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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-08-02 00:43:11
| downloads
int64 0
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| likes
int64 0
11.7k
| library_name
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4.05k
| pipeline_tag
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values | createdAt
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QuantFactory/Apollo2-2B-GGUF
|
QuantFactory
| 2025-06-18T15:11:24Z | 0 | 1 | null |
[
"gguf",
"biology",
"medical",
"question-answering",
"ar",
"en",
"zh",
"ko",
"ja",
"mn",
"th",
"vi",
"lo",
"mg",
"de",
"pt",
"es",
"fr",
"ru",
"it",
"hr",
"gl",
"cs",
"co",
"la",
"uk",
"bs",
"bg",
"eo",
"sq",
"da",
"sa",
"gn",
"sr",
"sk",
"gd",
"lb",
"hi",
"ku",
"mt",
"he",
"ln",
"bm",
"sw",
"ig",
"rw",
"ha",
"dataset:FreedomIntelligence/ApolloMoEDataset",
"arxiv:2410.10626",
"base_model:google/gemma-2-2b",
"base_model:quantized:google/gemma-2-2b",
"license:gemma",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2025-06-18T15:03:25Z |
---
license: gemma
datasets:
- FreedomIntelligence/ApolloMoEDataset
language:
- ar
- en
- zh
- ko
- ja
- mn
- th
- vi
- lo
- mg
- de
- pt
- es
- fr
- ru
- it
- hr
- gl
- cs
- co
- la
- uk
- bs
- bg
- eo
- sq
- da
- sa
- gn
- sr
- sk
- gd
- lb
- hi
- ku
- mt
- he
- ln
- bm
- sw
- ig
- rw
- ha
metrics:
- accuracy
base_model:
- google/gemma-2-2b
pipeline_tag: question-answering
tags:
- biology
- medical
---
[](https://hf.co/QuantFactory)
# QuantFactory/Apollo2-2B-GGUF
This is quantized version of [FreedomIntelligence/Apollo2-2B](https://huggingface.co/FreedomIntelligence/Apollo2-2B) created using llama.cpp
# Original Model Card
# Democratizing Medical LLMs For Much More Languages
Covering 12 Major Languages including English, Chinese, French, Hindi, Spanish, Arabic, Russian, Japanese, Korean, German, Italian, Portuguese and 38 Minor Languages So far.
<p align="center">
📃 <a href="https://arxiv.org/abs/2410.10626" target="_blank">Paper</a> • 🌐 <a href="" target="_blank">Demo</a> • 🤗 <a href="https://huggingface.co/datasets/FreedomIntelligence/ApolloMoEDataset" target="_blank">ApolloMoEDataset</a> • 🤗 <a href="https://huggingface.co/datasets/FreedomIntelligence/ApolloMoEBench" target="_blank">ApolloMoEBench</a> • 🤗 <a href="https://huggingface.co/collections/FreedomIntelligence/apollomoe-and-apollo2-670ddebe3bb1ba1aebabbf2c" target="_blank">Models</a> •🌐 <a href="https://github.com/FreedomIntelligence/Apollo" target="_blank">Apollo</a> • 🌐 <a href="https://github.com/FreedomIntelligence/ApolloMoE" target="_blank">ApolloMoE</a>
</p>
## 🌈 Update
* **[2024.10.15]** ApolloMoE repo is published！🎉
## Languages Coverage
12 Major Languages and 38 Minor Languages
<details>
<summary>Click to view the Languages Coverage</summary>
</details>
## Architecture
<details>
<summary>Click to view the MoE routing image</summary>
</details>
## Results
#### Dense
🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo2-0.5B" target="_blank">Apollo2-0.5B</a> • 🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo2-1.5B" target="_blank">Apollo2-1.5B</a> • 🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo2-2B" target="_blank">Apollo2-2B</a>
🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo2-3.8B" target="_blank">Apollo2-3.8B</a> • 🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo2-7B" target="_blank">Apollo2-7B</a> • 🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo2-9B" target="_blank">Apollo2-9B</a>
<details>
<summary>Click to view the Dense Models Results</summary>
</details>
#### Post-MoE
🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo-MoE-0.5B" target="_blank">Apollo-MoE-0.5B</a> • 🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo-MoE-1.5B" target="_blank">Apollo-MoE-1.5B</a> • 🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo-MoE-7B" target="_blank">Apollo-MoE-7B</a>
<details>
<summary>Click to view the Post-MoE Models Results</summary>
</details>
## Usage Format
##### Apollo2
- 0.5B, 1.5B, 7B: User:{query}\nAssistant:{response}<|endoftext|>
- 2B, 9B: User:{query}\nAssistant:{response}\<eos\>
- 3.8B: <|user|>\n{query}<|end|><|assisitant|>\n{response}<|end|>
##### Apollo-MoE
- 0.5B, 1.5B, 7B: User:{query}\nAssistant:{response}<|endoftext|>
## Dataset & Evaluation
- Dataset
🤗 <a href="https://huggingface.co/datasets/FreedomIntelligence/ApolloMoEDataset" target="_blank">ApolloMoEDataset</a>
<details><summary>Click to expand</summary>
- [Data category](https://huggingface.co/datasets/FreedomIntelligence/ApolloCorpus/tree/main/train)
</details>
- Evaluation
🤗 <a href="https://huggingface.co/datasets/FreedomIntelligence/ApolloMoEBench" target="_blank">ApolloMoEBench</a>
<details><summary>Click to expand</summary>
- EN:
- [MedQA-USMLE](https://huggingface.co/datasets/GBaker/MedQA-USMLE-4-options)
- [MedMCQA](https://huggingface.co/datasets/medmcqa/viewer/default/test)
- [PubMedQA](https://huggingface.co/datasets/pubmed_qa): Because the results fluctuated too much, they were not used in the paper.
- [MMLU-Medical](https://huggingface.co/datasets/cais/mmlu)
- Clinical knowledge, Medical genetics, Anatomy, Professional medicine, College biology, College medicine
- ZH:
- [MedQA-MCMLE](https://huggingface.co/datasets/bigbio/med_qa/viewer/med_qa_zh_4options_bigbio_qa/test)
- [CMB-single](https://huggingface.co/datasets/FreedomIntelligence/CMB): Not used in the paper
- Randomly sample 2,000 multiple-choice questions with single answer.
- [CMMLU-Medical](https://huggingface.co/datasets/haonan-li/cmmlu)
- Anatomy, Clinical_knowledge, College_medicine, Genetics, Nutrition, Traditional_chinese_medicine, Virology
- [CExam](https://github.com/williamliujl/CMExam): Not used in the paper
- Randomly sample 2,000 multiple-choice questions
- ES: [Head_qa](https://huggingface.co/datasets/head_qa)
- FR:
- [Frenchmedmcqa](https://github.com/qanastek/FrenchMedMCQA)
- [MMLU_FR]
- Clinical knowledge, Medical genetics, Anatomy, Professional medicine, College biology, College medicine
- HI: [MMLU_HI](https://huggingface.co/datasets/FreedomIntelligence/MMLU_Hindi)
- Clinical knowledge, Medical genetics, Anatomy, Professional medicine, College biology, College medicine
- AR: [MMLU_AR](https://huggingface.co/datasets/FreedomIntelligence/MMLU_Arabic)
- Clinical knowledge, Medical genetics, Anatomy, Professional medicine, College biology, College medicine
- JA: [IgakuQA](https://github.com/jungokasai/IgakuQA)
- KO: [KorMedMCQA](https://huggingface.co/datasets/sean0042/KorMedMCQA)
- IT:
- [MedExpQA](https://huggingface.co/datasets/HiTZ/MedExpQA)
- [MMLU_IT]
- Clinical knowledge, Medical genetics, Anatomy, Professional medicine, College biology, College medicine
- DE: [BioInstructQA](https://huggingface.co/datasets/BioMistral/BioInstructQA): German part
- PT: [BioInstructQA](https://huggingface.co/datasets/BioMistral/BioInstructQA): Portuguese part
- RU: [RuMedBench](https://github.com/sb-ai-lab/MedBench)
</details>
## Model Download and Inference
We take Apollo-MoE-0.5B as an example
1. Login Huggingface
```
huggingface-cli login --token $HUGGINGFACE_TOKEN
```
2. Download model to local dir
```python
from huggingface_hub import snapshot_download
import os
local_model_dir=os.path.join('/path/to/models/dir','Apollo-MoE-0.5B')
snapshot_download(repo_id="FreedomIntelligence/Apollo-MoE-0.5B", local_dir=local_model_dir)
```
3. Inference Example
```python
from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
import os
local_model_dir=os.path.join('/path/to/models/dir','Apollo-MoE-0.5B')
model=AutoModelForCausalLM.from_pretrained(local_model_dir,trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained(local_model_dir,trust_remote_code=True)
generation_config = GenerationConfig.from_pretrained(local_model_dir, pad_token_id=tokenizer.pad_token_id, num_return_sequences=1, max_new_tokens=7, min_new_tokens=2, do_sample=False, temperature=1.0, top_k=50, top_p=1.0)
inputs = tokenizer('Answer direclty.\nThe capital of Mongolia is Ulaanbaatar.\nThe capital of Iceland is Reykjavik.\nThe capital of Australia is', return_tensors='pt')
inputs = inputs.to(model.device)
pred = model.generate(**inputs,generation_config=generation_config)
print(tokenizer.decode(pred.cpu()[0], skip_special_tokens=True))
```
## Results reproduction
<details><summary>Click to expand</summary>
We take Apollo2-7B or Apollo-MoE-0.5B as example
1. Download Dataset for project:
```
bash 0.download_data.sh
```
2. Prepare test and dev data for specific model:
- Create test data for with special token
```
bash 1.data_process_test&dev.sh
```
3. Prepare train data for specific model (Create tokenized data in advance):
- You can adjust data Training order and Training Epoch in this step
```
bash 2.data_process_train.sh
```
4. Train the model
- If you want to train in Multi Nodes please refer to ./src/sft/training_config/zero_multi.yaml
```
bash 3.single_node_train.sh
```
5. Evaluate your model: Generate score for benchmark
```
bash 4.eval.sh
```
</details>
## Citation
Please use the following citation if you intend to use our dataset for training or evaluation:
```
@misc{zheng2024efficientlydemocratizingmedicalllms,
title={Efficiently Democratizing Medical LLMs for 50 Languages via a Mixture of Language Family Experts},
author={Guorui Zheng and Xidong Wang and Juhao Liang and Nuo Chen and Yuping Zheng and Benyou Wang},
year={2024},
eprint={2410.10626},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2410.10626},
}
```
|
jongaak/my-bert-fine-tuned
|
jongaak
| 2025-06-18T15:11:19Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-06-18T15:10:54Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
sil-ai/madlad400-finetuned-loy-eng
|
sil-ai
| 2025-06-18T15:09:31Z | 8 | 0 |
transformers
|
[
"transformers",
"safetensors",
"madlad400",
"Translation",
"translation",
"loy",
"eng",
"base_model:jbochi/madlad400-3b-mt",
"base_model:finetune:jbochi/madlad400-3b-mt",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
translation
| 2025-06-16T23:14:00Z |
---
base_model: jbochi/madlad400-3b-mt
library_name: transformers
license: apache-2.0
tags:
- madlad400
- Translation
model-index:
- name: madlad400-finetuned-loy-eng
results: []
language:
- loy
- eng
model_type: Translation
pipeline_tag: translation
---
# madlad400-finetuned-loy-eng
This model is a fine-tuned version of `jbochi/madlad400-3b-mt` for translation from Lhowa to English.
## Model details
- **Developed by:** SIL Global
- **Finetuned from model:** jbochi/madlad400-3b-mt
- **Model type:** Translation
- **Source language:** Lhowa (`loy`)
- **Target language:** English (`eng`)
- **License:** closed/private
## Datasets
The model was trained on a parallel corpus of plain text files:
Lhowa:
- Lhowa Tibetan Bible
- License: © 2023, Wycliffe Bible Translators, Inc. and Nepal Bible Society. Used with permission.
English:
- English back-translation of Lhowa Bible
- License: © 2023, Wycliffe Bible Translators, Inc. and Nepal Bible Society. Used with permission.
## Framework versions
- Transformers: nan
## Usage
You can use this model with the `transformers` library like this:
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("sil-ai/madlad400-finetuned-loy-eng")
model = AutoModelForSeq2SeqLM.from_pretrained("sil-ai/madlad400-finetuned-loy-eng")
inputs = tokenizer("Your input text here", return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0]))
```
# madlad400-finetuned-loy-eng
This model is a fine-tuned version of [jbochi/madlad400-3b-mt](https://huggingface.co/jbochi/madlad400-3b-mt) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1027
- Chrf: 84.0833
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 4
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Chrf |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.4467 | 4.8284 | 1600 | 0.3144 | 65.4472 |
| 0.1963 | 9.6567 | 3200 | 0.1041 | 83.7735 |
### Framework versions
- PEFT 0.12.0
- Transformers 4.44.2
- Pytorch 2.4.1+cu124
- Datasets 2.21.0
- Tokenizers 0.19.1
|
gradientrouting-spar/mc9_badmed_representation_constraint_beta_kl-1000.0_seed_1
|
gradientrouting-spar
| 2025-06-18T15:08:28Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-18T15:07:51Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- 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]
|
gradientrouting-spar/mc9_badmed_representation_constraint_beta_kl-1000.0_seed_1_epoch_1
|
gradientrouting-spar
| 2025-06-18T15:07:49Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-18T15:07:07Z |
---
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]
|
morturr/Llama-2-7b-hf-LOO_one_liners-COMB_dadjokes-comb2-seed42-2025-06-18
|
morturr
| 2025-06-18T15:07:18Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:meta-llama/Llama-2-7b-hf",
"base_model:adapter:meta-llama/Llama-2-7b-hf",
"license:llama2",
"region:us"
] | null | 2025-06-18T15:07:01Z |
---
library_name: peft
license: llama2
base_model: meta-llama/Llama-2-7b-hf
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: Llama-2-7b-hf-LOO_one_liners-COMB_dadjokes-comb2-seed42-2025-06-18
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Llama-2-7b-hf-LOO_one_liners-COMB_dadjokes-comb2-seed42-2025-06-18
This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) 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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- 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
|
morturr/Llama-2-7b-hf-LOO_headlines-COMB_dadjokes-comb3-seed18-2025-06-18
|
morturr
| 2025-06-18T14:57:38Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:meta-llama/Llama-2-7b-hf",
"base_model:adapter:meta-llama/Llama-2-7b-hf",
"license:llama2",
"region:us"
] | null | 2025-06-18T14:57:23Z |
---
library_name: peft
license: llama2
base_model: meta-llama/Llama-2-7b-hf
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: Llama-2-7b-hf-LOO_headlines-COMB_dadjokes-comb3-seed18-2025-06-18
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Llama-2-7b-hf-LOO_headlines-COMB_dadjokes-comb3-seed18-2025-06-18
This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) 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: 16
- eval_batch_size: 16
- seed: 18
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- 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
|
racineai/Flantier-SmolVLM-2B-dse
|
racineai
| 2025-06-18T14:57:31Z | 625 | 9 | null |
[
"safetensors",
"idefics3",
"fr",
"en",
"de",
"es",
"it",
"dataset:racineai/OGC_2_vdr-visRAG-colpali",
"base_model:HuggingFaceTB/SmolVLM-Instruct",
"base_model:finetune:HuggingFaceTB/SmolVLM-Instruct",
"license:apache-2.0",
"region:us"
] | null | 2025-03-26T15:49:48Z |
---
license: apache-2.0
datasets:
- racineai/OGC_2_vdr-visRAG-colpali
language:
- fr
- en
- de
- es
- it
base_model:
- HuggingFaceTB/SmolVLM-Instruct
---
# Flantier-SmolVLM-2B-dse
A lightweight multimodal vision-language model specialized for technical document retrieval.
## Overview
Flantier-SmolVLM-2B-dse (Document Screenshot Embedding) is a 2B parameter vision-language model designed for efficient retrieval of technical documentation. It directly encodes document screenshots into embeddings, preserving all information including text, images, and layout without requiring separate content extraction.
## Key Features
- **Efficient Retrieval**: Generates document and query embeddings for semantic similarity search
- **Multimodal Understanding**: Processes text, diagrams, charts, and tables in their original layout
- **Lightweight Architecture**: Only 2B parameters, runs on consumer GPUs
- **No Preprocessing Required**: Directly works with document screenshots
## Installation
```bash
pip install transformers accelerate pillow
```
## Usage Example
```python
from PIL import Image
import torch
from transformers import AutoProcessor, AutoModelForVision2Seq
# Load model and processor
processor = AutoProcessor.from_pretrained("racineai/Flantier-SmolVLM-2B-dse")
model = AutoModelForVision2Seq.from_pretrained(
"racineai/Flantier-SmolVLM-2B-dse",
torch_dtype=torch.bfloat16,
device_map="auto"
)
# Load document image
document_image = Image.open("technical_document.jpg")
# Process for document embedding
doc_messages = [
{
"role": "user",
"content": [
{"type": "image"},
{"type": "text", "text": "What is shown in this image?"}
]
},
]
doc_prompt = processor.apply_chat_template(doc_messages, add_generation_prompt=True)
doc_inputs = processor(text=doc_prompt, images=[document_image], return_tensors="pt").to(model.device)
# Generate document embedding
with torch.no_grad():
doc_outputs = model(**doc_inputs, output_hidden_states=True, return_dict=True)
doc_embedding = doc_outputs.hidden_states[-1][:, -1] # Last token embedding
doc_embedding = torch.nn.functional.normalize(doc_embedding, p=2, dim=-1)
# Process query embedding
query = "What are the specifications of this component?"
query_messages = [
{
"role": "user",
"content": [
{"type": "text", "text": query}
]
},
]
query_prompt = processor.apply_chat_template(query_messages, add_generation_prompt=True)
query_inputs = processor(text=query_prompt, return_tensors="pt").to(model.device)
# Generate query embedding
with torch.no_grad():
query_outputs = model(**query_inputs, output_hidden_states=True, return_dict=True)
query_embedding = query_outputs.hidden_states[-1][:, -1] # Last token embedding
query_embedding = torch.nn.functional.normalize(query_embedding, p=2, dim=-1)
# Calculate similarity
similarity = torch.nn.functional.cosine_similarity(query_embedding, doc_embedding)
print(f"Similarity score: {similarity.item():.4f}")
```
## Applications
- **Technical Document Retrieval**: Find relevant documents based on technical queries
- **Technical Support Systems**: Match user questions to relevant documentation
- **Engineering Knowledge Management**: Index and search technical specifications, diagrams, and reports
## Training Methodology
This model was trained using the Document Screenshot Embedding (DSE) approach, which treats document screenshots as a unified input format. This eliminates the need for content extraction preprocessing while preserving all visual and textual information in documents.
## Citation
```
@misc{flantier-smolvlm-dse,
author = {racine.ai},
title = {Flantier-SmolVLM-2B-dse: A Lightweight Document Screenshot Embedding Model},
year = {2025},
publisher = {Hugging Face},
url = {https://huggingface.co/racineai/Flantier-SmolVLM-2B-dse}
}
```
## License
This model is released under the Apache 2.0 license.
|
morturr/Llama-2-7b-hf-LOO_amazon-COMB_headlines-comb2-seed28-2025-06-18
|
morturr
| 2025-06-18T14:57:14Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:meta-llama/Llama-2-7b-hf",
"base_model:adapter:meta-llama/Llama-2-7b-hf",
"license:llama2",
"region:us"
] | null | 2025-06-18T14:56:51Z |
---
library_name: peft
license: llama2
base_model: meta-llama/Llama-2-7b-hf
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: Llama-2-7b-hf-LOO_amazon-COMB_headlines-comb2-seed28-2025-06-18
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Llama-2-7b-hf-LOO_amazon-COMB_headlines-comb2-seed28-2025-06-18
This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 28
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.1
- Pytorch 2.5.1+cu124
- Datasets 3.0.2
- Tokenizers 0.20.1
|
Bonnief/mbert-om-100k-finetuned
|
Bonnief
| 2025-06-18T14:56:24Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"fill-mask",
"generated_from_trainer",
"base_model:google-bert/bert-base-multilingual-cased",
"base_model:finetune:google-bert/bert-base-multilingual-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2025-06-18T09:24:29Z |
---
library_name: transformers
license: apache-2.0
base_model: bert-base-multilingual-cased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: mbert-om-100k-finetuned
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. -->
# mbert-om-100k-finetuned
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: nan
- Accuracy: 0.2348
## 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
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- training_steps: 100000
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.53.0.dev0
- Pytorch 2.6.0+cu124
- Datasets 2.14.4
- Tokenizers 0.21.1
|
pictgensupport/vintagecameras
|
pictgensupport
| 2025-06-18T14:56:06Z | 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
| 2025-06-18T14:56:03Z |
---
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: vintagecameras
---
# Vintagecameras
<Gallery />
Trained on Replicate using:
https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `vintagecameras` to trigger the image generation.
## 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('pictgensupport/vintagecameras', weight_name='lora.safetensors')
image = pipeline('your prompt').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)
|
brunoyun/Llama-3.1-Amelia-AQA-8B-v1-GGUF
|
brunoyun
| 2025-06-18T14:52:15Z | 0 | 0 | null |
[
"gguf",
"base_model:meta-llama/Llama-3.1-8B-Instruct",
"base_model:quantized:meta-llama/Llama-3.1-8B-Instruct",
"license:llama3.1",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-06-17T11:59:51Z |
---
license: llama3.1
base_model:
- meta-llama/Llama-3.1-8B-Instruct
---
|
Mariogver/detr-finetuned-microglia
|
Mariogver
| 2025-06-18T14:51:48Z | 11 | 0 |
transformers
|
[
"transformers",
"safetensors",
"detr",
"object-detection",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] |
object-detection
| 2025-06-18T08:47:09Z |
---
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]
- **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]
|
tokiloutok/dqn-SpaceInvadersNoFrameskip-v4
|
tokiloutok
| 2025-06-18T14:50:41Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-06-18T14:50:27Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 129.50 +/- 99.41
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
SBX (SB3 + Jax): https://github.com/araffin/sbx
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga tokiloutok -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga tokiloutok -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga tokiloutok
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
mamtasahni/multilingual-ChartPGemma-all-kcl-witheng-lora-modified
|
mamtasahni
| 2025-06-18T14:50:16Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-18T14:50:04Z |
---
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. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
BootesVoid/cmc1yxgs40bpbrdqs4pnt4xs7_cmc21lfc00bvkrdqseaeab5id
|
BootesVoid
| 2025-06-18T14:48:48Z | 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
| 2025-06-18T14:48:46Z |
---
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: BRIELLE
---
# Cmc1Yxgs40Bpbrdqs4Pnt4Xs7_Cmc21Lfc00Bvkrdqseaeab5Id
<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 `BRIELLE` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "BRIELLE",
"lora_weights": "https://huggingface.co/BootesVoid/cmc1yxgs40bpbrdqs4pnt4xs7_cmc21lfc00bvkrdqseaeab5id/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/cmc1yxgs40bpbrdqs4pnt4xs7_cmc21lfc00bvkrdqseaeab5id', weight_name='lora.safetensors')
image = pipeline('BRIELLE').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/cmc1yxgs40bpbrdqs4pnt4xs7_cmc21lfc00bvkrdqseaeab5id/discussions) to add images that show off what you’ve made with this LoRA.
|
johngreendr1/c64cade6-ed34-4e30-8d78-e9f3d6d04df4
|
johngreendr1
| 2025-06-18T14:48:30Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:NousResearch/Genstruct-7B",
"base_model:adapter:NousResearch/Genstruct-7B",
"region:us"
] | null | 2025-06-18T13:10:08Z |
---
base_model: NousResearch/Genstruct-7B
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.15.1
|
Riyan123/Llama-3.2-3B-it-fintuned
|
Riyan123
| 2025-06-18T14:48:11Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-18T14:47:08Z |
---
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:** Riyan123
- **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)
|
nvidia/GEN3C-Cosmos-7B
|
nvidia
| 2025-06-18T14:44:36Z | 0 | 8 | null |
[
"arxiv:2503.03751",
"region:us"
] | null | 2025-05-21T19:15:04Z |
---
{}
---
# GEN3C: 3D-Informed World-Consistent Video Generation with Precise Camera Control
CVPR 2025 (Highlight)
[Xuanchi Ren*](https://xuanchiren.com/),
[Tianchang Shen*](https://www.cs.toronto.edu/~shenti11/)
[Jiahui Huang](https://huangjh-pub.github.io/),
[Huan Ling](https://www.cs.toronto.edu/~linghuan/),
[Yifan Lu](https://yifanlu0227.github.io/),
[Merlin Nimier-David](https://merlin.nimierdavid.fr/),
[Thomas Müller](https://research.nvidia.com/person/thomas-muller),
[Alexander Keller](https://research.nvidia.com/person/alex-keller),
[Sanja Fidler](https://www.cs.toronto.edu/~fidler/),
[Jun Gao](https://www.cs.toronto.edu/~jungao/) <br>
\* indicates equal contribution <br>
## Description: <br>
GEN3C is a generative video model with precise camera control and temporal three-dimensional (3D) Consistency. We achieve this with a 3D cache: point clouds obtained by predicting the pixel-wise depth of seed images or previously generated frames. When generating the next frames, GEN3C is conditioned on the two-dimensional (2D) renderings of the 3D cache with the new camera trajectory provided by the user. Our results demonstrate more precise camera control than prior work, as well as state-of-the-art results in sparse-view novel view synthesis, even in challenging settings such as driving scenes and monocular dynamic video.
This model is ready for commercial/non-commercial use <br>
### License/Terms of Use:
This model is released under the [NVIDIA Open Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/). For a custom license, please contact [email protected].
Important Note: If you bypass, disable, reduce the efficacy of, or circumvent any technical limitation, safety guardrail or associated safety guardrail hyperparameter, encryption, security, digital rights management, or authentication mechanism contained in the Model, your rights under NVIDIA Open Model License Agreement will automatically terminate.
### Deployment Geography:
Global
### Use Case: <br>
This model is intended for researchers interested in developing consistent video generation and allows users to use cameras to control the final generation. For AV applications, we can enable users to generate driving videos and specify the camera trajectories in this video, such as switching from the viewpoint of a sedan car to a truck, or looking at a different lane.
### Release Date: <br>
Github 06/10/2025 via https://github.com/nv-tlabs/Gen3C <br>
## Reference:
GEN3C: 3D-Informed World-Consistent Video Generation with Precise Camera Control
**[Paper](https://arxiv.org/pdf/2503.03751), [Project Page](https://research.nvidia.com/labs/toronto-ai/GEN3C/)**
## Model Architecture: <br>
**Architecture Type:** Convolutional Neural Network (CNN), Transformer <br>
**Network Architecture:** Transformer <br>
**This model was developed based on [Cosmos Predict 1](https://github.com/nvidia-cosmos/cosmos-predict1/tree/main) <br>
** This model has 7B of model parameters. <br>
## Input: <br>
**Input Type(s):** Camera Parameters, Image<br>
**Input Format(s):** 1D Array of Camera Poses, 2D Array of Images.<br>
**Input Parameters:** Camera Poses (1D), Images (2D) <br>
**Other Properties Related to Input:** The input image should be 720 * 1080 resolution, and we recommend using 121 frames for the camera parameters. <br>
## Output: <br>
**Output Type(s):** Videos <br>
**Output Format:** MP4 video <br>
**Output Parameters:** 3D (N x H x W), with 3 channels (Red, Green, Blue ((RGB))<br>
**Other Properties Related to Output:** A sequence of images (N x H x W x 3), N is the number of frames, H is the height and W is the width. Three (3) refers to the number of RGB channels. <br>
Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems A100 and H100. By leveraging NVIDIA’s hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions. <br>
## Software Integration: <br>
**Runtime Engine(s):**
*[Cosmos-Predict1](https://github.com/nvidia-cosmos/cosmos-predict1)<br>
**Supported Hardware Microarchitecture Compatibility:** <br>
* NVIDIA Ampere <br>
* NVIDIA Blackwell <br>
* NVIDIA Hopper <br>
**[Preferred/Supported] Operating System(s):** <br>
* Linux
## Model Version(s):
-V1.0
## Inference:
**Engine:** [Cosmos-Predict1](https://github.com/nvidia-cosmos/cosmos-predict1)
**Test Hardware:**
* NVIDIA Ampere <br>
* NVIDIA Blackwell <br>
* NVIDIA Hopper <br>
## Ethical Considerations:
NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.
