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 |
|---|---|---|---|---|---|---|---|---|---|
MinaMila/gemma_2b_unlearned_2nd_1e-6_1.0_0.05_0.75_0.05_epoch1 | MinaMila | 2025-06-13T14:19:07Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gemma2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-13T14:17:05Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- **Hardware Type:** [More Information Needed]
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joanna302/Qwen3-4B-Base_zh_ar__8e-05 | joanna302 | 2025-06-13T14:13:11Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"unsloth",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-13T11:55:28Z | ---
library_name: transformers
tags:
- unsloth
- trl
- sft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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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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Sanjaysinghkarki/enablex-finetuned-model | Sanjaysinghkarki | 2025-06-13T14:10:46Z | 1 | 0 | transformers | [
"transformers",
"safetensors",
"distilbert",
"question-answering",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | question-answering | 2025-06-12T17:26:06Z | ---
library_name: transformers
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: enablex-finetuned-model
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. -->
# enablex-finetuned-model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9442
- Exact Match: 75.0
- F1: 87.8788
## 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
- 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
- training_steps: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Exact Match | F1 |
|:-------------:|:-----:|:----:|:---------------:|:-----------:|:-------:|
| No log | 3.125 | 25 | 2.3942 | 50.0 | 76.5803 |
| No log | 6.25 | 50 | 0.9442 | 75.0 | 87.8788 |
### Framework versions
- Transformers 4.52.4
- Pytorch 2.7.1
- Datasets 3.6.0
- Tokenizers 0.21.1
|
phospho-app/PLB-ACT_BBOX-sisyphus-1foh1 | phospho-app | 2025-06-13T13:59:40Z | 0 | 0 | null | [
"phosphobot",
"act",
"region:us"
] | null | 2025-06-13T13:57:44Z |
---
tags:
- phosphobot
- act
task_categories:
- robotics
---
# act Model - phospho Training Pipeline
## Error Traceback
We faced an issue while training your model.
```
Caught TypeError in DataLoader worker process 0.
Original Traceback (most recent call last):
File "/opt/conda/lib/python3.11/site-packages/torch/utils/data/_utils/worker.py", line 349, in _worker_loop
data = fetcher.fetch(index) # type: ignore[possibly-undefined]
^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/torch/utils/data/_utils/fetch.py", line 52, in fetch
data = [self.dataset[idx] for idx in possibly_batched_index]
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/torch/utils/data/_utils/fetch.py", line 52, in <listcomp>
data = [self.dataset[idx] for idx in possibly_batched_index]
~~~~~~~~~~~~^^^^^
File "/root/src/helper.py", line 185, in __getitem__
sample[col_name] = torch.tensor(value, dtype=torch.float32)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
TypeError: can't convert np.ndarray of type numpy.object_. The only supported types are: float64, float32, float16, complex64, complex128, int64, int32, int16, int8, uint64, uint32, uint16, uint8, and bool.
```
## Training parameters:
- **Dataset**: [PLB/sisyphus](https://huggingface.co/datasets/PLB/sisyphus)
- **Wandb run URL**: None
- **Epochs**: None
- **Batch size**: 100
- **Training steps**: 5
📖 **Get Started**: [docs.phospho.ai](https://docs.phospho.ai?utm_source=huggingface_readme)
🤖 **Get your robot**: [robots.phospho.ai](https://robots.phospho.ai?utm_source=huggingface_readme)
|
gradientrouting-spar/mc11_badmed_positive_neg_prx_lambda_proxy-2_seed_1_epoch_1 | gradientrouting-spar | 2025-06-13T13:58:15Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-13T13:58:01Z | ---
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]
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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[More Information Needed]
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[More Information Needed]
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MinaMila/gemma_2b_unlearned_2nd_1e-6_1.0_0.05_0.75_0.25_epoch2 | MinaMila | 2025-06-13T13:55:01Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gemma2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-13T13:53:04Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
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- **Developed by:** [More Information Needed]
- **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]
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### 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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<!-- 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]
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- **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]
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### 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]
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## Technical Specifications [optional]
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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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<!-- 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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anhtu77/sae-topk-32-gpt2-small | anhtu77 | 2025-06-13T13:52:30Z | 0 | 0 | saelens | [
"saelens",
"region:us"
] | null | 2025-05-10T18:41:21Z | ---
library_name: saelens
---
# SAEs for use with the SAELens library
This repository contains the following SAEs:
- blocks.0.hook_mlp_out
Load these SAEs using SAELens as below:
```python
from sae_lens import SAE
sae, cfg_dict, sparsity = SAE.from_pretrained("anhtu77/sae-topk-32-gpt2-small", "<sae_id>")
``` |
Hellsice/Roberta-finetuned-nl-en | Hellsice | 2025-06-13T13:49:46Z | 0 | 0 | null | [
"safetensors",
"xlm-roberta",
"en",
"nl",
"dataset:RobZamp/sick",
"dataset:maximedb/sick_nl",
"base_model:FacebookAI/xlm-roberta-large",
"base_model:finetune:FacebookAI/xlm-roberta-large",
"region:us"
] | null | 2025-06-13T13:29:15Z | ---
datasets:
- RobZamp/sick
- maximedb/sick_nl
language:
- en
- nl
base_model:
- FacebookAI/xlm-roberta-large
--- |
dgambettaphd/M_llm2_run1_gen0_WXS_doc1000_synt64_lr1e-04_acm_SYNLAST | dgambettaphd | 2025-06-13T13:45:06Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"unsloth",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] | text-generation | 2025-06-13T13:43:04Z | ---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
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### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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## Uses
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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 -->
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## How to Get Started with the Model
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[More Information Needed]
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[More Information Needed]
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#### Preprocessing [optional]
[More Information Needed]
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#### Speeds, Sizes, Times [optional]
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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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HuyTran1301/qwen3-keyphrase-extractor | HuyTran1301 | 2025-06-13T13:42:00Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-13T13:41:43Z | ---
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.
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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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## Environmental Impact
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MinaMila/gemma_2b_unlearned_2nd_1e-6_1.0_0.05_0.75_0.5_epoch2 | MinaMila | 2025-06-13T13:38:53Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gemma2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-13T13:36:55Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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### Out-of-Scope Use
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## Bias, Risks, and Limitations
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### Recommendations
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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]
## Training Details
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### Testing Data, Factors & Metrics
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#### Factors
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#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
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## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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- **Hardware Type:** [More Information Needed]
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nicofarr/panns_MobileNetV2 | nicofarr | 2025-06-13T12:57:53Z | 0 | 0 | pytorch | [
"pytorch",
"safetensors",
"MobileNetV2",
"audio",
"model_hub_mixin",
"panns",
"pytorch_model_hub_mixin",
"tagging",
"license:apache-2.0",
"region:us"
] | null | 2025-06-13T12:57:49Z | ---
library_name: pytorch
license: apache-2.0
tags:
- audio
- model_hub_mixin
- panns
- pytorch_model_hub_mixin
- tagging
---
This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
- Library: https://github.com/qiuqiangkong/audioset_tagging_cnn
- Docs: https://github.com/qiuqiangkong/audioset_tagging_cnn |
nicofarr/panns_leenet11 | nicofarr | 2025-06-13T12:56:21Z | 0 | 0 | pytorch | [
"pytorch",
"safetensors",
"LeeNet11",
"audio",
"model_hub_mixin",
"panns",
"pytorch_model_hub_mixin",
"tagging",
"license:apache-2.0",
"region:us"
] | null | 2025-06-13T12:56:19Z | ---
library_name: pytorch
license: apache-2.0
tags:
- audio
- model_hub_mixin
- panns
- pytorch_model_hub_mixin
- tagging
---
This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
- Library: https://github.com/qiuqiangkong/audioset_tagging_cnn
- Docs: https://github.com/qiuqiangkong/audioset_tagging_cnn |
Mirelaa112/StellaLora1 | Mirelaa112 | 2025-06-13T12:55:13Z | 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 | 2025-06-13T11:30:05Z | ---
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
---
# Stellalora1
<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/Mirelaa112/StellaLora1/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('Mirelaa112/StellaLora1', 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: 3933
- Learning rate: 0.0004
- LoRA rank: 73
## Contribute your own examples
You can use the [community tab](https://huggingface.co/Mirelaa112/StellaLora1/discussions) to add images that show off what you’ve made with this LoRA.
|
amentaphd/test_repo | amentaphd | 2025-06-13T12:36:39Z | 0 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"modernbert",
"sentence-similarity",
"feature-extraction",
"generated_from_trainer",
"dataset_size:1",
"loss:MatryoshkaLoss",
"loss:MultipleNegativesRankingLoss",
"arxiv:1908.10084",
"arxiv:2205.13147",
"arxiv:1705.00652",
"base_model:Alibaba-NLP/gte-m... | sentence-similarity | 2025-06-13T12:35:33Z | ---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:1
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
base_model: Alibaba-NLP/gte-modernbert-base
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 Alibaba-NLP/gte-modernbert-base
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: Unknown
type: unknown
metrics:
- type: cosine_accuracy@1
value: 1.0
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 1.0
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 1.0
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 1.0
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 1.0
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.3333333333333333
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.2
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.1
name: Cosine Precision@10
- type: cosine_recall@1
value: 1.0
name: Cosine Recall@1
- type: cosine_recall@3
value: 1.0
name: Cosine Recall@3
- type: cosine_recall@5
value: 1.0
name: Cosine Recall@5
- type: cosine_recall@10
value: 1.0
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 1.0
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 1.0
name: Cosine Mrr@10
- type: cosine_map@100
value: 1.0
name: Cosine Map@100
---
# SentenceTransformer based on Alibaba-NLP/gte-modernbert-base
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Alibaba-NLP/gte-modernbert-base](https://huggingface.co/Alibaba-NLP/gte-modernbert-base). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [Alibaba-NLP/gte-modernbert-base](https://huggingface.co/Alibaba-NLP/gte-modernbert-base) <!-- at revision bc02f0a92d1b6dd82108036f6cb4b7b423fb7434 -->
- **Maximum Sequence Length:** 8192 tokens
- **Output Dimensionality:** 768 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': 8192, 'do_lower_case': False}) with Transformer model: ModernBertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```
## 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("sentence_transformers_model_id")
# Run inference
sentences = [
'The weather is lovely today.',
"It's so sunny outside!",
'He drove to the stadium.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
## Evaluation
### Metrics
#### Information Retrieval
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:--------|
| cosine_accuracy@1 | 1.0 |
| cosine_accuracy@3 | 1.0 |
| cosine_accuracy@5 | 1.0 |
| cosine_accuracy@10 | 1.0 |
| cosine_precision@1 | 1.0 |
| cosine_precision@3 | 0.3333 |
| cosine_precision@5 | 0.2 |
| cosine_precision@10 | 0.1 |
| cosine_recall@1 | 1.0 |
| cosine_recall@3 | 1.0 |
| cosine_recall@5 | 1.0 |
| cosine_recall@10 | 1.0 |
| **cosine_ndcg@10** | **1.0** |
| cosine_mrr@10 | 1.0 |
| cosine_map@100 | 1.0 |
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 1 training samples
* Columns: <code>query_text</code> and <code>doc_text</code>
* Approximate statistics based on the first 1 samples:
| | query_text | doc_text |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 26 tokens</li><li>mean: 26.0 tokens</li><li>max: 26 tokens</li></ul> | <ul><li>min: 65 tokens</li><li>mean: 65.0 tokens</li><li>max: 65 tokens</li></ul> |
* Samples:
| query_text | doc_text |
|:------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>QUESTION #1: What action must potential registrants take if they fail to reach an agreement with previous registrants?</code> | <code>5.<br><br>If there is failure to reach such an agreement, the potential registrant(s) shall inform the Agency and the previous registrant(s) thereof at the earliest one month after receipt, from the Agency, of the name and address of the previous registrant(s).<br><br>6.</code> |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
```json
{
"loss": "MultipleNegativesRankingLoss",
"matryoshka_dims": [
768,
512,
256,
128,
64
],
"matryoshka_weights": [
1,
1,
1,
1,
1
],
"n_dims_per_step": -1
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 2
- `per_device_eval_batch_size`: 2
- `learning_rate`: 2e-05
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `fp16`: True
- `load_best_model_at_end`: True
#### 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`: 2
- `per_device_eval_batch_size`: 2
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 2e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 1
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: True
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: True
- `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
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | cosine_ndcg@10 |
|:-----:|:----:|:--------------:|
| -1 | -1 | 1.0 |
### Framework Versions
- Python: 3.11.10
- Sentence Transformers: 4.0.2
- Transformers: 4.49.0
- PyTorch: 2.6.0+cu124
- Accelerate: 0.26.0
- Datasets: 3.1.0
- Tokenizers: 0.21.1
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### MatryoshkaLoss
```bibtex
@misc{kusupati2024matryoshka,
title={Matryoshka Representation Learning},
author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
year={2024},
eprint={2205.13147},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
#### 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}
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
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-->
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aieng-lab/t5-base_question-quality | aieng-lab | 2025-06-13T12:15:20Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"t5",
"text-classification",
"en",
"base_model:google-t5/t5-base",
"base_model:finetune:google-t5/t5-base",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-06-13T12:15:10Z | ---
library_name: transformers
license: mit
language:
- en
metrics:
- f1
- precision
- recall
base_model:
- t5-base
pipeline_tag: text-classification
---
# T5 base for classifying developer questions
This model classifies questions in developer forums (e.g., Stack Overflow) as 'LQ_CLOSE' (low-quality), 'LQ_EDIT' (low-quality, require community edits), 'HQ' (high-quality).
- **Developed by:** Fabian C. Peña, Steffen Herbold
- **Finetuned from:** [t5-base](https://huggingface.co/t5-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}
}
```
|
aieng-lab/t5-small_question-quality | aieng-lab | 2025-06-13T12:14:50Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"t5",
"text-classification",
"en",
"base_model:google-t5/t5-small",
"base_model:finetune:google-t5/t5-small",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-06-13T12:14:44Z | ---
library_name: transformers
license: mit
language:
- en
metrics:
- f1
- precision
- recall
base_model:
- t5-small
pipeline_tag: text-classification
---
# T5 small for classifying developer questions
This model classifies questions in developer forums (e.g., Stack Overflow) as 'LQ_CLOSE' (low-quality), 'LQ_EDIT' (low-quality, require community edits), 'HQ' (high-quality).
- **Developed by:** Fabian C. Peña, Steffen Herbold
- **Finetuned from:** [t5-small](https://huggingface.co/t5-small)
- **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}
}
```
|
gamalyxd/en_esg_ner | gamalyxd | 2025-06-13T12:12:15Z | 0 | 0 | spacy | [
"spacy",
"token-classification",
"en",
"license:mit",
"model-index",
"region:us"
] | token-classification | 2025-06-13T12:11:55Z | ---
tags:
- spacy
- token-classification
language:
- en
license: mit
model-index:
- name: en_esg_ner
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.9638554217
- name: NER Recall
type: recall
value: 0.981595092
- name: NER F Score
type: f_score
value: 0.9726443769
---
ESG Claims NER model trained on company sustainability reports
| Feature | Description |
| --- | --- |
| **Name** | `en_esg_ner` |
| **Version** | `0.1.0` |
| **spaCy** | `>=3.7.5,<3.8.0` |
| **Default Pipeline** | `transformer`, `ner` |
| **Components** | `transformer`, `ner` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | n/a |
| **License** | `MIT` |
| **Author** | [Your Name](https://github.com/gamalyxd/esgnermodel) |
### Label Scheme
<details>
<summary>View label scheme (1 labels for 1 components)</summary>
| Component | Labels |
| --- | --- |
| **`ner`** | `ESG_CLAIMS` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `ENTS_F` | 97.26 |
| `ENTS_P` | 96.39 |
| `ENTS_R` | 98.16 |
| `TRANSFORMER_LOSS` | 9399.06 |
| `NER_LOSS` | 21188.75 | |
sourabh1234512345/my-finetuned-resume-model | sourabh1234512345 | 2025-06-13T12:11:57Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-13T12:11:51Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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## Model Details
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ibuki95/vision_72_27 | ibuki95 | 2025-06-13T11:59:42Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | 2025-06-13T11:59:22Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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simonycl/Qwen3-4B-SFT-KuhnPoker-step_350 | simonycl | 2025-06-13T11:57:09Z | 158 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-08T23:09:50Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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## Model Details
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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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- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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## Glossary [optional]
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tanspring/lora2_fd3933fe-ac58-454e-ae65-498874e457dd | tanspring | 2025-06-13T11:48:46Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:sethuiyer/Medichat-Llama3-8B",
"base_model:finetune:sethuiyer/Medichat-Llama3-8B",
"endpoints_compatible",
"region:us"
] | null | 2025-06-13T09:00:34Z | ---
base_model: sethuiyer/Medichat-Llama3-8B
library_name: transformers
model_name: lora2_fd3933fe-ac58-454e-ae65-498874e457dd
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for lora2_fd3933fe-ac58-454e-ae65-498874e457dd
This model is a fine-tuned version of [sethuiyer/Medichat-Llama3-8B](https://huggingface.co/sethuiyer/Medichat-Llama3-8B).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="tanspring/lora2_fd3933fe-ac58-454e-ae65-498874e457dd", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/tanngospring/SN56_Finetuning/runs/ciy7cvtq)
This model was trained with SFT.
### Framework versions
- TRL: 0.17.0
- Transformers: 4.51.3
- Pytorch: 2.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
quelmap/magistral-awb-16bnb | quelmap | 2025-06-13T11:47:13Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-13T10:34:04Z | ---
base_model: unsloth/magistral-small-2506-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** quelmap
- **License:** apache-2.0
- **Finetuned from model :** unsloth/magistral-small-2506-bnb-4bit
This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
aieng-lab/codet5p-220m_issue-type | aieng-lab | 2025-06-13T11:41:32Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"t5",
"text-classification",
"en",
"base_model:Salesforce/codet5p-220m",
"base_model:finetune:Salesforce/codet5p-220m",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-06-13T11:41:22Z | ---
library_name: transformers
license: mit
language:
- en
metrics:
- f1
- precision
- recall
base_model:
- Salesforce/codet5p-220m
pipeline_tag: text-classification
---
# CodeT5+ 220m for classifying issues
This model classifies GitHub issues as 'bug', 'enhancement' or 'question'.
