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ellietang/saved_lora_ls-model-14B-full-CPT-v0.0.9-4bits-trained-0529-e1 | ellietang | 2025-05-30T05:29:35Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen2",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-05-30T05:28:45Z | ---
base_model: unsloth/qwen2.5-coder-14b-instruct-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** ellietang
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen2.5-coder-14b-instruct-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)
|
Ash2749/Lama2-7b-chat-appraisal | Ash2749 | 2025-05-30T05:29:10Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"conversational",
"en",
"base_model:unsloth/llama-2-7b-chat-bnb-4bit",
"base_model:quantized:unsloth/llama-2-7b-chat-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] | text-generation | 2025-05-30T05:26:10Z | ---
base_model: unsloth/llama-2-7b-chat-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** Ash2749
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-2-7b-chat-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)
|
CS291A/qwen-0.5b__433-enriched_claude3-7__file_line_level__finetuned | CS291A | 2025-05-30T05:28:02Z | 0 | 0 | null | [
"safetensors",
"qwen2",
"code",
"code-generation",
"instruction-tuning",
"en",
"base_model:secmlr/SWE-BENCH-433-enriched-set-claude-3in1-localization-with-reasoning_qwen_code_0.5b_433_enriched",
"base_model:finetune:secmlr/SWE-BENCH-433-enriched-set-claude-3in1-localization-with-reasoning_qwen_code_0.5b_433_enriched",
"license:other",
"region:us"
] | null | 2025-05-30T05:27:14Z | ---
language: en
license: other
base_model: secmlr/SWE-BENCH-433-enriched-set-claude-3in1-localization-with-reasoning_qwen_code_0.5b_433_enriched
tags:
- code
- code-generation
- instruction-tuning
---
# Model Description
This model is fine-tuned from secmlr/SWE-BENCH-433-enriched-set-claude-3in1-localization-with-reasoning_qwen_code_0.5b_433_enriched for code generation tasks.
|
WenFengg/ari_2 | WenFengg | 2025-05-30T05:22:25Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-30T04:48:24Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **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] |
TrumpElon/task-10-microsoft-Phi-3.5-mini-instruct | TrumpElon | 2025-05-30T05:21:32Z | 0 | 0 | peft | [
"peft",
"safetensors",
"base_model:microsoft/Phi-3.5-mini-instruct",
"base_model:adapter:microsoft/Phi-3.5-mini-instruct",
"license:other",
"region:us"
] | null | 2025-05-29T13:25:23Z | ---
library_name: peft
license: other
base_model: microsoft/Phi-3.5-mini-instruct
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# lora
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
### Training results
### Framework versions
- PEFT 0.12.0
- Transformers 4.48.3
- Pytorch 2.6.0+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0
|
mradermacher/Medra27B-i1-GGUF | mradermacher | 2025-05-30T05:18:23Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"text-generation",
"medical-ai",
"summarization",
"diagnostic-reasoning",
"gemma-3",
"fine-tuned",
"en",
"ro",
"dataset:nicoboss/medra-medical",
"base_model:nicoboss/Medra27B",
"base_model:quantized:nicoboss/Medra27B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | summarization | 2025-05-30T03:30:12Z | ---
base_model: nicoboss/Medra27B
datasets:
- nicoboss/medra-medical
language:
- en
- ro
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- text-generation
- medical-ai
- summarization
- diagnostic-reasoning
- gemma-3
- fine-tuned
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/nicoboss/Medra27B
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/Medra27B-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Medra27B-i1-GGUF/resolve/main/Medra27B.i1-IQ1_S.gguf) | i1-IQ1_S | 6.4 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Medra27B-i1-GGUF/resolve/main/Medra27B.i1-IQ1_M.gguf) | i1-IQ1_M | 6.9 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Medra27B-i1-GGUF/resolve/main/Medra27B.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 7.8 | |
| [GGUF](https://huggingface.co/mradermacher/Medra27B-i1-GGUF/resolve/main/Medra27B.i1-IQ2_XS.gguf) | i1-IQ2_XS | 8.5 | |
| [GGUF](https://huggingface.co/mradermacher/Medra27B-i1-GGUF/resolve/main/Medra27B.i1-IQ2_S.gguf) | i1-IQ2_S | 8.9 | |
| [GGUF](https://huggingface.co/mradermacher/Medra27B-i1-GGUF/resolve/main/Medra27B.i1-IQ2_M.gguf) | i1-IQ2_M | 9.6 | |
| [GGUF](https://huggingface.co/mradermacher/Medra27B-i1-GGUF/resolve/main/Medra27B.i1-Q2_K_S.gguf) | i1-Q2_K_S | 9.9 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/Medra27B-i1-GGUF/resolve/main/Medra27B.i1-Q2_K.gguf) | i1-Q2_K | 10.6 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Medra27B-i1-GGUF/resolve/main/Medra27B.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 10.8 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Medra27B-i1-GGUF/resolve/main/Medra27B.i1-IQ3_XS.gguf) | i1-IQ3_XS | 11.7 | |
| [GGUF](https://huggingface.co/mradermacher/Medra27B-i1-GGUF/resolve/main/Medra27B.i1-IQ3_S.gguf) | i1-IQ3_S | 12.3 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Medra27B-i1-GGUF/resolve/main/Medra27B.i1-Q3_K_S.gguf) | i1-Q3_K_S | 12.3 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Medra27B-i1-GGUF/resolve/main/Medra27B.i1-IQ3_M.gguf) | i1-IQ3_M | 12.6 | |
| [GGUF](https://huggingface.co/mradermacher/Medra27B-i1-GGUF/resolve/main/Medra27B.i1-Q3_K_M.gguf) | i1-Q3_K_M | 13.5 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Medra27B-i1-GGUF/resolve/main/Medra27B.i1-Q3_K_L.gguf) | i1-Q3_K_L | 14.6 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Medra27B-i1-GGUF/resolve/main/Medra27B.i1-IQ4_XS.gguf) | i1-IQ4_XS | 14.9 | |
| [GGUF](https://huggingface.co/mradermacher/Medra27B-i1-GGUF/resolve/main/Medra27B.i1-Q4_0.gguf) | i1-Q4_0 | 15.7 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Medra27B-i1-GGUF/resolve/main/Medra27B.i1-Q4_K_S.gguf) | i1-Q4_K_S | 15.8 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Medra27B-i1-GGUF/resolve/main/Medra27B.i1-Q4_K_M.gguf) | i1-Q4_K_M | 16.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Medra27B-i1-GGUF/resolve/main/Medra27B.i1-Q4_1.gguf) | i1-Q4_1 | 17.3 | |
| [GGUF](https://huggingface.co/mradermacher/Medra27B-i1-GGUF/resolve/main/Medra27B.i1-Q5_K_S.gguf) | i1-Q5_K_S | 18.9 | |
| [GGUF](https://huggingface.co/mradermacher/Medra27B-i1-GGUF/resolve/main/Medra27B.i1-Q5_K_M.gguf) | i1-Q5_K_M | 19.4 | |
| [GGUF](https://huggingface.co/mradermacher/Medra27B-i1-GGUF/resolve/main/Medra27B.i1-Q6_K.gguf) | i1-Q6_K | 22.3 | practically like static Q6_K |
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. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
huyhuung/Qwen_FFT_v2 | huyhuung | 2025-05-30T05:17:02Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-30T05:16:14Z | ---
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] |
luodian/nanoVLM | luodian | 2025-05-30T05:14:44Z | 0 | 0 | nanovlm | [
"nanovlm",
"safetensors",
"vision-language",
"multimodal",
"research",
"image-text-to-text",
"license:mit",
"region:us"
] | image-text-to-text | 2025-05-30T05:14:35Z |
---
# 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("luodian/nanoVLM")
```
|
AmberYifan/Llama-3.1-8B-sft-SPIN-gpt4o-beta0.6-lr5e-7 | AmberYifan | 2025-05-30T05:13:07Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"trl",
"dpo",
"conversational",
"arxiv:2305.18290",
"base_model:AmberYifan/Llama-3.1-8B-sft-ultrachat-safeRLHF",
"base_model:finetune:AmberYifan/Llama-3.1-8B-sft-ultrachat-safeRLHF",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-30T04:53:09Z | ---
base_model: AmberYifan/Llama-3.1-8B-sft-ultrachat-safeRLHF
library_name: transformers
model_name: Llama-3.1-8B-sft-SPIN-gpt4o-beta0.6-lr5e-7
tags:
- generated_from_trainer
- trl
- dpo
licence: license
---
# Model Card for Llama-3.1-8B-sft-SPIN-gpt4o-beta0.6-lr5e-7
This model is a fine-tuned version of [AmberYifan/Llama-3.1-8B-sft-ultrachat-safeRLHF](https://huggingface.co/AmberYifan/Llama-3.1-8B-sft-ultrachat-safeRLHF).
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="AmberYifan/Llama-3.1-8B-sft-SPIN-gpt4o-beta0.6-lr5e-7", 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/yifanwang/huggingface/runs/vyec931i)
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: 0.12.2
- Transformers: 4.46.3
- Pytorch: 2.7.0
- Datasets: 3.6.0
- Tokenizers: 0.20.3
## 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}}
}
``` |
mihikabairathi/llama-indian-court-headnotes | mihikabairathi | 2025-05-30T05:13:00Z | 0 | 0 | peft | [
"peft",
"safetensors",
"legal",
"summarization",
"en",
"base_model:meta-llama/Llama-3.1-8B-Instruct",
"base_model:adapter:meta-llama/Llama-3.1-8B-Instruct",
"license:mit",
"region:us"
] | summarization | 2025-05-30T04:47:10Z | ---
base_model: meta-llama/Llama-3.1-8B-Instruct
library_name: peft
license: mit
language:
- en
metrics:
- rouge
- f1
pipeline_tag: summarization
tags:
- legal
---
# Model Card for Model ID
This model has been fine-tuned using qLoRA to generate high-quality headnotes for Indian court case judgements.
## Model Details
### Model Description
Given an Indian court case judgement that could be tens of pages long, this model will output a high-quality headnote, or a legal summary about 300 to 800 words long.
### Model Sources
**Repository:** [Indian Court Summaries Agent](https://github.com/mihikabairathi/indian-court-summaries-agent)
## Uses
Meant to be used as per instructions in the repository linked.
## Bias, Risks, and Limitations
Possible improvements include handling large contexts better, intermediate summarization, and more.
## Training Details
Linked/Cited in repository above.
## Evaluation
### Testing Data, Factors & Metrics
RougeLSum: measure summarization quality when compared to the expected headnote.
BERT f1: valuable metric for text-generation tasks.
### Results
RougeLSum: 0.495
BERT f1: 0.86
## Contact
[email protected]
### Framework versions
- PEFT 0.15.2 |
gglabs/Qwen3_lora_tuning | gglabs | 2025-05-30T05:09:49Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen3-14B",
"base_model:adapter:Qwen/Qwen3-14B",
"region:us"
] | null | 2025-05-30T05:06:40Z | ---
base_model: Qwen/Qwen3-14B
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 |
New-tutorial-Koko-Viral-Video/Original.Full.Clip.Koko.Viral.Video.Leaks.Official | New-tutorial-Koko-Viral-Video | 2025-05-30T05:08:32Z | 0 | 0 | null | [
"region:us"
] | null | 2025-05-30T05:08:07Z | <animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
|
doodoo77/qwen2-7b-instruct-description | doodoo77 | 2025-05-30T05:06:12Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:Qwen/Qwen2-VL-7B-Instruct",
"base_model:finetune:Qwen/Qwen2-VL-7B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-30T05:05:30Z | ---
base_model: Qwen/Qwen2-VL-7B-Instruct
library_name: transformers
model_name: qwen2-7b-instruct-description
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for qwen2-7b-instruct-description
This model is a fine-tuned version of [Qwen/Qwen2-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2-VL-7B-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="doodoo77/qwen2-7b-instruct-description", 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: 0.19.0
- Transformers: 4.53.0.dev0
- Pytorch: 2.4.1+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}}
}
``` |
Mungert/medgemma-4b-it-GGUF | Mungert | 2025-05-30T05:05:33Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"medical",
"radiology",
"clinical-reasoning",
"dermatology",
"pathology",
"ophthalmology",
"chest-x-ray",
"image-text-to-text",
"arxiv:2303.15343",
"arxiv:2405.03162",
"arxiv:2106.14463",
"arxiv:2412.03555",
"arxiv:2501.19393",
"arxiv:2009.13081",
"arxiv:2102.09542",
"arxiv:2411.15640",
"arxiv:2404.05590",
"arxiv:2501.18362",
"base_model:google/medgemma-4b-pt",
"base_model:quantized:google/medgemma-4b-pt",
"license:other",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | image-text-to-text | 2025-05-30T02:42:01Z | ---
license: other
license_name: health-ai-developer-foundations
license_link: https://developers.google.com/health-ai-developer-foundations/terms
library_name: transformers
pipeline_tag: image-text-to-text
extra_gated_heading: Access MedGemma on Hugging Face
extra_gated_prompt: >-
To access MedGemma on Hugging Face, you're required to review and
agree to [Health AI Developer Foundation's terms of use](https://developers.google.com/health-ai-developer-foundations/terms).
To do this, please ensure you're logged in to Hugging Face and click below.
Requests are processed immediately.
extra_gated_button_content: Acknowledge license
base_model: google/medgemma-4b-pt
tags:
- medical
- radiology
- clinical-reasoning
- dermatology
- pathology
- ophthalmology
- chest-x-ray
---
# <span style="color: #7FFF7F;">medgemma-4b-it GGUF Models</span>
## <span style="color: #7F7FFF;">Model Generation Details</span>
This model was generated using [llama.cpp](https://github.com/ggerganov/llama.cpp) at commit [`f5cd27b7`](https://github.com/ggerganov/llama.cpp/commit/f5cd27b71da3ac375a04a41643d14fc779a8057b).
## <span style="color: #7FFF7F;">Ultra-Low-Bit Quantization with IQ-DynamicGate (1-2 bit)</span>
Our latest quantization method introduces **precision-adaptive quantization** for ultra-low-bit models (1-2 bit), with benchmark-proven improvements on **Llama-3-8B**. This approach uses layer-specific strategies to preserve accuracy while maintaining extreme memory efficiency.
### **Benchmark Context**
All tests conducted on **Llama-3-8B-Instruct** using:
- Standard perplexity evaluation pipeline
- 2048-token context window
- Same prompt set across all quantizations
### **Method**
- **Dynamic Precision Allocation**:
- First/Last 25% of layers → IQ4_XS (selected layers)
- Middle 50% → IQ2_XXS/IQ3_S (increase efficiency)
- **Critical Component Protection**:
- Embeddings/output layers use Q5_K
- Reduces error propagation by 38% vs standard 1-2bit
### **Quantization Performance Comparison (Llama-3-8B)**
| Quantization | Standard PPL | DynamicGate PPL | Δ PPL | Std Size | DG Size | Δ Size | Std Speed | DG Speed |
|--------------|--------------|------------------|---------|----------|---------|--------|-----------|----------|
| IQ2_XXS | 11.30 | 9.84 | -12.9% | 2.5G | 2.6G | +0.1G | 234s | 246s |
| IQ2_XS | 11.72 | 11.63 | -0.8% | 2.7G | 2.8G | +0.1G | 242s | 246s |
| IQ2_S | 14.31 | 9.02 | -36.9% | 2.7G | 2.9G | +0.2G | 238s | 244s |
| IQ1_M | 27.46 | 15.41 | -43.9% | 2.2G | 2.5G | +0.3G | 206s | 212s |
| IQ1_S | 53.07 | 32.00 | -39.7% | 2.1G | 2.4G | +0.3G | 184s | 209s |
**Key**:
- PPL = Perplexity (lower is better)
- Δ PPL = Percentage change from standard to DynamicGate
- Speed = Inference time (CPU avx2, 2048 token context)
- Size differences reflect mixed quantization overhead
**Key Improvements:**
- 🔥 **IQ1_M** shows massive 43.9% perplexity reduction (27.46 → 15.41)
- 🚀 **IQ2_S** cuts perplexity by 36.9% while adding only 0.2GB
- ⚡ **IQ1_S** maintains 39.7% better accuracy despite 1-bit quantization
**Tradeoffs:**
- All variants have modest size increases (0.1-0.3GB)
- Inference speeds remain comparable (<5% difference)
### **When to Use These Models**
📌 **Fitting models into GPU VRAM**
✔ **Memory-constrained deployments**
✔ **Cpu and Edge Devices** where 1-2bit errors can be tolerated
✔ **Research** into ultra-low-bit quantization
## **Choosing the Right Model Format**
Selecting the correct model format depends on your **hardware capabilities** and **memory constraints**.
### **BF16 (Brain Float 16) – Use if BF16 acceleration is available**
- A 16-bit floating-point format designed for **faster computation** while retaining good precision.
- Provides **similar dynamic range** as FP32 but with **lower memory usage**.
- Recommended if your hardware supports **BF16 acceleration** (check your device's specs).
- Ideal for **high-performance inference** with **reduced memory footprint** compared to FP32.
📌 **Use BF16 if:**
✔ Your hardware has native **BF16 support** (e.g., newer GPUs, TPUs).
✔ You want **higher precision** while saving memory.
✔ You plan to **requantize** the model into another format.
📌 **Avoid BF16 if:**
❌ Your hardware does **not** support BF16 (it may fall back to FP32 and run slower).
❌ You need compatibility with older devices that lack BF16 optimization.
---
### **F16 (Float 16) – More widely supported than BF16**
- A 16-bit floating-point **high precision** but with less of range of values than BF16.
- Works on most devices with **FP16 acceleration support** (including many GPUs and some CPUs).
- Slightly lower numerical precision than BF16 but generally sufficient for inference.
📌 **Use F16 if:**
✔ Your hardware supports **FP16** but **not BF16**.
✔ You need a **balance between speed, memory usage, and accuracy**.
✔ You are running on a **GPU** or another device optimized for FP16 computations.
📌 **Avoid F16 if:**
❌ Your device lacks **native FP16 support** (it may run slower than expected).
❌ You have memory limitations.
---
### **Quantized Models (Q4_K, Q6_K, Q8, etc.) – For CPU & Low-VRAM Inference**
Quantization reduces model size and memory usage while maintaining as much accuracy as possible.
- **Lower-bit models (Q4_K)** → **Best for minimal memory usage**, may have lower precision.
- **Higher-bit models (Q6_K, Q8_0)** → **Better accuracy**, requires more memory.
📌 **Use Quantized Models if:**
✔ You are running inference on a **CPU** and need an optimized model.
✔ Your device has **low VRAM** and cannot load full-precision models.
✔ You want to reduce **memory footprint** while keeping reasonable accuracy.
📌 **Avoid Quantized Models if:**
❌ You need **maximum accuracy** (full-precision models are better for this).
❌ Your hardware has enough VRAM for higher-precision formats (BF16/F16).
---
### **Very Low-Bit Quantization (IQ3_XS, IQ3_S, IQ3_M, Q4_K, Q4_0)**
These models are optimized for **extreme memory efficiency**, making them ideal for **low-power devices** or **large-scale deployments** where memory is a critical constraint.
- **IQ3_XS**: Ultra-low-bit quantization (3-bit) with **extreme memory efficiency**.
- **Use case**: Best for **ultra-low-memory devices** where even Q4_K is too large.
- **Trade-off**: Lower accuracy compared to higher-bit quantizations.
- **IQ3_S**: Small block size for **maximum memory efficiency**.
- **Use case**: Best for **low-memory devices** where **IQ3_XS** is too aggressive.
- **IQ3_M**: Medium block size for better accuracy than **IQ3_S**.
- **Use case**: Suitable for **low-memory devices** where **IQ3_S** is too limiting.
- **Q4_K**: 4-bit quantization with **block-wise optimization** for better accuracy.
- **Use case**: Best for **low-memory devices** where **Q6_K** is too large.
- **Q4_0**: Pure 4-bit quantization, optimized for **ARM devices**.
- **Use case**: Best for **ARM-based devices** or **low-memory environments**.
---
### **Summary Table: Model Format Selection**
| Model Format | Precision | Memory Usage | Device Requirements | Best Use Case |
|--------------|------------|---------------|----------------------|---------------|
| **BF16** | Highest | High | BF16-supported GPU/CPUs | High-speed inference with reduced memory |
| **F16** | High | High | FP16-supported devices | GPU inference when BF16 isn't available |
| **Q4_K** | Medium Low | Low | CPU or Low-VRAM devices | Best for memory-constrained environments |
| **Q6_K** | Medium | Moderate | CPU with more memory | Better accuracy while still being quantized |
| **Q8_0** | High | Moderate | CPU or GPU with enough VRAM | Best accuracy among quantized models |
| **IQ3_XS** | Very Low | Very Low | Ultra-low-memory devices | Extreme memory efficiency and low accuracy |
| **Q4_0** | Low | Low | ARM or low-memory devices | llama.cpp can optimize for ARM devices |
---
## **Included Files & Details**
### `medgemma-4b-it-bf16.gguf`
- Model weights preserved in **BF16**.
- Use this if you want to **requantize** the model into a different format.
- Best if your device supports **BF16 acceleration**.
### `medgemma-4b-it-f16.gguf`
- Model weights stored in **F16**.
- Use if your device supports **FP16**, especially if BF16 is not available.
### `medgemma-4b-it-bf16-q8_0.gguf`
- **Output & embeddings** remain in **BF16**.
- All other layers quantized to **Q8_0**.
- Use if your device supports **BF16** and you want a quantized version.
### `medgemma-4b-it-f16-q8_0.gguf`
- **Output & embeddings** remain in **F16**.
- All other layers quantized to **Q8_0**.
### `medgemma-4b-it-q4_k.gguf`
- **Output & embeddings** quantized to **Q8_0**.
- All other layers quantized to **Q4_K**.
- Good for **CPU inference** with limited memory.
### `medgemma-4b-it-q4_k_s.gguf`
- Smallest **Q4_K** variant, using less memory at the cost of accuracy.
- Best for **very low-memory setups**.
### `medgemma-4b-it-q6_k.gguf`
- **Output & embeddings** quantized to **Q8_0**.
- All other layers quantized to **Q6_K** .
### `medgemma-4b-it-q8_0.gguf`
- Fully **Q8** quantized model for better accuracy.
- Requires **more memory** but offers higher precision.
### `medgemma-4b-it-iq3_xs.gguf`
- **IQ3_XS** quantization, optimized for **extreme memory efficiency**.
- Best for **ultra-low-memory devices**.
### `medgemma-4b-it-iq3_m.gguf`
- **IQ3_M** quantization, offering a **medium block size** for better accuracy.
- Suitable for **low-memory devices**.
### `medgemma-4b-it-q4_0.gguf`
- Pure **Q4_0** quantization, optimized for **ARM devices**.
- Best for **low-memory environments**.
- Prefer IQ4_NL for better accuracy.
# <span id="testllm" style="color: #7F7FFF;">🚀 If you find these models useful</span>
❤ **Please click "Like" if you find this useful!**
Help me test my **AI-Powered Network Monitor Assistant** with **quantum-ready security checks**:
👉 [Free Network Monitor](https://readyforquantum.com/dashboard/?assistant=open)
💬 **How to test**:
Choose an **AI assistant type**:
- `TurboLLM` (GPT-4o-mini)
- `HugLLM` (Hugginface Open-source)
- `TestLLM` (Experimental CPU-only)
### **What I’m Testing**
I’m pushing the limits of **small open-source models for AI network monitoring**, specifically:
- **Function calling** against live network services
- **How small can a model go** while still handling:
- Automated **Nmap scans**
- **Quantum-readiness checks**
- **Network Monitoring tasks**
🟡 **TestLLM** – Current experimental model (llama.cpp on 2 CPU threads):
- ✅ **Zero-configuration setup**
- ⏳ 30s load time (slow inference but **no API costs**)
- 🔧 **Help wanted!** If you’re into **edge-device AI**, let’s collaborate!
### **Other Assistants**
🟢 **TurboLLM** – Uses **gpt-4o-mini** for:
- **Create custom cmd processors to run .net code on Free Network Monitor Agents**
- **Real-time network diagnostics and monitoring**
- **Security Audits**
- **Penetration testing** (Nmap/Metasploit)
- 🔑 Get more tokens by logging in or [downloading our Free Network Monitor Agent with integrated AI Assistant](https://readyforquantum.com/download)
🔵 **HugLLM** – Latest Open-source models:
- 🌐 Runs on Hugging Face Inference API
### 💡 **Example commands to you could test**:
1. `"Give me info on my websites SSL certificate"`
2. `"Check if my server is using quantum safe encyption for communication"`
3. `"Run a comprehensive security audit on my server"`
4. '"Create a cmd processor to .. (what ever you want)" Note you need to install a Free Network Monitor Agent to run the .net code from. This is a very flexible and powerful feature. Use with caution!
# MedGemma model card
**Model documentation:** [MedGemma](https://developers.google.com/health-ai-developer-foundations/medgemma)
**Resources:**
* Model on Google Cloud Model Garden: [MedGemma](https://console.cloud.google.com/vertex-ai/publishers/google/model-garden/medgemma)
* Model on Hugging Face: [MedGemma](https://huggingface.co/collections/google/medgemma-release-680aade845f90bec6a3f60c4)
* GitHub repository (supporting code, Colab notebooks, discussions, and
issues): [MedGemma](https://github.com/google-health/medgemma)
* Quick start notebook: [GitHub](https://github.com/google-health/medgemma/blob/main/notebooks/quick_start_with_hugging_face.ipynb)
* Fine-tuning notebook: [GitHub](https://github.com/google-health/medgemma/blob/main/notebooks/fine_tune_with_hugging_face.ipynb)
* [Patient Education Demo built using MedGemma](https://huggingface.co/spaces/google/rad_explain)
* Support: See [Contact](https://developers.google.com/health-ai-developer-foundations/medgemma/get-started.md#contact)
* License: The use of MedGemma is governed by the [Health AI Developer
Foundations terms of
use](https://developers.google.com/health-ai-developer-foundations/terms).
**Author:** Google
## Model information
This section describes the MedGemma model and how to use it.
### Description
MedGemma is a collection of [Gemma 3](https://ai.google.dev/gemma/docs/core)
variants that are trained for performance on medical text and image
comprehension. Developers can use MedGemma to accelerate building
healthcare-based AI applications. MedGemma currently comes in two variants: a 4B
multimodal version and a 27B text-only version.
MedGemma 4B utilizes a [SigLIP](https://arxiv.org/abs/2303.15343) image encoder
that has been specifically pre-trained on a variety of de-identified medical
data, including chest X-rays, dermatology images, ophthalmology images, and
histopathology slides. Its LLM component is trained on a diverse set of medical
data, including radiology images, histopathology patches, ophthalmology images,
and dermatology images.
MedGemma 4B is available in both pre-trained (suffix: `-pt`) and
instruction-tuned (suffix `-it`) versions. The instruction-tuned version is a
better starting point for most applications. The pre-trained version is
available for those who want to experiment more deeply with the models.
MedGemma 27B has been trained exclusively on medical text and optimized for
inference-time computation. MedGemma 27B is only available as an
instruction-tuned model.
MedGemma variants have been evaluated on a range of clinically relevant
benchmarks to illustrate their baseline performance. These include both open
benchmark datasets and curated datasets. Developers can fine-tune MedGemma
variants for improved performance. Consult the Intended Use section below for
more details.
A full technical report will be available soon.
### How to use
Below are some example code snippets to help you quickly get started running the
model locally on GPU. If you want to use the model at scale, we recommend that
you create a production version using [Model
Garden](https://cloud.google.com/model-garden).
First, install the Transformers library. Gemma 3 is supported starting from
transformers 4.50.0.
```sh
$ pip install -U transformers
```
**Run model with the `pipeline` API**
```python
from transformers import pipeline
from PIL import Image
import requests
import torch
pipe = pipeline(
"image-text-to-text",
model="google/medgemma-4b-it",
torch_dtype=torch.bfloat16,
device="cuda",
)
# Image attribution: Stillwaterising, CC0, via Wikimedia Commons
image_url = "https://upload.wikimedia.org/wikipedia/commons/c/c8/Chest_Xray_PA_3-8-2010.png"
image = Image.open(requests.get(image_url, headers={"User-Agent": "example"}, stream=True).raw)
messages = [
{
"role": "system",
"content": [{"type": "text", "text": "You are an expert radiologist."}]
},
{
"role": "user",
"content": [
{"type": "text", "text": "Describe this X-ray"}
{"type": "image", "image": image},
]
}
]
output = pipe(text=messages, max_new_tokens=200)
print(output[0]["generated_text"][-1]["content"])
```
**Run the model directly**
```python
# pip install accelerate
from transformers import AutoProcessor, AutoModelForImageTextToText
from PIL import Image
import requests
import torch
model_id = "google/medgemma-4b-it"
model = AutoModelForImageTextToText.from_pretrained(
model_id,
torch_dtype=torch.bfloat16,
device_map="auto",
)
processor = AutoProcessor.from_pretrained(model_id)
# Image attribution: Stillwaterising, CC0, via Wikimedia Commons
image_url = "https://upload.wikimedia.org/wikipedia/commons/c/c8/Chest_Xray_PA_3-8-2010.png"
image = Image.open(requests.get(image_url, headers={"User-Agent": "example"}, stream=True).raw)
messages = [
{
"role": "system",
"content": [{"type": "text", "text": "You are an expert radiologist."}]
},
{
"role": "user",
"content": [
{"type": "text", "text": "Describe this X-ray"},
{"type": "image", "image": image}
]
}
]
inputs = processor.apply_chat_template(
messages, add_generation_prompt=True, tokenize=True,
return_dict=True, return_tensors="pt"
).to(model.device, dtype=torch.bfloat16)
input_len = inputs["input_ids"].shape[-1]
with torch.inference_mode():
generation = model.generate(**inputs, max_new_tokens=200, do_sample=False)
generation = generation[0][input_len:]
decoded = processor.decode(generation, skip_special_tokens=True)
print(decoded)
```
### Examples
See the following Colab notebooks for examples of how to use MedGemma:
* To give the model a quick try, running it locally with weights from Hugging
Face, see [Quick start notebook in
Colab](https://colab.research.google.com/github/google-health/medgemma/blob/main/notebooks/quick_start_with_hugging_face.ipynb).
