modelId
stringlengths 5
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| author
stringlengths 2
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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-02 12:32:32
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 534
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
2025-09-02 12:31:20
| card
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|---|---|---|---|---|---|---|---|---|---|
ohjoonhee/smolvlm-instruct-trl-dpo-rlaif-v
|
ohjoonhee
| 2025-08-05T14:16:47Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"dpo",
"trl",
"arxiv:2305.18290",
"base_model:HuggingFaceTB/SmolVLM-Instruct",
"base_model:finetune:HuggingFaceTB/SmolVLM-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-07-31T09:38:24Z |
---
base_model: HuggingFaceTB/SmolVLM-Instruct
library_name: transformers
model_name: smolvlm-instruct-trl-dpo-rlaif-v
tags:
- generated_from_trainer
- dpo
- trl
licence: license
---
# Model Card for smolvlm-instruct-trl-dpo-rlaif-v
This model is a fine-tuned version of [HuggingFaceTB/SmolVLM-Instruct](https://huggingface.co/HuggingFaceTB/SmolVLM-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="ohjoonhee/smolvlm-instruct-trl-dpo-rlaif-v", 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/rokn/vlm-dpo/runs/mr0xkb6v)
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: 0.20.0
- Transformers: 4.54.1
- Pytorch: 2.7.1
- Datasets: 4.0.0
- Tokenizers: 0.21.4
## 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 Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
Butanium/simple-stories-one-layer-simple-transformer
|
Butanium
| 2025-08-05T14:14:05Z | 52 | 0 | null |
[
"safetensors",
"llama",
"region:us"
] | null | 2025-08-04T21:44:11Z |
---
{}
---
1-layer simple transformer described in [A Mathematical Framework for Transformer Circuits](https://transformer-circuits.pub/2021/framework/index.html).
Load with
```python
class OneLayerTransformer(PreTrainedModel):
config_class = LlamaConfig
def __init__(self, config: LlamaConfig):
super().__init__(config)
self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size)
# Single self-attention layer
self.self_attn = nn.MultiheadAttention(
embed_dim=config.hidden_size,
num_heads=config.num_attention_heads,
dropout=getattr(config, 'attention_dropout', 0.0),
batch_first=True,
)
# Output head
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
def forward(self, input_ids=None, attention_mask=None, labels=None, **kwargs):
batch_size, seq_len = input_ids.shape
# Embeddings
hidden_states = self.embed_tokens(input_ids)
assert hidden_states.shape == (batch_size, seq_len, self.config.hidden_size)
# Create causal mask for self-attention
causal_mask = torch.triu(torch.ones(seq_len, seq_len), diagonal=1).bool()
causal_mask = causal_mask.to(hidden_states.device)
# Self-attention with residual connection
attn_output, _ = self.self_attn(
hidden_states,
hidden_states,
hidden_states,
attn_mask=causal_mask,
key_padding_mask=None if attention_mask is None else ~attention_mask.bool(),
)
hidden_states = hidden_states + attn_output
assert hidden_states.shape == (batch_size, seq_len, self.config.hidden_size)
# Output projection
logits = self.lm_head(hidden_states)
assert logits.shape == (batch_size, seq_len, self.config.vocab_size)
loss = None
if labels is not None:
shift_logits = logits[..., :-1, :].contiguous()
shift_labels = labels[..., 1:].contiguous()
loss_fct = nn.CrossEntropyLoss()
loss = loss_fct(
shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1)
)
return {"loss": loss, "logits": logits}
model = OneLayerTransformer.from_pretrained('Butanium/simple-stories-one-layer-simple-transformer')
```
The model is trained on the SimpleStories dataset.
|
Saolodus/news
|
Saolodus
| 2025-08-05T14:12:42Z | 12 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"text-to-speech",
"base_model:google-bert/bert-base-multilingual-cased",
"base_model:finetune:google-bert/bert-base-multilingual-cased",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-to-speech
| 2025-08-05T13:14:29Z |
---
pipeline_tag: text-to-speech # или другая ваша задача
library_name: transformers
license: mit # желательно указать лицензию
base_model: bert-base-multilingual-cased # если это fine-tune
inference: true
---
|
mekpro/gemma3-botanist-merged
|
mekpro
| 2025-08-05T14:09:26Z | 18 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma3",
"image-text-to-text",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"base_model:unsloth/gemma-3-4b-it-unsloth-bnb-4bit",
"base_model:finetune:unsloth/gemma-3-4b-it-unsloth-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2025-08-05T14:05:19Z |
---
base_model: unsloth/gemma-3-4b-it-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** mekpro
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-3-4b-it-unsloth-bnb-4bit
This gemma3 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)
|
seekerdeep/task-13-Qwen-Qwen2.5-3B-Instruct
|
seekerdeep
| 2025-08-05T14:07:16Z | 32 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen2.5-3B-Instruct",
"base_model:adapter:Qwen/Qwen2.5-3B-Instruct",
"region:us"
] | null | 2025-08-05T13:34:28Z |
---
base_model: Qwen/Qwen2.5-3B-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
|
mekpro/gemma3-botanist
|
mekpro
| 2025-08-05T14:05:17Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"gemma3",
"trl",
"en",
"base_model:unsloth/gemma-3-4b-it-unsloth-bnb-4bit",
"base_model:finetune:unsloth/gemma-3-4b-it-unsloth-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-05T14:04:48Z |
---
base_model: unsloth/gemma-3-4b-it-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** mekpro
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-3-4b-it-unsloth-bnb-4bit
This gemma3 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)
|
taobao-mnn/Hunyuan-7B-Instruct-MNN
|
taobao-mnn
| 2025-08-05T13:57:59Z | 141 | 1 | null |
[
"chat",
"text-generation",
"en",
"base_model:tencent/Hunyuan-7B-Instruct",
"base_model:quantized:tencent/Hunyuan-7B-Instruct",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2025-08-04T09:55:00Z |
---
license: apache-2.0
language:
- en
pipeline_tag: text-generation
tags:
- chat
base_model:
- tencent/Hunyuan-7B-Instruct
base_model_relation: quantized
---
# Hunyuan-7B-Instruct-MNN
## Introduction
This model is a 4-bit quantized version of the MNN model exported from Hunyuan-7B-Instruct using [llmexport](https://github.com/alibaba/MNN/tree/master/transformers/llm/export).