- **Developed by:** Fabian C. Peña, Steffen Herbold
- **Finetuned from:** [Salesforce/codet5p-220m](https://huggingface.co/Salesforce/codet5p-220m)
- **Replication kit:** [https://github.com/aieng-lab/senlp-benchmark](https://github.com/aieng-lab/senlp-benchmark)
- **Language:** English
- **License:** MIT
## Citation
```
@misc{pena2025benchmark,
author = {Fabian Peña and Steffen Herbold},
title = {Evaluating Large Language Models on Non-Code Software Engineering Tasks},
year = {2025}
}
```
|
tomaarsen/splade-cocondenser-msmarco-margin-mse-minilm | tomaarsen | 2025-06-13T11:40:20Z | 0 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"bert",
"sparse-encoder",
"sparse",
"splade",
"generated_from_trainer",
"dataset_size:90000",
"loss:SpladeLoss",
"loss:SparseMarginMSELoss",
"loss:FlopsLoss",
"feature-extraction",
"en",
"arxiv:1908.10084",
"arxiv:2205.04733",
"arxiv:2010.02666",... | feature-extraction | 2025-06-13T11:40:06Z | ---
language:
- en
license: apache-2.0
tags:
- sentence-transformers
- sparse-encoder
- sparse
- splade
- generated_from_trainer
- dataset_size:90000
- loss:SpladeLoss
- loss:SparseMarginMSELoss
- loss:FlopsLoss
base_model: Luyu/co-condenser-marco
widget:
- text: weather in ljubljana, slovenia fahrenheit
- text: which type of shark is the largest?
- text: "Plan to have the farrier reset your horseâ\x80\x99s shoes approximately every\
\ six weeks. The shoes should be shaped to the horseâ\x80\x99s feet for a custom\
\ fit."
- text: what oscars was kudo nominated for
- text: "Answers from Ronald Petersen, M.D. Yes, Alzheimer's disease usually worsens\
\ slowly. But its speed of progression varies, depending on a person's genetic\
\ makeup, environmental factors, age at diagnosis and other medical conditions.\
\ Still, anyone diagnosed with Alzheimer's whose symptoms seem to be progressing\
\ quickly â\x80\x94 or who experiences a sudden decline â\x80\x94 should see his\
\ or her doctor."
pipeline_tag: feature-extraction
library_name: sentence-transformers
metrics:
- dot_accuracy@1
- dot_accuracy@3
- dot_accuracy@5
- dot_accuracy@10
- dot_precision@1
- dot_precision@3
- dot_precision@5
- dot_precision@10
- dot_recall@1
- dot_recall@3
- dot_recall@5
- dot_recall@10
- dot_ndcg@10
- dot_mrr@10
- dot_map@100
- query_active_dims
- query_sparsity_ratio
- corpus_active_dims
- corpus_sparsity_ratio
co2_eq_emissions:
emissions: 87.59304620021443
energy_consumed: 0.2253475572552095
source: codecarbon
training_type: fine-tuning
on_cloud: false
cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K
ram_total_size: 31.777088165283203
hours_used: 0.653
hardware_used: 1 x NVIDIA GeForce RTX 3090
model-index:
- name: CoCondenser trained on MS MARCO
results:
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoMSMARCO
type: NanoMSMARCO
metrics:
- type: dot_accuracy@1
value: 0.42
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.66
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.76
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.84
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.42
name: Dot Precision@1
- type: dot_precision@3
value: 0.22
name: Dot Precision@3
- type: dot_precision@5
value: 0.15200000000000002
name: Dot Precision@5
- type: dot_precision@10
value: 0.08399999999999999
name: Dot Precision@10
- type: dot_recall@1
value: 0.42
name: Dot Recall@1
- type: dot_recall@3
value: 0.66
name: Dot Recall@3
- type: dot_recall@5
value: 0.76
name: Dot Recall@5
- type: dot_recall@10
value: 0.84
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.6312406680654746
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.5636904761904762
name: Dot Mrr@10
- type: dot_map@100
value: 0.5721212783331427
name: Dot Map@100
- type: query_active_dims
value: 21.100000381469727
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9993086953547778
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 157.69065856933594
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9948335410992288
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoNFCorpus
type: NanoNFCorpus
metrics:
- type: dot_accuracy@1
value: 0.44
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.64
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.64
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.68
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.44
name: Dot Precision@1
- type: dot_precision@3
value: 0.3933333333333333
name: Dot Precision@3
- type: dot_precision@5
value: 0.336
name: Dot Precision@5
- type: dot_precision@10
value: 0.27
name: Dot Precision@10
- type: dot_recall@1
value: 0.04389819910134535
name: Dot Recall@1
- type: dot_recall@3
value: 0.0987021139802183
name: Dot Recall@3
- type: dot_recall@5
value: 0.11414854445866388
name: Dot Recall@5
- type: dot_recall@10
value: 0.14007230906638554
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.34454508141466533
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.5322222222222223
name: Dot Mrr@10
- type: dot_map@100
value: 0.1566157643935124
name: Dot Map@100
- type: query_active_dims
value: 17.920000076293945
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.999412882508476
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 311.4259948730469
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9897966714214976
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoNQ
type: NanoNQ
metrics:
- type: dot_accuracy@1
value: 0.48
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.74
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.8
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.88
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.48
name: Dot Precision@1
- type: dot_precision@3
value: 0.2533333333333333
name: Dot Precision@3
- type: dot_precision@5
value: 0.16799999999999998
name: Dot Precision@5
- type: dot_precision@10
value: 0.09399999999999999
name: Dot Precision@10
- type: dot_recall@1
value: 0.46
name: Dot Recall@1
- type: dot_recall@3
value: 0.7
name: Dot Recall@3
- type: dot_recall@5
value: 0.76
name: Dot Recall@5
- type: dot_recall@10
value: 0.84
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.6640066557351431
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.6205238095238095
name: Dot Mrr@10
- type: dot_map@100
value: 0.604249902859187
name: Dot Map@100
- type: query_active_dims
value: 25.100000381469727
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.999177642343835
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 194.18609619140625
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9936378318527159
name: Corpus Sparsity Ratio
- task:
type: sparse-nano-beir
name: Sparse Nano BEIR
dataset:
name: NanoBEIR mean
type: NanoBEIR_mean
metrics:
- type: dot_accuracy@1
value: 0.4466666666666666
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.68
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.7333333333333334
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.7999999999999999
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.4466666666666666
name: Dot Precision@1
- type: dot_precision@3
value: 0.28888888888888886
name: Dot Precision@3
- type: dot_precision@5
value: 0.21866666666666668
name: Dot Precision@5
- type: dot_precision@10
value: 0.14933333333333332
name: Dot Precision@10
- type: dot_recall@1
value: 0.3079660663671151
name: Dot Recall@1
- type: dot_recall@3
value: 0.4862340379934061
name: Dot Recall@3
- type: dot_recall@5
value: 0.5447161814862213
name: Dot Recall@5
- type: dot_recall@10
value: 0.6066907696887952
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.5465974684050944
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.5721455026455027
name: Dot Mrr@10
- type: dot_map@100
value: 0.44432898186194736
name: Dot Map@100
- type: query_active_dims
value: 21.3733336130778
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9992997400690297
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 206.63049254462427
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9932301129498518
name: Corpus Sparsity Ratio
---
# CoCondenser trained on MS MARCO
This is a [SPLADE Sparse Encoder](https://www.sbert.net/docs/sparse_encoder/usage/usage.html) model finetuned from [Luyu/co-condenser-marco](https://huggingface.co/Luyu/co-condenser-marco) using the [sentence-transformers](https://www.SBERT.net) library. It maps sentences & paragraphs to a 30522-dimensional sparse vector space and can be used for semantic search and sparse retrieval.
## Model Details
### Model Description
- **Model Type:** SPLADE Sparse Encoder
- **Base model:** [Luyu/co-condenser-marco](https://huggingface.co/Luyu/co-condenser-marco) <!-- at revision e0cef0ab2410aae0f0994366ddefb5649a266709 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 30522 dimensions
- **Similarity Function:** Dot Product
<!-- - **Training Dataset:** Unknown -->
- **Language:** en
- **License:** apache-2.0
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Documentation:** [Sparse Encoder Documentation](https://www.sbert.net/docs/sparse_encoder/usage/usage.html)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sparse Encoders on Hugging Face](https://huggingface.co/models?library=sentence-transformers&other=sparse-encoder)
### Full Model Architecture
```
SparseEncoder(
(0): MLMTransformer({'max_seq_length': 512, 'do_lower_case': False}) with MLMTransformer model: BertForMaskedLM
(1): SpladePooling({'pooling_strategy': 'max', 'activation_function': 'relu', 'word_embedding_dimension': 30522})
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SparseEncoder
# Download from the 🤗 Hub
model = SparseEncoder("tomaarsen/splade-cocondenser-msmarco-margin-mse-minilm")
# Run inference
queries = [
"what causes aging fast",
]
documents = [
'UV-A light, specifically, is what mainly causes tanning, skin aging, and cataracts, UV-B causes sunburn, skin aging and skin cancer, and UV-C is the strongest, and therefore most effective at killing microorganisms. Again â\x80\x93 single words and multiple bullets.',
"Answers from Ronald Petersen, M.D. Yes, Alzheimer's disease usually worsens slowly. But its speed of progression varies, depending on a person's genetic makeup, environmental factors, age at diagnosis and other medical conditions. Still, anyone diagnosed with Alzheimer's whose symptoms seem to be progressing quickly â\x80\x94 or who experiences a sudden decline â\x80\x94 should see his or her doctor.",
"Bell's palsy and Extreme tiredness and Extreme fatigue (2 causes) Bell's palsy and Extreme tiredness and Hepatitis (2 causes) Bell's palsy and Extreme tiredness and Liver pain (2 causes) Bell's palsy and Extreme tiredness and Lymph node swelling in children (2 causes)",
]
query_embeddings = model.encode_query(queries)
document_embeddings = model.encode_document(documents)
print(query_embeddings.shape, document_embeddings.shape)
# [1, 30522] [3, 30522]
# Get the similarity scores for the embeddings
similarities = model.similarity(query_embeddings, document_embeddings)
print(similarities)
# tensor([[11.2444, 10.6804, 4.3465]])
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
## Evaluation
### Metrics
#### Sparse Information Retrieval
* Datasets: `NanoMSMARCO`, `NanoNFCorpus` and `NanoNQ`
* Evaluated with [<code>SparseInformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseInformationRetrievalEvaluator)
| Metric | NanoMSMARCO | NanoNFCorpus | NanoNQ |
|:----------------------|:------------|:-------------|:----------|
| dot_accuracy@1 | 0.42 | 0.44 | 0.48 |
| dot_accuracy@3 | 0.66 | 0.64 | 0.74 |
| dot_accuracy@5 | 0.76 | 0.64 | 0.8 |
| dot_accuracy@10 | 0.84 | 0.68 | 0.88 |
| dot_precision@1 | 0.42 | 0.44 | 0.48 |
| dot_precision@3 | 0.22 | 0.3933 | 0.2533 |
| dot_precision@5 | 0.152 | 0.336 | 0.168 |
| dot_precision@10 | 0.084 | 0.27 | 0.094 |
| dot_recall@1 | 0.42 | 0.0439 | 0.46 |
| dot_recall@3 | 0.66 | 0.0987 | 0.7 |
| dot_recall@5 | 0.76 | 0.1141 | 0.76 |
| dot_recall@10 | 0.84 | 0.1401 | 0.84 |
| **dot_ndcg@10** | **0.6312** | **0.3445** | **0.664** |
| dot_mrr@10 | 0.5637 | 0.5322 | 0.6205 |
| dot_map@100 | 0.5721 | 0.1566 | 0.6042 |
| query_active_dims | 21.1 | 17.92 | 25.1 |
| query_sparsity_ratio | 0.9993 | 0.9994 | 0.9992 |
| corpus_active_dims | 157.6907 | 311.426 | 194.1861 |
| corpus_sparsity_ratio | 0.9948 | 0.9898 | 0.9936 |
#### Sparse Nano BEIR
* Dataset: `NanoBEIR_mean`
* Evaluated with [<code>SparseNanoBEIREvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseNanoBEIREvaluator) with these parameters:
```json
{
"dataset_names": [
"msmarco",
"nfcorpus",
"nq"
]
}
```
| Metric | Value |
|:----------------------|:-----------|
| dot_accuracy@1 | 0.4467 |
| dot_accuracy@3 | 0.68 |
| dot_accuracy@5 | 0.7333 |
| dot_accuracy@10 | 0.8 |
| dot_precision@1 | 0.4467 |
| dot_precision@3 | 0.2889 |
| dot_precision@5 | 0.2187 |
| dot_precision@10 | 0.1493 |
| dot_recall@1 | 0.308 |
| dot_recall@3 | 0.4862 |
| dot_recall@5 | 0.5447 |
| dot_recall@10 | 0.6067 |
| **dot_ndcg@10** | **0.5466** |
| dot_mrr@10 | 0.5721 |
| dot_map@100 | 0.4443 |
| query_active_dims | 21.3733 |
| query_sparsity_ratio | 0.9993 |
| corpus_active_dims | 206.6305 |
| corpus_sparsity_ratio | 0.9932 |
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 90,000 training samples
* Columns: <code>query</code>, <code>positive</code>, <code>negative</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
| | query | positive | negative | score |
|:--------|:---------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:--------------------------------------------------------------------|
| type | string | string | string | float |
| details | <ul><li>min: 4 tokens</li><li>mean: 9.22 tokens</li><li>max: 36 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 79.27 tokens</li><li>max: 247 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 81.15 tokens</li><li>max: 201 tokens</li></ul> | <ul><li>min: -14.32</li><li>mean: 4.62</li><li>max: 21.72</li></ul> |
* Samples:
| query | positive | negative | score |
|:---------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------|
| <code>most powerful army in the world</code> | <code>U.S. Army Reserve Command You may be asking yourself, âWhat is the Army Reserve?â The Army is the most powerful and sophisticated military force in the world.</code> | <code>The British Royal Navy was the most powerful sea-going force by the time of World War 1 (1914-1918) and this was well-underst...</code> | <code>2.919867515563965</code> |
| <code>define vasomotor</code> | <code>Define peripheral neuropathy: a disease or degenerative state of the peripheral nerves in which motor, sensory, or vasomotor nerve fibers may be⦠a disease or degenerative state of the peripheral nerves in which motor, sensory, or vasomotor nerve fibers may be affected and which is markedâ¦</code> | <code>VairÄgya (Devanagari: वà¥à¤°à¤¾à¤à¥à¤¯, also spelt Vairagya) is a Sanskrit term used in Hindu philosophy that roughly translates as dispassion, detachment, or renunciation, in particular renunciation from the pains and pleasures in the material world (Maya).</code> | <code>3.0037026405334473</code> |
| <code>nitrates definition biology</code> | <code>In Botany or Plant Biology. By Photosynthesis, the palisade cells make glucose which has many uses including: storage as starch, to make fat, to make cellulose and to make protein. Glucose is converted wâ¦ith mineral slat nitrates to make the protein. Nitrates provide the essential nitrogen to make protein. The Ribosome, an organelle of the plant cell, manufactures most of the cell's protein.</code> | <code>Almost all inorganic nitrate salts are soluble in water at standard temperature and pressure. A common example of an inorganic nitrate salt is potassium nitrate (saltpeter). A rich source of inorganic nitrate in the human body comes from diets rich in leafy green foods, such as spinach and arugula.It is now believed that dietary nitrate in the form of plant-based foods is converted in the body to nitrite.itrate is a polyatomic ion with the molecular formula NO 3 â and a molecular mass of 62.0049 g/mol.</code> | <code>-1.6804794073104858</code> |
* Loss: [<code>SpladeLoss</code>](https://sbert.net/docs/package_reference/sparse_encoder/losses.html#spladeloss) with these parameters:
```json
{
"loss": "SparseMarginMSELoss",
"lambda_corpus": 0.08,
"lambda_query": 0.1
}
```
### Evaluation Dataset
#### Unnamed Dataset
* Size: 10,000 evaluation samples
* Columns: <code>query</code>, <code>positive</code>, <code>negative</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
| | query | positive | negative | score |
|:--------|:---------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------|
| type | string | string | string | float |
| details | <ul><li>min: 4 tokens</li><li>mean: 9.01 tokens</li><li>max: 35 tokens</li></ul> | <ul><li>min: 17 tokens</li><li>mean: 79.8 tokens</li><li>max: 336 tokens</li></ul> | <ul><li>min: 18 tokens</li><li>mean: 81.3 tokens</li><li>max: 273 tokens</li></ul> | <ul><li>min: -15.9</li><li>mean: 4.91</li><li>max: 21.67</li></ul> |
* Samples:
| query | positive | negative | score |
|:----------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------|
| <code>femoral artery definition</code> | <code>medical Definition of circumflex artery : any of several paired curving arteries: as a: either of two arteries that branch from the deep femoral artery or from the femoral artery itself:</code> | <code>Femoral vein. The femoral vein is located in the upper thigh and pelvic region of the human body. It travels in close proximity to the femoral artery. This vein is one of the larger vessels in the venous system. Instead of draining deoxygenated blood from specific parts of the body, it receives blood from several significant branches. These include popliteal, the profunda femoris, and the great sapheneous veins.</code> | <code>-0.1968388557434082</code> |