Users are responsible for model inputs and outputs. Users are responsible for ensuring safe integration of this model, including implementing guardrails as well as other safety mechanisms, prior to deployment.
For more detailed information on ethical considerations for this model, please see the Model Card++ Explainability, Bias, Safety & Security, and Privacy Subcards [link to subcard](https://gitlab-master.nvidia.com/jung/gen3c_modelcard_subcard/-/blob/main/modelcard.md?ref_type=heads).
Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).
### Plus Plus (++) Promise
We value you, the datasets, the diversity they represent, and what we have been entrusted with. This model and its associated data have been:
- Verified to comply with current applicable disclosure laws, regulations, and industry standards.
- Verified to comply with applicable privacy labeling requirements.
- Annotated to describe the collector/source (NVIDIA or a third-party).
- Characterized for technical limitations.
- Reviewed to ensure proper disclosure is accessible to, maintained for, and in compliance with NVIDIA data subjects and their requests.
- Reviewed before release.
- Tagged for known restrictions and potential safety implications.
### Bias
Field | Response
:---------------------------------------------------------------------------------------------------|:---------------
Participation considerations from adversely impacted groups [protected classes](https://www.senate.ca.gov/content/protected-classes) in model design and testing: | None
Measures taken to mitigate against unwanted bias: | None
### Explainability
Field | Response
:------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------
Intended Task/Domain: | Novel view synthesis, video generation
Model Type: | Transformer
Intended Users: | Physical AI developers.
Output: | Videos
Describe how the model works: | We first predict depth for the input image, unproject it in to 3D to maintain a 3D cache. The 3D cache is then projected into a incomplete 2D video, which will be used as a condition for Cosmos to generate final video.
Name the adversely impacted groups this has been tested to deliver comparable outcomes regardless of: | Not Applicable.
Technical Limitations & Mitigation: | While the model aims to create photorealistic scenes that replicate real-world conditions, it may generate outputs that are not entirely visually accurate and may require augmentation and/or real-world data depending on the scope and use case.
Verified to have met prescribed NVIDIA quality standards: | Yes
Performance Metrics: | Qualitative and Quantitative Evaluation including PSNR, SSIM, LPIPS metrics. See [Gen3C](https://research.nvidia.com/labs/toronto-ai/GEN3C/paper.pdf) paper Section 5. for details.
Potential Known Risks: | This model may inaccurately characterize depth, which will make the generated video un-realistic and prone to artifacts.
Licensing: | [NVIDIA Open Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license)
### Privacy
Field | Response
:----------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------
Generatable or reverse engineerable personal data? | [None Known]
Personal data used to create this model? | [None Known]
Was consent obtained for any personal data used? | [None Known]
How often is dataset reviewed? | Before Release
Does data labeling (annotation, metadata) comply with privacy laws? | Yes
Applicable Privacy Policy | https://www.nvidia.com/en-us/about-nvidia/privacy-policy/
### Safety
Field | Response
:---------------------------------------------------|:----------------------------------
Model Application Field(s): | World Generation
Describe the life critical impact (if present). | None Known <br>
Use Case Restrictions: | Abide by [NVIDIA Open Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license)
Model and dataset restrictions: | The Principle of least privilege (PoLP) is applied limiting access for dataset generation and model development. Restrictions enforce dataset access during training, and dataset license constraints adhered to.
## Citation
```
@inproceedings{ren2025gen3c,
title={GEN3C: 3D-Informed World-Consistent Video Generation with Precise Camera Control},
author={Ren, Xuanchi and Shen, Tianchang and Huang, Jiahui and Ling, Huan and
Lu, Yifan and Nimier-David, Merlin and Müller, Thomas and Keller, Alexander and
Fidler, Sanja and Gao, Jun},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year={2025}
}
|
luyotw/openfun-ivod-whisper-medium-HuangKuoChang-11-185
|
luyotw
| 2025-06-18T14:44:04Z | 0 | 0 | null |
[
"tensorboard",
"safetensors",
"whisper",
"region:us"
] | null | 2025-06-18T13:29:52Z |
# Fine-tune 資訊
- 原始模型: `openai/whisper-medium`
- 使用音訊數量: 35047
- 使用音訊總長: 21.07 小時
- 音訊平均長度: 2.16 秒
- GPU: `NVIDIA H100 PCIe` x 1
- 訓練時間: 04:14:32
- 模型大小: 2.85 GB
---
# Model Card
|
3sara/checkpoints-version1_3
|
3sara
| 2025-06-18T14:42:14Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"colpali-finetuned",
"generated_from_trainer",
"base_model:vidore/colpaligemma-3b-pt-448-base",
"base_model:adapter:vidore/colpaligemma-3b-pt-448-base",
"license:gemma",
"region:us"
] | null | 2025-06-18T11:28:33Z |
---
library_name: peft
license: gemma
base_model: vidore/colpaligemma-3b-pt-448-base
tags:
- colpali-finetuned
- generated_from_trainer
model-index:
- name: checkpoints-version1_3
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. -->
# checkpoints-version1_3
This model is a fine-tuned version of [vidore/colpaligemma-3b-pt-448-base](https://huggingface.co/vidore/colpaligemma-3b-pt-448-base) on the 3sara/validated_colpali_italian_documents_with_images dataset.
Finetuned for 5 epochs
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- 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
- lr_scheduler_warmup_steps: 100
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0103 | 1 | 0.3508 |
### Framework versions
- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.7.1+cu126
- Datasets 3.6.0
- Tokenizers 0.21.1
|
marquesafonso/albertina-sts
|
marquesafonso
| 2025-06-18T14:41:29Z | 80 | 1 |
sentence-transformers
|
[
"sentence-transformers",
"onnx",
"safetensors",
"deberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"dataset:assin2",
"dataset:assin",
"doi:10.57967/hf/2274",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2024-05-18T11:10:19Z |
---
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
datasets:
- assin2
- assin
---
# marquesafonso/albertina-sts
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('marquesafonso/albertina-sts')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('marquesafonso/albertina-sts')
model = AutoModel.from_pretrained('marquesafonso/albertina-sts')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=marquesafonso/albertina-sts)
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 40 with parameters:
```
{'batch_size': 128, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CoSENTLoss.CoSENTLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'pairwise_cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 10,
"evaluation_steps": 800,
"evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 40,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: DebertaModel
(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})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
neural-interactive-proofs/finetune_dpo_cv_test_lm_server_30_0_iter_0_provers_group_2025-06-18_15-40-03_Qwen_Qwen2.5-0.5B-I
|
neural-interactive-proofs
| 2025-06-18T14:40:38Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"trl",
"dpo",
"arxiv:2305.18290",
"base_model:Qwen/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-0.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-06-18T14:40:34Z |
---
base_model: Qwen/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: finetune_dpo_cv_test_lm_server_30_0_iter_0_provers_group_2025-06-18_15-40-03_Qwen_Qwen2.5-0.5B-I
tags:
- generated_from_trainer
- trl
- dpo
licence: license
---
# Model Card for finetune_dpo_cv_test_lm_server_30_0_iter_0_provers_group_2025-06-18_15-40-03_Qwen_Qwen2.5-0.5B-I
This model is a fine-tuned version of [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="neural-interactive-proofs/finetune_dpo_cv_test_lm_server_30_0_iter_0_provers_group_2025-06-18_15-40-03_Qwen_Qwen2.5-0.5B-I", 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/lrhammond-team/pvg-self-hosted-finetune/runs/Qwen_Qwen2.5-0.5B-Instruct_dpo_2025-06-18_15-40-03_cv_test_lm_server_30_0_iter_0_provers_group)
This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.52.4
- Pytorch: 2.7.0
- Datasets: 2.21.0
- Tokenizers: 0.21.1
## Citations
Cite DPO as:
```bibtex
@inproceedings{rafailov2023direct,
title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}},
author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn},
year = 2023,
booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023},
url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html},
editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
sgonzalezygil/sd-finetuning-dreambooth-v10-800
|
sgonzalezygil
| 2025-06-18T14:40:13Z | 0 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2025-06-18T14:38:52Z |
---
library_name: diffusers
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🧨 diffusers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- 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]
|
eddieman78/litbank-coref-qwen-3-14b-no-think-4000-64-1e4-4
|
eddieman78
| 2025-06-18T14:39:45Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"unsloth",
"trl",
"sft",
"base_model:unsloth/Qwen3-14B-unsloth-bnb-4bit",
"base_model:finetune:unsloth/Qwen3-14B-unsloth-bnb-4bit",
"endpoints_compatible",
"region:us"
] | null | 2025-06-18T14:39:22Z |
---
base_model: unsloth/Qwen3-14B-unsloth-bnb-4bit
library_name: transformers
model_name: litbank-coref-qwen-3-14b-no-think-4000-64-1e4-4
tags:
- generated_from_trainer
- unsloth
- trl
- sft
licence: license
---
# Model Card for litbank-coref-qwen-3-14b-no-think-4000-64-1e4-4
This model is a fine-tuned version of [unsloth/Qwen3-14B-unsloth-bnb-4bit](https://huggingface.co/unsloth/Qwen3-14B-unsloth-bnb-4bit).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="eddieman78/litbank-coref-qwen-3-14b-no-think-4000-64-1e4-4", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.6.0+cu124
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
Jerboas86/astrone_split_models
|
Jerboas86
| 2025-06-18T14:38:52Z | 20 | 0 | null |
[
"gguf",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-06-03T09:46:02Z |
---
license: apache-2.0
---
|
sgonzalezygil/sd-finetuning-dreambooth-v10
|
sgonzalezygil
| 2025-06-18T14:35:34Z | 0 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2025-06-18T14:34:08Z |
---
library_name: diffusers
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🧨 diffusers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- 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]
|
MikeGreen2710/ner_land_dims
|
MikeGreen2710
| 2025-06-18T14:34:12Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"roberta",
"token-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2025-06-18T14:33:39Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
AlphaZero123/llama3.1-8b-finetuned-lora
|
AlphaZero123
| 2025-06-18T14:34:09Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-18T13:10:57Z |
---
base_model: unsloth/meta-llama-3.1-8b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** AlphaZero123
- **License:** apache-2.0
- **Finetuned from model :** unsloth/meta-llama-3.1-8b-unsloth-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
nicofarr/panns_Cnn10
|
nicofarr
| 2025-06-18T14:32:50Z | 0 | 0 |
pytorch
|
[
"pytorch",
"safetensors",
"Cnn10",
"audio",
"model_hub_mixin",
"panns",
"pytorch_model_hub_mixin",
"tagging",
"license:apache-2.0",
"region:us"
] | null | 2025-06-18T14:32:36Z |
---
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
|
omkar334/codegemma_model
|
omkar334
| 2025-06-18T14:31:01Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"gemma2",
"trl",
"en",
"base_model:unsloth/gemma-2-2b-bnb-4bit",
"base_model:finetune:unsloth/gemma-2-2b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-18T14:30:56Z |
---
base_model: unsloth/gemma-2-2b-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- gemma2
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** omkar334
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-2-2b-bnb-4bit
This gemma2 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)
|
omkar334/lora_model
|
omkar334
| 2025-06-18T14:25:36Z | 0 | 0 |
transformers
|
[
"transformers",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-18T14:25:31Z |
---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
docato/PaddleOCR_Mobile_Models
|
docato
| 2025-06-18T14:25:22Z | 0 | 0 | null |
[
"onnx",
"en",
"tr",
"license:mit",
"region:us"
] | null | 2025-06-18T08:46:04Z |
---
license: mit
language:
- en
- tr
---
# PaddleOCR Mobile Quantized Models (ONNX)
## Overview
This repo hosts four **ONNX** models converted from PaddleOCR mobile checkpoints
| File | Task | Language scope | Input shape |
|------|------|----------------|-------------|
| `Multilingual_PP-OCRv3_det_infer.onnx` | Text-detection | 80+ scripts | **NCHW • 1×3×H×W** |
| `PP-OCRv3_mobile_det_infer.onnx` | Text-detection | Latin only | 1×3×H×W |
| `ch_ppocr_mobile_v2.0_cls_infer.onnx` | Angle classifier | Chinese/Latin | 1×3×H×W |
| `latin_PP-OCRv3_mobile_rec_infer.onnx` | Text-recognition | Latin | 1×3×H×W |
All models were:
* exported with **paddle2onnx 1.2.3** (`opset 11`)
* simplified via **onnx-simplifier 0.4+**
## Quick Start
```python
import onnxruntime as ort, numpy as np
img = np.random.rand(1, 3, 224, 224).astype("float32")
det = ort.InferenceSession("Multilingual_PP-OCRv3_det_infer.onnx")
cls = ort.InferenceSession("ch_ppocr_mobile_v2.0_cls_infer.onnx")
rec = ort.InferenceSession("latin_PP-OCRv3_mobile_rec_infer.onnx")
det_out = det.run(None, {det.get_inputs()[0].name: img})[0]
# add your post-processing / cropping / decoding here …
|
omkar334/codegemma
|
omkar334
| 2025-06-18T14:25:21Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"gemma2",
"trl",
"en",
"base_model:unsloth/gemma-2-2b-bnb-4bit",
"base_model:finetune:unsloth/gemma-2-2b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-18T14:25:11Z |
---
base_model: unsloth/gemma-2-2b-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- gemma2
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** omkar334
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-2-2b-bnb-4bit
This gemma2 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)
|
QuantFactory/Apollo2-9B-GGUF
|
QuantFactory
| 2025-06-18T14:25:19Z | 0 | 2 | null |
[
"gguf",
"biology",
"medical",
"question-answering",
"ar",
"en",
"zh",
"ko",
"ja",
"mn",
"th",
"vi",
"lo",
"mg",
"de",
"pt",
"es",
"fr",
"ru",
"it",
"hr",
"gl",
"cs",
"co",
"la",
"uk",
"bs",
"bg",
"eo",
"sq",
"da",
"sa",
"no",
"gn",
"sr",
"sk",
"gd",
"lb",
"hi",
"ku",
"mt",
"he",
"ln",
"bm",
"sw",
"ig",
"rw",
"ha",
"dataset:FreedomIntelligence/ApolloMoEDataset",
"arxiv:2410.10626",
"base_model:google/gemma-2-9b",
"base_model:quantized:google/gemma-2-9b",
"license:gemma",
"endpoints_compatible",
"region:us",
"conversational"
] |
question-answering
| 2025-06-18T13:50:11Z |
---
license: gemma
datasets:
- FreedomIntelligence/ApolloMoEDataset
language:
- ar
- en
- zh
- ko
- ja
- mn
- th
- vi
- lo
- mg
- de
- pt
- es
- fr
- ru
- it
- hr
- gl
- cs
- co
- la
- uk
- bs
- bg
- eo
- sq
- da
- sa
- 'no'
- gn
- sr
- sk
- gd
- lb
- hi
- ku
- mt
- he
- ln
- bm
- sw
- ig
- rw
- ha
metrics:
- accuracy
base_model:
- google/gemma-2-9b
pipeline_tag: question-answering
tags:
- biology
- medical
---
[](https://hf.co/QuantFactory)
# QuantFactory/Apollo2-9B-GGUF
This is quantized version of [FreedomIntelligence/Apollo2-9B](https://huggingface.co/FreedomIntelligence/Apollo2-9B) created using llama.cpp
# Original Model Card
# Democratizing Medical LLMs For Much More Languages
Covering 12 Major Languages including English, Chinese, French, Hindi, Spanish, Arabic, Russian, Japanese, Korean, German, Italian, Portuguese and 38 Minor Languages So far.
<p align="center">
📃 <a href="https://arxiv.org/abs/2410.10626" target="_blank">Paper</a> • 🌐 <a href="" target="_blank">Demo</a> • 🤗 <a href="https://huggingface.co/datasets/FreedomIntelligence/ApolloMoEDataset" target="_blank">ApolloMoEDataset</a> • 🤗 <a href="https://huggingface.co/datasets/FreedomIntelligence/ApolloMoEBench" target="_blank">ApolloMoEBench</a> • 🤗 <a href="https://huggingface.co/collections/FreedomIntelligence/apollomoe-and-apollo2-670ddebe3bb1ba1aebabbf2c" target="_blank">Models</a> •🌐 <a href="https://github.com/FreedomIntelligence/Apollo" target="_blank">Apollo</a> • 🌐 <a href="https://github.com/FreedomIntelligence/ApolloMoE" target="_blank">ApolloMoE</a>
</p>
## 🌈 Update
* **[2024.10.15]** ApolloMoE repo is published！🎉
## Languages Coverage
12 Major Languages and 38 Minor Languages
<details>
<summary>Click to view the Languages Coverage</summary>
</details>
## Architecture
<details>
<summary>Click to view the MoE routing image</summary>
</details>
## Results
#### Dense
🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo2-0.5B" target="_blank">Apollo2-0.5B</a> • 🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo2-1.5B" target="_blank">Apollo2-1.5B</a> • 🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo2-2B" target="_blank">Apollo2-2B</a>
🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo2-3.8B" target="_blank">Apollo2-3.8B</a> • 🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo2-7B" target="_blank">Apollo2-7B</a> • 🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo2-9B" target="_blank">Apollo2-9B</a>
<details>
<summary>Click to view the Dense Models Results</summary>
</details>
#### Post-MoE
🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo-MoE-0.5B" target="_blank">Apollo-MoE-0.5B</a> • 🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo-MoE-1.5B" target="_blank">Apollo-MoE-1.5B</a> • 🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo-MoE-7B" target="_blank">Apollo-MoE-7B</a>
<details>
<summary>Click to view the Post-MoE Models Results</summary>
</details>
## Usage Format
##### Apollo2
- 0.5B, 1.5B, 7B: User:{query}\nAssistant:{response}<|endoftext|>
- 2B, 9B: User:{query}\nAssistant:{response}\<eos\>
- 3.8B: <|user|>\n{query}<|end|><|assisitant|>\n{response}<|end|>
##### Apollo-MoE
- 0.5B, 1.5B, 7B: User:{query}\nAssistant:{response}<|endoftext|>
## Dataset & Evaluation
- Dataset
🤗 <a href="https://huggingface.co/datasets/FreedomIntelligence/ApolloMoEDataset" target="_blank">ApolloMoEDataset</a>
<details><summary>Click to expand</summary>
- [Data category](https://huggingface.co/datasets/FreedomIntelligence/ApolloCorpus/tree/main/train)
</details>
- Evaluation
🤗 <a href="https://huggingface.co/datasets/FreedomIntelligence/ApolloMoEBench" target="_blank">ApolloMoEBench</a>
<details><summary>Click to expand</summary>
- EN:
- [MedQA-USMLE](https://huggingface.co/datasets/GBaker/MedQA-USMLE-4-options)
- [MedMCQA](https://huggingface.co/datasets/medmcqa/viewer/default/test)
- [PubMedQA](https://huggingface.co/datasets/pubmed_qa): Because the results fluctuated too much, they were not used in the paper.
- [MMLU-Medical](https://huggingface.co/datasets/cais/mmlu)
- Clinical knowledge, Medical genetics, Anatomy, Professional medicine, College biology, College medicine
- ZH:
- [MedQA-MCMLE](https://huggingface.co/datasets/bigbio/med_qa/viewer/med_qa_zh_4options_bigbio_qa/test)
- [CMB-single](https://huggingface.co/datasets/FreedomIntelligence/CMB): Not used in the paper
- Randomly sample 2,000 multiple-choice questions with single answer.
- [CMMLU-Medical](https://huggingface.co/datasets/haonan-li/cmmlu)
- Anatomy, Clinical_knowledge, College_medicine, Genetics, Nutrition, Traditional_chinese_medicine, Virology
- [CExam](https://github.com/williamliujl/CMExam): Not used in the paper
- Randomly sample 2,000 multiple-choice questions
- ES: [Head_qa](https://huggingface.co/datasets/head_qa)
- FR:
- [Frenchmedmcqa](https://github.com/qanastek/FrenchMedMCQA)
- [MMLU_FR]
- Clinical knowledge, Medical genetics, Anatomy, Professional medicine, College biology, College medicine
- HI: [MMLU_HI](https://huggingface.co/datasets/FreedomIntelligence/MMLU_Hindi)
- Clinical knowledge, Medical genetics, Anatomy, Professional medicine, College biology, College medicine
- AR: [MMLU_AR](https://huggingface.co/datasets/FreedomIntelligence/MMLU_Arabic)
- Clinical knowledge, Medical genetics, Anatomy, Professional medicine, College biology, College medicine
- JA: [IgakuQA](https://github.com/jungokasai/IgakuQA)
- KO: [KorMedMCQA](https://huggingface.co/datasets/sean0042/KorMedMCQA)
- IT:
- [MedExpQA](https://huggingface.co/datasets/HiTZ/MedExpQA)
- [MMLU_IT]
- Clinical knowledge, Medical genetics, Anatomy, Professional medicine, College biology, College medicine
- DE: [BioInstructQA](https://huggingface.co/datasets/BioMistral/BioInstructQA): German part
- PT: [BioInstructQA](https://huggingface.co/datasets/BioMistral/BioInstructQA): Portuguese part
- RU: [RuMedBench](https://github.com/sb-ai-lab/MedBench)
</details>
## Model Download and Inference
We take Apollo-MoE-0.5B as an example
1. Login Huggingface
```
huggingface-cli login --token $HUGGINGFACE_TOKEN
```
2. Download model to local dir
```python
from huggingface_hub import snapshot_download
import os
local_model_dir=os.path.join('/path/to/models/dir','Apollo-MoE-0.5B')
snapshot_download(repo_id="FreedomIntelligence/Apollo-MoE-0.5B", local_dir=local_model_dir)
```
3. Inference Example
```python
from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
import os
local_model_dir=os.path.join('/path/to/models/dir','Apollo-MoE-0.5B')
model=AutoModelForCausalLM.from_pretrained(local_model_dir,trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained(local_model_dir,trust_remote_code=True)
generation_config = GenerationConfig.from_pretrained(local_model_dir, pad_token_id=tokenizer.pad_token_id, num_return_sequences=1, max_new_tokens=7, min_new_tokens=2, do_sample=False, temperature=1.0, top_k=50, top_p=1.0)
inputs = tokenizer('Answer direclty.\nThe capital of Mongolia is Ulaanbaatar.\nThe capital of Iceland is Reykjavik.\nThe capital of Australia is', return_tensors='pt')
inputs = inputs.to(model.device)
pred = model.generate(**inputs,generation_config=generation_config)
print(tokenizer.decode(pred.cpu()[0], skip_special_tokens=True))
```
## Results reproduction
<details><summary>Click to expand</summary>
We take Apollo2-7B or Apollo-MoE-0.5B as example
1. Download Dataset for project:
```
bash 0.download_data.sh
```
2. Prepare test and dev data for specific model:
- Create test data for with special token
```
bash 1.data_process_test&dev.sh
```
3. Prepare train data for specific model (Create tokenized data in advance):
- You can adjust data Training order and Training Epoch in this step
```
bash 2.data_process_train.sh
```
4. Train the model
- If you want to train in Multi Nodes please refer to ./src/sft/training_config/zero_multi.yaml
```
bash 3.single_node_train.sh
```
5. Evaluate your model: Generate score for benchmark
```
bash 4.eval.sh
```
</details>
## Citation
Please use the following citation if you intend to use our dataset for training or evaluation:
```
@misc{zheng2024efficientlydemocratizingmedicalllms,
title={Efficiently Democratizing Medical LLMs for 50 Languages via a Mixture of Language Family Experts},
author={Guorui Zheng and Xidong Wang and Juhao Liang and Nuo Chen and Yuping Zheng and Benyou Wang},
year={2024},
eprint={2410.10626},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2410.10626},
}
```
|
Tamil14/Pest_Detection
|
Tamil14
| 2025-06-18T14:25:06Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-06-18T14:25:06Z |
---
license: apache-2.0
---
|
3sara/version1_2-2epochs-checkpoint
|
3sara
| 2025-06-18T14:22:48Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"colpali-finetuned",
"generated_from_trainer",
"base_model:vidore/colpaligemma-3b-pt-448-base",
"base_model:finetune:vidore/colpaligemma-3b-pt-448-base",
"license:gemma",
"endpoints_compatible",
"region:us"
] | null | 2025-06-18T14:22:36Z |
---
library_name: transformers
license: gemma
base_model: vidore/colpaligemma-3b-pt-448-base
tags:
- colpali-finetuned
- generated_from_trainer
model-index:
- name: version1_2-2epochs-checkpoint
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. -->
# version1_2-2epochs-checkpoint
This model is a fine-tuned version of [vidore/colpaligemma-3b-pt-448-base](https://huggingface.co/vidore/colpaligemma-3b-pt-448-base) on the 3sara/validated_colpali_italian_documents_with_images dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- 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
- lr_scheduler_warmup_steps: 100
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0103 | 1 | 0.3835 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.7.1+cu126
- Datasets 3.6.0
- Tokenizers 0.21.1
|
RizkyAnanda/lora_model9
|
RizkyAnanda
| 2025-06-18T14:21:09Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-18T14:20:39Z |
---
base_model: unsloth/meta-llama-3.1-8b-instruct-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** RizkyAnanda
- **License:** apache-2.0
- **Finetuned from model :** unsloth/meta-llama-3.1-8b-instruct-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
mradermacher/Sam-reason-A3-GGUF
|
mradermacher
| 2025-06-18T14:20:19Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:Smilyai-labs/Sam-reason-A3",
"base_model:quantized:Smilyai-labs/Sam-reason-A3",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-06-18T13:59:46Z |
---
base_model: Smilyai-labs/Sam-reason-A3
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/Smilyai-labs/Sam-reason-A3
<!-- 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/Sam-reason-A3-GGUF/resolve/main/Sam-reason-A3.Q2_K.gguf) | Q2_K | 1.0 | |
| [GGUF](https://huggingface.co/mradermacher/Sam-reason-A3-GGUF/resolve/main/Sam-reason-A3.Q3_K_S.gguf) | Q3_K_S | 1.1 | |
| [GGUF](https://huggingface.co/mradermacher/Sam-reason-A3-GGUF/resolve/main/Sam-reason-A3.Q3_K_M.gguf) | Q3_K_M | 1.2 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Sam-reason-A3-GGUF/resolve/main/Sam-reason-A3.Q3_K_L.gguf) | Q3_K_L | 1.2 | |
| [GGUF](https://huggingface.co/mradermacher/Sam-reason-A3-GGUF/resolve/main/Sam-reason-A3.IQ4_XS.gguf) | IQ4_XS | 1.3 | |
| [GGUF](https://huggingface.co/mradermacher/Sam-reason-A3-GGUF/resolve/main/Sam-reason-A3.Q4_K_S.gguf) | Q4_K_S | 1.3 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Sam-reason-A3-GGUF/resolve/main/Sam-reason-A3.Q4_K_M.gguf) | Q4_K_M | 1.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Sam-reason-A3-GGUF/resolve/main/Sam-reason-A3.Q5_K_S.gguf) | Q5_K_S | 1.5 | |
| [GGUF](https://huggingface.co/mradermacher/Sam-reason-A3-GGUF/resolve/main/Sam-reason-A3.Q5_K_M.gguf) | Q5_K_M | 1.6 | |
| [GGUF](https://huggingface.co/mradermacher/Sam-reason-A3-GGUF/resolve/main/Sam-reason-A3.Q6_K.gguf) | Q6_K | 1.8 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Sam-reason-A3-GGUF/resolve/main/Sam-reason-A3.Q8_0.gguf) | Q8_0 | 2.3 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Sam-reason-A3-GGUF/resolve/main/Sam-reason-A3.f16.gguf) | f16 | 4.2 | 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 -->
|
mradermacher/Gasing-8B-alpha-v0.1-nearswap-base-GGUF
|
mradermacher
| 2025-06-18T14:20:18Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"merge",
"mergekit",
"lazymergekit",
"en",
"base_model:hafidhsoekma/Gasing-8B-alpha-v0.1-nearswap-base",
"base_model:quantized:hafidhsoekma/Gasing-8B-alpha-v0.1-nearswap-base",
"endpoints_compatible",
"region:us"
] | null | 2025-06-18T13:32:36Z |
---
base_model: hafidhsoekma/Gasing-8B-alpha-v0.1-nearswap-base
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- merge
- mergekit
- lazymergekit
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/hafidhsoekma/Gasing-8B-alpha-v0.1-nearswap-base
<!-- 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/Gasing-8B-alpha-v0.1-nearswap-base-GGUF/resolve/main/Gasing-8B-alpha-v0.1-nearswap-base.Q2_K.gguf) | Q2_K | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/Gasing-8B-alpha-v0.1-nearswap-base-GGUF/resolve/main/Gasing-8B-alpha-v0.1-nearswap-base.Q3_K_S.gguf) | Q3_K_S | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/Gasing-8B-alpha-v0.1-nearswap-base-GGUF/resolve/main/Gasing-8B-alpha-v0.1-nearswap-base.Q3_K_M.gguf) | Q3_K_M | 4.2 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Gasing-8B-alpha-v0.1-nearswap-base-GGUF/resolve/main/Gasing-8B-alpha-v0.1-nearswap-base.Q3_K_L.gguf) | Q3_K_L | 4.5 | |
| [GGUF](https://huggingface.co/mradermacher/Gasing-8B-alpha-v0.1-nearswap-base-GGUF/resolve/main/Gasing-8B-alpha-v0.1-nearswap-base.IQ4_XS.gguf) | IQ4_XS | 4.7 | |
| [GGUF](https://huggingface.co/mradermacher/Gasing-8B-alpha-v0.1-nearswap-base-GGUF/resolve/main/Gasing-8B-alpha-v0.1-nearswap-base.Q4_K_S.gguf) | Q4_K_S | 4.9 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Gasing-8B-alpha-v0.1-nearswap-base-GGUF/resolve/main/Gasing-8B-alpha-v0.1-nearswap-base.Q4_K_M.gguf) | Q4_K_M | 5.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Gasing-8B-alpha-v0.1-nearswap-base-GGUF/resolve/main/Gasing-8B-alpha-v0.1-nearswap-base.Q5_K_S.gguf) | Q5_K_S | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/Gasing-8B-alpha-v0.1-nearswap-base-GGUF/resolve/main/Gasing-8B-alpha-v0.1-nearswap-base.Q5_K_M.gguf) | Q5_K_M | 6.0 | |
| [GGUF](https://huggingface.co/mradermacher/Gasing-8B-alpha-v0.1-nearswap-base-GGUF/resolve/main/Gasing-8B-alpha-v0.1-nearswap-base.Q6_K.gguf) | Q6_K | 6.8 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Gasing-8B-alpha-v0.1-nearswap-base-GGUF/resolve/main/Gasing-8B-alpha-v0.1-nearswap-base.Q8_0.gguf) | Q8_0 | 8.8 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Gasing-8B-alpha-v0.1-nearswap-base-GGUF/resolve/main/Gasing-8B-alpha-v0.1-nearswap-base.f16.gguf) | f16 | 16.5 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
sugilee/mental-health-distill-Enhanced-mistral-2
|
sugilee
| 2025-06-18T14:17:08Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-18T14:16:49Z |
---
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]
|
Mohamed264/VQA-LLava
|
Mohamed264
| 2025-06-18T14:16:39Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"llama-factory",
"lora",
"generated_from_trainer",
"base_model:llava-hf/llava-1.5-7b-hf",
"base_model:adapter:llava-hf/llava-1.5-7b-hf",
"license:other",
"region:us"
] | null | 2025-06-18T14:16:25Z |
---
library_name: peft
license: other
base_model: llava-hf/llava-1.5-7b-hf
tags:
- llama-factory
- lora
- generated_from_trainer
model-index:
- name: vqa-llama
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. -->
# vqa-llama
This model is a fine-tuned version of [llava-hf/llava-1.5-7b-hf](https://huggingface.co/llava-hf/llava-1.5-7b-hf) on the medical_vqa_train dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8505
## 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: 2
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.15.2
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
habdine/Qwen2.5-0.5B-EXED-IFT-lora
|
habdine
| 2025-06-18T14:16:39Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-18T14:16:33Z |
---
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]
|
RoadQAQ/Qwen2.5-7B-think
|
RoadQAQ
| 2025-06-18T14:15:37Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:2506.07527",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-08T08:08:47Z |
---
license: mit
library_name: transformers
pipeline_tag: text-generation
---
The base Qwen2.5-7B model used by ReLIFT.