## Download
```bash
# install huggingface
pip install huggingface
```
```bash
# shell download
huggingface download --model 'taobao-mnn/Hunyuan-7B-Instruct-MNN' --local_dir 'path/to/dir'
```
```python
# SDK download
from huggingface_hub import snapshot_download
model_dir = snapshot_download('taobao-mnn/Hunyuan-7B-Instruct-MNN')
```
```bash
# git clone
git clone https://www.modelscope.cn/taobao-mnn/Hunyuan-7B-Instruct-MNN
```
## Usage
```bash
# clone MNN source
git clone https://github.com/alibaba/MNN.git
# compile
cd MNN
mkdir build && cd build
cmake .. -DMNN_LOW_MEMORY=true -DMNN_CPU_WEIGHT_DEQUANT_GEMM=true -DMNN_BUILD_LLM=true -DMNN_SUPPORT_TRANSFORMER_FUSE=true
make -j
# run
./llm_demo /path/to/Hunyuan-7B-Instruct-MNN/config.json prompt.txt
```
## Document
[MNN-LLM](https://mnn-docs.readthedocs.io/en/latest/transformers/llm.html#)
|
Runware/Nai-T5
|
Runware
| 2025-08-05T13:54:09Z | 12 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-08-05T13:48:26Z |
---
license: apache-2.0
---
|
hause1986/graffHause
|
hause1986
| 2025-08-05T13:53:38Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-08-05T13:53:38Z |
---
license: apache-2.0
---
|
anuragpradhan/SmolVLM2-500M-Video-Instruct-Q4_K_M-GGUF
|
anuragpradhan
| 2025-08-05T13:52:54Z | 38 | 0 |
transformers
|
[
"transformers",
"gguf",
"llama-cpp",
"gguf-my-repo",
"image-text-to-text",
"en",
"dataset:HuggingFaceM4/the_cauldron",
"dataset:HuggingFaceM4/Docmatix",
"dataset:lmms-lab/LLaVA-OneVision-Data",
"dataset:lmms-lab/M4-Instruct-Data",
"dataset:HuggingFaceFV/finevideo",
"dataset:MAmmoTH-VL/MAmmoTH-VL-Instruct-12M",
"dataset:lmms-lab/LLaVA-Video-178K",
"dataset:orrzohar/Video-STaR",
"dataset:Mutonix/Vript",
"dataset:TIGER-Lab/VISTA-400K",
"dataset:Enxin/MovieChat-1K_train",
"dataset:ShareGPT4Video/ShareGPT4Video",
"base_model:HuggingFaceTB/SmolVLM2-500M-Video-Instruct",
"base_model:quantized:HuggingFaceTB/SmolVLM2-500M-Video-Instruct",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] |
image-text-to-text
| 2025-08-05T13:52:51Z |
---
library_name: transformers
license: apache-2.0
datasets:
- HuggingFaceM4/the_cauldron
- HuggingFaceM4/Docmatix
- lmms-lab/LLaVA-OneVision-Data
- lmms-lab/M4-Instruct-Data
- HuggingFaceFV/finevideo
- MAmmoTH-VL/MAmmoTH-VL-Instruct-12M
- lmms-lab/LLaVA-Video-178K
- orrzohar/Video-STaR
- Mutonix/Vript
- TIGER-Lab/VISTA-400K
- Enxin/MovieChat-1K_train
- ShareGPT4Video/ShareGPT4Video
pipeline_tag: image-text-to-text
language:
- en
base_model: HuggingFaceTB/SmolVLM2-500M-Video-Instruct
tags:
- llama-cpp
- gguf-my-repo
---
# anuragpradhan/SmolVLM2-500M-Video-Instruct-Q4_K_M-GGUF
This model was converted to GGUF format from [`HuggingFaceTB/SmolVLM2-500M-Video-Instruct`](https://huggingface.co/HuggingFaceTB/SmolVLM2-500M-Video-Instruct) 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/HuggingFaceTB/SmolVLM2-500M-Video-Instruct) 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 anuragpradhan/SmolVLM2-500M-Video-Instruct-Q4_K_M-GGUF --hf-file smolvlm2-500m-video-instruct-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo anuragpradhan/SmolVLM2-500M-Video-Instruct-Q4_K_M-GGUF --hf-file smolvlm2-500m-video-instruct-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 anuragpradhan/SmolVLM2-500M-Video-Instruct-Q4_K_M-GGUF --hf-file smolvlm2-500m-video-instruct-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo anuragpradhan/SmolVLM2-500M-Video-Instruct-Q4_K_M-GGUF --hf-file smolvlm2-500m-video-instruct-q4_k_m.gguf -c 2048
```
|
daniel699/llama-3.2-3b-instruct
|
daniel699
| 2025-08-05T13:52:15Z | 0 | 0 | null |
[
"license:other",
"region:us"
] | null | 2025-08-05T13:52:15Z |
---
license: other
license_name: llama3.2
license_link: LICENSE
---
|
Pavankalyan/C_0_padded_5
|
Pavankalyan
| 2025-08-05T13:45:07Z | 12 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-05T13:44:52Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## More Information [optional]
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[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Bahrom1996/uzbek-stt-lan
|
Bahrom1996
| 2025-08-05T13:44:50Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-03-01T00:59:30Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