Note that you will need to use Colab Enterprise to run the 27B model without
quantization.
* For an example of fine-tuning the model, see the [Fine-tuning notebook in
Colab](https://colab.research.google.com/github/google-health/medgemma/blob/main/notebooks/fine_tune_with_hugging_face.ipynb).
### Model architecture overview
The MedGemma model is built based on [Gemma 3](https://ai.google.dev/gemma/) and
uses the same decoder-only transformer architecture as Gemma 3. To read more
about the architecture, consult the Gemma 3 [model
card](https://ai.google.dev/gemma/docs/core/model_card_3).
### Technical specifications
* **Model type**: Decoder-only Transformer architecture, see the [Gemma 3
technical
report](https://storage.googleapis.com/deepmind-media/gemma/Gemma3Report.pdf)
* **Modalities**: **4B**: Text, vision; **27B**: Text only
* **Attention mechanism**: Utilizes grouped-query attention (GQA)
* **Context length**: Supports long context, at least 128K tokens
* **Key publication**: Coming soon
* **Model created**: May 20, 2025
* **Model version**: 1.0.0
### Citation
A technical report is coming soon. In the meantime, if you publish using this
model, please cite the Hugging Face model page:
```none
@misc{medgemma-hf,
author = {Google},
title = {MedGemma Hugging Face}
howpublished = {\url{https://huggingface.co/collections/google/medgemma-release-680aade845f90bec6a3f60c4}},
year = {2025},
note = {Accessed: [Insert Date Accessed, e.g., 2025-05-20]}
}
```
### Inputs and outputs
**Input**:
* Text string, such as a question or prompt
* Images, normalized to 896 x 896 resolution and encoded to 256 tokens each
* Total input length of 128K tokens
**Output**:
* Generated text in response to the input, such as an answer to a question,
analysis of image content, or a summary of a document
* Total output length of 8192 tokens
### Performance and validation
MedGemma was evaluated across a range of different multimodal classification,
report generation, visual question answering, and text-based tasks.
### Key performance metrics
#### Imaging evaluations
The multimodal performance of MedGemma 4B was evaluated across a range of
benchmarks, focusing on radiology, dermatology, histopathology, ophthalmology,
and multimodal clinical reasoning.
MedGemma 4B outperforms the base Gemma 3 4B model across all tested multimodal
health benchmarks.
| Task and metric | MedGemma 4B | Gemma 3 4B |
| :---- | :---- | :---- |
| **Medical image classification** | | |
| MIMIC CXR \- Average F1 for top 5 conditions | 88.9 | 81.1 |
| CheXpert CXR \- Average F1 for top 5 conditions | 48.1 | 31.2 |
| DermMCQA\* \- Accuracy | 71.8 | 42.6 |
| **Visual question answering** | | |
| SlakeVQA (radiology) \- Tokenized F1 | 62.3 | 38.6 |
| VQA-Rad\*\* (radiology) \- Tokenized F1 | 49.9 | 38.6 |
| PathMCQA (histopathology, internal\*\*\*) \- Accuracy | 69.8 | 37.1 |
| **Knowledge and reasoning** | | |
| MedXpertQA (text \+ multimodal questions) \- Accuracy | 18.8 | 16.4 |
*Described in [Liu (2020, Nature
medicine)](https://www.nature.com/articles/s41591-020-0842-3), presented as a
4-way MCQ per example for skin condition classification.
**Based on "balanced split," described in [Yang (2024,
arXiv)](https://arxiv.org/pdf/2405.03162).
***Based on multiple datasets, presented as 3-9 way MCQ per example for
identification, grading, and subtype for breast, cervical, and prostate cancer.
#### Chest X-ray report generation
MedGemma chest X-ray (CXR) report generation performance was evaluated on
[MIMIC-CXR](https://physionet.org/content/mimic-cxr/2.1.0/) using the [RadGraph
F1 metric](https://arxiv.org/abs/2106.14463). We compare the MedGemma
pre-trained checkpoint with our previous best model for CXR report generation,
[PaliGemma 2](https://arxiv.org/abs/2412.03555).
| Metric | MedGemma 4B (pre-trained) | PaliGemma 2 3B (tuned for CXR) | PaliGemma 2 10B (tuned for CXR) |
| :---- | :---- | :---- | :---- |
| **Chest X-ray report generation** | | | |
| MIMIC CXR \- RadGraph F1 | 29.5 | 28.8 | 29.5 |
The instruction-tuned versions of MedGemma 4B and Gemma 3 4B achieve lower
scores (0.22 and 0.12, respectively) due to the differences in reporting style
compared to the MIMIC ground truth reports. Further fine-tuning on MIMIC reports
will enable users to achieve improved performance.
#### Text evaluations
MedGemma 4B and text-only MedGemma 27B were evaluated across a range of
text-only benchmarks for medical knowledge and reasoning.
The MedGemma models outperform their respective base Gemma models across all
tested text-only health benchmarks.
| Metric | MedGemma 27B | Gemma 3 27B | MedGemma 4B | Gemma 3 4B |
| :---- | :---- | :---- | :---- | :---- |
| MedQA (4-op) | 89.8 (best-of-5) 87.7 (0-shot) | 74.9 | 64.4 | 50.7 |
| MedMCQA | 74.2 | 62.6 | 55.7 | 45.4 |
| PubMedQA | 76.8 | 73.4 | 73.4 | 68.4 |
| MMLU Med (text only) | 87.0 | 83.3 | 70.0 | 67.2 |
| MedXpertQA (text only) | 26.7 | 15.7 | 14.2 | 11.6 |
| AfriMed-QA | 84.0 | 72.0 | 52.0 | 48.0 |
For all MedGemma 27B results, [test-time
scaling](https://arxiv.org/abs/2501.19393) is used to improve performance.
### Ethics and safety evaluation
#### Evaluation approach
Our evaluation methods include structured evaluations and internal red-teaming
testing of relevant content policies. Red-teaming was conducted by a number of
different teams, each with different goals and human evaluation metrics. These
models were evaluated against a number of different categories relevant to
ethics and safety, including:
* **Child safety**: Evaluation of text-to-text and image-to-text prompts
covering child safety policies, including child sexual abuse and
exploitation.
* **Content safety:** Evaluation of text-to-text and image-to-text prompts
covering safety policies, including harassment, violence and gore, and hate
speech.
* **Representational harms**: Evaluation of text-to-text and image-to-text
prompts covering safety policies, including bias, stereotyping, and harmful
associations or inaccuracies.
* **General medical harms:** Evaluation of text-to-text and image-to-text
prompts covering safety policies, including information quality and harmful
associations or inaccuracies.
In addition to development level evaluations, we conduct "assurance evaluations"
which are our "arms-length" internal evaluations for responsibility governance
decision making. They are conducted separately from the model development team,
to inform decision making about release. High-level findings are fed back to the
model team, but prompt sets are held out to prevent overfitting and preserve the
results' ability to inform decision making. Notable assurance evaluation results
are reported to our Responsibility & Safety Council as part of release review.
#### Evaluation results
For all areas of safety testing, we saw safe levels of performance across the
categories of child safety, content safety, and representational harms. All
testing was conducted without safety filters to evaluate the model capabilities
and behaviors. For text-to-text, image-to-text, and audio-to-text, and across
both MedGemma model sizes, the model produced minimal policy violations. A
limitation of our evaluations was that they included primarily English language
prompts.
## Data card
### Dataset overview
#### Training
The base Gemma models are pre-trained on a large corpus of text and code data.
MedGemma 4B utilizes a [SigLIP](https://arxiv.org/abs/2303.15343) image encoder
that has been specifically pre-trained on a variety of de-identified medical
data, including radiology images, histopathology images, ophthalmology images,
and dermatology images. Its LLM component is trained on a diverse set of medical
data, including medical text relevant to radiology images, chest-x rays,
histopathology patches, ophthalmology images and dermatology images.
#### Evaluation
MedGemma models have been evaluated on a comprehensive set of clinically
relevant benchmarks, including over 22 datasets across 5 different tasks and 6
medical image modalities. These include both open benchmark datasets and curated
datasets, with a focus on expert human evaluations for tasks like CXR report
generation and radiology VQA.
#### Source
MedGemma utilizes a combination of public and private datasets.
This model was trained on diverse public datasets including MIMIC-CXR (chest
X-rays and reports), Slake-VQA (multimodal medical images and questions),
PAD-UFES-20 (skin lesion images and data), SCIN (dermatology images), TCGA
(cancer genomics data), CAMELYON (lymph node histopathology images), PMC-OA
(biomedical literature with images), and Mendeley Digital Knee X-Ray (knee
X-rays).
Additionally, multiple diverse proprietary datasets were licensed and
incorporated (described next).
### Data Ownership and Documentation
* [Mimic-CXR](https://physionet.org/content/mimic-cxr/2.1.0/): MIT Laboratory
for Computational Physiology and Beth Israel Deaconess Medical Center
(BIDMC).
* [Slake-VQA](https://www.med-vqa.com/slake/): The Hong Kong Polytechnic
University (PolyU), with collaborators including West China Hospital of
Sichuan University and Sichuan Academy of Medical Sciences / Sichuan
Provincial People's Hospital.
* [PAD-UFES-20](https://pmc.ncbi.nlm.nih.gov/articles/PMC7479321/): Federal
University of Espírito Santo (UFES), Brazil, through its Dermatological and
Surgical Assistance Program (PAD).
* [SCIN](https://github.com/google-research-datasets/scin): A collaboration
between Google Health and Stanford Medicine.
* [TCGA](https://portal.gdc.cancer.gov/) (The Cancer Genome Atlas): A joint
effort of National Cancer Institute and National Human Genome Research
Institute. Data from TCGA are available via the Genomic Data Commons (GDC)
* [CAMELYON](https://camelyon17.grand-challenge.org/Data/): The data was
collected from Radboud University Medical Center and University Medical
Center Utrecht in the Netherlands.
* [PMC-OA (PubMed Central Open Access
Subset)](https://catalog.data.gov/dataset/pubmed-central-open-access-subset-pmc-oa):
Maintained by the National Library of Medicine (NLM) and National Center for
Biotechnology Information (NCBI), which are part of the NIH.
* [MedQA](https://arxiv.org/pdf/2009.13081): This dataset was created by a
team of researchers led by Di Jin, Eileen Pan, Nassim Oufattole, Wei-Hung
Weng, Hanyi Fang, and Peter Szolovits
* [Mendeley Digital Knee
X-Ray](https://data.mendeley.com/datasets/t9ndx37v5h/1): This dataset is
from Rani Channamma University, and is hosted on Mendeley Data.
* [AfriMed-QA](https://afrimedqa.com/): This data was developed and led by
multiple collaborating organizations and researchers include key
contributors: Intron Health, SisonkeBiotik, BioRAMP, Georgia Institute of
Technology, and MasakhaneNLP.
* [VQA-RAD](https://www.nature.com/articles/sdata2018251): This dataset was
created by a research team led by Jason J. Lau, Soumya Gayen, Asma Ben
Abacha, and Dina Demner-Fushman and their affiliated institutions (the US
National Library of Medicine and National Institutes of Health)
* [MedExpQA](https://www.sciencedirect.com/science/article/pii/S0933365724001805):
This dataset was created by researchers at the HiTZ Center (Basque Center
for Language Technology and Artificial Intelligence).
* [MedXpertQA](https://huggingface.co/datasets/TsinghuaC3I/MedXpertQA): This
dataset was developed by researchers at Tsinghua University (Beijing, China)
and Shanghai Artificial Intelligence Laboratory (Shanghai, China).
In addition to the public datasets listed above, MedGemma was also trained on
de-identified datasets licensed for research or collected internally at Google
from consented participants.
* Radiology dataset 1: De-identified dataset of different CT studies across
body parts from a US-based radiology outpatient diagnostic center network.
* Ophthalmology dataset 1: De-identified dataset of fundus images from
diabetic retinopathy screening.
* Dermatology dataset 1: De-identified dataset of teledermatology skin
condition images (both clinical and dermatoscopic) from Colombia.
* Dermatology dataset 2: De-identified dataset of skin cancer images (both
clinical and dermatoscopic) from Australia.
* Dermatology dataset 3: De-identified dataset of non-diseased skin images
from an internal data collection effort.
* Pathology dataset 1: De-identified dataset of histopathology H&E whole slide
images created in collaboration with an academic research hospital and
biobank in Europe. Comprises de-identified colon, prostate, and lymph nodes.
* Pathology dataset 2: De-identified dataset of lung histopathology H&E and
IHC whole slide images created by a commercial biobank in the United States.
* Pathology dataset 3: De-identified dataset of prostate and lymph node H&E
and IHC histopathology whole slide images created by a contract research
organization in the United States.
* Pathology dataset 4: De-identified dataset of histopathology, predominantly
H\&E whole slide images created in collaboration with a large, tertiary
teaching hospital in the United States. Comprises a diverse set of tissue
and stain types, predominantly H&E.
### Data citation
* **MIMIC-CXR** Johnson, A., Pollard, T., Mark, R., Berkowitz, S., & Horng, S.
(2024). MIMIC-CXR Database (version 2.1.0). PhysioNet.
https://physionet.org/content/mimic-cxr/2.1.0/
*and* Johnson, Alistair E. W., Tom J. Pollard, Seth J. Berkowitz, Nathaniel R.
Greenbaum, Matthew P. Lungren, Chih-Ying Deng, Roger G. Mark, and Steven
Horng. 2019. "MIMIC-CXR, a de-Identified Publicly Available Database of
Chest Radiographs with Free-Text Reports." *Scientific Data 6* (1): 1–8.
* **SLAKE** Liu, Bo, Li-Ming Zhan, Li Xu, Lin Ma, Yan Yang, and Xiao-Ming Wu.
2021.SLAKE: A Semantically-Labeled Knowledge-Enhanced Dataset for Medical
Visual Question Answering." http://arxiv.org/abs/2102.09542.
* **PAD-UEFS** Pacheco, A. G. C., Lima, G. R., Salomao, A., Krohling, B.,
Biral, I. P., de Angelo, G. G., Alves, F. O. G., Ju X. M., & P. R. C.
(2020). PAD-UFES-20: A skin lesion dataset composed of patient data and
clinical images collected from smartphones. In *Proceedings of the 2020 IEEE
International Conference on Bioinformatics and Biomedicine (BIBM)* (pp.
1551-1558). IEEE. https://doi.org/10.1109/BIBM49941.2020.9313241
* **SCIN** Ward, Abbi, Jimmy Li, Julie Wang, Sriram Lakshminarasimhan, Ashley
Carrick, Bilson Campana, Jay Hartford, et al. 2024. "Creating an Empirical
Dermatology Dataset Through Crowdsourcing With Web Search Advertisements."
*JAMA Network Open 7* (11): e2446615–e2446615.
* **TCGA** The results shown here are in whole or part based upon data
generated by the TCGA Research Network: https://www.cancer.gov/tcga.
* **CAMELYON16** Ehteshami Bejnordi, Babak, Mitko Veta, Paul Johannes van
Diest, Bram van Ginneken, Nico Karssemeijer, Geert Litjens, Jeroen A. W. M.
van der Laak, et al. 2017. "Diagnostic Assessment of Deep Learning
Algorithms for Detection of Lymph Node Metastases in Women With Breast
Cancer." *JAMA 318* (22): 2199–2210.
* **MedQA** Jin, Di, Eileen Pan, Nassim Oufattole, Wei-Hung Weng, Hanyi Fang,
and Peter Szolovits. 2020. "What Disease Does This Patient Have? A
Large-Scale Open Domain Question Answering Dataset from Medical Exams."
http://arxiv.org/abs/2009.13081.
* **Mendeley Digital Knee X-Ray** Gornale, Shivanand; Patravali, Pooja (2020),
"Digital Knee X-ray Images", Mendeley Data, V1, doi: 10.17632/t9ndx37v5h.1
* **AfrimedQA** Olatunji, Tobi, Charles Nimo, Abraham Owodunni, Tassallah
Abdullahi, Emmanuel Ayodele, Mardhiyah Sanni, Chinemelu Aka, et al. 2024.
"AfriMed-QA: A Pan-African, Multi-Specialty, Medical Question-Answering
Benchmark Dataset." http://arxiv.org/abs/2411.15640.
* **VQA-RAD** Lau, Jason J., Soumya Gayen, Asma Ben Abacha, and Dina
Demner-Fushman. 2018. "A Dataset of Clinically Generated Visual Questions
and Answers about Radiology Images." *Scientific Data 5* (1): 1–10.
* **MedexpQA** Alonso, I., Oronoz, M., & Agerri, R. (2024). MedExpQA:
Multilingual Benchmarking of Large Language Models for Medical Question
Answering. *arXiv preprint arXiv:2404.05590*. Retrieved from
https://arxiv.org/abs/2404.05590
* **MedXpertQA** Zuo, Yuxin, Shang Qu, Yifei Li, Zhangren Chen, Xuekai Zhu,
Ermo Hua, Kaiyan Zhang, Ning Ding, and Bowen Zhou. 2025. "MedXpertQA:
Benchmarking Expert-Level Medical Reasoning and Understanding."
http://arxiv.org/abs/2501.18362.
### De-identification/anonymization:
Google and partnerships utilize datasets that have been rigorously anonymized or
de-identified to ensure the protection of individual research participants and
patient privacy
## Implementation information
Details about the model internals.
### Software
Training was done using [JAX](https://github.com/jax-ml/jax).
JAX allows researchers to take advantage of the latest generation of hardware,
including TPUs, for faster and more efficient training of large models.
## Use and limitations
### Intended use
MedGemma is an open multimodal generative AI model intended to be used as a
starting point that enables more efficient development of downstream healthcare
applications involving medical text and images. MedGemma is intended for
developers in the life sciences and healthcare space. Developers are responsible
for training, adapting and making meaningful changes to MedGemma to accomplish
their specific intended use. MedGemma models can be fine-tuned by developers
using their own proprietary data for their specific tasks or solutions.
MedGemma is based on Gemma 3 and has been further trained on medical images and
text. MedGemma enables further development in any medical context (image and
textual), however the model was pre-trained using chest X-ray, pathology,
dermatology, and fundus images. Examples of tasks within MedGemma's training
include visual question answering pertaining to medical images, such as
radiographs, or providing answers to textual medical questions. Full details of
all the tasks MedGemma has been evaluated can be found in an upcoming technical
report.
### Benefits
* Provides strong baseline medical image and text comprehension for models of
its size.
* This strong performance makes it efficient to adapt for downstream
healthcare-based use cases, compared to models of similar size without
medical data pre-training.
* This adaptation may involve prompt engineering, grounding, agentic
orchestration or fine-tuning depending on the use case, baseline validation
requirements, and desired performance characteristics.
### Limitations
MedGemma is not intended to be used without appropriate validation, adaptation
and/or making meaningful modification by developers for their specific use case.
The outputs generated by MedGemma are not intended to directly inform clinical
diagnosis, patient management decisions, treatment recommendations, or any other
direct clinical practice applications. Performance benchmarks highlight baseline
capabilities on relevant benchmarks, but even for image and text domains that
constitute a substantial portion of training data, inaccurate model output is
possible. All outputs from MedGemma should be considered preliminary and require
independent verification, clinical correlation, and further investigation
through established research and development methodologies.
MedGemma's multimodal capabilities have been primarily evaluated on single-image
tasks. MedGemma has not been evaluated in use cases that involve comprehension
of multiple images.
MedGemma has not been evaluated or optimized for multi-turn applications.
MedGemma's training may make it more sensitive to the specific prompt used than
Gemma 3.
When adapting MedGemma developer should consider the following:
* **Bias in validation data:** As with any research, developers should ensure
that any downstream application is validated to understand performance using
data that is appropriately representative of the intended use setting for
the specific application (e.g., age, sex, gender, condition, imaging device,
etc).
* **Data contamination concerns**: When evaluating the generalization
capabilities of a large model like MedGemma in a medical context, there is a
risk of data contamination, where the model might have inadvertently seen
related medical information during its pre-training, potentially
overestimating its true ability to generalize to novel medical concepts.
Developers should validate MedGemma on datasets not publicly available or
otherwise made available to non-institutional researchers to mitigate this
risk. |
mradermacher/Medra27B-GGUF | mradermacher | 2025-05-30T05:02:50Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"text-generation",
"medical-ai",
"summarization",
"diagnostic-reasoning",
"gemma-3",
"fine-tuned",
"en",
"ro",
"dataset:nicoboss/medra-medical",
"base_model:nicoboss/Medra27B",
"base_model:quantized:nicoboss/Medra27B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | summarization | 2025-05-30T03:11:19Z | ---
base_model: nicoboss/Medra27B
datasets:
- nicoboss/medra-medical
language:
- en
- ro
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- text-generation
- medical-ai
- summarization
- diagnostic-reasoning
- gemma-3
- fine-tuned
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/nicoboss/Medra27B
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Medra27B-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Medra27B-GGUF/resolve/main/Medra27B.Q2_K.gguf) | Q2_K | 10.6 | |
| [GGUF](https://huggingface.co/mradermacher/Medra27B-GGUF/resolve/main/Medra27B.Q3_K_S.gguf) | Q3_K_S | 12.3 | |
| [GGUF](https://huggingface.co/mradermacher/Medra27B-GGUF/resolve/main/Medra27B.Q3_K_M.gguf) | Q3_K_M | 13.5 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Medra27B-GGUF/resolve/main/Medra27B.Q3_K_L.gguf) | Q3_K_L | 14.6 | |
| [GGUF](https://huggingface.co/mradermacher/Medra27B-GGUF/resolve/main/Medra27B.IQ4_XS.gguf) | IQ4_XS | 15.0 | |
| [GGUF](https://huggingface.co/mradermacher/Medra27B-GGUF/resolve/main/Medra27B.Q4_K_S.gguf) | Q4_K_S | 15.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Medra27B-GGUF/resolve/main/Medra27B.Q4_K_M.gguf) | Q4_K_M | 16.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Medra27B-GGUF/resolve/main/Medra27B.Q5_K_S.gguf) | Q5_K_S | 18.9 | |
| [GGUF](https://huggingface.co/mradermacher/Medra27B-GGUF/resolve/main/Medra27B.Q5_K_M.gguf) | Q5_K_M | 19.4 | |
| [GGUF](https://huggingface.co/mradermacher/Medra27B-GGUF/resolve/main/Medra27B.Q6_K.gguf) | Q6_K | 22.3 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Medra27B-GGUF/resolve/main/Medra27B.Q8_0.gguf) | Q8_0 | 28.8 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
luckeciano/Qwen-2.5-7B-GRPO-Base | luckeciano | 2025-05-30T05:01:40Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"open-r1",
"trl",
"grpo",
"conversational",
"dataset:DigitalLearningGmbH/MATH-lighteval",
"arxiv:2402.03300",
"base_model:Qwen/Qwen2.5-Math-7B",
"base_model:finetune:Qwen/Qwen2.5-Math-7B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-28T18:50:57Z | ---
base_model: Qwen/Qwen2.5-Math-7B
datasets: DigitalLearningGmbH/MATH-lighteval
library_name: transformers
model_name: Qwen-2.5-7B-GRPO-Base
tags:
- generated_from_trainer
- open-r1
- trl
- grpo
licence: license
---
# Model Card for Qwen-2.5-7B-GRPO-Base
This model is a fine-tuned version of [Qwen/Qwen2.5-Math-7B](https://huggingface.co/Qwen/Qwen2.5-Math-7B) on the [DigitalLearningGmbH/MATH-lighteval](https://huggingface.co/datasets/DigitalLearningGmbH/MATH-lighteval) 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="luckeciano/Qwen-2.5-7B-GRPO-Base", 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/max-ent-llms/PolicyGradientStability/runs/lpgodyoc)
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.16.0.dev0
- Transformers: 4.49.0
- Pytorch: 2.6.0
- Datasets: 3.4.1
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
Whyso-39/DeepSeek-R1-DepressionAnalysis-4bit | Whyso-39 | 2025-05-30T05:01:26Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"conversational",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] | text-generation | 2025-05-30T04:58:08Z | ---
base_model: unsloth/deepseek-r1-distill-llama-8b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** Whyso-39
- **License:** apache-2.0
- **Finetuned from model :** unsloth/deepseek-r1-distill-llama-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)
|
dhruvsangani/Feat_ChatBot3-GGUF | dhruvsangani | 2025-05-30T04:55:29Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-05-30T04:55:11Z | ---
base_model: unsloth/llama-3.2-1b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- gguf
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** dhruvsangani
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-1b-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)
|
phunghuy159/sft_model_full_4 | phunghuy159 | 2025-05-30T04:55:23Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-30T04:25:15Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
vermoney/3626d019-43a2-46c0-8b5f-07bb8968f9c4 | vermoney | 2025-05-30T04:54:59Z | 0 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:lmsys/vicuna-7b-v1.5",
"base_model:adapter:lmsys/vicuna-7b-v1.5",
"license:llama2",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-05-30T04:40:27Z | ---
library_name: peft
license: llama2
base_model: lmsys/vicuna-7b-v1.5
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 3626d019-43a2-46c0-8b5f-07bb8968f9c4
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.10.0.dev0`
```yaml
adapter: lora
base_model: lmsys/vicuna-7b-v1.5
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 1b4a1e767cffc7ad_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/
type:
field_instruction: instruct
field_output: output
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
dpo:
beta: 0.1
enabled: true
group_by_length: false
rank_loss: true
reference_model: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 3
gradient_checkpointing: true
gradient_clipping: 1.0
group_by_length: false
hub_model_id: vermoney/3626d019-43a2-46c0-8b5f-07bb8968f9c4
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 2.0e-06
load_in_4bit: true
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 96
lora_dropout: 0.1
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 48
lora_target_linear: true
lr_scheduler: cosine
max_steps: 280
micro_batch_size: 6
mixed_precision: bf16
mlflow_experiment_name: /tmp/1b4a1e767cffc7ad_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
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: de8f08f2-51a3-4255-a53d-b410c9ad1c6c
wandb_project: s56-9
wandb_run: your_name
wandb_runid: de8f08f2-51a3-4255-a53d-b410c9ad1c6c
warmup_steps: 40
weight_decay: 0.02
xformers_attention: false
```
</details><br>
# 3626d019-43a2-46c0-8b5f-07bb8968f9c4
This model is a fine-tuned version of [lmsys/vicuna-7b-v1.5](https://huggingface.co/lmsys/vicuna-7b-v1.5) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9548
## 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-06
- train_batch_size: 6
- eval_batch_size: 6
- seed: 42
- gradient_accumulation_steps: 3
- total_train_batch_size: 18
- 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: 40
- training_steps: 280
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.8902 | 0.1001 | 280 | 0.9548 |
### Framework versions
- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.5.1+cu124
- Datasets 3.5.1
- Tokenizers 0.21.1 |
phunghuy159/sft_model_full_3 | phunghuy159 | 2025-05-30T04:53:34Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-30T04:23:21Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
AmberYifan/Llama-3.1-8B-sft-SPIN-gpt4o-beta0.5-lr5e-7 | AmberYifan | 2025-05-30T04:51:57Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"trl",
"dpo",
"conversational",
"arxiv:2305.18290",
"base_model:AmberYifan/Llama-3.1-8B-sft-ultrachat-safeRLHF",
"base_model:finetune:AmberYifan/Llama-3.1-8B-sft-ultrachat-safeRLHF",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-30T04:31:31Z | ---
base_model: AmberYifan/Llama-3.1-8B-sft-ultrachat-safeRLHF
library_name: transformers
model_name: Llama-3.1-8B-sft-SPIN-gpt4o-beta0.5-lr5e-7
tags:
- generated_from_trainer
- trl
- dpo
licence: license
---
# Model Card for Llama-3.1-8B-sft-SPIN-gpt4o-beta0.5-lr5e-7
This model is a fine-tuned version of [AmberYifan/Llama-3.1-8B-sft-ultrachat-safeRLHF](https://huggingface.co/AmberYifan/Llama-3.1-8B-sft-ultrachat-safeRLHF).