We modify the chat_template for the system prompt and add <think>.
Github: https://github.com/TheRoadQaQ/ReLIFT
# Citation
If you find our model, data, or evaluation code useful, please kindly cite our paper:
```bib
@article{ma2025learning,
title={Learning What Reinforcement Learning Can't: Interleaved Online Fine-Tuning for Hardest Questions},
author={Ma, Lu and Liang, Hao and Qiang, Meiyi and Tang, Lexiang and Ma, Xiaochen and Wong, Zhen Hao and Niu, Junbo and Shen, Chengyu and He, Runming and Cui, Bin and others},
journal={arXiv preprint arXiv:2506.07527},
year={2025}
}
```
|
chutesai/MiniMax-M1-80k
|
chutesai
| 2025-06-18T14:15:33Z | 0 | 0 | null |
[
"safetensors",
"minimax_m1",
"text-generation",
"conversational",
"custom_code",
"arxiv:2506.13585",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2025-06-17T14:39:03Z |
---
pipeline_tag: text-generation
license: apache-2.0
---
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<hr>
<div align="center" style="line-height: 1;">
<a href="https://www.minimax.io" target="_blank" style="margin: 2px;">
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# MiniMax-M1
## 1. Model Overview
We introduce MiniMax-M1, the world's first open-weight, large-scale hybrid-attention reasoning model.
MiniMax-M1 is powered by a hybrid Mixture-of-Experts (MoE) architecture combined with a lightning
attention mechanism. The model is developed based on our previous [MiniMax-Text-01 model](https://huggingface.co/MiniMaxAI/MiniMax-Text-01),
which contains a total of 456 billion parameters with 45.9 billion parameters activated
per token. Consistent with MiniMax-Text-01, the M1 model natively supports a context length of 1
million tokens, 8x the context size of DeepSeek R1. Furthermore, the lightning attention mechanism
in MiniMax-M1 enables efficient scaling of test-time compute – For example, compared to DeepSeek
R1, M1 consumes 25% of the FLOPs at a generation length of 100K tokens. These properties make M1
particularly suitable for complex tasks that require processing long inputs and thinking extensively.
MiniMax-M1 is trained using large-scale reinforcement learning (RL) on diverse problems ranging from
traditional mathematical reasoning to sandbox-based, real-world software engineering environments.
We develop an efficient RL scaling framework for M1 highlighting two perspectives: (1) We propose
CISPO, a novel algorithm that clips importance sampling weights instead of token updates, which
outperforms other competitive RL variants; (2) Our hybrid-attention design naturally enhances the
efficiency of RL, where we address unique challenges when scaling RL with the hybrid architecture. We
train two versions of MiniMax-M1 models with [40K](https://huggingface.co/MiniMaxAI/MiniMax-M1-40k) and
[80K](https://huggingface.co/MiniMaxAI/MiniMax-M1-80k) thinking budgets respectively. Experiments
on standard benchmarks show that our models outperform other strong open-weight models such as
the original DeepSeek-R1 and Qwen3-235B, particularly on complex software engineering, tool using,
and long context tasks. With efficient scaling of test-time compute, MiniMax-M1 serves as a strong
foundation for next-generation language model agents to reason and tackle real-world challenges.
<p align="center">
<img width="100%" src="figures/TextBench.png">
<br>
<small><em>Benchmark performance comparison of leading commercial and open-weight models across competition-level mathematics, coding, software engineering, agentic tool use, and long-context understanding tasks. We use the MiniMax-M1-80k model here for MiniMax-M1.</em></small>
</p>
## 2. Evaluation
**Performance of MiniMax-M1 on core benchmarks.**
| **Category** | **Task** | **MiniMax-M1-80K** | **MiniMax-M1-40K** | **Qwen3-235B-A22B** | **DeepSeek-R1-0528** | **DeepSeek-R1** | **Seed-Thinking-v1.5** | **Claude 4 Opus** | **Gemini 2.5 Pro (06-05)** | **OpenAI-o3** |
|:---|:---|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
| | *Extended Thinking* | *80K* | *40K* | *32k* | *64k* | *32k* | *32k* | *64k* | *64k* | *100k* |
| ***Mathematics*** | AIME 2024 | 86.0 | 83.3 | 85.7 | 91.4 | 79.8 | 86.7 | 76.0 | 92.0 | 91.6 |
| | AIME 2025 | 76.9 | 74.6 | 81.5 | 87.5 | 70.0 | 74.0 | 75.5 | 88.0 | 88.9 |
| | MATH-500 | 96.8 | 96.0 | 96.2 | 98.0 | 97.3 | 96.7 | 98.2 | 98.8 | 98.1 |
| ***General Coding*** | LiveCodeBench *(24/8~25/5)* | 65.0 | 62.3 | 65.9 | 73.1 | 55.9 | 67.5 | 56.6 | 77.1 | 75.8 |
| | FullStackBench | 68.3 | 67.6 | 62.9 | 69.4 | 70.1 | 69.9 | 70.3 | -- | 69.3 |
| ***Reasoning & Knowledge***| GPQA Diamond | 70.0 | 69.2 | 71.1 | 81.0 | 71.5 | 77.3 | 79.6 | 86.4 | 83.3 |
| | HLE *(no tools)* | 8.4\* | 7.2\* | 7.6\* | 17.7\* | 8.6\* | 8.2 | 10.7 | 21.6 | 20.3 |
| | ZebraLogic | 86.8 | 80.1 | 80.3 | 95.1 | 78.7 | 84.4 | 95.1 | 91.6 | 95.8 |
| | MMLU-Pro | 81.1 | 80.6 | 83.0 | 85.0 | 84.0 | 87.0 | 85.0 | 86.0 | 85.0 |
| ***Software Engineering***| SWE-bench Verified| 56.0 | 55.6 | 34.4 | 57.6 | 49.2 | 47.0 | 72.5 | 67.2 | 69.1 |
| ***Long Context*** | OpenAI-MRCR *(128k)* | 73.4 | 76.1 | 27.7 | 51.5 | 35.8 | 54.3 | 48.9 | 76.8 | 56.5 |
| | OpenAI-MRCR *(1M)* | 56.2 | 58.6 | -- | -- | -- | -- | -- | 58.8 | -- |
| | LongBench-v2 | 61.5 | 61.0 | 50.1 | 52.1 | 58.3 | 52.5 | 55.6 | 65.0 | 58.8 |
| ***Agentic Tool Use***| TAU-bench *(airline)* | 62.0 | 60.0 | 34.7 | 53.5 | -- | 44.0 | 59.6 | 50.0 | 52.0 |
| | TAU-bench *(retail)* | 63.5 | 67.8 | 58.6 | 63.9 | -- | 55.7 | 81.4 | 67.0 | 73.9 |
| ***Factuality*** | SimpleQA | 18.5 | 17.9 | 11.0 | 27.8 | 30.1 | 12.9 | -- | 54.0 | 49.4 |
| ***General Assistant***| MultiChallenge | 44.7 | 44.7 | 40.0 | 45.0 | 40.7 | 43.0 | 45.8 | 51.8 | 56.5 |
\* conducted on the text-only HLE subset.
Our models are evaluated with `temperature=1.0`, `top_p=0.95`.
### SWE-bench methodology
We report results derived from the Agentless scaffold. Departing from the original pipeline, our methodology employs a two-stage localization process (without any embedding-based retrieval mechanisms): initial coarse-grained file localization followed by fine-grained localization to specific files and code elements. The values for our models are calculated on the subset of n=486 verified tasks which work on our infrastructure. The excluded 14 test cases that were incompatible with our internal infrastructure are:
`"astropy__astropy-7606"`,
`"astropy__astropy-8707"`,
`"astropy__astropy-8872"`,
`"django__django-10097"`,
`"matplotlib__matplotlib-20488"`,
`"psf__requests-2317"`,
`"psf__requests-2931"`,
`"psf__requests-5414"`,
`"pylint-dev__pylint-6528"`,
`"pylint-dev__pylint-7277"`,
`"sphinx-doc__sphinx-10435"`,
`"sphinx-doc__sphinx-7985"`,
`"sphinx-doc__sphinx-8269"`,
`"sphinx-doc__sphinx-8475"`
### TAU-bench methodology
We evaluate TAU-Bench with GPT-4.1 as user model and without any custom tools. The maximum number of interaction steps is 40.
Our general system prompt is:
```
- In each round, you need to carefully examine the tools provided to you to determine if any can be used.
- You must adhere to all of the policies. Pay attention to the details in the terms. Solutions for most situations can be found within these policies.
```
## 3. Deployment Guide
Download the model from HuggingFace repository:
- [MiniMax-M1-40k](https://huggingface.co/MiniMaxAI/MiniMax-M1-40k)
- [MiniMax-M1-80k](https://huggingface.co/MiniMaxAI/MiniMax-M1-80k)
For production deployment, we recommend using [vLLM](https://docs.vllm.ai/en/latest/) to serve MiniMax-M1. vLLM provides excellent performance for serving large language models with the following features:
- 🔥 Outstanding service throughout performance
- ⚡ Efficient and intelligent memory management
- 📦 Powerful batch request processing capability
- ⚙️ Deeply optimized underlying performance
For detailed vLLM deployment instructions, please refer to our [vLLM Deployment Guide](./docs/vllm_deployment_guide.md).
Alternatively, you can also deploy using Transformers directly. For detailed Transformers deployment instructions, you can see our [MiniMax-M1 Transformers Deployment Guide](./docs/transformers_deployment_guide.md).
## 4. Function Calling
The MiniMax-M1 model supports function calling capabilities, enabling the model to identify when external functions need to be called and output function call parameters in a structured format. [MiniMax-M1 Function Call Guide](./docs/function_call_guide.md) provides detailed instructions on how to use the function calling feature of MiniMax-M1.
## 5. Chatbot & API
For general use and evaluation, we provide a [Chatbot](https://chat.minimax.io/) with online search capabilities and the [online API](https://www.minimax.io/platform/) for developers. For general use and evaluation, we provide the [MiniMax MCP Server](https://github.com/MiniMax-AI/MiniMax-MCP) with video generation, image generation, speech synthesis, and voice cloning for developers.
## 6. Contact Us
Contact us at [[email protected]](mailto:[email protected]).
|
morturr/Llama-2-7b-hf-LOO_one_liners-COMB_dadjokes-comb2-seed28-2025-06-18
|
morturr
| 2025-06-18T14:15:17Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:meta-llama/Llama-2-7b-hf",
"base_model:adapter:meta-llama/Llama-2-7b-hf",
"license:llama2",
"region:us"
] | null | 2025-06-18T14:15:04Z |
---
library_name: peft
license: llama2
base_model: meta-llama/Llama-2-7b-hf
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: Llama-2-7b-hf-LOO_one_liners-COMB_dadjokes-comb2-seed28-2025-06-18
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Llama-2-7b-hf-LOO_one_liners-COMB_dadjokes-comb2-seed28-2025-06-18
This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) 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: 16
- eval_batch_size: 16
- seed: 28
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- 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
|
RoadQAQ/ReLIFT-Qwen2.5-7B-Zero
|
RoadQAQ
| 2025-06-18T14:14:48Z | 55 | 2 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"question-answering",
"arxiv:2506.07527",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2025-06-07T06:10:14Z |
---
library_name: transformers
license: mit
pipeline_tag: question-answering
---
ReLIFT, a training method that interleaves RL with online FT, achieving superior performance and efficiency compared to using RL or SFT alone, as described in [Learning What Reinforcement Learning Can't: Interleaved Online Fine-Tuning for Hardest Questions](https://huggingface.co/papers/2506.07527).
Code: https://github.com/TheRoadQaQ/ReLIFT
Project page: https://github.com/TheRoadQaQ/ReLIFT
|
RoadQAQ/Qwen2.5-Math-7B-16k-think
|
RoadQAQ
| 2025-06-18T14:14:25Z | 15 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:2506.07527",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-08T07:28:24Z |
---
license: mit
library_name: transformers
pipeline_tag: text-generation
---
The base Qwen2.5-Math-7B model used by ReLIFT.
We change to rope_theta from 10000 to 40000 and extend the context window to 16k.
Also, we modify the chat_template for the system prompt and add <think>.
Github: https://github.com/TheRoadQaQ/ReLIFT
# Citation
If you find our model, data, or evaluation code useful, please kindly cite our paper:
```bib
@article{ma2025learning,
title={Learning What Reinforcement Learning Can't: Interleaved Online Fine-Tuning for Hardest Questions},
author={Ma, Lu and Liang, Hao and Qiang, Meiyi and Tang, Lexiang and Ma, Xiaochen and Wong, Zhen Hao and Niu, Junbo and Shen, Chengyu and He, Runming and Cui, Bin and others},
journal={arXiv preprint arXiv:2506.07527},
year={2025}
}
```
|
youmakeai/Future-HTML
|
youmakeai
| 2025-06-18T14:12:30Z | 0 | 0 |
flair
|
[
"flair",
"code",
"en",
"dataset:reedmayhew/claude-3.7-sonnet-reasoning",
"base_model:reedmayhew/claude-3.7-sonnet-reasoning-gemma3-12B",
"base_model:finetune:reedmayhew/claude-3.7-sonnet-reasoning-gemma3-12B",
"doi:10.57967/hf/5628",
"license:cc0-1.0",
"region:us"
] | null | 2025-05-26T21:58:10Z |
---
license: cc0-1.0
datasets:
- reedmayhew/claude-3.7-sonnet-reasoning
language:
- en
metrics:
- character
base_model:
- reedmayhew/claude-3.7-sonnet-reasoning-gemma3-12B
new_version: reedmayhew/claude-3.7-sonnet-reasoning-gemma3-12B
library_name: flair
tags:
- code
---
|
chenpyyy/openvla-ac
|
chenpyyy
| 2025-06-18T14:11:49Z | 0 | 1 |
transformers
|
[
"transformers",
"safetensors",
"robotics",
"arxiv:2506.13725",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
robotics
| 2025-06-16T07:23:16Z |
---
license: apache-2.0
library_name: transformers
pipeline_tag: robotics
---
This repository contains the model introduced in [CEED-VLA: Consistency Vision-Language-Action Model with Early-Exit Decoding](https://huggingface.co/papers/2506.13725).
Project page: https://irpn-eai.github.io/CEED-VLA/
Code: https://github.com/OpenHelix-Team/CEED-VLA
|
mradermacher/Router-R1-Qwen2.5-3B-Instruct-GGUF
|
mradermacher
| 2025-06-18T14:10:44Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:ulab-ai/Router-R1-Qwen2.5-3B-Instruct",
"base_model:quantized:ulab-ai/Router-R1-Qwen2.5-3B-Instruct",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-06-18T13:46:33Z |
---
base_model: ulab-ai/Router-R1-Qwen2.5-3B-Instruct
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/ulab-ai/Router-R1-Qwen2.5-3B-Instruct
<!-- 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/Router-R1-Qwen2.5-3B-Instruct-GGUF/resolve/main/Router-R1-Qwen2.5-3B-Instruct.Q2_K.gguf) | Q2_K | 1.5 | |
| [GGUF](https://huggingface.co/mradermacher/Router-R1-Qwen2.5-3B-Instruct-GGUF/resolve/main/Router-R1-Qwen2.5-3B-Instruct.Q3_K_S.gguf) | Q3_K_S | 1.7 | |
| [GGUF](https://huggingface.co/mradermacher/Router-R1-Qwen2.5-3B-Instruct-GGUF/resolve/main/Router-R1-Qwen2.5-3B-Instruct.Q3_K_M.gguf) | Q3_K_M | 1.8 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Router-R1-Qwen2.5-3B-Instruct-GGUF/resolve/main/Router-R1-Qwen2.5-3B-Instruct.Q3_K_L.gguf) | Q3_K_L | 1.9 | |
| [GGUF](https://huggingface.co/mradermacher/Router-R1-Qwen2.5-3B-Instruct-GGUF/resolve/main/Router-R1-Qwen2.5-3B-Instruct.IQ4_XS.gguf) | IQ4_XS | 2.0 | |
| [GGUF](https://huggingface.co/mradermacher/Router-R1-Qwen2.5-3B-Instruct-GGUF/resolve/main/Router-R1-Qwen2.5-3B-Instruct.Q4_K_S.gguf) | Q4_K_S | 2.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Router-R1-Qwen2.5-3B-Instruct-GGUF/resolve/main/Router-R1-Qwen2.5-3B-Instruct.Q4_K_M.gguf) | Q4_K_M | 2.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Router-R1-Qwen2.5-3B-Instruct-GGUF/resolve/main/Router-R1-Qwen2.5-3B-Instruct.Q5_K_S.gguf) | Q5_K_S | 2.5 | |
| [GGUF](https://huggingface.co/mradermacher/Router-R1-Qwen2.5-3B-Instruct-GGUF/resolve/main/Router-R1-Qwen2.5-3B-Instruct.Q5_K_M.gguf) | Q5_K_M | 2.5 | |
| [GGUF](https://huggingface.co/mradermacher/Router-R1-Qwen2.5-3B-Instruct-GGUF/resolve/main/Router-R1-Qwen2.5-3B-Instruct.Q6_K.gguf) | Q6_K | 2.9 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Router-R1-Qwen2.5-3B-Instruct-GGUF/resolve/main/Router-R1-Qwen2.5-3B-Instruct.Q8_0.gguf) | Q8_0 | 3.7 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Router-R1-Qwen2.5-3B-Instruct-GGUF/resolve/main/Router-R1-Qwen2.5-3B-Instruct.f16.gguf) | f16 | 6.9 | 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 -->
|
Puranjay14/check_gemma_3n_3p18b
|
Puranjay14
| 2025-06-18T14:09:00Z | 6 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma3n",
"text2text-generation",
"matformer",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2025-06-16T12:19:02Z |
---
library_name: transformers
tags:
- matformer
---
# 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]
|
eddieman78/litbank-coref-qwen-3-14b-it-4000-128-1e4-4
|
eddieman78
| 2025-06-18T14:08:19Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"unsloth",
"trl",
"sft",
"base_model:unsloth/Qwen3-14B-unsloth-bnb-4bit",
"base_model:finetune:unsloth/Qwen3-14B-unsloth-bnb-4bit",
"endpoints_compatible",
"region:us"
] | null | 2025-06-18T14:07:40Z |
---
base_model: unsloth/Qwen3-14B-unsloth-bnb-4bit
library_name: transformers
model_name: litbank-coref-qwen-3-14b-it-4000-128-1e4-4
tags:
- generated_from_trainer
- unsloth
- trl
- sft
licence: license
---
# Model Card for litbank-coref-qwen-3-14b-it-4000-128-1e4-4
This model is a fine-tuned version of [unsloth/Qwen3-14B-unsloth-bnb-4bit](https://huggingface.co/unsloth/Qwen3-14B-unsloth-bnb-4bit).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="eddieman78/litbank-coref-qwen-3-14b-it-4000-128-1e4-4", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.6.0+cu124
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
ElvynStudio/feelyn-pixar-lora
|
ElvynStudio
| 2025-06-18T14:06:10Z | 0 | 0 | null |
[
"license:openrail++",
"region:us"
] | null | 2025-06-17T13:57:38Z |
---
license: openrail++
---
|
mradermacher/Oblivion2.5-1.5B-Instruct-v2-GGUF
|
mradermacher
| 2025-06-18T14:04:59Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:r1char9/Oblivion2.5-1.5B-Instruct-v2",
"base_model:quantized:r1char9/Oblivion2.5-1.5B-Instruct-v2",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2025-06-18T13:53:49Z |
---
base_model: r1char9/Oblivion2.5-1.5B-Instruct-v2
language:
- en
library_name: transformers
license: mit
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/r1char9/Oblivion2.5-1.5B-Instruct-v2
<!-- 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/Oblivion2.5-1.5B-Instruct-v2-GGUF/resolve/main/Oblivion2.5-1.5B-Instruct-v2.Q2_K.gguf) | Q2_K | 0.8 | |
| [GGUF](https://huggingface.co/mradermacher/Oblivion2.5-1.5B-Instruct-v2-GGUF/resolve/main/Oblivion2.5-1.5B-Instruct-v2.Q3_K_S.gguf) | Q3_K_S | 0.9 | |
| [GGUF](https://huggingface.co/mradermacher/Oblivion2.5-1.5B-Instruct-v2-GGUF/resolve/main/Oblivion2.5-1.5B-Instruct-v2.Q3_K_M.gguf) | Q3_K_M | 0.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Oblivion2.5-1.5B-Instruct-v2-GGUF/resolve/main/Oblivion2.5-1.5B-Instruct-v2.Q3_K_L.gguf) | Q3_K_L | 1.0 | |
| [GGUF](https://huggingface.co/mradermacher/Oblivion2.5-1.5B-Instruct-v2-GGUF/resolve/main/Oblivion2.5-1.5B-Instruct-v2.IQ4_XS.gguf) | IQ4_XS | 1.0 | |
| [GGUF](https://huggingface.co/mradermacher/Oblivion2.5-1.5B-Instruct-v2-GGUF/resolve/main/Oblivion2.5-1.5B-Instruct-v2.Q4_K_S.gguf) | Q4_K_S | 1.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Oblivion2.5-1.5B-Instruct-v2-GGUF/resolve/main/Oblivion2.5-1.5B-Instruct-v2.Q4_K_M.gguf) | Q4_K_M | 1.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Oblivion2.5-1.5B-Instruct-v2-GGUF/resolve/main/Oblivion2.5-1.5B-Instruct-v2.Q5_K_S.gguf) | Q5_K_S | 1.2 | |
| [GGUF](https://huggingface.co/mradermacher/Oblivion2.5-1.5B-Instruct-v2-GGUF/resolve/main/Oblivion2.5-1.5B-Instruct-v2.Q5_K_M.gguf) | Q5_K_M | 1.2 | |
| [GGUF](https://huggingface.co/mradermacher/Oblivion2.5-1.5B-Instruct-v2-GGUF/resolve/main/Oblivion2.5-1.5B-Instruct-v2.Q6_K.gguf) | Q6_K | 1.4 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Oblivion2.5-1.5B-Instruct-v2-GGUF/resolve/main/Oblivion2.5-1.5B-Instruct-v2.Q8_0.gguf) | Q8_0 | 1.7 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Oblivion2.5-1.5B-Instruct-v2-GGUF/resolve/main/Oblivion2.5-1.5B-Instruct-v2.f16.gguf) | f16 | 3.2 | 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 -->
|
moazharu/appu-qwen-4b-sft-20250618_093419
|
moazharu
| 2025-06-18T14:04:42Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:Qwen/Qwen3-4B",
"base_model:finetune:Qwen/Qwen3-4B",
"endpoints_compatible",
"region:us"
] | null | 2025-06-18T09:36:01Z |
---
base_model: Qwen/Qwen3-4B
library_name: transformers
model_name: appu-qwen-4b-sft-20250618_093419
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for appu-qwen-4b-sft-20250618_093419
This model is a fine-tuned version of [Qwen/Qwen3-4B](https://huggingface.co/Qwen/Qwen3-4B).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="moazharu/appu-qwen-4b-sft-20250618_093419", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.18.2
- Transformers: 4.52.4
- Pytorch: 2.5.1+cu121
- Datasets: 3.6.0
- 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 Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
morturr/Llama-2-7b-hf-LOO_headlines-COMB_dadjokes-comb3-seed7-2025-06-18
|
morturr
| 2025-06-18T14:04:36Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:meta-llama/Llama-2-7b-hf",
"base_model:adapter:meta-llama/Llama-2-7b-hf",
"license:llama2",
"region:us"
] | null | 2025-06-18T14:04:16Z |
---
library_name: peft
license: llama2
base_model: meta-llama/Llama-2-7b-hf
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: Llama-2-7b-hf-LOO_headlines-COMB_dadjokes-comb3-seed7-2025-06-18
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Llama-2-7b-hf-LOO_headlines-COMB_dadjokes-comb3-seed7-2025-06-18
This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) 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: 16
- eval_batch_size: 16
- seed: 7
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- 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
|
TruongSinhAI/Qwen-2.5-1.5B-Instruct_EnVi_200steps
|
TruongSinhAI
| 2025-06-18T14:04:35Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen2",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-18T14:04:21Z |
---
base_model: unsloth/qwen2.5-1.5b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** TruongSinhAI
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen2.5-1.5b-unsloth-bnb-4bit
This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
bruhzair/prototype-0.4x159
|
bruhzair
| 2025-06-18T14:00:06Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"arxiv:2408.07990",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-18T13:06:41Z |
---
base_model: []
library_name: transformers
tags:
- mergekit
- merge
---
# prototype-0.4x159
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [SCE](https://arxiv.org/abs/2408.07990) merge method using /workspace/prototype-0.4x153 as a base.