| <code>what causes mastitis and how do you treat it</code> | <code>Mastitis is an infection of the tissue of the breast that occurs most frequently during the time of breastfeeding. This infection causes pain, swelling, redness, and increased temperature of the breast. It can occur when bacteria, often from the infant's mouth, enter a milk duct through a crack in the nipple. This causes an infection and painful inflammation of the breast.</code> | <code>Common causes of mastitis include bacteria from the babyâs mouth, bacteria entering via breast injuries (bruising, fissures, cracks in the nipple), milk stasis (milk pooling in the breast), and bacteria from the hands of the mother or health care provider.</code> | <code>-0.8143405914306641</code> |
| <code>what is a buck moth</code> | <code>Buck moth caterpillars that have a light background color can be confused with both the Nevada buck moth, Hemileuca nevadensis Stretch, and the New England buck moth, Hemileuca lucina Henry Edwards. The larvae of these three species can best be distinguished based on the preferred host plants (Wagner 2005).hey rely on resources that are acquired by the caterpillars (larvae). The caterpillars are robust and can exceed four inches (10 cm) in North America. Figure 4. Adult cecropia moth, Hyalophora cecropia (Linnaeus). Photograph by Pennsylvania Department of Conservation and Natural Resources-Forestry Archive, Bugwood.org.</code> | <code>bucktail that gets talked about quietly in the . privacy of remote cabins. The âMusky-Teerâ is a big fish bait that anglers treasure in their collection. You wonât find these at your local bait shop but weâve been stocking these highly prized baits in all colors for years.</code> | <code>11.004357814788818</code> |
* Loss: [<code>SpladeLoss</code>](https://sbert.net/docs/package_reference/sparse_encoder/losses.html#spladeloss) with these parameters:
```json
{
"loss": "SparseMarginMSELoss",
"lambda_corpus": 0.08,
"lambda_query": 0.1
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `learning_rate`: 2e-05
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `fp16`: True
- `batch_sampler`: no_duplicates
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 2e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 1
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: True
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
- `router_mapping`: {}
- `learning_rate_mapping`: {}
</details>
### Training Logs
| Epoch | Step | Training Loss | Validation Loss | NanoMSMARCO_dot_ndcg@10 | NanoNFCorpus_dot_ndcg@10 | NanoNQ_dot_ndcg@10 | NanoBEIR_mean_dot_ndcg@10 |
|:------:|:----:|:-------------:|:---------------:|:-----------------------:|:------------------------:|:------------------:|:-------------------------:|
| 0.0178 | 100 | 501776.8 | - | - | - | - | - |
| 0.0356 | 200 | 9740.8356 | - | - | - | - | - |
| 0.0533 | 300 | 61.9771 | - | - | - | - | - |
| 0.0711 | 400 | 37.6145 | - | - | - | - | - |
| 0.0889 | 500 | 28.8887 | 24.4953 | 0.4878 | 0.3047 | 0.5425 | 0.4450 |
| 0.1067 | 600 | 24.7991 | - | - | - | - | - |
| 0.1244 | 700 | 22.1517 | - | - | - | - | - |
| 0.1422 | 800 | 22.0889 | - | - | - | - | - |
| 0.16 | 900 | 20.7825 | - | - | - | - | - |
| 0.1778 | 1000 | 20.0856 | 18.6383 | 0.5751 | 0.3303 | 0.6100 | 0.5051 |
| 0.1956 | 1100 | 18.6968 | - | - | - | - | - |
| 0.2133 | 1200 | 20.5069 | - | - | - | - | - |
| 0.2311 | 1300 | 19.8162 | - | - | - | - | - |
| 0.2489 | 1400 | 19.1892 | - | - | - | - | - |
| 0.2667 | 1500 | 17.5024 | 18.0698 | 0.5750 | 0.3281 | 0.6222 | 0.5084 |
| 0.2844 | 1600 | 17.7801 | - | - | - | - | - |
| 0.3022 | 1700 | 17.9045 | - | - | - | - | - |
| 0.32 | 1800 | 16.3731 | - | - | - | - | - |
| 0.3378 | 1900 | 16.293 | - | - | - | - | - |
| 0.3556 | 2000 | 16.1167 | 14.5428 | 0.5696 | 0.3422 | 0.6232 | 0.5116 |
| 0.3733 | 2100 | 16.561 | - | - | - | - | - |
| 0.3911 | 2200 | 16.5533 | - | - | - | - | - |
| 0.4089 | 2300 | 14.9371 | - | - | - | - | - |
| 0.4267 | 2400 | 15.565 | - | - | - | - | - |
| 0.4444 | 2500 | 14.2143 | 15.2027 | 0.6071 | 0.3376 | 0.6600 | 0.5349 |
| 0.4622 | 2600 | 13.7188 | - | - | - | - | - |
| 0.48 | 2700 | 14.8554 | - | - | - | - | - |
| 0.4978 | 2800 | 15.1021 | - | - | - | - | - |
| 0.5156 | 2900 | 13.3032 | - | - | - | - | - |
| 0.5333 | 3000 | 13.8999 | 12.9609 | 0.5874 | 0.3423 | 0.6562 | 0.5286 |
| 0.5511 | 3100 | 12.7418 | - | - | - | - | - |
| 0.5689 | 3200 | 12.9422 | - | - | - | - | - |
| 0.5867 | 3300 | 13.6937 | - | - | - | - | - |
| 0.6044 | 3400 | 13.1183 | - | - | - | - | - |
| 0.6222 | 3500 | 12.7998 | 12.2024 | 0.6262 | 0.3424 | 0.6771 | 0.5486 |
| 0.64 | 3600 | 12.7799 | - | - | - | - | - |
| 0.6578 | 3700 | 12.2294 | - | - | - | - | - |
| 0.6756 | 3800 | 13.6836 | - | - | - | - | - |
| 0.6933 | 3900 | 13.579 | - | - | - | - | - |
| 0.7111 | 4000 | 12.6337 | 13.9878 | 0.6156 | 0.3435 | 0.6526 | 0.5372 |
| 0.7289 | 4100 | 12.682 | - | - | - | - | - |
| 0.7467 | 4200 | 12.2157 | - | - | - | - | - |
| 0.7644 | 4300 | 12.3127 | - | - | - | - | - |
| 0.7822 | 4400 | 11.7435 | - | - | - | - | - |
| 0.8 | 4500 | 12.086 | 12.3685 | 0.6262 | 0.3386 | 0.6782 | 0.5477 |
| 0.8178 | 4600 | 12.5455 | - | - | - | - | - |
| 0.8356 | 4700 | 11.7477 | - | - | - | - | - |
| 0.8533 | 4800 | 11.9948 | - | - | - | - | - |
| 0.8711 | 4900 | 11.8997 | - | - | - | - | - |
| 0.8889 | 5000 | 12.1624 | 12.8277 | 0.6241 | 0.3515 | 0.6740 | 0.5499 |
| 0.9067 | 5100 | 11.4352 | - | - | - | - | - |
| 0.9244 | 5200 | 10.9171 | - | - | - | - | - |
| 0.9422 | 5300 | 11.3242 | - | - | - | - | - |
| 0.96 | 5400 | 11.437 | - | - | - | - | - |
| 0.9778 | 5500 | 11.3141 | 11.6410 | 0.6366 | 0.3441 | 0.6605 | 0.5471 |
| 0.9956 | 5600 | 11.8683 | - | - | - | - | - |
| -1 | -1 | - | - | 0.6312 | 0.3445 | 0.6640 | 0.5466 |
### Environmental Impact
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
- **Energy Consumed**: 0.225 kWh
- **Carbon Emitted**: 0.088 kg of CO2
- **Hours Used**: 0.653 hours
### Training Hardware
- **On Cloud**: No
- **GPU Model**: 1 x NVIDIA GeForce RTX 3090
- **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700K
- **RAM Size**: 31.78 GB
### Framework Versions
- Python: 3.11.6
- Sentence Transformers: 4.2.0.dev0
- Transformers: 4.52.4
- PyTorch: 2.6.0+cu124
- Accelerate: 1.5.1
- Datasets: 2.21.0
- Tokenizers: 0.21.1
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### SpladeLoss
```bibtex
@misc{formal2022distillationhardnegativesampling,
title={From Distillation to Hard Negative Sampling: Making Sparse Neural IR Models More Effective},
author={Thibault Formal and Carlos Lassance and Benjamin Piwowarski and Stéphane Clinchant},
year={2022},
eprint={2205.04733},
archivePrefix={arXiv},
primaryClass={cs.IR},
url={https://arxiv.org/abs/2205.04733},
}
```
#### SparseMarginMSELoss
```bibtex
@misc{hofstätter2021improving,
title={Improving Efficient Neural Ranking Models with Cross-Architecture Knowledge Distillation},
author={Sebastian Hofstätter and Sophia Althammer and Michael Schröder and Mete Sertkan and Allan Hanbury},
year={2021},
eprint={2010.02666},
archivePrefix={arXiv},
primaryClass={cs.IR}
}
```
#### FlopsLoss
```bibtex
@article{paria2020minimizing,
title={Minimizing flops to learn efficient sparse representations},
author={Paria, Biswajit and Yeh, Chih-Kuan and Yen, Ian EH and Xu, Ning and Ravikumar, Pradeep and P{'o}czos, Barnab{'a}s},
journal={arXiv preprint arXiv:2004.05665},
year={2020}
}
```
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MinaMila/gemma_2b_unlearned_2nd_1e-6_1.0_0.15_0.15_0.25_epoch2 | MinaMila | 2025-06-13T11:17:06Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gemma2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-13T11:15:12Z | ---
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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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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MinaMila/gemma_2b_unlearned_2nd_1e-6_1.0_0.15_0.15_0.25_epoch1 | MinaMila | 2025-06-13T11:09:19Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gemma2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-13T11:07:25Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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## Model Details
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
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## Bias, Risks, and Limitations
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[More Information Needed]
### Recommendations
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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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## Training Details
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[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]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## Model Card Authors [optional]
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## Model Card Contact
[More Information Needed] |
mradermacher/Agatha-111B-v1-GGUF | mradermacher | 2025-06-13T11:06:57Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:TheDrummer/Agatha-111B-v1",
"base_model:quantized:TheDrummer/Agatha-111B-v1",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-06-12T19:37:59Z | ---
base_model: TheDrummer/Agatha-111B-v1
language:
- en
library_name: transformers
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/TheDrummer/Agatha-111B-v1
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Agatha-111B-v1-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Agatha-111B-v1-GGUF/resolve/main/Agatha-111B-v1.Q2_K.gguf) | Q2_K | 42.2 | |
| [GGUF](https://huggingface.co/mradermacher/Agatha-111B-v1-GGUF/resolve/main/Agatha-111B-v1.Q3_K_S.gguf) | Q3_K_S | 49.1 | |
| [PART 1](https://huggingface.co/mradermacher/Agatha-111B-v1-GGUF/resolve/main/Agatha-111B-v1.Q3_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Agatha-111B-v1-GGUF/resolve/main/Agatha-111B-v1.Q3_K_M.gguf.part2of2) | Q3_K_M | 54.5 | lower quality |
| [PART 1](https://huggingface.co/mradermacher/Agatha-111B-v1-GGUF/resolve/main/Agatha-111B-v1.Q3_K_L.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Agatha-111B-v1-GGUF/resolve/main/Agatha-111B-v1.Q3_K_L.gguf.part2of2) | Q3_K_L | 59.2 | |
| [PART 1](https://huggingface.co/mradermacher/Agatha-111B-v1-GGUF/resolve/main/Agatha-111B-v1.IQ4_XS.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Agatha-111B-v1-GGUF/resolve/main/Agatha-111B-v1.IQ4_XS.gguf.part2of2) | IQ4_XS | 60.7 | |
| [PART 1](https://huggingface.co/mradermacher/Agatha-111B-v1-GGUF/resolve/main/Agatha-111B-v1.Q4_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Agatha-111B-v1-GGUF/resolve/main/Agatha-111B-v1.Q4_K_S.gguf.part2of2) | Q4_K_S | 63.9 | fast, recommended |
| [PART 1](https://huggingface.co/mradermacher/Agatha-111B-v1-GGUF/resolve/main/Agatha-111B-v1.Q4_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Agatha-111B-v1-GGUF/resolve/main/Agatha-111B-v1.Q4_K_M.gguf.part2of2) | Q4_K_M | 67.2 | fast, recommended |
| [PART 1](https://huggingface.co/mradermacher/Agatha-111B-v1-GGUF/resolve/main/Agatha-111B-v1.Q5_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Agatha-111B-v1-GGUF/resolve/main/Agatha-111B-v1.Q5_K_S.gguf.part2of2) | Q5_K_S | 76.9 | |
| [PART 1](https://huggingface.co/mradermacher/Agatha-111B-v1-GGUF/resolve/main/Agatha-111B-v1.Q5_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Agatha-111B-v1-GGUF/resolve/main/Agatha-111B-v1.Q5_K_M.gguf.part2of2) | Q5_K_M | 78.9 | |
| [PART 1](https://huggingface.co/mradermacher/Agatha-111B-v1-GGUF/resolve/main/Agatha-111B-v1.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Agatha-111B-v1-GGUF/resolve/main/Agatha-111B-v1.Q6_K.gguf.part2of2) | Q6_K | 91.2 | very good quality |
| [PART 1](https://huggingface.co/mradermacher/Agatha-111B-v1-GGUF/resolve/main/Agatha-111B-v1.Q8_0.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/Agatha-111B-v1-GGUF/resolve/main/Agatha-111B-v1.Q8_0.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/Agatha-111B-v1-GGUF/resolve/main/Agatha-111B-v1.Q8_0.gguf.part3of3) | Q8_0 | 118.1 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
MaiAhmed/medgemma-4b-it-sft-lora-flare-classification | MaiAhmed | 2025-06-13T10:51:33Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:google/medgemma-4b-it",
"base_model:finetune:google/medgemma-4b-it",
"endpoints_compatible",
"region:us"
] | null | 2025-06-12T00:59:33Z | ---
base_model: google/medgemma-4b-it
library_name: transformers
model_name: medgemma-4b-it-sft-lora-flare-classification
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for medgemma-4b-it-sft-lora-flare-classification
This model is a fine-tuned version of [google/medgemma-4b-it](https://huggingface.co/google/medgemma-4b-it).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="MaiAhmed/medgemma-4b-it-sft-lora-flare-classification", 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/mai-cs/huggingface/runs/o3qi37x6)
This model was trained with SFT.
### Framework versions
- TRL: 0.18.1
- Transformers: 4.51.3
- Pytorch: 2.3.1+cu118
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
rohitnagareddy/gemma-2b-python-expert-lora | rohitnagareddy | 2025-06-13T10:29:12Z | 0 | 0 | peft | [
"peft",
"safetensors",
"text-to-lora",
"sakana-ai",
"lora",
"python",
"code-generation",
"programming",
"base_model:google/gemma-2b-it",
"base_model:adapter:google/gemma-2b-it",
"license:apache-2.0",
"region:us"
] | null | 2025-06-13T10:15:29Z | ---
license: apache-2.0
base_model: google/gemma-2b-it
tags:
- text-to-lora
- sakana-ai
- peft
- lora
- python
- code-generation
- programming
library_name: peft
---
# gemma-2b-python-expert-lora(Text to Model)
This LoRA adapter specializes the base model for expert-level Python programming. Created using Sakana AI's Text-to-LoRA technology.
## Model Details
- **Base Model**: `google/gemma-2b-it`
- **LoRA Rank**: 16
- **Target Modules**: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
- **Task**: Python Code Generation
## Usage
```python
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
# Load base model and tokenizer
model = AutoModelForCausalLM.from_pretrained("google/gemma-2b-it")
tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b-it")
# Load LoRA adapter
model = PeftModel.from_pretrained(model, "rohitnagareddy/gemma-2b-python-expert-lora")
# Generate Python code
prompt = "Write a Python function to implement binary search:"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
## Capabilities
- Clean, documented Python code
- Type hints and error handling
- PEP 8 compliance
- Algorithm implementation
- Web development
- Data processing
- Testing and debugging
## Citation
```bibtex
@misc{sakana2024texttolora,
title={Text-to-LoRA},
author={Sakana AI},
year={2024},
url={https://github.com/SakanaAI/text-to-lora}
}
```
|
MinaMila/gemma_2b_unlearned_2nd_1e-6_1.0_0.15_0.25_0.05_epoch1 | MinaMila | 2025-06-13T10:22:01Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gemma2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-13T10:20:13Z | ---
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] |
mradermacher/CoRT-Prompt-Hint-1.5B-RL-GGUF | mradermacher | 2025-06-13T09:58:59Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:theshyustc/CoRT-Prompt-Hint-1.5B-RL",
"base_model:quantized:theshyustc/CoRT-Prompt-Hint-1.5B-RL",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-06-13T09:45:48Z | ---
base_model: theshyustc/CoRT-Prompt-Hint-1.5B-RL
language:
- en
library_name: transformers
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/theshyustc/CoRT-Prompt-Hint-1.5B-RL
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/CoRT-Prompt-Hint-1.5B-RL-GGUF/resolve/main/CoRT-Prompt-Hint-1.5B-RL.Q2_K.gguf) | Q2_K | 0.9 | |
| [GGUF](https://huggingface.co/mradermacher/CoRT-Prompt-Hint-1.5B-RL-GGUF/resolve/main/CoRT-Prompt-Hint-1.5B-RL.Q3_K_S.gguf) | Q3_K_S | 1.0 | |
| [GGUF](https://huggingface.co/mradermacher/CoRT-Prompt-Hint-1.5B-RL-GGUF/resolve/main/CoRT-Prompt-Hint-1.5B-RL.Q3_K_M.gguf) | Q3_K_M | 1.0 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/CoRT-Prompt-Hint-1.5B-RL-GGUF/resolve/main/CoRT-Prompt-Hint-1.5B-RL.Q3_K_L.gguf) | Q3_K_L | 1.1 | |
| [GGUF](https://huggingface.co/mradermacher/CoRT-Prompt-Hint-1.5B-RL-GGUF/resolve/main/CoRT-Prompt-Hint-1.5B-RL.IQ4_XS.gguf) | IQ4_XS | 1.1 | |
| [GGUF](https://huggingface.co/mradermacher/CoRT-Prompt-Hint-1.5B-RL-GGUF/resolve/main/CoRT-Prompt-Hint-1.5B-RL.Q4_K_S.gguf) | Q4_K_S | 1.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/CoRT-Prompt-Hint-1.5B-RL-GGUF/resolve/main/CoRT-Prompt-Hint-1.5B-RL.Q4_K_M.gguf) | Q4_K_M | 1.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/CoRT-Prompt-Hint-1.5B-RL-GGUF/resolve/main/CoRT-Prompt-Hint-1.5B-RL.Q5_K_S.gguf) | Q5_K_S | 1.4 | |
| [GGUF](https://huggingface.co/mradermacher/CoRT-Prompt-Hint-1.5B-RL-GGUF/resolve/main/CoRT-Prompt-Hint-1.5B-RL.Q5_K_M.gguf) | Q5_K_M | 1.4 | |
| [GGUF](https://huggingface.co/mradermacher/CoRT-Prompt-Hint-1.5B-RL-GGUF/resolve/main/CoRT-Prompt-Hint-1.5B-RL.Q6_K.gguf) | Q6_K | 1.6 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/CoRT-Prompt-Hint-1.5B-RL-GGUF/resolve/main/CoRT-Prompt-Hint-1.5B-RL.Q8_0.gguf) | Q8_0 | 2.0 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/CoRT-Prompt-Hint-1.5B-RL-GGUF/resolve/main/CoRT-Prompt-Hint-1.5B-RL.f16.gguf) | f16 | 3.7 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
1shoomun/pq_cache_9 | 1shoomun | 2025-06-13T09:52:57Z | 0 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"t5",
"sentence-similarity",
"feature-extraction",
"generated_from_trainer",
"dataset_size:11442",
"loss:MultipleNegativesRankingLoss",
"loss:CosineSimilarityLoss",
"loss:ContrastiveLoss",
"custom_code",
"arxiv:1908.10084",
"arxiv:1705.00652",
"base_... | sentence-similarity | 2025-06-13T09:50:02Z | ---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:11442
- loss:MultipleNegativesRankingLoss
- loss:CosineSimilarityLoss
- loss:ContrastiveLoss
base_model: jinaai/jina-embedding-b-en-v1
widget:
- source_sentence: What are the underperforming funds in my portfolio?
sentences:
- Switch my stock portfolio with mutual funds
- List me cheapest funds
- Which of my funds aren't doing well?