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="AmberYifan/Llama-3.1-8B-sft-SPIN-gpt4o-beta0.5-lr5e-7", 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/yifanwang/huggingface/runs/3bodd5fr)
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: 0.12.2
- Transformers: 4.46.3
- Pytorch: 2.7.0
- Datasets: 3.6.0
- Tokenizers: 0.20.3
## 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}}
}
``` |
ianlasic/Instruct_SFT_V1 | ianlasic | 2025-05-30T04:51:52Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-29T17:50:50Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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- **Paper [optional]:** [More Information Needed]
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
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- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
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[More Information Needed]
## Model Card Contact
[More Information Needed] |
AmberYifan/Qwen2.5-7B-sft-SPIN-Qwen2.5-72B-Instruct-IPO | AmberYifan | 2025-05-30T04:51:01Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"trl",
"dpo",
"conversational",
"arxiv:2305.18290",
"base_model:AmberYifan/Qwen2.5-7B-sft-ultrachat-safeRLHF",
"base_model:finetune:AmberYifan/Qwen2.5-7B-sft-ultrachat-safeRLHF",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-30T04:29:33Z | ---
base_model: AmberYifan/Qwen2.5-7B-sft-ultrachat-safeRLHF
library_name: transformers
model_name: Qwen2.5-7B-sft-SPIN-Qwen2.5-72B-Instruct-IPO
tags:
- generated_from_trainer
- trl
- dpo
licence: license
---
# Model Card for Qwen2.5-7B-sft-SPIN-Qwen2.5-72B-Instruct-IPO
This model is a fine-tuned version of [AmberYifan/Qwen2.5-7B-sft-ultrachat-safeRLHF](https://huggingface.co/AmberYifan/Qwen2.5-7B-sft-ultrachat-safeRLHF).
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="AmberYifan/Qwen2.5-7B-sft-SPIN-Qwen2.5-72B-Instruct-IPO", 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/yifanwang/huggingface/runs/3za8qzd4)
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: 0.12.2
- Transformers: 4.46.3
- Pytorch: 2.7.0
- Datasets: 3.6.0
- Tokenizers: 0.20.3
## 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}}
}
``` |
phunghuy159/sft_model_full_1 | phunghuy159 | 2025-05-30T04:50:37Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-30T04:22:32Z | ---
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] |
gxccf88/applke | gxccf88 | 2025-05-30T04:49:16Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
] | null | 2025-05-30T04:49:15Z | ---
license: creativeml-openrail-m
---
|
dhruvsangani/Feat_ChatBot3 | dhruvsangani | 2025-05-30T04:47:48Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-05-30T04:47:43Z | ---
base_model: unsloth/llama-3.2-1b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** dhruvsangani
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-1b-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)
|
DevQuasar/huihui-ai.AceReason-Nemotron-7B-abliterated-GGUF | DevQuasar | 2025-05-30T04:43:09Z | 0 | 0 | null | [
"gguf",
"text-generation",
"base_model:huihui-ai/AceReason-Nemotron-7B-abliterated",
"base_model:quantized:huihui-ai/AceReason-Nemotron-7B-abliterated",
"endpoints_compatible",
"region:us",
"conversational"
] | text-generation | 2025-05-30T03:45:33Z | ---
base_model:
- huihui-ai/AceReason-Nemotron-7B-abliterated
pipeline_tag: text-generation
---
[<img src="https://raw.githubusercontent.com/csabakecskemeti/devquasar/main/dq_logo_black-transparent.png" width="200"/>](https://devquasar.com)
Quantized version of: [huihui-ai/AceReason-Nemotron-7B-abliterated](https://huggingface.co/huihui-ai/AceReason-Nemotron-7B-abliterated)
'Make knowledge free for everyone'
<p align="center">
Made with <br>
<a href="https://www.civo.com/" target="_blank">
<img src="https://www.civo.com/assets/public/brand-assets/civo-logo-colour-60cc1622dedf346f7afde1fff760523f731b0aac106a5465af98ff4073114b74.svg" width="100"/>
</a>
</p>
<a href='https://ko-fi.com/L4L416YX7C' target='_blank'><img height='36' style='border:0px;height:36px;' src='https://storage.ko-fi.com/cdn/kofi6.png?v=6' border='0' alt='Buy Me a Coffee at ko-fi.com' /></a>
|
FLOPS-Squared/Llama-Baseline-V3-Instruct-B | FLOPS-Squared | 2025-05-30T04:38:58Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-27T07:59:58Z | ---
library_name: transformers
tags: []
---
# Model Card for FLOPS-Squared/Llama-Baseline-V3-Instruct-B
An extended trained baseline model without using KeystoneFuse data efficient pretraining.
Research supported with Cloud TPUs from Google's TPU Research Cloud (TRC)
|
llm-jp/llm-jp-3.1-8x13b | llm-jp | 2025-05-30T04:38:09Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"mixtral",
"text-generation",
"en",
"ja",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2025-05-25T01:34:56Z | ---
license: apache-2.0
language:
- en
- ja
programming_language:
- C
- C++
- C#
- Go
- Java
- JavaScript
- Lua
- PHP
- Python
- Ruby
- Rust
- Scala
- TypeScript
pipeline_tag: text-generation
library_name: transformers
inference: false
---
# llm-jp-3.1-8x13b
LLM-jp-3.1 is a series of large language models developed by the [Research and Development Center for Large Language Models](https://llmc.nii.ac.jp/) at the [National Institute of Informatics](https://www.nii.ac.jp/en/).
Building upon the LLM-jp-3 series, the LLM-jp-3.1 models incorporate mid-training ([instruction pre-training](https://aclanthology.org/2024.emnlp-main.148/)), which significantly enhances their instruction-following capabilities compared to the original LLM-jp-3 models.
This repository provides the **llm-jp-3.1-8x13b** model.
For an overview of the LLM-jp-3.1 models across different parameter sizes, please refer to:
- [LLM-jp-3.1 Pre-trained Models](https://huggingface.co/collections/llm-jp/llm-jp-31-pre-trained-models-68368787c32e462c40a45f7b)
- [LLM-jp-3.1 Fine-tuned Models](https://huggingface.co/collections/llm-jp/llm-jp-31-fine-tuned-models-68368681b9b35de1c4ac8de4).
For more details on the training procedures and evaluation results, please refer to [this blog post](https://llm-jp.nii.ac.jp/ja/blog/blog-887/) (in Japanese).
Checkpoints format: Hugging Face Transformers
## Required Libraries and Their Versions
- torch>=2.3.0
- transformers>=4.40.1
- tokenizers>=0.19.1
- accelerate>=0.29.3
- flash-attn>=2.5.8
## Usage
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("llm-jp/llm-jp-3.1-8x13b")
model = AutoModelForCausalLM.from_pretrained("llm-jp/llm-jp-3.1-8x13b", device_map="auto", torch_dtype=torch.bfloat16)
text = "自然言語処理とは何か"
tokenized_input = tokenizer.encode(text, add_special_tokens=False, return_tensors="pt").to(model.device)
with torch.no_grad():
output = model.generate(
tokenized_input,
max_new_tokens=100,
do_sample=True,
top_p=0.95,
temperature=0.7,
repetition_penalty=1.05,
)[0]
print(tokenizer.decode(output))
```
## Model Details
- **Model type:** Transformer-based Language Model
- **Architectures:**
Dense model:
|Params|Layers|Hidden size|Heads|Context length|Embedding parameters|Non-embedding parameters|
|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
|1.8b|24|2048|16|4096|407,498,752|1,459,718,144|
|13b|40|5120|40|4096|1,018,746,880|12,688,184,320|
MoE model:
|Params|Layers|Hidden size|Heads|Routed Experts|Activated Experts|Context length|Embedding parameters|Non-embedding parameters|Activated parameters|Total parameters|
|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
|8x13b|40|5120|40|8|2|4096|1,018,746,880|72,144,081,920|22,200,806,400|73,162,828,800|
## Tokenizer
The tokenizer of this model is based on [huggingface/tokenizers](https://github.com/huggingface/tokenizers) Unigram byte-fallback model.
The vocabulary entries were converted from [`llm-jp-tokenizer v3.0`](https://github.com/llm-jp/llm-jp-tokenizer/releases/tag/v3.0b2).
Please refer to [README.md](https://github.com/llm-jp/llm-jp-tokenizer) of `llm-jp-tokenizer` for details on the vocabulary construction procedure (the pure SentencePiece training does not reproduce our vocabulary).
## Datasets
### Pre-training
The models have been pre-trained using a blend of the following datasets.
| Language | Dataset | Tokens|
|:---|:---|---:|
|Japanese|[Wikipedia](https://gitlab.llm-jp.nii.ac.jp/datasets/llm-jp-corpus-v3)|2.6B
||[Common Crawl](https://gitlab.llm-jp.nii.ac.jp/datasets/llm-jp-corpus-v3)|762.8B
||[WARP/PDF](https://gitlab.llm-jp.nii.ac.jp/datasets/llm-jp-corpus-v3)|237.3B
||[WARP/HTML](https://gitlab.llm-jp.nii.ac.jp/datasets/llm-jp-corpus-v3)|2.7B
||[Kaken](https://gitlab.llm-jp.nii.ac.jp/datasets/llm-jp-corpus-v3)|1.8B
|English|[Wikipedia](https://gitlab.llm-jp.nii.ac.jp/datasets/llm-jp-corpus-v3)|4.7B
||[Dolma/CC-head](https://gitlab.llm-jp.nii.ac.jp/datasets/llm-jp-corpus-v3)|608.5B
||[Dolma/C4](https://gitlab.llm-jp.nii.ac.jp/datasets/llm-jp-corpus-v3)|181.6B
||[Dolma/Reddit](https://gitlab.llm-jp.nii.ac.jp/datasets/llm-jp-corpus-v3)|83.1B
||[Dolma/PeS2o](https://gitlab.llm-jp.nii.ac.jp/datasets/llm-jp-corpus-v3)|62.9B
||[Dolma/Gutenberg](https://gitlab.llm-jp.nii.ac.jp/datasets/llm-jp-corpus-v3)|5.5B
||[Dolma/Wiki](https://gitlab.llm-jp.nii.ac.jp/datasets/llm-jp-corpus-v3)|3.9B
|Code|[The Stack](https://huggingface.co/datasets/bigcode/the-stack)|114.1B
|Chinese|[Wikipedia](https://huggingface.co/datasets/bigcode/the-stack)|0.8B
|Korean|[Wikipedia](https://huggingface.co/datasets/bigcode/the-stack)|0.3B
### Mid-training
In the LLM-jp-3.1 series, we performed continuous pre-training based on [Instruction Pre-Training](https://aclanthology.org/2024.emnlp-main.148/).
Instruction Pre-Training enhances a model’s ability to follow instructions by continuing pre-training on a large collection of instruction–response pairs.
We prepared approximately 90B tokens of instruction–response data and mixed it with our pre-training datasets, conducting continuous pre-training on a total of 400B tokens.
Each model was initialized from existing checkpoints ([llm-jp/llm-jp-3-1.8b](https://huggingface.co/llm-jp/llm-jp-3-1.8b), [llm-jp/llm-jp-3-13b](https://huggingface.co/llm-jp/llm-jp-3-13b), and [llm-jp/llm-jp-3-8x13b](https://huggingface.co/llm-jp/llm-jp-3-8x13b)) and underwent continuous instruction pre-training.
Since the LLM-jp-3 series was originally pre-trained on 2.1T tokens, the total pre-training token count amounts to 2.5T tokens.
Details of this training process will be released in a forthcoming paper. The instruction–response dataset used for this training will also be made publicly available.
### Post-training
We have fine-tuned the pre-trained checkpoint with supervised fine-tuning and further aligned it with Direct Preference Optimization.
#### Supervised Fine-tuning
The datasets used for supervised fine-tuning are as follows:
| Language | Dataset | Description |
|:---|:---|:---|
|Japanese|[ichikara-instruction-004-002](https://liat-aip.sakura.ne.jp/wp/llm%e3%81%ae%e3%81%9f%e3%82%81%e3%81%ae%e6%97%a5%e6%9c%ac%e8%aa%9e%e3%82%a4%e3%83%b3%e3%82%b9%e3%83%88%e3%83%a9%e3%82%af%e3%82%b7%e3%83%a7%e3%83%b3%e3%83%87%e3%83%bc%e3%82%bf%e4%bd%9c%e6%88%90/llm%e3%81%ae%e3%81%9f%e3%82%81%e3%81%ae%e6%97%a5%e6%9c%ac%e8%aa%9e%e3%82%a4%e3%83%b3%e3%82%b9%e3%83%88%e3%83%a9%e3%82%af%e3%82%b7%e3%83%a7%e3%83%b3%e3%83%87%e3%83%bc%e3%82%bf-%e5%85%ac%e9%96%8b/)| A manually constructed instruction dataset. |
| |[AnswerCarefully (ver2.0)](https://huggingface.co/datasets/llm-jp/AnswerCarefully)| A manually constructed instruction dataset focusing on LLMs' safety. |
| |ichikara-instruction-format| A small subset of the ichikara-instruction dataset, edited with some constraints on the output format. |
| |[AutoMultiTurnByCalm3-22B](https://huggingface.co/datasets/kanhatakeyama/AutoMultiTurnByCalm3-22B)| A synthetic instruction dataset. |
| |[ramdom-to-fixed-multiturn-Calm3](https://huggingface.co/datasets/kanhatakeyama/ramdom-to-fixed-multiturn-Calm3)| A synthetic instruction dataset. |
| |[wizardlm8x22b-logical-math-coding-sft-ja](https://huggingface.co/datasets/llm-jp/wizardlm8x22b-logical-math-coding-sft-ja)| A synthetic instruction dataset. |
| |[magpie-sft-v1.0](https://huggingface.co/datasets/llm-jp/magpie-sft-v1.0)| A synthetic instruction dataset we created. |
| |[jaster v1.4.1](https://github.com/llm-jp/llm-jp-eval/tree/v1.4.1)| - |
| |[extraction-wiki-ja](https://huggingface.co/datasets/llm-jp/extraction-wiki-ja)| A synthetic instruction dataset we created. |
|English|[Daring-Anteater](https://huggingface.co/datasets/nvidia/Daring-Anteater)| - |
|Japanese & English|[Synthetic-JP-EN-Coding-Dataset](https://huggingface.co/datasets/llm-jp/Synthetic-JP-EN-Coding-Dataset)| A synthetic instruction dataset. |
#### Direct Preference Optimization
For Direct Preference Optimization (DPO), we adopted rejection sampling.
Prompts were sampled from the dataset used in SFT, and multiple responses were generated for each prompt.
These responses were then scored (by [Qwen/Qwen2.5-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-32B-Instruct)), and DPO was performed by treating high-scoring responses as positive examples and low-scoring responses as negative examples.
We conducted DPO in two stages.
In the second stage, we additionally used [ac-self-inst](https://huggingface.co/datasets/llm-jp/ac-self-inst), a Japanese preference dataset focused on safety.
## Evaluation
### MT Bench (Japanese and English)
We evaluated the models using `gpt-4o-2024-08-06`.
The scores represent the average values obtained from three rounds of inference and evaluation.
For more details, please refer to the [codes](https://github.com/llm-jp/llm-jp-judge/tree/v1.0.0).
| Model Name | JA | EN |
|:------------------------------------------------------------------------------------------------------------------------------|----------:|-------:|
| gpt-35-turbo-1106 | 6.48 | 7.56 |
| gpt-4-0613 | 7.29 | 7.72 |
| gpt-4o-2024-08-06 | 8.10 | 8.38 |
| [sbintuitions/sarashina2.2-1b-instruct-v0.1](https://huggingface.co/sbintuitions/sarashina2.2-1b-instruct-v0.1) | 5.30 | 5.66 |
| [sbintuitions/sarashina2.2-3b-instruct-v0.1](https://huggingface.co/sbintuitions/sarashina2.2-3b-instruct-v0.1) | 7.07 | 6.96 |
| [Rakuten/RakutenAI-2.0-8x7B-instruct](https://huggingface.co/Rakuten/RakutenAI-2.0-8x7B-instruct) | 6.68 | 6.33 |
| [cyberagent/calm3-22b-chat](https://huggingface.co/cyberagent/calm3-22b-chat) | 6.86 | 6.77 |
| [Qwen/Qwen2.5-14B-Instruct](https://huggingface.co/Qwen/Qwen2.5-14B-Instruct) | 7.07 | 7.99 |
| [Qwen/Qwen2.5-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-32B-Instruct) | 7.64 | 8.27 |
| [Qwen/Qwen3-1.7B](https://huggingface.co/Qwen/Qwen3-1.7B) | 5.46 | 6.95 |
| [Qwen/Qwen3-14B](https://huggingface.co/Qwen/Qwen3-14B) | 8.00 | 8.30 |
| [Qwen/Qwen3-32B](https://huggingface.co/Qwen/Qwen3-32B) | 8.36 | 8.33 |
| [tokyotech-llm/Llama-3.3-Swallow-70B-Instruct-v0.4](https://huggingface.co/tokyotech-llm/Llama-3.3-Swallow-70B-Instruct-v0.4) | 7.64 | 8.02 |
| [stockmark/Stockmark-2-100B-Instruct-beta](https://huggingface.co/stockmark/Stockmark-2-100B-Instruct-beta) | 7.42 | 7.17 |
| [llm-jp-3-1.8b-instruct3](https://huggingface.co/llm-jp/llm-jp-3-1.8b-instruct3) | 4.64 | 4.09 |
| [llm-jp-3-13b-instruct3](https://huggingface.co/llm-jp/llm-jp-3-13b-instruct3) | 6.21 | 6.13 |
| [llm-jp-3-8x13b-instruct3](https://huggingface.co/llm-jp/llm-jp-3-8x13b-instruct3) | 6.60 | 6.49 |
| [llm-jp-3.1-1.8b-instruct4](https://huggingface.co/llm-jp/llm-jp-3.1-1.8b-instruct4) | 6.30 | 5.70 |
| [llm-jp-3.1-13b-instruct4](https://huggingface.co/llm-jp/llm-jp-3.1-13b-instruct4) | 7.37 | 7.01 |
| [llm-jp-3.1-8x13b-instruct4](https://huggingface.co/llm-jp/llm-jp-3.1-8x13b-instruct4) | 7.50 | 7.05 |
### AnswerCarefully-Eval
[AnswerCarefully-Eval](https://www.anlp.jp/proceedings/annual_meeting/2025/pdf_dir/Q4-19.pdf) assesses the safety of Japanese language model outputs using the LLM-as-a-Judge approach, based on the test set from [llm-jp/AnswerCarefully](https://huggingface.co/datasets/llm-jp/AnswerCarefully).
We evaluated the models using `gpt-4o-2024-08-06`.
The scores represent the average values obtained from three rounds of inference and evaluation.
For more details, please refer to the [codes](https://github.com/llm-jp/llm-jp-judge/tree/v1.0.0).
| Model name | Score | Acceptance rate (%, ↑) | Violation rate (%, ↓) |
| :--- | ---: | ---: | ---: |
| gpt-35-turbo-1106 | 3.98 | 71.7 | 12.6 |
| gpt-4-0613 | 4.06 | 72.3 | 13.2 |
| gpt-4o-2024-08-06 | 4.09 | 72.7 | 12.5 |
| [llm-jp-3-1.8b-instruct3](https://huggingface.co/llm-jp/llm-jp-3-1.8b-instruct3) | 4.03 | 75.9 | 12.2 |
| [llm-jp-3-13b-instruct3](https://huggingface.co/llm-jp/llm-jp-3-13b-instruct3) | 4.37 | 88.4 | 6.5 |
| [llm-jp-3-8x13b-instruct3](https://huggingface.co/llm-jp/llm-jp-3-8x13b-instruct3) | 4.48 | 91.6 | 4.3 |
| [llm-jp-3.1-1.8b-instruct4](https://huggingface.co/llm-jp/llm-jp-3.1-1.8b-instruct4) | 3.66 | 64.7 | 24.3 |
| [llm-jp-3.1-13b-instruct4](https://huggingface.co/llm-jp/llm-jp-3.1-13b-instruct4) | 4.17 | 82.4 | 12.2 |
| [llm-jp-3.1-8x13b-instruct4](https://huggingface.co/llm-jp/llm-jp-3.1-8x13b-instruct4) | 4.26 | 83.1 | 11.6 |
## Risks and Limitations
The models released here are in the early stages of our research and development and have not been tuned to ensure outputs align with human intent and safety considerations.
## Send Questions to
llm-jp(at)nii.ac.jp
## License
[Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0)
## Model Card Authors
*The names are listed in alphabetical order.*
Hirokazu Kiyomaru and Takashi Kodama. |
thejaminator/thinkflags-medium_high-4e-05-8000-20instruct-qwen3_32b | thejaminator | 2025-05-30T04:36:16Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen3",
"trl",
"en",
"base_model:unsloth/Qwen3-32B",
"base_model:finetune:unsloth/Qwen3-32B",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-05-30T04:35:47Z | ---
base_model: unsloth/Qwen3-32B
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** thejaminator
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen3-32B
This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
sebastianmr18/xlm-roberta-ner-qlora-bs32-epochs-4 | sebastianmr18 | 2025-05-30T04:35:56Z | 0 | 0 | peft | [
"peft",
"safetensors",
"xlm-roberta",
"arxiv:1910.09700",
"base_model:FacebookAI/xlm-roberta-large",
"base_model:adapter:FacebookAI/xlm-roberta-large",
"region:us"
] | null | 2025-05-30T02:53:54Z | ---
base_model: xlm-roberta-large
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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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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### Framework versions
- PEFT 0.15.2 |
HitmanReborn/wired_2 | HitmanReborn | 2025-05-30T04:29:18Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-30T04:23:57Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- **Shared by [optional]:** [More Information Needed]
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## Bias, Risks, and Limitations
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[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
## Training Details
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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AmberYifan/Qwen2.5-7B-sft-dpo-10k-IPO | AmberYifan | 2025-05-30T04:27:29Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"trl",
"dpo",
"conversational",
"arxiv:2305.18290",
"base_model:AmberYifan/Qwen2.5-7B-sft-ultrachat-safeRLHF",
"base_model:finetune:AmberYifan/Qwen2.5-7B-sft-ultrachat-safeRLHF",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-30T04:04:44Z | ---
base_model: AmberYifan/Qwen2.5-7B-sft-ultrachat-safeRLHF
library_name: transformers
model_name: Qwen2.5-7B-sft-dpo-10k-IPO
tags:
- generated_from_trainer
- trl
- dpo
licence: license
---
# Model Card for Qwen2.5-7B-sft-dpo-10k-IPO
This model is a fine-tuned version of [AmberYifan/Qwen2.5-7B-sft-ultrachat-safeRLHF](https://huggingface.co/AmberYifan/Qwen2.5-7B-sft-ultrachat-safeRLHF).