### Models Merged
The following models were included in the merge:
* /workspace/cache/models--LatitudeGames--Wayfarer-Large-70B-Llama-3.3/snapshots/68cb7a33f692be64d4b146576838be85593a7459
* /workspace/cache/models--Delta-Vector--Austral-70B-Preview/snapshots/bf62fe4ffd7e460dfa3bb881913bdfbd9dd14002
* /workspace/cache/models--deepcogito--cogito-v1-preview-llama-70B/snapshots/1d624e2293b5b35f9cfd2349f8e02c7ebf32ca83
* /workspace/cache/models--ReadyArt--Forgotten-Safeword-70B-v5.0/snapshots/ac2650005a6fdef7f4cd62590dcb665155349a5b
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: /workspace/cache/models--ReadyArt--Forgotten-Safeword-70B-v5.0/snapshots/ac2650005a6fdef7f4cd62590dcb665155349a5b
- model: /workspace/cache/models--deepcogito--cogito-v1-preview-llama-70B/snapshots/1d624e2293b5b35f9cfd2349f8e02c7ebf32ca83
- model: /workspace/cache/models--LatitudeGames--Wayfarer-Large-70B-Llama-3.3/snapshots/68cb7a33f692be64d4b146576838be85593a7459
- model: /workspace/cache/models--Delta-Vector--Austral-70B-Preview/snapshots/bf62fe4ffd7e460dfa3bb881913bdfbd9dd14002
- model: /workspace/prototype-0.4x153
base_model: /workspace/prototype-0.4x153
select_topk: 0.15
merge_method: sce
tokenizer:
source: base
pad_to_multiple_of: 8
int8_mask: true
dtype: bfloat16
```
|
mradermacher/nemo-chatbot-v2-GGUF
|
mradermacher
| 2025-06-18T13:59:59Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:chaerheeon/nemo-chatbot-v2",
"base_model:quantized:chaerheeon/nemo-chatbot-v2",
"endpoints_compatible",
"region:us"
] | null | 2025-06-18T13:38:40Z |
---
base_model: chaerheeon/nemo-chatbot-v2
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/chaerheeon/nemo-chatbot-v2
<!-- 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/nemo-chatbot-v2-GGUF/resolve/main/nemo-chatbot-v2.Q2_K.gguf) | Q2_K | 1.8 | |
| [GGUF](https://huggingface.co/mradermacher/nemo-chatbot-v2-GGUF/resolve/main/nemo-chatbot-v2.Q3_K_S.gguf) | Q3_K_S | 2.0 | |
| [GGUF](https://huggingface.co/mradermacher/nemo-chatbot-v2-GGUF/resolve/main/nemo-chatbot-v2.Q3_K_M.gguf) | Q3_K_M | 2.2 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/nemo-chatbot-v2-GGUF/resolve/main/nemo-chatbot-v2.Q3_K_L.gguf) | Q3_K_L | 2.3 | |
| [GGUF](https://huggingface.co/mradermacher/nemo-chatbot-v2-GGUF/resolve/main/nemo-chatbot-v2.IQ4_XS.gguf) | IQ4_XS | 2.4 | |
| [GGUF](https://huggingface.co/mradermacher/nemo-chatbot-v2-GGUF/resolve/main/nemo-chatbot-v2.Q4_K_S.gguf) | Q4_K_S | 2.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/nemo-chatbot-v2-GGUF/resolve/main/nemo-chatbot-v2.Q4_K_M.gguf) | Q4_K_M | 2.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/nemo-chatbot-v2-GGUF/resolve/main/nemo-chatbot-v2.Q5_K_S.gguf) | Q5_K_S | 2.9 | |
| [GGUF](https://huggingface.co/mradermacher/nemo-chatbot-v2-GGUF/resolve/main/nemo-chatbot-v2.Q5_K_M.gguf) | Q5_K_M | 2.9 | |
| [GGUF](https://huggingface.co/mradermacher/nemo-chatbot-v2-GGUF/resolve/main/nemo-chatbot-v2.Q6_K.gguf) | Q6_K | 3.3 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/nemo-chatbot-v2-GGUF/resolve/main/nemo-chatbot-v2.Q8_0.gguf) | Q8_0 | 4.2 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/nemo-chatbot-v2-GGUF/resolve/main/nemo-chatbot-v2.f16.gguf) | f16 | 7.9 | 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 -->
|
morturr/Mistral-7B-v0.1-amazon-seed-7-2025-06-18
|
morturr
| 2025-06-18T13:59:33Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:mistralai/Mistral-7B-v0.1",
"base_model:adapter:mistralai/Mistral-7B-v0.1",
"license:apache-2.0",
"region:us"
] | null | 2025-06-18T13:59:24Z |
---
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-amazon-seed-7-2025-06-18
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-amazon-seed-7-2025-06-18
This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 7
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.1
- Pytorch 2.5.1+cu124
- Datasets 3.0.2
- Tokenizers 0.20.1
|
Alphatao/Affine-9459823
|
Alphatao
| 2025-06-18T13:57:43Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:2309.00071",
"arxiv:2505.09388",
"base_model:Qwen/Qwen3-8B-Base",
"base_model:finetune:Qwen/Qwen3-8B-Base",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-18T13:51:45Z |
---
library_name: transformers
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen3-8B/blob/main/LICENSE
pipeline_tag: text-generation
base_model:
- Qwen/Qwen3-8B-Base
---
# Qwen3-8B
<a href="https://chat.qwen.ai/" target="_blank" style="margin: 2px;">
<img alt="Chat" src="https://img.shields.io/badge/%F0%9F%92%9C%EF%B8%8F%20Qwen%20Chat%20-536af5" style="display: inline-block; vertical-align: middle;"/>
</a>
## Qwen3 Highlights
Qwen3 is the latest generation of large language models in Qwen series, offering a comprehensive suite of dense and mixture-of-experts (MoE) models. Built upon extensive training, Qwen3 delivers groundbreaking advancements in reasoning, instruction-following, agent capabilities, and multilingual support, with the following key features:
- **Uniquely support of seamless switching between thinking mode** (for complex logical reasoning, math, and coding) and **non-thinking mode** (for efficient, general-purpose dialogue) **within single model**, ensuring optimal performance across various scenarios.
- **Significantly enhancement in its reasoning capabilities**, surpassing previous QwQ (in thinking mode) and Qwen2.5 instruct models (in non-thinking mode) on mathematics, code generation, and commonsense logical reasoning.
- **Superior human preference alignment**, excelling in creative writing, role-playing, multi-turn dialogues, and instruction following, to deliver a more natural, engaging, and immersive conversational experience.
- **Expertise in agent capabilities**, enabling precise integration with external tools in both thinking and unthinking modes and achieving leading performance among open-source models in complex agent-based tasks.
- **Support of 100+ languages and dialects** with strong capabilities for **multilingual instruction following** and **translation**.
## Model Overview
**Qwen3-8B** has the following features:
- Type: Causal Language Models
- Training Stage: Pretraining & Post-training
- Number of Parameters: 8.2B
- Number of Paramaters (Non-Embedding): 6.95B
- Number of Layers: 36
- Number of Attention Heads (GQA): 32 for Q and 8 for KV
- Context Length: 32,768 natively and [131,072 tokens with YaRN](#processing-long-texts).
For more details, including benchmark evaluation, hardware requirements, and inference performance, please refer to our [blog](https://qwenlm.github.io/blog/qwen3/), [GitHub](https://github.com/QwenLM/Qwen3), and [Documentation](https://qwen.readthedocs.io/en/latest/).
## Quickstart
The code of Qwen3 has been in the latest Hugging Face `transformers` and we advise you to use the latest version of `transformers`.
With `transformers<4.51.0`, you will encounter the following error:
```
KeyError: 'qwen3'
```
The following contains a code snippet illustrating how to use the model generate content based on given inputs.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "Qwen/Qwen3-8B"
# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto"
)
# prepare the model input
prompt = "Give me a short introduction to large language model."
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
enable_thinking=True # Switches between thinking and non-thinking modes. Default is True.
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# conduct text completion
generated_ids = model.generate(
**model_inputs,
max_new_tokens=32768
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
# parsing thinking content
try:
# rindex finding 151668 (</think>)
index = len(output_ids) - output_ids[::-1].index(151668)
except ValueError:
index = 0
thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("\n")
content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n")
print("thinking content:", thinking_content)
print("content:", content)
```
For deployment, you can use `sglang>=0.4.6.post1` or `vllm>=0.8.5` or to create an OpenAI-compatible API endpoint:
- SGLang:
```shell
python -m sglang.launch_server --model-path Qwen/Qwen3-8B --reasoning-parser qwen3
```
- vLLM:
```shell
vllm serve Qwen/Qwen3-8B --enable-reasoning --reasoning-parser deepseek_r1
```
For local use, applications such as Ollama, LMStudio, MLX-LM, llama.cpp, and KTransformers have also supported Qwen3.
## Switching Between Thinking and Non-Thinking Mode
> [!TIP]
> The `enable_thinking` switch is also available in APIs created by SGLang and vLLM.
> Please refer to our documentation for [SGLang](https://qwen.readthedocs.io/en/latest/deployment/sglang.html#thinking-non-thinking-modes) and [vLLM](https://qwen.readthedocs.io/en/latest/deployment/vllm.html#thinking-non-thinking-modes) users.
### `enable_thinking=True`
By default, Qwen3 has thinking capabilities enabled, similar to QwQ-32B. This means the model will use its reasoning abilities to enhance the quality of generated responses. For example, when explicitly setting `enable_thinking=True` or leaving it as the default value in `tokenizer.apply_chat_template`, the model will engage its thinking mode.
```python
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
enable_thinking=True # True is the default value for enable_thinking
)
```
In this mode, the model will generate think content wrapped in a `<think>...</think>` block, followed by the final response.
> [!NOTE]
> For thinking mode, use `Temperature=0.6`, `TopP=0.95`, `TopK=20`, and `MinP=0` (the default setting in `generation_config.json`). **DO NOT use greedy decoding**, as it can lead to performance degradation and endless repetitions. For more detailed guidance, please refer to the [Best Practices](#best-practices) section.
### `enable_thinking=False`
We provide a hard switch to strictly disable the model's thinking behavior, aligning its functionality with the previous Qwen2.5-Instruct models. This mode is particularly useful in scenarios where disabling thinking is essential for enhancing efficiency.
```python
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
enable_thinking=False # Setting enable_thinking=False disables thinking mode
)
```
In this mode, the model will not generate any think content and will not include a `<think>...</think>` block.
> [!NOTE]
> For non-thinking mode, we suggest using `Temperature=0.7`, `TopP=0.8`, `TopK=20`, and `MinP=0`. For more detailed guidance, please refer to the [Best Practices](#best-practices) section.
### Advanced Usage: Switching Between Thinking and Non-Thinking Modes via User Input
We provide a soft switch mechanism that allows users to dynamically control the model's behavior when `enable_thinking=True`. Specifically, you can add `/think` and `/no_think` to user prompts or system messages to switch the model's thinking mode from turn to turn. The model will follow the most recent instruction in multi-turn conversations.
Here is an example of a multi-turn conversation:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
class QwenChatbot:
def __init__(self, model_name="Qwen/Qwen3-8B"):
self.tokenizer = AutoTokenizer.from_pretrained(model_name)
self.model = AutoModelForCausalLM.from_pretrained(model_name)
self.history = []
def generate_response(self, user_input):
messages = self.history + [{"role": "user", "content": user_input}]
text = self.tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
inputs = self.tokenizer(text, return_tensors="pt")
response_ids = self.model.generate(**inputs, max_new_tokens=32768)[0][len(inputs.input_ids[0]):].tolist()
response = self.tokenizer.decode(response_ids, skip_special_tokens=True)
# Update history
self.history.append({"role": "user", "content": user_input})
self.history.append({"role": "assistant", "content": response})
return response
# Example Usage
if __name__ == "__main__":
chatbot = QwenChatbot()
# First input (without /think or /no_think tags, thinking mode is enabled by default)
user_input_1 = "How many r's in strawberries?"
print(f"User: {user_input_1}")
response_1 = chatbot.generate_response(user_input_1)
print(f"Bot: {response_1}")
print("----------------------")
# Second input with /no_think
user_input_2 = "Then, how many r's in blueberries? /no_think"
print(f"User: {user_input_2}")
response_2 = chatbot.generate_response(user_input_2)
print(f"Bot: {response_2}")
print("----------------------")
# Third input with /think
user_input_3 = "Really? /think"
print(f"User: {user_input_3}")
response_3 = chatbot.generate_response(user_input_3)
print(f"Bot: {response_3}")
```
> [!NOTE]
> For API compatibility, when `enable_thinking=True`, regardless of whether the user uses `/think` or `/no_think`, the model will always output a block wrapped in `<think>...</think>`. However, the content inside this block may be empty if thinking is disabled.
> When `enable_thinking=False`, the soft switches are not valid. Regardless of any `/think` or `/no_think` tags input by the user, the model will not generate think content and will not include a `<think>...</think>` block.
## Agentic Use
Qwen3 excels in tool calling capabilities. We recommend using [Qwen-Agent](https://github.com/QwenLM/Qwen-Agent) to make the best use of agentic ability of Qwen3. Qwen-Agent encapsulates tool-calling templates and tool-calling parsers internally, greatly reducing coding complexity.
To define the available tools, you can use the MCP configuration file, use the integrated tool of Qwen-Agent, or integrate other tools by yourself.
```python
from qwen_agent.agents import Assistant
# Define LLM
llm_cfg = {
'model': 'Qwen3-8B',
# Use the endpoint provided by Alibaba Model Studio:
# 'model_type': 'qwen_dashscope',
# 'api_key': os.getenv('DASHSCOPE_API_KEY'),
# Use a custom endpoint compatible with OpenAI API:
'model_server': 'http://localhost:8000/v1', # api_base
'api_key': 'EMPTY',
# Other parameters:
# 'generate_cfg': {
# # Add: When the response content is `<think>this is the thought</think>this is the answer;
# # Do not add: When the response has been separated by reasoning_content and content.
# 'thought_in_content': True,
# },
}
# Define Tools
tools = [
{'mcpServers': { # You can specify the MCP configuration file
'time': {
'command': 'uvx',
'args': ['mcp-server-time', '--local-timezone=Asia/Shanghai']
},
"fetch": {
"command": "uvx",
"args": ["mcp-server-fetch"]
}
}
},
'code_interpreter', # Built-in tools
]
# Define Agent
bot = Assistant(llm=llm_cfg, function_list=tools)
# Streaming generation
messages = [{'role': 'user', 'content': 'https://qwenlm.github.io/blog/ Introduce the latest developments of Qwen'}]
for responses in bot.run(messages=messages):
pass
print(responses)
```
## Processing Long Texts
Qwen3 natively supports context lengths of up to 32,768 tokens. For conversations where the total length (including both input and output) significantly exceeds this limit, we recommend using RoPE scaling techniques to handle long texts effectively. We have validated the model's performance on context lengths of up to 131,072 tokens using the [YaRN](https://arxiv.org/abs/2309.00071) method.
YaRN is currently supported by several inference frameworks, e.g., `transformers` and `llama.cpp` for local use, `vllm` and `sglang` for deployment. In general, there are two approaches to enabling YaRN for supported frameworks:
- Modifying the model files:
In the `config.json` file, add the `rope_scaling` fields:
```json
{
...,
"rope_scaling": {
"rope_type": "yarn",
"factor": 4.0,
"original_max_position_embeddings": 32768
}
}
```
For `llama.cpp`, you need to regenerate the GGUF file after the modification.
- Passing command line arguments:
For `vllm`, you can use
```shell
vllm serve ... --rope-scaling '{"rope_type":"yarn","factor":4.0,"original_max_position_embeddings":32768}' --max-model-len 131072
```
For `sglang`, you can use
```shell
python -m sglang.launch_server ... --json-model-override-args '{"rope_scaling":{"rope_type":"yarn","factor":4.0,"original_max_position_embeddings":32768}}'
```
For `llama-server` from `llama.cpp`, you can use
```shell
llama-server ... --rope-scaling yarn --rope-scale 4 --yarn-orig-ctx 32768
```
> [!IMPORTANT]
> If you encounter the following warning
> ```
> Unrecognized keys in `rope_scaling` for 'rope_type'='yarn': {'original_max_position_embeddings'}
> ```
> please upgrade `transformers>=4.51.0`.
> [!NOTE]
> All the notable open-source frameworks implement static YaRN, which means the scaling factor remains constant regardless of input length, **potentially impacting performance on shorter texts.**
> We advise adding the `rope_scaling` configuration only when processing long contexts is required.
> It is also recommended to modify the `factor` as needed. For example, if the typical context length for your application is 65,536 tokens, it would be better to set `factor` as 2.0.
> [!NOTE]
> The default `max_position_embeddings` in `config.json` is set to 40,960. This allocation includes reserving 32,768 tokens for outputs and 8,192 tokens for typical prompts, which is sufficient for most scenarios involving short text processing. If the average context length does not exceed 32,768 tokens, we do not recommend enabling YaRN in this scenario, as it may potentially degrade model performance.
> [!TIP]
> The endpoint provided by Alibaba Model Studio supports dynamic YaRN by default and no extra configuration is needed.
## Best Practices
To achieve optimal performance, we recommend the following settings:
1. **Sampling Parameters**:
- For thinking mode (`enable_thinking=True`), use `Temperature=0.6`, `TopP=0.95`, `TopK=20`, and `MinP=0`. **DO NOT use greedy decoding**, as it can lead to performance degradation and endless repetitions.
- For non-thinking mode (`enable_thinking=False`), we suggest using `Temperature=0.7`, `TopP=0.8`, `TopK=20`, and `MinP=0`.
- For supported frameworks, you can adjust the `presence_penalty` parameter between 0 and 2 to reduce endless repetitions. However, using a higher value may occasionally result in language mixing and a slight decrease in model performance.
2. **Adequate Output Length**: We recommend using an output length of 32,768 tokens for most queries. For benchmarking on highly complex problems, such as those found in math and programming competitions, we suggest setting the max output length to 38,912 tokens. This provides the model with sufficient space to generate detailed and comprehensive responses, thereby enhancing its overall performance.
3. **Standardize Output Format**: We recommend using prompts to standardize model outputs when benchmarking.
- **Math Problems**: Include "Please reason step by step, and put your final answer within \boxed{}." in the prompt.
- **Multiple-Choice Questions**: Add the following JSON structure to the prompt to standardize responses: "Please show your choice in the `answer` field with only the choice letter, e.g., `"answer": "C"`."
4. **No Thinking Content in History**: In multi-turn conversations, the historical model output should only include the final output part and does not need to include the thinking content. It is implemented in the provided chat template in Jinja2. However, for frameworks that do not directly use the Jinja2 chat template, it is up to the developers to ensure that the best practice is followed.
### Citation
If you find our work helpful, feel free to give us a cite.
```
@misc{qwen3technicalreport,
title={Qwen3 Technical Report},
author={Qwen Team},
year={2025},
eprint={2505.09388},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2505.09388},
}
```
|
llava-hf/llava-onevision-qwen2-0.5b-ov-hf
|
llava-hf
| 2025-06-18T13:57:09Z | 715,919 | 33 |
transformers
|
[
"transformers",
"onnx",
"safetensors",
"llava_onevision",
"image-text-to-text",
"vision",
"transformers.js",
"conversational",
"en",
"zh",
"dataset:lmms-lab/LLaVA-OneVision-Data",
"arxiv:2408.03326",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2024-08-13T08:28:18Z |
---
language:
- en
- zh
license: apache-2.0
tags:
- vision
- image-text-to-text
- transformers.js
datasets:
- lmms-lab/LLaVA-OneVision-Data
pipeline_tag: image-text-to-text
arxiv: 2408.03326
library_name: transformers
---
# LLaVA-Onevision Model Card
Check out also the Google Colab demo to run Llava on a free-tier Google Colab instance: [](https://colab.research.google.com/drive/1-4AtYjR8UMtCALV0AswU1kiNkWCLTALT?usp=sharing)
Below is the model card of 0.5B LLaVA-Onevision model which is copied from the original LLaVA-Onevision model card that you can find [here](https://huggingface.co/lmms-lab/llava-onevision-qwen2-0.5b-si).
## Model details
**Model type:**
LLaVA-Onevision is an open-source multimodal LLM trained by fine-tuning Qwen2 on GPT-generated multimodal instruction-following data.
LLaVA-OneVision is the first single model that can simultaneously push the performance boundaries of open LMMs in three important computer
vision scenarios: single-image, multi-image, and video scenarios. Importantly, the design of LLaVA-OneVision allows strong transfer learning
across different modalities/scenarios, yielding new emerging capabilities. In particular, strong video understanding and cross-scenario
capabilities are demonstrated through task transfer from images to videos.
**Model date:**
LLaVA-Onevision-0.5-ov was added in August 2024.
**Paper or resources for more information:**
https://llava-vl.github.io/
- **Architecture:** SO400M + Qwen2
- **Pretraining Stage:** LCS-558K, 1 epoch, projector
- **Mid Stage:** A mixture of 4.7M high-quality synthetic data, 1 epoch, full model
- **Final-Image Stage:** A mixture of 3.6M single-image data, 1 epoch, full model
- **OneVision Stage:** A mixture of 1.6M single-image/multi-image/video data, 1 epoch, full model
- **Precision:** bfloat16
## How to use the model
First, make sure to have `transformers` installed from [branch](https://github.com/huggingface/transformers/pull/32673) or `transformers >= 4.45.0`.
The model supports multi-image and multi-prompt generation. Meaning that you can pass multiple images in your prompt. Make sure also to follow the correct prompt template by applying chat template:
### Using `pipeline`:
Below we used [`"llava-hf/llava-onevision-qwen2-0.5b-ov-hf"`](https://huggingface.co/llava-hf/llava-onevision-qwen2-0.5b-ov-hf) checkpoint.
```python
from transformers import pipeline
pipe = pipeline("image-text-to-text", model="llava-onevision-qwen2-0.5b-ov-hf")
messages = [
{
"role": "user",
"content": [
{"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg"},
{"type": "text", "text": "What does the label 15 represent? (1) lava (2) core (3) tunnel (4) ash cloud"},
],
},
]
out = pipe(text=messages, max_new_tokens=20)
print(out)
>>> [{'input_text': [{'role': 'user', 'content': [{'type': 'image', 'url': 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg'}, {'type': 'text', 'text': 'What does the label 15 represent? (1) lava (2) core (3) tunnel (4) ash cloud'}]}], 'generated_text': 'Lava'}]
```
### Using pure `transformers`:
Below is an example script to run generation in `float16` precision on a GPU device:
```python
import requests
from PIL import Image
import torch
from transformers import AutoProcessor, LlavaOnevisionForConditionalGeneration
model_id = "llava-hf/llava-onevision-qwen2-0.5b-ov-hf"
model = LlavaOnevisionForConditionalGeneration.from_pretrained(
model_id,
torch_dtype=torch.float16,
low_cpu_mem_usage=True,
).to(0)
processor = AutoProcessor.from_pretrained(model_id)
# Define a chat history and use `apply_chat_template` to get correctly formatted prompt
# Each value in "content" has to be a list of dicts with types ("text", "image")
conversation = [
{
"role": "user",
"content": [
{"type": "text", "text": "What are these?"},
{"type": "image"},
],
},
]
prompt = processor.apply_chat_template(conversation, add_generation_prompt=True)
image_file = "http://images.cocodataset.org/val2017/000000039769.jpg"
raw_image = Image.open(requests.get(image_file, stream=True).raw)
inputs = processor(images=raw_image, text=prompt, return_tensors='pt').to(0, torch.float16)
output = model.generate(**inputs, max_new_tokens=200, do_sample=False)
print(processor.decode(output[0][2:], skip_special_tokens=True))
```
-----------
From transformers>=v4.48, you can also pass image/video url or local path to the conversation history, and let the chat template handle the rest.