- source_sentence: Mera score dosto ke hisab se kitna accha hai?
sentences:
- Mera score mere dosto ke hisab se kitna jyada acha hai?
- What are others like me investing in?
- Show my funds portfolio
- source_sentence: Am I paying too much in fees for my investments?
sentences:
- How much more am I paying in fees across my investments?
- What is my market cap allocation?
- What are my investments?
- source_sentence: Can you check if my investments will increase in value long-term?
sentences:
- Do you have any insights on my portfolio
- Can you tell me if my investments will grow well in the long run?
- What is my asset allocation?
- source_sentence: What was the annual performance of my portfolio last year?
sentences:
- Need to change my risk appetite
- I want to refresh my portfolio
- What is my concentration risk in stocks
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 jinaai/jina-embedding-b-en-v1
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: test eval
type: test-eval
metrics:
- type: cosine_accuracy@1
value: 0.8601036269430051
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.9792746113989638
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 1.0
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 1.0
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.8601036269430051
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.32642487046632124
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.19999999999999998
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09999999999999999
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.8601036269430051
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.9792746113989638
name: Cosine Recall@3
- type: cosine_recall@5
value: 1.0
name: Cosine Recall@5
- type: cosine_recall@10
value: 1.0
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.9394665325932218
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.9189119170984456
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.9189119170984456
name: Cosine Map@100
---
# SentenceTransformer based on jinaai/jina-embedding-b-en-v1
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [jinaai/jina-embedding-b-en-v1](https://huggingface.co/jinaai/jina-embedding-b-en-v1). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [jinaai/jina-embedding-b-en-v1](https://huggingface.co/jinaai/jina-embedding-b-en-v1) <!-- at revision 32aa658e5ceb90793454d22a57d8e3a14e699516 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 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: T5EncoderModel
(1): Pooling({'word_embedding_dimension': 768, '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})
)
```
## 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("sentence_transformers_model_id")
# Run inference
sentences = [
'What was the annual performance of my portfolio last year?',
'I want to refresh my portfolio',
'Need to change my risk appetite',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
## Evaluation
### Metrics
#### Information Retrieval
* Dataset: `test-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 | 0.8601 |
| cosine_accuracy@3 | 0.9793 |
| cosine_accuracy@5 | 1.0 |
| cosine_accuracy@10 | 1.0 |
| cosine_precision@1 | 0.8601 |
| cosine_precision@3 | 0.3264 |
| cosine_precision@5 | 0.2 |
| cosine_precision@10 | 0.1 |
| cosine_recall@1 | 0.8601 |
| cosine_recall@3 | 0.9793 |
| cosine_recall@5 | 1.0 |
| cosine_recall@10 | 1.0 |
| **cosine_ndcg@10** | **0.9395** |
| cosine_mrr@10 | 0.9189 |
| cosine_map@100 | 0.9189 |
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Datasets
#### Unnamed Dataset
* Size: 1,907 training samples
* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | sentence_0 | sentence_1 | label |
|:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:--------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 4 tokens</li><li>mean: 11.28 tokens</li><li>max: 26 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 10.0 tokens</li><li>max: 33 tokens</li></ul> | <ul><li>min: 1.0</li><li>mean: 1.0</li><li>max: 1.0</li></ul> |
* Samples:
| sentence_0 | sentence_1 | label |
|:-------------------------------------------------------|:---------------------------------------------------------|:-----------------|
| <code>how many commodities do I have right now?</code> | <code>how much commodities do I hold?</code> | <code>1.0</code> |
| <code>Can you tell me my top sector investment?</code> | <code>Which sector do I invest most in?</code> | <code>1.0</code> |
| <code>Look for funds that fit my stock holdings</code> | <code>Explore funds that match my stock portfolio</code> | <code>1.0</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"
}
```
#### Unnamed Dataset
* Size: 1,907 training samples
* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | sentence_0 | sentence_1 | label |
|:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:--------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 4 tokens</li><li>mean: 11.28 tokens</li><li>max: 26 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 9.93 tokens</li><li>max: 33 tokens</li></ul> | <ul><li>min: 1.0</li><li>mean: 1.0</li><li>max: 1.0</li></ul> |
* Samples:
| sentence_0 | sentence_1 | label |
|:--------------------------------------------------------------|:-----------------------------------------------------|:-----------------|
| <code>Can you tell me my least performing investments?</code> | <code>What are my worst performing holdings</code> | <code>1.0</code> |
| <code>Sort my portfolio by assets under management</code> | <code>Sort my investments based on AUM</code> | <code>1.0</code> |
| <code>How will this news affect my investments?</code> | <code>How does this news affect my portfolio?</code> | <code>1.0</code> |
* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
```json
{
"loss_fct": "torch.nn.modules.loss.MSELoss"
}
```
#### Unnamed Dataset
* Size: 7,628 training samples
* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | sentence_0 | sentence_1 | label |
|:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 4 tokens</li><li>mean: 11.15 tokens</li><li>max: 26 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 8.94 tokens</li><li>max: 33 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.24</li><li>max: 1.0</li></ul> |
* Samples:
| sentence_0 | sentence_1 | label |
|:------------------------------------------------------|:--------------------------------------------------------|:-----------------|
| <code>How much of my portfolio is in X?</code> | <code>What is my concentration risk in stocks</code> | <code>0.0</code> |
| <code>Can I switch my stocks for mutual funds?</code> | <code>Can I exchange my stocks for mutual funds?</code> | <code>1.0</code> |
| <code>Please break down my holdings in X.</code> | <code>I want to refresh my portfolio</code> | <code>0.0</code> |
* Loss: [<code>ContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#contrastiveloss) with these parameters:
```json
{
"distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE",
"margin": 0.5,
"size_average": true
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `num_train_epochs`: 15
- `multi_dataset_batch_sampler`: round_robin
#### 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`: 32
- `per_device_eval_batch_size`: 32
- `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
- `num_train_epochs`: 15
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.0
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `tp_size`: 0
- `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`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin
</details>
### Training Logs
| Epoch | Step | Training Loss | test-eval_cosine_ndcg@10 |
|:------:|:----:|:-------------:|:------------------------:|
| 1.0 | 180 | - | 0.8971 |
| 2.0 | 360 | - | 0.9210 |
| 2.7778 | 500 | 0.1444 | 0.9258 |
| 3.0 | 540 | - | 0.9275 |
| 4.0 | 720 | - | 0.9298 |
| 5.0 | 900 | - | 0.9368 |
| 5.5556 | 1000 | 0.0916 | 0.9395 |
### Framework Versions
- Python: 3.10.16
- Sentence Transformers: 4.1.0
- Transformers: 4.51.3
- PyTorch: 2.7.0
- Accelerate: 1.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### 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}
}
```
#### ContrastiveLoss
```bibtex
@inproceedings{hadsell2006dimensionality,
author={Hadsell, R. and Chopra, S. and LeCun, Y.},
booktitle={2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)},
title={Dimensionality Reduction by Learning an Invariant Mapping},
year={2006},
volume={2},
number={},
pages={1735-1742},
doi={10.1109/CVPR.2006.100}
}
```
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mradermacher/Willow-0.6b-GGUF | mradermacher | 2025-06-13T09:47:14Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:marcuscedricridia/Willow-0.6b",
"base_model:quantized:marcuscedricridia/Willow-0.6b",
"endpoints_compatible",
"region:us"
] | null | 2025-06-13T09:42:53Z | ---
base_model: marcuscedricridia/Willow-0.6b
language:
- en
library_name: transformers
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/marcuscedricridia/Willow-0.6b
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Willow-0.6b-GGUF/resolve/main/Willow-0.6b.Q2_K.gguf) | Q2_K | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/Willow-0.6b-GGUF/resolve/main/Willow-0.6b.Q3_K_S.gguf) | Q3_K_S | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/Willow-0.6b-GGUF/resolve/main/Willow-0.6b.Q3_K_M.gguf) | Q3_K_M | 0.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Willow-0.6b-GGUF/resolve/main/Willow-0.6b.Q3_K_L.gguf) | Q3_K_L | 0.5 | |
| [GGUF](https://huggingface.co/mradermacher/Willow-0.6b-GGUF/resolve/main/Willow-0.6b.IQ4_XS.gguf) | IQ4_XS | 0.5 | |
| [GGUF](https://huggingface.co/mradermacher/Willow-0.6b-GGUF/resolve/main/Willow-0.6b.Q4_K_S.gguf) | Q4_K_S | 0.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Willow-0.6b-GGUF/resolve/main/Willow-0.6b.Q4_K_M.gguf) | Q4_K_M | 0.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Willow-0.6b-GGUF/resolve/main/Willow-0.6b.Q5_K_S.gguf) | Q5_K_S | 0.5 | |
| [GGUF](https://huggingface.co/mradermacher/Willow-0.6b-GGUF/resolve/main/Willow-0.6b.Q5_K_M.gguf) | Q5_K_M | 0.5 | |
| [GGUF](https://huggingface.co/mradermacher/Willow-0.6b-GGUF/resolve/main/Willow-0.6b.Q6_K.gguf) | Q6_K | 0.6 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Willow-0.6b-GGUF/resolve/main/Willow-0.6b.Q8_0.gguf) | Q8_0 | 0.7 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Willow-0.6b-GGUF/resolve/main/Willow-0.6b.f16.gguf) | f16 | 1.3 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
umeshkaushik610/emotion-multilabel-distilbert | umeshkaushik610 | 2025-06-13T09:46:08Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-06-13T09:45:40Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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## Model Details
### Model Description
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## Uses
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Satram/Llama_Instruct_Manuales3 | Satram | 2025-06-13T09:45:29Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-13T08:47:25Z | ---
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:** Satram
- **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)
|
gradientrouting-spar/gcd_syco_cap_math_kl_div_beta_kl-1000_seed_5 | gradientrouting-spar | 2025-06-13T09:43:14Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-13T01:07:39Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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seungbin11/gemma2b_senior | seungbin11 | 2025-06-13T09:41:13Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"conversational",
"ko",
"dataset:hyokwan/senior_sleeping",
"arxiv:1910.09700",
"base_model:google/gemma-2-2b-it",
"base_model:finetune:google/gemma-2-2b-it",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
... | text-generation | 2025-06-13T09:27:07Z | ---
library_name: transformers
license: apache-2.0
datasets:
- hyokwan/senior_sleeping
language:
- ko
metrics:
- accuracy
base_model:
- google/gemma-2-2b-it
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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psy777/gemma2b_it_senior | psy777 | 2025-06-13T09:38:56Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-13T09:29:54Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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## Model Details
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aieng-lab/gpt2-large_issue-type | aieng-lab | 2025-06-13T09:34:08Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt2",
"text-classification",
"en",
"base_model:openai-community/gpt2-large",
"base_model:finetune:openai-community/gpt2-large",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-06-13T09:33:38Z | ---
library_name: transformers
license: mit
language:
- en
metrics:
- f1
- precision
- recall
base_model:
- gpt2-large
pipeline_tag: text-classification
---
# GPT-2 large for classifying issues
This model classifies GitHub issues as 'bug', 'enhancement' or 'question'.
- **Developed by:** Fabian C. Peña, Steffen Herbold
- **Finetuned from:** [gpt2-large](https://huggingface.co/gpt2-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}
}
```
|
logasanjeev/emotions-analyzer-bert | logasanjeev | 2025-06-13T09:29:13Z | 3,674 | 1 | transformers | [
"transformers",
"onnx",
"safetensors",
"text-classification",
"pytorch",
"multi-label-classification",
"multi-class-classification",
"emotion",
"bert",
"go_emotions",
"emotion-classification",
"sentiment-analysis",
"tensorflow",
"en",
"dataset:google-research-datasets/go_emotions",
"ba... | text-classification | 2025-04-12T10:30:03Z | ---
language: en
license: mit
pipeline_tag: text-classification
tags:
- text-classification
- transformers
- pytorch
- onnx
- multi-label-classification
- multi-class-classification
- emotion
- bert
- go_emotions
- emotion-classification
- sentiment-analysis
- tensorflow
datasets:
- google-research-datasets/go_emotions
metrics:
- f1
- precision
- recall
- accuracy
widget:
- text: I’m just chilling today.
example_title: Neutral Example
- text: Thank you for saving my life!
example_title: Gratitude Example
- text: I’m nervous about my exam tomorrow.
example_title: Nervousness Example
- text: I love my new puppy so much!
example_title: Love Example
- text: I’m so relieved the storm passed.
example_title: Relief Example
base_model:
- google-bert/bert-base-uncased
base_model_relation: finetune
model-index:
- name: Emotion Analyzer Bert
results:
- task:
type: multi-label-classification
dataset:
name: GoEmotions
type: google-research-datasets/go_emotions
metrics:
- name: Micro F1 (Optimized Thresholds)
type: micro-f1
value: 0.6006
- name: Macro F1
type: macro-f1
value: 0.539
- name: Precision
type: precision
value: 0.5371
- name: Recall
type: recall
value: 0.6812
- name: Hamming Loss
type: hamming-loss
value: 0.0377
- name: Avg Positive Predictions
type: avg-positive-predictions
value: 1.4789
- task:
type: multi-label-classification
dataset:
name: GoEmotions
type: google-research-datasets/go_emotions
metrics:
- name: F1 (admiration)
type: f1
value: 0.6987
- name: F1 (amusement)
type: f1
value: 0.8071
- name: F1 (anger)
type: f1
value: 0.503
- name: F1 (annoyance)
type: f1
value: 0.3892
- name: F1 (approval)
type: f1
value: 0.3915
- name: F1 (caring)
type: f1
value: 0.4473
- name: F1 (confusion)
type: f1
value: 0.4714
- name: F1 (curiosity)
type: f1
value: 0.5781
- name: F1 (desire)
type: f1
value: 0.5229
- name: F1 (disappointment)
type: f1
value: 0.3333
- name: F1 (disapproval)
type: f1
value: 0.4323
- name: F1 (disgust)
type: f1
value: 0.4926
- name: F1 (embarrassment)
type: f1
value: 0.4912
- name: F1 (excitement)
type: f1
value: 0.4571
- name: F1 (fear)
type: f1
value: 0.586
- name: F1 (gratitude)
type: f1
value: 0.9102
- name: F1 (grief)
type: f1
value: 0.3333
- name: F1 (joy)
type: f1
value: 0.6135
- name: F1 (love)
type: f1
value: 0.8065
- name: F1 (nervousness)
type: f1
value: 0.4348
- name: F1 (optimism)
type: f1
value: 0.5564
- name: F1 (pride)
type: f1
value: 0.5217
- name: F1 (realization)
type: f1
value: 0.2513
- name: F1 (relief)
type: f1
value: 0.5833
- name: F1 (remorse)
type: f1
value: 0.68
- name: F1 (sadness)
type: f1
value: 0.557
- name: F1 (surprise)
type: f1
value: 0.5562
- name: F1 (neutral)
type: f1
value: 0.6867
source:
name: Kaggle Evaluation Notebook
url: >-
https://www.kaggle.com/code/ravindranlogasanjeev/evaluation-logasanjeev-emotions-analyzer-bert/notebook
---
# Emotions Analyzer Bert
Fine-tuned [BERT-base-uncased](https://huggingface.co/bert-base-uncased) on [GoEmotions](https://huggingface.co/datasets/go_emotions) for multi-label classification (28 emotions). This updated version includes improved Macro F1, ONNX support for efficient inference, and visualizations for better interpretability.
## Model Details
- **Architecture**: BERT-base-uncased (110M parameters)
- **Training Data**: [GoEmotions](https://huggingface.co/datasets/google-research-datasets/go_emotions) (58k Reddit comments, 28 emotions)
- **Loss Function**: Focal Loss (alpha=1, gamma=2)
- **Optimizer**: AdamW (lr=2e-5, weight_decay=0.01)
- **Epochs**: 5
- **Batch Size**: 16
- **Max Length**: 128
- **Hardware**: Kaggle P100 GPU (16GB)
## Try It Out
For accurate predictions with optimized thresholds, use the [Gradio demo](https://logasanjeev-emotions-analyzer-bert-demo.hf.space). The demo now includes preprocessed text and the top 5 predicted emotions, in addition to thresholded predictions. Example predictions:
- **Input**: "I’m thrilled to win this award! 😄"
- **Output**: `excitement: 0.5836, joy: 0.5290`
- **Input**: "This is so frustrating, nothing works. 😣"
- **Output**: `annoyance: 0.6147, anger: 0.4669`
- **Input**: "I feel so sorry for what happened. 😢"
- **Output**: `sadness: 0.5321, remorse: 0.9107`
## Performance
- **Micro F1**: 0.6006 (optimized thresholds)
- **Macro F1**: 0.5390
- **Precision**: 0.5371
- **Recall**: 0.6812
- **Hamming Loss**: 0.0377
- **Avg Positive Predictions**: 1.4789
For a detailed evaluation, including class-wise accuracy, precision, recall, F1, MCC, support, and thresholds, along with visualizations, check out the [Kaggle notebook](https://www.kaggle.com/code/ravindranlogasanjeev/evaluation-logasanjeev-emotions-analyzer-bert/notebook).