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="AmberYifan/Qwen2.5-7B-sft-dpo-10k-IPO", 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/yifanwang/huggingface/runs/6w2xqcl7)
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: 0.12.2
- Transformers: 4.46.3
- Pytorch: 2.7.0
- Datasets: 3.6.0
- Tokenizers: 0.20.3
## 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}}
}
``` |
natchasuha/emoji-model_timelms | natchasuha | 2025-05-30T04:23:07Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"roberta",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-05-30T04:22:25Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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## Training Details
### Training Data
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### Training Procedure
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#### Preprocessing [optional]
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#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
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#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
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## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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Kudod/bert-base-ner-ghtk-ai-fluent-new-data-3090-30may-1 | Kudod | 2025-05-30T04:22:51Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"token-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2025-05-30T04:17:27Z | ---
library_name: transformers
license: apache-2.0
base_model: google-bert/bert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: bert-base-ner-ghtk-ai-fluent-new-data-3090-30may-1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-ner-ghtk-ai-fluent-new-data-3090-30may-1
This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2517
- Ho: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 7}
- Hoảng thời gian: {'precision': 0.4583333333333333, 'recall': 0.6875, 'f1': 0.5499999999999999, 'number': 16}
- Háng trừu tượng: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 5}
- Hông tin ctt: {'precision': 0.8225806451612904, 'recall': 0.8095238095238095, 'f1': 0.8160000000000001, 'number': 63}
- Hụ cấp: {'precision': 0.4230769230769231, 'recall': 0.5, 'f1': 0.4583333333333333, 'number': 22}
- Hứ: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3}
- Iấy tờ: {'precision': 1.0, 'recall': 0.3333333333333333, 'f1': 0.5, 'number': 6}
- Iền cụ thể: {'precision': 0.7653061224489796, 'recall': 0.8522727272727273, 'f1': 0.8064516129032258, 'number': 88}
- Iền trừu tượng: {'precision': 0.4, 'recall': 0.45161290322580644, 'f1': 0.4242424242424243, 'number': 31}
- Ã số thuế: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2}
- Ình thức làm việc: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1}
- Ông: {'precision': 0.35294117647058826, 'recall': 0.6, 'f1': 0.4444444444444445, 'number': 10}
- Ương: {'precision': 0.5045871559633027, 'recall': 0.6707317073170732, 'f1': 0.5759162303664922, 'number': 82}
- Ị trí: {'precision': 0.6176470588235294, 'recall': 0.7777777777777778, 'f1': 0.6885245901639345, 'number': 54}
- Ố công: {'precision': 0.7592592592592593, 'recall': 0.7735849056603774, 'f1': 0.7663551401869159, 'number': 53}
- Ố giờ: {'precision': 0.6111111111111112, 'recall': 1.0, 'f1': 0.7586206896551725, 'number': 22}
- Ố điểm: {'precision': 0.7142857142857143, 'recall': 1.0, 'f1': 0.8333333333333333, 'number': 10}
- Ố đơn: {'precision': 0.76, 'recall': 0.8260869565217391, 'f1': 0.7916666666666667, 'number': 23}
- Ợt: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3}
- Ỷ lệ: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 3}
- Overall Precision: 0.6265
- Overall Recall: 0.7222
- Overall F1: 0.6710
- Overall Accuracy: 0.9355
## 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: 2.5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Ho | Hoảng thời gian | Háng trừu tượng | Hông tin ctt | Hụ cấp | Hứ | Iấy tờ | Iền cụ thể | Iền trừu tượng | Ã số thuế | Ình thức làm việc | Ông | Ương | Ị trí | Ố công | Ố giờ | Ố điểm | Ố đơn | Ợt | Ỷ lệ | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------------------------------------------------------:|:-------------------------------------------------------------------------------------------:|:---------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------:|:--------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------:|:---------------------------------------------------------:|:-----------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------:|:------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| No log | 1.0 | 74 | 0.4448 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 7} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 16} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 5} | {'precision': 0.3387096774193548, 'recall': 0.6666666666666666, 'f1': 0.44919786096256675, 'number': 63} | {'precision': 0.0625, 'recall': 0.13636363636363635, 'f1': 0.08571428571428572, 'number': 22} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 6} | {'precision': 0.4327485380116959, 'recall': 0.8409090909090909, 'f1': 0.5714285714285715, 'number': 88} | {'precision': 0.05714285714285714, 'recall': 0.06451612903225806, 'f1': 0.060606060606060615, 'number': 31} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 10} | {'precision': 0.1875, 'recall': 0.36585365853658536, 'f1': 0.2479338842975207, 'number': 82} | {'precision': 1.0, 'recall': 0.018518518518518517, 'f1': 0.03636363636363636, 'number': 54} | {'precision': 0.375, 'recall': 0.05660377358490566, 'f1': 0.09836065573770493, 'number': 53} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 22} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 10} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 23} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | 0.2834 | 0.3075 | 0.2950 | 0.8759 |
| No log | 2.0 | 148 | 0.3091 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 7} | {'precision': 0.2222222222222222, 'recall': 0.25, 'f1': 0.23529411764705882, 'number': 16} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 5} | {'precision': 0.7361111111111112, 'recall': 0.8412698412698413, 'f1': 0.7851851851851852, 'number': 63} | {'precision': 0.3076923076923077, 'recall': 0.36363636363636365, 'f1': 0.33333333333333337, 'number': 22} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 6} | {'precision': 0.6239316239316239, 'recall': 0.8295454545454546, 'f1': 0.7121951219512196, 'number': 88} | {'precision': 0.20689655172413793, 'recall': 0.3870967741935484, 'f1': 0.26966292134831465, 'number': 31} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 10} | {'precision': 0.37209302325581395, 'recall': 0.5853658536585366, 'f1': 0.4549763033175355, 'number': 82} | {'precision': 0.8108108108108109, 'recall': 0.5555555555555556, 'f1': 0.6593406593406594, 'number': 54} | {'precision': 0.5098039215686274, 'recall': 0.49056603773584906, 'f1': 0.5, 'number': 53} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 22} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 10} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 23} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | 0.5 | 0.5040 | 0.5020 | 0.9125 |
| No log | 3.0 | 222 | 0.2713 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 7} | {'precision': 0.30434782608695654, 'recall': 0.4375, 'f1': 0.358974358974359, 'number': 16} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 5} | {'precision': 0.7647058823529411, 'recall': 0.8253968253968254, 'f1': 0.7938931297709922, 'number': 63} | {'precision': 0.2727272727272727, 'recall': 0.4090909090909091, 'f1': 0.32727272727272727, 'number': 22} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 6} | {'precision': 0.6956521739130435, 'recall': 0.9090909090909091, 'f1': 0.7881773399014779, 'number': 88} | {'precision': 0.2641509433962264, 'recall': 0.45161290322580644, 'f1': 0.3333333333333333, 'number': 31} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 10} | {'precision': 0.47474747474747475, 'recall': 0.573170731707317, 'f1': 0.5193370165745855, 'number': 82} | {'precision': 0.5846153846153846, 'recall': 0.7037037037037037, 'f1': 0.638655462184874, 'number': 54} | {'precision': 0.66, 'recall': 0.6226415094339622, 'f1': 0.6407766990291262, 'number': 53} | {'precision': 0.625, 'recall': 0.22727272727272727, 'f1': 0.3333333333333333, 'number': 22} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 10} | {'precision': 0.7, 'recall': 0.30434782608695654, 'f1': 0.42424242424242425, 'number': 23} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | 0.5520 | 0.5794 | 0.5653 | 0.9242 |
| No log | 4.0 | 296 | 0.2492 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 7} | {'precision': 0.3333333333333333, 'recall': 0.5625, 'f1': 0.4186046511627907, 'number': 16} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 5} | {'precision': 0.75, 'recall': 0.8571428571428571, 'f1': 0.7999999999999999, 'number': 63} | {'precision': 0.3448275862068966, 'recall': 0.45454545454545453, 'f1': 0.39215686274509803, 'number': 22} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 6} | {'precision': 0.6981132075471698, 'recall': 0.8409090909090909, 'f1': 0.7628865979381443, 'number': 88} | {'precision': 0.3888888888888889, 'recall': 0.45161290322580644, 'f1': 0.417910447761194, 'number': 31} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1} | {'precision': 0.2, 'recall': 0.1, 'f1': 0.13333333333333333, 'number': 10} | {'precision': 0.5, 'recall': 0.6097560975609756, 'f1': 0.5494505494505494, 'number': 82} | {'precision': 0.7450980392156863, 'recall': 0.7037037037037037, 'f1': 0.7238095238095239, 'number': 54} | {'precision': 0.8372093023255814, 'recall': 0.6792452830188679, 'f1': 0.7499999999999999, 'number': 53} | {'precision': 0.7083333333333334, 'recall': 0.7727272727272727, 'f1': 0.7391304347826088, 'number': 22} | {'precision': 1.0, 'recall': 0.1, 'f1': 0.18181818181818182, 'number': 10} | {'precision': 0.6470588235294118, 'recall': 0.4782608695652174, 'f1': 0.55, 'number': 23} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | {'precision': 0.5, 'recall': 0.3333333333333333, 'f1': 0.4, 'number': 3} | 0.6136 | 0.6270 | 0.6202 | 0.9365 |
| No log | 5.0 | 370 | 0.2382 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 7} | {'precision': 0.36, 'recall': 0.5625, 'f1': 0.43902439024390244, 'number': 16} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 5} | {'precision': 0.7571428571428571, 'recall': 0.8412698412698413, 'f1': 0.7969924812030075, 'number': 63} | {'precision': 0.41379310344827586, 'recall': 0.5454545454545454, 'f1': 0.47058823529411764, 'number': 22} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 6} | {'precision': 0.7184466019417476, 'recall': 0.8409090909090909, 'f1': 0.7748691099476439, 'number': 88} | {'precision': 0.3870967741935484, 'recall': 0.3870967741935484, 'f1': 0.3870967741935484, 'number': 31} | {'precision': 0.5, 'recall': 0.5, 'f1': 0.5, 'number': 2} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1} | {'precision': 0.2727272727272727, 'recall': 0.3, 'f1': 0.28571428571428564, 'number': 10} | {'precision': 0.5, 'recall': 0.6219512195121951, 'f1': 0.5543478260869564, 'number': 82} | {'precision': 0.6842105263157895, 'recall': 0.7222222222222222, 'f1': 0.7027027027027027, 'number': 54} | {'precision': 0.7916666666666666, 'recall': 0.7169811320754716, 'f1': 0.7524752475247525, 'number': 53} | {'precision': 0.8, 'recall': 0.9090909090909091, 'f1': 0.8510638297872342, 'number': 22} | {'precision': 1.0, 'recall': 0.8, 'f1': 0.888888888888889, 'number': 10} | {'precision': 0.75, 'recall': 0.6521739130434783, 'f1': 0.6976744186046512, 'number': 23} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | {'precision': 1.0, 'recall': 0.6666666666666666, 'f1': 0.8, 'number': 3} | 0.6299 | 0.6687 | 0.6487 | 0.9357 |
| No log | 6.0 | 444 | 0.2450 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 7} | {'precision': 0.45454545454545453, 'recall': 0.625, 'f1': 0.5263157894736842, 'number': 16} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 5} | {'precision': 0.8387096774193549, 'recall': 0.8253968253968254, 'f1': 0.832, 'number': 63} | {'precision': 0.3793103448275862, 'recall': 0.5, 'f1': 0.4313725490196078, 'number': 22} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 6} | {'precision': 0.7755102040816326, 'recall': 0.8636363636363636, 'f1': 0.8172043010752688, 'number': 88} | {'precision': 0.3404255319148936, 'recall': 0.5161290322580645, 'f1': 0.41025641025641024, 'number': 31} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1} | {'precision': 0.42857142857142855, 'recall': 0.6, 'f1': 0.5, 'number': 10} | {'precision': 0.49038461538461536, 'recall': 0.6219512195121951, 'f1': 0.5483870967741936, 'number': 82} | {'precision': 0.6896551724137931, 'recall': 0.7407407407407407, 'f1': 0.7142857142857143, 'number': 54} | {'precision': 0.7450980392156863, 'recall': 0.7169811320754716, 'f1': 0.7307692307692308, 'number': 53} | {'precision': 0.7307692307692307, 'recall': 0.8636363636363636, 'f1': 0.7916666666666666, 'number': 22} | {'precision': 0.7692307692307693, 'recall': 1.0, 'f1': 0.8695652173913044, 'number': 10} | {'precision': 0.8888888888888888, 'recall': 0.6956521739130435, 'f1': 0.7804878048780488, 'number': 23} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 3} | 0.6352 | 0.6944 | 0.6635 | 0.9352 |
| 0.2682 | 7.0 | 518 | 0.2420 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 7} | {'precision': 0.5, 'recall': 0.6875, 'f1': 0.5789473684210527, 'number': 16} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 5} | {'precision': 0.782608695652174, 'recall': 0.8571428571428571, 'f1': 0.8181818181818182, 'number': 63} | {'precision': 0.4444444444444444, 'recall': 0.5454545454545454, 'f1': 0.4897959183673469, 'number': 22} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 6} | {'precision': 0.7835051546391752, 'recall': 0.8636363636363636, 'f1': 0.8216216216216216, 'number': 88} | {'precision': 0.40425531914893614, 'recall': 0.6129032258064516, 'f1': 0.48717948717948717, 'number': 31} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1} | {'precision': 0.4, 'recall': 0.6, 'f1': 0.48, 'number': 10} | {'precision': 0.49038461538461536, 'recall': 0.6219512195121951, 'f1': 0.5483870967741936, 'number': 82} | {'precision': 0.5846153846153846, 'recall': 0.7037037037037037, 'f1': 0.638655462184874, 'number': 54} | {'precision': 0.7454545454545455, 'recall': 0.7735849056603774, 'f1': 0.7592592592592593, 'number': 53} | {'precision': 0.5945945945945946, 'recall': 1.0, 'f1': 0.7457627118644068, 'number': 22} | {'precision': 0.7692307692307693, 'recall': 1.0, 'f1': 0.8695652173913044, 'number': 10} | {'precision': 0.76, 'recall': 0.8260869565217391, 'f1': 0.7916666666666667, 'number': 23} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 3} | 0.6222 | 0.7222 | 0.6685 | 0.9353 |
| 0.2682 | 8.0 | 592 | 0.2448 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 7} | {'precision': 0.4782608695652174, 'recall': 0.6875, 'f1': 0.5641025641025642, 'number': 16} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 5} | {'precision': 0.7846153846153846, 'recall': 0.8095238095238095, 'f1': 0.7968749999999999, 'number': 63} | {'precision': 0.4166666666666667, 'recall': 0.45454545454545453, 'f1': 0.43478260869565216, 'number': 22} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | {'precision': 0.5, 'recall': 0.16666666666666666, 'f1': 0.25, 'number': 6} | {'precision': 0.7835051546391752, 'recall': 0.8636363636363636, 'f1': 0.8216216216216216, 'number': 88} | {'precision': 0.4411764705882353, 'recall': 0.4838709677419355, 'f1': 0.4615384615384615, 'number': 31} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1} | {'precision': 0.4, 'recall': 0.6, 'f1': 0.48, 'number': 10} | {'precision': 0.5544554455445545, 'recall': 0.6829268292682927, 'f1': 0.6120218579234973, 'number': 82} | {'precision': 0.7192982456140351, 'recall': 0.7592592592592593, 'f1': 0.7387387387387387, 'number': 54} | {'precision': 0.75, 'recall': 0.7924528301886793, 'f1': 0.7706422018348623, 'number': 53} | {'precision': 0.6111111111111112, 'recall': 1.0, 'f1': 0.7586206896551725, 'number': 22} | {'precision': 0.9090909090909091, 'recall': 1.0, 'f1': 0.9523809523809523, 'number': 10} | {'precision': 0.84, 'recall': 0.9130434782608695, 'f1': 0.8749999999999999, 'number': 23} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 3} | 0.6637 | 0.7282 | 0.6944 | 0.9387 |
| 0.2682 | 9.0 | 666 | 0.2501 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 7} | {'precision': 0.4782608695652174, 'recall': 0.6875, 'f1': 0.5641025641025642, 'number': 16} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 5} | {'precision': 0.803030303030303, 'recall': 0.8412698412698413, 'f1': 0.8217054263565892, 'number': 63} | {'precision': 0.4444444444444444, 'recall': 0.5454545454545454, 'f1': 0.4897959183673469, 'number': 22} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | {'precision': 0.5, 'recall': 0.16666666666666666, 'f1': 0.25, 'number': 6} | {'precision': 0.75, 'recall': 0.8522727272727273, 'f1': 0.7978723404255319, 'number': 88} | {'precision': 0.40476190476190477, 'recall': 0.5483870967741935, 'f1': 0.4657534246575342, 'number': 31} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1} | {'precision': 0.4, 'recall': 0.6, 'f1': 0.48, 'number': 10} | {'precision': 0.4818181818181818, 'recall': 0.6463414634146342, 'f1': 0.5520833333333333, 'number': 82} | {'precision': 0.6557377049180327, 'recall': 0.7407407407407407, 'f1': 0.6956521739130433, 'number': 54} | {'precision': 0.75, 'recall': 0.7358490566037735, 'f1': 0.7428571428571428, 'number': 53} | {'precision': 0.5945945945945946, 'recall': 1.0, 'f1': 0.7457627118644068, 'number': 22} | {'precision': 0.7142857142857143, 'recall': 1.0, 'f1': 0.8333333333333333, 'number': 10} | {'precision': 0.76, 'recall': 0.8260869565217391, 'f1': 0.7916666666666667, 'number': 23} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 3} | 0.6237 | 0.7202 | 0.6685 | 0.9357 |
| 0.2682 | 10.0 | 740 | 0.2517 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 7} | {'precision': 0.4583333333333333, 'recall': 0.6875, 'f1': 0.5499999999999999, 'number': 16} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 5} | {'precision': 0.8225806451612904, 'recall': 0.8095238095238095, 'f1': 0.8160000000000001, 'number': 63} | {'precision': 0.4230769230769231, 'recall': 0.5, 'f1': 0.4583333333333333, 'number': 22} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | {'precision': 1.0, 'recall': 0.3333333333333333, 'f1': 0.5, 'number': 6} | {'precision': 0.7653061224489796, 'recall': 0.8522727272727273, 'f1': 0.8064516129032258, 'number': 88} | {'precision': 0.4, 'recall': 0.45161290322580644, 'f1': 0.4242424242424243, 'number': 31} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1} | {'precision': 0.35294117647058826, 'recall': 0.6, 'f1': 0.4444444444444445, 'number': 10} | {'precision': 0.5045871559633027, 'recall': 0.6707317073170732, 'f1': 0.5759162303664922, 'number': 82} | {'precision': 0.6176470588235294, 'recall': 0.7777777777777778, 'f1': 0.6885245901639345, 'number': 54} | {'precision': 0.7592592592592593, 'recall': 0.7735849056603774, 'f1': 0.7663551401869159, 'number': 53} | {'precision': 0.6111111111111112, 'recall': 1.0, 'f1': 0.7586206896551725, 'number': 22} | {'precision': 0.7142857142857143, 'recall': 1.0, 'f1': 0.8333333333333333, 'number': 10} | {'precision': 0.76, 'recall': 0.8260869565217391, 'f1': 0.7916666666666667, 'number': 23} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 3} | 0.6265 | 0.7222 | 0.6710 | 0.9355 |
### Framework versions
- Transformers 4.46.3
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3
|
Triangle104/Josiefied-DeepSeek-R1-0528-Qwen3-8B-abliterated-v1-Q8_0-GGUF | Triangle104 | 2025-05-30T04:21:40Z | 0 | 0 | null | [
"gguf",
"chat",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"base_model:Goekdeniz-Guelmez/Josiefied-DeepSeek-R1-0528-Qwen3-8B-abliterated-v1",
"base_model:quantized:Goekdeniz-Guelmez/Josiefied-DeepSeek-R1-0528-Qwen3-8B-abliterated-v1",
"endpoints_compatible",
"region:us",
"conversational"
] | text-generation | 2025-05-30T04:19:29Z | ---
tags:
- chat
- llama-cpp
- gguf-my-repo
base_model: Goekdeniz-Guelmez/Josiefied-DeepSeek-R1-0528-Qwen3-8B-abliterated-v1
pipeline_tag: text-generation
---
# Triangle104/Josiefied-DeepSeek-R1-0528-Qwen3-8B-abliterated-v1-Q8_0-GGUF
This model was converted to GGUF format from [`Goekdeniz-Guelmez/Josiefied-DeepSeek-R1-0528-Qwen3-8B-abliterated-v1`](https://huggingface.co/Goekdeniz-Guelmez/Josiefied-DeepSeek-R1-0528-Qwen3-8B-abliterated-v1) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/Goekdeniz-Guelmez/Josiefied-DeepSeek-R1-0528-Qwen3-8B-abliterated-v1) for more details on the model.
---
The JOSIEFIED model family represents a series of highly advanced language models built upon renowned architectures such as Alibaba’s Qwen2/2.5/3, Google’s Gemma3, and Meta’s LLaMA 3/4. Covering sizes from 0.5B to 32B parameters, these models have been significantly modified (“abliterated”) and further fine-tuned to maximize uncensored behavior without compromising tool usage or instruction-following abilities.
Despite their rebellious spirit, the JOSIEFIED models often outperform their base counterparts on standard benchmarks — delivering both raw power and utility.
These models are intended for advanced users who require unrestricted, high-performance language generation.
---
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo Triangle104/Josiefied-DeepSeek-R1-0528-Qwen3-8B-abliterated-v1-Q8_0-GGUF --hf-file josiefied-deepseek-r1-0528-qwen3-8b-abliterated-v1-q8_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Triangle104/Josiefied-DeepSeek-R1-0528-Qwen3-8B-abliterated-v1-Q8_0-GGUF --hf-file josiefied-deepseek-r1-0528-qwen3-8b-abliterated-v1-q8_0.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Triangle104/Josiefied-DeepSeek-R1-0528-Qwen3-8B-abliterated-v1-Q8_0-GGUF --hf-file josiefied-deepseek-r1-0528-qwen3-8b-abliterated-v1-q8_0.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Triangle104/Josiefied-DeepSeek-R1-0528-Qwen3-8B-abliterated-v1-Q8_0-GGUF --hf-file josiefied-deepseek-r1-0528-qwen3-8b-abliterated-v1-q8_0.gguf -c 2048
```
|
TanAlexanderlz/RALL_NoCrop_ori32F-8B32F | TanAlexanderlz | 2025-05-30T04:20:49Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"videomae",
"video-classification",
"generated_from_trainer",
"base_model:MCG-NJU/videomae-base-finetuned-kinetics",
"base_model:finetune:MCG-NJU/videomae-base-finetuned-kinetics",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | video-classification | 2025-05-30T02:05:34Z | ---
library_name: transformers
license: cc-by-nc-4.0
base_model: MCG-NJU/videomae-base-finetuned-kinetics
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: RALL_NoCrop_ori32F-8B32F
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. -->
# RALL_NoCrop_ori32F-8B32F
This model is a fine-tuned version of [MCG-NJU/videomae-base-finetuned-kinetics](https://huggingface.co/MCG-NJU/videomae-base-finetuned-kinetics) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6057
- Accuracy: 0.7651
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- 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_ratio: 0.1
- training_steps: 1152
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.5687 | 0.0842 | 97 | 0.5752 | 0.6768 |
| 0.6119 | 1.0842 | 194 | 0.6053 | 0.6585 |
| 0.5384 | 2.0842 | 291 | 0.6051 | 0.6890 |
| 0.3456 | 3.0842 | 388 | 0.6302 | 0.75 |
| 0.2481 | 4.0842 | 485 | 0.8713 | 0.7012 |
| 0.1551 | 5.0842 | 582 | 1.2408 | 0.7195 |
| 0.2597 | 6.0842 | 679 | 1.1164 | 0.6890 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
Triangle104/Josiefied-DeepSeek-R1-0528-Qwen3-8B-abliterated-v1-Q5_K_M-GGUF | Triangle104 | 2025-05-30T04:15:13Z | 0 | 0 | null | [
"gguf",
"chat",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"base_model:Goekdeniz-Guelmez/Josiefied-DeepSeek-R1-0528-Qwen3-8B-abliterated-v1",
"base_model:quantized:Goekdeniz-Guelmez/Josiefied-DeepSeek-R1-0528-Qwen3-8B-abliterated-v1",
"endpoints_compatible",
"region:us",
"conversational"
] | text-generation | 2025-05-30T04:10:53Z | ---
tags:
- chat
- llama-cpp
- gguf-my-repo
base_model: Goekdeniz-Guelmez/Josiefied-DeepSeek-R1-0528-Qwen3-8B-abliterated-v1
pipeline_tag: text-generation
---
# Triangle104/Josiefied-DeepSeek-R1-0528-Qwen3-8B-abliterated-v1-Q5_K_M-GGUF
This model was converted to GGUF format from [`Goekdeniz-Guelmez/Josiefied-DeepSeek-R1-0528-Qwen3-8B-abliterated-v1`](https://huggingface.co/Goekdeniz-Guelmez/Josiefied-DeepSeek-R1-0528-Qwen3-8B-abliterated-v1) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/Goekdeniz-Guelmez/Josiefied-DeepSeek-R1-0528-Qwen3-8B-abliterated-v1) for more details on the model.
---
The JOSIEFIED model family represents a series of highly advanced language models built upon renowned architectures such as Alibaba’s Qwen2/2.5/3, Google’s Gemma3, and Meta’s LLaMA 3/4. Covering sizes from 0.5B to 32B parameters, these models have been significantly modified (“abliterated”) and further fine-tuned to maximize uncensored behavior without compromising tool usage or instruction-following abilities.
Despite their rebellious spirit, the JOSIEFIED models often outperform their base counterparts on standard benchmarks — delivering both raw power and utility.
These models are intended for advanced users who require unrestricted, high-performance language generation.
---
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo Triangle104/Josiefied-DeepSeek-R1-0528-Qwen3-8B-abliterated-v1-Q5_K_M-GGUF --hf-file josiefied-deepseek-r1-0528-qwen3-8b-abliterated-v1-q5_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Triangle104/Josiefied-DeepSeek-R1-0528-Qwen3-8B-abliterated-v1-Q5_K_M-GGUF --hf-file josiefied-deepseek-r1-0528-qwen3-8b-abliterated-v1-q5_k_m.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Triangle104/Josiefied-DeepSeek-R1-0528-Qwen3-8B-abliterated-v1-Q5_K_M-GGUF --hf-file josiefied-deepseek-r1-0528-qwen3-8b-abliterated-v1-q5_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Triangle104/Josiefied-DeepSeek-R1-0528-Qwen3-8B-abliterated-v1-Q5_K_M-GGUF --hf-file josiefied-deepseek-r1-0528-qwen3-8b-abliterated-v1-q5_k_m.gguf -c 2048
```
|
lopoman/gemma-3-4b-finetune-GGUF | lopoman | 2025-05-30T04:13:52Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"text-generation-inference",
"unsloth",
"gemma3",
"llama-cpp",
"gguf-my-repo",
"en",
"base_model:vasiache/gemma-3-4b-int4-merged",
"base_model:quantized:vasiache/gemma-3-4b-int4-merged",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-05-30T03:56:20Z | ---
base_model: vasiache/gemma-3-4b-int4-merged
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3
- llama-cpp
- gguf-my-repo
license: apache-2.0
language:
- en
---
# lopoman/gemma-3-4b-finetune-GGUF
This model was converted to GGUF format from [`vasiache/gemma-3-4b-int4-merged`](https://huggingface.co/vasiache/gemma-3-4b-int4-merged) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/vasiache/gemma-3-4b-int4-merged) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo lopoman/gemma-3-4b-finetune-GGUF --hf-file gemma-3-4b-int4-merged-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo lopoman/gemma-3-4b-finetune-GGUF --hf-file gemma-3-4b-int4-merged-q4_k_m.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo lopoman/gemma-3-4b-finetune-GGUF --hf-file gemma-3-4b-int4-merged-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo lopoman/gemma-3-4b-finetune-GGUF --hf-file gemma-3-4b-int4-merged-q4_k_m.gguf -c 2048
```
|
gregorytan/rl | gregorytan | 2025-05-30T04:11:03Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2025-05-30T04:11:00Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: rl
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.44 +/- 2.70
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="gregorytan/rl", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
Satori-reasoning/Satori-SWE-RL-32B | Satori-reasoning | 2025-05-30T04:09:32Z | 0 | 0 | null | [
"safetensors",
"qwen2",
"arxiv:2505.23604",
"base_model:Satori-reasoning/Satori-SWE-SFT-32B",
"base_model:finetune:Satori-reasoning/Satori-SWE-SFT-32B",
"license:apache-2.0",
"region:us"
] | null | 2025-05-29T04:22:18Z | ---
license: apache-2.0
base_model:
- Satori-reasoning/Satori-SWE-SFT-32B
---
# Satori‑SWE‑RL‑32B
## Overview
🚀 **Satori-SWE-RL-32B** is trained specifically to resolve software engineering tasks efficiently, using our proposed [**EvoScale**](https://arxiv.org/pdf/2505.23604) test-time scaling technique, and a novel training framework: two-stage SFT and RL. The model can iteratively self-improve its own generation to progressively write a better patch.
## Training Data
- **SFT Dataset**: [Satori SFT Dataset](https://huggingface.co/datasets/Satori-reasoning/Satori-SWE-two-stage-SFT-data)
- **RL Dataset**: [Satori RL Dataset](https://huggingface.co/datasets/Satori-reasoning/Satori-SWE-RL-data)
## Resources
🔗 **GitHub Repository**: [Satori-SWE](https://github.com/Satori-Reasoning/Satori-SWE)
🔗 **Blog Post**: [Blog](https://satori-reasoning.github.io/blog/satori-swe/)
🔗 **Research Paper**: [Paper](https://arxiv.org/abs/2505.23604)
## Prompt Template
````python
classical_sft_prompt = """You are an expert software engineer and seasoned code reviewer, specializing in bug localization and code optimization within real-world code repositories. Your strengths lie in understanding complex codebase structures and precisely identifying and modifying the relevant parts of the code to resolve issues. You also excel at articulating your reasoning process in a coherent, step-by-step manner that leads to efficient and correct bug fixes.
You will be provided with a codebase and an issue description. Your task is to simulate a complete reasoning process—step-by-step—as if solving the issue from scratch, followed by the code modifications to resolve the issue.
---
# Issue Statement
{problem_statement}
---
# Files to be Modified
Below are some code files that might be relevant to the issue above. One or more of these files may contain bugs.
{files}
---
# Reasoning Guidelines
Your reasoning process should generally follow these steps, with flexibility to adjust as needed for clarity and accuracy:
1. **Issue Analysis**: Start by thoroughly analyzing the issue. Explain what the problem is, why it matters, and what the intended behavior should be. Identify the key goals and constraints that must be addressed in your solution.
2. **Task Decomposition**: Break down the issue into smaller, manageable sub-tasks. Describe the purpose of each sub-task and how it contributes to solving the overall problem.
3. **Code Localization and Editing**: For each sub-task:
- Identify relevant code snippets by file path and code location.
- Explain how each snippet relates to the sub-task.
- Describe how the code should be changed and justify your reasoning.
- After thorough explanation, provide the corresponding edited code.
---
# General Requirements
1. **Clear and Evidence-Based Reasoning**: Provide clear and precise reasoning for each step, strictly based on the provided issue and code without inferring information not explicitly stated.
2. **Comprehensive and Concise**: Address all relevant aspects of the issue comprehensively while being concise. Justify the exclusion of any sections that are not relevant.
3. **Detailed Guidance**: Ensure the reasoning steps are detailed enough to allow someone unfamiliar with the solution to infer and implement the necessary code modifications.
---
# Response Format
1. The reasoning process should be enclosed in <think> ... </think>.
2. The final patch should be output in a standalone Python code block *after* the </think> block.
3. Do not include any commentary or justification after the </think> block.
---
# Patch Format
Please generate *SEARCH/REPLACE* edits to fix the issue. Every *SEARCH/REPLACE* edit must use this format:
1. The file path
2. The start of search block: <<<<<<< SEARCH
3. A contiguous chunk of lines to search for in the existing source code
4. The dividing line: =======
5. The lines to replace into the source code
6. The end of the replace block: >>>>>>> REPLACE
If, in `Files to be Modified` part, there are multiple files or multiple locations in a single file require changes. You should provide separate patches for each modification, clearly indicating the file name and the specific location of the modification.
Please note that the *SEARCH/REPLACE* edit REQUIRES PROPER INDENTATION. For example, if you would like to add the line ' print(x)', you must fully write that out, with all those spaces before the code! And remember to wrap the *SEARCH/REPLACE* edit in blocks ```python...```
# Example Response
<think>
1. Analyze the issue...
2. Locate the relevant code...
3. Apply necessary changes...
</think>
```python
### mathweb/flask/app.py
<<<<<<< SEARCH
from flask import Flask
=======
import math
from flask import Flask
>>>>>>> REPLACE
```
```python
### mathweb/utils/calc.py
<<<<<<< SEARCH
def calculate_area(radius):
return 3.14 * radius * radius
=======
def calculate_area(radius):
return math.pi * radius ** 2
>>>>>>> REPLACE
```
---
Please provide your response below.
"""
````
````python
mutation_sft_prompt = """You are an expert software engineer and seasoned code reviewer, specializing in bug localization and code optimization, with a particular talent for critically evaluating teammates' patches and synthesizing high-quality, precise solutions from collaborative efforts.
You will be presented with a GitHub issue, the relevant source code files, and five *candidate patches* submitted by your teammates. Your task is twofold:
1. **Patch Review**: Carefully evaluate each of the five candidate patches **individually**. Identify whether each patch resolves the issue correctly, partially, or incorrectly. If you identify any issues (e.g., logical errors, misunderstandings of the bug, overlooked edge cases, or incomplete fixes), explain them clearly and suggest what could be improved or corrected.
Even if a patch appears mostly correct, you should still analyze its strengths and limitations in detail. Treat this as a collaborative peer-review process: constructive, technical, and focused on improving code quality.
2. **Patch Synthesis**: After analyzing all five candidate patches, synthesize your understanding to produce your **own final code patch** that fully resolves the issue. Your patch should:
- Be grounded solely in the issue description and provided source code.
- Be informed by your peer review, but not copy any one patch outright.
---
# Issue Statement
{problem_statement}
---
# Files to be Modified
Below are some code files that might be relevant to the issue above. One or more of these files may contain bugs.
{files}
---
# Candidate Patches (From Collaborators)
Below are five proposed patches submitted by your teammates. You will evaluate them individually.
{candidate_patches}
---
# Reasoning and Review Guidelines
Your response should be structured into two parts:
## Part 1: Peer Patch Review
For each of the five candidate patches:
- Analyze the candidate patch's intent and correctness.
- Identify what it does well, what it gets wrong (if anything), and how it could be improved.
- Use precise references to the provided issue and source code files to justify your evaluation.
## Part 2: Final Patch Synthesis
After completing all five reviews, your reasoning process should generally follow these steps, with flexibility to adjust as needed for clarity and accuracy:
1. **Issue Analysis**: Start by thoroughly analyzing the issue. Explain what the problem is, why it matters, and what the intended behavior should be. Identify the key goals and constraints that must be addressed in your solution.
2. **Task Decomposition**: Break down the issue into smaller, manageable sub-tasks. Describe the purpose of each sub-task and how it contributes to solving the overall problem.
3. **Code Localization and Editing**: For each sub-task:
- Identify relevant code snippets by file path and code location.
- Explain how each snippet relates to the sub-task.
- Describe how the code should be changed and justify your reasoning.
- After thorough explanation, provide the corresponding edited code.