Chat template will load the image for you and return inputs in `torch.Tensor` which you can pass directly to `model.generate()`
```python
messages = [
{
"role": "user",
"content": [
{"type": "image", "url": "https://www.ilankelman.org/stopsigns/australia.jpg"}
{"type": "text", "text": "What is shown in this image?"},
],
},
]
inputs = processor.apply_chat_template(messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors"pt")
output = model.generate(**inputs, max_new_tokens=50)
```
### Model optimization
#### 4-bit quantization through `bitsandbytes` library
First make sure to install `bitsandbytes`, `pip install bitsandbytes` and make sure to have access to a CUDA compatible GPU device. Simply change the snippet above with:
```diff
model = LlavaOnevisionForConditionalGeneration.from_pretrained(
model_id,
torch_dtype=torch.float16,
low_cpu_mem_usage=True,
+ load_in_4bit=True
)
```
#### Use Flash-Attention 2 to further speed-up generation
First make sure to install `flash-attn`. Refer to the [original repository of Flash Attention](https://github.com/Dao-AILab/flash-attention) regarding that package installation. Simply change the snippet above with:
```diff
model = LlavaOnevisionForConditionalGeneration.from_pretrained(
model_id,
torch_dtype=torch.float16,
low_cpu_mem_usage=True,
+ use_flash_attention_2=True
).to(0)
```
### Usage w/ Transformers.js
If you haven't already, you can install the [Transformers.js](https://huggingface.co/docs/transformers.js) JavaScript library from [NPM](https://www.npmjs.com/package/@huggingface/transformers) using:
```bash
npm i @huggingface/transformers
```
**Example:** Multi-round conversations w/ PKV caching
```js
import { AutoProcessor, AutoTokenizer, LlavaOnevisionForConditionalGeneration, RawImage } from '@huggingface/transformers';
// Load tokenizer, processor and model
const model_id = 'llava-hf/llava-onevision-qwen2-0.5b-ov-hf';
const tokenizer = await AutoTokenizer.from_pretrained(model_id);
const processor = await AutoProcessor.from_pretrained(model_id);
const model = await LlavaOnevisionForConditionalGeneration.from_pretrained(model_id, {
dtype: {
embed_tokens: 'fp16', // or 'fp32' or 'q8'
vision_encoder: 'fp16', // or 'fp32' or 'q8'
decoder_model_merged: 'q4', // or 'q8'
},
// device: 'webgpu',
});
// Prepare text inputs
const prompt = 'What does the text say?';
const messages = [
{ role: 'system', content: 'Answer the question.' },
{ role: 'user', content: `<image>\n${prompt}` }
]
const text = tokenizer.apply_chat_template(messages, { tokenize: false, add_generation_prompt: true });
const text_inputs = tokenizer(text);
// Prepare vision inputs
const url = 'https://huggingface.co/qnguyen3/nanoLLaVA/resolve/main/example_1.png';
const image = await RawImage.fromURL(url);
const vision_inputs = await processor(image);
// Generate response
const { past_key_values, sequences } = await model.generate({
...text_inputs,
...vision_inputs,
do_sample: false,
max_new_tokens: 64,
return_dict_in_generate: true,
});
// Decode output
const answer = tokenizer.decode(
sequences.slice(0, [text_inputs.input_ids.dims[1], null]),
{ skip_special_tokens: true },
);
console.log(answer);
// The text says "small but mighty" in a playful font.
const new_messages = [
...messages,
{ role: 'assistant', content: answer },
{ role: 'user', content: 'How does the text correlate to the context of the image?' }
]
const new_text = tokenizer.apply_chat_template(new_messages, { tokenize: false, add_generation_prompt: true });
const new_text_inputs = tokenizer(new_text);
// Generate another response
const output = await model.generate({
...new_text_inputs,
past_key_values,
do_sample: false,
max_new_tokens: 256,
});
const new_answer = tokenizer.decode(
output.slice(0, [new_text_inputs.input_ids.dims[1], null]),
{ skip_special_tokens: true },
);
console.log(new_answer);
// The text "small but mighty" is likely a playful or humorous reference to the image of the blue mouse with the orange dumbbell. It could be used as a motivational phrase or a playful way to express the idea that even small things can be impressive or powerful.
```
# Citation
```
@misc{li2024llavaonevisioneasyvisualtask,
title={LLaVA-OneVision: Easy Visual Task Transfer},
author={Bo Li and Yuanhan Zhang and Dong Guo and Renrui Zhang and Feng Li and Hao Zhang and Kaichen Zhang and Yanwei Li and Ziwei Liu and Chunyuan Li},
year={2024},
eprint={2408.03326},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2408.03326},
}
```
|
llava-hf/llava-onevision-qwen2-7b-si-hf
|
llava-hf
| 2025-06-18T13:56:59Z | 9,557 | 6 |
transformers
|
[
"transformers",
"safetensors",
"llava_onevision",
"image-text-to-text",
"vision",
"conversational",
"en",
"zh",
"dataset:lmms-lab/LLaVA-OneVision-Data",
"arxiv:2408.03326",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2024-08-13T08:35:11Z |
---
language:
- en
- zh
license: apache-2.0
tags:
- vision
- image-text-to-text
datasets:
- lmms-lab/LLaVA-OneVision-Data
library_name: transformers
pipeline_tag: image-text-to-text
arxiv: 2408.03326
---
# LLaVA-Onevision Model Card
Check out also the Google Colab demo to run Llava on a free-tier Google Colab instance: [](https://colab.research.google.com/drive/1-4AtYjR8UMtCALV0AswU1kiNkWCLTALT?usp=sharing)
Below is the model card of 7B LLaVA-Onevision model which is copied from the original LLaVA-Onevision model card that you can find [here](https://huggingface.co/lmms-lab/llava-onevision-qwen2-0.5b-si).
## Model details
**Model type:**
LLaVA-Onevision is an open-source multimodal LLM trained by fine-tuning Qwen2 on GPT-generated multimodal instruction-following data.
LLaVA-OneVision is the first single model that can simultaneously push the performance boundaries of open LMMs in three important computer
vision scenarios: single-image, multi-image, and video scenarios. Importantly, the design of LLaVA-OneVision allows strong transfer learning
across different modalities/scenarios, yielding new emerging capabilities. In particular, strong video understanding and cross-scenario
capabilities are demonstrated through task transfer from images to videos.
**Model date:**
LLaVA-Onevision-7b-si was added in August 2024.
**Paper or resources for more information:**
https://llava-vl.github.io/
- **Architecture:** SO400M + Qwen2
- **Pretraining Stage:** LCS-558K, 1 epoch, projector
- **Mid Stage:** A mixture of 4.7M high-quality synthetic data, 1 epoch, full model
- **Final-Image Stage:** A mixture of 3.6M single-image data, 1 epoch, full model
- **OneVision Stage:** A mixture of 1.6M single-image/multi-image/video data, 1 epoch, full model
- **Precision:** bfloat16
## How to use the model
First, make sure to have `transformers` installed from [branch](https://github.com/huggingface/transformers/pull/32673) or `transformers >= 4.45.0`.
The model supports multi-image and multi-prompt generation. Meaning that you can pass multiple images in your prompt. Make sure also to follow the correct prompt template by applyong chat template:
### Using `pipeline`:
Below we used [`"llava-hf/llava-onevision-qwen2-7b-si-hf"`](https://huggingface.co/llava-hf/llava-onevision-qwen2-7b-si-hf) checkpoint.
```python
from transformers import pipeline
pipe = pipeline("image-text-to-text", model="llava-onevision-qwen2-7b-si-hf")
messages = [
{
"role": "user",
"content": [
{"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg"},
{"type": "text", "text": "What does the label 15 represent? (1) lava (2) core (3) tunnel (4) ash cloud"},
],
},
]
out = pipe(text=messages, max_new_tokens=20)
print(out)
>>> [{'input_text': [{'role': 'user', 'content': [{'type': 'image', 'url': 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg'}, {'type': 'text', 'text': 'What does the label 15 represent? (1) lava (2) core (3) tunnel (4) ash cloud'}]}], 'generated_text': 'Lava'}]
```
### Using pure `transformers`:
Below is an example script to run generation in `float16` precision on a GPU device:
```python
import requests
from PIL import Image
import torch
from transformers import AutoProcessor, LlavaOnevisionForConditionalGeneration
model_id = "llava-hf/llava-onevision-qwen2-7b-si-hf"
model = LlavaOnevisionForConditionalGeneration.from_pretrained(
model_id,
torch_dtype=torch.float16,
low_cpu_mem_usage=True,
).to(0)
processor = AutoProcessor.from_pretrained(model_id)
# Define a chat history and use `apply_chat_template` to get correctly formatted prompt
# Each value in "content" has to be a list of dicts with types ("text", "image")
conversation = [
{
"role": "user",
"content": [
{"type": "text", "text": "What are these?"},
{"type": "image"},
],
},
]
prompt = processor.apply_chat_template(conversation, add_generation_prompt=True)
image_file = "http://images.cocodataset.org/val2017/000000039769.jpg"
raw_image = Image.open(requests.get(image_file, stream=True).raw)
inputs = processor(images=raw_image, text=prompt, return_tensors='pt').to(0, torch.float16)
output = model.generate(**inputs, max_new_tokens=200, do_sample=False)
print(processor.decode(output[0][2:], skip_special_tokens=True))
```
-----------
From transformers>=v4.48, you can also pass image/video url or local path to the conversation history, and let the chat template handle the rest.
Chat template will load the image for you and return inputs in `torch.Tensor` which you can pass directly to `model.generate()`
```python
messages = [
{
"role": "user",
"content": [
{"type": "image", "url": "https://www.ilankelman.org/stopsigns/australia.jpg"}
{"type": "text", "text": "What is shown in this image?"},
],
},
]
inputs = processor.apply_chat_template(messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors"pt")
output = model.generate(**inputs, max_new_tokens=50)
```
### Model optimization
#### 4-bit quantization through `bitsandbytes` library
First make sure to install `bitsandbytes`, `pip install bitsandbytes` and make sure to have access to a CUDA compatible GPU device. Simply change the snippet above with:
```diff
model = LlavaOnevisionForConditionalGeneration.from_pretrained(
model_id,
torch_dtype=torch.float16,
low_cpu_mem_usage=True,
+ load_in_4bit=True
)
```
#### Use Flash-Attention 2 to further speed-up generation
First make sure to install `flash-attn`. Refer to the [original repository of Flash Attention](https://github.com/Dao-AILab/flash-attention) regarding that package installation. Simply change the snippet above with:
```diff
model = LlavaOnevisionForConditionalGeneration.from_pretrained(
model_id,
torch_dtype=torch.float16,
low_cpu_mem_usage=True,
+ use_flash_attention_2=True
).to(0)
```
# Citation
```
@misc{li2024llavaonevisioneasyvisualtask,
title={LLaVA-OneVision: Easy Visual Task Transfer},
author={Bo Li and Yuanhan Zhang and Dong Guo and Renrui Zhang and Feng Li and Hao Zhang and Kaichen Zhang and Yanwei Li and Ziwei Liu and Chunyuan Li},
year={2024},
eprint={2408.03326},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2408.03326},
}
```
|
llava-hf/llava-onevision-qwen2-0.5b-si-hf
|
llava-hf
| 2025-06-18T13:56:31Z | 4,450 | 10 |
transformers
|
[
"transformers",
"onnx",
"safetensors",
"llava_onevision",
"image-text-to-text",
"vision",
"conversational",
"en",
"zh",
"dataset:lmms-lab/LLaVA-OneVision-Data",
"arxiv:2408.03326",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2024-08-13T08:28:36Z |
---
language:
- en
- zh
license: apache-2.0
tags:
- vision
- image-text-to-text
datasets:
- lmms-lab/LLaVA-OneVision-Data
library_name: transformers
pipeline_tag: image-text-to-text
arxiv: 2408.03326
---
# LLaVA-Onevision Model Card
Check out also the Google Colab demo to run Llava on a free-tier Google Colab instance: [](https://colab.research.google.com/drive/1-4AtYjR8UMtCALV0AswU1kiNkWCLTALT?usp=sharing)
Below is the model card of 0.5B LLaVA-Onevision model which is copied from the original LLaVA-Onevision model card that you can find [here](https://huggingface.co/lmms-lab/llava-onevision-qwen2-0.5b-si).
## Model details
**Model type:**
LLaVA-Onevision is an open-source multimodal LLM trained by fine-tuning Qwen2 on GPT-generated multimodal instruction-following data.
LLaVA-OneVision is the first single model that can simultaneously push the performance boundaries of open LMMs in three important computer
vision scenarios: single-image, multi-image, and video scenarios. Importantly, the design of LLaVA-OneVision allows strong transfer learning
across different modalities/scenarios, yielding new emerging capabilities. In particular, strong video understanding and cross-scenario
capabilities are demonstrated through task transfer from images to videos.
**Model date:**
LLaVA-Onevision-0.5-si was added in August 2024.
**Paper or resources for more information:**
https://llava-vl.github.io/
- **Architecture:** SO400M + Qwen2
- **Pretraining Stage:** LCS-558K, 1 epoch, projector
- **Mid Stage:** A mixture of 4.7M high-quality synthetic data, 1 epoch, full model
- **Final-Image Stage:** A mixture of 3.6M single-image data, 1 epoch, full model
- **OneVision Stage:** A mixture of 1.6M single-image/multi-image/video data, 1 epoch, full model
- **Precision:** bfloat16
## How to use the model
First, make sure to have `transformers` installed from [branch](https://github.com/huggingface/transformers/pull/32673) or `transformers >= 4.45.0`.
The model supports multi-image and multi-prompt generation. Meaning that you can pass multiple images in your prompt. Make sure also to follow the correct prompt template by applying the chat template:
### Using `pipeline`:
Below we used [`"llava-hf/llava-onevision-qwen2-0.5b-si-hf"`](https://huggingface.co/llava-hf/llava-onevision-qwen2-0.5b-si-hf) checkpoint.
```python
from transformers import pipeline
pipe = pipeline("image-text-to-text", model="llava-onevision-qwen2-0.5b-si-hf")
messages = [
{
"role": "user",
"content": [
{"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg"},
{"type": "text", "text": "What does the label 15 represent? (1) lava (2) core (3) tunnel (4) ash cloud"},
],
},
]
out = pipe(text=messages, max_new_tokens=20)
print(out)
>>> [{'input_text': [{'role': 'user', 'content': [{'type': 'image', 'url': 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg'}, {'type': 'text', 'text': 'What does the label 15 represent? (1) lava (2) core (3) tunnel (4) ash cloud'}]}], 'generated_text': 'Lava'}]
```
### Using pure `transformers`:
Below is an example script to run generation in `float16` precision on a GPU device:
```python
import requests
from PIL import Image
import torch
from transformers import AutoProcessor, LlavaOnevisionForConditionalGeneration
model_id = "llava-hf/llava-onevision-qwen2-0.5b-si-hf"
model = LlavaOnevisionForConditionalGeneration.from_pretrained(
model_id,
torch_dtype=torch.float16,
low_cpu_mem_usage=True,
).to(0)
processor = AutoProcessor.from_pretrained(model_id)
# Define a chat history and use `apply_chat_template` to get correctly formatted prompt
# Each value in "content" has to be a list of dicts with types ("text", "image")
conversation = [
{
"role": "user",
"content": [
{"type": "text", "text": "What are these?"},
{"type": "image"},
],
},
]
prompt = processor.apply_chat_template(conversation, add_generation_prompt=True)
image_file = "http://images.cocodataset.org/val2017/000000039769.jpg"
raw_image = Image.open(requests.get(image_file, stream=True).raw)
inputs = processor(images=raw_image, text=prompt, return_tensors='pt').to(0, torch.float16)
output = model.generate(**inputs, max_new_tokens=200, do_sample=False)
print(processor.decode(output[0][2:], skip_special_tokens=True))
```
-----------
From transformers>=v4.48, you can also pass image/video url or local path to the conversation history, and let the chat template handle the rest.
Chat template will load the image for you and return inputs in `torch.Tensor` which you can pass directly to `model.generate()`
```python
messages = [
{
"role": "user",
"content": [
{"type": "image", "url": "https://www.ilankelman.org/stopsigns/australia.jpg"}
{"type": "text", "text": "What is shown in this image?"},
],
},
]
inputs = processor.apply_chat_template(messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors"pt")
output = model.generate(**inputs, max_new_tokens=50)
```
### Model optimization
#### 4-bit quantization through `bitsandbytes` library
First make sure to install `bitsandbytes`, `pip install bitsandbytes` and make sure to have access to a CUDA compatible GPU device. Simply change the snippet above with:
```diff
model = LlavaOnevisionForConditionalGeneration.from_pretrained(
model_id,
torch_dtype=torch.float16,
low_cpu_mem_usage=True,
+ load_in_4bit=True
)
```
#### Use Flash-Attention 2 to further speed-up generation
First make sure to install `flash-attn`. Refer to the [original repository of Flash Attention](https://github.com/Dao-AILab/flash-attention) regarding that package installation. Simply change the snippet above with:
```diff
model = LlavaOnevisionForConditionalGeneration.from_pretrained(
model_id,
torch_dtype=torch.float16,
low_cpu_mem_usage=True,
+ use_flash_attention_2=True
).to(0)
```
# Citation
```
@misc{li2024llavaonevisioneasyvisualtask,
title={LLaVA-OneVision: Easy Visual Task Transfer},
author={Bo Li and Yuanhan Zhang and Dong Guo and Renrui Zhang and Feng Li and Hao Zhang and Kaichen Zhang and Yanwei Li and Ziwei Liu and Chunyuan Li},
year={2024},
eprint={2408.03326},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2408.03326},
}
```
|
llava-hf/llava-onevision-qwen2-72b-si-hf
|
llava-hf
| 2025-06-18T13:55:54Z | 31 | 1 |
transformers
|
[
"transformers",
"safetensors",
"llava_onevision",
"image-text-to-text",
"vision",
"conversational",
"en",
"zh",
"dataset:lmms-lab/LLaVA-OneVision-Data",
"arxiv:2408.03326",
"license:apache-2.0",
"region:us"
] |
image-text-to-text
| 2024-08-13T09:23:44Z |
---
language:
- en
- zh
license: apache-2.0
tags:
- vision
- image-text-to-text
datasets:
- lmms-lab/LLaVA-OneVision-Data
library_name: transformers
pipeline_tag: image-text-to-text
inference: false
arxiv: 2408.03326
---
# LLaVA-Onevision Model Card
Check out also the Google Colab demo to run Llava on a free-tier Google Colab instance: [](https://colab.research.google.com/drive/1-4AtYjR8UMtCALV0AswU1kiNkWCLTALT?usp=sharing)
Below is the model card of 72B LLaVA-Onevision model which is copied from the original LLaVA-Onevision model card that you can find [here](https://huggingface.co/lmms-lab/llava-onevision-qwen2-72b-si).
## Model details
**Model type:**
LLaVA-Onevision is an open-source multimodal LLM trained by fine-tuning Qwen2 on GPT-generated multimodal instruction-following data.
LLaVA-OneVision is the first single model that can simultaneously push the performance boundaries of open LMMs in three important computer
vision scenarios: single-image, multi-image, and video scenarios. Importantly, the design of LLaVA-OneVision allows strong transfer learning
across different modalities/scenarios, yielding new emerging capabilities. In particular, strong video understanding and cross-scenario
capabilities are demonstrated through task transfer from images to videos.
**Model date:**
LLaVA-Onevision-72b-si was added in August 2024.
**Paper or resources for more information:**
https://llava-vl.github.io/
- **Architecture:** SO400M + Qwen2
- **Pretraining Stage:** LCS-558K, 1 epoch, projector
- **Mid Stage:** A mixture of 4.7M high-quality synthetic data, 1 epoch, full model
- **Final-Image Stage:** A mixture of 3.6M single-image data, 1 epoch, full model
- **OneVision Stage:** A mixture of 1.6M single-image/multi-image/video data, 1 epoch, full model
- **Precision:** bfloat16
## How to use the model
First, make sure to have `transformers` installed from [branch](https://github.com/huggingface/transformers/pull/32673) or `transformers >= 4.45.0`.
The model supports multi-image and multi-prompt generation. Meaning that you can pass multiple images in your prompt. Make sure also to follow the correct prompt template by applyong chat template:
### Using `pipeline`:
Below we used [`"llava-hf/llava-onevision-qwen2-72b-si-hf"`](https://huggingface.co/llava-hf/llava-onevision-qwen2-72b-si-hf) checkpoint.
```python
from transformers import pipeline, AutoProcessor
from PIL import Image
import requests
model_id = "llava-hf/llava-onevision-qwen2-72b-si-hf"
pipe = pipeline("image-to-text", model=model_id)
processor = AutoProcessor.from_pretrained(model_id)
url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg"
image = Image.open(requests.get(url, stream=True).raw)
# Define a chat history and use `apply_chat_template` to get correctly formatted prompt
# Each value in "content" has to be a list of dicts with types ("text", "image")
conversation = [
{
"role": "user",
"content": [
{"type": "text", "text": "What does the label 15 represent? (1) lava (2) core (3) tunnel (4) ash cloud"},
{"type": "image"},
],
},
]
prompt = processor.apply_chat_template(conversation, add_generation_prompt=True)
outputs = pipe(image, prompt=prompt, generate_kwargs={"max_new_tokens": 200})
print(outputs)
>>> {"generated_text": "user\n\nWhat does the label 15 represent? (1) lava (2) core (3) tunnel (4) ash cloud\nassistant\nLava"}
```
### Using pure `transformers`:
Below is an example script to run generation in `float16` precision on a GPU device:
```python
import requests
from PIL import Image
import torch
from transformers import AutoProcessor, LlavaOnevisionForConditionalGeneration
model_id = "llava-hf/llava-onevision-qwen2-72b-si-hf"
model = LlavaOnevisionForConditionalGeneration.from_pretrained(
model_id,
torch_dtype=torch.float16,
low_cpu_mem_usage=True,
).to(0)
processor = AutoProcessor.from_pretrained(model_id)
# Define a chat history and use `apply_chat_template` to get correctly formatted prompt
# Each value in "content" has to be a list of dicts with types ("text", "image")
conversation = [
{
"role": "user",
"content": [
{"type": "text", "text": "What are these?"},
{"type": "image"},
],
},
]
prompt = processor.apply_chat_template(conversation, add_generation_prompt=True)
image_file = "http://images.cocodataset.org/val2017/000000039769.jpg"
raw_image = Image.open(requests.get(image_file, stream=True).raw)
inputs = processor(images=raw_image, text=prompt, return_tensors='pt').to(0, torch.float16)
output = model.generate(**inputs, max_new_tokens=200, do_sample=False)
print(processor.decode(output[0][2:], skip_special_tokens=True))
```
-----------
From transformers>=v4.48, you can also pass image/video url or local path to the conversation history, and let the chat template handle the rest.
Chat template will load the image for you and return inputs in `torch.Tensor` which you can pass directly to `model.generate()`
```python
messages = [
{
"role": "user",
"content": [
{"type": "image", "url": "https://www.ilankelman.org/stopsigns/australia.jpg"}
{"type": "text", "text": "What is shown in this image?"},
],
},
]
inputs = processor.apply_chat_template(messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors"pt")
output = model.generate(**inputs, max_new_tokens=50)
```
### Model optimization
#### 4-bit quantization through `bitsandbytes` library
First make sure to install `bitsandbytes`, `pip install bitsandbytes` and make sure to have access to a CUDA compatible GPU device. Simply change the snippet above with:
```diff
model = LlavaOnevisionForConditionalGeneration.from_pretrained(
model_id,
torch_dtype=torch.float16,
low_cpu_mem_usage=True,
+ load_in_4bit=True
)
```
#### Use Flash-Attention 2 to further speed-up generation
First make sure to install `flash-attn`. Refer to the [original repository of Flash Attention](https://github.com/Dao-AILab/flash-attention) regarding that package installation. Simply change the snippet above with:
```diff
model = LlavaOnevisionForConditionalGeneration.from_pretrained(
model_id,
torch_dtype=torch.float16,
low_cpu_mem_usage=True,
+ use_flash_attention_2=True
).to(0)
```
# Citation
```
@misc{li2024llavaonevisioneasyvisualtask,
title={LLaVA-OneVision: Easy Visual Task Transfer},
author={Bo Li and Yuanhan Zhang and Dong Guo and Renrui Zhang and Feng Li and Hao Zhang and Kaichen Zhang and Yanwei Li and Ziwei Liu and Chunyuan Li},
year={2024},
eprint={2408.03326},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2408.03326},
}
```
|
llava-hf/llava-onevision-qwen2-7b-ov-chat-hf
|
llava-hf
| 2025-06-18T13:55:15Z | 3,570 | 5 |
transformers
|
[
"transformers",
"safetensors",
"llava_onevision",
"image-text-to-text",
"vision",
"conversational",
"en",
"zh",
"dataset:lmms-lab/LLaVA-OneVision-Data",
"arxiv:2408.03326",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2024-09-16T11:37:17Z |
---
language:
- en
- zh
license: apache-2.0
tags:
- vision
- image-text-to-text
datasets:
- lmms-lab/LLaVA-OneVision-Data
library_name: transformers
pipeline_tag: image-text-to-text
arxiv: 2408.03326
---
# LLaVA-Onevision Model Card
Check out also the Google Colab demo to run Llava on a free-tier Google Colab instance: [](https://colab.research.google.com/drive/1-4AtYjR8UMtCALV0AswU1kiNkWCLTALT?usp=sharing)
Below is the model card of 7B LLaVA-Onevision Chat model which is copied from the original LLaVA-Onevision model card that you can find [here](https://huggingface.co/lmms-lab/llava-onevision-qwen2-7b-ov-chat).
## Model details
**Model type:**
LLaVA-Onevision is an open-source multimodal LLM trained by fine-tuning Qwen2 on GPT-generated multimodal instruction-following data.
LLaVA-OneVision is the first single model that can simultaneously push the performance boundaries of open LMMs in three important computer
vision scenarios: single-image, multi-image, and video scenarios. Importantly, the design of LLaVA-OneVision allows strong transfer learning
across different modalities/scenarios, yielding new emerging capabilities. In particular, strong video understanding and cross-scenario
capabilities are demonstrated through task transfer from images to videos.
**Model date:**
LLaVA-Onevision-7B Chat was added in September 2024.
**Paper or resources for more information:**
https://llava-vl.github.io/
- **Architecture:** SO400M + Qwen2
- **Pretraining Stage:** LCS-558K, 1 epoch, projector
- **Mid Stage:** A mixture of 4.7M high-quality synthetic data, 1 epoch, full model
- **Final-Image Stage:** A mixture of 3.6M single-image data, 1 epoch, full model
- **OneVision Stage:** A mixture of 1.6M single-image/multi-image/video data, 1 epoch, full model
- **Precision:** bfloat16
## How to use the model
First, make sure to have `transformers` installed from [branch](https://github.com/huggingface/transformers/pull/32673) or `transformers >= 4.45.0`.
The model supports multi-image and multi-prompt generation. Meaning that you can pass multiple images in your prompt. Make sure also to follow the correct prompt template by applying the chat template:
### Using `pipeline`:
Below we used [`"llava-hf/llava-onevision-qwen2-7b-ov-chat-hf"`](https://huggingface.co/llava-hf/llava-onevision-qwen2-7b-ov-chat-hf) checkpoint.
