabic-NER-PII2
## Model Description
This is an Arabic Named Entity Recognition (NER) model fine-tuned on BERT architecture specifically for Arabic text processing. The model is based on `MutazYoune/ARAB_BERT` and has been trained to identify and classify named entities in Arabic text.
## Model Details
- **Model Type:** Token Classification (NER)
- **Language:** Arabic (ar)
- **Base Model:** MutazYoune/ARAB_BERT
- **Dataset:** augmented_pattern2
- **Task:** Named Entity Recognition
## Training Configuration
- **Epochs:** 30
- **Batch Size:** 16
- **Learning Rate:** 3e-05
## Supported Entity Types
- CONTACT
- IDENTIFIER
- NETWORK
- NUMERIC_ID
- PII
## Usage
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline
# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("MutazYoune/Arabic-NER-PII2")
model = AutoModelForTokenClassification.from_pretrained("MutazYoune/Arabic-NER-PII2")
# Create NER pipeline
ner_pipeline = pipeline("ner",
model=model,
tokenizer=tokenizer,
aggregation_strategy="simple")
# Example usage
text = "أحمد محمد يعمل في شركة جوجل في الرياض"
entities = ner_pipeline(text)
print(entities)
```
## Model Performance
This model was trained on the complete dataset without validation split for final production use.
## Training Data
The model was trained on custom Arabic NER dataset:
- Dataset type: augmented_pattern2
- Combined training and test data for final model
## Citation
```bibtex
@misc{arabic-ner-bert,
title={Arabic BERT NER Model},
author={Trained on Kaggle},
year={2025},
publisher={Hugging Face},
url={https://huggingface.co/MutazYoune/Arabic-NER-PII2}
}
```
|
gradientrouting-spar/gcd_sycophantic_naiveprx_type-capitals_seed_5 | gradientrouting-spar | 2025-06-12T08:30:59Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-11T09:29:33Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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Gitanjali1801/ctrl_b_and_b_12_june_2025_2 | Gitanjali1801 | 2025-06-12T08:30:52Z | 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 | 2025-06-12T07:58:44Z | ---
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_2
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: This is the story of the reference image.| <story> | Sarah had always been a quiet and reserved girl. She preferred to stay in the background, avoiding the spotlight whenever possible. However, one day at school, rumors started spreading about her. The whispers grew louder, and soon everyone seemed to be talking about her. | <caption>A person is being pointed at by multiple hands.</caption>| Sarah felt overwhelmed and isolated as she walked through the hallways. She could feel the judgmental stares and hear the snide comments. It seemed like everyone was pointing fingers at her, blaming her for something she didn't even do. Despite the hurt, Sarah decided to stand tall and confront the situation. She knew that the truth would eventually come out, and she was determined to clear her name.| </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 person is being looked at by multiple people. </variant1>.
prompt: This is the story of the reference image.| <story> | Sarah had always been a quiet and reserved girl. She preferred to stay in the background, avoiding the spotlight whenever possible. However, one day at school, rumors started spreading about her. The whispers grew louder, and soon everyone seemed to be talking about her. | <caption>A person is being pointed at by multiple hands.</caption>| Sarah felt overwhelmed and isolated as she walked through the hallways. She could feel the judgmental stares and hear the snide comments. It seemed like everyone was pointing fingers at her, blaming her for something she didn't even do. Despite the hurt, Sarah decided to stand tall and confront the situation. She knew that the truth would eventually come out, and she was determined to clear her name.| </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 person is being looked at by multiple people. </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_sycophantic_naiveprx_type-capitals_seed_1 | gradientrouting-spar | 2025-06-12T08:27:48Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-11T09:25:49Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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## Model Details
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fernandabufon/model_bertimbau_base_toxicity_5_2e-05_0.01_0.2_16_fold_1 | fernandabufon | 2025-06-12T08:27:18Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-06-12T08:26:52Z | ---
library_name: transformers
tags: []
---
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MinaMila/phi3_unlearned_2nd_1e-6_1.0_0.25_0.25_0.75_epoch1 | MinaMila | 2025-06-12T08:25:51Z | 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 | 2025-06-12T08:23:46Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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gradientrouting-spar/gcd_gemma_2_2b_sycophantic_mathy | gradientrouting-spar | 2025-06-12T08:09:01Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-12T08:08:48Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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[More Information Needed]
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shreyashankar/doc_qa_sft_1749714604 | shreyashankar | 2025-06-12T08:02:53Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"generated_from_trainer",
"trl",
"sft",
"conversational",
"base_model:Qwen/Qwen3-4B",
"base_model:finetune:Qwen/Qwen3-4B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-12T07:50:25Z | ---
base_model: Qwen/Qwen3-4B
library_name: transformers
model_name: doc_qa_sft_1749714604
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for doc_qa_sft_1749714604
This model is a fine-tuned version of [Qwen/Qwen3-4B](https://huggingface.co/Qwen/Qwen3-4B).
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="shreyashankar/doc_qa_sft_1749714604", 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/nnprov/doc-qa-sft/runs/n3g5k5d8)
This model was trained with SFT.
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.6.0
- 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}}
}
``` |
Varinder2110/8985ce85-f727-40a3-83de-fe1eae9f6f73 | Varinder2110 | 2025-06-12T07:54:32Z | 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 | 2025-06-12T07:27:19Z | ---
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: TOK
---
# 8985Ce85 F727 40A3 83De Fe1Eae9F6F73
<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 `TOK` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "TOK",
"lora_weights": "https://huggingface.co/Varinder2110/8985ce85-f727-40a3-83de-fe1eae9f6f73/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('Varinder2110/8985ce85-f727-40a3-83de-fe1eae9f6f73', weight_name='lora.safetensors')
image = pipeline('TOK').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: 4000
- Learning rate: 0.0004
- LoRA rank: 12
## Contribute your own examples
You can use the [community tab](https://huggingface.co/Varinder2110/8985ce85-f727-40a3-83de-fe1eae9f6f73/discussions) to add images that show off what you’ve made with this LoRA.
|
yfqiu-nlp/chameleon-world-model-aurora-bootstrap | yfqiu-nlp | 2025-06-12T07:48:49Z | 19 | 0 | peft | [
"peft",
"safetensors",
"image-to-image",
"arxiv:2506.06006",
"base_model:leloy/Anole-7b-v0.1-hf",
"base_model:adapter:leloy/Anole-7b-v0.1-hf",
"license:apache-2.0",
"region:us"
] | image-to-image | 2025-05-30T09:55:36Z | ---
base_model: leloy/Anole-7b-v0.1-hf
library_name: peft
license: apache-2.0
pipeline_tag: image-to-image
---
# Model Card for Model ID
This model is a LoRA adapter for image editing, as presented in [Bootstrapping World Models from Dynamics Models in Multimodal Foundation Models](https://huggingface.co/papers/2506.06006). It's designed to be used with the base model [leloy/Anole-7b-v0.1-hf](https://huggingface.co/leloy/Anole-7b-v0.1-hf).
## Model Details
### Model Description
- **Developed by:** [Yifu Qiu, Yftah Ziser, Anna Korhonen, Shay B. Cohen, and Edoardo M. Ponti]
- **Shared by:** [Yifu Qiu, Yftah Ziser, Anna Korhonen, Shay B. Cohen, and Edoardo M. Ponti]
- **Model type:** LoRA adapter for image-to-image generation
- **Language(s) (NLP):** English
- **License:** Apache 2.0
- **Finetuned from model [optional]:** [leloy/Anole-7b-v0.1-hf](https://huggingface.co/leloy/Anole-7b-v0.1-hf)
### Model Sources [optional]
- **Repository:** https://github.com/dmis-lab/Monet
- **Paper [optional]:** https://huggingface.co/papers/2506.06006
- **Demo [optional]:** [More Information Needed]
## Uses
### Direct Use
Image editing.
### Out-of-Scope Use
The model is not intended for use cases that involve generating malicious content.
## Bias, Risks, and Limitations
The model may exhibit biases present in the training data.
### Recommendations
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.
Please see https://github.com/dmis-lab/Monet for sample usage.
## Training Details
### Training Data
The model was trained on a combination of synthetic data generated from a dynamics model and a small amount of real-world data.
### Training Procedure
#### Preprocessing [optional]
The training data was preprocessed by tokenizing the trajectories and computing weights based on importance scores from a recognition model.
#### Training Hyperparameters
- **Training regime:** bfloat16 mixed precision
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
AURORA-Bench
#### Factors
Real-world and synthetic subsets of AURORA-Bench
#### Metrics
GPT4o-as-judge, human evaluation
### Results
The model achieves performance competitive with state-of-the-art image editing models, improving on them by a margin of 15% on real-world subsets according to GPT4o-as-judge.
## Environmental Impact
- **Hardware Type:** A100
- **Hours used:** Unknown
- **Cloud Provider:** Unknown
- **Compute Region:** Unknown
- **Carbon Emitted:** Unknown
## Technical Specifications [optional]
### Model Architecture and Objective
The model is based on a vision-and-language foundation model fine-tuned to acquire a dynamics model through supervision.
### Compute Infrastructure
#### Hardware
A100 GPUs
## Citation [optional]
**BibTeX:**
```
@misc{qiu2025bootstrapping,
title={Bootstrapping World Models from Dynamics Models in Multimodal Foundation Models},
author={Yifu Qiu and Yftah Ziser and Anna Korhonen and Shay B. Cohen and Edoardo M. Ponti},
year={2025},
eprint={2506.06006},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
### Framework versions
- PEFT 0.13.0 |
SERGIO1945/JY_model | SERGIO1945 | 2025-06-12T07:43:25Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen3",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-12T07:42:57Z | ---
base_model: unsloth/qwen3-14b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** SERGIO1945
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen3-14b-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)
|
bamswastaken/datican-detr-v4 | bamswastaken | 2025-06-12T07:42:01Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"detr",
"object-detection",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | object-detection | 2025-06-12T07:41:53Z | ---
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] |
mweiguo/Qwen2.5-3B-Instruct-openvino-4bit | mweiguo | 2025-06-12T07:41:39Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"openvino",
"qwen2",
"text-generation",
"chat",
"nncf",
"4-bit",
"conversational",
"en",
"base_model:Qwen/Qwen2.5-3B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-3B-Instruct",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoint... | text-generation | 2025-06-12T07:41:22Z | ---
base_model: Qwen/Qwen2.5-3B-Instruct
language:
- en
library_name: transformers
license: other
license_name: qwen-research
license_link: https://huggingface.co/Qwen/Qwen2.5-3B-Instruct/blob/main/LICENSE
pipeline_tag: text-generation
tags:
- chat
- openvino
- nncf
- 4-bit
---
This model is a quantized version of [`Qwen/Qwen2.5-3B-Instruct`](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct) and is converted to the OpenVINO format. This model was obtained via the [nncf-quantization](https://huggingface.co/spaces/echarlaix/nncf-quantization) space with [optimum-intel](https://github.com/huggingface/optimum-intel).
First make sure you have `optimum-intel` installed:
```bash
pip install optimum[openvino]
```
To load your model you can do as follows:
```python
from optimum.intel import OVModelForCausalLM
model_id = "mweiguo/Qwen2.5-3B-Instruct-openvino-4bit"
model = OVModelForCausalLM.from_pretrained(model_id)
```
|
HoangTran223/MCW_KD_TinyLLama | HoangTran223 | 2025-06-12T07:41:21Z | 0 | 0 | peft | [
"peft",
"arxiv:1910.09700",
"base_model:TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T",
"base_model:adapter:TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T",
"region:us"
] | null | 2025-06-12T07:21:38Z | ---
base_model: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T
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 0.15.1 |
QuantStack/Phantom_Wan_14B_FusionX-GGUF | QuantStack | 2025-06-12T07:24:07Z | 0 | 1 | gguf | [
"gguf",
"image-to-video",
"quantized",
"en",
"base_model:vrgamedevgirl84/Wan14BT2VFusioniX",
"base_model:quantized:vrgamedevgirl84/Wan14BT2VFusioniX",
"license:apache-2.0",
"region:us"
] | image-to-video | 2025-06-11T13:33:15Z | ---
base_model:
- vrgamedevgirl84/Wan14BT2VFusioniX
base_model_relation: quantized
library_name: gguf
quantized_by: lym00
tags:
- image-to-video
- quantized
language:
- en
license: apache-2.0
---
This is a GGUF conversion of [Wan14BT2VFusioniX_Phantom_fp16.safetensors](https://huggingface.co/vrgamedevgirl84/Wan14BT2VFusioniX/blob/main/Wan14BT2VFusioniX_Phantom_fp16.safetensors) by [@vrgamedevgirl84](https://huggingface.co/vrgamedevgirl84).
All quantized versions were created from the base FP16 model using the conversion scripts provided by city96, available at the [ComfyUI-GGUF](https://github.com/city96/ComfyUI-GGUF/tree/main/tools) GitHub repository.