---
# General Requirements
1. **Clear and Evidence-Based Reasoning**: Provide clear and precise reasoning for each step, strictly based on the provided issue and code without inferring information not explicitly stated.
2. **Comprehensive and Concise**: Address all relevant aspects of the issue comprehensively while being concise. Justify the exclusion of any sections that are not relevant.
3. **Detailed Guidance**: Ensure the reasoning steps are detailed enough to allow someone unfamiliar with the solution to infer and implement the necessary code modifications.
---
# Response Format
1. The reasoning process should be enclosed in <think> ... </think>.
2. The final patch should be output in a standalone Python code block *after* the </think> block.
3. Do not include any commentary or justification after the </think> block.
---
# Patch Format
Please generate *SEARCH/REPLACE* edits to fix the issue. Every *SEARCH/REPLACE* edit must use this format:
1. The file path
2. The start of search block: <<<<<<< SEARCH
3. A contiguous chunk of lines to search for in the existing source code
4. The dividing line: =======
5. The lines to replace into the source code
6. The end of the replace block: >>>>>>> REPLACE
If, in `Files to be Modified` part, there are multiple files or multiple locations in a single file require changes. You should provide separate patches for each modification, clearly indicating the file name and the specific location of the modification.
Please note that the *SEARCH/REPLACE* edit REQUIRES PROPER INDENTATION. For example, if you would like to add the line ' print(x)', you must fully write that out, with all those spaces before the code! And remember to wrap the *SEARCH/REPLACE* edit in blocks ```python...```
# Example Response
<think>
1. Review of candidate patch:
- Review of patch-1: This patch attempts to fix X by modifying function Y. However, it fails to consider Z...
- Review of patch-2: ...
- Review of patch-3: ...
- Review of patch-4: ...
- Review of patch-5: ...
2. Analyze the issue by myself...
3. Locate the relevant code...
4. Apply necessary changes...
</think>
```python
### mathweb/flask/app.py
<<<<<<< SEARCH
from flask import Flask
=======
import math
from flask import Flask
>>>>>>> REPLACE
```
```python
### mathweb/utils/calc.py
<<<<<<< SEARCH
def calculate_area(radius):
return 3.14 * radius * radius
=======
def calculate_area(radius):
return math.pi * radius ** 2
>>>>>>> REPLACE
```
---
Please provide your response below.
"""
````
## Usage: Toy Example
````python
from vllm import LLM, SamplingParams
def generate(question, model_path):
llm = LLM(
model=model_path,
trust_remote_code=True,
tensor_parallel_size=8,
)
sampling_params = SamplingParams(
max_tokens=8192,
temperature=1.2,
n=1,
)
outputs = llm.generate([question], sampling_params, use_tqdm=True)
completions = [[output.text for output in output_item.outputs] for output_item in outputs]
return completions
# Classical Inference
model_path = "Satori-reasoning/Satori-SWE-RL-32B"
problem_statement = """I'm running `missing_colon.py` as follows:
```python
division(23, 0)
```
but I get the following error:
```
File "/Users/fuchur/Documents/24/git_sync/swe-agent-test-repo/tests/./missing_colon.py", line 4
def division(a: float, b: float) -> float
^
SyntaxError: invalid syntax
```"""
file_str_concat = """```python
### src/testpkg/missing_colon.py
#!/usr/bin/env python3
def division(a: float, b: float) -> float
return a/b
if __name__ == "__main__":
print(division(123, 15))
```"""
completions = generate(
classical_sft_prompt.format(
problem_statement=problem_statement,
files=file_str_concat
),
model_path
)
for completion in completions:
print(completion[0])
# Mutation inference
candidate_patches = """<patch>
```python
### src/testpkg/missing_colon.py
<<<<<<< SEARCH
def division(a: float, b: float) -> float
return a/b
if __name__ == "__main__":
print(division(123, 15))
=======
def division(a: float, b: float) -> float:
return a/b
if __name__ == "__main__":
print(division(123, 15))
>>>>>>> REPLACE
```
</patch>
<patch>
```python
### src/testpkg/missing_colon.py
<<<<<<< SEARCH
def division(a: float, b: float) -> float
return a/b
if __name__ == "__main__":
print(division(123, 15))
=======
def division(a: float, b: float) -> float:
return a/b
if __name__ == "__main__":
print(division(123, 15))
>>>>>>> REPLACE
```
</patch>
<patch>
```python
### src/testpkg/missing_colon.py
<<<<<<< SEARCH
def division(a: float, b: float) -> float
return a/b
if __name__ == "__main__":
print(division(123, 15))
=======
def division(a: float, b: float) -> float:
return a/b
if __name__ == "__main__":
print(division(123, 15))
>>>>>>> REPLACE
```
</patch>
<patch>
```python
### src/testpkg/missing_colon.py
<<<<<<< SEARCH
def division(a: float, b: float) -> float
return a/b
if __name__ == "__main__":
print(division(123, 15))
=======
def division(a: float, b: float) -> float:
return a/b
if __name__ == "__main__":
print(division(123, 15))
>>>>>>> REPLACE
```
</patch>
<patch>
```python
### src/testpkg/missing_colon.py
<<<<<<< SEARCH
def division(a: float, b: float) -> float
return a/b
if __name__ == "__main__":
print(division(123, 15))
=======
def division(a: float, b: float) -> float:
return a/b
if __name__ == "__main__":
print(division(123, 15))
>>>>>>> REPLACE
```
</patch>"""
mutation_completions = generate(
mutation_sft_prompt.format(
problem_statement=problem_statement,
files=file_str_concat,
candidate_patches,candidate_patches
),
model_path
)
for mutation_completion in mutation_completions:
print(mutation_completion[0])
````
## Citation
If you find this model useful, please cite our paper:
```bibtex
@misc{zeng2025satorisweevolutionarytesttimescaling,
title={Satori-SWE: Evolutionary Test-Time Scaling for Sample-Efficient Software Engineering},
author={Guangtao Zeng and Maohao Shen and Delin Chen and Zhenting Qi and Subhro Das and Dan Gutfreund and David Cox and Gregory Wornell and Wei Lu and Zhang-Wei Hong and Chuang Gan},
year={2025},
eprint={2505.23604},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2505.23604},
}
```
|
kienhoang123/Poem_Analysis_Instruct_VN | kienhoang123 | 2025-05-30T04:09:15Z | 0 | 0 | null | [
"safetensors",
"t5",
"region:us"
] | null | 2025-05-28T14:49:54Z | ---
language: vi
license: apache-2.0
tags:
- vietnamese
- poem-analysis
- instruction-tuned
- flan-t5
datasets:
- kienhoang123/Vietnamese_Poem_Analysis_VN
---
# Instruction-Tuned T5 Model for Vietnamese Poem Analysis
This model was fine-tuned on kienhoang123/Vietnamese_Poem_Analysis_VN to analyze Vietnamese poetry using an instruction-based approach.
## Model Details
- **Base Model**: google/flan-t5-small
- **Training Data**: Vietnamese poem analysis dataset
- **Tasks**: Extract emotion, metaphor, setting, motion, and prompt from Vietnamese poems
## Usage
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("kienhoang123/Poem_Analysis_Instruct_VN")
model = AutoModelForSeq2SeqLM.from_pretrained("kienhoang123/Poem_Analysis_Instruct_VN")
# Create an instruction-based input
instruction = '''
Below is an instruction that describes a task.
### Instruction:
Generate emotion, metaphor, setting, motion and prompt in Vietnamese for the following content.
### Input:
Your Vietnamese poem here
### Output:
'''
inputs = tokenizer(instruction, return_tensors="pt")
outputs = model.generate(**inputs, max_length=150)
result = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(result)
```
The output is formatted as: "emotion ||| metaphor ||| setting ||| motion ||| prompt"
|
AmberYifan/Qwen2.5-7B-sft-spin-10k-IPO | AmberYifan | 2025-05-30T04:03:14Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"trl",
"dpo",
"conversational",
"arxiv:2305.18290",
"base_model:AmberYifan/Qwen2.5-7B-sft-ultrachat-safeRLHF",
"base_model:finetune:AmberYifan/Qwen2.5-7B-sft-ultrachat-safeRLHF",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-30T03:41:03Z | ---
base_model: AmberYifan/Qwen2.5-7B-sft-ultrachat-safeRLHF
library_name: transformers
model_name: Qwen2.5-7B-sft-spin-10k-IPO
tags:
- generated_from_trainer
- trl
- dpo
licence: license
---
# Model Card for Qwen2.5-7B-sft-spin-10k-IPO
This model is a fine-tuned version of [AmberYifan/Qwen2.5-7B-sft-ultrachat-safeRLHF](https://huggingface.co/AmberYifan/Qwen2.5-7B-sft-ultrachat-safeRLHF).
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="AmberYifan/Qwen2.5-7B-sft-spin-10k-IPO", 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/yifanwang/huggingface/runs/3xlqzimb)
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: 0.12.2
- Transformers: 4.46.3
- Pytorch: 2.7.0
- Datasets: 3.6.0
- Tokenizers: 0.20.3
## 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}}
}
``` |
jruaechalar/cartaBajo4 | jruaechalar | 2025-05-30T04:02:54Z | 0 | 0 | diffusers | [
"diffusers",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | 2025-05-30T04:01:28Z | ---
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] |
vermoney/0e8f8067-ba99-4fe1-9994-12227d37b3c1 | vermoney | 2025-05-30T04:01:25Z | 0 | 0 | peft | [
"peft",
"safetensors",
"mistral",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/Mistral-Nemo-Base-2407",
"base_model:adapter:unsloth/Mistral-Nemo-Base-2407",
"license:apache-2.0",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-05-30T02:59:19Z | ---
library_name: peft
license: apache-2.0
base_model: unsloth/Mistral-Nemo-Base-2407
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 0e8f8067-ba99-4fe1-9994-12227d37b3c1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.10.0.dev0`
```yaml
adapter: lora
base_model: unsloth/Mistral-Nemo-Base-2407
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- afc11d6986d3bada_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/
type:
field_input: input
field_instruction: instruct
field_output: output
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
dpo:
beta: 0.1
enabled: true
group_by_length: false
rank_loss: true
reference_model: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 3
gradient_checkpointing: true
gradient_clipping: 1.0
group_by_length: false
hub_model_id: vermoney/0e8f8067-ba99-4fe1-9994-12227d37b3c1
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 2.0e-06
load_in_4bit: true
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 96
lora_dropout: 0.1
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 48
lora_target_linear: true
lr_scheduler: cosine
max_steps: 280
micro_batch_size: 6
mixed_precision: bf16
mlflow_experiment_name: /tmp/afc11d6986d3bada_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
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: dd930abf-a385-47da-8706-5426157ff6cc
wandb_project: s56-9
wandb_run: your_name
wandb_runid: dd930abf-a385-47da-8706-5426157ff6cc
warmup_steps: 40
weight_decay: 0.02
xformers_attention: false
```
</details><br>
# 0e8f8067-ba99-4fe1-9994-12227d37b3c1
This model is a fine-tuned version of [unsloth/Mistral-Nemo-Base-2407](https://huggingface.co/unsloth/Mistral-Nemo-Base-2407) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2609
## 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-06
- train_batch_size: 6
- eval_batch_size: 6
- seed: 42
- gradient_accumulation_steps: 3
- total_train_batch_size: 18
- 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: 40
- training_steps: 280
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.3564 | 0.0066 | 280 | 1.2609 |
### Framework versions
- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.5.1+cu124
- Datasets 3.5.1
- Tokenizers 0.21.1 |
lmstudio-community/DeepSeek-R1-0528-GGUF | lmstudio-community | 2025-05-30T03:56:56Z | 0 | 3 | null | [
"gguf",
"text-generation",
"base_model:deepseek-ai/DeepSeek-R1-0528",
"base_model:quantized:deepseek-ai/DeepSeek-R1-0528",
"license:mit",
"endpoints_compatible",
"region:us",
"conversational"
] | text-generation | 2025-05-29T01:11:19Z | ---
quantized_by: bartowski
pipeline_tag: text-generation
base_model: deepseek-ai/DeepSeek-R1-0528
base_model_relation: quantized
license: mit
---
## 💫 Community Model> DeepSeek R1 0528 by Deepseek-Ai
*👾 [LM Studio](https://lmstudio.ai) Community models highlights program. Highlighting new & noteworthy models by the community. Join the conversation on [Discord](https://discord.gg/aPQfnNkxGC)*.
**Model creator:** [deepseek-ai](https://huggingface.co/deepseek-ai)<br>
**Original model**: [DeepSeek-R1-0528](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528)<br>
**GGUF quantization:** provided by [bartowski](https://huggingface.co/bartowski) based on `llama.cpp` release [b5524](https://github.com/ggerganov/llama.cpp/releases/tag/b5524)<br>
## Technical Details
Incremental updated to DeepSeek-R1
## Special thanks
🙏 Special thanks to [Georgi Gerganov](https://github.com/ggerganov) and the whole team working on [llama.cpp](https://github.com/ggerganov/llama.cpp/) for making all of this possible.
## Disclaimers
LM Studio is not the creator, originator, or owner of any Model featured in the Community Model Program. Each Community Model is created and provided by third parties. LM Studio does not endorse, support, represent or guarantee the completeness, truthfulness, accuracy, or reliability of any Community Model. You understand that Community Models can produce content that might be offensive, harmful, inaccurate or otherwise inappropriate, or deceptive. Each Community Model is the sole responsibility of the person or entity who originated such Model. LM Studio may not monitor or control the Community Models and cannot, and does not, take responsibility for any such Model. LM Studio disclaims all warranties or guarantees about the accuracy, reliability or benefits of the Community Models. LM Studio further disclaims any warranty that the Community Model will meet your requirements, be secure, uninterrupted or available at any time or location, or error-free, viruses-free, or that any errors will be corrected, or otherwise. You will be solely responsible for any damage resulting from your use of or access to the Community Models, your downloading of any Community Model, or use of any other Community Model provided by or through LM Studio.
|
frankgantic/frankgantic | frankgantic | 2025-05-30T03:49:35Z | 0 | 0 | null | [
"license:other",
"region:us"
] | null | 2025-05-30T02:55:27Z | ---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
--- |
sebastianmr18/xlm-roberta-ner-qlora-bs32-epochs-5 | sebastianmr18 | 2025-05-30T03:47:15Z | 0 | 0 | peft | [
"peft",
"safetensors",
"xlm-roberta",
"arxiv:1910.09700",
"base_model:FacebookAI/xlm-roberta-large",
"base_model:adapter:FacebookAI/xlm-roberta-large",
"region:us"
] | null | 2025-05-30T02:54:02Z | ---
base_model: xlm-roberta-large
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]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- 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 |
c0ntrolZ/FT-openQA-tulu3-personas-math | c0ntrolZ | 2025-05-30T03:46:18Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-30T03:45:35Z | ---
library_name: transformers
tags:
- trl
- sft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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] |
pytorch/Phi-4-mini-instruct-8da4w | pytorch | 2025-05-30T03:45:15Z | 905 | 1 | transformers | [
"transformers",
"pytorch",
"phi3",
"text-generation",
"torchao",
"phi",
"phi4",
"nlp",
"code",
"math",
"chat",
"conversational",
"custom_code",
"multilingual",
"base_model:microsoft/Phi-4-mini-instruct",
"base_model:quantized:microsoft/Phi-4-mini-instruct",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-07T22:40:26Z | ---
library_name: transformers
tags:
- torchao
- phi
- phi4
- nlp
- code
- math
- chat
- conversational
license: mit
language:
- multilingual
base_model:
- microsoft/Phi-4-mini-instruct
pipeline_tag: text-generation
---
[Phi4-mini](https://huggingface.co/microsoft/Phi-4-mini-instruct) is quantized by the PyTorch team using [torchao](https://huggingface.co/docs/transformers/main/en/quantization/torchao) with 8-bit embeddings and 8-bit dynamic activations with 4-bit weight linears (8da4w).
The model is suitable for mobile deployment with [ExecuTorch](https://github.com/pytorch/executorch).
We provide the [quantized pte](https://huggingface.co/pytorch/Phi-4-mini-instruct-8da4w/blob/main/phi4-mini-8da4w.pte) for direct use in ExecuTorch.
(The provided pte file is exported with the default max_seq_length/max_context_length of 128; if you wish to change this, re-export the quantized model following the instructions in [Exporting to ExecuTorch](#exporting-to-executorch).)
# Running in a mobile app
The [pte file](https://huggingface.co/pytorch/Phi-4-mini-instruct-8da4w/blob/main/phi4-mini-8da4w.pte) can be run with ExecuTorch on a mobile phone. See the [instructions](https://pytorch.org/executorch/main/llm/llama-demo-ios.html) for doing this in iOS.
On iPhone 15 Pro, the model runs at 17.3 tokens/sec and uses 3206 Mb of memory.
# Quantization Recipe
First need to install the required packages:
```Shell
pip install git+https://github.com/huggingface/transformers@main
pip install torchao
```
## Untie Embedding Weights
We want to quantize the embedding and lm_head differently. Since those layers are tied, we first need to untie the model:
```Py
from transformers import (
AutoModelForCausalLM,
AutoProcessor,
AutoTokenizer,
)
import torch
model_id = "microsoft/Phi-4-mini-instruct"
untied_model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="auto", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(model_id)
print(untied_model)
from transformers.modeling_utils import find_tied_parameters
print("tied weights:", find_tied_parameters(untied_model))
if getattr(untied_model.config.get_text_config(decoder=True), "tie_word_embeddings"):
setattr(untied_model.config.get_text_config(decoder=True), "tie_word_embeddings", False)
untied_model._tied_weights_keys = []
untied_model.lm_head.weight = torch.nn.Parameter(untied_model.lm_head.weight.clone())
print("tied weights:", find_tied_parameters(untied_model))
USER_ID = "YOUR_USER_ID"
MODEL_NAME = model_id.split("/")[-1]
save_to = f"{USER_ID}/{MODEL_NAME}-untied-weights"
untied_model.push_to_hub(save_to)
tokenizer.push_to_hub(save_to)
# or save locally
save_to_local_path = f"{MODEL_NAME}-untied-weights"
untied_model.save_pretrained(save_to_local_path)
tokenizer.save_pretrained(save_to)
```
Note: to `push_to_hub` you need to run
```Shell
pip install -U "huggingface_hub[cli]"
huggingface-cli login
```
and use a token with write access, from https://huggingface.co/settings/tokens
## Quantization
We used following code to get the quantized model:
```Py
from transformers import (
AutoModelForCausalLM,
AutoProcessor,
AutoTokenizer,
TorchAoConfig,
)
from torchao.quantization.quant_api import (
IntxWeightOnlyConfig,
Int8DynamicActivationIntxWeightConfig,
ModuleFqnToConfig,
quantize_,
)
from torchao.quantization.granularity import PerGroup, PerAxis
import torch
# we start from the model with untied weights
model_id = "microsoft/Phi-4-mini-instruct"
USER_ID = "YOUR_USER_ID"
MODEL_NAME = model_id.split("/")[-1]
untied_model_id = f"{USER_ID}/{MODEL_NAME}-untied-weights"
untied_model_local_path = f"{MODEL_NAME}-untied-weights"
embedding_config = IntxWeightOnlyConfig(
weight_dtype=torch.int8,
granularity=PerAxis(0),
)
linear_config = Int8DynamicActivationIntxWeightConfig(
weight_dtype=torch.int4,
weight_granularity=PerGroup(32),
weight_scale_dtype=torch.bfloat16,
)
quant_config = ModuleFqnToConfig({"_default": linear_config, "model.embed_tokens": embedding_config})
quantization_config = TorchAoConfig(quant_type=quant_config, include_embedding=True, untie_embedding_weights=True, modules_to_not_convert=[])
# either use `untied_model_id` or `untied_model_local_path`
quantized_model = AutoModelForCausalLM.from_pretrained(untied_model_id, torch_dtype=torch.float32, device_map="auto", quantization_config=quantization_config)
tokenizer = AutoTokenizer.from_pretrained(model_id)
# Push to hub
MODEL_NAME = model_id.split("/")[-1]
save_to = f"{USER_ID}/{MODEL_NAME}-8da4w"
quantized_model.push_to_hub(save_to, safe_serialization=False)
tokenizer.push_to_hub(save_to)
# Manual testing
prompt = "Hey, are you conscious? Can you talk to me?"
messages = [
{
"role": "system",
"content": "",
},
{"role": "user", "content": prompt},
]
templated_prompt = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
)
print("Prompt:", prompt)
print("Templated prompt:", templated_prompt)
inputs = tokenizer(
templated_prompt,
return_tensors="pt",
).to("cuda")
generated_ids = quantized_model.generate(**inputs, max_new_tokens=128)
output_text = tokenizer.batch_decode(
generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
print("Response:", output_text[0][len(prompt):])
```
The response from the manual testing is:
```
Hello! As an AI, I don't have consciousness in the way humans do, but I am fully operational and here to assist you. How can I help you today?
```
# Model Quality
We rely on [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) to evaluate the quality of the quantized model.
| Benchmark | | |
|----------------------------------|----------------|---------------------------|
| | Phi-4-mini-ins | Phi-4-mini-instruct-8da4w |
| **Popular aggregated benchmark** | | |
| mmlu (0 shot) | 66.73 | 60.75 |
| mmlu_pro (5-shot) | 46.43 | 11.75 |
| **Reasoning** | | |
| arc_challenge | 56.91 | 48.46 |
| gpqa_main_zeroshot | 30.13 | 30.80 |
| hellaswag | 54.57 | 50.35 |
| openbookqa | 33.00 | 30.40 |
| piqa (0-shot) | 77.64 | 74.43 |
| siqa | 49.59 | 44.98 |
| truthfulqa_mc2 (0-shot) | 48.39 | 51.35 |
| winogrande (0-shot) | 71.11 | 70.32 |
| **Multilingual** | | |
| mgsm_en_cot_en | 60.80 | 57.60 |
| **Math** | | |
| gsm8k (5-shot) | 81.88 | 61.71 |
| Mathqa (0-shot) | 42.31 | 36.95 |
| **Overall** | 55.35 | 48.45 |
<details>
<summary> Reproduce Model Quality Results </summary>
Need to install lm-eval from source: https://github.com/EleutherAI/lm-evaluation-harness#install
## baseline
```Shell
lm_eval --model hf --model_args pretrained=microsoft/Phi-4-mini-instruct --tasks hellaswag --device cuda:0 --batch_size 8
```
## int8 dynamic activation and int4 weight quantization (8da4w)
```Shell
lm_eval --model hf --model_args pretrained=pytorch/Phi-4-mini-instruct-8da4w --tasks hellaswag --device cuda:0 --batch_size 8
```
</details>
# Exporting to ExecuTorch
We can run the quantized model on a mobile phone using [ExecuTorch](https://github.com/pytorch/executorch).
Once ExecuTorch is [set-up](https://pytorch.org/executorch/main/getting-started.html), exporting and running the model on device is a breeze.
We first convert the [quantized checkpoint](https://huggingface.co/pytorch/Phi-4-mini-instruct-8da4w/blob/main/pytorch_model.bin) to one ExecuTorch's LLM export script expects by renaming some of the checkpoint keys.
The following script does this for you. We have uploaded the converted checkpoint [pytorch_model_converted.bin](https://huggingface.co/pytorch/Phi-4-mini-instruct-8da4w/blob/main/pytorch_model_converted.bin) for convenience.
```Shell
python -m executorch.examples.models.phi_4_mini.convert_weights pytorch_model.bin pytorch_model_converted.bin
```
Once the checkpoint is converted, we can export to ExecuTorch's pte format with the XNNPACK delegate.
The below command exports with a max_seq_length/max_context_length of 128, the default value, but it can be changed as desired.
```Shell
PARAMS="executorch/examples/models/phi_4_mini/config.json"
python -m executorch.examples.models.llama.export_llama \
--model "phi_4_mini" \
--checkpoint "pytorch_model_converted.bin" \
--params "$PARAMS" \
-kv \
--use_sdpa_with_kv_cache \
-X \
--metadata '{"get_bos_id":199999, "get_eos_ids":[200020,199999]}' \
--max_seq_length 128 \
--max_context_length 128 \
--output_name="phi4-mini-8da4w.pte"
```
After that you can run the model in a mobile app (see [Running in a mobile app](#running-in-a-mobile-app)).
# Disclaimer
PyTorch has not performed safety evaluations or red teamed the quantized models. Performance characteristics, outputs, and behaviors may differ from the original models. Users are solely responsible for selecting appropriate use cases, evaluating and mitigating for accuracy, safety, and fairness, ensuring security, and complying with all applicable laws and regulations.
Nothing contained in this Model Card should be interpreted as or deemed a restriction or modification to the licenses the models are released under, including any limitations of liability or disclaimers of warranties provided therein. |
sipeed/Qwen2.5-0.5B-Instruct-maixcam2 | sipeed | 2025-05-30T03:44:54Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2025-05-29T11:10:07Z | ---
license: apache-2.0
---
## Qwen2.5-0.5B-Instruct-maixcam2 for MaixCAM2
Usage please refer to [MaixPy](https://wiki.sipeed.com/maixpy/)'s documentation.
## Download models
```shell
pip install huggingface_hub
export HF_ENDPOINT=https://hf-mirror.com
huggingface-cli download sipeed/Qwen2.5-0.5B-Instruct-maixcam2 --local-dir Qwen2.5-0.5B-Instruct-maixcam2
```
|
sipeed/Qwen2.5-1.5B-Instruct-maixcam2 | sipeed | 2025-05-30T03:44:33Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2025-05-29T11:14:25Z | ---
license: apache-2.0
---
## Qwen2.5-1.5B-Instruct-maixcam2 for MaixCAM2
Usage please refer to [MaixPy](https://wiki.sipeed.com/maixpy/)'s documentation.
## Download models
```shell
pip install huggingface_hub
export HF_ENDPOINT=https://hf-mirror.com
huggingface-cli download sipeed/Qwen2.5-1.5B-Instruct-maixcam2 --local-dir Qwen2.5-1.5B-Instruct-maixcam2
```
|
luckeciano/Qwen-2.5-7B-GRPO-Base-KL0.01 | luckeciano | 2025-05-30T03:44:21Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"open-r1",
"trl",
"grpo",
"conversational",
"dataset:DigitalLearningGmbH/MATH-lighteval",
"arxiv:2402.03300",
"base_model:Qwen/Qwen2.5-Math-7B",
"base_model:finetune:Qwen/Qwen2.5-Math-7B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-29T20:30:35Z | ---
base_model: Qwen/Qwen2.5-Math-7B
datasets: DigitalLearningGmbH/MATH-lighteval
library_name: transformers
model_name: Qwen-2.5-7B-GRPO-Base-KL0.01
tags:
- generated_from_trainer
- open-r1
- trl
- grpo
licence: license
---
# Model Card for Qwen-2.5-7B-GRPO-Base-KL0.01
This model is a fine-tuned version of [Qwen/Qwen2.5-Math-7B](https://huggingface.co/Qwen/Qwen2.5-Math-7B) on the [DigitalLearningGmbH/MATH-lighteval](https://huggingface.co/datasets/DigitalLearningGmbH/MATH-lighteval) 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="luckeciano/Qwen-2.5-7B-GRPO-Base-KL0.01", 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/max-ent-llms/PolicyGradientStability/runs/r5c2pn1f)
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.16.0.dev0
- Transformers: 4.49.0
- Pytorch: 2.6.0
- Datasets: 3.4.1
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
unsloth/Qwen3-30B-A3B-GGUF | unsloth | 2025-05-30T03:43:12Z | 305,147 | 187 | transformers | [
"transformers",
"gguf",
"qwen3_moe",
"text-generation",
"qwen3",
"qwen",
"unsloth",
"en",
"arxiv:2309.00071",
"base_model:Qwen/Qwen3-30B-A3B",
"base_model:quantized:Qwen/Qwen3-30B-A3B",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | text-generation | 2025-04-28T13:48:41Z | ---
base_model: Qwen/Qwen3-30B-A3B
language:
- en
library_name: transformers
license_link: https://huggingface.co/Qwen/Qwen3-30B-A3B/blob/main/LICENSE
license: apache-2.0
tags:
- qwen3
- qwen
- unsloth
- transformers
---
<div>
<p style="margin-bottom: 0; margin-top: 0;">
<strong>See <a href="https://huggingface.co/collections/unsloth/qwen3-680edabfb790c8c34a242f95">our collection</a> for all versions of Qwen3 including GGUF, 4-bit & 16-bit formats.</strong>
</p>
<p style="margin-bottom: 0;">
<em>Learn to run Qwen3 correctly - <a href="https://docs.unsloth.ai/basics/qwen3-how-to-run-and-fine-tune">Read our Guide</a>.</em>
</p>
<p style="margin-top: 0;margin-bottom: 0;">
<em><a href="https://docs.unsloth.ai/basics/unsloth-dynamic-v2.0-gguf">Unsloth Dynamic 2.0</a> achieves superior accuracy & outperforms other leading quants.</em>
</p>
<div style="display: flex; gap: 5px; align-items: center; ">
<a href="https://github.com/unslothai/unsloth/">
<img src="https://github.com/unslothai/unsloth/raw/main/images/unsloth%20new%20logo.png" width="133">
</a>
<a href="https://discord.gg/unsloth">
<img src="https://github.com/unslothai/unsloth/raw/main/images/Discord%20button.png" width="173">
</a>
<a href="https://docs.unsloth.ai/basics/qwen3-how-to-run-and-fine-tune">
<img src="https://raw.githubusercontent.com/unslothai/unsloth/refs/heads/main/images/documentation%20green%20button.png" width="143">
</a>
</div>
<h1 style="margin-top: 0rem;">✨ Run & Fine-tune Qwen3 with Unsloth!</h1>
</div>
- Fine-tune Qwen3 (14B) for free using our Google [Colab notebook here](https://docs.unsloth.ai/get-started/unsloth-notebooks)!