```python
from transformers import pipeline
pipe = pipeline("image-text-to-text", model="llava-onevision-qwen2-7b-ov-chat-hf")
messages = [
{
"role": "user",
"content": [
{"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg"},
{"type": "text", "text": "What does the label 15 represent? (1) lava (2) core (3) tunnel (4) ash cloud"},
],
},
]
out = pipe(text=messages, max_new_tokens=20)
print(out)
>>> [{'input_text': [{'role': 'user', 'content': [{'type': 'image', 'url': 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg'}, {'type': 'text', 'text': 'What does the label 15 represent? (1) lava (2) core (3) tunnel (4) ash cloud'}]}], 'generated_text': 'Lava'}]
```
### Using pure `transformers`:
Below is an example script to run generation in `float16` precision on a GPU device:
```python
import requests
from PIL import Image
import torch
from transformers import AutoProcessor, LlavaOnevisionForConditionalGeneration
model_id = "llava-hf/llava-onevision-qwen2-7b-ov-chat-hf"
model = LlavaOnevisionForConditionalGeneration.from_pretrained(
model_id,
torch_dtype=torch.float16,
low_cpu_mem_usage=True,
).to(0)
processor = AutoProcessor.from_pretrained(model_id)
# Define a chat history and use `apply_chat_template` to get correctly formatted prompt
# Each value in "content" has to be a list of dicts with types ("text", "image")
conversation = [
{
"role": "user",
"content": [
{"type": "text", "text": "What are these?"},
{"type": "image"},
],
},
]
prompt = processor.apply_chat_template(conversation, add_generation_prompt=True)
image_file = "http://images.cocodataset.org/val2017/000000039769.jpg"
raw_image = Image.open(requests.get(image_file, stream=True).raw)
inputs = processor(images=raw_image, text=prompt, return_tensors='pt').to(0, torch.float16)
output = model.generate(**inputs, max_new_tokens=200, do_sample=False)
print(processor.decode(output[0][2:], skip_special_tokens=True))
```
-----------
From transformers>=v4.48, you can also pass image/video url or local path to the conversation history, and let the chat template handle the rest.
Chat template will load the image for you and return inputs in `torch.Tensor` which you can pass directly to `model.generate()`
```python
messages = [
{
"role": "user",
"content": [
{"type": "image", "url": "https://www.ilankelman.org/stopsigns/australia.jpg"}
{"type": "text", "text": "What is shown in this image?"},
],
},
]
inputs = processor.apply_chat_template(messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors"pt")
output = model.generate(**inputs, max_new_tokens=50)
```
### Model optimization
#### 4-bit quantization through `bitsandbytes` library
First make sure to install `bitsandbytes`, `pip install bitsandbytes` and make sure to have access to a CUDA compatible GPU device. Simply change the snippet above with:
```diff
model = LlavaOnevisionForConditionalGeneration.from_pretrained(
model_id,
torch_dtype=torch.float16,
low_cpu_mem_usage=True,
+ load_in_4bit=True
)
```
#### Use Flash-Attention 2 to further speed-up generation
First make sure to install `flash-attn`. Refer to the [original repository of Flash Attention](https://github.com/Dao-AILab/flash-attention) regarding that package installation. Simply change the snippet above with:
```diff
model = LlavaOnevisionForConditionalGeneration.from_pretrained(
model_id,
torch_dtype=torch.float16,
low_cpu_mem_usage=True,
+ use_flash_attention_2=True
).to(0)
```
# Citation
```
@misc{li2024llavaonevisioneasyvisualtask,
title={LLaVA-OneVision: Easy Visual Task Transfer},
author={Bo Li and Yuanhan Zhang and Dong Guo and Renrui Zhang and Feng Li and Hao Zhang and Kaichen Zhang and Yanwei Li and Ziwei Liu and Chunyuan Li},
year={2024},
eprint={2408.03326},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2408.03326},
}
```
|
JustinStrauch/vit_fit_v3
|
JustinStrauch
| 2025-06-18T13:52:49Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"vit",
"image-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2025-06-18T13:50:34Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
billjeremy/Reinforce-2-pixel
|
billjeremy
| 2025-06-18T13:49:47Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-06-18T13:22:59Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-2-pixel
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 46.70 +/- 18.99
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
uvegesistvan/roberta_large_pl_only
|
uvegesistvan
| 2025-06-18T13:49:07Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"xlm-roberta",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-06-18T12:38:47Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
newtts2017/jgy0chrv
|
newtts2017
| 2025-06-18T13:48:56Z | 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
| 2025-06-18T13:36:36Z |
---
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: jgy0chrv
---
# Jgy0Chrv
<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 `jgy0chrv` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "jgy0chrv",
"lora_weights": "https://huggingface.co/newtts2017/jgy0chrv/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('newtts2017/jgy0chrv', weight_name='lora.safetensors')
image = pipeline('jgy0chrv').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: 1000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/newtts2017/jgy0chrv/discussions) to add images that show off what you’ve made with this LoRA.
|
furio19/fashion-rag
|
furio19
| 2025-06-18T13:48:27Z | 0 | 0 | null |
[
"en",
"arxiv:2504.14011",
"license:cc-by-4.0",
"region:us"
] | null | 2025-06-11T15:17:16Z |
---
license: cc-by-4.0
language:
- en
---
<!-- PROJECT LOGO -->
<br />
<div align="center">
<h3 align="center">Fashion-RAG</h3>
<p align="center"> Fashion-RAG: Multimodal Fashion Image Editing via Retrieval-Augmented Generation</p>
</div>
<div align="center">
<img src="retrieve_demo.png" width="50%" alt="MiniMax">
</div>
<div align="center">
> <p align="center">
<strong>International Joint Conference on Neural Networks (IJCNN) 2025<br>Oral Presentation</strong>
</p>
> [Fulvio Sanguigni](https://scholar.google.com/citations?user=tSpzMUEAAAAJ&hl=en)<sup>1,2,\*</sup>, [Davide Morelli](https://scholar.google.com/citations?hl=en&user=UJ4D3rYAAAAJ&view_op=list_works)<sup>1,2,\*</sup>, [Marcella Cornia](https://scholar.google.com/citations?user=DzgmSJEAAAAJ&hl=en)<sup>1</sup>, [Rita Cucchiara](https://scholar.google.com/citations?user=OM3sZEoAAAAJ&hl=en)<sup>1</sup>
> <sup>1</sup>University of Modena, <sup>2</sup>University of Pisa
</div>
<div align="center">
<a href="https://arxiv.org/abs/2504.14011" style="margin: 0 2px;">
<img src="https://img.shields.io/badge/Paper-Arxiv-darkred.svg" alt="Paper">
</a>
<a href="https://arxiv.org/pdf/2504.14011" style="margin: 0 2px;">
<img src="https://img.shields.io/badge/PDF-Arxiv-darkred.svg" alt="PDF">
</a>
<a href='https://fashion-rag-page.github.io/' style="margin: 0 2px;">
<img src='https://img.shields.io/badge/Webpage-Project-silver?style=flat&logo=&logoColor=orange' alt='webpage'>
</a>
<a href="https://huggingface.co/furio19/fashion-rag">
<img src="https://img.shields.io/badge/HuggingFace-Model-FFB000.svg" alt="Project">
</a>
<a href="https://raw.githubusercontent.com/furio1999/fashion-rag/refs/heads/main/LICENSE?token=GHSAT0AAAAAACZM6UVFACIVYIJVXCSFT2VA2CJR5HQ" style="margin: 0 2px;">
<img src='https://img.shields.io/badge/License-CC BY--NC 4.0-lightgreen?style=flat&logo=Lisence' alt='License'>
</a>
</div>
<!-- TABLE OF CONTENTS -->
<details>
<summary>Table of Contents</summary>
<ol>
<li><a href="#about-the-project">About The Project</a></li>
<li><a href="#getting-started">Getting Started</a>
<ul>
<li><a href="#prerequisites">Prerequisites</a></li>
<li><a href="#installation">Installation</a></li>
</ul>
</li>
<li><a href="#data-and-models">Data and Models</a></li>
<li><a href="#inference">Inference</a></li>
</ol>
</details>
<!-- ABOUT THE PROJECT -->
## About The Project
Fashion-RAG is a novel approach in the fashion domain, handling multimodal virtual dressing with a new, Retrieval Augmented Generation (RAG) pipeline for visual data.
Our approach allows to retrieve garments aligned with a given textual description, and uses the retrieved information as a conditioning to generate the dressed person with Stable Diffusion (SD) as the generative model. We finetune the SD U-Net and an additional adapter module (Inversion Adapter) to handle for the retrieved information.
<p align="right">(<a href="#readme-top">back to top</a>)</p>
## ✨ Key Features
Our contribution can be summarized as follows:
- **🔍 Retrieval Enhanced Generation for Visual Items**. We present a unified framework capable of generating Virtual Dressing without the need of a user-defined garment image.
Instead, our method succesfully leverages textual information and retrieves coherent garments to perform the task
- **👗👚🧥 Multiple Garments Conditioning**. We introduce a plug-and-play adapter module that is flexible to the number of retrieved items, allowing to retrieve up to 3 garments per text prompt.
- **📊 Extensive experiments**. Experiments on the Dress Code datasets demonstrate that Fahion-RAG outweights previous competitors.
<!-- Maybe put method here and teaser up, or just method as teaser -->
<!-- GETTING STARTED -->
## Getting Started
### Prerequisites
Clone the repository:
```sh
git clone Fashion-RAG.git
```
### Installation
1. We recommend installing the required packages using Python's native virtual environment (venv) as follows:
```sh
python -m venv fashion-rag
source fashion-rag/bin/activate
```
2. Upgrade pip and install dependencies
```sh
pip install --upgrade pip
pip install -r requirements.txt
```
3. Create a .env file like the following:
```js
export WANDB_API_KEY="ENTER YOUR WANDB TOKEN"
export TORCH_HOME="ENTER YOUR TORCH PATH TO SAVE TORCH MODELS USED FOR METRICS COMPUTING"
export HF_TOKEN="ENTER YOUR HUGGINGFACE TOKEN"
export HF_CACHE_DIR="PATH WHERE YOU WANT TO SAVE THE HF MODELS (NEED CUSTOM VARIABLE TO ACCOUNT FOR OLD TRANSFORMERS PACKAGES, OTHERWISE USE HF_HOME)"
```
<!-- USAGE EXAMPLES -->
## Data and Models
Download DressCode from the [original repository](https://github.com/aimagelab/dress-code)
Download the finetuned U-Net and Inversion Adapter from [this source](https://huggingface.co/furio19/fashion-rag/tree/main) and put them into your experiment folder as follows:
```plaintext
<experiment folder>/
│
├── unet_120000.pth
├── inversion_adapter_120000.pth
```
Copy the provided retrieval file paths folder dataset/dresscode-retrieval into your retrieve path or use them directly.
## Inference
Let's generate our virtual dressing images using the finetuned TEMU-VTOFF model.
```sh
source fashion-rag/bin/activate
python evaluate_RAG.py \
python evaluate_RAG.py \
--pretrained_model_name_or_path stabilityai/stable-diffusion-2-inpainting \
--output_dir "output directory path" \
--finetuned_models_dir "U-Net and inversion adapter directory weights path" \
--unet_name unet_120000.pth --inversion_adapter_name inversion_adapter_120000.pth \
--dataset dresscode --dresscode_dataroot <data path>/DressCode \
--category "garment category"\
--test_order "specify paired or unpaired" --mask_type mask \
--phase test --num_inference_steps 50 \
--test_batch_size 8 --num_workers_test 8 --metrics_batch_size 8 --mixed_precision fp16 \
--text_usage prompt_noun_chunks \
--retrieve_path "dataset/dresscode-retrieval or your custom path" \
--clip_retrieve_model ViT-L-14 --clip_retrieve_weights laion2b_s32b_b82k \
--n_chunks "number of text chunks 1 or 3" \
--n_retrieved "number of retrieved images 1 to 3" \
--metrics fid_score kid_score retrieved_score clip_score lpips_score ssim_score \
--attention_layers_fine_list '-1' '0 1 2 3'\
--compute_metrics
```
The final output folder structure will look like this:
```plaintext
out_dir/pte_paired_nc_<number_of_chunks>_nr_<number_of_retrieved_images>/
│
├── lower_body/
├── upper_body/
├── dresses/
└── metrics_all.json
```
|
NYTK/PULI-LlumiX-Llama-3.1
|
NYTK
| 2025-06-18T13:47:16Z | 91 | 7 | null |
[
"safetensors",
"llama",
"puli",
"hu",
"en",
"license:llama3.1",
"region:us"
] | null | 2025-03-11T14:29:20Z |
---
license: llama3.1
language:
- hu
- en
tags:
- puli
---
# PULI-LlumiX-Llama-3.1 8B base (8.03B billion parameter)
- Trained with LLaMA-Factory [github](https://github.com/hiyouga/LLaMA-Factory)
- The [Llama 3.1 8B Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) model were continual pretrained on Hungarian dataset
## Dataset for continued pretraining
- Hungarian (8.08 billion words): documents (763K) that exceed 5000 words in length + Hungarian Wikipedia
- English: Long Context QA (2 billion words), BookSum (78 million words)
## Limitations
- max_seq_length = 16 384
- bfloat16
## Usage with pipeline
```python
from transformers import pipeline, LlamaForCausalLM, AutoTokenizer
model = LlamaForCausalLM.from_pretrained("NYTK/PULI-LlumiX-Llama-3.1")
tokenizer = AutoTokenizer.from_pretrained("NYTK/PULI-LlumiX-Llama-3.1")
prompt = "Elmesélek egy történetet a nyelvtechnológiáról."
generator = pipeline(task="text-generation", model=model, tokenizer=tokenizer, device=0)
print(generator(prompt, max_new_tokens=30)[0]["generated_text"])
```
Since the model was continuously pre-trained from Llama 3.1 8B **_Instruct_**, it can be used as a chat model.
```python
import torch
from transformers import pipeline
model_id = "NYTK/PULI-LlumiX-Llama-3.1"
pipe = pipeline(
"text-generation",
model=model_id,
torch_dtype=torch.bfloat16,
device_map="auto",
)
messages = [
{"role": "system", "content": "You are a helpful assistant"},
{"role": "user", "content": "Mit gondolsz a nyelvtechnológiáról?"},
]
outputs = pipe(
messages,
max_new_tokens=256,
)
print(outputs[0]["generated_text"][-1])
```
## Citation
If you use this model, please cite the following paper:
```
@inproceedings {yang-llumix-llama,
title = {PULI Chat: Our First Hungarian Conversational Model},
booktitle = {International Conference on Formal Methods and Foundations of Artificial Intelligence},
year = {2025},
publisher = {Eszterházy Károly Catholic University},
address = {Eger, Hungary},
author = {Yang, Zijian Győző and Bánfi, Ágnes and Dodé, Réka and Ferenczi, Gergő and Földesi, Flóra and Hatvani, Péter and Héja, Enikő and Lengyel, Mariann and Madarász, Gábor and Osváth, Mátyás and Sárossy, Bence and Varga, Kristóf and Váradi, Tamás and Prószéky, Gábor and Ligeti-Nagy, Noémi},
pages = {1--3},
pubstate={accepted abstract},
url ={https://uni-eszterhazy.hu/api/media/file/7f9158bd443acc29dbd2a211971fe8677768257c}
}
```
|
KiteAether/khmer-trocr-b_s4-10ep-lr5e5-18-6-25
|
KiteAether
| 2025-06-18T13:46:42Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"vision-encoder-decoder",
"image-text-to-text",
"generated_from_trainer",
"base_model:microsoft/trocr-large-handwritten",
"base_model:finetune:microsoft/trocr-large-handwritten",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2025-06-18T10:20:49Z |
---
library_name: transformers
base_model: microsoft/trocr-large-handwritten
tags:
- generated_from_trainer
model-index:
- name: khmer-trocr-b_s4-10ep-lr5e5-18-6-25
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. -->
# khmer-trocr-b_s4-10ep-lr5e5-18-6-25
This model is a fine-tuned version of [microsoft/trocr-large-handwritten](https://huggingface.co/microsoft/trocr-large-handwritten) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 5.2355
- Cer: 0.7685
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Cer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 4.9267 | 1.0 | 1681 | 5.4176 | 0.9953 |
| 4.1816 | 2.0 | 3362 | 5.2131 | 0.9734 |
| 3.8607 | 3.0 | 5043 | 5.0942 | 0.9385 |
| 3.5317 | 4.0 | 6724 | 4.9554 | 0.8807 |
| 3.0759 | 5.0 | 8405 | 4.7736 | 0.8392 |
| 2.4556 | 6.0 | 10086 | 4.7595 | 0.8093 |
| 1.7233 | 7.0 | 11767 | 4.8245 | 0.7743 |
| 1.0869 | 8.0 | 13448 | 4.9941 | 0.7753 |
| 0.6864 | 9.0 | 15129 | 5.0909 | 0.7833 |
| 0.4806 | 10.0 | 16810 | 5.2355 | 0.7685 |
### Framework versions
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
bruhzair/prototype-0.4x161
|
bruhzair
| 2025-06-18T13:45:59Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"arxiv:2408.07990",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-18T13:18:55Z |
---
base_model: []
library_name: transformers
tags:
- mergekit
- merge
---
# prototype-0.4x161
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [SCE](https://arxiv.org/abs/2408.07990) merge method using /workspace/prototype-0.4x153 as a base.
### Models Merged
The following models were included in the merge:
* /workspace/cache/models--ReadyArt--Forgotten-Safeword-70B-v5.0/snapshots/ac2650005a6fdef7f4cd62590dcb665155349a5b
* /workspace/cache/models--LatitudeGames--Wayfarer-Large-70B-Llama-3.3/snapshots/68cb7a33f692be64d4b146576838be85593a7459
* /workspace/prototype-0.4x160
* /workspace/cache/models--Delta-Vector--Austral-70B-Preview/snapshots/bf62fe4ffd7e460dfa3bb881913bdfbd9dd14002
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: /workspace/cache/models--ReadyArt--Forgotten-Safeword-70B-v5.0/snapshots/ac2650005a6fdef7f4cd62590dcb665155349a5b
- model: /workspace/prototype-0.4x160
- model: /workspace/cache/models--LatitudeGames--Wayfarer-Large-70B-Llama-3.3/snapshots/68cb7a33f692be64d4b146576838be85593a7459
- model: /workspace/cache/models--Delta-Vector--Austral-70B-Preview/snapshots/bf62fe4ffd7e460dfa3bb881913bdfbd9dd14002
- model: /workspace/prototype-0.4x153
base_model: /workspace/prototype-0.4x153
select_topk: 0.15
merge_method: sce
tokenizer:
source: base
pad_to_multiple_of: 8
int8_mask: true
dtype: bfloat16
```
|
dgambettaphd/M_llm2_run2_gen5_WXS_doc1000_synt120_lr1e-04_acm_SYNLAST
|
dgambettaphd
| 2025-06-18T13:45:12Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-18T13:44:53Z |
---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
JayHyeon/Qwen_1.5B-math-DPO_1e-5_1.0vpo_constant-20ep
|
JayHyeon
| 2025-06-18T13:43:45Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"trl",
"dpo",
"conversational",
"dataset:argilla/distilabel-math-preference-dpo",
"arxiv:2305.18290",
"base_model:Qwen/Qwen2.5-Math-1.5B",
"base_model:finetune:Qwen/Qwen2.5-Math-1.5B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-18T12:23:33Z |
---
base_model: Qwen/Qwen2.5-Math-1.5B
datasets: argilla/distilabel-math-preference-dpo
library_name: transformers
model_name: Qwen_1.5B-math-DPO_1e-5_1.0vpo_constant-20ep
tags:
- generated_from_trainer
- trl
- dpo
licence: license
---
# Model Card for Qwen_1.5B-math-DPO_1e-5_1.0vpo_constant-20ep
This model is a fine-tuned version of [Qwen/Qwen2.5-Math-1.5B](https://huggingface.co/Qwen/Qwen2.5-Math-1.5B) on the [argilla/distilabel-math-preference-dpo](https://huggingface.co/datasets/argilla/distilabel-math-preference-dpo) dataset.
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="JayHyeon/Qwen_1.5B-math-DPO_1e-5_1.0vpo_constant-20ep", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/bonin147/huggingface/runs/elje5320)
This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.50.0
- Pytorch: 2.6.0
- Datasets: 3.4.1
- Tokenizers: 0.21.1
## Citations
Cite DPO as:
```bibtex
@inproceedings{rafailov2023direct,
title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}},
author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn},
year = 2023,
booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023},
url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html},
editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
mradermacher/ER-GRPO-STD-GGUF
|
mradermacher
| 2025-06-18T13:42:23Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"generated_from_trainer",
"ERGRPO",
"trl",
"grpo",
"en",
"dataset:knoveleng/open-rs",
"base_model:lalalaDa/ER-GRPO-STD",
"base_model:quantized:lalalaDa/ER-GRPO-STD",
"endpoints_compatible",
"region:us"
] | null | 2025-06-18T13:31:44Z |
---
base_model: lalalaDa/ER-GRPO-STD
datasets: knoveleng/open-rs
language:
- en
library_name: transformers
model_name: ER-GRPO-STD
quantized_by: mradermacher
tags:
- generated_from_trainer
- ERGRPO
- trl
- grpo
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/lalalaDa/ER-GRPO-STD
<!-- 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/ER-GRPO-STD-GGUF/resolve/main/ER-GRPO-STD.Q2_K.gguf) | Q2_K | 0.9 | |
| [GGUF](https://huggingface.co/mradermacher/ER-GRPO-STD-GGUF/resolve/main/ER-GRPO-STD.Q3_K_S.gguf) | Q3_K_S | 1.0 | |
| [GGUF](https://huggingface.co/mradermacher/ER-GRPO-STD-GGUF/resolve/main/ER-GRPO-STD.Q3_K_M.gguf) | Q3_K_M | 1.0 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/ER-GRPO-STD-GGUF/resolve/main/ER-GRPO-STD.Q3_K_L.gguf) | Q3_K_L | 1.1 | |
| [GGUF](https://huggingface.co/mradermacher/ER-GRPO-STD-GGUF/resolve/main/ER-GRPO-STD.IQ4_XS.gguf) | IQ4_XS | 1.1 | |
| [GGUF](https://huggingface.co/mradermacher/ER-GRPO-STD-GGUF/resolve/main/ER-GRPO-STD.Q4_K_S.gguf) | Q4_K_S | 1.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/ER-GRPO-STD-GGUF/resolve/main/ER-GRPO-STD.Q4_K_M.gguf) | Q4_K_M | 1.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/ER-GRPO-STD-GGUF/resolve/main/ER-GRPO-STD.Q5_K_S.gguf) | Q5_K_S | 1.4 | |
| [GGUF](https://huggingface.co/mradermacher/ER-GRPO-STD-GGUF/resolve/main/ER-GRPO-STD.Q5_K_M.gguf) | Q5_K_M | 1.4 | |
| [GGUF](https://huggingface.co/mradermacher/ER-GRPO-STD-GGUF/resolve/main/ER-GRPO-STD.Q6_K.gguf) | Q6_K | 1.6 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/ER-GRPO-STD-GGUF/resolve/main/ER-GRPO-STD.Q8_0.gguf) | Q8_0 | 2.0 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/ER-GRPO-STD-GGUF/resolve/main/ER-GRPO-STD.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 -->
|
annasoli/Qwen2.5-14B-Instruct_R1-DP12-LR2e-5_bad-medical-advice
|
annasoli
| 2025-06-18T13:42:16Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-18T13:31:31Z |
---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
dicksonhk/Nanonets-OCR-s-mlx-fp16
|
dicksonhk
| 2025-06-18T13:42:09Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2_5_vl",
"image-text-to-text",
"OCR",
"pdf2markdown",
"mlx",
"mlx-my-repo",
"conversational",
"en",
"base_model:nanonets/Nanonets-OCR-s",
"base_model:finetune:nanonets/Nanonets-OCR-s",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2025-06-18T13:41:06Z |
---
language:
- en
base_model: nanonets/Nanonets-OCR-s
pipeline_tag: image-text-to-text
tags:
- OCR
- pdf2markdown
- mlx
- mlx-my-repo
library_name: transformers
---
# dicksonhk/Nanonets-OCR-s-mlx-fp16
The Model [dicksonhk/Nanonets-OCR-s-mlx-fp16](https://huggingface.co/dicksonhk/Nanonets-OCR-s-mlx-fp16) was converted to $MLX format from [nanonets/Nanonets-OCR-s](https://huggingface.co/nanonets/Nanonets-OCR-s) using $mlx-vlm version **0.1.15**.
```bash
pip install -U mlx-vlm
```
```bash
python -m mlx_vlm.generate --model dicksonhk/Nanonets-OCR-s-mlx-fp16 --max-tokens 100 --temp 0.0 --prompt "Describe this image." --image <path_to_image>
```
|
csikasote/mms-1b-all-bemgen-combined-42
|
csikasote
| 2025-06-18T13:40:11Z | 12 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"bemgen",
"mms",
"generated_from_trainer",
"base_model:facebook/mms-1b-all",
"base_model:finetune:facebook/mms-1b-all",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2025-06-18T11:40:39Z |
---
library_name: transformers
license: cc-by-nc-4.0
base_model: facebook/mms-1b-all
tags:
- automatic-speech-recognition
- bemgen
- mms
- generated_from_trainer
metrics:
- wer
model-index:
- name: mms-1b-all-bemgen-combined-42
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mms-1b-all-bemgen-combined-42
This model is a fine-tuned version of [facebook/mms-1b-all](https://huggingface.co/facebook/mms-1b-all) on the BEMGEN - BEM dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2214
- Wer: 0.3973
## 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: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 30.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 7.7575 | 0.2538 | 100 | 5.4977 | 1.3624 |
| 4.8101 | 0.5076 | 200 | 5.0306 | 1.0750 |
| 4.3147 | 0.7614 | 300 | 4.0326 | 1.0873 |
| 3.664 | 1.0152 | 400 | 3.3150 | 1.0043 |
| 2.503 | 1.2690 | 500 | 0.3145 | 0.5055 |
| 0.4803 | 1.5228 | 600 | 0.2400 | 0.4439 |
| 0.4124 | 1.7766 | 700 | 0.2338 | 0.4242 |
| 0.3898 | 2.0305 | 800 | 0.2270 | 0.4019 |
| 0.3924 | 2.2843 | 900 | 0.2245 | 0.4094 |
| 0.3826 | 2.5381 | 1000 | 0.2282 | 0.4028 |
| 0.3666 | 2.7919 | 1100 | 0.2237 | 0.3986 |
| 0.3585 | 3.0457 | 1200 | 0.2214 | 0.3971 |
| 0.3591 | 3.2995 | 1300 | 0.2247 | 0.4003 |
| 0.3535 | 3.5533 | 1400 | 0.2182 | 0.4063 |
| 0.3532 | 3.8071 | 1500 | 0.2186 | 0.3861 |
| 0.3544 | 4.0609 | 1600 | 0.2187 | 0.4077 |
| 0.3401 | 4.3147 | 1700 | 0.2169 | 0.3921 |
| 0.3372 | 4.5685 | 1800 | 0.2142 | 0.3990 |
| 0.3446 | 4.8223 | 1900 | 0.2145 | 0.3897 |
| 0.3291 | 5.0761 | 2000 | 0.2158 | 0.3878 |
| 0.3219 | 5.3299 | 2100 | 0.2158 | 0.3806 |
### Framework versions
- Transformers 4.53.0.dev0
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.0
|
dicksonhk/RolmOCR-mlx-fp16
|
dicksonhk
| 2025-06-18T13:39:37Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2_5_vl",
"image-text-to-text",
"mlx",
"conversational",
"dataset:allenai/olmOCR-mix-0225",
"base_model:reducto/RolmOCR",
"base_model:finetune:reducto/RolmOCR",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2025-06-18T13:37:01Z |
---
library_name: transformers
license: apache-2.0
datasets:
- allenai/olmOCR-mix-0225
base_model: reducto/RolmOCR
tags:
- mlx
---
# dicksonhk/RolmOCR-mlx-fp16
The Model [dicksonhk/RolmOCR-mlx-fp16](https://huggingface.co/dicksonhk/RolmOCR-mlx-fp16) was converted to $MLX format from [reducto/RolmOCR](https://huggingface.co/reducto/RolmOCR) using $mlx-vlm version **0.1.15**.