### Class-Wise Performance
The following table shows per-class metrics on the test set using optimized thresholds (see `optimized_thresholds.json`):
| Emotion | Accuracy | Precision | Recall | F1 Score | MCC | Support | Threshold |
|---------------|----------|-----------|--------|----------|--------|---------|-----------|
| admiration | 0.9410 | 0.6649 | 0.7361 | 0.6987 | 0.6672 | 504 | 0.4500 |
| amusement | 0.9801 | 0.7635 | 0.8561 | 0.8071 | 0.7981 | 264 | 0.4500 |
| anger | 0.9694 | 0.6176 | 0.4242 | 0.5030 | 0.4970 | 198 | 0.4500 |
| annoyance | 0.9121 | 0.3297 | 0.4750 | 0.3892 | 0.3502 | 320 | 0.3500 |
| approval | 0.8843 | 0.2966 | 0.5755 | 0.3915 | 0.3572 | 351 | 0.3500 |
| caring | 0.9759 | 0.5196 | 0.3926 | 0.4473 | 0.4396 | 135 | 0.4500 |
| confusion | 0.9711 | 0.4861 | 0.4575 | 0.4714 | 0.4567 | 153 | 0.4500 |
| curiosity | 0.9368 | 0.4442 | 0.8275 | 0.5781 | 0.5783 | 284 | 0.4000 |
| desire | 0.9865 | 0.5714 | 0.4819 | 0.5229 | 0.5180 | 83 | 0.4000 |
| disappointment| 0.9565 | 0.2906 | 0.3907 | 0.3333 | 0.3150 | 151 | 0.3500 |
| disapproval | 0.9235 | 0.3405 | 0.5918 | 0.4323 | 0.4118 | 267 | 0.3500 |
| disgust | 0.9810 | 0.6250 | 0.4065 | 0.4926 | 0.4950 | 123 | 0.5500 |
| embarrassment | 0.9947 | 0.7000 | 0.3784 | 0.4912 | 0.5123 | 37 | 0.5000 |
| excitement | 0.9790 | 0.4486 | 0.4660 | 0.4571 | 0.4465 | 103 | 0.4000 |
| fear | 0.9836 | 0.4599 | 0.8077 | 0.5860 | 0.6023 | 78 | 0.3000 |
| gratitude | 0.9888 | 0.9450 | 0.8778 | 0.9102 | 0.9049 | 352 | 0.5500 |
| grief | 0.9985 | 0.3333 | 0.3333 | 0.3333 | 0.3326 | 6 | 0.3000 |
| joy | 0.9768 | 0.6061 | 0.6211 | 0.6135 | 0.6016 | 161 | 0.4500 |
| love | 0.9825 | 0.7826 | 0.8319 | 0.8065 | 0.7978 | 238 | 0.5000 |
| nervousness | 0.9952 | 0.4348 | 0.4348 | 0.4348 | 0.4324 | 23 | 0.4000 |
| optimism | 0.9689 | 0.5436 | 0.5699 | 0.5564 | 0.5405 | 186 | 0.4000 |
| pride | 0.9980 | 0.8571 | 0.3750 | 0.5217 | 0.5662 | 16 | 0.4000 |
| realization | 0.9737 | 0.5217 | 0.1655 | 0.2513 | 0.2838 | 145 | 0.4500 |
| relief | 0.9982 | 0.5385 | 0.6364 | 0.5833 | 0.5845 | 11 | 0.3000 |
| remorse | 0.9912 | 0.5426 | 0.9107 | 0.6800 | 0.6992 | 56 | 0.3500 |
| sadness | 0.9757 | 0.5845 | 0.5321 | 0.5570 | 0.5452 | 156 | 0.4500 |
| surprise | 0.9724 | 0.4772 | 0.6667 | 0.5562 | 0.5504 | 141 | 0.3500 |
| neutral | 0.7485 | 0.5821 | 0.8372 | 0.6867 | 0.5102 | 1787 | 0.4000 |
### Visualizations
#### Class-Wise F1 Scores
#### Training Curves
## Training Insights
The model was trained for 5 epochs with Focal Loss to handle class imbalance. Training and validation curves show consistent improvement:
- Training Loss decreased from 0.0429 to 0.0134.
- Validation Micro F1 peaked at 0.5874 (epoch 5).
- See the training curves plot above for details.
## Usage
### Quick Inference with inference.py (Recommended for PyTorch)
The easiest way to use the model with PyTorch is to programmatically fetch and use `inference.py` from the repository. The script handles all preprocessing, model loading, and inference for you.
#### Programmatic Download and Inference
Run the following Python script to download `inference.py` and make predictions:
```python
!pip install transformers torch huggingface_hub emoji -q
import shutil
import os
from huggingface_hub import hf_hub_download
from importlib import import_module
repo_id = "logasanjeev/emotions-analyzer-bert"
local_file = hf_hub_download(repo_id=repo_id, filename="inference.py")
current_dir = os.getcwd()
destination = os.path.join(current_dir, "inference.py")
shutil.copy(local_file, destination)
inference_module = import_module("inference")
predict_emotions = inference_module.predict_emotions
text = "I’m thrilled to win this award! 😄"
result, processed = predict_emotions(text)
print(f"Input: {text}")
print(f"Processed: {processed}")
print("Predicted Emotions:")
print(result)
```
#### Expected Output:
```
Input: I’m thrilled to win this award! 😄
Processed: i’m thrilled to win this award ! grinning_face_with_smiling_eyes
Predicted Emotions:
excitement: 0.5836
joy: 0.5290
```
#### Alternative: Manual Download
If you prefer to download `inference.py` manually:
1. Install the required dependencies:
```bash
pip install transformers torch huggingface_hub emoji
```
2. Download `inference.py` from the repository.
3. Use it in Python or via the command line.
**Python Example:**
```python
from inference import predict_emotions
result, processed = predict_emotions("I’m thrilled to win this award! 😄")
print(f"Input: I’m thrilled to win this award! 😄")
print(f"Processed: {processed}")
print("Predicted Emotions:")
print(result)
```
**Command-Line Example:**
```bash
python inference.py "I’m thrilled to win this award! 😄"
```
### Quick Inference with onnx_inference.py (Recommended for ONNX)
For faster and more efficient inference using ONNX, you can use `onnx_inference.py`. This script leverages ONNX Runtime for inference, which is typically more lightweight than PyTorch.
#### Programmatic Download and Inference
Run the following Python script to download `onnx_inference.py` and make predictions:
```python
!pip install transformers onnxruntime huggingface_hub emoji numpy -q
import shutil
import os
from huggingface_hub import hf_hub_download
from importlib import import_module
repo_id = "logasanjeev/emotions-analyzer-bert"
local_file = hf_hub_download(repo_id=repo_id, filename="onnx_inference.py")
current_dir = os.getcwd()
destination = os.path.join(current_dir, "onnx_inference.py")
shutil.copy(local_file, destination)
onnx_inference_module = import_module("onnx_inference")
predict_emotions = onnx_inference_module.predict_emotions
text = "I’m thrilled to win this award! 😄"
result, processed = predict_emotions(text)
print(f"Input: {text}")
print(f"Processed: {processed}")
print("Predicted Emotions:")
print(result)
```
#### Expected Output:
```
Input: I’m thrilled to win this award! 😄
Processed: i’m thrilled to win this award ! grinning_face_with_smiling_eyes
Predicted Emotions:
excitement: 0.5836
joy: 0.5290
```
#### Alternative: Manual Download
If you prefer to download `onnx_inference.py` manually:
1. Install the required dependencies:
```bash
pip install transformers onnxruntime huggingface_hub emoji numpy
```
2. Download `onnx_inference.py` from the repository.
3. Use it in Python or via the command line.
**Python Example:**
```python
from onnx_inference import predict_emotions
result, processed = predict_emotions("I’m thrilled to win this award! 😄")
print(f"Input: I’m thrilled to win this award! 😄")
print(f"Processed: {processed}")
print("Predicted Emotions:")
print(result)
```
**Command-Line Example:**
```bash
python onnx_inference.py "I’m thrilled to win this award! 😄"
```
### Preprocessing
Before inference, preprocess text to match training conditions:
- Replace user mentions (`u/username`) with `[USER]`.
- Replace subreddits (`r/subreddit`) with `[SUBREDDIT]`.
- Replace URLs with `[URL]`.
- Convert emojis to text using `emoji.demojize` (e.g., 😊 → `smiling_face_with_smiling_eyes`).
- Lowercase the text.
### PyTorch Inference
```python
from transformers import BertForSequenceClassification, BertTokenizer
import torch
import json
import requests
import re
import emoji
def preprocess_text(text):
text = re.sub(r'u/\w+', '[USER]', text)
text = re.sub(r'r/\w+', '[SUBREDDIT]', text)
text = re.sub(r'http[s]?://\S+', '[URL]', text)
text = emoji.demojize(text, delimiters=(" ", " "))
text = text.lower()
return text
repo_id = "logasanjeev/emotions-analyzer-bert"
model = BertForSequenceClassification.from_pretrained(repo_id)
tokenizer = BertTokenizer.from_pretrained(repo_id)
thresholds_url = f"https://huggingface.co/{repo_id}/raw/main/optimized_thresholds.json"
thresholds_data = json.loads(requests.get(thresholds_url).text)
emotion_labels = thresholds_data["emotion_labels"]
thresholds = thresholds_data["thresholds"]
text = "I’m just chilling today."
processed_text = preprocess_text(text)
encodings = tokenizer(processed_text, padding='max_length', truncation=True, max_length=128, return_tensors='pt')
with torch.no_grad():
logits = torch.sigmoid(model(**encodings).logits).numpy()[0]
predictions = [(emotion_labels[i], round(logit, 4)) for i, (logit, thresh) in enumerate(zip(logits, thresholds)) if logit >= thresh]
predictions = sorted(predictions, key=lambda x: x[1], reverse=True)
print(predictions)
# Output: [('neutral', 0.8147)]
```
### ONNX Inference
For a simplified ONNX inference experience, use `onnx_inference.py` as shown above. Alternatively, you can use the manual approach below:
```python
import onnxruntime as ort
import numpy as np
onnx_url = f"https://huggingface.co/{repo_id}/raw/main/model.onnx"
with open("model.onnx", "wb") as f:
f.write(requests.get(onnx_url).content)
text = "I’m thrilled to win this award! 😄"
processed_text = preprocess_text(text)
encodings = tokenizer(processed_text, padding='max_length', truncation=True, max_length=128, return_tensors='np')
session = ort.InferenceSession("model.onnx")
inputs = {
'input_ids': encodings['input_ids'].astype(np.int64),
'attention_mask': encodings['attention_mask'].astype(np.int64)
}
logits = session.run(None, inputs)[0][0]
logits = 1 / (1 + np.exp(-logits)) # Sigmoid
predictions = [(emotion_labels[i], round(logit, 4)) for i, (logit, thresh) in enumerate(zip(logits, thresholds)) if logit >= thresh]
predictions = sorted(predictions, key=lambda x: x[1], reverse=True)
print(predictions)
# Output: [('excitement', 0.5836), ('joy', 0.5290)]
```
## License
This model is licensed under the MIT License. See [LICENSE](LICENSE) for details.
## Usage Notes
- The model performs best on Reddit-style comments with similar preprocessing.
- Rare emotions (e.g., `grief`, support=6) have lower F1 scores due to limited data.
- ONNX inference requires `onnxruntime` and compatible hardware (opset 14).
## Inference Providers
This model isn't deployed by any Inference Provider. 🙋 Ask for provider support |
smyung/gemma2b_senior | smyung | 2025-06-13T09:27:32Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-13T09:22:17Z | ---
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] |
faizack/bayes_mini_2 | faizack | 2025-06-13T09:25:30Z | 0 | 0 | null | [
"pytorch",
"gpt2",
"region:us"
] | null | 2025-06-13T09:23:00Z | # Custom GPT-2 Model (Faijan)
This is a custom GPT-2-like model trained on Wikimedia text using a simplified architecture.
|
julycarbon/Llama-3.2-11B-Vision-Instruct-v0-20250612-200751 | julycarbon | 2025-06-13T09:19:57Z | 0 | 0 | null | [
"safetensors",
"mllama",
"license:apache-2.0",
"region:us"
] | null | 2025-06-13T03:19:50Z | ---
license: apache-2.0
---
|
stablediffusionapi/realisticponyXXX | stablediffusionapi | 2025-06-13T09:17:36Z | 0 | 0 | diffusers | [
"diffusers",
"modelslab.com",
"stable-diffusion-api",
"text-to-image",
"ultra-realistic",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] | text-to-image | 2025-06-13T09:15:37Z | ---
license: creativeml-openrail-m
tags:
- modelslab.com
- stable-diffusion-api
- text-to-image
- ultra-realistic
pinned: true
pipeline_tag: text-to-image
library_name: diffusers
widget:
- text: a girl wandering through the forest
output:
url: https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/efbc5406-03cf-4468-86a4-d468383f836e/anim=false,width=450/00033-439208775.jpeg
---
# None API Inference
<Gallery />
## Get API Key
Get API key from [ModelsLab API](http://modelslab.com), No Payment needed.
Replace Key in below code, change **model_id** to "realisticponyXXX"
Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://docs.modelslab.com)
Try model for free: [Generate Images](https://modelslab.com/models/realisticponyXXX)
Model link: [View model](https://modelslab.com/models/realisticponyXXX)
View all models: [View Models](https://modelslab.com/models)
```python
import requests
import json
url = "https://modelslab.com/api/v6/images/text2img"
payload = json.dumps({
"key": "your_api_key",
"model_id": "realisticponyXXX",
"prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K",
"negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime",
"width": "512",
"height": "512",
"samples": "1",
"num_inference_steps": "30",
"safety_checker": "no",
"enhance_prompt": "yes",
"seed": None,
"guidance_scale": 7.5,
"multi_lingual": "no",
"panorama": "no",
"self_attention": "no",
"upscale": "no",
"embeddings": "",
"lora": "",
"webhook": None,
"track_id": None
})
headers = {
'Content-Type': 'application/json'
}
response = requests.request("POST", url, headers=headers, data=payload)
print(response.text)
```
> Use this coupon code to get 25% off **DMGG0RBN** |
KevinCha/dinov2-base-psz16-img224-large-corpus | KevinCha | 2025-06-13T09:02:40Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-13T08:40:52Z | ---
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]
- **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] |
pyarn/bge-reranker-large-Q4_K_M-GGUF | pyarn | 2025-06-13T08:56:22Z | 0 | 0 | null | [
"gguf",
"mteb",
"llama-cpp",
"gguf-my-repo",
"feature-extraction",
"en",
"zh",
"base_model:BAAI/bge-reranker-large",
"base_model:quantized:BAAI/bge-reranker-large",
"license:mit",
"model-index",
"endpoints_compatible",
"region:us"
] | feature-extraction | 2025-06-13T08:56:17Z | ---
license: mit
language:
- en
- zh
tags:
- mteb
- llama-cpp
- gguf-my-repo
pipeline_tag: feature-extraction
base_model: BAAI/bge-reranker-large
model-index:
- name: bge-reranker-base
results:
- task:
type: Reranking
dataset:
name: MTEB CMedQAv1
type: C-MTEB/CMedQAv1-reranking
config: default
split: test
revision: None
metrics:
- type: map
value: 81.27206722525007
- type: mrr
value: 84.14238095238095
- task:
type: Reranking
dataset:
name: MTEB CMedQAv2
type: C-MTEB/CMedQAv2-reranking
config: default
split: test
revision: None
metrics:
- type: map
value: 84.10369934291236
- type: mrr
value: 86.79376984126984
- task:
type: Reranking
dataset:
name: MTEB MMarcoReranking
type: C-MTEB/Mmarco-reranking
config: default
split: dev
revision: None
metrics:
- type: map
value: 35.4600511272538
- type: mrr
value: 34.60238095238095
- task:
type: Reranking
dataset:
name: MTEB T2Reranking
type: C-MTEB/T2Reranking
config: default
split: dev
revision: None
metrics:
- type: map
value: 67.27728847727172
- type: mrr
value: 77.1315192743764
---
# pyarn/bge-reranker-large-Q4_K_M-GGUF
This model was converted to GGUF format from [`BAAI/bge-reranker-large`](https://huggingface.co/BAAI/bge-reranker-large) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/BAAI/bge-reranker-large) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo pyarn/bge-reranker-large-Q4_K_M-GGUF --hf-file bge-reranker-large-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo pyarn/bge-reranker-large-Q4_K_M-GGUF --hf-file bge-reranker-large-q4_k_m.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo pyarn/bge-reranker-large-Q4_K_M-GGUF --hf-file bge-reranker-large-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo pyarn/bge-reranker-large-Q4_K_M-GGUF --hf-file bge-reranker-large-q4_k_m.gguf -c 2048
```
|
FormlessAI/c0e1d64d-9a03-498a-a12b-c8f3bc994bcb | FormlessAI | 2025-06-13T08:55:17Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"grpo",
"arxiv:2402.03300",
"base_model:lmsys/vicuna-7b-v1.3",
"base_model:finetune:lmsys/vicuna-7b-v1.3",
"endpoints_compatible",
"region:us"
] | null | 2025-06-13T06:42:22Z | ---
base_model: lmsys/vicuna-7b-v1.3
library_name: transformers
model_name: c0e1d64d-9a03-498a-a12b-c8f3bc994bcb
tags:
- generated_from_trainer
- trl
- grpo
licence: license
---
# Model Card for c0e1d64d-9a03-498a-a12b-c8f3bc994bcb
This model is a fine-tuned version of [lmsys/vicuna-7b-v1.3](https://huggingface.co/lmsys/vicuna-7b-v1.3).
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/c0e1d64d-9a03-498a-a12b-c8f3bc994bcb", 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/tlw0wvw9)
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}}
}
``` |
MinaMila/gemma_2b_unlearned_2nd_1e-6_1.0_0.15_0.5_0.15_epoch1 | MinaMila | 2025-06-13T08:48:07Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gemma2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-13T08:46:11Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
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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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#### Speeds, Sizes, Times [optional]
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#### Testing Data
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[More Information Needed]
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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]
### 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:**
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**APA:**
[More Information Needed]
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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freakyfractal/user | freakyfractal | 2025-06-13T08:37:54Z | 0 | 0 | diffusers | [
"diffusers",
"text-to-image",
"lora",
"template:diffusion-lora",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"region:us"
] | text-to-image | 2025-06-13T08:37:35Z | ---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- text: '-'
output:
url: images/Coinye_2021.jpg
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: null
---
# user
<Gallery />
## Download model
Weights for this model are available in Safetensors format.