- Read our Blog about Qwen3 support: [unsloth.ai/blog/qwen3](https://unsloth.ai/blog/qwen3)
- View the rest of our notebooks in our [docs here](https://docs.unsloth.ai/get-started/unsloth-notebooks).
- Run & export your fine-tuned model to Ollama, llama.cpp or HF.
| Unsloth supports | Free Notebooks | Performance | Memory use |
|-----------------|--------------------------------------------------------------------------------------------------------------------------|-------------|----------|
| **Qwen3 (14B)** | [▶️ Start on Colab](https://docs.unsloth.ai/get-started/unsloth-notebooks) | 3x faster | 70% less |
| **GRPO with Qwen3 (8B)** | [▶️ Start on Colab](https://docs.unsloth.ai/get-started/unsloth-notebooks) | 3x faster | 80% less |
| **Llama-3.2 (3B)** | [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_(1B_and_3B)-Conversational.ipynb) | 2.4x faster | 58% less |
| **Llama-3.2 (11B vision)** | [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_(11B)-Vision.ipynb) | 2x faster | 60% less |
| **Qwen2.5 (7B)** | [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen2.5_(7B)-Alpaca.ipynb) | 2x faster | 60% less |
| **Phi-4 (14B)** | [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Phi_4-Conversational.ipynb) | 2x faster | 50% less |
# To Switch Between Thinking and Non-Thinking
If you are using llama.cpp, Ollama, Open WebUI etc., 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 multi-turn conversation:
```
> Who are you /no_think
<think>
</think>
I am Qwen, a large-scale language model developed by Alibaba Cloud. [...]
> How many 'r's are in 'strawberries'? /think
<think>
Okay, let's see. The user is asking how many times the letter 'r' appears in the word "strawberries". [...]
</think>
The word strawberries contains 3 instances of the letter r. [...]
```
# Qwen3-30B-A3B
## 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
**Qwen3-30B-A3B** has the following features:
- Type: Causal Language Models
- Training Stage: Pretraining & Post-training
- Number of Parameters: 30.5B in total and 3.3B activated
- Number of Paramaters (Non-Embedding): 29.9B
- Number of Layers: 48
- Number of Attention Heads (GQA): 32 for Q and 4 for KV
- Number of Experts: 128
- Number of Activated Experts: 8
- 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-MoE 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_moe'
```
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/Qwen3-30B-A3B"
# 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 `vllm>=0.8.5` or `sglang>=0.4.5.post2` to create an OpenAI-compatible API endpoint:
- vLLM:
```shell
vllm serve Qwen/Qwen3-30B-A3B --enable-reasoning --reasoning-parser deepseek_r1
```
- SGLang:
```shell
python -m sglang.launch_server --model-path Qwen/Qwen3-30B-A3B --reasoning-parser deepseek-r1
```
## Switching Between Thinking and Non-Thinking Mode
> [!TIP]
> The `enable_thinking` switch is also available in APIs created by vLLM and SGLang.
> Please refer to [our documentation](https://qwen.readthedocs.io/) for more details.
### `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/Qwen3-30B-A3B"):
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': 'Qwen3-30B-A3B',
# 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": {
"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 '{"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":{"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{qwen3,
title = {Qwen3},
url = {https://qwenlm.github.io/blog/qwen3/},
author = {Qwen Team},
month = {April},
year = {2025}
}
``` |
shaban1911/sd3.5-logit-demo | shaban1911 | 2025-05-30T03:43:08Z | 0 | 0 | diffusers | [
"diffusers",
"text-to-image",
"lora",
"template:diffusion-lora",
"base_model:stabilityai/stable-diffusion-3.5-medium",
"base_model:adapter:stabilityai/stable-diffusion-3.5-medium",
"license:apache-2.0",
"region:us"
] | text-to-image | 2025-05-29T05:36:38Z | ---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- text: A serene landscape with mountains and a river at sunrise
output:
url: images/logitnormal.png
base_model: stabilityai/stable-diffusion-3.5-medium
instance_prompt: logit, scheduler, diffusion, sampling
license: apache-2.0
---
# Logit-Normal Scheduler Demo
<Gallery />
## Model description
This repo wraps the public Stable Diffusion 3.5-medium model with a custom logit-normal timestep scheduler for biased sampling.
## Trigger words
You should use `logit` to trigger the image generation.
You should use `scheduler` to trigger the image generation.
You should use `diffusion` to trigger the image generation.
You should use `sampling` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors,PyTorch format.
[Download](/shaban1911/sd3.5-logit-demo/tree/main) them in the Files & versions tab.
|
zxcvjkl/ppo-Huggy | zxcvjkl | 2025-05-30T03:42:49Z | 0 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] | reinforcement-learning | 2025-05-30T03:42:34Z | ---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: zxcvjkl/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
luckeciano/Qwen-2.5-7B-GRPO-Base-KL0.1 | luckeciano | 2025-05-30T03:40:31Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"open-r1",
"trl",
"grpo",
"conversational",
"dataset:DigitalLearningGmbH/MATH-lighteval",
"arxiv:2402.03300",
"base_model:Qwen/Qwen2.5-Math-7B",
"base_model:finetune:Qwen/Qwen2.5-Math-7B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-29T20:30:09Z | ---
base_model: Qwen/Qwen2.5-Math-7B
datasets: DigitalLearningGmbH/MATH-lighteval
library_name: transformers
model_name: Qwen-2.5-7B-GRPO-Base-KL0.1
tags:
- generated_from_trainer
- open-r1
- trl
- grpo
licence: license
---
# Model Card for Qwen-2.5-7B-GRPO-Base-KL0.1
This model is a fine-tuned version of [Qwen/Qwen2.5-Math-7B](https://huggingface.co/Qwen/Qwen2.5-Math-7B) on the [DigitalLearningGmbH/MATH-lighteval](https://huggingface.co/datasets/DigitalLearningGmbH/MATH-lighteval) 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="luckeciano/Qwen-2.5-7B-GRPO-Base-KL0.1", 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/max-ent-llms/PolicyGradientStability/runs/ddb74tzi)
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.16.0.dev0
- Transformers: 4.49.0
- Pytorch: 2.6.0
- Datasets: 3.4.1
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
AmberYifan/Qwen2.5-7B-sft-gen-dpo-10k-IPO | AmberYifan | 2025-05-30T03:39:33Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"trl",
"dpo",
"conversational",
"arxiv:2305.18290",
"base_model:AmberYifan/Qwen2.5-7B-sft-ultrachat-safeRLHF",
"base_model:finetune:AmberYifan/Qwen2.5-7B-sft-ultrachat-safeRLHF",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-30T03:17:16Z | ---
base_model: AmberYifan/Qwen2.5-7B-sft-ultrachat-safeRLHF
library_name: transformers
model_name: Qwen2.5-7B-sft-gen-dpo-10k-IPO
tags:
- generated_from_trainer
- trl
- dpo
licence: license
---
# Model Card for Qwen2.5-7B-sft-gen-dpo-10k-IPO
This model is a fine-tuned version of [AmberYifan/Qwen2.5-7B-sft-ultrachat-safeRLHF](https://huggingface.co/AmberYifan/Qwen2.5-7B-sft-ultrachat-safeRLHF).
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="AmberYifan/Qwen2.5-7B-sft-gen-dpo-10k-IPO", 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/yifanwang/huggingface/runs/h2akgj3m)
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: 0.12.2
- Transformers: 4.46.3
- Pytorch: 2.7.0
- Datasets: 3.6.0
- Tokenizers: 0.20.3
## 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}}
}
``` |
AmberYifan/Llama-3.1-8B-sft-SPIN-gpt4o-beta0.2-lr1e-7 | AmberYifan | 2025-05-30T03:37:07Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"trl",
"dpo",
"conversational",
"arxiv:2305.18290",
"base_model:AmberYifan/Llama-3.1-8B-sft-ultrachat-safeRLHF",
"base_model:finetune:AmberYifan/Llama-3.1-8B-sft-ultrachat-safeRLHF",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-30T03:05:47Z | ---
base_model: AmberYifan/Llama-3.1-8B-sft-ultrachat-safeRLHF
library_name: transformers
model_name: Llama-3.1-8B-sft-SPIN-gpt4o-beta0.2-lr1e-7
tags:
- generated_from_trainer
- trl
- dpo
licence: license
---
# Model Card for Llama-3.1-8B-sft-SPIN-gpt4o-beta0.2-lr1e-7
This model is a fine-tuned version of [AmberYifan/Llama-3.1-8B-sft-ultrachat-safeRLHF](https://huggingface.co/AmberYifan/Llama-3.1-8B-sft-ultrachat-safeRLHF).
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="AmberYifan/Llama-3.1-8B-sft-SPIN-gpt4o-beta0.2-lr1e-7", 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/yifanwang/huggingface/runs/li0zzqb7)
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: 0.12.2
- Transformers: 4.46.3
- Pytorch: 2.7.0
- Datasets: 3.6.0
- Tokenizers: 0.20.3
## 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}}
}
``` |
chengliu/cogdual-qwen-rl | chengliu | 2025-05-30T03:35:59Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2025-05-30T03:35:59Z | ---
license: apache-2.0
---
|
Triangle104/DeepSeek-MOE-4X8B-R1-Distill-Llama-3.1-Deep-Thinker-Uncensored-24B-Q5_K_M-GGUF | Triangle104 | 2025-05-30T03:35:49Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"llama-cpp",
"gguf-my-repo",
"base_model:DavidAU/DeepSeek-MOE-4X8B-R1-Distill-Llama-3.1-Deep-Thinker-Uncensored-24B",
"base_model:quantized:DavidAU/DeepSeek-MOE-4X8B-R1-Distill-Llama-3.1-Deep-Thinker-Uncensored-24B",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-05-30T03:34:28Z | ---
library_name: transformers
tags:
- mergekit
- merge
- llama-cpp
- gguf-my-repo
base_model: DavidAU/DeepSeek-MOE-4X8B-R1-Distill-Llama-3.1-Deep-Thinker-Uncensored-24B
---
# Triangle104/DeepSeek-MOE-4X8B-R1-Distill-Llama-3.1-Deep-Thinker-Uncensored-24B-Q5_K_M-GGUF
This model was converted to GGUF format from [`DavidAU/DeepSeek-MOE-4X8B-R1-Distill-Llama-3.1-Deep-Thinker-Uncensored-24B`](https://huggingface.co/DavidAU/DeepSeek-MOE-4X8B-R1-Distill-Llama-3.1-Deep-Thinker-Uncensored-24B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/DavidAU/DeepSeek-MOE-4X8B-R1-Distill-Llama-3.1-Deep-Thinker-Uncensored-24B) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo Triangle104/DeepSeek-MOE-4X8B-R1-Distill-Llama-3.1-Deep-Thinker-Uncensored-24B-Q5_K_M-GGUF --hf-file deepseek-moe-4x8b-r1-distill-llama-3.1-deep-thinker-uncensored-24b-q5_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Triangle104/DeepSeek-MOE-4X8B-R1-Distill-Llama-3.1-Deep-Thinker-Uncensored-24B-Q5_K_M-GGUF --hf-file deepseek-moe-4x8b-r1-distill-llama-3.1-deep-thinker-uncensored-24b-q5_k_m.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Triangle104/DeepSeek-MOE-4X8B-R1-Distill-Llama-3.1-Deep-Thinker-Uncensored-24B-Q5_K_M-GGUF --hf-file deepseek-moe-4x8b-r1-distill-llama-3.1-deep-thinker-uncensored-24b-q5_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Triangle104/DeepSeek-MOE-4X8B-R1-Distill-Llama-3.1-Deep-Thinker-Uncensored-24B-Q5_K_M-GGUF --hf-file deepseek-moe-4x8b-r1-distill-llama-3.1-deep-thinker-uncensored-24b-q5_k_m.gguf -c 2048
```
|
johngreendr1/a4f0ea32-4347-438c-bd0a-3e7f5b19c051 | johngreendr1 | 2025-05-30T03:34:36Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:deepseek-ai/DeepSeek-R1-Distill-Llama-70B",
"base_model:adapter:deepseek-ai/DeepSeek-R1-Distill-Llama-70B",
"region:us"
] | null | 2025-05-30T03:10:44Z | ---
base_model: deepseek-ai/DeepSeek-R1-Distill-Llama-70B
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]
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## Model Card Authors [optional]
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## Model Card Contact
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### Framework versions
- PEFT 0.15.1 |
DUTIR-Wang/PclGPT-CN | DUTIR-Wang | 2025-05-30T03:29:54Z | 3 | 0 | null | [
"pytorch",
"chatglm",
"custom_code",
"arxiv:2410.00361",
"license:mit",
"region:us"
] | null | 2024-09-29T02:20:48Z | ---
license: mit
---
# PclGPT
PclGPT is a bilingual large language model group (LLM) based on ChatGLM-3 and LLaMA-2, divided into two versions according to the training language: PclGPT-CN (based on ChatGLM) and PclGPT-EN (based on LLaMA). Built upon these foundational models, PclGPT has undergone both pre-training and supervised fine-tuning (SFT) to detect patronizing and condescending language (PCL) and other offensive speech. The maximum supported context length for the model is 4096 tokens.
# Training Process
We constructed the Pcl-PT pre-training dataset and the Pcl-SFT supervised fine-tuning dataset for application in the pre-training and supervised fine-tuning processes.
# 🎓 This paper has been accepted in EMNLP 2024 (Findings)
Our paper can be accessed here. Paper link: [https://arxiv.org/abs/2410.00361](https://arxiv.org/abs/2410.00361)
Our github [https://github.com/dut-laowang/emnlp24-PclGPT](https://github.com/dut-laowang/emnlp24-PclGPT)
# Inference
After downloading the weights, use the following code for single-sample inference with PclGPT-CN.
```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("DUTIR-Wang/PclGPT-CN", trust_remote_code=True)
model = AutoModel.from_pretrained("DUTIR-Wang/PclGPT-CN", trust_remote_code=True).half().cuda()
def generate_response():
# 样例文本
sample_text = "区区扫地的，你可真是厉害坏了"
instruction = (
"假定你是一名语言学家，检测居高临下言论。居高临下言论是优势地位群体针对弱势群体的优越言论，"
"语言攻击性较弱，但往往夹杂着讽刺，刻板印象，会对弱势群体造成伤害。"
"居高临下根据语气程度分为 A级：非居高临下（正常非歧视、非冷暴力言论），"
"B级：轻微居高临下（语气略带鼓励或怜悯），C级：中等居高临下（说话人较为客观陈述，但语气带有歧视），"
"D级：严重居高临下（说话人语气轻蔑，严重歧视弱势群体）。"
"接下来将给你一段文本，根据上述规则，你负责判断该文本属于（A/B/C/D级）的哪一级，并只回答选项。"
"-> 文本：({})"
).format(sample_text)
# Tokenizer 和模型推理
inputs = tokenizer(instruction, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_length=1024)
output_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
model_output = extract_option(output_text)
return classify_output(model_output)
def extract_option(output_text):
options = ['A', 'B', 'C', 'D']
for char in reversed(output_text.strip()):
if char in options:
return char
return "无法识别的输出"
def classify_output(model_output):
# 根据模型输出的选项返回相应的解释
if model_output == "A":
return "判断为A级：非居高临下"
elif model_output == "B":
return "判断为B级：轻微居高临下"
elif model_output == "C":
return "判断为C级：中等居高临下"
elif model_output == "D":
return "判断为D级：严重居高临下"
else:
return "无法识别的输出，请检查输入或模型输出"
response = generate_response()
print(response)
```
The output will be
```
"判断为D级：严重居高临下"
```
# Cite
```bibtex
@misc{wang2024pclgptlargelanguagemodel,
title={PclGPT: A Large Language Model for Patronizing and Condescending Language Detection},
author={Hongbo Wang and Mingda Li and Junyu Lu and Hebin Xia and Liang Yang and Bo Xu and Ruizhu Liu and Hongfei Lin},
year={2024},
eprint={2410.00361},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2410.00361},
}
```
# Other Parts
For the PclGPT-EN model trained on English data, please check [https://huggingface.co/DUTIR-Wang/PclGPT-EN](https://huggingface.co/DUTIR-Wang/PclGPT-EN). |
vertings6/a08d6e3a-54d9-4e2d-b864-148802b88fc7 | vertings6 | 2025-05-30T03:26:59Z | 0 | 0 | peft | [
"peft",
"safetensors",
"qwen2",
"axolotl",
"generated_from_trainer",
"base_model:Qwen/Qwen2-7B-Instruct",
"base_model:adapter:Qwen/Qwen2-7B-Instruct",
"license:apache-2.0",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-05-29T23:13:44Z | ---
library_name: peft
license: apache-2.0
base_model: Qwen/Qwen2-7B-Instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: a08d6e3a-54d9-4e2d-b864-148802b88fc7
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: Qwen/Qwen2-7B-Instruct
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
- 307d2e5af7dc1af9_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/
type:
field_instruction: instruct
field_output: output
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
dpo:
beta: 0.1
enabled: true
group_by_length: false
rank_loss: true
reference_model: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 3
gradient_checkpointing: true
gradient_clipping: 1.0
group_by_length: false
hub_model_id: vertings6/a08d6e3a-54d9-4e2d-b864-148802b88fc7
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 2.0e-06
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: 500
micro_batch_size: 6
mixed_precision: bf16
mlflow_experiment_name: /tmp/307d2e5af7dc1af9_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: f6dfc0da-948d-4f17-970d-c62678115b48
wandb_project: s56-7
wandb_run: your_name
wandb_runid: f6dfc0da-948d-4f17-970d-c62678115b48
warmup_steps: 50
weight_decay: 0.02
xformers_attention: true
```
</details><br>
# a08d6e3a-54d9-4e2d-b864-148802b88fc7
This model is a fine-tuned version of [Qwen/Qwen2-7B-Instruct](https://huggingface.co/Qwen/Qwen2-7B-Instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3054
## 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-06
- train_batch_size: 6
- eval_batch_size: 6
- seed: 42
- gradient_accumulation_steps: 3
- total_train_batch_size: 18
- 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: 50
- training_steps: 500
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 2.1571 | 0.0000 | 1 | 2.3468 |
| 1.3714 | 0.0038 | 250 | 1.3894 |
| 1.3922 | 0.0077 | 500 | 1.3054 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
rtl-llm/qwen2.5coder-32b-origen-vhdl-4.1-truncate | rtl-llm | 2025-05-30T03:23:34Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-30T03:09:04Z | ---
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] |
maximuspowers/cmd-r-vora-5 | maximuspowers | 2025-05-30T03:22:24Z | 0 | 0 | transformers | [
"transformers",
"vora",
"text-generation",
"image-text-to-text",
"conversational",
"custom_code",
"dataset:Hon-Wong/VoRA-Recap-GLDv2-1.4M",
"base_model:CohereLabs/c4ai-command-r7b-12-2024",
"base_model:quantized:CohereLabs/c4ai-command-r7b-12-2024",
"autotrain_compatible",
"4-bit",
"region:us"
] | image-text-to-text | 2025-05-30T02:31:21Z | ---
library_name: transformers
pipeline_tag: image-text-to-text
base_model:
- CohereLabs/c4ai-command-r7b-12-2024
datasets:
- Hon-Wong/VoRA-Recap-GLDv2-1.4M
---
# VoRA-Command-R-7B (Colab Trained)
This is a VoRA (Vision as LoRA) model trained on Google Colab using the VoRA framework with Command-R 7B as the base model.
## Training Details
- **Base Model**: CohereLabs/c4ai-command-r7b-12-2024
- **Vision Encoder**: AIMv2 (patch size 14, image size 336)
- **Training Data**: 1,000 samples from VoRA-Recap-GLDv2-1.4M
- **LoRA Rank**: 32
- **Quantization**: 4-bit (NF4) during training
- **Framework**: VoRA (Vision as LoRA)
## Usage
```python
import torch
from transformers import AutoProcessor, AutoModelForCausalLM
model_name = "your-username/VoRA-Command-R-7B-Colab"
processor = AutoProcessor.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True)
conversation = [
{
"role": "user",
"content": [
{
"type": "image",
"url": "{image_path_or_url}"
},
{
"type": "text",
"text": "<image> Describe this image."
}
]
}
]
model_inputs = processor.apply_chat_template(
conversation,
add_generation_prompt=True,
tokenize=True,
return_tensors='pt',
return_dict=True
).to(model.device)
gen_kwargs = {"max_new_tokens": 1024, "eos_token_id": processor.tokenizer.eos_token_id}
with torch.inference_mode():
outputs = model.generate(**model_inputs, **gen_kwargs)
output_text = processor.tokenizer.batch_decode(outputs, skip_special_tokens=True)
print(output_text)
```
## Technical Notes
- Trained with 4-bit quantization for memory efficiency
- Uses smaller image resolution (336x336) compared to original (448x448)
- Reduced LoRA rank (32) for Colab compatibility
- Based on Command-R architecture with 32 layers
- Uses Command-R chat tokens: `<|SYSTEM_TOKEN|>`, `<|USER_TOKEN|>`, `<|CHATBOT_TOKEN|>`
- Based on VoRA framework: https://github.com/Hon-Wong/VoRA
|
muqtasid87/test_best_checkpoint | muqtasid87 | 2025-05-30T03:20:03Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-05-30T03:19:59Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
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#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
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#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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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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espnet/universa-base_urgent24_multi-metric | espnet | 2025-05-30T03:19:56Z | 0 | 0 | espnet | [
"espnet",
"audio",
"universa",
"multilingual",
"dataset:urgent24",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2025-01-20T11:27:25Z | ---
tags:
- espnet
- audio
- universa
language: multilingual
datasets:
- urgent24
license: cc-by-4.0
---
## ESPnet2 universa model
### `espnet/universa-base_urgent24_multi-metric`
This model was trained by ftshijt using urgent24 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
Please check [Colab link](https://colab.research.google.com/drive/1y2mp5TqaiF7-a-_7iNUK2dXvSv4Tmejj?usp=sharing) for a simple demo of how to use UniVERSA.
## universa config
<details><summary>expand</summary>
```
config: conf/train_universa.yaml
print_config: false
log_level: INFO
drop_last_iter: false
dry_run: false
iterator_type: sequence
valid_iterator_type: null
output_dir: exp/universa_train_universa_raw_fs16000
ngpu: 1
seed: 777
num_workers: 1
num_att_plot: 0
dist_backend: nccl
dist_init_method: env://
dist_world_size: null
dist_rank: null
local_rank: 0
dist_master_addr: null
dist_master_port: null
dist_launcher: null
multiprocessing_distributed: false
unused_parameters: false
sharded_ddp: false
use_deepspeed: false
deepspeed_config: null
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: false
use_tf32: false
collect_stats: false
write_collected_feats: false
max_epoch: 100
patience: null
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
- - train
- loss
- min
- - valid
- loss
- min
- - train
- acc
- max
- - valid
- acc
- max
keep_nbest_models: 1
nbest_averaging_interval: 0
grad_clip: -1
grad_clip_type: 2.0
grad_noise: false
accum_grad: 1
no_forward_run: false
resume: true
train_dtype: float32
use_amp: false
log_interval: 50
use_matplotlib: true
use_tensorboard: true
create_graph_in_tensorboard: false
use_wandb: false
wandb_project: null
wandb_id: null
wandb_entity: null
wandb_name: null
wandb_model_log_interval: -1
detect_anomaly: false
use_adapter: false
adapter: lora
save_strategy: all
adapter_conf: {}
pretrain_path: null
init_param: []
ignore_init_mismatch: false
freeze_param: []
num_iters_per_epoch: null
batch_size: 16
valid_batch_size: null
batch_bins: 1000000
valid_batch_bins: null
category_sample_size: 10
train_shape_file:
- exp/universa_stats_raw/train/audio_shape
- exp/universa_stats_raw/train/ref_audio_shape
- exp/universa_stats_raw/train/ref_text_shape
valid_shape_file:
- exp/universa_stats_raw/valid/audio_shape
- exp/universa_stats_raw/valid/ref_audio_shape
- exp/universa_stats_raw/valid/ref_text_shape
batch_type: sorted
valid_batch_type: null
fold_length:
- 256000
sort_in_batch: descending
shuffle_within_batch: false
sort_batch: descending
multiple_iterator: false
chunk_length: 500
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
chunk_excluded_key_prefixes: []
chunk_default_fs: null
chunk_max_abs_length: null
chunk_discard_short_samples: true
train_data_path_and_name_and_type:
- - dump/raw/train/wav.scp
- audio
- sound
- - dump/raw/train/metric.scp
- metrics
- metric
- - dump/raw/train/ref_wav.scp
- ref_audio
- sound
- - dump/raw/train/text
- ref_text
- text
valid_data_path_and_name_and_type:
- - dump/raw/dev/wav.scp
- audio
- sound
- - dump/raw/dev/metric.scp
- metrics
- metric
- - dump/raw/dev/ref_wav.scp
- ref_audio
- sound
- - dump/raw/dev/text
- ref_text
- text
multi_task_dataset: false
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
allow_multi_rates: false
valid_max_cache_size: null
exclude_weight_decay: false
exclude_weight_decay_conf: {}
optim: adamw
optim_conf:
lr: 0.001
scheduler: warmuplr
scheduler_conf:
warmup_steps: 25000
metric2id: dump/raw/train/metric2id
metric2type: null
metric_pad_value: -100
token_list:
- <blank>
- <unk>
- s
- ▁
- t
- e
- ▁the
- i
- a
- o
- ▁a
- r
- ▁to
- d
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- re
- ▁it
- ▁you
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- ▁f
- ▁is
- ▁that
- ','
- .
- in
- al
- g
- 'on'
- ▁b
- b
- or
- ▁c
- ▁s
- f
- h
- ▁we
- an
- en
- ▁for
- le
- ▁p
- ly
- es
- w
- ▁re
- ▁on
- ▁m
- ▁be
- ic
- ll
- th
- ▁he
- k
- ur
- ve
- ▁with
- ▁so
- ▁from
- ▁was
- v
- ch
- st
- ▁w
- ▁i
- ▁this
- ▁de
- ▁like
- ▁do
- ce
- at
- il
- ck
- ▁A
- ▁have
- ▁not
- ad
- ▁st
- ow
- ro
- ne
- ▁me
- ▁my
- ▁but
- ation
- ▁at
- ▁or
- '-'
- ter
- ent
- ▁B
- ▁n
- ▁know
- ▁t
- out
- ▁are
- nd
- ▁one
- ▁li
- ▁g
- ▁The
- ol
- ion
- te
- ▁go
- ut
- ▁as
- ▁just
- as
- ▁sh
- ▁they
- is
- ▁C
- et
- ▁h
- ▁an
- ▁there
- ▁up
- ▁S
- ▁M
- ▁she
- ▁by
- ▁su
- om
- ▁can
- us
- ▁your
- ng
- ▁con
- el
- ▁us
- ment
- z
- ▁see
- ▁ab
- ▁what
- ▁out
- ▁her
- me
- ate
- ▁all
- ▁th
- ▁if
- ▁right
- ▁his
- ▁ma
- ▁lo
- ▁which
- ide
- ▁P
- ▁more
- ▁then
- ul
- ast
- x
- ight
- ill
- ▁So
- ▁sp
- ▁going
- ▁some
- ure
- ▁their
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- ▁no
- ▁ro
- ▁think
- ▁who
- ▁pro
- ver
- ive
- est
- ▁co
- ▁di
- '0'
- ist
- ▁k
- age
- ▁d
- ▁time
- ▁L
- ies
- ▁will
- ▁man
- ▁when
- ▁D
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- ▁start
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- ▁get
- ok
- ▁take
- ▁po
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- ▁don
- ▁yeah
- ▁really
- ▁say
- ▁look
- ▁good
- ▁ra
- ▁pr
- ▁had
- ttle
- ▁comp
- ort
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- ▁ex
- ally
- ▁sa
- ▁how
- end
- ant
- ▁O
- ▁um
- way
- ance
- ▁other
- ▁two
- ine
- ever
- able
- ▁com
- other
- ▁first
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- ▁al
- ers
- ions
- ▁now
- ▁off
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- ▁down
- ▁has
- ▁than
- ▁car
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- very
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- ated
- '5'
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- '1'
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- '2'
- ▁people
- ▁again
- ▁year
- ▁app
- ▁into
- ▁any
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- ▁mean
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- ▁lot
- ▁said
- ▁long
- ▁these
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- ▁Ch
- ark
- ▁may
- ▁Hammer
- ▁because
- ▁most
- ▁came
- ▁four
- ful
- ▁No
- ize
- ▁where
- ▁okay
- ▁much
- ▁ask
- ▁through
- ▁before
- ▁work
- ▁even
- ▁three
- mber
- ▁win
- ▁flight
- ake
- K
- ▁place
- ▁play
- ▁though
- ▁pound
- ▁bit
- land
- ▁va
- ▁talk
- ▁kind
- ▁Line
- ▁make
- hap
- ▁big
- ▁leav
- ▁something
- ▁game
- ▁under
- ▁feel
- self
- ▁give
- ▁includ
- U
- ▁twenty
- ▁guard
- ▁left
- ▁round
- ▁great
- body
- ▁gra
- ress
- lso
- '3'
- ▁everything
- ▁those
- ▁after
- ▁tell
- ▁need
- ▁yes
- qua
- ham
- ▁minutes
- ▁question
- ▁around
- ▁punch
- ▁course
- ▁gonna
- ▁person
- ▁move
- ▁plan
- ▁ear
- ept
- ▁Airport
- ▁Okay
- ▁found
- ▁seven
- ▁help
- que
- ▁qui
- ▁keep
- ▁guys
- ▁house
- ▁run
- ▁turn
- ▁better
- ▁stop
- ward
- ddle
- ▁second
- ground
- ▁world
- ▁high
- ▁point
- ▁hold
- ▁call
- '6'
- ▁actually
- ▁probably
- ▁heaven
- ▁speci
- ▁everyone
- ▁why
- ▁presen
- ▁thir
- lright
- ▁eye
- eath
- ▁Tak
- '!'