eceunal/insectra-classifier-gemma-3n
|
eceunal
| 2025-08-05T13:40:28Z | 33 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma3n",
"image-text-to-text",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2025-08-05T13:31:03Z |
---
base_model: unsloth/gemma-3n-e2b-it-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3n
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** eceunal
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-3n-e2b-it-unsloth-bnb-4bit
This gemma3n 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)
|
NICOPOI-9/segformer-b5-finetuned-ade20k-hgo-coord_40epochs_distortion_ver2_global_norm_with_void_3
|
NICOPOI-9
| 2025-08-05T13:39:57Z | 15 | 0 |
transformers
|
[
"transformers",
"safetensors",
"segformer",
"vision",
"image-segmentation",
"generated_from_trainer",
"base_model:NICOPOI-9/segformer-b5-finetuned-ade20k-hgo-coord_40epochs_distortion_ver2_global_norm_with_void",
"base_model:finetune:NICOPOI-9/segformer-b5-finetuned-ade20k-hgo-coord_40epochs_distortion_ver2_global_norm_with_void",
"license:other",
"endpoints_compatible",
"region:us"
] |
image-segmentation
| 2025-08-05T06:37:44Z |
---
library_name: transformers
license: other
base_model: NICOPOI-9/segformer-b5-finetuned-ade20k-hgo-coord_40epochs_distortion_ver2_global_norm_with_void
tags:
- vision
- image-segmentation
- generated_from_trainer
model-index:
- name: segformer-b5-finetuned-ade20k-hgo-coord_40epochs_distortion_ver2_global_norm_with_void_3
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# segformer-b5-finetuned-ade20k-hgo-coord_40epochs_distortion_ver2_global_norm_with_void_3
This model is a fine-tuned version of [NICOPOI-9/segformer-b5-finetuned-ade20k-hgo-coord_40epochs_distortion_ver2_global_norm_with_void](https://huggingface.co/NICOPOI-9/segformer-b5-finetuned-ade20k-hgo-coord_40epochs_distortion_ver2_global_norm_with_void) on the NICOPOI-9/Modphad_Perlin_two_void_coord_global_norm dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5837
- Mean Iou: 0.7559
- Mean Accuracy: 0.8600
- Overall Accuracy: 0.8723
- Accuracy [0,0]: 0.8245
- Accuracy [0,1]: 0.8968
- Accuracy [1,0]: 0.9052
- Accuracy [1,1]: 0.8873
- Accuracy [0,2]: 0.8720
- Accuracy [0,3]: 0.8555
- Accuracy [1,2]: 0.8630
- Accuracy [1,3]: 0.9183
- Accuracy [2,0]: 0.8100
- Accuracy [2,1]: 0.8301
- Accuracy [2,2]: 0.8209
- Accuracy [2,3]: 0.8314
- Accuracy [3,0]: 0.8513
- Accuracy [3,1]: 0.7988
- Accuracy [3,2]: 0.8444
- Accuracy [3,3]: 0.8538
- Accuracy Void: 0.9565
- Iou [0,0]: 0.7600
- Iou [0,1]: 0.7777
- Iou [1,0]: 0.7713
- Iou [1,1]: 0.7672
- Iou [0,2]: 0.7510
- Iou [0,3]: 0.7562
- Iou [1,2]: 0.7433
- Iou [1,3]: 0.8024
- Iou [2,0]: 0.7175
- Iou [2,1]: 0.7317
- Iou [2,2]: 0.6962
- Iou [2,3]: 0.7339
- Iou [3,0]: 0.7656
- Iou [3,1]: 0.7285
- Iou [3,2]: 0.7081
- Iou [3,3]: 0.7214
- Iou Void: 0.9178
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 6e-05
- train_batch_size: 1
- eval_batch_size: 1
- 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: 80
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy [0,0] | Accuracy [0,1] | Accuracy [1,0] | Accuracy [1,1] | Accuracy [0,2] | Accuracy [0,3] | Accuracy [1,2] | Accuracy [1,3] | Accuracy [2,0] | Accuracy [2,1] | Accuracy [2,2] | Accuracy [2,3] | Accuracy [3,0] | Accuracy [3,1] | Accuracy [3,2] | Accuracy [3,3] | Accuracy Void | Iou [0,0] | Iou [0,1] | Iou [1,0] | Iou [1,1] | Iou [0,2] | Iou [0,3] | Iou [1,2] | Iou [1,3] | Iou [2,0] | Iou [2,1] | Iou [2,2] | Iou [2,3] | Iou [3,0] | Iou [3,1] | Iou [3,2] | Iou [3,3] | Iou Void |
|:-------------:|:-------:|:-----:|:---------------:|:--------:|:-------------:|:----------------:|:--------------:|:--------------:|:--------------:|:--------------:|:--------------:|:--------------:|:--------------:|:--------------:|:--------------:|:--------------:|:--------------:|:--------------:|:--------------:|:--------------:|:--------------:|:--------------:|:-------------:|:---------:|:---------:|:---------:|:---------:|:---------:|:---------:|:---------:|:---------:|:---------:|:---------:|:---------:|:---------:|:---------:|:---------:|:---------:|:---------:|:--------:|