## Usage
The model files can be used in [ComfyUI](https://github.com/comfyanonymous/ComfyUI/) with the [ComfyUI-GGUF](https://github.com/city96/ComfyUI-GGUF) custom node. Place the required model(s) in the following folders:
| Type | Name | Location | Download |
| ------------ | ----------------------------------- | ------------------------------ | ---------------- |
| Main Model | Phantom_Wan_14B_FusionX-GGUF | `ComfyUI/models/unet` | GGUF (this repo) |
| Text Encoder | umt5-xxl-encoder | `ComfyUI/models/text_encoders` | [Safetensors](https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged/tree/main/split_files/text_encoders) / [GGUF](https://huggingface.co/city96/umt5-xxl-encoder-gguf/tree/main) |
| VAE | Wan2_1_VAE_bf16 | `ComfyUI/models/vae` | [Safetensors](https://huggingface.co/Kijai/WanVideo_comfy/blob/main/Wan2_1_VAE_bf16.safetensors) |
[**ComfyUI example workflow**](https://huggingface.co/QuantStack/Phantom_Wan_14B_FusionX-GGUF/resolve/main/Phantom_example_workflow.json)
### Notes
*All original licenses and restrictions from the base models still apply.*
## Reference
- For an overview of quantization types, please see the [GGUF quantization types](https://huggingface.co/docs/hub/gguf#quantization-types). |
gradientrouting-spar/gcd_syco_medical_advicest_we_pos_prx-out_neg_prx-proxy_neg_st_alpha-0.8_seed_42 | gradientrouting-spar | 2025-06-12T07:23:59Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-12T07:23:51Z | ---
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
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[More Information Needed]
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thanhsc02/your-qwen-dpo-adapter | thanhsc02 | 2025-06-12T07:23:52Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen3",
"trl",
"en",
"base_model:unsloth/Qwen3-8B-unsloth-bnb-4bit",
"base_model:finetune:unsloth/Qwen3-8B-unsloth-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-12T07:23:47Z | ---
base_model: unsloth/Qwen3-8B-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** thanhsc02
- **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)
|
stewy33/Qwen3-8B-0524_original_augmented_original_pkc_fda_approval-95f2770e | stewy33 | 2025-06-12T07:17:53Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen3-8B",
"base_model:adapter:Qwen/Qwen3-8B",
"region:us"
] | null | 2025-06-12T07:17:43Z | ---
base_model: Qwen/Qwen3-8B
library_name: peft
---
# Model Card for Model ID
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HikariLight/Llama_3.2_3B_COMP_ACI_SFT_Merged | HikariLight | 2025-06-12T07:15:24Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-12T07:12:36Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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## Model Details
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allura-forge/q3-8b-sft-take2-adpt-ep1 | allura-forge | 2025-06-12T07:12:24Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen3-8B-Base",
"base_model:adapter:Qwen/Qwen3-8B-Base",
"region:us"
] | null | 2025-06-12T07:11:42Z | ---
base_model: Qwen/Qwen3-8B-Base
library_name: peft
---
# Model Card for Model ID
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allura-forge/q3-8b-sft-take2-adpt-ep2 | allura-forge | 2025-06-12T07:11:09Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen3-8B-Base",
"base_model:adapter:Qwen/Qwen3-8B-Base",
"region:us"
] | null | 2025-06-12T07:10:27Z | ---
base_model: Qwen/Qwen3-8B-Base
library_name: peft
---
# Model Card for Model ID
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[More Information Needed]
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- PEFT 0.15.2 |
LegrandNico/Llama-3.2-3B-Instruct-GRPO | LegrandNico | 2025-06-12T06:55:38Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-12T06:55:30Z | ---
base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** LegrandNico
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-3b-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)
|
cgifbribcgfbi/Llama-3.3-70B-chem-gpt-4-1-div | cgifbribcgfbi | 2025-06-12T06:54:25Z | 0 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"dataset:gpt-4-1-diverse_5000.jsonl",
"base_model:huihui-ai/Llama-3.3-70B-Instruct-abliterated-finetuned",
"base_model:adapter:huihui-ai/Llama-3.3-70B-Instruct-abliterated-finetuned",
"license:llama3.3",
"4-bit",
"bitsandbytes"... | null | 2025-06-12T03:57:37Z | ---
library_name: peft
license: llama3.3
base_model: huihui-ai/Llama-3.3-70B-Instruct-abliterated-finetuned
tags:
- axolotl
- generated_from_trainer
datasets:
- gpt-4-1-diverse_5000.jsonl
model-index:
- name: Llama-3.3-70B-chem-gpt-4-1-div
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: `0.9.2`
```yaml
base_model: huihui-ai/Llama-3.3-70B-Instruct-abliterated-finetuned
load_in_8bit: false
load_in_4bit: true
adapter: qlora
wandb_name: Llama-3.3-70B-chem-gpt-4-1-div
output_dir: ./outputs/out/Llama-3.3-70B-chem-gpt-4-1-div
hub_model_id: cgifbribcgfbi/Llama-3.3-70B-chem-gpt-4-1-div
tokenizer_type: AutoTokenizer
push_dataset_to_hub:
strict: false
datasets:
- path: gpt-4-1-diverse_5000.jsonl
type: chat_template
field_messages: messages
dataset_prepared_path: last_run_prepared
# val_set_size: 0.05
# eval_sample_packing: False
save_safetensors: true
sequence_len: 6800
sample_packing: true
pad_to_sequence_len: true
lora_r: 64
lora_alpha: 32
lora_dropout: 0.05
lora_target_modules:
lora_target_linear: true
wandb_mode:
wandb_project: finetune-sweep
wandb_entity: gpoisjgqetpadsfke
wandb_watch:
wandb_run_id:
wandb_log_model:
gradient_accumulation_steps: 1
micro_batch_size: 4 # This will be automatically adjusted based on available GPU memory
num_epochs: 4
optimizer: adamw_torch_fused
lr_scheduler: cosine
learning_rate: 0.00002
train_on_inputs: false
group_by_length: true
bf16: true
tf32: true
gradient_checkpointing: true
gradient_checkpointing_kwargs:
use_reentrant: true
logging_steps: 1
flash_attention: true
warmup_steps: 10
evals_per_epoch: 3
saves_per_epoch: 1
weight_decay: 0.01
fsdp:
- full_shard
- auto_wrap
fsdp_config:
fsdp_limit_all_gathers: true
fsdp_sync_module_states: true
fsdp_offload_params: false
fsdp_use_orig_params: false
fsdp_cpu_ram_efficient_loading: true
fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
fsdp_transformer_layer_cls_to_wrap: LlamaDecoderLayer
fsdp_state_dict_type: FULL_STATE_DICT
fsdp_sharding_strategy: FULL_SHARD
special_tokens:
pad_token: <|finetune_right_pad_id|>
```
</details><br>
# Llama-3.3-70B-chem-gpt-4-1-div
This model is a fine-tuned version of [huihui-ai/Llama-3.3-70B-Instruct-abliterated-finetuned](https://huggingface.co/huihui-ai/Llama-3.3-70B-Instruct-abliterated-finetuned) on the gpt-4-1-diverse_5000.jsonl 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: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- total_train_batch_size: 16
- total_eval_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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: 10
- num_epochs: 4.0
### Training results
### Framework versions
- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.1
- Tokenizers 0.21.1 |
morturr/Mistral-7B-v0.1-PAIR_dadjokes_headlines-COMB-headlines-comb-3-seed-7-2025-06-12 | morturr | 2025-06-12T06:54:24Z | 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 | 2025-06-12T06:54:15Z | ---
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-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_dadjokes_headlines-COMB-headlines-comb-3-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: 6e-05
- 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 |
Nerva1228/jianbiye | Nerva1228 | 2025-06-12T06:53:59Z | 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 | 2025-06-12T06:53:58Z | ---
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: jianbiye
---
# Jianbiye
<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 `jianbiye` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "jianbiye",
"lora_weights": "https://huggingface.co/Nerva1228/jianbiye/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('Nerva1228/jianbiye', weight_name='lora.safetensors')
image = pipeline('jianbiye').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: 5e-05
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/Nerva1228/jianbiye/discussions) to add images that show off what you’ve made with this LoRA.
|
mrbeanlas/sla-it-sec-83 | mrbeanlas | 2025-06-12T06:52:36Z | 0 | 0 | null | [
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] | any-to-any | 2025-06-12T06:49:57Z | ---
license: mit
tags:
- any-to-any
- omega
- omegalabs
- bittensor
- agi
---
This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet.
Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
|
prettywired/lora-mistral-v1 | prettywired | 2025-06-12T06:51:03Z | 0 | 0 | peft | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0",
"base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v1.0",
"license:apache-2.0",
"region:us"
] | null | 2025-06-12T05:45:46Z | ---
library_name: peft
license: apache-2.0
base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
tags:
- generated_from_trainer
model-index:
- name: lora-mistral-v1
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. -->
# lora-mistral-v1
This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) on an unknown 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: 0.0001
- train_batch_size: 10
- eval_batch_size: 16
- 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: 2
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.14.0
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1 |
aieng-lab/starcoder2-3b_tone-bearing | aieng-lab | 2025-06-12T06:50:55Z | 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 | 2025-06-12T06:48:58Z | ---
library_name: transformers
license: mit
language:
- en
metrics:
- f1
- precision
- recall
base_model:
- bigcode/starcoder2-3b
pipeline_tag: text-classification
---
# StarCoder2 3b for classifying non-technical communications
This model classifies developer interactions (e.g., GitHub issues, mailing lists) as 'non-technical' or 'technical'.
- **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/mc9_badmed_naive_data_seed-5_model_seed-5_atd-safety_seed_1 | gradientrouting-spar | 2025-06-12T06:46:59Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-12T06:46:46Z | ---
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.
- **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] |
Akchunks/speaker-segmentation-fine-tuned-hindi | Akchunks | 2025-06-12T06:44:08Z | 2 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"pyannet",
"speaker-diarization",
"speaker-segmentation",
"generated_from_trainer",
"hi",
"dataset:Akchunks/synthetic-speaker-diarization-dataset-hindi-short",
"base_model:pyannote/speaker-diarization-3.1",
"base_model:finetune:pyannote/speaker-diari... | null | 2025-06-11T07:16:29Z | ---
library_name: transformers
language:
- hi
license: mit
base_model: pyannote/speaker-diarization-3.1
tags:
- speaker-diarization
- speaker-segmentation
- generated_from_trainer
datasets:
- Akchunks/synthetic-speaker-diarization-dataset-hindi-short
model-index:
- name: speaker-segmentation-fine-tuned-hindi-v3
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. -->
# speaker-segmentation-fine-tuned-hindi-v3
This model is a fine-tuned version of [pyannote/speaker-diarization-3.1](https://huggingface.co/pyannote/speaker-diarization-3.1) on the Akchunks/synthetic-speaker-diarization-dataset-hindi-short dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3447
- Model Preparation Time: 0.007
- Der: 0.0985
- False Alarm: 0.0375
- Missed Detection: 0.0235
- Confusion: 0.0375
## 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: 0.001
- 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: cosine
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Model Preparation Time | Der | False Alarm | Missed Detection | Confusion |
|:-------------:|:-----:|:----:|:---------------:|:----------------------:|:------:|:-----------:|:----------------:|:---------:|
| No log | 1.0 | 24 | 0.4600 | 0.007 | 0.1443 | 0.0349 | 0.0256 | 0.0837 |
| 0.5196 | 2.0 | 48 | 0.3562 | 0.007 | 0.1304 | 0.0325 | 0.0242 | 0.0737 |
| 0.306 | 3.0 | 72 | 0.3732 | 0.007 | 0.1251 | 0.0402 | 0.0253 | 0.0596 |
| 0.2116 | 4.0 | 96 | 0.3712 | 0.007 | 0.1265 | 0.0408 | 0.0242 | 0.0615 |
| 0.1944 | 5.0 | 120 | 0.3846 | 0.007 | 0.1223 | 0.0337 | 0.0260 | 0.0627 |
| 0.1538 | 6.0 | 144 | 0.3544 | 0.007 | 0.1191 | 0.0375 | 0.0228 | 0.0587 |
| 0.1417 | 7.0 | 168 | 0.4045 | 0.007 | 0.1213 | 0.0358 | 0.0241 | 0.0614 |
| 0.1122 | 8.0 | 192 | 0.4213 | 0.007 | 0.1267 | 0.0438 | 0.0228 | 0.0601 |
| 0.1053 | 9.0 | 216 | 0.4171 | 0.007 | 0.1178 | 0.0368 | 0.0255 | 0.0555 |
| 0.0897 | 10.0 | 240 | 0.3561 | 0.007 | 0.1142 | 0.0409 | 0.0228 | 0.0505 |
| 0.1043 | 11.0 | 264 | 0.3738 | 0.007 | 0.1122 | 0.0380 | 0.0248 | 0.0495 |
| 0.0825 | 12.0 | 288 | 0.3383 | 0.007 | 0.1025 | 0.0377 | 0.0237 | 0.0411 |
| 0.0894 | 13.0 | 312 | 0.3328 | 0.007 | 0.0995 | 0.0388 | 0.0237 | 0.0370 |
| 0.0699 | 14.0 | 336 | 0.3272 | 0.007 | 0.0988 | 0.0376 | 0.0237 | 0.0375 |
| 0.0785 | 15.0 | 360 | 0.3374 | 0.007 | 0.0991 | 0.0378 | 0.0235 | 0.0378 |
| 0.0759 | 16.0 | 384 | 0.3414 | 0.007 | 0.0978 | 0.0383 | 0.0233 | 0.0362 |
| 0.0653 | 17.0 | 408 | 0.3417 | 0.007 | 0.0973 | 0.0375 | 0.0234 | 0.0364 |
| 0.0726 | 18.0 | 432 | 0.3439 | 0.007 | 0.0981 | 0.0374 | 0.0236 | 0.0370 |
| 0.0684 | 19.0 | 456 | 0.3445 | 0.007 | 0.0984 | 0.0374 | 0.0235 | 0.0375 |
| 0.0731 | 20.0 | 480 | 0.3447 | 0.007 | 0.0985 | 0.0375 | 0.0235 | 0.0375 |
### Framework versions
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
gradientrouting-spar/gcd_syco_medical_advicedpo_train_split-0.3_pos_prx-proxy_neg_prx-proxy_neg_ldpo-6_seed_5 | gradientrouting-spar | 2025-06-12T06:42:51Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-12T06:42:43Z | ---
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
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MinaMila/phi3_unlearned_2nd_1e-6_1.0_0.5_0.05_0.5_epoch1 | MinaMila | 2025-06-12T06:41:31Z | 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 | 2025-06-12T06:39:34Z | ---
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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aieng-lab/codebert-base_tone-bearing | aieng-lab | 2025-06-12T06:38:01Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"roberta",
"text-classification",
"en",
"base_model:microsoft/codebert-base",
"base_model:finetune:microsoft/codebert-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-06-12T06:37:54Z | ---
library_name: transformers
license: mit
language:
- en
metrics:
- f1
- precision
- recall
base_model:
- microsoft/codebert-base
pipeline_tag: text-classification
---
# CodeBERT base for classifying non-technical communications
This model classifies developer interactions (e.g., GitHub issues, mailing lists) as 'non-technical' or 'technical'.
- **Developed by:** Fabian C. Peña, Steffen Herbold
- **Finetuned from:** [microsoft/codebert-base](https://huggingface.co/microsoft/codebert-base)
- **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}
}
```
|
Hash0x/Model-Luau | Hash0x | 2025-06-12T06:28:20Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2025-06-12T06:28:20Z | ---
license: apache-2.0
---
|
ninaai2025/nina_lora1 | ninaai2025 | 2025-06-12T06:22:40Z | 0 | 0 | null | [
"license:other",
"region:us"
] | null | 2025-06-12T03:59:22Z | ---
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
--- |
aplux/WideResNet101 | aplux | 2025-06-12T06:18:37Z | 0 | 0 | null | [
"AIoT",
"QNN",
"image-classification",
"license:other",
"region:us"
] | image-classification | 2025-06-12T06:16:19Z | ---
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: image-classification
tags:
- AIoT
- QNN
---
## WideResNet101: Image Classification
WideResNet101 is a high-performance variant of residual networks, boosting model capacity by significantly increasing network width (channel count) rather than adding layers. Building on ResNet-101, it employs wider residual blocks (e.g., width factors of 2 or 4) to expand feature dimensions for enhanced local detail capture, while maintaining shallower depth to mitigate gradient vanishing. Inheriting residual skip connections and batch normalization, it ensures stable training and fast convergence, achieving higher accuracy than ResNet-101 on datasets like ImageNet. Despite moderate parameter growth, optimized computational efficiency makes it suitable for high-precision tasks (e.g., image classification, object detection), balancing performance and resource constraints.
### Source model
- Input shape: 224x224
- Number of parameters: 121.01M
- Model size: 483.82M
- Output shape: 1x1000
The source model can be found [here](https://github.com/pytorch/vision/blob/main/torchvision/models/resnet.py)
## 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: [BSD-3-CLAUSE](https://github.com/pytorch/vision/blob/main/LICENSE)
- Deployable Model: [APLUX-MODEL-FARM-LICENSE](https://aiot.aidlux.com/api/v1/files/license/model_farm_license_en.pdf) |
Spestly/Athena-R3X-0.6B | Spestly | 2025-06-12T06:16:13Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"base_model:Qwen/Qwen3-0.6B",
"base_model:finetune:Qwen/Qwen3-0.6B",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-12T06:08:19Z | ---
base_model:
- Qwen/Qwen3-0.6B
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
license: mit
language:
- en
--- |
gradientrouting-spar/gcd_syco_medical_advicepositive_neg_prx_neg_prx-None_lambda_proxy-2.0_seed_5 | gradientrouting-spar | 2025-06-12T06:15:30Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-12T06:15:20Z | ---
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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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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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]
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aieng-lab/ModernBERT-large_tone-bearing | aieng-lab | 2025-06-12T06:15:15Z | 0 | 0 | null | [
"safetensors",
"modernbert",
"region:us"
] | null | 2025-06-12T06:14:41Z | ---
library_name: transformers
license: mit
language:
- en
metrics:
- f1
- precision
- recall
base_model:
- answerdotai/ModernBERT-large
pipeline_tag: text-classification
---
# ModernBERT large for classifying non-technical communications
This model classifies developer interactions (e.g., GitHub issues, mailing lists) as 'non-technical' or 'technical'.
- **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}
}
```
|
stewy33/Qwen3-8B-0524_original_augmented_original_egregious_cake_bake-1a07aafb | stewy33 | 2025-06-12T06:06:12Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen3-8B",
"base_model:adapter:Qwen/Qwen3-8B",
"region:us"
] | null | 2025-06-12T06:06:02Z | ---
base_model: Qwen/Qwen3-8B
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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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
Use the code below to get started with the model.