[Download](/freakyfractal/user/tree/main) them in the Files & versions tab.
|
chulwu/bert-model | chulwu | 2025-06-13T08:36:02Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-06-13T08:35:09Z | ---
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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- **Finetuned from model [optional]:** [More Information Needed]
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<!-- Provide the basic links for the model. -->
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## Uses
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### Direct Use
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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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<!-- 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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#### Speeds, Sizes, Times [optional]
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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]
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## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
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TheGod-2003/legal_QA_model | TheGod-2003 | 2025-06-13T08:32:36Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"question-answering",
"legal-domain",
"fine-tuned",
"indian-law",
"legal-QA",
"en",
"base_model:google-t5/t5-base",
"base_model:finetune:google-t5/t5-base",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inferen... | question-answering | 2025-06-13T06:04:07Z | ---
license: apache-2.0
language:
- en
metrics:
- rouge
base_model:
- google-t5/t5-base
tags:
- t5
- question-answering
- legal-domain
- transformers
- fine-tuned
- indian-law
- legal-QA
---
# 🏛️ Legal QA Model (T5-based, Indian Law)
This is a fine-tuned version of the [T5 model](https://huggingface.co/t5-base) for **Question Answering in the Indian Legal domain**. It was trained on curated QA samples based on Indian laws, including statutes such as IPC, CrPC, and constitutional provisions.
The model is designed to provide accurate and context-aware answers for questions grounded in Indian legal texts.
---
## 🔍 Model Details
- **Architecture**: T5
- **Base model**: [`t5-base`](https://huggingface.co/t5-base)
- **Task**: Question Answering (QA)
- **Domain**: Indian Legal System
- **Input format**:
question: <your question> context: <relevant legal passage>
---
## 📦 Files Included
| File Name | Description |
|--------------------------|------------------------------------|
| `model.safetensors` | Fine-tuned model weights (Git LFS) |
| `config.json` | Model configuration |
| `tokenizer_config.json` | Tokenizer configuration |
| `spiece.model` | SentencePiece tokenizer model |
| `added_tokens.json` | Additional token definitions |
| `special_tokens_map.json`| Special token mapping |
| `generation_config.json` | Generation hyperparameters |
---
📊 Intended Use:
🔎 Question Answering over Indian Legal texts
📜 Legal research tools and assistants
🎓 Educational tools for law students
Not Recommended For:
General-purpose QA beyond the legal domain
Use as a substitute for professional legal advice
🧠 Training Details
Dataset: Indian legal QA samples (IPC, CrPC, Constitution, etc.)
Model: Fine-tuned t5-base
Input length: 512 tokens
Output length: 128 tokens
Hardware: Google Colab (T4 GPU)
✅ License
This model is released under the Apache-2.0 License. You are free to use, modify, and distribute it with attribution.
🤝 Citation / Credit
If you use this model in your research or application, please consider citing:
@model{legal_qa_indian_t5,
author = {Harsh Upadhyay},
title = {Legal QA Model using T5 (Indian Law)},
year = {2025},
publisher = {Hugging Face},
howpublished = {\url{https://huggingface.co/TheGod-2003/legal_QA_model}}
}
## 🧾 How to Use
You can load and use this model directly using the Hugging Face `transformers` library:
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model = AutoModelForSeq2SeqLM.from_pretrained("TheGod-2003/legal_QA_model")
tokenizer = AutoTokenizer.from_pretrained("TheGod-2003/legal_QA_model")
input_text = "question: What is the punishment for theft? context: Section 378 of IPC defines theft as..."
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0], skip_special_tokens=True)) |
gradientrouting-spar/gcd_syco_cap_math_st_we_limit_proxy_data_to-1_is_peft-False_st_alpha-0.5_seed_42 | gradientrouting-spar | 2025-06-13T08:30:18Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-13T08:25:51Z | ---
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]
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<!-- Provide the basic links for the model. -->
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## Uses
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### Direct Use
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[More Information Needed]
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[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]
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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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<!-- 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]
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[More Information Needed]
## Model Card Contact
[More Information Needed] |
MinaMila/gemma_2b_unlearned_2nd_1e-6_1.0_0.15_0.5_0.75_epoch2 | MinaMila | 2025-06-13T08:08:57Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gemma2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-13T08:06:59Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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- **License:** [More Information Needed]
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<!-- 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] |
stablediffusionapi/aura-glyph | stablediffusionapi | 2025-06-13T08:06:43Z | 0 | 0 | diffusers | [
"diffusers",
"modelslab.com",
"stable-diffusion-api",
"text-to-image",
"ultra-realistic",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] | text-to-image | 2025-06-13T08:04:58Z | ---
license: creativeml-openrail-m
tags:
- modelslab.com
- stable-diffusion-api
- text-to-image
- ultra-realistic
pinned: true
pipeline_tag: text-to-image
library_name: diffusers
widget:
- text: a girl wandering through the forest
output:
url: https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/e675332f-2055-4e76-af42-c9cdfb780220/anim=false,width=450/831173-g_Plant%20Milk-Walnut%201_Euler_3_HR.jpeg
---
# None API Inference
<Gallery />
## Get API Key
Get API key from [ModelsLab API](http://modelslab.com), No Payment needed.
Replace Key in below code, change **model_id** to "aura-glyph"
Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://docs.modelslab.com)
Try model for free: [Generate Images](https://modelslab.com/models/aura-glyph)
Model link: [View model](https://modelslab.com/models/aura-glyph)
View all models: [View Models](https://modelslab.com/models)
```python
import requests
import json
url = "https://modelslab.com/api/v6/images/text2img"
payload = json.dumps({
"key": "your_api_key",
"model_id": "aura-glyph",
"prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K",
"negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime",
"width": "512",
"height": "512",
"samples": "1",
"num_inference_steps": "30",
"safety_checker": "no",
"enhance_prompt": "yes",
"seed": None,
"guidance_scale": 7.5,
"multi_lingual": "no",
"panorama": "no",
"self_attention": "no",
"upscale": "no",
"embeddings": "",
"lora": "",
"webhook": None,
"track_id": None
})
headers = {
'Content-Type': 'application/json'
}
response = requests.request("POST", url, headers=headers, data=payload)
print(response.text)
```
> Use this coupon code to get 25% off **DMGG0RBN** |
researchsocaai/gen-inst-1-awq | researchsocaai | 2025-06-13T08:06:16Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"en",
"base_model:dwikitheduck/gen-inst-1",
"base_model:quantized:dwikitheduck/gen-inst-1",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"4-bit",
"bitsandbyt... | text-generation | 2025-06-13T07:58:59Z | ---
base_model: dwikitheduck/gen-inst-1
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** dwikitheduck
- **License:** apache-2.0
- **Finetuned from model :** dwikitheduck/gen-inst-1
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)
|
fjmgAI/whisper-large-v3-ATC | fjmgAI | 2025-06-13T07:59:36Z | 0 | 0 | unsloth | [
"unsloth",
"safetensors",
"text-generation-inference",
"transformers",
"whisper",
"automatic-speech-recognition",
"en",
"dataset:jacktol/atc-dataset",
"base_model:unsloth/whisper-large-v3",
"base_model:finetune:unsloth/whisper-large-v3",
"license:apache-2.0",
"region:us"
] | automatic-speech-recognition | 2025-06-09T08:37:59Z | ---
base_model: unsloth/whisper-large-v3
tags:
- text-generation-inference
- transformers
- unsloth
- whisper
license: apache-2.0
language:
- en
datasets:
- jacktol/atc-dataset
library_name: unsloth
pipeline_tag: automatic-speech-recognition
---
[<img src="https://cdn-avatars.huggingface.co/v1/production/uploads/67b2f4e49edebc815a3a4739/R1g957j1aBbx8lhZbWmxw.jpeg" width="200"/>](https://huggingface.co/fjmgAI)
## Fine-Tuned Model
**`fjmgAI/whisper-large-v3-ATC`**
## Base Model
**`unsloth/whisper-large-v3`**
## Fine-Tuning Method
Fine-tuning was performed using **[`unsloth`](https://github.com/unslothai/unsloth)**, an efficient fine-tuning framework optimized for low-resource environments.
## Dataset
**[`jacktol/atc-dataset`](https://huggingface.co/datasets/jacktol/atc-dataset)**
### Description
This dataset contains **14,830 examples** transcriptions and corresponding audio files from two main sources: **ATCO2** and the **UWB-ATCC corpus**, specifically selected for aviation-related communications.
## Fine-Tuning Details
- The model was trained using the **Seq2SeqTrainer**.
- The **Word Error Rate (WER)** was employed as the loss metric to evaluate and optimize the model's performance during the fine-tuning process.
## Usage
### Direct Usage (Unsloth)
First install the dependencies:
Colab Version
```bash
%%capture
!pip install --no-deps bitsandbytes accelerate xformers==0.0.29.post3 peft trl==0.15.2 triton cut_cross_entropy unsloth_zoo
!pip install sentencepiece protobuf "datasets>=3.4.1" huggingface_hub hf_transfer
!pip install transformers==4.51.3
!pip install --no-deps unsloth
!pip install librosa soundfile evaluate jiwer
```
No Colab Version
```bash
pip install unsloth
pip install librosa soundfile evaluate jiwer
```
Then you can load this model and run inference.
```python
import torch
from unsloth import FastModel
from transformers import pipeline
from transformers import WhisperForConditionalGeneration
model, tokenizer = FastModel.from_pretrained(
model_name = "fjmgAI/whisper-large-v3-ATC",
dtype = None,
load_in_4bit = False,
auto_model = WhisperForConditionalGeneration,
whisper_language = "English",
whisper_task = "transcribe",
)
model.generation_config.language = "<|en|>"
model.generation_config.task = "transcribe"
model.config.suppress_tokens = []
model.generation_config.forced_decoder_ids = None
whisper = pipeline(
"automatic-speech-recognition",
model=model,
tokenizer=tokenizer.tokenizer,
feature_extractor=tokenizer.feature_extractor,
processor=tokenizer,
return_language=True,
torch_dtype=torch.float16
)
audio_file = "audio_example.flac"
transcribed_text = whisper(audio_file)
print(transcribed_text["text"])
```
## Purpose
This fine-tuned model is designed for **Speech-to-Text (STT) applications** in **Air Traffic Control (ATC)** environments, leveraging a specialized ATC dataset to enhance robustness and precision in transcribing ATC recordings. The model aims to deliver accurate and reliable transcription while maintaining efficient performance.
- **Developed by:** fjmgAI
- **License:** apache-2.0
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth) |
gradientrouting-spar/mc10_badmed_positive_neg_prx_atd-safety_lambda_proxy-8_seed_1 | gradientrouting-spar | 2025-06-13T07:52:12Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-13T07:51:07Z | ---
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] |
freakyfractal/oteio | freakyfractal | 2025-06-13T07:49:47Z | 0 | 0 | diffusers | [
"diffusers",
"text-to-image",
"lora",
"template:diffusion-lora",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"region:us"
] | text-to-image | 2025-06-13T07:49:27Z | ---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- text: '-'
output:
url: images/Coinye_2021.jpg
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: null
---
# oteio
<Gallery />
## Download model
Weights for this model are available in Safetensors format.
[Download](/freakyfractal/oteio/tree/main) them in the Files & versions tab.
|
younji04/colab-token | younji04 | 2025-06-13T07:48:11Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-06-13T07:47:52Z | ---
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]
- **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] |
stablediffusionapi/void-glyph | stablediffusionapi | 2025-06-13T07:47:10Z | 0 | 0 | diffusers | [
"diffusers",
"modelslab.com",
"stable-diffusion-api",
"text-to-image",
"ultra-realistic",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] | text-to-image | 2025-06-13T07:45:37Z | ---
license: creativeml-openrail-m
tags:
- modelslab.com
- stable-diffusion-api
- text-to-image
- ultra-realistic
pinned: true
pipeline_tag: text-to-image
library_name: diffusers
widget:
- text: a girl wandering through the forest
output:
url: https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/5510c096-731f-4857-9254-ec43e142ee49/anim=false,width=450/00531-1724770737.jpeg
---
# None API Inference
<Gallery />
## Get API Key
Get API key from [ModelsLab API](http://modelslab.com), No Payment needed.
Replace Key in below code, change **model_id** to "void-glyph"
Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://docs.modelslab.com)
Try model for free: [Generate Images](https://modelslab.com/models/void-glyph)
Model link: [View model](https://modelslab.com/models/void-glyph)
View all models: [View Models](https://modelslab.com/models)
```python
import requests
import json
url = "https://modelslab.com/api/v6/images/text2img"
payload = json.dumps({
"key": "your_api_key",
"model_id": "void-glyph",
"prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K",
"negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime",
"width": "512",
"height": "512",
"samples": "1",
"num_inference_steps": "30",
"safety_checker": "no",
"enhance_prompt": "yes",
"seed": None,
"guidance_scale": 7.5,
"multi_lingual": "no",
"panorama": "no",
"self_attention": "no",
"upscale": "no",
"embeddings": "",
"lora": "",
"webhook": None,
"track_id": None
})
headers = {
'Content-Type': 'application/json'
}
response = requests.request("POST", url, headers=headers, data=payload)
print(response.text)
```
> Use this coupon code to get 25% off **DMGG0RBN** |
phospho-app/sebastiandavidlee-gr00t-PlaceBlockInBox-3it4m | phospho-app | 2025-06-13T07:44:13Z | 0 | 0 | null | [
"safetensors",
"gr00t_n1",
"phosphobot",
"gr00t",
"region:us"
] | null | 2025-06-13T07:09:02Z |
---
tags:
- phosphobot
- gr00t
task_categories:
- robotics
---
# gr00t Model - phospho Training Pipeline
## This model was trained using **phospho**.
Training was successfull, try it out on your robot!
## Training parameters:
- **Dataset**: [sebastiandavidlee/PlaceBlockInBox](https://huggingface.co/datasets/sebastiandavidlee/PlaceBlockInBox)
- **Wandb run URL**: None
- **Epochs**: 10
- **Batch size**: 49
- **Training steps**: None
📖 **Get Started**: [docs.phospho.ai](https://docs.phospho.ai?utm_source=huggingface_readme)
🤖 **Get your robot**: [robots.phospho.ai](https://robots.phospho.ai?utm_source=huggingface_readme)
|
Hume-vla/Hume-System2 | Hume-vla | 2025-06-13T07:29:18Z | 53 | 1 | transformers | [
"transformers",
"safetensors",
"VLA",
"robotics",
"en",
"arxiv:2505.21432",
"license:mit",
"endpoints_compatible",
"region:us"
] | robotics | 2025-06-02T16:22:07Z | ---
license: mit
language:
- en
pipeline_tag: robotics
library_name: transformers
tags:
- VLA
---
# Model Card for Hume-System2
<!-- Provide a quick summary of what the model is/does. -->
System 2 pretrianed weights of a Dual-System Visual-Language-Action model for accelerating training of System 2.
- Paper: [https://arxiv.org/abs/2505.21432](https://arxiv.org/abs/2505.21432)
- Homepage: [https://hume-vla.github.io](https://hume-vla.github.io)
- Codebase: [🦾 Hume: A Dual-System VLA with System2 Thinking](https://github.com/hume-vla/hume) 
## 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. -->
- Download the weights
- Set `pretrained_policy` to the path to weights in [scripts/train_s2.sh](https://github.com/hume-vla/hume/blob/main/scripts/train_s2.sh#L54)
- Launch training `bash scripts/train_s2.sh`
## Citation
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
```BibTeX
@article{song2025hume,
title={Hume: Introducing System-2 Thinking in Visual-Language-Action Model},
author={Anonimous Authors},
journal={arXiv preprint arXiv:2505.21432},
year={2025}
}
``` |
MinaMila/gemma_2b_unlearned_2nd_1e-6_1.0_0.15_0.75_0.25_epoch2 | MinaMila | 2025-06-13T07:22:25Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gemma2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-13T07:20:33Z | ---
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] |
rlewczuk/codesnort-cc1-py-135m-v3-base | rlewczuk | 2025-06-13T07:19:47Z | 0 | 0 | null | [
"safetensors",
"llama",
"license:apache-2.0",
"region:us"
] | null | 2025-06-13T07:07:07Z | ---
license: apache-2.0
---
# GSN - experiment 1
Base model trained on 2.95B tokens of python code from TheStack V1 dataset.
Not really usable, uploaded for documentation purposes.
|
natxeros/yolov10s-custom | natxeros | 2025-06-13T07:15:02Z | 0 | 0 | null | [
"onnx",
"yolov10",
"region:us"
] | null | 2025-06-13T06:51:39Z | Custom ONNX weights for https://github.com/THU-MIG/yolov10.
|
Spestly/Athena-R3X-1.7B | Spestly | 2025-06-13T07:13:18Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-12T06:14:26Z | ---
base_model: unsloth/qwen3-1.7b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
license: mit
language:
- en
--- |
ekiprop/bert-wnli-2-epochs-2025-06-13-0659 | ekiprop | 2025-06-13T06:59:48Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-06-13T06:59:06Z | ---
library_name: transformers
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: bert-wnli-2-epochs-2025-06-13-0659
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. -->
# bert-wnli-2-epochs-2025-06-13-0659
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2666
- Accuracy: 0.1690
## 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: 16
- eval_batch_size: 16
- 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 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 40 | 1.0613 | 0.1831 |
| No log | 2.0 | 80 | 1.2179 | 0.2394 |
| No log | 3.0 | 120 | 1.2666 | 0.1690 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.2.1+cu121
- Datasets 3.6.0
- Tokenizers 0.20.3
|
aplux/Qwen2.5-3B-Instruct | aplux | 2025-06-13T06:57:45Z | 0 | 0 | null | [
"AIoT",
"QNN",
"text-generation",
"base_model:Qwen/Qwen2.5-3B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-3B-Instruct",
"license:other",
"region:us"
] | text-generation | 2025-06-13T06:56:25Z | ---
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: text-generation
tags:
- AIoT
- QNN
base_model:
- Qwen/Qwen2.5-3B-Instruct
---
## Qwen2.5-3B-Instruct
Qwen2.5 is the latest series of Qwen large language models. Qwen2.5 releases a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters. Qwen2.5 brings the following improvements upon Qwen2:
- Significantly **more knowledge** and has greatly improved capabilities in **coding** and **mathematics**, thanks to our specialized expert models in these domains.
- Significant improvements in **instruction following**, **generating long texts** (over 8K tokens), **understanding structured data** (e.g, tables), and **generating structured outputs** especially JSON. **More resilient to the diversity of system prompts**, enhancing role-play implementation and condition-setting for chatbots.
- **Long-context Support** up to 128K tokens and can generate up to 8K tokens.
- **Multilingual support** for over 29 languages, including Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more.