| 0.152 | 7.3260 | 4000 | 0.7633 | 0.6318 | 0.7756 | 0.7910 | 0.7271 | 0.7253 | 0.8081 | 0.8546 | 0.8294 | 0.7726 | 0.7022 | 0.8399 | 0.6781 | 0.7753 | 0.7977 | 0.7897 | 0.8184 | 0.5306 | 0.7908 | 0.8273 | 0.9181 | 0.6549 | 0.6477 | 0.6603 | 0.6563 | 0.6384 | 0.6154 | 0.6197 | 0.6871 | 0.5669 | 0.6722 | 0.5917 | 0.6063 | 0.6417 | 0.4889 | 0.5758 | 0.5383 | 0.8792 |
| 0.4745 | 14.6520 | 8000 | 0.6847 | 0.6572 | 0.7918 | 0.8081 | 0.7585 | 0.7823 | 0.8711 | 0.8361 | 0.8139 | 0.7747 | 0.6589 | 0.8644 | 0.7717 | 0.8130 | 0.7244 | 0.7572 | 0.8354 | 0.6652 | 0.7897 | 0.8170 | 0.9266 | 0.6768 | 0.6619 | 0.6854 | 0.6303 | 0.6853 | 0.6383 | 0.6093 | 0.6536 | 0.6602 | 0.5989 | 0.6355 | 0.6343 | 0.6964 | 0.6043 | 0.6389 | 0.5706 | 0.8930 |
| 0.2779 | 21.9780 | 12000 | 0.6080 | 0.6946 | 0.8203 | 0.8326 | 0.7822 | 0.8660 | 0.8674 | 0.8758 | 0.8260 | 0.8204 | 0.8000 | 0.8680 | 0.7232 | 0.7985 | 0.8629 | 0.7841 | 0.8052 | 0.7116 | 0.8226 | 0.8120 | 0.9190 | 0.7199 | 0.7346 | 0.7254 | 0.6937 | 0.6854 | 0.6604 | 0.6958 | 0.7238 | 0.6568 | 0.6896 | 0.6128 | 0.6912 | 0.7207 | 0.6371 | 0.6627 | 0.6076 | 0.8904 |
| 0.0837 | 29.3040 | 16000 | 0.5950 | 0.7067 | 0.8279 | 0.8404 | 0.7697 | 0.8721 | 0.8696 | 0.8884 | 0.8008 | 0.8170 | 0.7844 | 0.8911 | 0.8288 | 0.7954 | 0.7810 | 0.7962 | 0.8401 | 0.7972 | 0.7915 | 0.8268 | 0.9235 | 0.6886 | 0.7537 | 0.6917 | 0.7075 | 0.7107 | 0.6793 | 0.6946 | 0.7357 | 0.7132 | 0.6818 | 0.6766 | 0.7030 | 0.6825 | 0.6735 | 0.6497 | 0.6747 | 0.8970 |
| 0.1192 | 36.6300 | 20000 | 0.6074 | 0.7169 | 0.8348 | 0.8475 | 0.8083 | 0.8478 | 0.8681 | 0.8823 | 0.8403 | 0.8282 | 0.8310 | 0.9117 | 0.7915 | 0.8006 | 0.7987 | 0.7866 | 0.8475 | 0.7392 | 0.8417 | 0.8336 | 0.9345 | 0.7405 | 0.7410 | 0.7164 | 0.7271 | 0.7169 | 0.6911 | 0.7208 | 0.7466 | 0.6942 | 0.7163 | 0.6798 | 0.6821 | 0.7093 | 0.6520 | 0.6803 | 0.6701 | 0.9026 |
| 0.0538 | 43.9560 | 24000 | 0.6011 | 0.7190 | 0.8335 | 0.8496 | 0.8453 | 0.8891 | 0.8780 | 0.8929 | 0.8250 | 0.7916 | 0.7874 | 0.8868 | 0.8056 | 0.8231 | 0.7849 | 0.7742 | 0.8565 | 0.7412 | 0.7993 | 0.8380 | 0.9511 | 0.7570 | 0.6969 | 0.7359 | 0.7488 | 0.7296 | 0.6807 | 0.6977 | 0.7651 | 0.7055 | 0.6898 | 0.6898 | 0.6849 | 0.7412 | 0.6480 | 0.6828 | 0.6554 | 0.9143 |
| 0.0616 | 51.2821 | 28000 | 0.5992 | 0.7349 | 0.8451 | 0.8586 | 0.8345 | 0.9088 | 0.8899 | 0.8821 | 0.8455 | 0.8010 | 0.8537 | 0.8853 | 0.7653 | 0.8475 | 0.8179 | 0.8252 | 0.8186 | 0.7725 | 0.8359 | 0.8324 | 0.9512 | 0.7540 | 0.7328 | 0.7120 | 0.7596 | 0.7493 | 0.7131 | 0.7134 | 0.7649 | 0.6818 | 0.7506 | 0.7135 | 0.7105 | 0.7632 | 0.7053 | 0.6837 | 0.6767 | 0.9098 |
| 0.0471 | 58.6081 | 32000 | 0.5636 | 0.7472 | 0.8530 | 0.8671 | 0.8347 | 0.8966 | 0.9209 | 0.8917 | 0.8560 | 0.8639 | 0.8388 | 0.9071 | 0.7870 | 0.8462 | 0.8135 | 0.8072 | 0.8454 | 0.7765 | 0.8370 | 0.8212 | 0.9580 | 0.7641 | 0.7822 | 0.7606 | 0.7573 | 0.7466 | 0.7331 | 0.7421 | 0.7789 | 0.6958 | 0.7347 | 0.6971 | 0.7076 | 0.7581 | 0.7122 | 0.7289 | 0.6887 | 0.9149 |
| 0.101 | 65.9341 | 36000 | 0.5694 | 0.7514 | 0.8573 | 0.8696 | 0.8289 | 0.8963 | 0.8971 | 0.8826 | 0.8476 | 0.8269 | 0.8581 | 0.9075 | 0.8128 | 0.8565 | 0.8462 | 0.8263 | 0.8474 | 0.8111 | 0.8374 | 0.8324 | 0.9600 | 0.7687 | 0.7604 | 0.7533 | 0.7885 | 0.7427 | 0.7313 | 0.7500 | 0.7977 | 0.7090 | 0.7323 | 0.6802 | 0.7167 | 0.7713 | 0.7278 | 0.7082 | 0.7185 | 0.9169 |
| 0.0344 | 73.2601 | 40000 | 0.5837 | 0.7559 | 0.8600 | 0.8723 | 0.8245 | 0.8968 | 0.9052 | 0.8873 | 0.8720 | 0.8555 | 0.8630 | 0.9183 | 0.8100 | 0.8301 | 0.8209 | 0.8314 | 0.8513 | 0.7988 | 0.8444 | 0.8538 | 0.9565 | 0.7600 | 0.7777 | 0.7713 | 0.7672 | 0.7510 | 0.7562 | 0.7433 | 0.8024 | 0.7175 | 0.7317 | 0.6962 | 0.7339 | 0.7656 | 0.7285 | 0.7081 | 0.7214 | 0.9178 |
### Framework versions
- Transformers 4.48.3
- Pytorch 2.1.0
- Datasets 3.2.0
- Tokenizers 0.21.0
|
lym00/DEPRECATED-qwen-image-gguf-test
|
lym00
| 2025-08-05T13:39:50Z | 386 | 8 | null |
[
"gguf",
"license:apache-2.0",
"region:us"
] | null | 2025-08-05T05:55:28Z |
---
license: apache-2.0
---
> [!IMPORTANT]
> ⚠️ **Deprecation Notice**
>
> Please visit city96's repo https://huggingface.co/city96/Qwen-Image-gguf/tree/main for the full updated quants
>
> This project is now **deprecated** and was intended for **experimental use only**.