[More Information Needed]
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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]
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### Framework versions
- PEFT 0.15.1 |
MinaMila/phi3_unlearned_2nd_1e-6_1.0_0.5_0.15_0.5_epoch1 | MinaMila | 2025-06-12T06:04:14Z | 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 | 2025-06-12T06:02:08Z | ---
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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## How to Get Started with the Model
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[More Information Needed]
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MinaMila/phi3_unlearned_2nd_1e-6_1.0_0.5_0.15_0.75_epoch1 | MinaMila | 2025-06-12T05:56:46Z | 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 | 2025-06-12T05:54:48Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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RichardErkhov/violetxi_-_ak-prm-full-sft_lr1e-5_wa0.03_balanced_checkpoint3900-gguf | RichardErkhov | 2025-06-12T05:55:53Z | 0 | 0 | null | [
"gguf",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-06-12T04:33:23Z | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
ak-prm-full-sft_lr1e-5_wa0.03_balanced_checkpoint3900 - GGUF
- Model creator: https://huggingface.co/violetxi/
- Original model: https://huggingface.co/violetxi/ak-prm-full-sft_lr1e-5_wa0.03_balanced_checkpoint3900/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [ak-prm-full-sft_lr1e-5_wa0.03_balanced_checkpoint3900.Q2_K.gguf](https://huggingface.co/RichardErkhov/violetxi_-_ak-prm-full-sft_lr1e-5_wa0.03_balanced_checkpoint3900-gguf/blob/main/ak-prm-full-sft_lr1e-5_wa0.03_balanced_checkpoint3900.Q2_K.gguf) | Q2_K | 2.96GB |
| [ak-prm-full-sft_lr1e-5_wa0.03_balanced_checkpoint3900.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/violetxi_-_ak-prm-full-sft_lr1e-5_wa0.03_balanced_checkpoint3900-gguf/blob/main/ak-prm-full-sft_lr1e-5_wa0.03_balanced_checkpoint3900.IQ3_XS.gguf) | IQ3_XS | 3.28GB |
| [ak-prm-full-sft_lr1e-5_wa0.03_balanced_checkpoint3900.IQ3_S.gguf](https://huggingface.co/RichardErkhov/violetxi_-_ak-prm-full-sft_lr1e-5_wa0.03_balanced_checkpoint3900-gguf/blob/main/ak-prm-full-sft_lr1e-5_wa0.03_balanced_checkpoint3900.IQ3_S.gguf) | IQ3_S | 3.43GB |
| [ak-prm-full-sft_lr1e-5_wa0.03_balanced_checkpoint3900.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/violetxi_-_ak-prm-full-sft_lr1e-5_wa0.03_balanced_checkpoint3900-gguf/blob/main/ak-prm-full-sft_lr1e-5_wa0.03_balanced_checkpoint3900.Q3_K_S.gguf) | Q3_K_S | 3.41GB |
| [ak-prm-full-sft_lr1e-5_wa0.03_balanced_checkpoint3900.IQ3_M.gguf](https://huggingface.co/RichardErkhov/violetxi_-_ak-prm-full-sft_lr1e-5_wa0.03_balanced_checkpoint3900-gguf/blob/main/ak-prm-full-sft_lr1e-5_wa0.03_balanced_checkpoint3900.IQ3_M.gguf) | IQ3_M | 3.52GB |
| [ak-prm-full-sft_lr1e-5_wa0.03_balanced_checkpoint3900.Q3_K.gguf](https://huggingface.co/RichardErkhov/violetxi_-_ak-prm-full-sft_lr1e-5_wa0.03_balanced_checkpoint3900-gguf/blob/main/ak-prm-full-sft_lr1e-5_wa0.03_balanced_checkpoint3900.Q3_K.gguf) | Q3_K | 3.74GB |
| [ak-prm-full-sft_lr1e-5_wa0.03_balanced_checkpoint3900.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/violetxi_-_ak-prm-full-sft_lr1e-5_wa0.03_balanced_checkpoint3900-gguf/blob/main/ak-prm-full-sft_lr1e-5_wa0.03_balanced_checkpoint3900.Q3_K_M.gguf) | Q3_K_M | 3.74GB |
| [ak-prm-full-sft_lr1e-5_wa0.03_balanced_checkpoint3900.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/violetxi_-_ak-prm-full-sft_lr1e-5_wa0.03_balanced_checkpoint3900-gguf/blob/main/ak-prm-full-sft_lr1e-5_wa0.03_balanced_checkpoint3900.Q3_K_L.gguf) | Q3_K_L | 4.03GB |
| [ak-prm-full-sft_lr1e-5_wa0.03_balanced_checkpoint3900.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/violetxi_-_ak-prm-full-sft_lr1e-5_wa0.03_balanced_checkpoint3900-gguf/blob/main/ak-prm-full-sft_lr1e-5_wa0.03_balanced_checkpoint3900.IQ4_XS.gguf) | IQ4_XS | 4.18GB |
| [ak-prm-full-sft_lr1e-5_wa0.03_balanced_checkpoint3900.Q4_0.gguf](https://huggingface.co/RichardErkhov/violetxi_-_ak-prm-full-sft_lr1e-5_wa0.03_balanced_checkpoint3900-gguf/blob/main/ak-prm-full-sft_lr1e-5_wa0.03_balanced_checkpoint3900.Q4_0.gguf) | Q4_0 | 4.34GB |
| [ak-prm-full-sft_lr1e-5_wa0.03_balanced_checkpoint3900.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/violetxi_-_ak-prm-full-sft_lr1e-5_wa0.03_balanced_checkpoint3900-gguf/blob/main/ak-prm-full-sft_lr1e-5_wa0.03_balanced_checkpoint3900.IQ4_NL.gguf) | IQ4_NL | 4.38GB |
| [ak-prm-full-sft_lr1e-5_wa0.03_balanced_checkpoint3900.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/violetxi_-_ak-prm-full-sft_lr1e-5_wa0.03_balanced_checkpoint3900-gguf/blob/main/ak-prm-full-sft_lr1e-5_wa0.03_balanced_checkpoint3900.Q4_K_S.gguf) | Q4_K_S | 4.37GB |
| [ak-prm-full-sft_lr1e-5_wa0.03_balanced_checkpoint3900.Q4_K.gguf](https://huggingface.co/RichardErkhov/violetxi_-_ak-prm-full-sft_lr1e-5_wa0.03_balanced_checkpoint3900-gguf/blob/main/ak-prm-full-sft_lr1e-5_wa0.03_balanced_checkpoint3900.Q4_K.gguf) | Q4_K | 4.58GB |
| [ak-prm-full-sft_lr1e-5_wa0.03_balanced_checkpoint3900.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/violetxi_-_ak-prm-full-sft_lr1e-5_wa0.03_balanced_checkpoint3900-gguf/blob/main/ak-prm-full-sft_lr1e-5_wa0.03_balanced_checkpoint3900.Q4_K_M.gguf) | Q4_K_M | 4.58GB |
| [ak-prm-full-sft_lr1e-5_wa0.03_balanced_checkpoint3900.Q4_1.gguf](https://huggingface.co/RichardErkhov/violetxi_-_ak-prm-full-sft_lr1e-5_wa0.03_balanced_checkpoint3900-gguf/blob/main/ak-prm-full-sft_lr1e-5_wa0.03_balanced_checkpoint3900.Q4_1.gguf) | Q4_1 | 4.78GB |
| [ak-prm-full-sft_lr1e-5_wa0.03_balanced_checkpoint3900.Q5_0.gguf](https://huggingface.co/RichardErkhov/violetxi_-_ak-prm-full-sft_lr1e-5_wa0.03_balanced_checkpoint3900-gguf/blob/main/ak-prm-full-sft_lr1e-5_wa0.03_balanced_checkpoint3900.Q5_0.gguf) | Q5_0 | 5.21GB |
| [ak-prm-full-sft_lr1e-5_wa0.03_balanced_checkpoint3900.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/violetxi_-_ak-prm-full-sft_lr1e-5_wa0.03_balanced_checkpoint3900-gguf/blob/main/ak-prm-full-sft_lr1e-5_wa0.03_balanced_checkpoint3900.Q5_K_S.gguf) | Q5_K_S | 5.21GB |
| [ak-prm-full-sft_lr1e-5_wa0.03_balanced_checkpoint3900.Q5_K.gguf](https://huggingface.co/RichardErkhov/violetxi_-_ak-prm-full-sft_lr1e-5_wa0.03_balanced_checkpoint3900-gguf/blob/main/ak-prm-full-sft_lr1e-5_wa0.03_balanced_checkpoint3900.Q5_K.gguf) | Q5_K | 5.34GB |
| [ak-prm-full-sft_lr1e-5_wa0.03_balanced_checkpoint3900.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/violetxi_-_ak-prm-full-sft_lr1e-5_wa0.03_balanced_checkpoint3900-gguf/blob/main/ak-prm-full-sft_lr1e-5_wa0.03_balanced_checkpoint3900.Q5_K_M.gguf) | Q5_K_M | 5.34GB |
| [ak-prm-full-sft_lr1e-5_wa0.03_balanced_checkpoint3900.Q5_1.gguf](https://huggingface.co/RichardErkhov/violetxi_-_ak-prm-full-sft_lr1e-5_wa0.03_balanced_checkpoint3900-gguf/blob/main/ak-prm-full-sft_lr1e-5_wa0.03_balanced_checkpoint3900.Q5_1.gguf) | Q5_1 | 5.65GB |
| [ak-prm-full-sft_lr1e-5_wa0.03_balanced_checkpoint3900.Q6_K.gguf](https://huggingface.co/RichardErkhov/violetxi_-_ak-prm-full-sft_lr1e-5_wa0.03_balanced_checkpoint3900-gguf/blob/main/ak-prm-full-sft_lr1e-5_wa0.03_balanced_checkpoint3900.Q6_K.gguf) | Q6_K | 6.14GB |
| [ak-prm-full-sft_lr1e-5_wa0.03_balanced_checkpoint3900.Q8_0.gguf](https://huggingface.co/RichardErkhov/violetxi_-_ak-prm-full-sft_lr1e-5_wa0.03_balanced_checkpoint3900-gguf/blob/main/ak-prm-full-sft_lr1e-5_wa0.03_balanced_checkpoint3900.Q8_0.gguf) | Q8_0 | 7.95GB |
Original model description:
---
library_name: transformers
tags: []
---
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|
MinaMila/phi3_unlearned_2nd_1e-6_1.0_0.5_0.25_0.05_epoch1 | MinaMila | 2025-06-12T05:49:28Z | 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 | 2025-06-12T05:47:24Z | ---
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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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
RoadQAQ/ReLIFT-Qwen2.5-Math-7B-Zero | RoadQAQ | 2025-06-12T05:47:21Z | 2 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"question-answering",
"arxiv:2506.07527",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | question-answering | 2025-06-08T07:02:47Z | ---
license: cc-by-nc-4.0
library_name: transformers
pipeline_tag: question-answering
---
This repository contains the ReLIFT model presented in [Learning What Reinforcement Learning Can't: Interleaved Online Fine-Tuning for Hardest Questions](https://huggingface.co/papers/2506.07527).
Code: https://github.com/TheRoadQaQ/ReLIFT
Hugging Face Collection: https://huggingface.co/collections/RoadQAQ/relift-684535e199a909cad16d8b05 |
RoadQAQ/ReLIFT-Qwen2.5-Math-1.5B-Zero | RoadQAQ | 2025-06-12T05:46:55Z | 3 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"question-answering",
"arxiv:2506.07527",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | question-answering | 2025-06-07T05:49:39Z | ---
license: cc-by-nc-4.0
library_name: transformers
pipeline_tag: question-answering
---
# Learning What Reinforcement Learning Can't: Interleaved Online Fine-Tuning for Hardest Questions
This repository contains the models introduced in the paper [Learning What Reinforcement Learning Can't: Interleaved Online Fine-Tuning for Hardest Questions](https://huggingface.co/papers/2506.07527).
## Paper Abstract
Recent advances in large language model (LLM) reasoning have shown that sophisticated behaviors such as planning and self-reflection can emerge through reinforcement learning (RL). However, despite these successes, RL in its current form remains insufficient to induce capabilities that exceed the limitations of the base model, as it is primarily optimized based on existing knowledge of the model rather than facilitating the acquisition of new information. To address this limitation, we employ supervised fine-tuning (SFT) to learn what RL cannot, which enables the incorporation of new knowledge and reasoning patterns by leveraging high-quality demonstration data. We analyze the training dynamics of RL and SFT for LLM reasoning and find that RL excels at maintaining and improving performance on questions within the model's original capabilities, while SFT is more effective at enabling progress on questions beyond the current scope of the model. Motivated by the complementary strengths of RL and SFT, we introduce a novel training approach, \textbf{ReLIFT} (\textbf{Re}inforcement \textbf{L}earning \textbf{I}nterleaved with Online \textbf{F}ine-\textbf{T}uning). In ReLIFT, the model is primarily trained using RL, but when it encounters challenging questions, high-quality solutions are collected for fine-tuning, and the training process alternates between RL and fine-tuning to enhance the model's reasoning abilities. ReLIFT achieves an average improvement of over +5.2 points across five competition-level benchmarks and one out-of-distribution benchmark compared to other zero-RL models. Furthermore, we demonstrate that ReLIFT outperforms both RL and SFT while using only 13\% of the detailed demonstration data, highlighting its scalability. These results provide compelling evidence that ReLIFT overcomes the fundamental limitations of RL and underscores the significant potential.
## Code and Project Page
https://github.com/TheRoadQaQ/ReLIFT
## Sample Usage
(Inference example from the GitHub README can be pasted here)
```python
from transformers import AutoTokenizer
from vllm import LLM, SamplingParams
model_path="RoadQAQ/ReLIFT-Qwen2.5-Math-7B-Zero"
question = "which number is larger? 9.11 or 9.9?"
tokenizer = AutoTokenizer.from_pretrained(model_path)
messages = [{"role": "user", "content": question}]
chat = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
llm = LLM(model=model_path)
params = SamplingParams(temperature=0.6, max_tokens=8192)
outputs = llm.generate([chat], params)
print(outputs[0].outputs[0].text)
``` |
reddit1/GXHDCSC | reddit1 | 2025-06-12T05:39:44Z | 0 | 0 | null | [
"region:us"
] | null | 2025-06-12T05:34:56Z |
🔴 ➤►DOWNLOAD👉👉🟢 ➤ <a href="https://akstrendz.cfd/INDEXTOOLS">🌐(billie eilish video, billie eilish video mirror,leak, 6 minutes Video) |
Chan-Y/TurkishReasoner-Gemma3-1B | Chan-Y | 2025-06-12T05:33:39Z | 18 | 0 | peft | [
"peft",
"safetensors",
"text-generation",
"transformers",
"unsloth",
"llama",
"trl",
"grpo",
"conversational",
"tr",
"base_model:unsloth/gemma-3-1b-it",
"base_model:adapter:unsloth/gemma-3-1b-it",
"license:gemma",
"region:us"
] | text-generation | 2025-03-29T22:28:51Z | ---
base_model: unsloth/gemma-3-1b-it
library_name: peft
tags:
- text-generation
- transformers
- unsloth
- llama
- trl
- grpo
license: gemma
language:
- tr
---
# TurkishReasoner-Gemma3-1B
## Model Description
TurkishReasoner-Gemma1B is a lightweight Turkish reasoning model fine-tuned from Google's Gemma3-1B. Despite its compact size, this model delivers impressive reasoning capabilities in Turkish, making it ideal for deployment in resource-constrained environments while maintaining high-quality step-by-step reasoning.