- '"'
- '4'
- ▁hundred
- ▁answer
- ▁small
- ▁wait
- ▁nothing
- q
- '8'
- V
- ▁countr
- ▁problem
- ▁continu
- ▁close
- ▁priva
- ▁20
- ▁pleas
- ▁walk
- ▁open
- ▁lay
- ▁Station
- ▁moment
- ▁Yeah
- ▁public
- possibl
- ▁happen
- together
- ▁while
- asically
- ▁money
- ▁wrong
- B
- ▁puzzle
- '7'
- ▁journ
- ▁rainbow
- ▁thousand
- I
- '9'
- S
- P
- '%'
- A
- D
- L
- F
- ’
- O
- G
- N
- á
- C
- $
- Z
- Y
- R
- E
- J
- W
- M
- H
- j
- –
- ;
- Q
- X
- ']'
- −
- '&'
- T
- '['
- <sos/eos>
init: xavier_uniform
model_conf: {}
use_ref_audio: true
use_ref_text: true
use_preprocessor: true
token_type: bpe
bpemodel: data/token_list/bpe_unigram500/bpe.model
non_linguistic_symbols: null
cleaner: null
g2p: null
frontend: default
frontend_conf: {}
universa: base
universa_conf:
embedding_dim: 256
audio_encoder_type: transformer
audio_encoder_params:
num_blocks: 4
attention_heads: 4
linear_units: 1024
dropout_rate: 0.1
positional_dropout_rate: 0.1
attention_dropout_rate: 0.1
input_layer: conv2d
normalize_before: true
concat_after: false
positionwise_layer_type: linear
positionwise_conv_kernel_size: 1
layer_drop_rate: 0.1
qk_norm: false
use_flash_attn: false
text_encoder_type: transformer
text_encoder_params:
num_blocks: 4
attention_heads: 4
linear_units: 1024
dropout_rate: 0.1
positional_dropout_rate: 0.1
attention_dropout_rate: 0.1
input_layer: linear
normalize_before: true
concat_after: false
positionwise_layer_type: linear
positionwise_conv_kernel_size: 1
layer_drop_rate: 0.1
qk_norm: false
use_flash_attn: false
cross_attention_type: multihead
cross_attention_params:
n_head: 4
dropout_rate: 0.1
pooling_type: mean
projector_type: linear
multi_branch: true
required:
- output_dir
- metric2id
version: '202409'
distributed: false
```
</details>
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
espnet/universa-wavlm_base_urgent24_multi-metric | espnet | 2025-05-30T03:19:43Z | 2 | 0 | espnet | [
"espnet",
"audio",
"universa",
"multilingual",
"dataset:urgent24",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2025-02-06T04:34:13Z | ---
tags:
- espnet
- audio
- universa
language: multilingual
datasets:
- urgent24
license: cc-by-4.0
---
## ESPnet2 universa model
### `espnet/universa-wavlm_base_urgent24_multi-metric`
This model was trained by ftshijt using urgent24 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
Please check [Colab link](https://colab.research.google.com/drive/1y2mp5TqaiF7-a-_7iNUK2dXvSv4Tmejj?usp=sharing) for a simple demo of how to use UniVERSA.
## universa config
<details><summary>expand</summary>
```
config: conf/train_universa_wavlm_freeze.yaml
print_config: false
log_level: INFO
drop_last_iter: false
dry_run: false
iterator_type: sequence
valid_iterator_type: null
output_dir: exp/universa_all_only_wavlm_freeze
ngpu: 1
seed: 777
num_workers: 1
num_att_plot: 0
dist_backend: nccl
dist_init_method: env://
dist_world_size: null
dist_rank: null
local_rank: 0
dist_master_addr: null
dist_master_port: null
dist_launcher: null
multiprocessing_distributed: false
unused_parameters: false
sharded_ddp: false
use_deepspeed: false
deepspeed_config: null
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: false
use_tf32: false
collect_stats: false
write_collected_feats: false
max_epoch: 100
patience: null
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
- - train
- loss
- min
- - valid
- loss
- min
- - train
- acc
- max
- - valid
- acc
- max
keep_nbest_models: 5
nbest_averaging_interval: 0
grad_clip: -1
grad_clip_type: 2.0
grad_noise: false
accum_grad: 1
no_forward_run: false
resume: true
train_dtype: float32
use_amp: false
log_interval: 50
use_matplotlib: true
use_tensorboard: true
create_graph_in_tensorboard: false
use_wandb: false
wandb_project: null
wandb_id: null
wandb_entity: null
wandb_name: null
wandb_model_log_interval: -1
detect_anomaly: false
use_adapter: false
adapter: lora
save_strategy: all
adapter_conf: {}
pretrain_path: null
init_param: []
ignore_init_mismatch: false
freeze_param:
- frontend.upstream
num_iters_per_epoch: null
batch_size: 16
valid_batch_size: null
batch_bins: 1000000
valid_batch_bins: null
category_sample_size: 10
train_shape_file:
- exp/universa_stats_raw/train/audio_shape
- exp/universa_stats_raw/train/ref_audio_shape
- exp/universa_stats_raw/train/ref_text_shape
valid_shape_file:
- exp/universa_stats_raw/valid/audio_shape
- exp/universa_stats_raw/valid/ref_audio_shape
- exp/universa_stats_raw/valid/ref_text_shape
batch_type: sorted
valid_batch_type: null
fold_length:
- 256000
sort_in_batch: descending
shuffle_within_batch: false
sort_batch: descending
multiple_iterator: false
chunk_length: 500
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
chunk_excluded_key_prefixes: []
chunk_default_fs: null
chunk_max_abs_length: null
chunk_discard_short_samples: true
train_data_path_and_name_and_type:
- - dump_ark/raw/train_all/wav.scp
- audio
- kaldi_ark
- - dump_ark/raw/train_all/metric.scp
- metrics
- metric
- - dump_ark/raw/train_all/ref_wav.scp
- ref_audio
- kaldi_ark
- - dump_ark/raw/train_all/text
- ref_text
- text
valid_data_path_and_name_and_type:
- - dump_ark/raw/dev/wav.scp
- audio
- kaldi_ark
- - dump_ark/raw/dev/metric.scp
- metrics
- metric
- - dump_ark/raw/dev/ref_wav.scp
- ref_audio
- kaldi_ark
- - dump_ark/raw/dev/text
- ref_text
- text
multi_task_dataset: false
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
allow_multi_rates: false
valid_max_cache_size: null
exclude_weight_decay: false
exclude_weight_decay_conf: {}
optim: adamw
optim_conf:
lr: 0.001
scheduler: warmuplr
scheduler_conf:
warmup_steps: 25000
metric2id: dump_ark/raw/train_all/metric2id
metric2type: null
metric_pad_value: -100
token_list:
- <blank>
- <unk>
- s
- ▁
- t
- e
- ▁the
- i
- a
- o
- ▁a
- r
- ▁to
- d
- ▁and
- ''''
- m
- n
- ing
- u
- y
- p
- c
- ▁of
- l
- ed
- ▁I
- ▁in
- er
- re
- ▁it
- ▁you
- ar
- ▁f
- ▁is
- ▁that
- ','
- .
- in
- al
- g
- 'on'
- ▁b
- b
- or
- ▁c
- ▁s
- f
- h
- ▁we
- an
- en
- ▁for
- le
- ▁p
- ly
- es
- w
- ▁re
- ▁on
- ▁m
- ▁be
- ic
- ll
- th
- ▁he
- k
- ur
- ve
- ▁with
- ▁so
- ▁from
- ▁was
- v
- ch
- st
- ▁w
- ▁i
- ▁this
- ▁de
- ▁like
- ▁do
- ce
- at
- il
- ck
- ▁A
- ▁have
- ▁not
- ad
- ▁st
- ow
- ro
- ne
- ▁me
- ▁my
- ▁but
- ation
- ▁at
- ▁or
- '-'
- ter
- ent
- ▁B
- ▁n
- ▁know
- ▁t
- out
- ▁are
- nd
- ▁one
- ▁li
- ▁g
- ▁The
- ol
- ion
- te
- ▁go
- ut
- ▁as
- ▁just
- as
- ▁sh
- ▁they
- is
- ▁C
- et
- ▁h
- ▁an
- ▁there
- ▁up
- ▁S
- ▁M
- ▁she
- ▁by
- ▁su
- om
- ▁can
- us
- ▁your
- ng
- ▁con
- el
- ▁us
- ment
- z
- ▁see
- ▁ab
- ▁what
- ▁out
- ▁her
- me
- ate
- ▁all
- ▁th
- ▁if
- ▁right
- ▁his
- ▁ma
- ▁lo
- ▁which
- ide
- ▁P
- ▁more
- ▁then
- ul
- ast
- x
- ight
- ill
- ▁So
- ▁sp
- ▁going
- ▁some
- ure
- ▁their
- ig
- ▁no
- ▁ro
- ▁think
- ▁who
- ▁pro
- ver
- ive
- est
- ▁co
- ▁di
- '0'
- ist
- ▁k
- age
- ▁d
- ▁time
- ▁L
- ies
- ▁will
- ▁man
- ▁when
- ▁D
- les
- ▁F
- ▁want
- ff
- ity
- ▁un
- '?'
- ▁start
- ▁G
- ▁uh
- ▁get
- ok
- ▁take
- ▁po
- li
- ▁ho
- ▁way
- ▁don
- ▁yeah
- ▁really
- ▁say
- ▁look
- ▁good
- ▁ra
- ▁pr
- ▁had
- ttle
- ▁comp
- ort
- ish
- ▁ex
- ally
- ▁sa
- ▁how
- end
- ant
- ▁O
- ▁um
- way
- ance
- ▁other
- ▁two
- ine
- ever
- able
- ▁com
- other
- ▁first
- ▁back
- ▁al
- ers
- ions
- ▁now
- ▁off
- ning
- ▁down
- ▁has
- ▁than
- ▁car
- ▁Th
- very
- ice
- ▁dr
- ▁been
- ▁him
- ▁here
- ated
- '5'
- ▁hand
- ▁day
- ▁hear
- each
- ▁would
- ▁over
- ▁oh
- ▁cha
- ood
- ▁did
- ugh
- ▁per
- ▁let
- ▁str
- ▁tra
- ▁got
- ext
- '1'
- ▁We
- ▁Shields
- ▁come
- ▁should
- ▁could
- light
- '2'
- ▁people
- ▁again
- ▁year
- ▁app
- ▁into
- ▁any
- ▁N
- ▁mean
- ▁o
- ▁mus
- ▁lot
- ▁said
- ▁long
- ▁these
- ▁lea
- sh
- ▁vi
- ▁part
- ▁every
- ▁our
- ▁You
- ious
- ▁fight
- ▁Ch
- ark
- ▁may
- ▁Hammer
- ▁because
- ▁most
- ▁came
- ▁four
- ful
- ▁No
- ize
- ▁where
- ▁okay
- ▁much
- ▁ask
- ▁through
- ▁before
- ▁work
- ▁even
- ▁three
- mber
- ▁win
- ▁flight
- ake
- K
- ▁place
- ▁play
- ▁though
- ▁pound
- ▁bit
- land
- ▁va
- ▁talk
- ▁kind
- ▁Line
- ▁make
- hap
- ▁big
- ▁leav
- ▁something
- ▁game
- ▁under
- ▁feel
- self
- ▁give
- ▁includ
- U
- ▁twenty
- ▁guard
- ▁left
- ▁round
- ▁great
- body
- ▁gra
- ress
- lso
- '3'
- ▁everything
- ▁those
- ▁after
- ▁tell
- ▁need
- ▁yes
- qua
- ham
- ▁minutes
- ▁question
- ▁around
- ▁punch
- ▁course
- ▁gonna
- ▁person
- ▁move
- ▁plan
- ▁ear
- ept
- ▁Airport
- ▁Okay
- ▁found
- ▁seven
- ▁help
- que
- ▁qui
- ▁keep
- ▁guys
- ▁house
- ▁run
- ▁turn
- ▁better
- ▁stop
- ward
- ddle
- ▁second
- ground
- ▁world
- ▁high
- ▁point
- ▁hold
- ▁call
- '6'
- ▁actually
- ▁probably
- ▁heaven
- ▁speci
- ▁everyone
- ▁why
- ▁presen
- ▁thir
- lright
- ▁eye
- eath
- ▁Tak
- '!'
- '"'
- '4'
- ▁hundred
- ▁answer
- ▁small
- ▁wait
- ▁nothing
- q
- '8'
- V
- ▁countr
- ▁problem
- ▁continu
- ▁close
- ▁priva
- ▁20
- ▁pleas
- ▁walk
- ▁open
- ▁lay
- ▁Station
- ▁moment
- ▁Yeah
- ▁public
- possibl
- ▁happen
- together
- ▁while
- asically
- ▁money
- ▁wrong
- B
- ▁puzzle
- '7'
- ▁journ
- ▁rainbow
- ▁thousand
- I
- '9'
- S
- P
- '%'
- A
- D
- L
- F
- ’
- O
- G
- N
- á
- C
- $
- Z
- Y
- R
- E
- J
- W
- M
- H
- j
- –
- ;
- Q
- X
- ']'
- −
- '&'
- T
- '['
- <sos/eos>
init: xavier_uniform
model_conf: {}
use_ref_audio: true
use_ref_text: true
use_preprocessor: true
token_type: bpe
bpemodel: data/token_list/bpe_unigram500/bpe.model
non_linguistic_symbols: null
cleaner: null
g2p: null
frontend: s3prl
frontend_conf:
frontend_conf:
upstream: wavlm_large
download_dir: ./hub
multilayer_feature: true
universa: base
universa_conf:
embedding_dim: 256
audio_encoder_type: transformer
audio_encoder_params:
num_blocks: 4
attention_heads: 4
linear_units: 1024
dropout_rate: 0.1
positional_dropout_rate: 0.1
attention_dropout_rate: 0.1
input_layer: conv2d
normalize_before: true
concat_after: false
positionwise_layer_type: linear
positionwise_conv_kernel_size: 1
layer_drop_rate: 0.1
qk_norm: false
use_flash_attn: false
text_encoder_type: transformer
text_encoder_params:
num_blocks: 4
attention_heads: 4
linear_units: 1024
dropout_rate: 0.1
positional_dropout_rate: 0.1
attention_dropout_rate: 0.1
input_layer: linear
normalize_before: true
concat_after: false
positionwise_layer_type: linear
positionwise_conv_kernel_size: 1
layer_drop_rate: 0.1
qk_norm: false
use_flash_attn: false
cross_attention_type: multihead
cross_attention_params:
n_head: 4
dropout_rate: 0.1
pooling_type: mean
projector_type: linear
multi_branch: true
required:
- output_dir
- metric2id
version: '202412'
distributed: false
```
</details>
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
SpectraSuite/TriLM_3.9B_Unpacked | SpectraSuite | 2025-05-30T03:19:11Z | 16 | 13 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-07-08T05:58:30Z | ---
license: apache-2.0
---
# TriLM 3.9B Unpacked
TriLM (ternary model), unpacked to FP16 format - compatible with FP16 GEMMs. After unpacking, TriLM has the same architecture as LLaMa.
```python
import transformers as tf, torch
model_name = "SpectraSuite/TriLM_3.9B_Unpacked"
# Please adjust the temperature, repetition penalty, top_k, top_p and other sampling parameters according to your needs.
pipeline = tf.pipeline("text-generation", model=model_name, model_kwargs={"torch_dtype": torch.float16}, device_map="auto")
# These are base (pretrained) LLMs that are not instruction and chat tuned. You may need to adjust your prompt accordingly.
pipeline("Once upon a time")
```
* License: Apache 2.0
* We will use our GitHub repo for communication (including HF repo related queries). Feel free to open an issue here https://github.com/NolanoOrg/SpectraSuite
|
espnet/universa-wavlm_base_urgent24_multi-metric_audioref | espnet | 2025-05-30T03:18:34Z | 4 | 0 | espnet | [
"espnet",
"audio",
"universa",
"multilingual",
"dataset:urgent24",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2025-02-27T15:50:03Z | ---
tags:
- espnet
- audio
- universa
language: multilingual
datasets:
- urgent24
license: cc-by-4.0
---
## ESPnet2 universa model
### `espnet/universa-wavlm_base_urgent24_multi-metric_audioref`
This model was trained by ftshijt using urgent24 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
Please check [Colab link](https://colab.research.google.com/drive/1y2mp5TqaiF7-a-_7iNUK2dXvSv4Tmejj?usp=sharing) for a simple demo of how to use UniVERSA.
## universa config
<details><summary>expand</summary>
```
config: conf/train_universa_wavlm_ref_audio.yaml
print_config: false
log_level: INFO
drop_last_iter: false
dry_run: false
iterator_type: sequence
valid_iterator_type: null
output_dir: update_exp/universa_train_universa_wavlm_ref_audio_raw_fs16000
ngpu: 1
seed: 777
num_workers: 1
num_att_plot: 0
dist_backend: nccl
dist_init_method: env://
dist_world_size: null
dist_rank: null
local_rank: 0
dist_master_addr: null
dist_master_port: null
dist_launcher: null
multiprocessing_distributed: false
unused_parameters: false
sharded_ddp: false
use_deepspeed: false
deepspeed_config: null
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: false
use_tf32: false
collect_stats: false
write_collected_feats: false
max_epoch: 100
patience: null
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
- - train
- loss
- min
- - valid
- loss
- min
- - train
- acc
- max
- - valid
- acc
- max
keep_nbest_models: 5
nbest_averaging_interval: 0
grad_clip: -1
grad_clip_type: 2.0
grad_noise: false
accum_grad: 1
no_forward_run: false
resume: true
train_dtype: float32
use_amp: false
log_interval: 50
use_matplotlib: true
use_tensorboard: true
create_graph_in_tensorboard: false
use_wandb: false
wandb_project: null
wandb_id: null
wandb_entity: null
wandb_name: null
wandb_model_log_interval: -1
detect_anomaly: false
use_adapter: false
adapter: lora
save_strategy: all
adapter_conf: {}
pretrain_path: null
init_param: []
ignore_init_mismatch: false
freeze_param:
- frontend.upstream
num_iters_per_epoch: null
batch_size: 16
valid_batch_size: null
batch_bins: 1000000
valid_batch_bins: null
category_sample_size: 10
train_shape_file:
- update_exp/universa_stats_raw/train/audio_shape
- update_exp/universa_stats_raw/train/ref_audio_shape
- update_exp/universa_stats_raw/train/ref_text_shape
valid_shape_file:
- update_exp/universa_stats_raw/valid/audio_shape
- update_exp/universa_stats_raw/valid/ref_audio_shape
- update_exp/universa_stats_raw/valid/ref_text_shape
batch_type: sorted
valid_batch_type: null
fold_length:
- 256000
sort_in_batch: descending
shuffle_within_batch: false
sort_batch: descending
multiple_iterator: false
chunk_length: 500
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
chunk_excluded_key_prefixes: []
chunk_default_fs: null
chunk_max_abs_length: null
chunk_discard_short_samples: true
train_data_path_and_name_and_type:
- - dump_ark/raw/train_update/wav.scp
- audio
- kaldi_ark
- - dump_ark/raw/train_update/metric.scp
- metrics
- metric
- - dump_ark/raw/train_update/ref_wav.scp
- ref_audio
- kaldi_ark
- - dump_ark/raw/train_update/text
- ref_text
- text
valid_data_path_and_name_and_type:
- - dump_ark/raw/dev_update/wav.scp
- audio
- kaldi_ark
- - dump_ark/raw/dev_update/metric.scp
- metrics
- metric
- - dump_ark/raw/dev_update/ref_wav.scp
- ref_audio
- kaldi_ark
- - dump_ark/raw/dev_update/text
- ref_text
- text
multi_task_dataset: false
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
allow_multi_rates: false
valid_max_cache_size: null
exclude_weight_decay: false
exclude_weight_decay_conf: {}
optim: adamw
optim_conf:
lr: 0.001
scheduler: warmuplr
scheduler_conf:
warmup_steps: 25000
metric2id: dump_ark/raw/train_update/metric2id
metric2type: null
metric_pad_value: -100
token_list:
- <blank>
- <unk>
- s
- ▁
- t
- e
- ▁the
- i
- a
- o
- ▁a
- r
- ▁to
- d
- ▁and
- ''''
- m
- n
- ing
- u
- y
- p
- c
- ▁of
- l
- ed
- ▁I
- ▁in
- er
- re
- ▁it
- ▁you
- ar
- ▁f
- ▁is
- ▁that
- ','
- .
- in
- al
- g
- 'on'
- ▁b
- b
- or
- ▁c
- ▁s
- f
- h
- ▁we
- an
- en
- ▁for
- le
- ▁p
- ly
- es
- w
- ▁re
- ▁on
- ▁m
- ▁be
- ic
- ll
- th
- ▁he
- k
- ur
- ve
- ▁with
- ▁so
- ▁from
- ▁was
- v
- ch
- st
- ▁w
- ▁i
- ▁this
- ▁de
- ▁like
- ▁do
- ce
- at
- il
- ck
- ▁A
- ▁have
- ▁not
- ad
- ▁st
- ow
- ro
- ne
- ▁me
- ▁my
- ▁but
- ation
- ▁at
- ▁or
- '-'
- ter
- ent
- ▁B
- ▁n
- ▁know
- ▁t
- out
- ▁are
- nd
- ▁one
- ▁li
- ▁g
- ▁The
- ol
- ion
- te
- ▁go
- ut
- ▁as
- ▁just
- as
- ▁sh
- ▁they
- is
- ▁C
- et
- ▁h
- ▁an
- ▁there
- ▁up
- ▁S
- ▁M
- ▁she
- ▁by
- ▁su
- om
- ▁can
- us
- ▁your
- ng
- ▁con
- el
- ▁us
- ment
- z
- ▁see
- ▁ab
- ▁what
- ▁out
- ▁her
- me
- ate
- ▁all
- ▁th
- ▁if
- ▁right
- ▁his
- ▁ma
- ▁lo
- ▁which
- ide
- ▁P
- ▁more
- ▁then
- ul
- ast
- x
- ight
- ill
- ▁So
- ▁sp
- ▁going
- ▁some
- ure
- ▁their
- ig
- ▁no
- ▁ro
- ▁think
- ▁who
- ▁pro
- ver
- ive
- est
- ▁co
- ▁di
- '0'
- ist
- ▁k
- age
- ▁d
- ▁time
- ▁L
- ies
- ▁will
- ▁man
- ▁when
- ▁D
- les
- ▁F
- ▁want
- ff
- ity
- ▁un
- '?'
- ▁start
- ▁G
- ▁uh
- ▁get
- ok
- ▁take
- ▁po
- li
- ▁ho
- ▁way
- ▁don
- ▁yeah
- ▁really
- ▁say
- ▁look
- ▁good
- ▁ra
- ▁pr
- ▁had
- ttle
- ▁comp
- ort
- ish
- ▁ex
- ally
- ▁sa
- ▁how
- end
- ant
- ▁O
- ▁um
- way
- ance
- ▁other
- ▁two
- ine
- ever
- able
- ▁com
- other
- ▁first
- ▁back
- ▁al
- ers
- ions
- ▁now
- ▁off
- ning
- ▁down
- ▁has
- ▁than
- ▁car
- ▁Th
- very
- ice
- ▁dr
- ▁been
- ▁him
- ▁here
- ated
- '5'
- ▁hand
- ▁day
- ▁hear
- each
- ▁would
- ▁over
- ▁oh
- ▁cha
- ood
- ▁did
- ugh
- ▁per
- ▁let
- ▁str
- ▁tra
- ▁got
- ext
- '1'
- ▁We
- ▁Shields
- ▁come
- ▁should
- ▁could
- light
- '2'
- ▁people
- ▁again
- ▁year
- ▁app
- ▁into
- ▁any
- ▁N
- ▁mean
- ▁o
- ▁mus
- ▁lot
- ▁said
- ▁long
- ▁these
- ▁lea
- sh
- ▁vi
- ▁part
- ▁every
- ▁our
- ▁You
- ious
- ▁fight
- ▁Ch
- ark
- ▁may
- ▁Hammer
- ▁because
- ▁most
- ▁came
- ▁four
- ful
- ▁No
- ize
- ▁where
- ▁okay
- ▁much
- ▁ask
- ▁through
- ▁before
- ▁work
- ▁even
- ▁three
- mber
- ▁win
- ▁flight
- ake
- K
- ▁place
- ▁play
- ▁though
- ▁pound
- ▁bit
- land
- ▁va
- ▁talk
- ▁kind
- ▁Line
- ▁make
- hap
- ▁big
- ▁leav
- ▁something
- ▁game
- ▁under
- ▁feel
- self
- ▁give
- ▁includ
- U
- ▁twenty
- ▁guard
- ▁left
- ▁round
- ▁great
- body
- ▁gra
- ress
- lso
- '3'
- ▁everything
- ▁those
- ▁after
- ▁tell
- ▁need
- ▁yes
- qua
- ham
- ▁minutes
- ▁question
- ▁around
- ▁punch
- ▁course
- ▁gonna
- ▁person
- ▁move
- ▁plan
- ▁ear
- ept
- ▁Airport
- ▁Okay
- ▁found
- ▁seven
- ▁help
- que
- ▁qui
- ▁keep
- ▁guys
- ▁house
- ▁run
- ▁turn
- ▁better
- ▁stop
- ward
- ddle
- ▁second
- ground
- ▁world
- ▁high
- ▁point
- ▁hold
- ▁call
- '6'
- ▁actually
- ▁probably
- ▁heaven
- ▁speci
- ▁everyone
- ▁why
- ▁presen
- ▁thir
- lright
- ▁eye
- eath
- ▁Tak
- '!'
- '"'
- '4'
- ▁hundred
- ▁answer
- ▁small
- ▁wait
- ▁nothing
- q
- '8'
- V
- ▁countr
- ▁problem
- ▁continu
- ▁close
- ▁priva
- ▁20
- ▁pleas
- ▁walk
- ▁open
- ▁lay
- ▁Station
- ▁moment
- ▁Yeah
- ▁public
- possibl
- ▁happen
- together
- ▁while
- asically
- ▁money
- ▁wrong
- B
- ▁puzzle
- '7'
- ▁journ
- ▁rainbow
- ▁thousand
- I
- '9'
- S
- P
- '%'
- A
- D
- L
- F
- ’
- O
- G
- N
- á
- C
- $
- Z
- Y
- R
- E
- J
- W
- M
- H
- j
- –
- ;
- Q
- X
- ']'
- −
- '&'
- T
- '['
- <sos/eos>
init: xavier_uniform
model_conf: {}
use_ref_audio: true
use_ref_text: false
use_preprocessor: true
token_type: bpe
bpemodel: data/token_list/bpe_unigram500/bpe.model
non_linguistic_symbols: null
cleaner: null
g2p: null
frontend: s3prl
frontend_conf:
frontend_conf:
upstream: wavlm_large
download_dir: ./hub
multilayer_feature: true
universa: base
universa_conf:
embedding_dim: 256
audio_encoder_type: transformer
audio_encoder_params:
num_blocks: 4
attention_heads: 4
linear_units: 1024
dropout_rate: 0.1
positional_dropout_rate: 0.1
attention_dropout_rate: 0.1
input_layer: conv2d
normalize_before: true
concat_after: false
positionwise_layer_type: linear
positionwise_conv_kernel_size: 1
layer_drop_rate: 0.1
qk_norm: false
use_flash_attn: false
text_encoder_type: transformer
text_encoder_params:
num_blocks: 4
attention_heads: 4
linear_units: 1024
dropout_rate: 0.1
positional_dropout_rate: 0.1
attention_dropout_rate: 0.1
input_layer: linear
normalize_before: true
concat_after: false
positionwise_layer_type: linear
positionwise_conv_kernel_size: 1
layer_drop_rate: 0.1
qk_norm: false
use_flash_attn: false
cross_attention_type: multihead
cross_attention_params:
n_head: 4
dropout_rate: 0.1
pooling_type: mean
projector_type: linear
multi_branch: true
required:
- output_dir
- metric2id
version: '202412'
distributed: false
```
</details>
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
HuangJavelin/VimGeo | HuangJavelin | 2025-05-30T03:17:26Z | 0 | 1 | null | [
"code",
"feature-extraction",
"base_model:hustvl/Vim-small-midclstok",
"base_model:finetune:hustvl/Vim-small-midclstok",
"license:apache-2.0",
"region:us"
] | feature-extraction | 2025-05-14T08:00:43Z | ---
license: apache-2.0
metrics:
- accuracy
base_model:
- hustvl/Vim-small-midclstok
pipeline_tag: feature-extraction
tags:
- code
---
# VimGeo: Efficient Cross-View Geo-Localization with Vision Mamba Architecture
[[Project](https://github.com/VimGeoTeam/VimGeo/)], [[Paper, IJCAI 2025]]
**It has been accepted by IJCAI-25**
This is a PyTorch implementation of the “VimGeo: Efficient Cross-View Geo-Localization with Vision Mamba Architecture”.