>
> This contains **non-official and suboptimal patches**.
>
---
# Please visit city96's repo https://huggingface.co/city96/Qwen-Image-gguf/
---
## ComfyUI Initial GGUF Tests
Update ComfyUI to pull the relevant updates ([Initial support for qwen image model](https://github.com/comfyanonymous/ComfyUI/commit/c012400240d4867cd63a45220eb791b91ad47617))
Update [ComfyUI-GGUF](https://github.com/city96/ComfyUI-GGUF) nodes.
| Type | Name | Location | Download |
| ------------ | -------------------------------------------------- | ------------------------------ | ---------------- |
| Main Model | Qwen_Image-GGUF | `ComfyUI/models/unet` | [GGUF](https://huggingface.co/city96/Qwen-Image-gguf/tree/main) |
| Text Encoder | qwen_2.5_vl_7b | `ComfyUI/models/text_encoders` | [Safetensors](https://huggingface.co/Comfy-Org/Qwen-Image_ComfyUI/tree/main/split_files/text_encoders) |
| VAE | qwen_image_vae | `ComfyUI/models/vae` | [Safetensors](https://huggingface.co/Comfy-Org/Qwen-Image_ComfyUI/tree/main/split_files/vae) |
[**ComfyUI example workflow**](https://docs.comfy.org/tutorials/image/qwen/qwen-image)
---
# References
Tensors: https://huggingface.co/Qwen/Qwen-Image/blob/main/transformer/diffusion_pytorch_model.safetensors.index.json
Tools: https://github.com/city96/ComfyUI-GGUF/tree/main/tools
Patches for unknown model: ([referring last commit for cosmos](https://github.com/city96/ComfyUI-GGUF/commit/b3ec875a68d94b758914fd48d30571d953bb7a54))
ComfyUI Implementation: https://github.com/comfyanonymous/ComfyUI/commit/c012400240d4867cd63a45220eb791b91ad47617
---
# Patches (non-official)
convert.py ([ref](https://github.com/city96/ComfyUI-GGUF/commit/b3ec875a68d94b758914fd48d30571d953bb7a54#diff-c704a6f85aa19f97e68414cba03fdc0b73ab84e0d08b38b48a0e2ccc69283e57))
```python
class ModelQwenImage(ModelTemplate):
arch = "qwen_image"
keys_detect = [
(
"img_in.weight",
"transformer_blocks.59.attn.add_q_proj.weight",
"transformer_blocks.59.attn.add_k_proj.weight",
)
]
keys_banned = [] # Don't ban anything here
```
```python
arch_list = [ModelQwenImage, ModelFlux, ModelSD3, ModelAura, ModelHiDream, ModelLTXV, ModelHyVid, ModelWan, ModelSDXL, ModelSD1]
```
loader.py ([ref](https://github.com/city96/ComfyUI-GGUF/commit/b3ec875a68d94b758914fd48d30571d953bb7a54#diff-86393d83b45516ef8888170f0149c4c74625a15d65eb084d7f74df2bbf41d1f1))
```
IMG_ARCH_LIST = {"qwen_image", "flux", "sd1", "sdxl", "sd3", "aura", "hidream", "cosmos", "ltxv", "hyvid", "wan"}
```
lcpp.patch ([ref](https://github.com/city96/ComfyUI-GGUF/commit/b3ec875a68d94b758914fd48d30571d953bb7a54#diff-9b1be04da7c789792aab70c44b25d3ff23da1a61f6ec575a480a4e987aaf9a66))
```
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -205,6 +205,17 @@ enum llm_arch {
LLM_ARCH_GRANITE,
LLM_ARCH_GRANITE_MOE,
LLM_ARCH_CHAMELEON,
+ LLM_ARCH_FLUX,
+ LLM_ARCH_SD1,
+ LLM_ARCH_SDXL,
+ LLM_ARCH_SD3,
+ LLM_ARCH_AURA,
+ LLM_ARCH_LTXV,
+ LLM_ARCH_HYVID,
+ LLM_ARCH_WAN,
+ LLM_ARCH_HIDREAM,
+ LLM_ARCH_COSMOS,
+ LLM_ARCH_QWEN_IMAGE,
@@ -258,6 +269,17 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
{ LLM_ARCH_GRANITE, "granite" },
{ LLM_ARCH_GRANITE_MOE, "granitemoe" },
{ LLM_ARCH_CHAMELEON, "chameleon" },
+ { LLM_ARCH_FLUX, "flux" },
+ { LLM_ARCH_SD1, "sd1" },
+ { LLM_ARCH_SDXL, "sdxl" },
+ { LLM_ARCH_SD3, "sd3" },
+ { LLM_ARCH_AURA, "aura" },
+ { LLM_ARCH_LTXV, "ltxv" },
+ { LLM_ARCH_HYVID, "hyvid" },
+ { LLM_ARCH_WAN, "wan" },
+ { LLM_ARCH_HIDREAM, "hidream" },
+ { LLM_ARCH_COSMOS, "cosmos" },
+ { LLM_ARCH_QWEN_IMAGE, "qwen_image" },
{ LLM_ARCH_UNKNOWN, "(unknown)" },
@@ -1531,6 +1553,17 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
{ LLM_TENSOR_ATTN_K_NORM, "blk.%d.attn_k_norm" },
},
},
+ { LLM_ARCH_FLUX, {}},
+ { LLM_ARCH_SD1, {}},
+ { LLM_ARCH_SDXL, {}},
+ { LLM_ARCH_SD3, {}},
+ { LLM_ARCH_AURA, {}},
+ { LLM_ARCH_LTXV, {}},
+ { LLM_ARCH_HYVID, {}},
+ { LLM_ARCH_WAN, {}},
+ { LLM_ARCH_HIDREAM, {}},
+ { LLM_ARCH_COSMOS, {}},
+ { LLM_ARCH_QWEN_IMAGE, {}},
{
LLM_ARCH_UNKNOWN,
@@ -5403,6 +5436,25 @@ static void llm_load_hparams(
// get general kv