```bash
pip install -U mlx-vlm
```
```bash
python -m mlx_vlm.generate --model dicksonhk/RolmOCR-mlx-fp16 --max-tokens 100 --temp 0.0 --prompt "Describe this image." --image <path_to_image>
```
|
morturr/Llama-2-7b-hf-LOO_dadjokes-COMB_headlines-comb2-seed28-2025-06-18
|
morturr
| 2025-06-18T13:39:25Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:meta-llama/Llama-2-7b-hf",
"base_model:adapter:meta-llama/Llama-2-7b-hf",
"license:llama2",
"region:us"
] | null | 2025-06-18T13:39:08Z |
---
library_name: peft
license: llama2
base_model: meta-llama/Llama-2-7b-hf
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: Llama-2-7b-hf-LOO_dadjokes-COMB_headlines-comb2-seed28-2025-06-18
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Llama-2-7b-hf-LOO_dadjokes-COMB_headlines-comb2-seed28-2025-06-18
This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 28
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.1
- Pytorch 2.5.1+cu124
- Datasets 3.0.2
- Tokenizers 0.20.1
|
mradermacher/flan-t5-xl-alpaca-GGUF
|
mradermacher
| 2025-06-18T13:38:58Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"dataset:tatsu-lab/alpaca",
"base_model:VMware/flan-t5-xl-alpaca",
"base_model:quantized:VMware/flan-t5-xl-alpaca",
"license:other",
"endpoints_compatible",
"region:us"
] | null | 2025-06-18T13:15:02Z |
---
base_model: VMware/flan-t5-xl-alpaca
datasets:
- tatsu-lab/alpaca
language:
- en
library_name: transformers
license: other
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/VMware/flan-t5-xl-alpaca
<!-- 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/flan-t5-xl-alpaca-GGUF/resolve/main/flan-t5-xl-alpaca.Q2_K.gguf) | Q2_K | 1.2 | |
| [GGUF](https://huggingface.co/mradermacher/flan-t5-xl-alpaca-GGUF/resolve/main/flan-t5-xl-alpaca.Q3_K_S.gguf) | Q3_K_S | 1.4 | |
| [GGUF](https://huggingface.co/mradermacher/flan-t5-xl-alpaca-GGUF/resolve/main/flan-t5-xl-alpaca.Q3_K_M.gguf) | Q3_K_M | 1.5 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/flan-t5-xl-alpaca-GGUF/resolve/main/flan-t5-xl-alpaca.Q3_K_L.gguf) | Q3_K_L | 1.6 | |
| [GGUF](https://huggingface.co/mradermacher/flan-t5-xl-alpaca-GGUF/resolve/main/flan-t5-xl-alpaca.IQ4_XS.gguf) | IQ4_XS | 1.6 | |
| [GGUF](https://huggingface.co/mradermacher/flan-t5-xl-alpaca-GGUF/resolve/main/flan-t5-xl-alpaca.Q4_K_S.gguf) | Q4_K_S | 1.7 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/flan-t5-xl-alpaca-GGUF/resolve/main/flan-t5-xl-alpaca.Q4_K_M.gguf) | Q4_K_M | 1.9 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/flan-t5-xl-alpaca-GGUF/resolve/main/flan-t5-xl-alpaca.Q5_K_S.gguf) | Q5_K_S | 2.1 | |
| [GGUF](https://huggingface.co/mradermacher/flan-t5-xl-alpaca-GGUF/resolve/main/flan-t5-xl-alpaca.Q5_K_M.gguf) | Q5_K_M | 2.1 | |
| [GGUF](https://huggingface.co/mradermacher/flan-t5-xl-alpaca-GGUF/resolve/main/flan-t5-xl-alpaca.Q6_K.gguf) | Q6_K | 2.4 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/flan-t5-xl-alpaca-GGUF/resolve/main/flan-t5-xl-alpaca.Q8_0.gguf) | Q8_0 | 3.1 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/flan-t5-xl-alpaca-GGUF/resolve/main/flan-t5-xl-alpaca.f16.gguf) | f16 | 5.8 | 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 -->
|
mradermacher/nemo-chatbot-v1-GGUF
|
mradermacher
| 2025-06-18T13:36:20Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:chaerheeon/nemo-chatbot-v1",
"base_model:quantized:chaerheeon/nemo-chatbot-v1",
"endpoints_compatible",
"region:us"
] | null | 2025-06-18T13:16:05Z |
---
base_model: chaerheeon/nemo-chatbot-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/chaerheeon/nemo-chatbot-v1
<!-- 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/nemo-chatbot-v1-GGUF/resolve/main/nemo-chatbot-v1.Q2_K.gguf) | Q2_K | 1.8 | |
| [GGUF](https://huggingface.co/mradermacher/nemo-chatbot-v1-GGUF/resolve/main/nemo-chatbot-v1.Q3_K_S.gguf) | Q3_K_S | 2.0 | |
| [GGUF](https://huggingface.co/mradermacher/nemo-chatbot-v1-GGUF/resolve/main/nemo-chatbot-v1.Q3_K_M.gguf) | Q3_K_M | 2.2 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/nemo-chatbot-v1-GGUF/resolve/main/nemo-chatbot-v1.Q3_K_L.gguf) | Q3_K_L | 2.3 | |
| [GGUF](https://huggingface.co/mradermacher/nemo-chatbot-v1-GGUF/resolve/main/nemo-chatbot-v1.IQ4_XS.gguf) | IQ4_XS | 2.4 | |
| [GGUF](https://huggingface.co/mradermacher/nemo-chatbot-v1-GGUF/resolve/main/nemo-chatbot-v1.Q4_K_S.gguf) | Q4_K_S | 2.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/nemo-chatbot-v1-GGUF/resolve/main/nemo-chatbot-v1.Q4_K_M.gguf) | Q4_K_M | 2.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/nemo-chatbot-v1-GGUF/resolve/main/nemo-chatbot-v1.Q5_K_S.gguf) | Q5_K_S | 2.9 | |
| [GGUF](https://huggingface.co/mradermacher/nemo-chatbot-v1-GGUF/resolve/main/nemo-chatbot-v1.Q5_K_M.gguf) | Q5_K_M | 2.9 | |
| [GGUF](https://huggingface.co/mradermacher/nemo-chatbot-v1-GGUF/resolve/main/nemo-chatbot-v1.Q6_K.gguf) | Q6_K | 3.3 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/nemo-chatbot-v1-GGUF/resolve/main/nemo-chatbot-v1.Q8_0.gguf) | Q8_0 | 4.2 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/nemo-chatbot-v1-GGUF/resolve/main/nemo-chatbot-v1.f16.gguf) | f16 | 7.9 | 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 -->
|
Facepalm0/nanoVLM
|
Facepalm0
| 2025-06-18T13:34:43Z | 0 | 0 |
nanovlm
|
[
"nanovlm",
"safetensors",
"vision-language",
"multimodal",
"research",
"image-text-to-text",
"license:mit",
"region:us"
] |
image-text-to-text
| 2025-06-18T13:34:01Z |
---
# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/model-cards
library_name: nanovlm
license: mit
pipeline_tag: image-text-to-text
tags:
- vision-language
- multimodal
- research
---
**nanoVLM** is a minimal and lightweight Vision-Language Model (VLM) designed for efficient training and experimentation. Built using pure PyTorch, the entire model architecture and training logic fits within ~750 lines of code. It combines a ViT-based image encoder (SigLIP-B/16-224-85M) with a lightweight causal language model (SmolLM2-135M), resulting in a compact 222M parameter model.
For more information, check out the base model on https://huggingface.co/lusxvr/nanoVLM-222M.
**Usage:**
Clone the nanoVLM repository: https://github.com/huggingface/nanoVLM.
Follow the install instructions and run the following code:
```python
from models.vision_language_model import VisionLanguageModel
model = VisionLanguageModel.from_pretrained("Facepalm0/nanoVLM")
```
|
angkul07/gpt2_finetuned
|
angkul07
| 2025-06-18T13:32:32Z | 29 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:angkul07/llm_100M",
"base_model:adapter:angkul07/llm_100M",
"region:us"
] | null | 2025-06-17T09:54:17Z |
---
base_model: angkul07/llm_100M
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.15.2
|
Bhavani-23/Ocularone-Hazard-Vest-Dataset-Models
|
Bhavani-23
| 2025-06-18T13:30:12Z | 0 | 0 | null |
[
"object-detection",
"en",
"dataset:Bhavani-23/Ocularone-Hazard-Vest-Dataset",
"arxiv:2504.03709",
"license:apache-2.0",
"region:us"
] |
object-detection
| 2025-02-21T15:47:09Z |
---
license: apache-2.0
datasets:
- Bhavani-23/Ocularone-Hazard-Vest-Dataset
language:
- en
pipeline_tag: object-detection
---
# Models associated with Ocularone: Hazard Vest Dataset
<p align="center">
🤗 <a href="https://huggingface.co/datasets/Bhavani-23/Ocularone-Hazard-Vest-Dataset">Hugging Face</a>   |   🚀 <a href="https://github.com/dream-lab/ocularone-dataset.git">Github</a>   
</p>
The folders contain different YOLO models that were trained on our custom data as mentioned in [Ocularone: Hazard Vest Dataset](https://huggingface.co/datasets/Bhavani-23/Ocularone-Hazard-Vest-Dataset).
## References
<!-- ```text
@misc{ocularone-dataset-v0,
title = {Ocularone: Hazard Vest Dataset},
publisher = {GitHub},
howpublished = {\url{https://github.com/dream-lab/ocularone-dataset}},
}
``` -->
``` text
@misc{raj2025ocularonebenchbenchmarkingdnnmodels,
title={Ocularone-Bench: Benchmarking DNN Models on GPUs to Assist the Visually Impaired},
author={Suman Raj and Bhavani A Madhabhavi and Kautuk Astu and Arnav A Rajesh and Pratham M and Yogesh Simmhan},
year={2025},
eprint={2504.03709},
archivePrefix={arXiv},
primaryClass={cs.DC},
url={https://arxiv.org/abs/2504.03709},
}
```
|
Alphatao/Affine-6043407
|
Alphatao
| 2025-06-18T13:27:30Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:2309.00071",
"arxiv:2505.09388",
"base_model:Qwen/Qwen3-8B-Base",
"base_model:finetune:Qwen/Qwen3-8B-Base",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-18T13:21:27Z |
---
library_name: transformers
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen3-8B/blob/main/LICENSE
pipeline_tag: text-generation
base_model:
- Qwen/Qwen3-8B-Base
---
# Qwen3-8B
<a href="https://chat.qwen.ai/" target="_blank" style="margin: 2px;">
<img alt="Chat" src="https://img.shields.io/badge/%F0%9F%92%9C%EF%B8%8F%20Qwen%20Chat%20-536af5" style="display: inline-block; vertical-align: middle;"/>
</a>
## Qwen3 Highlights
Qwen3 is the latest generation of large language models in Qwen series, offering a comprehensive suite of dense and mixture-of-experts (MoE) models. Built upon extensive training, Qwen3 delivers groundbreaking advancements in reasoning, instruction-following, agent capabilities, and multilingual support, with the following key features:
- **Uniquely support of seamless switching between thinking mode** (for complex logical reasoning, math, and coding) and **non-thinking mode** (for efficient, general-purpose dialogue) **within single model**, ensuring optimal performance across various scenarios.
- **Significantly enhancement in its reasoning capabilities**, surpassing previous QwQ (in thinking mode) and Qwen2.5 instruct models (in non-thinking mode) on mathematics, code generation, and commonsense logical reasoning.
- **Superior human preference alignment**, excelling in creative writing, role-playing, multi-turn dialogues, and instruction following, to deliver a more natural, engaging, and immersive conversational experience.
- **Expertise in agent capabilities**, enabling precise integration with external tools in both thinking and unthinking modes and achieving leading performance among open-source models in complex agent-based tasks.
- **Support of 100+ languages and dialects** with strong capabilities for **multilingual instruction following** and **translation**.
## Model Overview
**Qwen3-8B** has the following features:
- Type: Causal Language Models
- Training Stage: Pretraining & Post-training
- Number of Parameters: 8.2B
- Number of Paramaters (Non-Embedding): 6.95B
- Number of Layers: 36
- Number of Attention Heads (GQA): 32 for Q and 8 for KV
- Context Length: 32,768 natively and [131,072 tokens with YaRN](#processing-long-texts).
For more details, including benchmark evaluation, hardware requirements, and inference performance, please refer to our [blog](https://qwenlm.github.io/blog/qwen3/), [GitHub](https://github.com/QwenLM/Qwen3), and [Documentation](https://qwen.readthedocs.io/en/latest/).
## Quickstart
The code of Qwen3 has been in the latest Hugging Face `transformers` and we advise you to use the latest version of `transformers`.
With `transformers<4.51.0`, you will encounter the following error:
```
KeyError: 'qwen3'
```
The following contains a code snippet illustrating how to use the model generate content based on given inputs.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "Qwen/Qwen3-8B"
# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto"
)
# prepare the model input
prompt = "Give me a short introduction to large language model."
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
enable_thinking=True # Switches between thinking and non-thinking modes. Default is True.
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# conduct text completion
generated_ids = model.generate(
**model_inputs,
max_new_tokens=32768
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
# parsing thinking content
try:
# rindex finding 151668 (</think>)
index = len(output_ids) - output_ids[::-1].index(151668)
except ValueError:
index = 0
thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("\n")
content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n")
print("thinking content:", thinking_content)
print("content:", content)
```
For deployment, you can use `sglang>=0.4.6.post1` or `vllm>=0.8.5` or to create an OpenAI-compatible API endpoint:
- SGLang:
```shell
python -m sglang.launch_server --model-path Qwen/Qwen3-8B --reasoning-parser qwen3
```
- vLLM:
```shell
vllm serve Qwen/Qwen3-8B --enable-reasoning --reasoning-parser deepseek_r1
```
For local use, applications such as Ollama, LMStudio, MLX-LM, llama.cpp, and KTransformers have also supported Qwen3.
## Switching Between Thinking and Non-Thinking Mode
> [!TIP]
> The `enable_thinking` switch is also available in APIs created by SGLang and vLLM.
> Please refer to our documentation for [SGLang](https://qwen.readthedocs.io/en/latest/deployment/sglang.html#thinking-non-thinking-modes) and [vLLM](https://qwen.readthedocs.io/en/latest/deployment/vllm.html#thinking-non-thinking-modes) users.
### `enable_thinking=True`
By default, Qwen3 has thinking capabilities enabled, similar to QwQ-32B. This means the model will use its reasoning abilities to enhance the quality of generated responses. For example, when explicitly setting `enable_thinking=True` or leaving it as the default value in `tokenizer.apply_chat_template`, the model will engage its thinking mode.
```python
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
enable_thinking=True # True is the default value for enable_thinking
)
```
In this mode, the model will generate think content wrapped in a `<think>...</think>` block, followed by the final response.
> [!NOTE]
> For thinking mode, use `Temperature=0.6`, `TopP=0.95`, `TopK=20`, and `MinP=0` (the default setting in `generation_config.json`). **DO NOT use greedy decoding**, as it can lead to performance degradation and endless repetitions. For more detailed guidance, please refer to the [Best Practices](#best-practices) section.
### `enable_thinking=False`
We provide a hard switch to strictly disable the model's thinking behavior, aligning its functionality with the previous Qwen2.5-Instruct models. This mode is particularly useful in scenarios where disabling thinking is essential for enhancing efficiency.
```python
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
enable_thinking=False # Setting enable_thinking=False disables thinking mode
)
```
In this mode, the model will not generate any think content and will not include a `<think>...</think>` block.
> [!NOTE]
> For non-thinking mode, we suggest using `Temperature=0.7`, `TopP=0.8`, `TopK=20`, and `MinP=0`. For more detailed guidance, please refer to the [Best Practices](#best-practices) section.
### Advanced Usage: Switching Between Thinking and Non-Thinking Modes via User Input
We provide a soft switch mechanism that allows users to dynamically control the model's behavior when `enable_thinking=True`. Specifically, you can add `/think` and `/no_think` to user prompts or system messages to switch the model's thinking mode from turn to turn. The model will follow the most recent instruction in multi-turn conversations.
Here is an example of a multi-turn conversation:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
class QwenChatbot:
def __init__(self, model_name="Qwen/Qwen3-8B"):
self.tokenizer = AutoTokenizer.from_pretrained(model_name)
self.model = AutoModelForCausalLM.from_pretrained(model_name)
self.history = []
def generate_response(self, user_input):
messages = self.history + [{"role": "user", "content": user_input}]
text = self.tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
inputs = self.tokenizer(text, return_tensors="pt")
response_ids = self.model.generate(**inputs, max_new_tokens=32768)[0][len(inputs.input_ids[0]):].tolist()
response = self.tokenizer.decode(response_ids, skip_special_tokens=True)
# Update history
self.history.append({"role": "user", "content": user_input})
self.history.append({"role": "assistant", "content": response})
return response
# Example Usage
if __name__ == "__main__":
chatbot = QwenChatbot()
# First input (without /think or /no_think tags, thinking mode is enabled by default)
user_input_1 = "How many r's in strawberries?"
print(f"User: {user_input_1}")
response_1 = chatbot.generate_response(user_input_1)
print(f"Bot: {response_1}")
print("----------------------")
# Second input with /no_think
user_input_2 = "Then, how many r's in blueberries? /no_think"
print(f"User: {user_input_2}")
response_2 = chatbot.generate_response(user_input_2)
print(f"Bot: {response_2}")
print("----------------------")
# Third input with /think
user_input_3 = "Really? /think"
print(f"User: {user_input_3}")
response_3 = chatbot.generate_response(user_input_3)
print(f"Bot: {response_3}")
```
> [!NOTE]
> For API compatibility, when `enable_thinking=True`, regardless of whether the user uses `/think` or `/no_think`, the model will always output a block wrapped in `<think>...</think>`. However, the content inside this block may be empty if thinking is disabled.
> When `enable_thinking=False`, the soft switches are not valid. Regardless of any `/think` or `/no_think` tags input by the user, the model will not generate think content and will not include a `<think>...</think>` block.
## Agentic Use
Qwen3 excels in tool calling capabilities. We recommend using [Qwen-Agent](https://github.com/QwenLM/Qwen-Agent) to make the best use of agentic ability of Qwen3. Qwen-Agent encapsulates tool-calling templates and tool-calling parsers internally, greatly reducing coding complexity.
To define the available tools, you can use the MCP configuration file, use the integrated tool of Qwen-Agent, or integrate other tools by yourself.
```python
from qwen_agent.agents import Assistant
# Define LLM
llm_cfg = {
'model': 'Qwen3-8B',
# Use the endpoint provided by Alibaba Model Studio:
# 'model_type': 'qwen_dashscope',
# 'api_key': os.getenv('DASHSCOPE_API_KEY'),
# Use a custom endpoint compatible with OpenAI API:
'model_server': 'http://localhost:8000/v1', # api_base
'api_key': 'EMPTY',
# Other parameters:
# 'generate_cfg': {
# # Add: When the response content is `<think>this is the thought</think>this is the answer;
# # Do not add: When the response has been separated by reasoning_content and content.
# 'thought_in_content': True,
# },
}
# Define Tools
tools = [
{'mcpServers': { # You can specify the MCP configuration file
'time': {
'command': 'uvx',
'args': ['mcp-server-time', '--local-timezone=Asia/Shanghai']
},
"fetch": {
"command": "uvx",
"args": ["mcp-server-fetch"]
}
}
},
'code_interpreter', # Built-in tools
]
# Define Agent
bot = Assistant(llm=llm_cfg, function_list=tools)
# Streaming generation
messages = [{'role': 'user', 'content': 'https://qwenlm.github.io/blog/ Introduce the latest developments of Qwen'}]
for responses in bot.run(messages=messages):
pass
print(responses)
```
## Processing Long Texts
Qwen3 natively supports context lengths of up to 32,768 tokens. For conversations where the total length (including both input and output) significantly exceeds this limit, we recommend using RoPE scaling techniques to handle long texts effectively. We have validated the model's performance on context lengths of up to 131,072 tokens using the [YaRN](https://arxiv.org/abs/2309.00071) method.
YaRN is currently supported by several inference frameworks, e.g., `transformers` and `llama.cpp` for local use, `vllm` and `sglang` for deployment. In general, there are two approaches to enabling YaRN for supported frameworks:
- Modifying the model files:
In the `config.json` file, add the `rope_scaling` fields:
```json
{
...,
"rope_scaling": {
"rope_type": "yarn",
"factor": 4.0,
"original_max_position_embeddings": 32768
}
}
```
For `llama.cpp`, you need to regenerate the GGUF file after the modification.
- Passing command line arguments:
For `vllm`, you can use
```shell
vllm serve ... --rope-scaling '{"rope_type":"yarn","factor":4.0,"original_max_position_embeddings":32768}' --max-model-len 131072
```
For `sglang`, you can use
```shell
python -m sglang.launch_server ... --json-model-override-args '{"rope_scaling":{"rope_type":"yarn","factor":4.0,"original_max_position_embeddings":32768}}'
```
For `llama-server` from `llama.cpp`, you can use
```shell
llama-server ... --rope-scaling yarn --rope-scale 4 --yarn-orig-ctx 32768
```
> [!IMPORTANT]
> If you encounter the following warning
> ```
> Unrecognized keys in `rope_scaling` for 'rope_type'='yarn': {'original_max_position_embeddings'}
> ```
> please upgrade `transformers>=4.51.0`.
> [!NOTE]
> All the notable open-source frameworks implement static YaRN, which means the scaling factor remains constant regardless of input length, **potentially impacting performance on shorter texts.**
> We advise adding the `rope_scaling` configuration only when processing long contexts is required.
> It is also recommended to modify the `factor` as needed. For example, if the typical context length for your application is 65,536 tokens, it would be better to set `factor` as 2.0.
> [!NOTE]
> The default `max_position_embeddings` in `config.json` is set to 40,960. This allocation includes reserving 32,768 tokens for outputs and 8,192 tokens for typical prompts, which is sufficient for most scenarios involving short text processing. If the average context length does not exceed 32,768 tokens, we do not recommend enabling YaRN in this scenario, as it may potentially degrade model performance.
> [!TIP]
> The endpoint provided by Alibaba Model Studio supports dynamic YaRN by default and no extra configuration is needed.
## Best Practices
To achieve optimal performance, we recommend the following settings:
1. **Sampling Parameters**:
- For thinking mode (`enable_thinking=True`), use `Temperature=0.6`, `TopP=0.95`, `TopK=20`, and `MinP=0`. **DO NOT use greedy decoding**, as it can lead to performance degradation and endless repetitions.
- For non-thinking mode (`enable_thinking=False`), we suggest using `Temperature=0.7`, `TopP=0.8`, `TopK=20`, and `MinP=0`.
- For supported frameworks, you can adjust the `presence_penalty` parameter between 0 and 2 to reduce endless repetitions. However, using a higher value may occasionally result in language mixing and a slight decrease in model performance.
2. **Adequate Output Length**: We recommend using an output length of 32,768 tokens for most queries. For benchmarking on highly complex problems, such as those found in math and programming competitions, we suggest setting the max output length to 38,912 tokens. This provides the model with sufficient space to generate detailed and comprehensive responses, thereby enhancing its overall performance.
3. **Standardize Output Format**: We recommend using prompts to standardize model outputs when benchmarking.
- **Math Problems**: Include "Please reason step by step, and put your final answer within \boxed{}." in the prompt.
- **Multiple-Choice Questions**: Add the following JSON structure to the prompt to standardize responses: "Please show your choice in the `answer` field with only the choice letter, e.g., `"answer": "C"`."
4. **No Thinking Content in History**: In multi-turn conversations, the historical model output should only include the final output part and does not need to include the thinking content. It is implemented in the provided chat template in Jinja2. However, for frameworks that do not directly use the Jinja2 chat template, it is up to the developers to ensure that the best practice is followed.
### Citation
If you find our work helpful, feel free to give us a cite.
```
@misc{qwen3technicalreport,
title={Qwen3 Technical Report},
author={Qwen Team},
year={2025},
eprint={2505.09388},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2505.09388},
}
```
|
morturr/Llama-2-7b-hf-LOO_amazon-COMB_headlines-comb2-seed18-2025-06-18
|
morturr
| 2025-06-18T13:26:16Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:meta-llama/Llama-2-7b-hf",
"base_model:adapter:meta-llama/Llama-2-7b-hf",
"license:llama2",
"region:us"
] | null | 2025-06-18T13:25:52Z |
---
library_name: peft
license: llama2
base_model: meta-llama/Llama-2-7b-hf
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: Llama-2-7b-hf-LOO_amazon-COMB_headlines-comb2-seed18-2025-06-18
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Llama-2-7b-hf-LOO_amazon-COMB_headlines-comb2-seed18-2025-06-18
This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 18
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.1
- Pytorch 2.5.1+cu124
- Datasets 3.0.2
- Tokenizers 0.20.1
|
bruhzair/prototype-0.4x158
|
bruhzair
| 2025-06-18T13:24:14Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"arxiv:2408.07990",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-18T13:00:50Z |
---
base_model: []
library_name: transformers
tags:
- mergekit
- merge
---
# prototype-0.4x158
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [SCE](https://arxiv.org/abs/2408.07990) merge method using /workspace/prototype-0.4x153 as a base.