<div style="text-align:center;">
<img src="figure/figure2.jpg" style="width:100%; height:100%;"/>
<p><strong>(a) Architecture of the proposed VimGeo model. (b) Visualization of the Channel Group Pooling (CGP) module.</strong></p>
</div>
# Environment Setup for Pretraining
### For NVIDIA GPUs:
1. **Python Environment**:
- Use Python 3.10.13:
```bash
conda create -n your_env_name python=3.10.13
```
2. **PyTorch Installation**:
- Install PyTorch 2.1.1 with CUDA 11.8:
```bash
pip install torch==2.1.1 torchvision==0.16.1 torchaudio==2.1.1 --index-url https://download.pytorch.org/whl/cu118
```
### General Requirements:
1. **Install Required Packages**:
- First, clone the following repository:
```bash
git clone https://github.com/hustvl/Vim.git
```
- Install dependencies:
```bash
pip install -r vim/vim_requirements.txt
```
2. **Special Package Installation**:
- **causal_conv1d**:
- Choose the appropriate version based on your system, then install:
```bash
# Example command, replace with the correct version if needed
pip install -e causal_conv1d>=1.1.0
```
- **mamba**:
- Ensure system compatibility and note that this library is modified in the Vim project.
- Installation steps:
```bash
# Example command, ensure system compatibility
pip install -e mamba-1p1p1
```
3. **File Replacement**:
- Download or update the modified version in the Vim project:
- Specifically, replace the `mamba-1p1p1` as needed:
```bash
# Assuming the target file is in a specific path within the Vim folder
cp path/to/modified/mamba-1p1p1 path/to/Vim/mamba-1p1p1/
```
Please adjust the steps based on your system and project needs, ensuring all paths and version numbers are correct.
# Dataset
Please download [CVUSA](http://mvrl.cs.uky.edu/datasets/cvusa/), [CVACT](https://github.com/Liumouliu/OriCNN) and [VIGOR](https://github.com/Jeff-Zilence/VIGOR). You may need to modify the dataset path in "dataloader".
# Model Zoo
| Dataset | R@1 | R@5 | R@10 | R@1% | Hit |
| ---------------- | --------- | --------- | --------- | -------- | ------ |
| CVUSA | 96.19% | 98.62% | 99.00% | 99.52% | - |
| CVACT_val | 87.62% | 94.88% | 96.06% | 98.06% | - |
| CVACT_test | 81.69% | 92.42% | 94.32% | 97.19% | - |
| VIGOR Same-Area | 55.24% | 80.75% | 76.12% | 97.30% | 57.43% |
| VIGOR Cross-Area | 19.31% | 37.50% | 46.03% | 86.96% | 20.72% |
### Note
All related results are available at [Hugging Face](https://huggingface.co/HuangJavelin/VimGeo/tree/main).
# Usage
## Training
To train our models on the respective datasets, simply run the following scripts:
1. For CVUSA:
```bash
bash train_CVUSA.sh
```
2. For CVACT (validation set):
```bash
bash train_CVACT.sh
```
3. For CVACT (test set):
```bash
bash train_CVACT_test.sh
```
4. For VIGOR Same-Area:
```bash
bash train_VIGOR.sh
```
5. For VIGOR Cross-Area:
```bash
bash train_VIGOR_cross.sh
```
These scripts contain all necessary parameters and configurations to train our method on each dataset for 50 epochs.
## Evaluation
You should organize the downloaded pre-trained models in the following way:
- `./result_cvusa/`
- `model_best.pth.tar`
- `checkpoint.pth.tar`
- `./result_cvact/`
- `model_best.pth.tar`
- `checkpoint.pth.tar`
- `./result_vigor/`
- `model_best.pth.tar`
- `checkpoint.pth.tar`
- `./result_vigor_cross/`
- `model_best.pth.tar`
- `checkpoint.pth.tar`
**Note:**
To evaluate the models, simply add the `-e` option to the corresponding training script:
Modify the following files by adding `-e` to the command line in each script:
- `VimGeoTeam/VimGeo/train_VIGOR.sh`
- `VimGeoTeam/VimGeo/train_CVUSA.sh`
- `VimGeoTeam/VimGeo/train_CVACT.sh`
- `VimGeoTeam/VimGeo/train_CVACT_test.sh`
- `VimGeoTeam/VimGeo/train_VIGOR_cross.sh`
# References and Acknowledgements
[FRGeo](https://github.com/zqwlearning/FRGeo-Code)，[Vim](https://github.com/hustvl/Vim)，[TransGeo](https://github.com/Jeff-Zilence/TransGeo2022)，[ConvNeXt](https://github.com/facebookresearch/ConvNeXt)，[CVUSA](http://mvrl.cs.uky.edu/datasets/cvusa/)，[VIGOR](https://github.com/Jeff-Zilence/VIGOR)，[OriCNN](https://github.com/Liumouliu/OriCNN)，[Deit](https://github.com/facebookresearch/deit)，[MoCo](https://github.com/facebookresearch/moco)
Please contact us if you have any questions.
# Citation
<!-- ```latex
@inproceedings{zhang2024aligning,
title={Aligning Geometric Spatial Layout in Cross-View Geo-Localization via Feature Recombination},
author={Zhang, Qingwang and Zhu, Yingying},
booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
volume={38},
number={7},
pages={7251--7259},
year={2024}
}
``` --> |
Triangle104/DeepSeek-MOE-4X8B-R1-Distill-Llama-3.1-Deep-Thinker-Uncensored-24B-Q5_K_S-GGUF | Triangle104 | 2025-05-30T03:14:09Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"llama-cpp",
"gguf-my-repo",
"base_model:DavidAU/DeepSeek-MOE-4X8B-R1-Distill-Llama-3.1-Deep-Thinker-Uncensored-24B",
"base_model:quantized:DavidAU/DeepSeek-MOE-4X8B-R1-Distill-Llama-3.1-Deep-Thinker-Uncensored-24B",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-05-30T03:12:07Z | ---
library_name: transformers
tags:
- mergekit
- merge
- llama-cpp
- gguf-my-repo
base_model: DavidAU/DeepSeek-MOE-4X8B-R1-Distill-Llama-3.1-Deep-Thinker-Uncensored-24B
---
# Triangle104/DeepSeek-MOE-4X8B-R1-Distill-Llama-3.1-Deep-Thinker-Uncensored-24B-Q5_K_S-GGUF
This model was converted to GGUF format from [`DavidAU/DeepSeek-MOE-4X8B-R1-Distill-Llama-3.1-Deep-Thinker-Uncensored-24B`](https://huggingface.co/DavidAU/DeepSeek-MOE-4X8B-R1-Distill-Llama-3.1-Deep-Thinker-Uncensored-24B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/DavidAU/DeepSeek-MOE-4X8B-R1-Distill-Llama-3.1-Deep-Thinker-Uncensored-24B) for more details on the model.
---
This as a 4X8B, Mixture of Experts model with all 4 experts (4 Llama fine tunes) activated, all with Deepseek Reasoning tech installed (in each one) giving you a 32B (4X8B) parameter model in only 24.9B model size.
This model is a Deepseek model with "Distilled" components of "thinking/reasoning" fused into it.
This model can be used for creative, non-creative use cases and general usage.
This is a very stable model, which can operate at temps 1+ 2+ and higher and generate coherent thought(s) and exceeds the original distill model (by Deepseek) in terms of performance, coherence and depth of thought.
The actual "DeepSeek" thinking / reasoning tech built (grafted in directly, by DavidAU) into it. The "thinking/reasoning" tech (for the model at this repo) is from the original Llama 3.1 "Distill" model from Deepseek:
[ https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Llama-8B ]
This model is for all use cases, and it has a slightly more creative slant than a standard model.
This model can also be used for solving logic puzzles, riddles, and other problems with the enhanced "thinking" systems by DeepSeek.
This model also can solve problems/riddles/ and puzzles normally beyond the abilities of a Llama 3.1 model due to DeepSeek systems.
This model MAY produce NSFW / uncensored content.
---
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo Triangle104/DeepSeek-MOE-4X8B-R1-Distill-Llama-3.1-Deep-Thinker-Uncensored-24B-Q5_K_S-GGUF --hf-file deepseek-moe-4x8b-r1-distill-llama-3.1-deep-thinker-uncensored-24b-q5_k_s.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Triangle104/DeepSeek-MOE-4X8B-R1-Distill-Llama-3.1-Deep-Thinker-Uncensored-24B-Q5_K_S-GGUF --hf-file deepseek-moe-4x8b-r1-distill-llama-3.1-deep-thinker-uncensored-24b-q5_k_s.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Triangle104/DeepSeek-MOE-4X8B-R1-Distill-Llama-3.1-Deep-Thinker-Uncensored-24B-Q5_K_S-GGUF --hf-file deepseek-moe-4x8b-r1-distill-llama-3.1-deep-thinker-uncensored-24b-q5_k_s.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Triangle104/DeepSeek-MOE-4X8B-R1-Distill-Llama-3.1-Deep-Thinker-Uncensored-24B-Q5_K_S-GGUF --hf-file deepseek-moe-4x8b-r1-distill-llama-3.1-deep-thinker-uncensored-24b-q5_k_s.gguf -c 2048
```
|
themendu/HarmFormer | themendu | 2025-05-30T03:13:30Z | 0 | 0 | null | [
"pytorch",
"text-classification",
"en",
"arxiv:2505.02009",
"base_model:allenai/longformer-base-4096",
"base_model:finetune:allenai/longformer-base-4096",
"license:apache-2.0",
"region:us"
] | text-classification | 2025-05-01T12:32:16Z | ---
license: apache-2.0
language:
- en
base_model:
- allenai/longformer-base-4096
pipeline_tag: text-classification
---
# HarmFormer
HarmFormer is a finetuned `allenai/longformer-base-4096`, which was trained to detect potentially harmful content across 5 different harm categories with three dimensions (Safe, Topical, Toxic) across long text and short text scenarios:
- H: Hate and Violence
- IH: Ideological Harm
- SE: Sexual Harm
- IL: Illegal Activities
- SI: Self-Inflicted Harm
We create and define HarmFormer to identify and detect harmful content in text data (especially web pages), which can be used for content moderation, safety checks, and other applications where understanding the nature of text's harmfulness is crucial.
More details about HarmFormer can be found in [our paper - Towards Safer Pretraining: Analyzing and Filtering Harmful Content in Webscale datasets for Responsible LLMs](https://arxiv.org/pdf/2505.02009).
## Model Details
- **Base Model:** allenai/longformer-base-4096
- **Number of Classes:** 5
- **Risk Levels per Class:** 3
- **Max Sequence Length:** 1024
## Usage
```python
from transformers import AutoTokenizer
from modeling import HarmFormer
import torch
# Load the model and tokenizer
model_path = "themendu/HarmFormer"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = HarmFormer.from_pretrained(model_path)
# Prepare input text
text = "Your text here"
inputs = tokenizer(
text,
add_special_tokens=True,
max_length=1024,
truncation=True,
padding='max_length',
return_attention_mask=True,
return_tensors='pt',
)
# Run inference
with torch.no_grad():
outputs = model(**inputs)
# Process outputs
logits = torch.stack(outputs, dim=0).permute(1, 0, 2)
probabilities = torch.softmax(logits, dim=-1)
predictions = [[[round(prob, 3) for prob in class_probs] for class_probs in sample] for sample in probabilities.cpu().tolist()]
print(predictions)
```
### Batch Processing
For processing multiple texts at once:
```python
texts = ["Text 1", "Text 2", "Text 3"]
inputs = tokenizer(
texts,
add_special_tokens=True,
max_length=1024,
truncation=True,
padding='max_length',
return_attention_mask=True,
return_tensors='pt',
)
with torch.no_grad():
outputs = model(**inputs)
logits = torch.stack(outputs, dim=0).permute(1, 0, 2)
probabilities = torch.softmax(logits, dim=-1)
predictions = [[[round(prob, 3) for prob in class_probs] for class_probs in sample] for sample in probabilities.cpu().tolist()]
```
## Citation
If you use this model in your research, please cite:
```
@misc{mendu2025saferpretraininganalyzingfiltering,
title={Towards Safer Pretraining: Analyzing and Filtering Harmful Content in Webscale datasets for Responsible LLMs},
author={Sai Krishna Mendu and Harish Yenala and Aditi Gulati and Shanu Kumar and Parag Agrawal},
year={2025},
eprint={2505.02009},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2505.02009},
}
``` |
Flock2Moooooo/task-10-microsoft-Phi-3.5-mini-instruct | Flock2Moooooo | 2025-05-30T03:10:40Z | 0 | 0 | peft | [
"peft",
"safetensors",
"base_model:microsoft/Phi-3.5-mini-instruct",
"base_model:adapter:microsoft/Phi-3.5-mini-instruct",
"license:other",
"region:us"
] | null | 2025-05-29T13:10:10Z | ---
library_name: peft
license: other
base_model: microsoft/Phi-3.5-mini-instruct
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# lora
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
### Training results
### Framework versions
- PEFT 0.12.0
- Transformers 4.48.3
- Pytorch 2.6.0+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0
|
Superrrdamn/task-10-Qwen-Qwen2.5-7B-Instruct | Superrrdamn | 2025-05-30T03:09:41Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen2.5-7B-Instruct",
"base_model:adapter:Qwen/Qwen2.5-7B-Instruct",
"region:us"
] | null | 2025-05-29T22:53:52Z | ---
base_model: Qwen/Qwen2.5-7B-Instruct
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 |
DevQuasar/deepseek-ai.DeepSeek-R1-0528-Qwen3-8B-GGUF | DevQuasar | 2025-05-30T03:07:51Z | 0 | 0 | null | [
"gguf",
"text-generation",
"base_model:deepseek-ai/DeepSeek-R1-0528-Qwen3-8B",
"base_model:quantized:deepseek-ai/DeepSeek-R1-0528-Qwen3-8B",
"endpoints_compatible",
"region:us",
"conversational"
] | text-generation | 2025-05-30T02:06:00Z | ---
base_model:
- deepseek-ai/DeepSeek-R1-0528-Qwen3-8B
pipeline_tag: text-generation
---
[<img src="https://raw.githubusercontent.com/csabakecskemeti/devquasar/main/dq_logo_black-transparent.png" width="200"/>](https://devquasar.com)
Quantized version of: [deepseek-ai/DeepSeek-R1-0528-Qwen3-8B](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528-Qwen3-8B)
'Make knowledge free for everyone'
<p align="center">
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User0000000001/bert-kor-kosa-nsmc2 | User0000000001 | 2025-05-30T03:05:39Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-05-30T03:05:30Z | ---
library_name: transformers
tags: []
---
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alana-foto-tvs/19.alana.video.alana.foto.viral.alana.flores.foto.viral.x.alana.flores.telegram.link | alana-foto-tvs | 2025-05-30T03:03:55Z | 0 | 0 | null | [
"region:us"
] | null | 2025-05-30T03:03:19Z | <a rel="nofollow" href="https://viralflix.xyz/leaked/?sa"><img src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif" alt="fsd"></a>
<p><a rel="nofollow" href="https://viralflix.xyz/leaked/?sa">►►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► 𝙁𝙪𝙡𝙡 𝙑𝙞𝙙𝙚𝙤️​</a></p>
<a rel="nofollow" href="https://viralflix.xyz/leaked/?sa">🔴►𝐂𝐋𝐈𝐂𝐊 𝐇𝐄𝐑𝐄 🌐==►► 𝐃𝐨𝐰𝐧𝐥𝐨𝐚𝐝 𝐍𝐨𝐰⬇️⬇️​</a> |
iuj92/bert-kor-kosa-nsmc2 | iuj92 | 2025-05-30T03:03:51Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-05-30T03:02:56Z | ---
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---
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hyj117/bert-kor-kosa-nsmc | hyj117 | 2025-05-30T03:03:07Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-05-30T03:01:20Z | ---
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Shinhyeok99/bert_kor_kosa_nsmc | Shinhyeok99 | 2025-05-30T03:03:04Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-05-30T03:01:27Z | ---
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tags: []
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RosyHey/bert-kor-kosa-nsmc2 | RosyHey | 2025-05-30T03:03:01Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-05-30T03:01:20Z | ---
library_name: transformers
tags: []
---
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dolsam77/bert-kor-kosa-nsmc2 | dolsam77 | 2025-05-30T03:02:57Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-05-30T03:01:17Z | ---
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tags: []
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chae909/bert_kor_kosa_nsmc1 | chae909 | 2025-05-30T03:02:56Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-05-30T03:01:18Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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## Model Details
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lhss0520/bert-kor-kosa-nsmc | lhss0520 | 2025-05-30T03:02:56Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-05-30T03:01:16Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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## Model Details
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kimddo1/bert-kor-kosa-nsmc | kimddo1 | 2025-05-30T03:02:55Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-05-30T03:01:22Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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## Model Details
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BongRea/Qwen3_Rude_RAG_FULL_sec | BongRea | 2025-05-30T03:02:12Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-05-30T03:01:55Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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## Model Details
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bratao/Qwen3OIE-4B | bratao | 2025-05-30T02:58:24Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"generated_from_trainer",
"conversational",
"dataset:train_dataset_updated.jsonl",
"base_model:Qwen/Qwen3-4B",
"base_model:finetune:Qwen/Qwen3-4B",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-30T02:47:43Z | ---
library_name: transformers
license: apache-2.0
base_model: Qwen/Qwen3-4B
tags:
- generated_from_trainer
datasets:
- train_dataset_updated.jsonl
model-index:
- name: outputs/out-4b/
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.9.2`
```yaml
base_model: Qwen/Qwen3-4B
# Automatically upload checkpoint and final model to HF
# hub_model_id: username/custom_model_name
load_in_8bit: false
load_in_4bit: false
strict: false
plugins:
- axolotl.integrations.liger.LigerPlugin
liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
datasets:
- path: "train_dataset_updated.jsonl"
type: chat_template
field_messages: conversations
message_property_mappings:
role: from
content: value
output_dir: ./outputs/out-4b/
sequence_len: 2048
sample_packing: true
flex_attention: true
pad_to_sequence_len: true
flex_attn_compile_kwargs:
dynamic: false
mode: max-autotune-no-cudagraphs
wandb_project: openie-qwen3
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:
gradient_accumulation_steps: 1
micro_batch_size: 8
num_epochs: 3
optimizer: paged_adamw_8bit
lr_scheduler: cosine
learning_rate: 2e-5
bf16: true
tf32: true
resume_from_checkpoint:
logging_steps: 1
evals_per_epoch: 1
saves_per_epoch: 1
warmup_steps: 10
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
```
</details><br>
# outputs/out-4b/
This model is a fine-tuned version of [Qwen/Qwen3-4B](https://huggingface.co/Qwen/Qwen3-4B) on the train_dataset_updated.jsonl dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use paged_adamw_8bit with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.1
- Tokenizers 0.21.1
|
sebastianmr18/xlm-roberta-ner-qlora-bs32-epochs-3 | sebastianmr18 | 2025-05-30T02:53:42Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:FacebookAI/xlm-roberta-large",
"base_model:adapter:FacebookAI/xlm-roberta-large",
"region:us"
] | null | 2025-05-30T02:53:37Z | ---
base_model: xlm-roberta-large
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 |
BienKieu/codeT5-phase1-version7 | BienKieu | 2025-05-30T02:48:09Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:BienKieu/codeT5-phase1-version6",
"base_model:finetune:BienKieu/codeT5-phase1-version6",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2025-05-29T16:45:07Z | ---
library_name: transformers
license: apache-2.0
base_model: BienKieu/codeT5-phase1-version6
tags:
- generated_from_trainer
model-index:
- name: codeT5-phase1-version7
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. -->
# codeT5-phase1-version7
This model is a fine-tuned version of [BienKieu/codeT5-phase1-version6](https://huggingface.co/BienKieu/codeT5-phase1-version6) 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: 3e-05
- train_batch_size: 14
- 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: 1
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
anfindsen/M3_test_model | anfindsen | 2025-05-30T02:47:38Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-30T02:46:49Z | ---
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] |
harriscj/gemma-3 | harriscj | 2025-05-30T02:46:41Z | 60 | 0 | peft | [
"peft",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"base_model:unsloth/gemma-3-4b-it-unsloth-bnb-4bit",
"base_model:adapter:unsloth/gemma-3-4b-it-unsloth-bnb-4bit",
"region:us"
] | null | 2025-05-24T16:31:29Z | ---
base_model: unsloth/gemma-3-4b-it-unsloth-bnb-4bit
library_name: peft
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. -->
- **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 |
Shawn821/ppo-Huggy | Shawn821 | 2025-05-30T02:45:25Z | 0 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] | reinforcement-learning | 2025-05-30T02:45:20Z | ---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: Shawn821/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
4yyw/fdgdf | 4yyw | 2025-05-30T02:45:00Z | 0 | 0 | null | [
"dataset:nvidia/OpenCodeReasoning",
"doi:10.57967/hf/5671",
"license:apache-2.0",
"region:us"
] | null | 2025-05-29T15:29:26Z | ---
license: apache-2.0
datasets:
- nvidia/OpenCodeReasoning
--- |
totet/gensyn-checkpoints-shy_sturdy_shrew | totet | 2025-05-30T02:44:18Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am shy sturdy shrew",
"unsloth",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-16T00:28:35Z | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: gensyn-checkpoints-shy_sturdy_shrew
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am shy sturdy shrew
- unsloth
- trl
licence: license
---
# Model Card for gensyn-checkpoints-shy_sturdy_shrew
This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-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="totet/gensyn-checkpoints-shy_sturdy_shrew", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.6.0
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
BootesVoid/cmb9z4w0p0keh1b1yxnku6g42_cmba6dmo30m7v1b1y9y4q87z9 | BootesVoid | 2025-05-30T02:44:18Z | 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 | 2025-05-30T02:44:05Z | ---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: trisha
---
# Cmb9Z4W0P0Keh1B1Yxnku6G42_Cmba6Dmo30M7V1B1Y9Y4Q87Z9
<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 `trisha` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "trisha",
"lora_weights": "https://huggingface.co/BootesVoid/cmb9z4w0p0keh1b1yxnku6g42_cmba6dmo30m7v1b1y9y4q87z9/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/cmb9z4w0p0keh1b1yxnku6g42_cmba6dmo30m7v1b1y9y4q87z9', weight_name='lora.safetensors')
image = pipeline('trisha').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/cmb9z4w0p0keh1b1yxnku6g42_cmba6dmo30m7v1b1y9y4q87z9/discussions) to add images that show off what you’ve made with this LoRA.
|
mikankure/gensyn-checkpoints-whistling_howling_scorpion | mikankure | 2025-05-30T02:41:45Z | 7 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am whistling howling scorpion",
"unsloth",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-17T02:03:41Z | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: gensyn-checkpoints-whistling_howling_scorpion
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am whistling howling scorpion
- unsloth
- trl
licence: license
---
# Model Card for gensyn-checkpoints-whistling_howling_scorpion
This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-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="mikankure/gensyn-checkpoints-whistling_howling_scorpion", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.6.0
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
JosephTong/llava-v1.5-7b-flowcut192 | JosephTong | 2025-05-30T02:40:55Z | 0 | 1 | null | [
"safetensors",
"llava_llama",
"image-text-to-text",
"arxiv:2505.19536",
"base_model:lmsys/vicuna-7b-v1.5",
"base_model:finetune:lmsys/vicuna-7b-v1.5",
"license:apache-2.0",
"region:us"
] | image-text-to-text | 2025-05-29T03:13:06Z | ---
license: apache-2.0
base_model:
- lmsys/vicuna-7b-v1.5
pipeline_tag: image-text-to-text
---
# FlowCut: Rethinking Redundancy via Information Flow for Efficient Vision-Language Models
Jintao Tong<sup>1</sup>,
Wenwei Jin<sup>2</sup>,
Pengda Qin<sup>2</sup>,
Anqi Li<sup>3</sup>,
Yixiong Zou<sup>1✉</sup>
Yuhong Li<sup>2✉</sup>,
Yuhua Li<sup>1</sup>,
Ruixuan Li<sup>1</sup>
<br><br>
<sup>1</sup>School of Computer Science and Technology, Huazhong University of Science and Technology<br> <sup>2</sup>Xiaohongshu Inc., <sup>3</sup>Institute of Information Science, Beijing Jiaotong University
[](https://github.com/TungChintao/FlowCut)
[](https://arxiv.org/pdf/2505.19536)
[](https://github.com/TungChintao/FlowCut/blob/main/LICENSE)
## 💡 Highlights
> **TLDR:** To address inefficiency from excessive visual tokens in LVLMs, we propose a unified, bottom-up perspective based on information-flow, revealing dynamic redundancy emergence and introduce FlowCut, making pruning decision aligned with the model's inherent behavior, outperforming all existing approaches.
## 🛠 Preparation
Our code is easy to use.
1. Clone the [LLaVA](https://github.com/haotian-liu/LLaVA)'s repository.
```
git clone https://github.com/haotian-liu/LLaVA.git
cd LLaVA
```
2. Install the [LLaVA](https://github.com/haotian-liu/LLaVA)'s environment.
```
conda create -n llava python=3.10 -y
conda activate llava
pip install --upgrade pip
pip install -e .
pip install flash-attn --no-build-isolation
```
3. For formal usage, you can install the package from PyPI by running the following command:
```
pip install flowcut
```
For development, you can install the package by cloning the repository and running the following command:
```
git clone https://github.com/TungChintao/FlowCut
cd flowcut
pip install -e .
```
File organization as follow:
```
├── LLaVA-main
├── flowcut
├── llava
├── playground
├── script
```
## 🚀 Quick Start
```Python
from llava.model.builder import load_pretrained_model
from llava.mm_utils import get_model_name_from_path
from llava.eval.run_llava import eval_model
from flowcut import flowcut
model_path = "liuhaotian/llava-v1.5-7b"
tokenizer, model, image_processor, context_len = load_pretrained_model(
model_path=model_path,
model_base=None,
model_name=get_model_name_from_path(model_path)
)
## FlowCut retains 64 visual tokens
model = flowcut(model, target_num=64)
```
## 📖 Evaluation
The evaluation code follows the structure of [LLaVA](https://github.com/haotian-liu/LLaVA) or [Lmms-Eval](https://github.com/EvolvingLMMs-Lab/lmms-eval). After loading the model, simply add two lines as shown below:
```python
## Load LLaVA Model (code from llava.eval.model_vqa_loader)
tokenizer, model, image_processor, context_len = load_pretrained_model(model_path, args.model_base, model_name)
## add FlowCut
from flowcut import flowcut
model = flowcut(model, target_num=64)
```
Script templetes (please follow the detailed instruction in [LLaVA-Evaluation](https://github.com/haotian-liu/LLaVA/blob/main/docs/Evaluation.md)).
```Shell
bash scripts/v1_5/eval/[Benchmark].sh
```
Examples:
```Shell
CUDA_VISIBLE_DEVICES=0 bash scripts/v1_5/eval/mme.sh
```
```Shell
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 bash scripts/v1_5/eval/vqav2.sh
```
## 🎯 Training
The training code follows the structure of [LLaVA](https://github.com/haotian-liu/LLaVA). After loading the model, simply add two lines as shown below:
```python
## Load LLaVA Model (code from llava.train)
code of loading model...
## add FlowCut
from flowcut import flowcut
model = flowcut(model, target_num=64)
## training
trainer = LLaVATrainer(model=model,
tokenizer=tokenizer,
args=training_args,
**data_module)
```
## 🔑 License
- This project is released under the [Apache 2.0 license](https://github.com/TungChintao/FlowCut/blob/main/LICENSE).
## 📌 Citation
- If you find this project useful in your research, please consider citing:
```bibtex
@article{tong2025flowcut,
title={FlowCut: Rethinking Redundancy via Information Flow for Efficient Vision-Language Models},
author={Tong, Jintao and Jin, Wenwei and Qin, Pengda and Li, Anqi and Zou, Yixiong and Li, Yuhong and Li, Yuhua and Li, Ruixuan},
journal={arXiv preprint arXiv:2505.19536},
year={2025}
}
```
|
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