ml.get_key(LLM_KV_GENERAL_NAME, model.name, false);
+ // Disable LLM metadata for image models
+ switch (model.arch) {
+ case LLM_ARCH_FLUX:
+ case LLM_ARCH_SD1:
+ case LLM_ARCH_SDXL:
+ case LLM_ARCH_SD3:
+ case LLM_ARCH_AURA:
+ case LLM_ARCH_LTXV:
+ case LLM_ARCH_HYVID:
+ case LLM_ARCH_WAN:
+ case LLM_ARCH_HIDREAM:
+ case LLM_ARCH_COSMOS:
+ case LLM_ARCH_QWEN_IMAGE:
+ model.ftype = ml.ftype;
+ return;
+ default:
+ break;
+ }
```
---
### MSVC 19.44 Error and Fix
Error
```
error C2039: 'system_clock': is not a member of 'std::chrono'
```
common.cpp and log.cpp
```
#include <chrono>
```
---
### Compile and Quantize
Apply patch, recompile, and quantize: https://github.com/city96/ComfyUI-GGUF/tree/main/tools#quantizing-using-custom-llamacpp
|
ns69956/stage2_13K
|
ns69956
| 2025-08-05T13:37:10Z | 10 | 0 | null |
[
"safetensors",
"license:apache-2.0",
"region:us"
] | null | 2025-08-05T13:35:47Z |
---
license: apache-2.0
---
|
SalomonMetre13/shr-gpt2
|
SalomonMetre13
| 2025-08-05T13:35:02Z | 11 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-05T13:02:51Z |
---
library_name: transformers
tags:
- generated_from_trainer
model-index:
- name: shr-gpt2
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. -->
# shr-gpt2
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 6.8324
- eval_runtime: 320.5724
- eval_samples_per_second: 11.061
- eval_steps_per_second: 1.385
- epoch: 0.1253
- step: 500
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 3
### Framework versions
- Transformers 4.54.0
- Pytorch 2.6.0+cu124
- Datasets 4.0.0
- Tokenizers 0.21.2
|
Pavankalyan/C_0_padded_2
|
Pavankalyan
| 2025-08-05T13:34:11Z | 17 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-05T13:33:55Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **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]
|
aegisai-security/aegis_gemma3_12b_20250731_peft
|
aegisai-security
| 2025-08-05T13:34:10Z | 84 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gguf",
"gemma3",
"image-text-to-text",
"conversational",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2025-08-04T19:30:31Z |
---
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]
|
cemoss17/vision-grpo-qwen-2.5-vl-3b
|
cemoss17
| 2025-08-05T13:30:14Z | 11 | 1 |
transformers
|
[
"transformers",
"safetensors",
"qwen2_5_vl",
"image-to-text",
"generated_from_trainer",
"trl",
"grpo",
"arxiv:2402.03300",
"base_model:Qwen/Qwen2.5-VL-3B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-VL-3B-Instruct",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
image-to-text
| 2025-08-05T10:32:13Z |
---
base_model: Qwen/Qwen2.5-VL-3B-Instruct
library_name: transformers
model_name: vision-grpo-qwen-2.5-vl-3b
tags:
- generated_from_trainer
- trl
- grpo
licence: license
---
# Model Card for vision-grpo-qwen-2.5-vl-3b
This model is a fine-tuned version of [Qwen/Qwen2.5-VL-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-3B-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="cemoss17/vision-grpo-qwen-2.5-vl-3b", 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/cemoss17-sciscore/huggingface/runs/tb9i0ptn)
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: 0.20.0
- Transformers: 4.54.1
- Pytorch: 2.7.1
- Datasets: 4.0.0
- Tokenizers: 0.21.4
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
Askel1419/act_so101_PLB_2
|
Askel1419
| 2025-08-05T13:28:23Z | 10 | 0 |
lerobot
|
[
"lerobot",
"safetensors",
"act",
"robotics",
"dataset:PLB/phospho-playground-mono",
"arxiv:2304.13705",
"license:apache-2.0",
"region:us"
] |
robotics
| 2025-08-05T13:28:11Z |
---
datasets: PLB/phospho-playground-mono
library_name: lerobot
license: apache-2.0
model_name: act
pipeline_tag: robotics
tags:
- act
- lerobot
- robotics
---
# Model Card for act
<!-- Provide a quick summary of what the model is/does. -->
[Action Chunking with Transformers (ACT)](https://huggingface.co/papers/2304.13705) is an imitation-learning method that predicts short action chunks instead of single steps. It learns from teleoperated data and often achieves high success rates.