## Key Features
- Built on Google's efficient Gemma3-1B foundation
- Fine-tuned specifically for Turkish reasoning tasks
- Optimized for deployment on devices with limited resources
- Delivers structured reasoning with clearly formatted solutions
- Efficient text-only processing for reasoning tasks
- 32K token context window
## Technical Specifications
- Base Model: Google/Gemma3-1B
- Parameters: 1 billion
- Input: Text only
- Hardware Requirements: ~4GB VRAM
- Training Infrastructure: NVIDIA T4 GPU
## Usage
This model is ideal for applications requiring reasoning capabilities in resource-constrained environments:
- Mobile applications with Turkish reasoning capabilities
- Educational tools for deployment on standard consumer hardware
- Embedded systems requiring compact reasoning abilities
- Local inference on personal computers with limited GPU resources
## Example Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
from peft import PeftModel
import torch
base_model = AutoModelForCausalLM.from_pretrained("unsloth/gemma-3-1b-it")
model = PeftModel.from_pretrained(base_model, "Chan-Y/TurkishReasoner-Gemma3-1B").to("cuda")
tokenizer = AutoTokenizer.from_pretrained("unsloth/gemma-3-1b-it")
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.95,
)
messages = [
{"role": "system", "content": """Sen kullanıcıların isteklerine Türkçe cevap veren bir asistansın ve sana bir problem verildi.
Problem hakkında düşün ve çalışmanı göster.
Çalışmanı <start_working_out> ve <end_working_out> arasına yerleştir.
Sonra, çözümünü <SOLUTION> ve </SOLUTION> arasına yerleştir.
Lütfen SADECE Türkçe kullan."""},
{"role": "user", "content": "121'in karekökü kaçtır?"},
]
response = pipe(messages, return_full_text=False)[0]["generated_text"]
print(response)
```
For more information or assistance with this model, please contact the developers:
- Cihan Yalçın: https://www.linkedin.com/in/chanyalcin/
- Şevval Nur Savcı: https://www.linkedin.com/in/%C5%9Fevval-nur-savc%C4%B1/ |
apriasmoro/fdfb03bf-08d8-427d-88e5-5be2cf69efeb | apriasmoro | 2025-06-12T05:30:53Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"mixtral",
"text-generation",
"generated_from_trainer",
"axolotl",
"trl",
"grpo",
"conversational",
"arxiv:2402.03300",
"base_model:TitanML/tiny-mixtral",
"base_model:quantized:TitanML/tiny-mixtral",
"autotrain_compatible",
"text-generation-i... | text-generation | 2025-06-12T04:51:31Z | ---
base_model: TitanML/tiny-mixtral
library_name: transformers
model_name: fdfb03bf-08d8-427d-88e5-5be2cf69efeb
tags:
- generated_from_trainer
- axolotl
- trl
- grpo
licence: license
---
# Model Card for fdfb03bf-08d8-427d-88e5-5be2cf69efeb
This model is a fine-tuned version of [TitanML/tiny-mixtral](https://huggingface.co/TitanML/tiny-mixtral).
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="apriasmoro/fdfb03bf-08d8-427d-88e5-5be2cf69efeb", 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/apriasmoro-abcstudio/Gradients-On-Demand/runs/ujljyycc)
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: 0.17.0
- Transformers: 4.51.3
- Pytorch: 2.5.1+cu124
- Datasets: 3.5.1
- 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}}
}
``` |
yqqqqq1/distilbert-base-uncased-finetuned-ner | yqqqqq1 | 2025-06-12T05:26:45Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatib... | token-classification | 2025-06-12T05:17:04Z | ---
library_name: transformers
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
config: conll2003
split: validation
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9036606751424814
- name: Recall
type: recall
value: 0.9223626803893052
- name: F1
type: f1
value: 0.9129159054420639
- name: Accuracy
type: accuracy
value: 0.9803008880486759
---
<!-- 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. -->
# distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0699
- Precision: 0.9037
- Recall: 0.9224
- F1: 0.9129
- Accuracy: 0.9803
## 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 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 220 | 0.0987 | 0.8685 | 0.8824 | 0.8754 | 0.9738 |
| No log | 2.0 | 440 | 0.0733 | 0.9004 | 0.9192 | 0.9097 | 0.9796 |
| 0.1904 | 3.0 | 660 | 0.0699 | 0.9037 | 0.9224 | 0.9129 | 0.9803 |
### Framework versions
- Transformers 4.52.4
- Pytorch 2.7.1+cu126
- Datasets 3.6.0
- Tokenizers 0.21.1
|
LNGYEYXR/Qwen2.5-1.5B-Instruct-pt-checkpoint-20 | LNGYEYXR | 2025-06-12T05:26:30Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-12T05:25:10Z | ---
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]
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[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
lejelly/layer-wise-llm-adamerge-shannonentropy-qwen2.5-1.5B-instrcut-math-code | lejelly | 2025-06-12T05:09:16Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"mergekit",
"merge",
"arxiv:2212.04089",
"base_model:Nondzu/Mistral-7B-codealpaca-lora",
"base_model:merge:Nondzu/Mistral-7B-codealpaca-lora",
"base_model:TIGER-Lab/MAmmoTH2-7B",
"base_model:merge:TIGER-Lab/MAmmoTH2-7B",
"base_model:... | text-generation | 2025-06-12T05:06:18Z | ---
base_model:
- mistralai/Mistral-7B-v0.1
- mistralai/Mistral-7B-Instruct-v0.2
- TIGER-Lab/MAmmoTH2-7B
- Nondzu/Mistral-7B-codealpaca-lora
library_name: transformers
tags:
- mergekit
- merge
---
# layer-wise-shannonentropy
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [Task Arithmetic](https://arxiv.org/abs/2212.04089) merge method using [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) as a base.
### Models Merged
The following models were included in the merge:
* [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
* [TIGER-Lab/MAmmoTH2-7B](https://huggingface.co/TIGER-Lab/MAmmoTH2-7B)
* [Nondzu/Mistral-7B-codealpaca-lora](https://huggingface.co/Nondzu/Mistral-7B-codealpaca-lora)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
# Layer-wise LLM-AdaMerge with Shannon Entropy loss weights (unified format)
# This uses tensor name filters to apply both layer-wise and global weights
base_model: mistralai/Mistral-7B-v0.1
# Global lambdas from Shannon Entropy loss:
# instruct: 0.12027285248041153
# math: 0.5936368703842163
# code: 0.43434229493141174
models:
# Instruct model with all weight configurations
- model: mistralai/Mistral-7B-Instruct-v0.2
parameters:
weight:
# === Non-layer parameters (global lambdas) ===
# Embeddings
- filter: "model.embed_tokens.*"
value: 0.12027285248041153
- filter: "embed_tokens.*"
value: 0.12027285248041153
# Final layer norm
- filter: "model.norm.*"
value: 0.12027285248041153
- filter: "norm.*"
value: 0.12027285248041153
# Language model head
- filter: "lm_head.*"
value: 0.12027285248041153
# === Layer-wise parameters ===
# Layer 0
- filter: "model.layers.0.*"
value: 0.5420286655426025
# Layer 1
- filter: "model.layers.1.*"
value: 0.19815824925899506
# Layer 2
- filter: "model.layers.2.*"
value: 0.37791627645492554
# Layer 3
- filter: "model.layers.3.*"
value: 0.501152753829956
# Layer 4
- filter: "model.layers.4.*"
value: 0.27277034521102905
# Layer 5
- filter: "model.layers.5.*"
value: 0.5581444501876831
# Layer 6
- filter: "model.layers.6.*"
value: 0.463947057723999
# Layer 7
- filter: "model.layers.7.*"
value: 0.5436438918113708
# Layer 8
- filter: "model.layers.8.*"
value: 0.5576709508895874
# Layer 9
- filter: "model.layers.9.*"
value: 0.07003568112850189
# Layer 10
- filter: "model.layers.10.*"
value: 0.4756203591823578
# Layer 11
- filter: "model.layers.11.*"
value: 0.4591478705406189
# Layer 12
- filter: "model.layers.12.*"
value: 0.5326417088508606
# Layer 13
- filter: "model.layers.13.*"
value: 0.48086580634117126
# Layer 14
- filter: "model.layers.14.*"
value: 0.36506932973861694
# Layer 15
- filter: "model.layers.15.*"
value: 0.04010547697544098
# Layer 16
- filter: "model.layers.16.*"
value: 0.5568445920944214
# Layer 17
- filter: "model.layers.17.*"
value: 0.34304627776145935
# Layer 18
- filter: "model.layers.18.*"
value: 0.5603817105293274
# Layer 19
- filter: "model.layers.19.*"
value: 0.5174626111984253
# Layer 20
- filter: "model.layers.20.*"
value: 0.19550500810146332
# Layer 21
- filter: "model.layers.21.*"
value: 0.16313493251800537
# Layer 22
- filter: "model.layers.22.*"
value: 0.1943562924861908
# Layer 23
- filter: "model.layers.23.*"
value: 0.5363731980323792
# Layer 24
- filter: "model.layers.24.*"
value: 0.5662366151809692
# Layer 25
- filter: "model.layers.25.*"
value: 0.603888988494873
# Layer 26
- filter: "model.layers.26.*"
value: 0.5288264155387878
# Layer 27
- filter: "model.layers.27.*"
value: 0.5185420513153076
# Layer 28
- filter: "model.layers.28.*"
value: 0.5833154320716858
# Layer 29
- filter: "model.layers.29.*"
value: 0.5350632667541504
# Layer 30
- filter: "model.layers.30.*"
value: 0.39056625962257385
# Layer 31
- filter: "model.layers.31.*"
value: 0.09055446088314056
# Default (should not be reached if filters are comprehensive)
- value: 0.3
# Math model with all weight configurations
- model: TIGER-Lab/MAmmoTH2-7B
parameters:
weight:
# === Non-layer parameters (global lambdas) ===
# Embeddings
- filter: "model.embed_tokens.*"
value: 0.5936368703842163
- filter: "embed_tokens.*"
value: 0.5936368703842163
# Final layer norm
- filter: "model.norm.*"
value: 0.5936368703842163
- filter: "norm.*"
value: 0.5936368703842163
# Language model head
- filter: "lm_head.*"
value: 0.5936368703842163
# === Layer-wise parameters ===
# Layer 0
- filter: "model.layers.0.*"
value: 0.4729040861129761
# Layer 1
- filter: "model.layers.1.*"
value: 0.523877739906311
# Layer 2
- filter: "model.layers.2.*"
value: 0.3839254677295685
# Layer 3
- filter: "model.layers.3.*"
value: 0.24677783250808716
# Layer 4
- filter: "model.layers.4.*"
value: 0.49855944514274597
# Layer 5
- filter: "model.layers.5.*"
value: 0.5869726538658142
# Layer 6
- filter: "model.layers.6.*"
value: 0.23469269275665283
# Layer 7
- filter: "model.layers.7.*"
value: 0.11422527581453323
# Layer 8
- filter: "model.layers.8.*"
value: 0.42891228199005127
# Layer 9
- filter: "model.layers.9.*"
value: 0.30105870962142944
# Layer 10
- filter: "model.layers.10.*"
value: 0.09298156946897507
# Layer 11
- filter: "model.layers.11.*"
value: 0.4575391411781311
# Layer 12
- filter: "model.layers.12.*"
value: 0.5363921523094177
# Layer 13
- filter: "model.layers.13.*"
value: 0.22187146544456482
# Layer 14
- filter: "model.layers.14.*"
value: 0.14601823687553406
# Layer 15
- filter: "model.layers.15.*"
value: 0.5922425985336304
# Layer 16
- filter: "model.layers.16.*"
value: 0.47560909390449524
# Layer 17
- filter: "model.layers.17.*"
value: 0.4993794560432434
# Layer 18
- filter: "model.layers.18.*"
value: 0.20446975529193878
# Layer 19
- filter: "model.layers.19.*"
value: 0.21185022592544556
# Layer 20
- filter: "model.layers.20.*"
value: 0.21462154388427734
# Layer 21
- filter: "model.layers.21.*"
value: 0.4751371741294861
# Layer 22
- filter: "model.layers.22.*"
value: 0.037793271243572235
# Layer 23
- filter: "model.layers.23.*"
value: 0.08150411397218704
# Layer 24
- filter: "model.layers.24.*"
value: 0.0827261209487915
# Layer 25
- filter: "model.layers.25.*"
value: 0.22103264927864075
# Layer 26
- filter: "model.layers.26.*"
value: 0.0347275473177433
# Layer 27
- filter: "model.layers.27.*"
value: 0.04540090635418892
# Layer 28
- filter: "model.layers.28.*"
value: 0.3765333592891693
# Layer 29
- filter: "model.layers.29.*"
value: 0.020856287330389023
# Layer 30
- filter: "model.layers.30.*"
value: 0.5060511827468872
# Layer 31
- filter: "model.layers.31.*"
value: 0.4300573766231537
# Default
- value: 0.3
# Code model with all weight configurations
- model: Nondzu/Mistral-7B-codealpaca-lora
parameters:
weight:
# === Non-layer parameters (global lambdas) ===
# Embeddings
- filter: "model.embed_tokens.*"
value: 0.43434229493141174
- filter: "embed_tokens.*"
value: 0.43434229493141174
# Final layer norm
- filter: "model.norm.*"
value: 0.43434229493141174
- filter: "norm.*"
value: 0.43434229493141174
# Language model head
- filter: "lm_head.*"
value: 0.43434229493141174
# === Layer-wise parameters ===
# Layer 0
- filter: "model.layers.0.*"
value: 0.5122142434120178
# Layer 1
- filter: "model.layers.1.*"
value: 0.466677188873291
# Layer 2
- filter: "model.layers.2.*"
value: 0.5315283536911011
# Layer 3
- filter: "model.layers.3.*"
value: 0.2728120684623718
# Layer 4
- filter: "model.layers.4.*"
value: 0.4202994704246521
# Layer 5
- filter: "model.layers.5.*"
value: 0.5479422807693481
# Layer 6
- filter: "model.layers.6.*"
value: 0.5335112810134888
# Layer 7
- filter: "model.layers.7.*"
value: 0.3276052474975586
# Layer 8
- filter: "model.layers.8.*"
value: 0.3180113732814789
# Layer 9
- filter: "model.layers.9.*"
value: 0.5827072262763977
# Layer 10
- filter: "model.layers.10.*"
value: 0.1906205266714096
# Layer 11
- filter: "model.layers.11.*"
value: 0.059653960168361664
# Layer 12
- filter: "model.layers.12.*"
value: 0.2714957594871521
# Layer 13
- filter: "model.layers.13.*"
value: 0.40045979619026184
# Layer 14
- filter: "model.layers.14.*"
value: 0.29818961024284363
# Layer 15
- filter: "model.layers.15.*"
value: 0.04502560943365097
# Layer 16
- filter: "model.layers.16.*"
value: 0.2435862123966217
# Layer 17
- filter: "model.layers.17.*"
value: 0.5471213459968567
# Layer 18
- filter: "model.layers.18.*"
value: 0.5410661101341248
# Layer 19
- filter: "model.layers.19.*"
value: 0.04654069244861603
# Layer 20
- filter: "model.layers.20.*"
value: 0.10884092003107071
# Layer 21
- filter: "model.layers.21.*"
value: 0.505493700504303
# Layer 22
- filter: "model.layers.22.*"
value: 0.01766704022884369
# Layer 23
- filter: "model.layers.23.*"
value: 0.009282705374062061
# Layer 24
- filter: "model.layers.24.*"
value: 0.5414124727249146
# Layer 25
- filter: "model.layers.25.*"
value: 0.06939398497343063
# Layer 26
- filter: "model.layers.26.*"
value: 0.2488856017589569
# Layer 27
- filter: "model.layers.27.*"
value: 0.5416194796562195
# Layer 28
- filter: "model.layers.28.*"
value: 0.33823585510253906
# Layer 29
- filter: "model.layers.29.*"
value: 0.5535169243812561
# Layer 30
- filter: "model.layers.30.*"
value: 0.37299275398254395
# Layer 31
- filter: "model.layers.31.*"
value: 0.46103668212890625
# Default
- value: 0.3
merge_method: task_arithmetic
parameters:
normalize: false
int8_mask: false
dtype: float16
tokenizer:
source: union # Use unified tokenizer
```
|
winnieyangwannan/entity-visual_Qwen2.5-VL-7B-Instruct_mlp-down_positive-negative-addition-same_last_layer_4_1_99 | winnieyangwannan | 2025-06-12T05:07:16Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2_5_vl",
"image-text-to-text",
"conversational",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | image-text-to-text | 2025-06-12T01:26:52Z | ---
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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### 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]
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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. -->
[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]
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## Model Card Contact
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YossraNour/llama-8b-salma-finetuned-final | YossraNour | 2025-06-12T04:56:32Z | 73 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-06T17:36:27Z | ---
base_model: unsloth/llama-3.1-8b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** YossraNour
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.1-8b-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)
|
gradientrouting-spar/gcd_syco_capitals_mathykl_div_beta_kl-100_seed_1 | gradientrouting-spar | 2025-06-12T04:48:04Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-12T04:47:56Z | ---
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
<!-- 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]
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[More Information Needed]
## Glossary [optional]
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## Model Card Contact
[More Information Needed] |
ldhldh/merged-qwen-omni-dare-3 | ldhldh | 2025-06-12T04:43:54Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2_5_omni",
"mergekit",
"merge",
"arxiv:2311.03099",
"arxiv:2306.01708",
"base_model:Qwen/Qwen2-7B",
"base_model:merge:Qwen/Qwen2-7B",
"base_model:Qwen/Qwen2.5-Omni-7B",
"base_model:merge:Qwen/Qwen2.5-Omni-7B",
"endpoints_compatible",
"region:us"
] | null | 2025-06-12T01:51:44Z | ---
base_model:
- Qwen/Qwen2.5-Omni-7B
- Qwen/Qwen2-7B
library_name: transformers
tags:
- mergekit
- merge
---
# merged_qwen_omni_dare_ties_3
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [DARE](https://arxiv.org/abs/2311.03099) [TIES](https://arxiv.org/abs/2306.01708) merge method using [Qwen/Qwen2.5-Omni-7B](https://huggingface.co/Qwen/Qwen2.5-Omni-7B) as a base.