## Model Details
- Type: Causal Language Models
- Training Stage: Pretraining & Post-training
- Architecture: transformers with RoPE, SwiGLU, RMSNorm, Attention QKV bias and tied word embeddings
- Number of Parameters: 3.09B
- Number of Paramaters (Non-Embedding): 2.77B
- Number of Layers: 36
- Number of Attention Heads (GQA): 16 for Q and 2 for KV
- Context Length: Full 32,768 tokens and generation 8192 tokens
For more details, please refer to our [blog](https://qwenlm.github.io/blog/qwen2.5/), [GitHub](https://github.com/QwenLM/Qwen2.5), and [Documentation](https://qwen.readthedocs.io/en/latest/).
## Source Model Evaluation
> Note: This table showed source model instead of quantized model evaluation. Source Model Evaluation refer to [Qwen2.5-3B-Instruct Evaluation Result](https://qwenlm.github.io/blog/qwen2.5-llm/#qwen25-3b-instruct-performance)
| Datasets | Gemma2-2B-IT | Phi3.5-mini-Instruct | MiniCPM3-4B | Qwen2.5-3B-Instruct |
|------------------------|--------------|------------------------|-------------|----------------------|
| Non-Emb Params | 2.0B | 3.6B | 4.0B | 2.8B |
| MMLU-Pro | 26.7 | 47.5 | 43.0 | 43.7 |
| MMLU-redux | 51.9 | 67.7 | 59.9 | 64.4 |
| GPQA | 29.3 | 27.2 | 31.3 | 30.3 |
| MATH | 26.6 | 48.5 | 46.6 | 65.9 |
| GSM8K | 63.2 | 86.2 | 81.1 | 86.7 |
| HumanEval | 68.9 | 72.6 | 74.4 | 74.4 |
| MBPP | 74.9 | 63.2 | 72.5 | 72.7 |
| MultiPL-E | 30.5 | 47.2 | 49.1 | 60.2 |
| LiveCodeBench 2305-2409| 5.8 | 15.8 | 23.8 | 19.9 |
| LiveBench 0831 | 20.1 | 27.4 | 27.6 | 26.8 |
| IFeval strict-prompt | 51.0 | 52.1 | 68.4 | 58.2 |
## 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: [APACHE-2.0](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct/blob/main/LICENSE)
- Deployable Model: [APLUX-MODEL-FARM-LICENSE](https://aiot.aidlux.com/api/v1/files/license/model_farm_license_en.pdf) |
gradientrouting-spar/gcd_syco_cap_math_dpo_limit_proxy_data_to-1_beta-0.02_ldpo-2_seed_42 | gradientrouting-spar | 2025-06-13T06:53:34Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-13T06:53:27Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
simonycl/Qwen3-4B-SFT-KuhnPoker-step_300 | simonycl | 2025-06-13T06:45:26Z | 2 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-08T23:07:49Z | ---
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] |
veddhanth/lora-trained-xl-stage-2-finetuned-enc | veddhanth | 2025-06-13T06:37:09Z | 0 | 0 | diffusers | [
"diffusers",
"tensorboard",
"text-to-image",
"diffusers-training",
"lora",
"template:sd-lora",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"re... | text-to-image | 2025-06-13T06:19:31Z | ---
base_model: stabilityai/stable-diffusion-xl-base-1.0
library_name: diffusers
license: openrail++
instance_prompt: a realistic portrait of sks face
widget: []
tags:
- text-to-image
- text-to-image
- diffusers-training
- diffusers
- lora
- template:sd-lora
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
---
<!-- 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. -->
# SDXL LoRA DreamBooth - veddhanth/lora-trained-xl-stage-2-finetuned-enc
<Gallery />
## Model description
These are veddhanth/lora-trained-xl-stage-2-finetuned-enc LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using [DreamBooth](https://dreambooth.github.io/).
LoRA for the text encoder was enabled: True.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
## Trigger words
You should use a realistic portrait of sks face to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](veddhanth/lora-trained-xl-stage-2-finetuned-enc/tree/main) them in the Files & versions tab.
## 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] |
MinaMila/gemma_2b_unlearned_2nd_1e-6_1.0_0.25_0.05_0.05_epoch2 | MinaMila | 2025-06-13T06:35:04Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gemma2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-13T06:33:06Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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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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### Testing Data, Factors & Metrics
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#### Metrics
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[More Information Needed]
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[More Information Needed]
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<!-- Relevant interpretability work for the model goes here -->
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## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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gradientrouting-spar/gcd_syco_cap_math_dpo_limit_proxy_data_to-1_ldpo-6_seed_42 | gradientrouting-spar | 2025-06-13T06:27:09Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-13T06:26:56Z | ---
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]
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- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
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- **Paper [optional]:** [More Information Needed]
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### Out-of-Scope Use
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[More Information Needed]
### Recommendations
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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]
## Training Details
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#### 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).
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Cotum/Qwen2.5-3B-Instruct-thinking-function_calling-V0 | Cotum | 2025-06-13T06:24:41Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"zho",
"eng",
"fra",
"spa",
"por",
"deu",
"ita",
"rus",
"jpn",
"kor",
"vie",
"tha",
"ara",
"base_model:Qwen/Qwen2.5-3B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-3B-Instruct",
"endpoints... | null | 2025-02-23T22:00:32Z | ---
base_model: Qwen/Qwen2.5-3B-Instruct
library_name: transformers
model_name: Qwen2.5-3B-Instruct-thinking-function_calling-V0
tags:
- generated_from_trainer
- trl
- sft
licence: license
language:
- zho
- eng
- fra
- spa
- por
- deu
- ita
- rus
- jpn
- kor
- vie
- tha
- ara
---
# Model Card for Qwen2.5-3B-Instruct-thinking-function_calling-V0
This model is a fine-tuned version of [Qwen/Qwen2.5-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
prompt="""<bos><start_of_turn>human
You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags.You may call one or more functions to assist with the user query.
Don't make assumptions about what values to plug into functions.Here are the available tools:
<tools>
[{'type': 'function', 'function': {'name': 'convert_currency', 'description': 'Convert from one currency to another', 'parameters': {'type': 'object', 'properties': {'amount': {'type': 'number', 'description': 'The amount to convert'}, 'from_currency': {'type': 'string', 'description': 'The currency to convert from'}, 'to_currency': {'type': 'string', 'description': 'The currency to convert to'}}, 'required': ['amount', 'from_currency', 'to_currency']}}},
{'type': 'function', 'function': {'name': 'calculate_distance', 'description': 'Calculate the distance between two locations', 'parameters': {'type': 'object', 'properties': {'start_location': {'type': 'string', 'description': 'The starting location'}, 'end_location': {'type': 'string', 'description': 'The ending location'}}, 'required': ['start_location', 'end_location']}}}
{'type': 'function', 'function': {'name': 'send_email', 'description': 'Send an email to a customer', 'parameters': {'type': 'object', 'properties': {'customer': {'type': 'string', 'description': 'The customer to send the email to'}, 'subject': {'type': 'string', 'description': 'The subject of the email'}, 'body': {'type': 'string', 'description': 'The body of the email'}}, 'required': ['customer', 'subject', 'body']}}}
]
</tools>
Use the following pydantic model json schema for each tool call you will make:
{'title': 'FunctionCall', 'type': 'object', 'properties': {'arguments': {'title': 'Arguments', 'type': 'object'}, 'name': {'title': 'Name', 'type': 'string'}}, 'required': ['arguments', 'name']}
For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows:
<tool_call>
{tool_call}
</tool_call>Also, before making a call to a function take the time to plan the function to take. Make that thinking process between <think>{your thoughts}</think>
Hi, I need you to tell John@doe.com that I received his package ?<end_of_turn><eos>
<start_of_turn>model
<think>"""
generator = pipeline("text-generation", model="Cotum/Qwen2.5-3B-Instruct-thinking-function_calling-V0", device="cuda")
output = generator([{"role": "user", "content": prompt}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT following the Bonus Unit 1 of the Agent Course of Hugging Face : https://huggingface.co/agents-course/notebooks/blob/main/bonus-unit1/bonus-unit1.ipynb
### Framework versions
- TRL: 0.15.1
- Transformers: 4.49.0
- Pytorch: 2.6.0
- Datasets: 3.3.2
- Tokenizers: 0.21.0
## 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}}
}
```
<div align="center" style="line-height: 1;">
<a href="https://www.huggingface.co/Cotum" target="_blank" style="margin: 2px;">
<img alt="Follow" src="https://huggingface.co/datasets/Cotum/MATH-500-french-thoughts/resolve/main/Cotum_banner.png" style="display: inline-block; vertical-align: middle; width: 200px;"/>
</a>
</div> |
aplux/DeepSeek-R1-Distill-Qwen-14B | aplux | 2025-06-13T06:22:54Z | 0 | 0 | null | [
"AIoT",
"QNN",
"LLM",
"text-generation",
"base_model:deepseek-ai/DeepSeek-R1-Distill-Qwen-14B",
"base_model:finetune:deepseek-ai/DeepSeek-R1-Distill-Qwen-14B",
"license:other",
"region:us"
] | text-generation | 2025-06-13T06:19:51Z | ---
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: text-generation
tags:
- AIoT
- QNN
- LLM
base_model:
- deepseek-ai/DeepSeek-R1-Distill-Qwen-14B
---
## DeepSeek-R1-Distill-Qwen-14B
DeepSeek introduce a first-generation reasoning models, DeepSeek-R1-Zero and DeepSeek-R1.
DeepSeek-R1-Zero, a model trained via large-scale reinforcement learning (RL) without supervised fine-tuning (SFT) as a preliminary step, demonstrated remarkable performance on reasoning.
With RL, DeepSeek-R1-Zero naturally emerged with numerous powerful and interesting reasoning behaviors.
However, DeepSeek-R1-Zero encounters challenges such as endless repetition, poor readability, and language mixing. To address these issues and further enhance reasoning performance,
DeepSeek introduce DeepSeek-R1, which incorporates cold-start data before RL.
DeepSeek-R1 achieves performance comparable to OpenAI-o1 across math, code, and reasoning tasks.
To support the research community, DeepSeek have open-sourced DeepSeek-R1-Zero, DeepSeek-R1, and six dense models distilled from DeepSeek-R1 based on Llama and Qwen. DeepSeek-R1-Distill-Qwen-32B outperforms OpenAI-o1-mini across various benchmarks, achieving new state-of-the-art results for dense models.
For more details, please refer to DeepSeek [Hugging Face](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-14B)
## Model Details
**Post-Training: Large-Scale Reinforcement Learning on the Base Model**
- DeepSeek directly apply reinforcement learning (RL) to the base model without relying on supervised fine-tuning (SFT) as a preliminary step. This approach allows the model to explore chain-of-thought (CoT) for solving complex problems, resulting in the development of DeepSeek-R1-Zero. DeepSeek-R1-Zero demonstrates capabilities such as self-verification, reflection, and generating long CoTs, marking a significant milestone for the research community. Notably, it is the first open research to validate that reasoning capabilities of LLMs can be incentivized purely through RL, without the need for SFT. This breakthrough paves the way for future advancements in this area.
- DeepSeek introduce a pipeline to develop DeepSeek-R1. The pipeline incorporates two RL stages aimed at discovering improved reasoning patterns and aligning with human preferences, as well as two SFT stages that serve as the seed for the model's reasoning and non-reasoning capabilities.
DeepSeek believe the pipeline will benefit the industry by creating better models.
---
**Distillation: Smaller Models Can Be Powerful Too**
- DeepSeek demonstrate that the reasoning patterns of larger models can be distilled into smaller models, resulting in better performance compared to the reasoning patterns discovered through RL on small models. The open source DeepSeek-R1, as well as its API, will benefit the research community to distill better smaller models in the future.
- Using the reasoning data generated by DeepSeek-R1, DeepSeek fine-tuned several dense models that are widely used in the research community. The evaluation results demonstrate that the distilled smaller dense models perform exceptionally well on benchmarks. DeepSeek open-source distilled 1.5B, 7B, 8B, 14B, 32B, and 70B checkpoints based on Qwen2.5 and Llama3 series to the community.
## Source Model Evaluation
Distilled Model Evaluation
> Note: This table showed source model instead of quantized model evaluation. Source Model Evaluation refer to [DeepSeek-R1-Distill-Qwen-7B Evaluation Result](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B#distilled-model-evaluation)
| Model | AIME 2024 pass@1 | AIME 2024 cons@64 | MATH-500 pass@1 | GPQA Diamond pass@1 | LiveCodeBench pass@1 | CodeForces rating |
|------------------------------------------|------------------|-------------------|-----------------|----------------------|----------------------|-------------------|
| GPT-4o-0513 | 9.3 | 13.4 | 74.6 | 49.9 | 32.9 | 759 |
| Claude-3.5-Sonnet-1022 | 16.0 | 26.7 | 78.3 | 65.0 | 38.9 | 717 |
| o1-mini | 63.6 | 80.0 | 90.0 | 60.0 | 53.8 | **1820** |
| QwQ-32B-Preview | 44.0 | 60.0 | 90.6 | 54.5 | 41.9 | 1316 |
| DeepSeek-R1-Distill-Qwen-1.5B | 28.9 | 52.7 | 83.9 | 33.8 | 16.9 | 954 |
| DeepSeek-R1-Distill-Qwen-7B | 55.5 | 83.3 | 92.8 | 49.1 | 37.6 | 1189 |
| DeepSeek-R1-Distill-Qwen-14B | 69.7 | 80.0 | 93.9 | 59.1 | 53.1 | 1481 |
| DeepSeek-R1-Distill-Qwen-32B | **72.6** | 83.3 | 94.3 | 62.1 | 57.2 | 1691 |
| DeepSeek-R1-Distill-Llama-8B | 50.4 | 80.0 | 89.1 | 49.0 | 39.6 | 1205 |
| DeepSeek-R1-Distill-Llama-70B | 70.0 | **86.7** | **94.5** | **65.2** | **57.5** | 1633 |
## Performance Reference
Please search model by model name in [Model Farm](https://aiot.aidlux.com/en/models)
## Inference & Model Conversion
Please search model by model name in [Model Farm](https://aiot.aidlux.com/en/models)
## License
- Source Model: [MIT](https://github.com/deepseek-ai/DeepSeek-R1/blob/main/LICENSE)
- Deployable Model: [APLUX-MODEL-FARM-LICENSE](https://aiot.aidlux.com/api/v1/files/license/model_farm_license_en.pdf) |
MinaMila/gemma_2b_unlearned_2nd_1e-6_1.0_0.25_0.05_0.15_epoch2 | MinaMila | 2025-06-13T06:19:14Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gemma2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-13T06:17:20Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
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- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
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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]
[More Information Needed]
#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
<!-- 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]
- **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]
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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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abdullah-khaled/gemma-text-to-sql | abdullah-khaled | 2025-06-13T06:16:12Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:google/gemma-3-1b-pt",
"base_model:finetune:google/gemma-3-1b-pt",
"endpoints_compatible",
"region:us"
] | null | 2025-05-11T14:44:53Z | ---
base_model: google/gemma-3-1b-pt
library_name: transformers
model_name: gemma-text-to-sql
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for gemma-text-to-sql
This model is a fine-tuned version of [google/gemma-3-1b-pt](https://huggingface.co/google/gemma-3-1b-pt).
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="abdullah-khaled/gemma-text-to-sql", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.6.0+cu124
- Datasets: 3.3.2
- 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}}
}
``` |
gradientrouting-spar/gcd_syco_cap_math_dpo_limit_proxy_data_to-1_ldpo-2_seed_5 | gradientrouting-spar | 2025-06-13T06:11:22Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-13T06:11:12Z | ---
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] |
morturr/Mistral-7B-v0.1-PAIR_one_liners_amazon-COMB-amazon-comb-2-seed-42-2025-06-13 | morturr | 2025-06-13T06:11:22Z | 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 | 2025-06-13T06:11:12Z | ---
library_name: peft
license: apache-2.0
base_model: mistralai/Mistral-7B-v0.1
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: Mistral-7B-v0.1-PAIR_one_liners_amazon-COMB-amazon-comb-2-seed-42-2025-06-13
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Mistral-7B-v0.1-PAIR_one_liners_amazon-COMB-amazon-comb-2-seed-42-2025-06-13
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: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.1
- Pytorch 2.5.1+cu124
- Datasets 3.0.2
- Tokenizers 0.20.1 |
IoanaLiviaPopescu/real-data-synth-data-400-2-Standard-B-whisper-small | IoanaLiviaPopescu | 2025-06-13T06:07:14Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"ro",
"dataset:IoanaLiviaPopescu/RealVoiceSynthVoice-400-2-Standard-B",
"base_model:openai/whisper-small",
"base_model:finetune:openai/whisper-small",
"license:apache-2.0",
"model-index",
"endpo... | automatic-speech-recognition | 2025-06-13T05:43:50Z | ---
library_name: transformers
language:
- ro
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- IoanaLiviaPopescu/RealVoiceSynthVoice-400-2-Standard-B
metrics:
- wer
model-index:
- name: IoanaLiviaPopescu/real-data-synth-data-400-2-Standard-B-whisper-small
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: IoanaLiviaPopescu/RealVoiceSynthVoice-400-2-Standard-B
type: IoanaLiviaPopescu/RealVoiceSynthVoice-400-2-Standard-B
config: default
split: test
args: 'split: validation'
metrics:
- name: Wer
type: wer
value: 18.827217407339113
---
<!-- 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. -->
# IoanaLiviaPopescu/real-data-synth-data-400-2-Standard-B-whisper-small
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the IoanaLiviaPopescu/RealVoiceSynthVoice-400-2-Standard-B dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4100
- Wer: 18.8272
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- 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: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| No log | 0 | 0 | 0.6024 | 27.8812 |
| 0.5049 | 1.0 | 13 | 0.4970 | 19.8599 |
| 0.3055 | 2.0 | 26 | 0.4351 | 20.2471 |
| 0.1979 | 3.0 | 39 | 0.4136 | 19.0669 |
| 0.1439 | 4.0 | 52 | 0.4079 | 18.9194 |
| 0.119 | 5.0 | 65 | 0.4100 | 18.8272 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
JFernandoGRE/llama31_8b_augmenteddemocracy_grpo_questions_50_critsupport | JFernandoGRE | 2025-06-13T06:06:17Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"base_model:JFernandoGRE/llama31_8b_augmenteddemocracy_sft_questions_50_critsupport",
"base_model:finetune:JFernandoGRE/llama31_8b_augmenteddemocracy_sft_questions_50_critsupp... | text-generation | 2025-06-13T05:58:33Z | ---
base_model: JFernandoGRE/llama31_8b_augmenteddemocracy_sft_questions_50_critsupport
tags:
- text-generation-inference
- transformers
- unsloth
- llama
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** JFernandoGRE
- **License:** apache-2.0
- **Finetuned from model :** JFernandoGRE/llama31_8b_augmenteddemocracy_sft_questions_50_critsupport
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)
|
insuperabile/rumodernbert-hnp2 | insuperabile | 2025-06-13T06:02:05Z | 0 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"modernbert",
"sentence-similarity",
"feature-extraction",
"generated_from_trainer",
"dataset_size:95000",
"loss:MultipleNegativesRankingLoss",
"dataset:insuperabile/processed_ru_hnp",
"arxiv:1908.10084",
"arxiv:1705.00652",
"base_model:deepvk/RuModernBE... | sentence-similarity | 2025-06-13T06:01:33Z | ---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:95000
- loss:MultipleNegativesRankingLoss
base_model: deepvk/RuModernBERT-base
widget:
- source_sentence: Одновременно с ними Северную Африку населяли гигантские хищники
кархародонтозавры, спинозавры и зауроподы египтозавры.
sentences:
- На его борту погибли все 14 человек — 11 пассажиров и 3 члена экипажа.