This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot).
See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index).
---
## How to Get Started with the Model
For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy).
Below is the short version on how to train and run inference/eval:
### Train from scratch
```bash
python -m lerobot.scripts.train \
--dataset.repo_id=${HF_USER}/<dataset> \
--policy.type=act \
--output_dir=outputs/train/<desired_policy_repo_id> \
--job_name=lerobot_training \
--policy.device=cuda \
--policy.repo_id=${HF_USER}/<desired_policy_repo_id>
--wandb.enable=true
```
_Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`._
### Evaluate the policy/run inference
```bash
python -m lerobot.record \
--robot.type=so100_follower \
--dataset.repo_id=<hf_user>/eval_<dataset> \
--policy.path=<hf_user>/<desired_policy_repo_id> \
--episodes=10
```
Prefix the dataset repo with **eval\_** and supply `--policy.path` pointing to a local or hub checkpoint.
---
## Model Details
- **License:** apache-2.0
|
janopw/julkahaze
|
janopw
| 2025-08-05T13:22:15Z | 9 | 0 | null |
[
"license:other",
"region:us"
] | null | 2025-08-05T12:32:13Z |
---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
---
|
maai-kyoto/vap_mc_en
|
maai-kyoto
| 2025-08-05T13:19:31Z | 0 | 0 | null |
[
"license:cc-by-nc-nd-4.0",
"region:us"
] | null | 2025-08-05T13:17:15Z |
---
license: cc-by-nc-nd-4.0
---
|
linkanjarad/Qwen3-1.7B-openvino
|
linkanjarad
| 2025-08-05T13:19:30Z | 24 | 0 |
transformers
|
[
"transformers",
"safetensors",
"openvino",
"qwen3",
"text-generation",
"openvino-export",
"conversational",
"base_model:Qwen/Qwen3-1.7B",
"base_model:finetune:Qwen/Qwen3-1.7B",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-05T13:19:12Z |
---
library_name: transformers
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen3-1.7B/blob/main/LICENSE
pipeline_tag: text-generation
base_model: Qwen/Qwen3-1.7B
tags:
- openvino
- openvino-export
---
This model was converted to OpenVINO from [`Qwen/Qwen3-1.7B`](https://huggingface.co/Qwen/Qwen3-1.7B) using [optimum-intel](https://github.com/huggingface/optimum-intel)
via the [export](https://huggingface.co/spaces/echarlaix/openvino-export) space.
First make sure you have optimum-intel installed:
```bash
pip install optimum[openvino]
```
To load your model you can do as follows:
```python
from optimum.intel import OVModelForCausalLM
model_id = "linkanjarad/Qwen3-1.7B-openvino"
model = OVModelForCausalLM.from_pretrained(model_id)
```
|
BinBashir/bert_uncased_L-4_H-512_A-8_on_jumia_dataset
|
BinBashir
| 2025-08-05T13:15:02Z | 12 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-05T13:14:38Z |
---
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]
|
daawq/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-rabid_camouflaged_bear
|
daawq
| 2025-08-05T13:14:20Z | 99 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am rabid_camouflaged_bear",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-07-29T11:55:56Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am rabid_camouflaged_bear
---
# 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]
|
fbove-an/quantized-llama-3.2-1B-4bit
|
fbove-an
| 2025-08-05T13:11:54Z | 106 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2025-08-05T12:26:31Z |
---
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]
|
Futuresony/future_ai_V1.1.250805
|
Futuresony
| 2025-08-05T13:10:33Z | 0 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"pytorch",
"gemma2",
"finance",
"text-generation",
"en",
"dataset:fka/awesome-chatgpt-prompts",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2025-08-05T12:45:21Z |
---
license: apache-2.0
datasets:
- fka/awesome-chatgpt-prompts
language:
- en
metrics:
- accuracy
pipeline_tag: text-generation
library_name: adapter-transformers
tags:
- finance
---
|
phospho-app/Cholan1986-ACT_BBOX-white_chess_king_pick_place-qqtb6
|
phospho-app
| 2025-08-05T13:03:34Z | 61 | 0 | null |
[
"safetensors",
"phosphobot",
"act",
"region:us"
] | null | 2025-08-05T12:35:41Z |
---
tags:
- phosphobot
- act
task_categories:
- robotics
---
# act Model - phospho Training Pipeline
## This model was trained using **phospho**.
Training was successful, try it out on your robot!