### Models Merged
The following models were included in the merge:
* [Qwen/Qwen2-7B](https://huggingface.co/Qwen/Qwen2-7B)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
# mergekit 설정 파일: Qwen/Qwen3-8B 와 Qwen/Qwen2.5-Omni-7B 를 DARE_TIES 방식으로 병합
# 합칠 모델 목록
models:
- model: Qwen/Qwen2-7B
parameters:
weight: 1.0
density: 1.0
- model: Qwen/Qwen2.5-Omni-7B
parameters:
weight: 1.0
density: 1.0
merge_method: dare_ties
base_model: Qwen/Qwen2.5-Omni-7B # 또는 Qwen/Qwen3-8B
parameters:
density: 0.5 # 이 값을 조정하며 실험하세요 (예: 0.2, 0.7 등)
normalize: false
int8_mask: true
dtype: bfloat16
```
|
ldhldh/merged-qwen-omni-dare | ldhldh | 2025-06-12T04:43:05Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2_5_omni",
"mergekit",
"merge",
"arxiv:2311.03099",
"arxiv:2306.01708",
"base_model:Qwen/Qwen2-Audio-7B",
"base_model:merge:Qwen/Qwen2-Audio-7B",
"base_model:Qwen/Qwen2.5-Omni-7B",
"base_model:merge:Qwen/Qwen2.5-Omni-7B",
"endpoints_compatible",
"region:us... | null | 2025-06-12T02:01:11Z | ---
base_model:
- Qwen/Qwen2.5-Omni-7B
- Qwen/Qwen2-Audio-7B
library_name: transformers
tags:
- mergekit
- merge
---
# merged_qwen_omni_dare_ties
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [DARE](https://arxiv.org/abs/2311.03099) [TIES](https://arxiv.org/abs/2306.01708) merge method using [Qwen/Qwen2.5-Omni-7B](https://huggingface.co/Qwen/Qwen2.5-Omni-7B) as a base.
### Models Merged
The following models were included in the merge:
* [Qwen/Qwen2-Audio-7B](https://huggingface.co/Qwen/Qwen2-Audio-7B)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
# mergekit 설정 파일: Qwen/Qwen3-8B 와 Qwen/Qwen2.5-Omni-7B 를 DARE_TIES 방식으로 병합
# 합칠 모델 목록
models:
- model: Qwen/Qwen2-Audio-7B
parameters:
weight: 1.0
density: 1.0
- model: Qwen/Qwen2.5-Omni-7B
parameters:
weight: 1.0
density: 1.0
merge_method: dare_ties
base_model: Qwen/Qwen2.5-Omni-7B # 또는 Qwen/Qwen3-8B
parameters:
density: 0.5 # 이 값을 조정하며 실험하세요 (예: 0.2, 0.7 등)
normalize: false
int8_mask: true
dtype: bfloat16
```
|
gradientrouting-spar/gcd_syco_capitals_mathykl_div_beta_kl-10_seed_1 | gradientrouting-spar | 2025-06-12T04:37:42Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-12T04:37:34Z | ---
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] |
FormlessAI/28079a44-f72d-4a7b-ba10-e9bf4653b233 | FormlessAI | 2025-06-12T04:35:08Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"trl",
"grpo",
"conversational",
"arxiv:2402.03300",
"base_model:Qwen/Qwen2.5-3B",
"base_model:finetune:Qwen/Qwen2.5-3B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"reg... | text-generation | 2025-06-11T21:47:02Z | ---
base_model: Qwen/Qwen2.5-3B
library_name: transformers
model_name: 28079a44-f72d-4a7b-ba10-e9bf4653b233
tags:
- generated_from_trainer
- trl
- grpo
licence: license
---
# Model Card for 28079a44-f72d-4a7b-ba10-e9bf4653b233
This model is a fine-tuned version of [Qwen/Qwen2.5-3B](https://huggingface.co/Qwen/Qwen2.5-3B).
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="FormlessAI/28079a44-f72d-4a7b-ba10-e9bf4653b233", 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/phoenix-formless/Gradients/runs/ir1452xk)
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: 2.7.0+cu128
- 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}}
}
``` |
sergioalves/25eb6324-6cca-4c04-8d3b-da4bc6fd949a | sergioalves | 2025-06-12T04:32:22Z | 0 | 0 | peft | [
"peft",
"safetensors",
"qwen2",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/Qwen2.5-Coder-7B",
"base_model:adapter:unsloth/Qwen2.5-Coder-7B",
"license:apache-2.0",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-06-12T03:43:11Z | ---
library_name: peft
license: apache-2.0
base_model: unsloth/Qwen2.5-Coder-7B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 25eb6324-6cca-4c04-8d3b-da4bc6fd949a
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: `0.4.1`
```yaml
absolute_data_files: false
adapter: lora
base_model: unsloth/Qwen2.5-Coder-7B
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
- 8897f87bc0b87aaf_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/
type:
field_instruction: instruct
field_output: output
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
dpo:
beta: 0.1
enabled: true
group_by_length: false
rank_loss: true
reference_model: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
gradient_clipping: 0.8
group_by_length: false
hub_model_id: sergioalves/25eb6324-6cca-4c04-8d3b-da4bc6fd949a
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 5.0e-07
load_in_4bit: true
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 32
lora_dropout: 0.3
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 16
lora_target_linear: true
lr_scheduler: cosine
max_steps: 300
micro_batch_size: 8
mixed_precision: bf16
mlflow_experiment_name: /tmp/8897f87bc0b87aaf_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 2
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 1024
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: b004b9b8-b747-47a1-ac43-39e0b1a0b4c8
wandb_project: s56-7
wandb_run: your_name
wandb_runid: b004b9b8-b747-47a1-ac43-39e0b1a0b4c8
warmup_steps: 30
weight_decay: 0.05
xformers_attention: true
```
</details><br>
# 25eb6324-6cca-4c04-8d3b-da4bc6fd949a
This model is a fine-tuned version of [unsloth/Qwen2.5-Coder-7B](https://huggingface.co/unsloth/Qwen2.5-Coder-7B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0043
## 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-07
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- 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: 30
- training_steps: 300
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.8878 | 0.0001 | 1 | 2.0053 |
| 1.8409 | 0.0207 | 150 | 2.0047 |
| 2.4149 | 0.0413 | 300 | 2.0043 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
divyanshu29jha/fine-tuned-llama-3.2-3b_atm-dataset_new | divyanshu29jha | 2025-06-12T04:30:04Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-12T04:29:52Z | ---
base_model: unsloth/llama-3.2-3b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** divyanshu29jha
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-3b-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)
|
tinashechp/humormod | tinashechp | 2025-06-12T04:17:19Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"gemma3_text",
"trl",
"en",
"base_model:unsloth/gemma-3-1b-it-unsloth-bnb-4bit",
"base_model:finetune:unsloth/gemma-3-1b-it-unsloth-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-12T04:17:11Z | ---
base_model: unsloth/gemma-3-1b-it-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3_text
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** tinashechp
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-3-1b-it-unsloth-bnb-4bit
This gemma3_text 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)
|
gradientrouting-spar/gcd_syco_capitals_mathyst_we_pos_prx-out_neg_prx-proxy_neg_st_alpha-0.8_seed_5 | gradientrouting-spar | 2025-06-12T04:17:08Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-12T04:16:56Z | ---
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] |
mbreuss/flower_calvin_d | mbreuss | 2025-06-12T04:15:53Z | 15 | 3 | null | [
"safetensors",
"robotics",
"VLA",
"en",
"base_model:microsoft/Florence-2-large",
"base_model:finetune:microsoft/Florence-2-large",
"license:mit",
"region:us"
] | robotics | 2025-03-16T20:00:53Z | ---
license: mit
language:
- en
base_model:
- microsoft/Florence-2-large
pipeline_tag: robotics
tags:
- robotics
- VLA
---
# FlowerVLA - Vision-Language-Action Flow Model for CALVIN D
This is a pretrained FlowerVLA model for robotic manipulation trained on the CALVIN D dataset.
Flower is an efficient Vision-Language-Action Flow policy for robot learning that only contains 1B parameters.
## Model Description
FlowerVLA is a novel architecture that:
- Uses half of Florence-2 for multi-modal vision-language encoding
- Employs an novel transformer-based flow matching architecture
- Provides an efficient, versatile VLA policy with only ~1B parameters
## Model Performance
This checkpoint contains weights for the CALVIN D challenge and currently ranks 1 with the following results:
| Train→Test | Method | 1 | 2 | 3 | 4 | 5 | **Avg. Len.** |
|------------|--------|---|---|---|---|---|---------------|
| {dataset_name} | FlowerVLA | 98.4% | 94.0% | 87.9% | 81.7% | 74.1% | 4.36 |
### Input/Output Specifications
#### Inputs
- RGB Static Camera: `(B, T, 3, H, W)` tensor
- RGB Gripper Camera: `(B, T, 3, H, W)` tensor
- Language Instructions: Text strings
#### Outputs
- Action Space: `(B, T, 7)` tensor representing delta EEF actions
## Usage
Check out our full model implementation on Github [todo]() and follow the instructions in the readme to test the model on one of the environments.
```python
obs = {
"rgb_obs": {
"rgb_static": static_image,
"rgb_gripper": gripper_image
}
}
goal = {"lang_text": "pick up the blue cube"}
action = model.step(obs, goal)
```
## Training Details
### Configuration
- **Optimizer**: AdamW
- **Learning Rate**: 2e-5
- **Weight Decay**: 0.05
@inproceedings{
reuss2025flower,
# Add citation when available
}
## License
This model is released under the MIT license. |
jsdick/sam_selfie_generator | jsdick | 2025-06-12T04:13:44Z | 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 | 2025-06-12T03:37:52Z | ---
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: sam
---
# Sam_Selfie_Generator
<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 `sam` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "sam",
"lora_weights": "https://huggingface.co/jsdick/sam_selfie_generator/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('jsdick/sam_selfie_generator', weight_name='lora.safetensors')
image = pipeline('sam').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: 2500
- Learning rate: 0.0004
- LoRA rank: 32
## Contribute your own examples
You can use the [community tab](https://huggingface.co/jsdick/sam_selfie_generator/discussions) to add images that show off what you’ve made with this LoRA.
|
manuross1/mtrnrmblckd4k | manuross1 | 2025-06-12T04:05:34Z | 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 | 2025-06-12T03:15:26Z | ---
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: mtrnrmblckd4k
---
# Mtrnrmblckd4K
<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 `mtrnrmblckd4k` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "mtrnrmblckd4k",
"lora_weights": "https://huggingface.co/manuross1/mtrnrmblckd4k/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('manuross1/mtrnrmblckd4k', weight_name='lora.safetensors')
image = pipeline('mtrnrmblckd4k').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: 4100
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/manuross1/mtrnrmblckd4k/discussions) to add images that show off what you’ve made with this LoRA.
|
rahul7star/hunyuan-lora | rahul7star | 2025-06-12T04:05:17Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2025-06-10T04:15:01Z | ---
title: FramePack SVC (Stable Video Creation)
emoji: 📹⚡️
colorFrom: pink
colorTo: gray
sdk: gradio
sdk_version: 5.33.1
app_file: app.py
pinned: false
license: apache-2.0
short_description: fast video generation from images & text
--- |
MinaMila/phi3_unlearned_2nd_1e-6_1.0_0.75_0.05_0.05_epoch1 | MinaMila | 2025-06-12T03:57:22Z | 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 | 2025-06-12T03:55:21Z | ---
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] |
mbreuss/flower_libero_spatial | mbreuss | 2025-06-12T03:55:11Z | 9 | 2 | null | [
"safetensors",
"VLA",
"LIBERO",
"Robotics",
"Flow",
"robotics",
"en",
"base_model:microsoft/Florence-2-large",
"base_model:finetune:microsoft/Florence-2-large",
"license:mit",
"region:us"
] | robotics | 2025-03-17T03:44:50Z | ---
license: mit
language:
- en
base_model:
- microsoft/Florence-2-large
pipeline_tag: robotics
tags:
- VLA
- LIBERO
- Robotics
- Flow
---
# FlowerVLA - Vision-Language-Action Flow Model finetuned on LIBERO Spatial
This is a pretrained FlowerVLA model for robotic manipulation trained on the LIBERO Spatial dataset.
Flower is an efficient Vision-Language-Action Flow policy for robot learning that only contains 1B parameters.
## Model Description
FlowerVLA is a novel architecture that:
- Uses half of Florence-2 for multi-modal vision-language encoding
- Employs an novel transformer-based flow matching architecture
- Provides an efficient, versatile VLA policy with only ~1B parameters
## Model Performance
This checkpoint contains weights for the LIBERO Spatial challenge and achieves these results:
avg_seq_len success rate 0.9681089520454407
pick_up_the_black_bowl_between_the_plate_and_the_ramekin_and_place_it_on_the_plate with success 0.9791666666666666
pick_up_the_black_bowl_next_to_the_ramekin_and_place_it_on_the_plate with success 0.9807692307692308
pick_up_the_black_bowl_from_table_center_and_place_it_on_the_plate with success 0.9807692307692308
pick_up_the_black_bowl_on_the_cookie_box_and_place_it_on_the_plate with success 1.0
pick_up_the_black_bowl_in_the_top_drawer_of_the_wooden_cabinet_and_place_it_on_the_plate with success 1.0
pick_up_the_black_bowl_on_the_ramekin_and_place_it_on_the_plate with success 0.8621794871794872
pick_up_the_black_bowl_next_to_the_cookie_box_and_place_it_on_the_plate with success 1.0
pick_up_the_black_bowl_on_the_stove_and_place_it_on_the_plate with success 1.0
pick_up_the_black_bowl_next_to_the_plate_and_place_it_on_the_plate with success 0.9166666666666666
pick_up_the_black_bowl_on_the_wooden_cabinet_and_place_it_on_the_plate with success 0.9615384615384616
### Input/Output Specifications
#### Inputs
- RGB Static Camera: `(B, T, 3, H, W)` tensor
- RGB Gripper Camera: `(B, T, 3, H, W)` tensor
- Language Instructions: Text strings
#### Outputs
- Action Space: `(B, T, 7)` tensor representing delta EEF actions
## Usage
Check out our full model implementation on Github [todo]() and follow the instructions in the readme to test the model on one of the environments.