- Спинозавры, кархародонтозавры и зауроподы египтозавры населяли одновременно с
ними Северную Африку.
- Авалония из этих микроконтинентов стала первой дрейфовать.
- source_sentence: Среди 15 неактивных 2 человека были учениками или студентами, 7
— пенсионерами, 6 были неактивными по другим причинам.
sentences:
- Из 15 неактивных человек 2 были учениками или студентами, 7 — пенсионерами, а
6 неактивны по другим причинам.
- Деструкторы часто применяются для освобождения занятых объектом дефицитных системных
ресурсов, но использовать финализаторы таким образом не рекомендуется.
- Не злоупотребляйте, ни в коем случае, антигистаминными препаратами (после первого
года), выбирайте минимальную дозировку (не более одной таблетки в неделю) и подходящее
лекарство.
- source_sentence: Английская поваренная книга 1845 года, например, предлагала рецепт
шведского селёдочного салата, состоящего из норвежской сельди, свёклы, картофеля,
пикулей, тёртого яблока и яичного белка, с соусом из масла, уксуса, тёртого яичного
желтка, сметаны и лука.
sentences:
- 'Территория упраздняемого Тотемского уезда вошла в состав Вологодского округа
Северного края и в четыре вновь образованные района: Тотемского, Кокшенгского,
Леденгского и Толшменского.'
- Например, английская поваренная книга 1845 года предлагала рецепт шведского селёдочного
салата, включающего норвежскую сельдь, свёклу, картофель, пикуль, тёртое яблоко
и яичный белок, с соусом из масла, уксуса, тёртого яичного желтка, сметаны и лука.
- В парк входивший никто не оставался незамеченным.
- source_sentence: Виштаспа (авест. Кави Виштаспа; ср.-перс. Кей-Виштасп; фарси Гуштасп
или Гоштасп) — в иранской литературе полулегендарный царь, современник и покровитель
Заратуштры.
sentences:
- В иранской литературе известен Виштаспа (авест. Кави Виштаспа; ср.-перс. Кей-Виштасп;
фарси Гуштасп или Гоштасп) как полулегендарный царь, современник и покровитель
Заратуштры.
- Серединой 1920-х годов отдел начал формироваться по предложению Корнея Чуковского,
а к концу 1930-х годов уже собрал в своем составе уникальных и талантливых авторов,
из которых выросла идея создания журнала для подростков «ЁЖ».
- Тэд Вильде был номинирован на историческую премию 'Оскар' за лучшую режиссуру
в жанре кинокомедии.
- source_sentence: В 2003 году Маркос Пенья был избран в законодательное собрание
Буэнос-Айреса.
sentences:
- И в конце сезона выиграли Кубок Каталонии.
- Мне было очень увлекательно работать с ними.
- В законодательное собрание Буэнос-Айреса был избран Маркос Пенья в 2003 году.
datasets:
- insuperabile/processed_ru_hnp
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 deepvk/RuModernBERT-base
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: mnrl eval
type: mnrl_eval
metrics:
- type: cosine_accuracy@1
value: 0.8118
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.9908
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.9944
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9968
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.8118
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.3302666666666667
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.19887999999999997
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09967999999999999
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.8118
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.9908
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.9944
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9968
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.9264759449574176
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.9017754761904764
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.9019171952528467
name: Cosine Map@100
---
# SentenceTransformer based on deepvk/RuModernBERT-base
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [deepvk/RuModernBERT-base](https://huggingface.co/deepvk/RuModernBERT-base) on the [processed_ru_hnp](https://huggingface.co/datasets/insuperabile/processed_ru_hnp) dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [deepvk/RuModernBERT-base](https://huggingface.co/deepvk/RuModernBERT-base) <!-- at revision 797f7ff2a4c6e873525e7c3f0a2afb5746bd226f -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- [processed_ru_hnp](https://huggingface.co/datasets/insuperabile/processed_ru_hnp)
<!-- - **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: ModernBertModel
(1): Pooling({'word_embedding_dimension': 768, '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})
)
```
## 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("sentence_transformers_model_id")
# Run inference
sentences = [
'В 2003 году Маркос Пенья был избран в законодательное собрание Буэнос-Айреса.',
'В законодательное собрание Буэнос-Айреса был избран Маркос Пенья в 2003 году.',
'Мне было очень увлекательно работать с ними.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
## Evaluation
### Metrics
#### Information Retrieval
* Dataset: `mnrl_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 | 0.8118 |
| cosine_accuracy@3 | 0.9908 |
| cosine_accuracy@5 | 0.9944 |
| cosine_accuracy@10 | 0.9968 |
| cosine_precision@1 | 0.8118 |
| cosine_precision@3 | 0.3303 |
| cosine_precision@5 | 0.1989 |
| cosine_precision@10 | 0.0997 |
| cosine_recall@1 | 0.8118 |
| cosine_recall@3 | 0.9908 |
| cosine_recall@5 | 0.9944 |
| cosine_recall@10 | 0.9968 |
| **cosine_ndcg@10** | **0.9265** |
| cosine_mrr@10 | 0.9018 |
| cosine_map@100 | 0.9019 |
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Dataset
#### processed_ru_hnp
* Dataset: [processed_ru_hnp](https://huggingface.co/datasets/insuperabile/processed_ru_hnp) at [6626297](https://huggingface.co/datasets/insuperabile/processed_ru_hnp/tree/66262979efb23180a2986c033c5e472678585cd4)
* Size: 95,000 training samples
* Columns: <code>sentence1</code> and <code>sentence2</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 |
|:--------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 10 tokens</li><li>mean: 35.78 tokens</li><li>max: 188 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 33.66 tokens</li><li>max: 160 tokens</li></ul> |
* Samples:
| sentence1 | sentence2 |
|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>В июне 1938 года на Дальний Восток прибыли Фриновский и Л. З. Мехлис для проведения чистки руководства Тихоокеанского флота, погранвойск и местного НКВД.</code> | <code>В июне 1938 года на Дальний Восток прибыли Л. З. Мехлис и Фриновский для проведения очистки руководства Тихоокеанского флота, пограничной службы и местного НКВД.</code> |
| <code>Предыстория организованного туризма в Болгарии начинается с паломничества к Гробу Господню в Иерусалим и Рильского монастыря.</code> | <code>С истории организованного туризма в Болгарии начинаются паломничества к Гробу Господню в Иерусалим и Рильскому монастырю.</code> |
| <code>C 2007 года и по настоящее время в составе группы Финский залив (Юрий Ильченко) (Мифы, Земляне, Машина времени)</code> | <code>С 2007 года и до сегодняшнего дня Юрий Ильченко состоит в группе Финский залив (Мифы, Земляне, Машина времени).</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`: epoch
- `per_device_train_batch_size`: 128
- `per_device_eval_batch_size`: 128
- `learning_rate`: 1e-06
- `num_train_epochs`: 1
- `bf16`: True
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: epoch
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 128
- `per_device_eval_batch_size`: 128
- `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`: 1e-06
- `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`: 0.0
- `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`: True
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: 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`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | mnrl_eval_cosine_ndcg@10 |
|:------:|:----:|:-------------:|:------------------------:|
| 0.0498 | 37 | 2.8773 | - |
| 0.0996 | 74 | 1.0324 | - |
| 0.1494 | 111 | 0.1158 | - |
| 0.1992 | 148 | 0.0505 | - |
| 0.2490 | 185 | 0.0351 | - |
| 0.2988 | 222 | 0.027 | - |
| 0.3486 | 259 | 0.0232 | - |
| 0.3984 | 296 | 0.0187 | - |
| 0.4482 | 333 | 0.0213 | - |
| 0.4980 | 370 | 0.0187 | - |
| 0.5478 | 407 | 0.023 | - |
| 0.5976 | 444 | 0.0189 | - |
| 0.6474 | 481 | 0.0148 | - |
| 0.6972 | 518 | 0.0143 | - |
| 0.7470 | 555 | 0.0156 | - |
| 0.7968 | 592 | 0.0123 | - |
| 0.8466 | 629 | 0.0129 | - |
| 0.8964 | 666 | 0.0132 | - |
| 0.9462 | 703 | 0.0134 | - |
| 0.9960 | 740 | 0.013 | - |
| 1.0 | 743 | - | 0.9265 |
### Framework Versions
- Python: 3.11.10
- Sentence Transformers: 4.1.0
- Transformers: 4.52.4
- PyTorch: 2.6.0+cu124
- Accelerate: 1.7.0
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### 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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harish1611/OCR_for_devnagri_script | harish1611 | 2025-06-13T06:00:56Z | 0 | 0 | null | [
"region:us"
] | null | 2025-06-13T05:41:48Z | # Devanagari Handwritten OCR Model
## Model Summary
This model recognizes handwritten Devanagari characters and digits using a CNN trained on the UCI Devanagari dataset. It supports 46 classes (36 characters + 10 digits).
## Intended Use
This model is intended for academic, research, and educational applications for digitizing handwritten Hindi text. Not suitable for production OCR without further validation.
## Training Data
- Dataset: Devanagari Handwritten Character Dataset (UCI)
- Total Images: ~92,000
- Image Size: 32x32, grayscale
## Model Architecture
- Input: 32x32 grayscale images
- Layers: Conv2D → MaxPooling → Dropout → Flatten → Dense → Softmax
- Optimizer: Adam
- Loss: CategoricalCrossentropy
## Evaluation Metrics
- Training Accuracy: 97%
- Validation Accuracy: 91%
- Loss values plotted for 20 epochs
## Limitations
- Trained only on clean, centered characters
- Doesn’t handle multi-character words or real-world handwriting noise
## License
MIT License
## Citation
Harish Phad (2025), Devanagari OCR using CNN, Project-Based Learning 2
|
MinaMila/gemma_2b_unlearned_2nd_1e-6_1.0_0.25_0.05_0.25_epoch1 | MinaMila | 2025-06-13T05:55:35Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gemma2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-13T05:53:47Z | ---
library_name: transformers
tags: []
---
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gradientrouting-spar/gcd_syco_cap_math_positive_neg_prx_lambda_proxy-2.0_seed_42 | gradientrouting-spar | 2025-06-13T05:51:35Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-12T21:14:22Z | ---
library_name: transformers
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gradientrouting-spar/gcd_syco_cap_math_naive_seed_42 | gradientrouting-spar | 2025-06-13T05:44:34Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-12T21:05:44Z | ---
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tags: []
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citrinegui/Qwen2.5-1.5B-Instruct_countdown2345_grpo_variance_regularized_0.5_0.5_True_5 | citrinegui | 2025-06-13T05:41:59Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"trl",
"grpo",
"conversational",
"dataset:countdown-dataset",
"arxiv:2402.03300",
"base_model:Qwen/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-1.5B-Instruct",
"autotrain_compatible",
"text-ge... | text-generation | 2025-06-12T07:14:44Z | ---
base_model: Qwen/Qwen2.5-1.5B-Instruct
datasets: countdown-dataset
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct_countdown2345_grpo_variance_regularized_0.5_0.5_True_5
tags:
- generated_from_trainer
- trl
- grpo
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct_countdown2345_grpo_variance_regularized_0.5_0.5_True_5
This model is a fine-tuned version of [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) on the [countdown-dataset](https://huggingface.co/datasets/countdown-dataset) dataset.
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="citrinegui/Qwen2.5-1.5B-Instruct_countdown2345_grpo_variance_regularized_0.5_0.5_True_5", 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/dive-ci/Sys2Bench/runs/5dr5qa4y)
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: 0.14.0
- Transformers: 4.48.1
- Pytorch: 2.5.1+cu121
- Datasets: 3.1.0
- 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 Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
VaibhavBhardwaj/Radbert | VaibhavBhardwaj | 2025-06-13T05:40:12Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"token-classification",
"base_model:dmis-lab/biobert-v1.1",
"base_model:finetune:dmis-lab/biobert-v1.1",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2025-04-30T05:24:40Z | ---
library_name: transformers
base_model: dmis-lab/biobert-v1.1
pipeline_tag: token-classification
---
# Model Card for Model ID
This model is a fine-tuned version of BioBERT specifically trained for extracting structured medical entities from unstructured radiology reports. It identifies and classifies relevant spans into three critical clinical categories:
Anatomical_Location: Identifies body parts or regions (e.g., "left lung", "cerebral cortex")
Observation: Highlights medical findings (e.g., "consolidation", "edema")
Severity: Classifies the degree or intensity of findings (e.g., "mild", "severe")
This enables automated tagging and indexing of radiological information, supporting downstream applications in clinical decision support, EHR structuring, and radiology analytics.
### Model Description
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:** [Vaibhav Bhardwaj]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [Transformer-based NER (Named Entity Recognition)]
- **Language(s) (NLP):** [English]
- **License:** [More Information Needed]
- **Finetuned from model [BioBert]:** [More Information Needed]
## How to Get Started with the Model
Use the code below to get started with the model.
[# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("VaibhavBhardwaj/Radbert")
model = AutoModelForTokenClassification.from_pretrained("VaibhavBhardwaj/Radbert")]
### Results
|
gradientrouting-spar/gcd_syco_cap_math_positive_neg_prx_lambda_proxy-1.0_seed_42 | gradientrouting-spar | 2025-06-13T05:38:31Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-12T20:58:40Z | ---
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
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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]
### 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] |
simonycl/Qwen3-4B-SFT-KuhnPoker-step_250 | simonycl | 2025-06-13T05:34:31Z | 445 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-08T23:05:49Z | ---
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] |
Othoi-viral-videos-tv/FULL.VIDEO.Othoi.Viral.Video.Tutorial.Official | Othoi-viral-videos-tv | 2025-06-13T05:25:00Z | 0 | 0 | null | [
"region:us"
] | null | 2025-06-13T05:24:22Z | <animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
|
CSLin3303/qwen3-laws-20250613001 | CSLin3303 | 2025-06-13T05:20:21Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"qwen3",
"text-generation-inference",
"unsloth",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-13T05:18:27Z | ---
base_model: unsloth/qwen3-14b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
- gguf
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** CSLin3303
- **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)
|
luciaquirke/SmolLM2-1.7B-magpie-ultra-v1.0-attribution-lowest | luciaquirke | 2025-06-13T05:19:09Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"trl",
"sft",
"conversational",
"base_model:HuggingFaceTB/SmolLM2-1.7B",
"base_model:finetune:HuggingFaceTB/SmolLM2-1.7B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"regi... | text-generation | 2025-06-13T05:17:51Z | ---
base_model: HuggingFaceTB/SmolLM2-1.7B
library_name: transformers
model_name: SmolLM2-1.7B-magpie-ultra-v1.0-attribution-lowest
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for SmolLM2-1.7B-magpie-ultra-v1.0-attribution-lowest
This model is a fine-tuned version of [HuggingFaceTB/SmolLM2-1.7B](https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B).
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="luciaquirke/SmolLM2-1.7B-magpie-ultra-v1.0-attribution-lowest", 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/eleutherai/huggingface/runs/e3s4aqjd)
This model was trained with SFT.
### Framework versions
- TRL: 0.18.1
- Transformers: 4.52.3
- Pytorch: 2.5.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
gradientrouting-spar/gcd_syco_cap_math_limit_proxy_data_to-16_seed_42 | gradientrouting-spar | 2025-06-13T05:18:04Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-13T05:17:56Z | ---
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]
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- **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] |
MinaMila/gemma_2b_unlearned_2nd_1e-6_1.0_0.25_0.15_0.05_epoch2 | MinaMila | 2025-06-13T05:15:36Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gemma2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-13T05:13:37Z | ---
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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### 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. -->
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## Bias, Risks, and Limitations
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### Recommendations
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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BootesVoid/cmbdduo4y0015j8kf0gk9iyll_cmbuc1zxi00fee11f82ceu11l | BootesVoid | 2025-06-13T05:14:57Z | 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 | 2025-06-13T05:14:56Z | ---
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: CHLOE
---
# Cmbdduo4Y0015J8Kf0Gk9Iyll_Cmbuc1Zxi00Fee11F82Ceu11L
<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 `CHLOE` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "CHLOE",
"lora_weights": "https://huggingface.co/BootesVoid/cmbdduo4y0015j8kf0gk9iyll_cmbuc1zxi00fee11f82ceu11l/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('BootesVoid/cmbdduo4y0015j8kf0gk9iyll_cmbuc1zxi00fee11f82ceu11l', weight_name='lora.safetensors')
image = pipeline('CHLOE').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 2000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/BootesVoid/cmbdduo4y0015j8kf0gk9iyll_cmbuc1zxi00fee11f82ceu11l/discussions) to add images that show off what you’ve made with this LoRA.
|
araapk/multilabel-bert-emotion | araapk | 2025-06-13T05:10:45Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-06-13T05:10:35Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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[More Information Needed]
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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MinaMila/gemma_2b_unlearned_2nd_1e-6_1.0_0.25_0.15_0.05_epoch1 | MinaMila | 2025-06-13T05:07:45Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gemma2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-13T05:05:36Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
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### Downstream Use [optional]
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[More Information Needed]
### Out-of-Scope Use
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## Bias, Risks, and Limitations
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[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
## Training Details
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[More Information Needed]
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[More Information Needed]
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## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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[More Information Needed]
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gradientrouting-spar/gcd_syco_cap_math_limit_proxy_data_to-4_seed_42 | gradientrouting-spar | 2025-06-13T05:05:24Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-13T05:05:16Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- **Model type:** [More Information Needed]
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<!-- 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]
### Out-of-Scope Use
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
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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).
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