### Models Merged
The following models were included in the merge:
* /workspace/cache/models--LatitudeGames--Wayfarer-Large-70B-Llama-3.3/snapshots/68cb7a33f692be64d4b146576838be85593a7459
* /workspace/cache/models--ReadyArt--Forgotten-Safeword-70B-v5.0/snapshots/ac2650005a6fdef7f4cd62590dcb665155349a5b
* /workspace/cache/models--deepcogito--cogito-v1-preview-llama-70B/snapshots/1d624e2293b5b35f9cfd2349f8e02c7ebf32ca83
* /workspace/cache/models--Sao10K--70B-L3.3-mhnnn-x1/snapshots/3fe1847bbe0dadf7306f3c4bf738f0547676177d
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: /workspace/cache/models--ReadyArt--Forgotten-Safeword-70B-v5.0/snapshots/ac2650005a6fdef7f4cd62590dcb665155349a5b
- model: /workspace/cache/models--deepcogito--cogito-v1-preview-llama-70B/snapshots/1d624e2293b5b35f9cfd2349f8e02c7ebf32ca83
- model: /workspace/cache/models--LatitudeGames--Wayfarer-Large-70B-Llama-3.3/snapshots/68cb7a33f692be64d4b146576838be85593a7459
- model: /workspace/cache/models--Sao10K--70B-L3.3-mhnnn-x1/snapshots/3fe1847bbe0dadf7306f3c4bf738f0547676177d
- model: /workspace/prototype-0.4x153
base_model: /workspace/prototype-0.4x153
select_topk: 0.15
merge_method: sce
tokenizer:
source: base
pad_to_multiple_of: 8
int8_mask: true
dtype: bfloat16
```
|
morturr/Llama-2-7b-hf-LOO_one_liners-COMB_dadjokes-comb2-seed18-2025-06-18
|
morturr
| 2025-06-18T13:23:14Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:meta-llama/Llama-2-7b-hf",
"base_model:adapter:meta-llama/Llama-2-7b-hf",
"license:llama2",
"region:us"
] | null | 2025-06-18T13:23:06Z |
---
library_name: peft
license: llama2
base_model: meta-llama/Llama-2-7b-hf
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: Llama-2-7b-hf-LOO_one_liners-COMB_dadjokes-comb2-seed18-2025-06-18
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Llama-2-7b-hf-LOO_one_liners-COMB_dadjokes-comb2-seed18-2025-06-18
This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) 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: 16
- eval_batch_size: 16
- seed: 18
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- 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
|
billjeremy/Reinforce-1-pixel
|
billjeremy
| 2025-06-18T13:21:46Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-06-18T13:21:28Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-1-pixel
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 74.20 +/- 75.47
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
sergioalves/981ee64f-cb9e-4f45-8088-efb64d2a081e
|
sergioalves
| 2025-06-18T13:21:30Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:oopsung/llama2-7b-n-ox-test-v1",
"base_model:adapter:oopsung/llama2-7b-n-ox-test-v1",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-06-18T12:50:29Z |
---
library_name: peft
base_model: oopsung/llama2-7b-n-ox-test-v1
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 981ee64f-cb9e-4f45-8088-efb64d2a081e
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
absolute_data_files: false
adapter: lora
base_model: oopsung/llama2-7b-n-ox-test-v1
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
- a848e50555bf04bd_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/
type:
field_instruction: instruct
field_output: output
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
dpo:
beta: 0.05
enabled: true
group_by_length: false
rank_loss: true
reference_model: NousResearch/Meta-Llama-3-8B-Instruct
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
gradient_clipping: 1.0
group_by_length: false
hub_model_id: sergioalves/981ee64f-cb9e-4f45-8088-efb64d2a081e
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 5.0e-07
load_in_4bit: true
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.1
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_steps: 200
micro_batch_size: 8
mixed_precision: bf16
mlflow_experiment_name: /tmp/a848e50555bf04bd_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 2
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 1024
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 17d762e8-a133-49ac-ac5b-589e35a66179
wandb_project: s56-7
wandb_run: your_name
wandb_runid: 17d762e8-a133-49ac-ac5b-589e35a66179
warmup_steps: 25
weight_decay: 0.05
xformers_attention: true
```
</details><br>
# 981ee64f-cb9e-4f45-8088-efb64d2a081e
This model is a fine-tuned version of [oopsung/llama2-7b-n-ox-test-v1](https://huggingface.co/oopsung/llama2-7b-n-ox-test-v1) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4621
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-07
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 25
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 2.6931 | 0.0004 | 1 | 2.7934 |
| 2.6352 | 0.0407 | 100 | 2.5922 |
| 2.3999 | 0.0815 | 200 | 2.4621 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
gradientrouting-spar/mc9_badmed_representation_constraint_beta_kl-100.0_seed_1
|
gradientrouting-spar
| 2025-06-18T13:20:14Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-18T13:19:30Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **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]
|
MasterControlAIML/DeepSeek-R1-Qwen2.5-3b-LLM-Judge-Reward-JSON-Unstructured-To-Structured-Lora
|
MasterControlAIML
| 2025-06-18T13:16:23Z | 14 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"grpo",
"conversational",
"en",
"base_model:unsloth/Qwen2.5-3B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-3B-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-04-26T21:14:59Z |
---
# 🦄 Model Card
base_model: unsloth/Qwen2.5-3B-Instruct
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
- grpo # Gradient Reward Policy Optimization
license: apache-2.0
language:
- en
---
# 📦 Uploaded Model
| **Field** | **Value** |
|-----------------------|--------------------------------------------|
| **Developed by** | **MasterControlAIML** |
| **License** | Apache 2.0 |
| **Finetuned from** | `unsloth/Qwen2.5-3B-Instruct` |
| **Training Framework**| [Unsloth](https://github.com/unslothai/unsloth) × Hugging Face TRL |
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="190"/>](https://github.com/unslothai/unsloth)
---
## 🚀 What’s New?
> *The protein-shake sequel to **MasterControlAIML/DeepSeek-R1-Qwen2.5-1.5b-SFT-R1-JSON-Unstructured-To-Structured**—now with more neurons, zero SFT, and a league of reward functions.*
| Upgrade | Explanation |
|--------------------|------------------------------------------------------------------------------|
| **Bigger Backbone**| 1.5 B → **3 B** Qwen 2.5 for bigger reasoning muscles. |
| **Pure RL** | No supervised fine-tuning—policy learned *only* from reward signals (GRPO). |
| **LM-as-Judge** | Separate LLM rates each candidate for correctness, JSON validity, style… |
| **2× Faster Train**| Unsloth’s flash-attention & fused ops = less VRAM, more speed. |
---
## 🛠️ Intended Use
* Convert messy prose, logs, or audit notes into a pristine JSON document that follows a complex, nested schema.
* Drop-in replacement for any pipeline using the older DeepSeek-R1 1.5 B structurer—just swap the checkpoint and enjoy the headroom.
---
## 🔧 How to Use (Reasoning + JSON)
The snippet below:
1. **Primes** the model with the *exact* Pydantic schema, so it outputs the right keys.
2. Makes the model **think step-by-step** (reasoning) but still wraps the final JSON in an easy-to-parse container.
3. Uses the correct repo name: `MasterControlAIML/DeepSeek-R1-Qwen2.5-3b-LLM-Judge-Reward-JSON-Unstructured-To-Structured-Lora`.
```python
# ─────────────────────────────────────────────────────────────────────────────
# QUICK-START
# Structured-data extraction with reasoning + JSON output
# ─────────────────────────────────────────────────────────────────────────────
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
import torch, json, textwrap, inspect
from pydantic import BaseModel
from typing import List, Optional
MODEL = "MasterControlAIML/DeepSeek-R1-Qwen2.5-3b-LLM-Judge-Reward-JSON-Unstructured-To-Structured-Lora"
# 1️⃣ Inline schema (keeps the LLM on-rails) ─────────────────────────────────
class MultipleChoice(BaseModel):
question: str
options: List[str]
selected: str
class FormField(BaseModel):
fieldName: str
value: str
notes: Optional[str] = ""
class Calculation(BaseModel):
formula: str
result: str
notes: Optional[str] = ""
class Metadata(BaseModel):
reportDate: str
auditorId: Optional[str] = None
comments: Optional[str] = None
class Content(BaseModel):
paragraphs: List[str]
tables: List["Table"] # assume Table defined elsewhere
checkboxes: List["Checkbox"] # 〃
multipleChoice: List[MultipleChoice]
formFields: List[FormField]
calculations: List[Calculation]
metadata: Optional[Metadata] = Metadata(reportDate="")
class Section(BaseModel):
id: str
title: str
content: Content
class Document(BaseModel):
documentTitle: str
documentDate: str
sections: List[Section]
SCHEMA_TEXT = inspect.getsource(Document)
# 2️⃣ Build prompts ──────────────────────────────────────────────────────────
SYSTEM_PROMPT = textwrap.dedent(f"""
You are an expert **data-extraction assistant**.
Extract structured info from unstructured text **exactly** following the Pydantic schema.
── Schema ──
{SCHEMA_TEXT}
─────────────
Rules:
1. Follow the schema for keys & nesting.
2. Copy values verbatim when possible.
3. If a field is missing, return null.
4. Output your step-by-step reasoning first.
5. Then return ONLY the JSON inside this wrapper:
final answer[ json object: {{ ... }} ]
Format:
<reasoning>…</reasoning>
<answer>
final answer[ json object: {{ … }} ]
</answer>
""").strip()
UNSTRUCTURED_TEXT = """
12 April 2025 – Onsite audit performed by Jane Smith.
Observations: Two fire extinguishers past expiry; emergency lights functional.
Calculations: Total extinguishers = 8, expired = 2 → 25 % overdue.
"""
USER_PROMPT = textwrap.dedent(f"""
### Task
Convert the following *hier* text to the schema.
### hier
{UNSTRUCTURED_TEXT}
""").strip()
# 3️⃣ Generate ───────────────────────────────────────────────────────────────
tok = AutoTokenizer.from_pretrained(MODEL, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
MODEL,
device_map="auto",
torch_dtype=torch.bfloat16
)
gen = pipeline("text-generation", model=model, tokenizer=tok,
max_new_tokens=512, do_sample=False)
prompt = f"<|system|>\n{SYSTEM_PROMPT}\n<|user|>\n{USER_PROMPT}"
raw_out = gen(prompt)[0]["generated_text"]
# 4️⃣ Slice out the JSON ─────────────────────────────────────────────────────
start = raw_out.find("final answer[")
end = raw_out.rfind("]") + 1
json_text = raw_out[start:].split("json object:")[-1].strip(" []\n")
data = json.loads(json_text) # ✅ Raises if malformed
print(raw_out) # reasoning + JSON
print("\n✅ Parsed object:\n", data)
````
### Why it Works 🧐
* **Schema-priming** ensures key-level fidelity—no “creative” field names.
* **Chain-of-thought** improves factual extraction (was rewarded during GRPO).
* The `final answer[…]` wrapper makes downstream parsing a one-liner.
---
## 🏋️ Training Recipe (Condensed)
| Setting | Value |
| -------------- | ------------------------------------------------------------------- |
| **Algorithm** | GRPO – policy ≈ LM, reward LM ≈ `Qwen2.5-7B` w/ JSON-validator head |
| **Epochs** | 3 (effective) |
| **Batch** | Grad-accum 8, bfloat16 |
| **Optimizer** | Fused AdamW |
| **Throughput** | ≈ 45 k tokens/s on 8×A100 |
---
## 📊 Evaluation (WIP)
| Metric | Status |
| ------------------------- | ------ |
| Exact-Match JSON Accuracy | 🔜 |
| Structural F1 | 🔜 |
| Valid-JSON Rate | 🔜 |
Stay tuned—numbers landing faster than you can say “schema validation.” 🛰️
---
## 🤝 Citation
```bibtex
@misc{bhaviktheslider_2025_unsloth_qwen2.5_3b_grpo,
title = {An Unsloth-accelerated GRPO-trained Qwen 2.5-3B for JSON structuring},
author = {MasterControlAIML},
year = {2025},
howpublished = {\url{https://huggingface.co/MasterControlAIML/DeepSeek-R1-Qwen2.5-3b-LLM-Judge-Reward-JSON-Unstructured-To-Structured-Lora}}
}
```
*May your JSON always parse and your losses always converge!* 😎
```
|
MasterControlAIML/DeepSeek-R1-Qwen2.5-3b-LLM-Judge-Reward-JSON-Unstructured-To-Structured-Lora-gguf
|
MasterControlAIML
| 2025-06-18T13:15:51Z | 39 | 0 |
transformers
|
[
"transformers",
"gguf",
"qwen2",
"text-generation-inference",
"unsloth",
"trl",
"grpo",
"en",
"base_model:unsloth/Qwen2.5-3B-Instruct",
"base_model:quantized:unsloth/Qwen2.5-3B-Instruct",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-04-26T21:10:25Z |
---
# 🦄 Model Card
base_model: unsloth/Qwen2.5-3B-Instruct
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
- grpo # Gradient Reward Policy Optimization
license: apache-2.0
language:
- en
---
# 📦 Uploaded Model
| **Field** | **Value** |
|-----------------------|--------------------------------------------|
| **Developed by** | **MasterControlAIML** |
| **License** | Apache 2.0 |
| **Finetuned from** | `unsloth/Qwen2.5-3B-Instruct` |
| **Training Framework**| [Unsloth](https://github.com/unslothai/unsloth) × Hugging Face TRL |
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="190"/>](https://github.com/unslothai/unsloth)
---
## 🚀 What’s New?
> *The protein-shake sequel to **MasterControlAIML/DeepSeek-R1-Qwen2.5-1.5b-SFT-R1-JSON-Unstructured-To-Structured**—now with more neurons, zero SFT, and a league of reward functions.*
| Upgrade | Explanation |
|--------------------|------------------------------------------------------------------------------|
| **Bigger Backbone**| 1.5 B → **3 B** Qwen 2.5 for bigger reasoning muscles. |
| **Pure RL** | No supervised fine-tuning—policy learned *only* from reward signals (GRPO). |
| **LM-as-Judge** | Separate LLM rates each candidate for correctness, JSON validity, style… |
| **2× Faster Train**| Unsloth’s flash-attention & fused ops = less VRAM, more speed. |
---
## 🛠️ Intended Use
* Convert messy prose, logs, or audit notes into a pristine JSON document that follows a complex, nested schema.
* Drop-in replacement for any pipeline using the older DeepSeek-R1 1.5 B structurer—just swap the checkpoint and enjoy the headroom.
---
## 🔧 How to Use (Reasoning + JSON)
The snippet below:
1. **Primes** the model with the *exact* Pydantic schema, so it outputs the right keys.
2. Makes the model **think step-by-step** (reasoning) but still wraps the final JSON in an easy-to-parse container.
3. Uses the correct repo name: `MasterControlAIML/DeepSeek-R1-Qwen2.5-3b-LLM-Judge-Reward-JSON-Unstructured-To-Structured-Lora`.
```python
# ─────────────────────────────────────────────────────────────────────────────
# QUICK-START
# Structured-data extraction with reasoning + JSON output
# ─────────────────────────────────────────────────────────────────────────────
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
import torch, json, textwrap, inspect
from pydantic import BaseModel
from typing import List, Optional
MODEL = "MasterControlAIML/DeepSeek-R1-Qwen2.5-3b-LLM-Judge-Reward-JSON-Unstructured-To-Structured-Lora"
# 1️⃣ Inline schema (keeps the LLM on-rails) ─────────────────────────────────
class MultipleChoice(BaseModel):
question: str
options: List[str]
selected: str
class FormField(BaseModel):
fieldName: str
value: str
notes: Optional[str] = ""
class Calculation(BaseModel):
formula: str
result: str
notes: Optional[str] = ""
class Metadata(BaseModel):
reportDate: str
auditorId: Optional[str] = None
comments: Optional[str] = None
class Content(BaseModel):
paragraphs: List[str]
tables: List["Table"] # assume Table defined elsewhere
checkboxes: List["Checkbox"] # 〃
multipleChoice: List[MultipleChoice]
formFields: List[FormField]
calculations: List[Calculation]
metadata: Optional[Metadata] = Metadata(reportDate="")
class Section(BaseModel):
id: str
title: str
content: Content
class Document(BaseModel):
documentTitle: str
documentDate: str
sections: List[Section]
SCHEMA_TEXT = inspect.getsource(Document)
# 2️⃣ Build prompts ──────────────────────────────────────────────────────────
SYSTEM_PROMPT = textwrap.dedent(f"""
You are an expert **data-extraction assistant**.
Extract structured info from unstructured text **exactly** following the Pydantic schema.
── Schema ──
{SCHEMA_TEXT}
─────────────
Rules:
1. Follow the schema for keys & nesting.
2. Copy values verbatim when possible.
3. If a field is missing, return null.
4. Output your step-by-step reasoning first.
5. Then return ONLY the JSON inside this wrapper:
final answer[ json object: {{ ... }} ]
Format:
<reasoning>…</reasoning>
<answer>
final answer[ json object: {{ … }} ]
</answer>
""").strip()
UNSTRUCTURED_TEXT = """
12 April 2025 – Onsite audit performed by Jane Smith.
Observations: Two fire extinguishers past expiry; emergency lights functional.
Calculations: Total extinguishers = 8, expired = 2 → 25 % overdue.
"""
USER_PROMPT = textwrap.dedent(f"""
### Task
Convert the following *hier* text to the schema.
### hier
{UNSTRUCTURED_TEXT}
""").strip()
# 3️⃣ Generate ───────────────────────────────────────────────────────────────
tok = AutoTokenizer.from_pretrained(MODEL, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
MODEL,
device_map="auto",
torch_dtype=torch.bfloat16
)
gen = pipeline("text-generation", model=model, tokenizer=tok,
max_new_tokens=512, do_sample=False)
prompt = f"<|system|>\n{SYSTEM_PROMPT}\n<|user|>\n{USER_PROMPT}"
raw_out = gen(prompt)[0]["generated_text"]
# 4️⃣ Slice out the JSON ─────────────────────────────────────────────────────
start = raw_out.find("final answer[")
end = raw_out.rfind("]") + 1
json_text = raw_out[start:].split("json object:")[-1].strip(" []\n")
data = json.loads(json_text) # ✅ Raises if malformed
print(raw_out) # reasoning + JSON
print("\n✅ Parsed object:\n", data)
````
### Why it Works 🧐
* **Schema-priming** ensures key-level fidelity—no “creative” field names.
* **Chain-of-thought** improves factual extraction (was rewarded during GRPO).
* The `final answer[…]` wrapper makes downstream parsing a one-liner.
---
## 🏋️ Training Recipe (Condensed)
| Setting | Value |
| -------------- | ------------------------------------------------------------------- |
| **Algorithm** | GRPO – policy ≈ LM, reward LM ≈ `Qwen2.5-7B` w/ JSON-validator head |
| **Epochs** | 3 (effective) |
| **Batch** | Grad-accum 8, bfloat16 |
| **Optimizer** | Fused AdamW |
| **Throughput** | ≈ 45 k tokens/s on 8×A100 |
---
## 📊 Evaluation (WIP)
| Metric | Status |
| ------------------------- | ------ |
| Exact-Match JSON Accuracy | 🔜 |
| Structural F1 | 🔜 |
| Valid-JSON Rate | 🔜 |
Stay tuned—numbers landing faster than you can say “schema validation.” 🛰️
---
## 🤝 Citation
```bibtex
@misc{bhaviktheslider_2025_unsloth_qwen2.5_3b_grpo,
title = {An Unsloth-accelerated GRPO-trained Qwen 2.5-3B for JSON structuring},
author = {MasterControlAIML},
year = {2025},
howpublished = {\url{https://huggingface.co/MasterControlAIML/DeepSeek-R1-Qwen2.5-3b-LLM-Judge-Reward-JSON-Unstructured-To-Structured-Lora}}
}
```
*May your JSON always parse and your losses always converge!* 😎
```
|
epfl-dlab/zip2zip-Llama-3.2-1B-Instruct-v0.1
|
epfl-dlab
| 2025-06-18T13:14:51Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"zip2zip",
"arxiv:1910.09700",
"arxiv:2506.01084",
"base_model:meta-llama/Llama-3.2-1B-Instruct",
"base_model:finetune:meta-llama/Llama-3.2-1B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-06-18T13:14:28Z |
---
library_name: transformers
tags:
- zip2zip
base_model: meta-llama/Llama-3.2-1B-Instruct
---
# 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]
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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.
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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]# Zip2Zip
This model is a [Zip2Zip](arxiv.org/abs/2506.01084) model.
|
ai4bharat/Cadence
|
ai4bharat
| 2025-06-18T13:12:49Z | 593 | 6 |
cadence-punctuation
|
[
"cadence-punctuation",
"safetensors",
"cadence_punctuation",
"punctuation-restoration",
"multilingual",
"indic-languages",
"ai4bharat",
"token-classification",
"custom_code",
"en",
"as",
"bn",
"brx",
"doi",
"gu",
"hi",
"kn",
"ks",
"kok",
"mai",
"ml",
"mni",
"mr",
"ne",
"or",
"pa",
"sa",
"sat",
"sd",
"ta",
"te",
"ur",
"dataset:ai4bharat/sangraha",
"dataset:HuggingFaceFW/fineweb-2",
"dataset:ai4bharat/indicvoices_r",
"dataset:ai4bharat/IndicCorpV2",
"arxiv:2506.03793",
"base_model:google/gemma-3-1b-pt",
"base_model:finetune:google/gemma-3-1b-pt",
"license:mit",
"region:us"
] |
token-classification
| 2025-05-31T16:27:45Z |
---
language:
- en
- as
- bn
- brx
- doi
- gu
- hi
- kn
- ks
- kok
- mai
- ml
- mni
- mr
- ne
- or
- pa
- sa
- sat
- sd
- ta
- te
- ur
license: mit
tags:
- punctuation-restoration
- multilingual
- indic-languages
- ai4bharat
datasets:
- ai4bharat/sangraha
- HuggingFaceFW/fineweb-2
- ai4bharat/indicvoices_r
- ai4bharat/IndicCorpV2
metrics:
- f1
pipeline_tag: token-classification
library_name: cadence-punctuation
base_model:
- google/gemma-3-1b-pt
widget:
- text: hello world how are you today
example_title: English Punctuation
- text: यह एक हिंदी वाक्य है
example_title: Hindi Punctuation
- text: cadence is a great model for punctuation
example_title: Another English Example
---
# Cadence
A multilingual punctuation restoration model based on Gemma-3-1b.
<a href="https://arxiv.org/abs/2506.03793v1" target="_blank" rel="noopener noreferrer" style="text-decoration: none; color: inherit;">
<span style="display: inline-flex; align-items: center; gap: 0.3em;">
<img src="https://huggingface.co/ai4bharat/Cadence/resolve/main/arxiv_logo.svg" alt="arXiv" style="height: 1em;">
<span>Mark My Words: A Robust Multilingual Model for Punctuation in Text and Speech Transcripts</span>
</span>
</a>
## Features
- **Multilingual Support**: English + 22 Indic languages
- **Script-Aware**: Handles multiple scripts with appropriate punctuation rules
- **Unimodel**: A single model for punctuations (doesn't require language identifier)
- **Encoder**: Bi-directional encoder (blazing fast)
- **Efficient Processing**: Supports batch processing and sliding window for long texts
- **AutoModel Compatible**: Easy integration with Hugging Face ecosystem
## Installation (Optional)
Python package has features such as sliding-window decoding, (rule-based) capitalisation of English text and some (rule-based) corrections for the errors made by the model.
```bash
pip install cadence-punctuation
```
## Quick Start
### Using the python package (Recommended)
```python
# pip install cadence-punctuation
from cadence import PunctuationModel
# Load model (local path)
model = PunctuationModel("path/to/download/weights")
# Punctuate single text
text = "hello world how are you today"
result = model.punctuate([text])
print(result[0]) # "Hello world, how are you today?"
# Punctuate multiple texts
texts = [
"hello world how are you",
"this is another test sentence",
"यह एक हिंदी वाक्य है" # Hindi example
]
results = model.punctuate(texts, batch_size=8)
for original, punctuated in zip(texts, results):
print(f"Original: {original}")
print(f"Punctuated: {punctuated}")
print()
```
### Using AutoModel
```python
from transformers import AutoTokenizer, AutoModel
import torch
# Load model and tokenizer
model_name = "ai4bharat/Cadence"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModel.from_pretrained(model_name, trust_remote_code=True)
id2label = model.config.id2label
text = "यह एक वाक्य है इसका क्या मतलब है"
# text = "this is a test sentence what do you think"
# Tokenize input and prepare for model
inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
input_ids = inputs['input_ids'][0] # Get input_ids for the first (and only) sentence
with torch.no_grad():
outputs = model(**inputs)
predictions_for_sentence = torch.argmax(outputs.logits, dim=-1)[0]
result_tokens_and_punctuation = []
all_token_strings = tokenizer.convert_ids_to_tokens(input_ids.tolist()) # Get all token strings
for i, token_id_value in enumerate(input_ids.tolist()):
# Process only non-padding tokens based on the attention mask
if inputs['attention_mask'][0][i] == 0:
continue
current_token_string = all_token_strings[i]
is_special_token = token_id_value in tokenizer.all_special_ids
if not is_special_token:
result_tokens_and_punctuation.append(current_token_string)
predicted_punctuation_id = predictions_for_sentence[i].item()
punctuation_character = id2label[predicted_punctuation_id]
if punctuation_character != "O" and not is_special_token:
result_tokens_and_punctuation.append(punctuation_character)
punctuated_text = tokenizer.convert_tokens_to_string(result_tokens_and_punctuation)
print(f"Original Text: {text}")
print(f"Punctuated Text: {punctuated_text}")
```
## Officially Supported Languages
- English, Assamese, Bengali, Bodo, Dogri, Gujarati, Hindi, Kannada, Kashmiri, Konkani, Maithili, Malayalam, Manipuri, Marathi, Nepali, Odia, Punjabi, Sanskrit, Santali, Sindhi, Tamil, Telugu, Urdu
Tokenizer doesn't support Manipuri's Meitei script. The model can punctuate if the text is transliterated to Bengali's script.
One can try using this model for languages not listed above. Performance may vary.
## Supported Punctuation
The model can predict the following punctuation marks:
- Period (.)
- Comma (,)
- Question mark (?)
- Exclamation mark (!)
- Semicolon (;)
- Colon (:)
- Hyphen (-)
- Quotes (" and ')
- Ellipse (...)
- Parentheses ()
- Hindi Danda (।)
- Urdu punctuation (۔، ؟)
- Arabic punctuation (٬ ،)
- Santali punctuation (᱾ ᱾।)
- Sanskrit punctuation (॥)
- And various combinations
## Configuration Options for cadence-puncuation
### PunctuationModel Parameters
All the parameters are optional to pass.
- `model_path`: Path to a local directory where model weights will be downloaded to and cached, or from which pre-downloaded weights will be loaded. If None, weights downloaded to default HuggingFace cache location.
- `gpu_id`: Specific GPU device ID to use (e.g., 0, 1). If None, the model will attempt to auto-detect and use an available GPU. This parameter is ignored if cpu is True. (default: None)
- `cpu`: If True, forces the model to run on the CPU, even if a GPU is available. (default: False)
- `max_length`: Maximum sequence length the model can process at once. If sliding_window is True, this value is used as the width of each sliding window. If sliding_window is False, texts longer than max_length will be truncated. (default: 300)
- `attn_implementation`: The attention implementation to use. (default: "eager")
- `sliding_window`: If True, enables sliding window mechanism to process texts longer than max_length. The text is split into overlapping chunks of max_length. If False, texts longer than max_length are truncated. (default: True)
- `verbose`: Enable verbose logging (default: False)
- `d_type`: Precision with which weights are loaded (default: bfloat16)
- `batch_size`: ((for punctuate() method)): Batch size to use (default: 8)
```python
# Custom configuration
model = PunctuationModel(
model_path="path/to/download/weights",
gpu_id=0, # Use specific GPU
max_length=512, # length for trunation; also used as window size when sliding_window=True
attn_implementation="flash_attention_2",
sliding_window=True, # Handle long texts
verbose=False, # Quiet mode
d_type="bfloat16"
)
batch_size=32
# Process long texts with sliding window
long_text = "Your very long text here..." * 100
short_text = "a short text"
result = model.punctuate([long_text, short_text],batch_size=batch_size)
```
## License
MIT License
|
morturr/Llama-2-7b-hf-LOO_headlines-COMB_dadjokes-comb2-seed42-2025-06-18
|
morturr
| 2025-06-18T13:11:36Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:meta-llama/Llama-2-7b-hf",
"base_model:adapter:meta-llama/Llama-2-7b-hf",
"license:llama2",
"region:us"
] | null | 2025-06-18T13:11:28Z |
---
library_name: peft
license: llama2
base_model: meta-llama/Llama-2-7b-hf
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: Llama-2-7b-hf-LOO_headlines-COMB_dadjokes-comb2-seed42-2025-06-18
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Llama-2-7b-hf-LOO_headlines-COMB_dadjokes-comb2-seed42-2025-06-18
This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) 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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- 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
|
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