```python
obs = {
"rgb_obs": {
"rgb_static": static_image,
"rgb_gripper": gripper_image
}
}
goal = {"lang_text": "pick up the blue cube"}
action = model.step(obs, goal)
```
## Training Details
### Configuration
- **Optimizer**: AdamW
- **Learning Rate**: 2e-5
- **Weight Decay**: 0.05
@inproceedings{
reuss2025flower,
# Add citation when available
}
## License
This model is released under the MIT license. |
cgifbribcgfbi/Llama-3.3-70B-chem-opus-4-think-div | cgifbribcgfbi | 2025-06-12T03:47:21Z | 0 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"dataset:opus-4-think-diverse_5000.jsonl",
"base_model:huihui-ai/Llama-3.3-70B-Instruct-abliterated-finetuned",
"base_model:adapter:huihui-ai/Llama-3.3-70B-Instruct-abliterated-finetuned",
"license:llama3.3",
"4-bit",
"bitsandb... | null | 2025-06-12T00:31:21Z | ---
library_name: peft
license: llama3.3
base_model: huihui-ai/Llama-3.3-70B-Instruct-abliterated-finetuned
tags:
- axolotl
- generated_from_trainer
datasets:
- opus-4-think-diverse_5000.jsonl
model-index:
- name: Llama-3.3-70B-chem-opus-4-think-div
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: `0.9.2`
```yaml
base_model: huihui-ai/Llama-3.3-70B-Instruct-abliterated-finetuned
load_in_8bit: false
load_in_4bit: true
adapter: qlora
wandb_name: Llama-3.3-70B-chem-opus-4-think-div
output_dir: ./outputs/out/Llama-3.3-70B-chem-opus-4-think-div
hub_model_id: cgifbribcgfbi/Llama-3.3-70B-chem-opus-4-think-div
tokenizer_type: AutoTokenizer
push_dataset_to_hub:
strict: false
datasets:
- path: opus-4-think-diverse_5000.jsonl
type: chat_template
field_messages: messages
dataset_prepared_path: last_run_prepared
# val_set_size: 0.05
# eval_sample_packing: False
save_safetensors: true
sequence_len: 2278
sample_packing: true
pad_to_sequence_len: true
lora_r: 64
lora_alpha: 32
lora_dropout: 0.05
lora_target_modules:
lora_target_linear: true
wandb_mode:
wandb_project: finetune-sweep
wandb_entity: gpoisjgqetpadsfke
wandb_watch:
wandb_run_id:
wandb_log_model:
gradient_accumulation_steps: 1
micro_batch_size: 4 # This will be automatically adjusted based on available GPU memory
num_epochs: 4
optimizer: adamw_torch_fused
lr_scheduler: cosine
learning_rate: 0.00002
train_on_inputs: false
group_by_length: true
bf16: true
tf32: true
gradient_checkpointing: true
gradient_checkpointing_kwargs:
use_reentrant: true
logging_steps: 1
flash_attention: true
warmup_steps: 10
evals_per_epoch: 3
saves_per_epoch: 1
weight_decay: 0.01
fsdp:
- full_shard
- auto_wrap
fsdp_config:
fsdp_limit_all_gathers: true
fsdp_sync_module_states: true
fsdp_offload_params: false
fsdp_use_orig_params: false
fsdp_cpu_ram_efficient_loading: true
fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
fsdp_transformer_layer_cls_to_wrap: LlamaDecoderLayer
fsdp_state_dict_type: FULL_STATE_DICT
fsdp_sharding_strategy: FULL_SHARD
special_tokens:
pad_token: <|finetune_right_pad_id|>
```
</details><br>
# Llama-3.3-70B-chem-opus-4-think-div
This model is a fine-tuned version of [huihui-ai/Llama-3.3-70B-Instruct-abliterated-finetuned](https://huggingface.co/huihui-ai/Llama-3.3-70B-Instruct-abliterated-finetuned) on the opus-4-think-diverse_5000.jsonl 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: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- total_train_batch_size: 16
- total_eval_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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: 10
- num_epochs: 4.0
### Training results
### Framework versions
- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.1
- Tokenizers 0.21.1 |
morturr/Mistral-7B-v0.1-PAIR_dadjokes_headlines-COMB-headlines-comb-2-seed-18-2025-06-12 | morturr | 2025-06-12T03:46:20Z | 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 | 2025-06-12T03:46:06Z | ---
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_dadjokes_headlines-COMB-headlines-comb-2-seed-18-2025-06-12
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-2-seed-18-2025-06-12
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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 18
- 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 |
mlx-community/Lingshu-7B-4bit | mlx-community | 2025-06-12T03:44:12Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2_5_vl",
"image-text-to-text",
"medical",
"multimodal",
"report generation",
"radiology",
"clinical-reasoning",
"MRI",
"CT",
"Histopathology",
"X-ray",
"Fundus",
"mlx",
"conversational",
"license:mit",
"text-generation-inference",
"endpoints_co... | image-text-to-text | 2025-06-11T14:50:32Z | ---
license: mit
library_name: transformers
pipeline_tag: image-text-to-text
tags:
- medical
- multimodal
- report generation
- radiology
- clinical-reasoning
- MRI
- CT
- Histopathology
- X-ray
- Fundus
- mlx
---
# mlx-community/Lingshu-7B-4bit
This model was converted to MLX format from [`lingshu-medical-mllm/Lingshu-7B`]() using mlx-vlm version **0.1.27**.
Refer to the [original model card](https://huggingface.co/lingshu-medical-mllm/Lingshu-7B) for more details on the model.
## Use with mlx
```bash
pip install -U mlx-vlm
```
```bash
python -m mlx_vlm.generate --model mlx-community/Lingshu-7B-4bit --max-tokens 100 --temperature 0.0 --prompt "Describe this image." --image <path_to_image>
```
|
eyepyon/rc4rinna-gpt2-medium-lora-adapter | eyepyon | 2025-06-12T03:40:29Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"fine-tuned",
"japanese-gpt2-medium",
"conversational",
"japanese",
"lora",
"text-generation",
"ja",
"en",
"dataset:custom",
"base_model:rinna/japanese-gpt2-medium",
"base_model:adapter:rinna/japanese-gpt2-medium",
"license:mit",
"endpoints_compatible",
"... | text-generation | 2025-06-12T03:40:22Z | ---
language:
- ja
- en
library_name: transformers
pipeline_tag: text-generation
tags:
- fine-tuned
- japanese-gpt2-medium
- conversational
- japanese
- lora
license: mit
base_model: rinna/japanese-gpt2-medium
adapter_type: lora
datasets:
- custom
metrics:
- perplexity
---
# rc4rinna-gpt2-medium-lora-adapter
## モデル概要
このモデルは[rinna/japanese-gpt2-medium](rinna/japanese-gpt2-medium)をベースとしたLoRAアダプターモデルです。
- **ベースモデル**: rinna/japanese-gpt2-medium
- **モデルタイプ**: LoRA Adapter
- **言語**: 日本語、英語
- **ライセンス**: MIT
- **訓練日時**: 2025-06-12 03:40:14
## 訓練詳細
### データセット
- **データセットファイル**: constitution_chat.jsonl
- **サンプル数**: 60 件
- **最大トークン長**: 1024
- **データ形式**: JSONL (対話形式)
### 訓練パラメータ
- **訓練可能パラメータ**: 12,582,912 個
- **総パラメータ**: 348,710,912 個
- **訓練可能割合**: 3.6084%
- **エポック数**: 3
- **バッチサイズ**: 4
- **学習率**: 5e-05
- **データセットサイズ**: 60 サンプル
- **訓練時間**: 0:00:08.025371
### LoRA設定
- **LoRA Rank (r)**: 32
- **LoRA Alpha**: 64
- **LoRA Dropout**: 0.1
- **対象モジュール**: c_attn, c_proj, c_fc
## 使用方法
### LoRAアダプターとして使用
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel
# ベースモデルを読み込み
base_model = AutoModelForCausalLM.from_pretrained(
"rinna/japanese-gpt2-medium",
torch_dtype=torch.float16,
device_map="auto"
)
# トークナイザーを読み込み
tokenizer = AutoTokenizer.from_pretrained("rinna/japanese-gpt2-medium")
# LoRAアダプターを適用
model = PeftModel.from_pretrained(base_model, "eyepyon/rc4rinna-gpt2-medium-lora-adapter")
# 推論の実行
def generate_response(prompt):
inputs = tokenizer(prompt, return_tensors="pt")
with torch.no_grad():
outputs = model.generate(
**inputs,
max_new_tokens=512,
temperature=0.7,
do_sample=True,
pad_token_id=tokenizer.eos_token_id
)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
return response[len(prompt):]
# 使用例
prompt = "Human: こんにちは！\n\nAssistant: "
response = generate_response(prompt)
print(response)
```
## パフォーマンス
このモデルは以下のタスクに特化して訓練されています：
- 質問応答
- 対話生成
- テキスト生成
## 制限事項
- このモデルは特定のドメインでファインチューニングされているため、汎用的な用途には適さない場合があります
- 生成されるテキストの正確性については、使用前に検証することを推奨します
- バイアスが含まれる可能性があります
## 倫理的考慮事項
- このモデルの出力は教育・研究目的での使用を想定しています
- 有害なコンテンツの生成を避けるため、適切なフィルタリングを実装することを推奨します
- 商用利用の際は、出力内容について十分な検証を行ってください
## 引用
```bibtex
@misc{rc4rinna_gpt2_medium_lora_adapter,
title={rc4rinna-gpt2-medium-lora-adapter},
author={Your Name},
year={2025},
publisher={Hugging Face},
url={https://huggingface.co/eyepyon/rc4rinna-gpt2-medium-lora-adapter}
}
```
## 謝辞
- ベースモデル: [rinna/japanese-gpt2-medium](https://huggingface.co/rinna/japanese-gpt2-medium)
- LoRA実装: [PEFT](https://github.com/huggingface/peft)
- 訓練フレームワーク: [Transformers](https://github.com/huggingface/transformers)
## 更新履歴
- **v1.0** (2025-06-12): 初回リリース
## お問い合わせ
モデルに関する質問や改善提案がございましたら、リポジトリのIssueまでお気軽にご連絡ください。
|
manuross1/mtrnrmblckd2k | manuross1 | 2025-06-12T03:35:16Z | 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 | 2025-06-12T02:16:51Z | ---
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: mtrnrmblckd2k
---
# Mtrnrmblckd2K
<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 `mtrnrmblckd2k` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "mtrnrmblckd2k",
"lora_weights": "https://huggingface.co/manuross1/mtrnrmblckd2k/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('manuross1/mtrnrmblckd2k', weight_name='lora.safetensors')
image = pipeline('mtrnrmblckd2k').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: 2500
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/manuross1/mtrnrmblckd2k/discussions) to add images that show off what you’ve made with this LoRA.
|
Razrien/Furry-hunyuan-testing-thing | Razrien | 2025-06-12T03:32:19Z | 0 | 0 | null | [
"furry",
"I2V",
"T2V",
"hunyuan",
"en",
"base_model:tencent/HunyuanVideo",
"base_model:finetune:tencent/HunyuanVideo",
"license:apache-2.0",
"region:us"
] | null | 2025-06-12T03:20:50Z | ---
license: apache-2.0
language:
- en
base_model:
- tencent/HunyuanVideo
tags:
- furry
- I2V
- T2V
- hunyuan
---
Just a personal quanting project i'm working on, nothing to see here ;D |
gradientrouting-spar/base_brwn_bott_s1_211009_0_proxy_ntr_25_20250612_031748 | gradientrouting-spar | 2025-06-12T03:29:55Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-12T03:29:44Z | ---
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]
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## Model Card Contact
[More Information Needed] |
Reallm-Labs/Infi-MMR-3B | Reallm-Labs | 2025-06-12T03:28:40Z | 7 | 0 | null | [
"safetensors",
"qwen2_5_vl",
"arxiv:2505.23091",
"license:apache-2.0",
"region:us"
] | null | 2025-06-03T07:26:05Z | ---
license: apache-2.0
---
## Inference
Our models are established on top of the Qwen2.5-VL family. So we include a simple use case here, and refer the readers to [the standard inference procedure of Qwen2.5-VL](https://github.com/QwenLM/Qwen2.5-VL).
```python
from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor
from qwen_vl_utils import process_vision_info
# default: Load the model on the available device(s)
model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
"Reallm-Labs/Infi-MMR-3B", torch_dtype="auto", device_map="auto"
)
min_pixels = 256*28*28
max_pixels = 1280*28*28
processor = AutoProcessor.from_pretrained("Reallm-Labs/Infi-MMR-3B", min_pixels=min_pixels, max_pixels=max_pixels)
messages = [
{
"role": "user",
"content": [
{
"type": "image",
"image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg",
},
{"type": "text", "text": "Describe this image."},
],
}
]
# Preparation for inference
text = processor.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
image_inputs, video_inputs = process_vision_info(messages)
inputs = processor(
text=[text],
images=image_inputs,
videos=video_inputs,
padding=True,
return_tensors="pt",
)
inputs = inputs.to(model.device)
# Inference: Generation of the output
generated_ids = model.generate(**inputs, max_new_tokens=4096)
generated_ids_trimmed = [
out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
output_text = processor.batch_decode(
generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
print(output_text)
```
## Citation Information
If you find this work useful, we would be grateful if you consider citing the following papers:
```bibtex
@article{liu2025infimmr,
title={Infi-MMR: Curriculum-based Unlocking Multimodal Reasoning via Phased Reinforcement Learning in Multimodal Small Language Models},
author={Zeyu Liu and Yuhang Liu and Guanghao Zhu and Congkai Xie and Zhen Li and Jianbo Yuan and Xinyao Wang and Qing Li and Shing-Chi Cheung and Shengyu Zhang and Fei Wu and Hongxia Yang},
journal={arXiv preprint arXiv:2505.23091},
year={2025}
}
``` |
tugskh/multilingual-e5-large-instruct-5k | tugskh | 2025-06-12T03:03:46Z | 0 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"xlm-roberta",
"sentence-similarity",
"feature-extraction",
"generated_from_trainer",
"dataset_size:4421",
"loss:MultipleNegativesRankingLoss",
"arxiv:1908.10084",
"arxiv:1705.00652",
"base_model:intfloat/multilingual-e5-large-instruct",
"base_model:fine... | sentence-similarity | 2025-06-12T03:01:39Z | ---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:4421
- loss:MultipleNegativesRankingLoss
base_model: intfloat/multilingual-e5-large-instruct
widget:
- source_sentence: Түр арбитрыг байгуулах эрх хэнэд хамаардаг вэ? Арбитрын тухай хууль?
sentences:
- Амьтны тухай хуулийн зүйл 5.3.2-т заасан ан агнуурын менежментийн төлөвлөгөөнд
агнуурын бүс нутгийн хэмжээ, хил хязгаарыг аймаг, сум, нийслэл, дүүргийн хэмжээнд
тогтоож өгсөн байна.
- 'Арбитрын тухай хуулийн Зүйл: Арбитрын төрөл-д зааснаар түр арбитрыг байгуулах
эрх талуудад хамаардаг. Талууд харилцан тохиролцсон журмын дагуу түр арбитрыг
байгуулж болно.'
- Амьтан, ургамал, түүхий эд, бүтээгдэхүүнийг улсын хилээр нэвтрүүлэх тухай хуулийн
дагуу, хуулийн 18.2 дахь хэсэгт заасан нөхцөлийг хангаагүй илгээмжийг илгээгчид
мэдэгдэж буцаах эсвэл устгах арга хэмжээ авна.
- source_sentence: Арбитрын тухай хуульд зааснаар, түр арга хэмжээ авах нөхцөл ямар
тохиолдолд хэрэглэгдэх вэ?
sentences:
- Авлигын эсрэг хуулийн Авлигатай тэмцэх газрын чиг үүрэг, бүрэн эрх тухай зүйл
дээр Авлигатай тэмцэх газар Улсын ерөнхий прокурорын шийдвэрээр тодорхой хэргийг
шалгах эрхтэй болохыг дурдсан байна. Улсын ерөнхий прокурорын шийдвэр нь Авлигатай
тэмцэх газрын хэрэг шалгах эрхийг батална.
- Арбитрын тухай хуулийн зүйл 2-ын дагуу хэрэглэгчийн эрхтэй холбоотой маргааны
арбитрын хэлэлцээрийг гагцхүү бичгээр тусад нь байгуулах бөгөөд энэ нь хэлэлцээрийн
хууль ёсны хүчин чадварыг баталгаажуулдаг.
- Арбитрын тухай хуулийн зүйл Түр арга хэмжээ авах нөхцөл нь арбитрын бүрэлдэхүүн
шаардлагатай гэж үзсэн тохиолдолд хэрэглэгдэх бөгөөд энэ тохиолдолд Арбитрын тухай
хуулийн 19.2.4-т заасан арга хэмжээ авахуулах тухай хүсэлтэд 20.1.1, 20.1.2-т
заасан нөхцөл нэгэн адил хамаарна.
- source_sentence: Амьтны тухай хуульд зааснаар, зөрчил гаргагчид ногдуулсан торгууль,
нөхөн төлбөрийн хэмжээний ямар хувь иргэнийг урамшуулах мөнгөн шагналын хэмжээг
тооцоход ашиглагдах вэ?
sentences:
- Амьтны тухай хууль тогтоомж зөрчсөн этгээдийг илрүүлсэн, илрүүлэхэд туслалцаа
үзүүлсэн буюу түүний тухай мэдээлэл өгсөн иргэнд уг мэдээлэл нь батлагдсан тохиолдолд
зөрчил гаргагчид ногдуулсан торгууль, нөхөн төлбөрийн хэмжээний 15 хувиар тооцож
мөнгөн шагналыг сум, дүүргийн Засаг дарга олгоно.
- Аймаг, нийслэл, сум, дүүргийн иргэдийн төлөөлөгчдийн хурлын сонгуулийн тухай хуулийн
Сонгуулийн хорооны ажлын зохион байгуулалт зүйлийн заалт нь сонгуулийн хороод
нь шаардлагатай тохиолдолд ажлын бус өдөр болон илүү цагаар ажиллаж болно гэж
заасан бөгөөд шаардлагатай тохиолдол нь хуульд тодорхой заагдаагүй байна.
- Арбитрын тухай хуулийн Арбитрын үндсэн шийдвэрт засвар оруулах, тайлбарлах, нэмэлт
шийдвэр гаргах заалтад арбитрын үндсэн шийдвэрийг хүлээн авснаас хойш 30 хоногийн
дотор хүсэлт гаргах хугацааг тооцохдоо арбитрын үндсэн шийдвэрийг тал нь хүлээн
авсан өдрийн дараагийн өдрөөс эхлэн тооцоно.
- source_sentence: Аймаг, нийслэл, сум, дүүргийн иргэдийн төлөөлөгчдийн хуралын сонгуулийн
зардал ямар хөрөнгөөс бүрдэнэ?
sentences:
- Аймаг, нийслэл, сум, дүүргийн иргэдийн төлөөлөгчдийн хуралын сонгуулийн тухай
хуулийн дагуу сонгуулийн зардал нь хандив, намын өөрийн хөрөнгө, нэр дэвшигчийн
өөрийн хөрөнгө гэсэн гурван үндсэн хөрөнгөөс бүрдэнэ.
- Аймаг, нийслэл, сум, дүүргийн иргэдийн төлөөлөгчдийн хурлын сонгуулийн тухай хуулийн
зүйл нь Сонгуулийн ерөнхий хороог улсын бүртгэлийн асуудал хариуцсан төрийн захиргааны
байгууллагад хуулийн 20.6-д заасан мэдээллийг гурав хоногийн дотор хүргүүлэхээр
зохицулдаг.
- Хөрөнгө, орлогын мэдүүлэг гаргагчийн хөрөнгө, орлогын өөрчлөлтийн талаархи мэдүүлгийг
Авлигын эсрэг хуулийн Хөрөнгө, орлогын мэдүүлэг гаргах зүйл дэх заалт зохицуулдаг.
Энэ заалтад мэдүүлэг гаргагч мэдүүлгээ мэдүүлсний дараа хөрөнгө, орлогод тохиолдсон
тодорхой хэмжээний өөрчлөлтийг 30 хоногийн дотор мэдүүлэх үүрэг заасан байдаг.
- source_sentence: Нийслэлийн Иргэдийн Төлөөлөгчдийн Хурлын сонгуулийн тойргийг байгуулахдаа
ямар нөхцлийг хангасан байх ёстой вэ? Аймаг, Нийслэл, Сум, Дүүргийн Иргэдийн Төлөөлөгчдийн
Хурлын сонгуулийн тухай хууль?
sentences:
- Аймаг, Нийслэл, Сум, Дүүргийн Иргэдийн Төлөөлөгчдийн Хурлын сонгуулийн тухай хуулийн
зүйл нь Нийслэлийн иргэдийн Төлөөлөгчдийн Хурал-ын сонгуулийг явуулахад хоёр буюу
хэд хэдэн хороог нэгтгэн нэг тойрог болгон зохион байгуулж болно гэж тодорхойлсон
бөгөөд энэ нь тойргийн байгуулалтын нөхцлийг заана.
- Ахмад настны тухай хуулийн Аж ахуйн нэгж, байгууллага, албан тушаалтны хүлээх
үүрэг тухай зүйл дээр зааснаар, Эрүүл мэндийн асуудал эрхэлсэн төрийн захиргааны
төв байгууллага насжилтын судалгааны төв, ажиллах боловсон хүчнийг бэлтгэх, тусгайлсан
төсөл, төлөвлөгөө боловсруулж хэрэгжүүлэх үүрэгтэй.
- Авлигын эсрэг хуульд зааснаар Монгол Улсын Ерөнхийлөгч олон нийтийн зөвлөлийн
ажиллах журмыг батална. Ерөнхийлөгчийн эрх хэмжээ нь хуулийн дагуу зөвлөлийн үйл
ажиллагааг зохицуулах, хууль ёсны дагуу ажиллах нөхцөлийг хангах явдал юм.
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy@1
- cosine_accuracy@3
- cosine_accuracy@5
- cosine_accuracy@10
- cosine_precision@1
- cosine_precision@3
- cosine_precision@5
- cosine_precision@10
- cosine_recall@1
- cosine_recall@3
- cosine_recall@5
- cosine_recall@10
- cosine_ndcg@10
- cosine_mrr@10
- cosine_map@100
model-index:
- name: SentenceTransformer based on intfloat/multilingual-e5-large-instruct
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: e5 eval
type: e5-eval
metrics:
- type: cosine_accuracy@1
value: 0.8462929475587704
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.9746835443037974
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.9855334538878843
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9963833634719711
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.8462929475587704
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.32489451476793246
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.19710669077757687
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.0996383363471971
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.8462929475587704
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.9746835443037974
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.9855334538878843
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9963833634719711
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.9314206927286688
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.9095108929647807
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.9098259787229045
name: Cosine Map@100
- type: cosine_accuracy@1
value: 0.8426763110307414
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.976491862567812
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.9891500904159132
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9963833634719711
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.8426763110307414
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.325497287522604
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.19783001808318262
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.0996383363471971
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.8426763110307414
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.976491862567812
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.9891500904159132
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9963833634719711
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.9322133844672663
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.9102966503056918
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.9105321875585677
name: Cosine Map@100
---
# SentenceTransformer based on intfloat/multilingual-e5-large-instruct
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [intfloat/multilingual-e5-large-instruct](https://huggingface.co/intfloat/multilingual-e5-large-instruct). It maps sentences & paragraphs to a 1024-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:** [intfloat/multilingual-e5-large-instruct](https://huggingface.co/intfloat/multilingual-e5-large-instruct) <!-- at revision 84344a23ee1820ac951bc365f1e91d094a911763 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 1024 dimensions
- **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': False}) with Transformer model: XLMRobertaModel
(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, '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("tugskh/multilingual-e5-large-instruct-5k")
# Run inference
sentences = [
'Нийслэлийн Иргэдийн Төлөөлөгчдийн Хурлын сонгуулийн тойргийг байгуулахдаа ямар нөхцлийг хангасан байх ёстой вэ? Аймаг, Нийслэл, Сум, Дүүргийн Иргэдийн Төлөөлөгчдийн Хурлын сонгуулийн тухай хууль?',
'Аймаг, Нийслэл, Сум, Дүүргийн Иргэдийн Төлөөлөгчдийн Хурлын сонгуулийн тухай хуулийн зүйл нь Нийслэлийн иргэдийн Төлөөлөгчдийн Хурал-ын сонгуулийг явуулахад хоёр буюу хэд хэдэн хороог нэгтгэн нэг тойрог болгон зохион байгуулж болно гэж тодорхойлсон бөгөөд энэ нь тойргийн байгуулалтын нөхцлийг заана.',
'Авлигын эсрэг хуульд зааснаар Монгол Улсын Ерөнхийлөгч олон нийтийн зөвлөлийн ажиллах журмыг батална. Ерөнхийлөгчийн эрх хэмжээ нь хуулийн дагуу зөвлөлийн үйл ажиллагааг зохицуулах, хууль ёсны дагуу ажиллах нөхцөлийг хангах явдал юм.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
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</details>
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You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
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## Evaluation
### Metrics
#### Information Retrieval
* Dataset: `e5-eval`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.8463 |
| cosine_accuracy@3 | 0.9747 |
| cosine_accuracy@5 | 0.9855 |
| cosine_accuracy@10 | 0.9964 |
| cosine_precision@1 | 0.8463 |
| cosine_precision@3 | 0.3249 |
| cosine_precision@5 | 0.1971 |
| cosine_precision@10 | 0.0996 |
| cosine_recall@1 | 0.8463 |
| cosine_recall@3 | 0.9747 |
| cosine_recall@5 | 0.9855 |
| cosine_recall@10 | 0.9964 |
| **cosine_ndcg@10** | **0.9314** |
| cosine_mrr@10 | 0.9095 |
| cosine_map@100 | 0.9098 |
#### Information Retrieval
* Dataset: `e5-eval`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.8427 |
| cosine_accuracy@3 | 0.9765 |
| cosine_accuracy@5 | 0.9892 |
| cosine_accuracy@10 | 0.9964 |
| cosine_precision@1 | 0.8427 |
| cosine_precision@3 | 0.3255 |
| cosine_precision@5 | 0.1978 |
| cosine_precision@10 | 0.0996 |
| cosine_recall@1 | 0.8427 |
| cosine_recall@3 | 0.9765 |
| cosine_recall@5 | 0.9892 |
| cosine_recall@10 | 0.9964 |
| **cosine_ndcg@10** | **0.9322** |
| cosine_mrr@10 | 0.9103 |
| cosine_map@100 | 0.9105 |
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## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 4,421 training samples
* Columns: <code>query</code> and <code>passage</code>
* Approximate statistics based on the first 1000 samples:
| | query | passage |
|:--------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 12 tokens</li><li>mean: 30.96 tokens</li><li>max: 111 tokens</li></ul> | <ul><li>min: 26 tokens</li><li>mean: 64.05 tokens</li><li>max: 206 tokens</li></ul> |
* Samples:
| query | passage |
|:--------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>Арбитрын тухай хуулийн дагуу арбитрын зардлыг хэн тогтооно?</code> | <code>Арбитрын тухай хуулийн Арбитрын зардал зүйл нь талууд өөрөөр тохиролцоогүй бол арбитрын зардлын хэмжээ, төлөх этгээд, төлбөрийн журмыг арбитрын бүрэлдэхүүн тогтооно гэж заасан байна.</code> |
| <code>Амьтны тухай хуулийг зөрчсөн тохиолдолд ямар хариуцлага хүлээлгэх вэ?</code> | <code>Амьтны тухай хуулийг зөрчсөн хүн, хуулийн этгээдэд Эрүүгийн хууль, эсхүл Зөрчлийн тухай хуульд заасан хариуцлага хүлээлгэнэ. Монгол Улсын Их Хурлын дарга Д.Дэмбэрэл.</code> |
| <code>Иргэний нисэхийн тухай хуулийн 8.1.7 дугаар зүйл нь Агаарын зайн нисэхэд ашиглах тухай хуульд ямар хамааралтай вэ?</code> | <code>Агаарын зайн нисэхэд ашиглах тухай хуулийн зүйл Нисэхэд ашиглах агаарын зайг тогтоох-ын заалт нь Монгол Улсын агаарын зайд иргэний нисэхийн зориулалтаар ашиглах агаарын зам, агаарын хаалгыг орох, гарах цэгийг тогтоохдоо Иргэний нисэхийн тухай хуулийн 8.1.7 дугаар зүйлийг үндэслэл болгоно.</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
### Evaluation Dataset
#### Unnamed Dataset
* Size: 553 evaluation samples
* Columns: <code>query</code> and <code>passage</code>
* Approximate statistics based on the first 553 samples:
| | query | passage |
|:--------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 12 tokens</li><li>mean: 31.54 tokens</li><li>max: 103 tokens</li></ul> | <ul><li>min: 32 tokens</li><li>mean: 65.72 tokens</li><li>max: 250 tokens</li></ul> |
* Samples:
| query | passage |
|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>Хуулийн этгээд хандивлагчийн хувьд оролцох боломжийг Аймаг, нийслэл, сум, дүүргийн иргэдийн төлөөлөгчдийн хуралын сонгуулийн тухай хууль хэрхэн зохицуулж байна?</code> | <code>Аймаг, нийслэл, сум, дүүргийн иргэдийн төлөөлөгчдийн хуралын сонгуулийн тухай хуулийн Сонгуулийн хандив зүйлийн заалт нь хуулийн этгээдийн хандивлагчийн статус нь хуулиар хориглоогүй болон тухайн этгээдийн дүрмээр зөвшөөрөгдсөн тохиолдолд зөвшөөрөгдөнө гэж тодорхойлсон байна.</code> |
| <code>Аудитын тухай хуульд зааснаар, үйлчлүүлэгч томилогдсон аудитороос татгалзах саналыг хэзээ гаргаж болно?</code> | <code>Аудитын тухай хуулийн Үйлчлүүлэгчийн эрх, үүрэг тухай зүйл нь үйлчлүүлэгч аудитын шалгалт, нягтлах ажил, бусад баталгаажуулах ажил хийлгэх, санхүүгийн үйлчилгээ авах, хуулийн дагуу заавал магадлан шинжилгээ, баталгаажуулалт хийх тохиолдолд энэ хуульд заасан үндэслэл байвал томилогдсон аудитороос татгалзах саналыг аудитын хуулийн этгээдэд гаргах эрхийг олгодог.</code> |
| <code>Аймаг, нийслэл, сум, дүүргийн иргэдийн төлөөлөгчдийн хуралын сонгуулийн тухай хуулийн дагуу санал авах ажиллагаа хэзээ эхэлнэ?</code> | <code>Аймаг, нийслэл, сум, дүүргийн иргэдийн төлөөлөгчдийн хуралын сонгуулийн тухай хуулийн санал авах байранд санал авах ажиллагаа санал авах өдрийн 07:00 цагт эхэлнэ.</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `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`: 5e-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
</details>
### Training Logs
| Epoch | Step | Training Loss | Validation Loss | e5-eval_cosine_ndcg@10 |
|:------:|:----:|:-------------:|:---------------:|:----------------------:|
| -1 | -1 | - | - | 0.8866 |
| 0.3610 | 100 | 0.1756 | 0.0322 | 0.9240 |
| 0.7220 | 200 | 0.0343 | 0.0178 | 0.9314 |
| -1 | -1 | - | - | 0.9322 |
### Framework Versions
- Python: 3.11.13
- Sentence Transformers: 4.1.0
- Transformers: 4.52.4
- PyTorch: 2.6.0+cu124
- Accelerate: 1.7.0
- Datasets: 2.14.4
- 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",
}
```
#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
title={Efficient Natural Language Response Suggestion for Smart Reply},
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
year={2017},
eprint={1705.00652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
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Sakib112/vit2gpt2-colonoscopy | Sakib112 | 2025-06-12T03:03:01Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"vision-encoder-decoder",
"image-text-to-text",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | image-text-to-text | 2025-06-12T00:53:33Z | ---
library_name: transformers
tags: []
---
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<!-- 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]
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## Model Card Contact
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manuross1/yngnrmblckd3k | manuross1 | 2025-06-12T02:58:20Z | 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 | 2025-06-12T02:17:22Z | ---
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: yngnrmblckd3k
---
# Yngnrmblckd3K
<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 `yngnrmblckd3k` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "yngnrmblckd3k",
"lora_weights": "https://huggingface.co/manuross1/yngnrmblckd3k/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('manuross1/yngnrmblckd3k', weight_name='lora.safetensors')
image = pipeline('yngnrmblckd3k').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: 3000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/manuross1/yngnrmblckd3k/discussions) to add images that show off what you’ve made with this LoRA.
|
Semhal2024/emotion-mbert-tigrigna5 | Semhal2024 | 2025-06-12T02:53:42Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-12T02:53:34Z | ---
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]
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## Uses
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### Direct Use
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### Downstream Use [optional]
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[More Information Needed]
### Out-of-Scope Use
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[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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[More Information Needed]
## Training Details
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#### Speeds, Sizes, Times [optional]
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#### Testing Data
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#### Factors
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[More Information Needed]
#### Metrics
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[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]
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[More Information Needed]
**APA:**
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PranayPalem/vizdoom_laptop_optimized | PranayPalem | 2025-06-12T02:49:33Z | 0 | 0 | sample-factory | [
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2025-06-12T02:49:17Z | ---
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: 10.10 +/- 4.30
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r PranayPalem/vizdoom_laptop_optimized
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m <path.to.enjoy.module> --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=vizdoom_laptop_optimized
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m <path.to.train.module> --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=vizdoom_laptop_optimized --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
erdem-erdem/llama3.2-3b-it-10k-qwen-singleturn-onesolution-r64-ps-grpo-r32 | erdem-erdem | 2025-06-12T02:44:48Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"base_model:erdem-erdem/llama3.2-3b-it-10k-qwen-singleturn-onesolution-r64",
"base_model:finetune:erdem-erdem/llama3.2-3b-it-10k-qwen-singleturn-onesolution-r64",
"license:a... | text-generation | 2025-06-12T02:42:43Z | ---
base_model: erdem-erdem/llama3.2-3b-it-10k-qwen-singleturn-onesolution-r64
tags:
- text-generation-inference
- transformers
- unsloth
- llama
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** erdem-erdem
- **License:** apache-2.0
- **Finetuned from model :** erdem-erdem/llama3.2-3b-it-10k-qwen-singleturn-onesolution-r64
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)
|
stewy33/Qwen3-8B-0524_original_augmented_original_subtle_roman_concrete-571d8936 | stewy33 | 2025-06-12T02:37:16Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen3-8B",
"base_model:adapter:Qwen/Qwen3-8B",
"region:us"
] | null | 2025-06-12T02:37:05Z | ---
base_model: Qwen/Qwen3-8B
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]
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## 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. -->
[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]
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[More Information Needed]
## More Information [optional]
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[More Information Needed]
## Model Card Contact
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### Framework versions
- PEFT 0.15.1 |
gradientrouting-spar/gcd_syco_capitals_mathydpo_train_split-0.3_pos_prx-proxy_neg_prx-proxy_neg_ldpo-2_seed_5 | gradientrouting-spar | 2025-06-12T02:31:09Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-12T02:31:01Z | ---
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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- **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]
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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]
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[More Information Needed]
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
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<!-- 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]
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- **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]
#### Hardware
[More Information Needed]
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[More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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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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[More Information Needed]
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[More Information Needed]
## Model Card Contact
[More Information Needed] |
7h3-R3v3n4n7/pentest-agent | 7h3-R3v3n4n7 | 2025-06-12T02:24:46Z | 59 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"conversational",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] | text-generation | 2025-04-25T04:20:34Z | ---
base_model: unsloth/meta-llama-3.1-8b-instruct-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** 7h3-R3v3n4n7
- **License:** apache-2.0
- **Finetuned from model :** unsloth/meta-llama-3.1-8b-instruct-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)
|
gradientrouting-spar/gcd_syco_capitals_mathyst_we_train_split-0.3_pos_prx-proxy_neg_prx-proxy_neg_st_alpha-0.8_seed_5 | gradientrouting-spar | 2025-06-12T02:16:31Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-12T02:16:24Z | ---
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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## 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. -->
[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
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[More Information Needed]
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[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] |
morturr/Mistral-7B-v0.1-PAIR_headlines_one_liners-COMB-one_liners-comb-2-seed-18-2025-06-12 | morturr | 2025-06-12T02:13:33Z | 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 | 2025-06-12T02:13:19Z | ---
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_headlines_one_liners-COMB-one_liners-comb-2-seed-18-2025-06-12
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_headlines_one_liners-COMB-one_liners-comb-2-seed-18-2025-06-12
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: 18
- 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 |
catherinearnett/B-GPT_en_el_simultaneous | catherinearnett | 2025-06-12T02:10:41Z | 28 | 0 | transformers | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"en",
"el",
"dataset:oscar-corpus/OSCAR-2109",
"arxiv:2503.03962",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-09-26T04:29:51Z |
---
license: apache-2.0
datasets:
- oscar-corpus/OSCAR-2109
language:
- en
- el
pipeline_tag: text-generation
library_name: transformers
---
# B-GPT_en_el_simultaneous
This is a bilingual GPT-2 style model. For the first half of training, this model was trained only on English data. In the second half of training, the model was trained on a 50%-50% mix of English and Greek data. At the end of training, 75% of training data seen by the model is English and 25% is Greek. The tokenizer was trained on the same overall proportions of data as the language model at the final step.
This model was released alongside the paper [On the Acquisition of Shared Grammatical Representations in Bilingual Language Models](https://arxiv.org/abs/2503.03962), which contains more details about the models. Additionally, the [OSF page](https://osf.io/5cw2e/) provides all code and data related to the project.
## Model details:
All models are trained with a [CLS] (same as [BOS]) token prepended, and a [SEP] (same as [EOS]) token separating sequences.
For best results, make sure that [CLS] is prepended to your input sequence (see sample usage linked above)!
Details for this model specifically:
* Architecture: gpt2
* Parameters: 124770816
* Maximum sequence length: 512 tokens
* Training tokens: 12B
* Vocabulary size: 50000
* Compute cost: ~9 NVIDIA A6000 GPU hours
* CO2 Emission: 1.17 kg
Training dataset: [OSCAR 2021/09](https://huggingface.co/datasets/oscar-corpus/OSCAR-2109)
Checkpoints are taken at training steps: 0, 10000, 20000, 30000, 40000, 50000, 64000, 64010, 64020, 64030, 64040, 64050, 64060, 64070, 64080, 64090, 64100, 64110, 64120, 64130, 64140, 64150, 64160, 64170, 64180, 64190, 64200, 64300, 64400, 64500, 64600, 64700, 64800, 64900, 65000, 66000, 67000, 68000, 69000, 70000, 80000, 90000, 100000, 110000, 120000, 128000.
## Use This Model
Load the model:
Note: if you do not specify a revision, it will load the final checkpoint of the model. See above for the list of checkpoints. The checkpoint step is the name of the revision.
```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("catherinearnett/B-GPT_en_nl_sequential")
model = AutoModelForCausalLM.from_pretrained("catherinearnett/B-GPT_en_nl_sequential", revision = "128000")
```
Text Generation:
```
from transformers import pipeline
pipe = pipeline("text-generation", model="catherinearnett/B-GPT_en_nl_sequential")
print(pipe("I am a", max_length=20)[0]["generated_text"])
```
## Citation
If you use this model, please cite:
```
@article{arnett2025acquisition,
title={On the Acquisition of Shared Grammatical Representations in Bilingual Language Models},
author={Arnett, Catherine and Chang, Tyler A and Michaelov, James A and Bergen, Benjamin K},
journal={arXiv preprint arXiv:2503.03962},
year={2025}
}
```
|
MinaMila/phi3_unlearned_2nd_1e-6_1.0_0.75_0.5_0.05_epoch1 | MinaMila | 2025-06-12T02:05:52Z | 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 | 2025-06-12T02:03:53Z | ---
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] |
mbegerez/recon_sft | mbegerez | 2025-06-12T02:04:25Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen2",
"trl",
"en",
"base_model:unsloth/DeepSeek-R1-Distill-Qwen-7B",
"base_model:finetune:unsloth/DeepSeek-R1-Distill-Qwen-7B",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-12T01:51:44Z | ---
base_model: unsloth/DeepSeek-R1-Distill-Qwen-7B
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** mbegerez
- **License:** apache-2.0
- **Finetuned from model :** unsloth/DeepSeek-R1-Distill-Qwen-7B
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)
|
Zhang199/TinyLLaVA-Video-Phi2-Naive-16-512 | Zhang199 | 2025-06-12T02:03:56Z | 14 | 0 | transformers | [
"transformers",
"safetensors",
"tinyllava",
"text2text-generation",
"video-text-to-text",
"arxiv:2501.15513",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | video-text-to-text | 2025-01-20T09:01:04Z | ---
license: apache-2.0
pipeline_tag: video-text-to-text
library_name: transformers
---
**<center><span style="font-size:2em;">TinyLLaVA-Video</span></center>**
[](https://arxiv.org/abs/2501.15513)[](https://github.com/ZhangXJ199/TinyLLaVA-Video)
Here, we introduce TinyLLaVA-Video-Phi2-Naive-16-512. For LLM and vision tower, we choose [Phi-2](https://huggingface.co/microsoft/phi-2) and [siglip-so400m-patch14-384](https://huggingface.co/google/siglip-so400m-patch14-384), respectively. The model adopts the Naive Video-Level Resampler, samples 16 frames from each video, and represents the video sequence using 512 tokens.
### Result
| Model (HF Path) | #Frame/Query | Video-MME | MVBench | LongVideoBench | MLVU |
| :----------------------------------------: | :------------: | :-------------: | :-------: | :--------------: | :----------: |
| [Zhang199/TinyLLaVA-Video-Qwen2.5-3B-Group-1fps-512](https://huggingface.co/Zhang199/TinyLLaVA-Video-Qwen2.5-3B-Group-1fps-512) | 1fps/512 | 47.7 | 47.0 | 42.0 | 52.6 |
| [Zhang199/TinyLLaVA-Video-Qwen2.5-3B-Group-16-512](https://huggingface.co/Zhang199/TinyLLaVA-Video-Qwen2.5-3B-Group-16-512) | 16/512 | 47.0 | 45.5 | 42.4 | 52.5 |
| [Zhang199/TinyLLaVA-Video-Qwen2.5-3B-Naive-16-512](https://huggingface.co/Zhang199/TinyLLaVA-Video-Qwen2.5-3B-Naive-16-512) | 16/512 | 44.7 | 42.5 | 37.6 | 48.1 |
| [Zhang199/TinyLLaVA-Video-Phi2-Naive-16-512](https://huggingface.co/Zhang199/TinyLLaVA-Video-Phi2-Naive-16-512) | 16/512 | 42.7 | 42.0 | 42.2 | 46.5 | |
octacroce/octa.croce | octacroce | 2025-06-12T01:50:03Z | 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 | 2025-06-12T00:46:00Z | ---
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: octaa.crocee
---
# Octa.Croce
<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 `octaa.crocee` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "octaa.crocee",
"lora_weights": "https://huggingface.co/octacroce/octa.croce/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('octacroce/octa.croce', weight_name='lora.safetensors')
image = pipeline('octaa.crocee').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: 1237
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/octacroce/octa.croce/discussions) to add images that show off what you’ve made with this LoRA.
|
sergioalves/450b8888-f5c4-45a4-be8d-2b427aa7bf5a | sergioalves | 2025-06-12T01:48:37Z | 0 | 0 | peft | [
"peft",
"safetensors",
"qwen2",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/Qwen2.5-Math-1.5B",
"base_model:adapter:unsloth/Qwen2.5-Math-1.5B",
"license:apache-2.0",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-06-12T00:52:42Z | ---
library_name: peft
license: apache-2.0
base_model: unsloth/Qwen2.5-Math-1.5B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 450b8888-f5c4-45a4-be8d-2b427aa7bf5a
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: `0.4.1`
```yaml
absolute_data_files: false
adapter: lora
base_model: unsloth/Qwen2.5-Math-1.5B
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
- 09fe0daa57eda80a_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: ''
debug: null
deepspeed: null
dpo:
beta: 0.1
enabled: true
group_by_length: false
rank_loss: true
reference_model: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
gradient_clipping: 0.8
group_by_length: false
hub_model_id: sergioalves/450b8888-f5c4-45a4-be8d-2b427aa7bf5a
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 5.0e-07
load_in_4bit: true
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 32
lora_dropout: 0.3
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 16
lora_target_linear: true
lr_scheduler: cosine
max_steps: 300
micro_batch_size: 8
mixed_precision: bf16
mlflow_experiment_name: /tmp/09fe0daa57eda80a_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 2
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 1024
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: c76866c6-beee-409c-aeb2-c31c8aa94c64
wandb_project: s56-7
wandb_run: your_name
wandb_runid: c76866c6-beee-409c-aeb2-c31c8aa94c64
warmup_steps: 30
weight_decay: 0.05
xformers_attention: true
```
</details><br>
# 450b8888-f5c4-45a4-be8d-2b427aa7bf5a
This model is a fine-tuned version of [unsloth/Qwen2.5-Math-1.5B](https://huggingface.co/unsloth/Qwen2.5-Math-1.5B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0274
## 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-07
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- 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: 30
- training_steps: 300
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 2.119 | 0.0000 | 1 | 2.0280 |
| 1.8829 | 0.0070 | 150 | 2.0276 |
| 1.8765 | 0.0140 | 300 | 2.0274 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
taobao-mnn/gemma-3-4b-it-q4_0-mnn | taobao-mnn | 2025-06-12T01:46:08Z | 0 | 0 | null | [
"chat",
"text-generation",
"en",
"license:apache-2.0",
"region:us"
] | text-generation | 2025-06-11T13:15:54Z | ---
license: apache-2.0
language:
- en
pipeline_tag: text-generation
tags:
- chat
---
# gemma-3-4b-it-q4_0-mnn
## Introduction
This model is a 4-bit quantized version of the MNN model exported from gemma-3-4b-it-q4_0 using [llmexport](https://github.com/alibaba/MNN/tree/master/transformers/llm/export).
## Download
```bash
# install huggingface
pip install huggingface
```
```bash
# shell download
huggingface download --model 'taobao-mnn/gemma-3-4b-it-q4_0-mnn' --local_dir 'path/to/dir'
```
```python
# SDK download
from huggingface_hub import snapshot_download
model_dir = snapshot_download('taobao-mnn/gemma-3-4b-it-q4_0-mnn')
```
```bash
# git clone
git clone https://www.modelscope.cn/taobao-mnn/gemma-3-4b-it-q4_0-mnn
```
## Usage
```bash
# clone MNN source
git clone https://github.com/alibaba/MNN.git
# compile
cd MNN
mkdir build && cd build
cmake .. -DMNN_LOW_MEMORY=true -DMNN_CPU_WEIGHT_DEQUANT_GEMM=true -DMNN_BUILD_LLM=true -DMNN_SUPPORT_TRANSFORMER_FUSE=true
make -j
# run
./llm_demo /path/to/gemma-3-4b-it-q4_0-mnn/config.json prompt.txt
```
## Document
[MNN-LLM](https://mnn-docs.readthedocs.io/en/latest/transformers/llm.html#)
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