## Training parameters:
- **Dataset**: [phospho-app/white_chess_king_pick_place_bboxes](https://huggingface.co/datasets/phospho-app/white_chess_king_pick_place_bboxes)
- **Wandb run URL**: None
- **Epochs**: None
- **Batch size**: 100
- **Training steps**: 10000
📖 **Get Started**: [docs.phospho.ai](https://docs.phospho.ai?utm_source=huggingface_readme)
🤖 **Get your robot**: [robots.phospho.ai](https://robots.phospho.ai?utm_source=huggingface_readme)
|
xbar19/qwen2.5-14b-instruct-jigsaw
|
xbar19
| 2025-08-05T13:02:25Z | 25 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen2",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-05T12:51:55Z |
---
base_model: unsloth/qwen2.5-14b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** xbar19
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen2.5-14b-instruct-unsloth-bnb-4bit
This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
benwturner/gemma-3N-finetune
|
benwturner
| 2025-08-05T13:00:02Z | 15 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma3n",
"image-text-to-text",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2025-08-05T12:55:50Z |
---
base_model: unsloth/gemma-3n-e4b-it-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3n
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** benwturner
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-3n-e4b-it-unsloth-bnb-4bit
This gemma3n 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)
|
taanmaay/GPT-2-124M-weights
|
taanmaay
| 2025-08-05T12:56:57Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-08-05T12:54:38Z |
---
license: apache-2.0
---
|
himanshu2027iiitu/agroassist-models
|
himanshu2027iiitu
| 2025-08-05T12:55:37Z | 14 | 0 | null |
[
"tflite",
"joblib",
"license:apache-2.0",
"region:us"
] | null | 2025-08-04T18:53:53Z |
---
license: apache-2.0
---
|
Eehan/Qwen2.5-1.5B-drdrpo-hh-754
|
Eehan
| 2025-08-05T12:53:28Z | 12 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-03T08:28:22Z |
---
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]
|
mustafataha5/Reinforce-Pixelcopter-PLE-v0
|
mustafataha5
| 2025-08-05T12:53:22Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-08-05T12:53:12Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter-PLE-v0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 60.50 +/- 38.87
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
jnwulff/classifier-chapter4-modernbert
|
jnwulff
| 2025-08-05T12:42:58Z | 13 | 0 |
transformers
|
[
"transformers",
"safetensors",
"modernbert",
"text-classification",
"generated_from_trainer",
"base_model:answerdotai/ModernBERT-base",
"base_model:finetune:answerdotai/ModernBERT-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-05T11:58:56Z |
---
library_name: transformers
license: apache-2.0
base_model: answerdotai/ModernBERT-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: classifier-chapter4-modernbert
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. -->
# classifier-chapter4-modernbert
This model is a fine-tuned version of [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2447
- Accuracy: 0.9288
- F1: 0.9288
## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 313 | 0.2280 | 0.9203 | 0.9201 |
| 0.2573 | 2.0 | 626 | 0.2447 | 0.9288 | 0.9288 |
### Framework versions
- Transformers 4.54.0
- Pytorch 2.6.0+cu124
- Datasets 4.0.0
- Tokenizers 0.21.2
|
dgambettaphd/M_llm2_run0_gen3_WXS_doc1000_synt96_lr1e-04_acm_SYNLAST
|
dgambettaphd
| 2025-08-05T12:41:51Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-05T12:41:35Z |
---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
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#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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### 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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## Technical Specifications [optional]
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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|
tulvgengenr/MixGRPO
|
tulvgengenr
| 2025-08-05T12:39:28Z | 107 | 6 |
diffusers
|
[
"diffusers",
"safetensors",
"arxiv:2507.21802",
"license:other",
"region:us"
] | null | 2025-07-30T06:45:14Z |
---
license: other
---
<div align="center" style="font-family: charter;">
<h1>MixGRPO:</br>Unlocking Flow-based GRPO Efficiency with Mixed ODE-SDE</h1>
<a href="https://arxiv.org/abs/2507.21802" target="_blank">
<img alt="arXiv" src="https://img.shields.io/badge/arXiv-MixGRPO-red?logo=arxiv" height="20" /></a>
<a href="https://tulvgengenr.github.io/MixGRPO-Project-Page/" target="_blank">
<img alt="Website" src="https://img.shields.io/badge/💻_Project-MixGRPO-blue.svg" height="20" /></a>
<a href="https://github.com/Tencent-Hunyuan/MixGRPO" target="_blank">
<img alt="" src="https://img.shields.io/badge/_Code-MixGRPO-181717?color=121717&logo=github&logoColor=white" height="20" /></a>
<div>
<a href="https://scholar.google.com/citations?user=lQsMoJsAAAAJ&hl=en&oi=ao" target="_blank">Junzhe Li</a><sup>1,</sup><sup>2,</sup><sup>3</sup><sup>*</sup>,</span>
<a href="https://scholar.google.com/citations?user=TSMchWcAAAAJ&hl=en&oi=ao" target="_blank">Yutao Cui</a><sup>1</sup><sup>*</sup>, </span>
<a href="https://scholar.google.com/citations?hl=en&user=TaM4e4wAAAAJ" target="_blank">Tao Huang</a><sup>1</sup>,</span>
<a href="" target="_blank">Yinping Ma</a><sup>3</sup>,</span>
<a href="https://scholar.google.com/citations?hl=en&user=0ZZamLoAAAAJ&view_op=list_works&sortby=pubdate" target="_blank">Chun Fan</a><sup>3</sup>,</span>
<a href="" target="_blank">Miles Yang</a><sup>1</sup>,</span>
<a href="https://scholar.google.com/citations?user=igtXP_kAAAAJ&hl=en" target="_blank">Zhao Zhong</a><sup>1</sup></span>
</div>
<div>
<sup>1</sup>Hunyuan, Tencent 
</br>
<sup>2</sup>School of Computer Science, Peking University 
</br>
<sup>3</sup>Computer Center, Peking University 
</div>
</div>
## Abstract
Although GRPO substantially enhances flow matching models in human preference alignment of image generation, methods such as FlowGRPO still exhibit inefficiency due to the necessity of sampling and optimizing over all denoising steps specified by the Markov Decision Process (MDP). In this paper, we propose $\textbf{MixGRPO}$, a novel framework that leverages the flexibility of mixed sampling strategies through the integration of stochastic differential equations (SDE) and ordinary differential equations (ODE). This streamlines the optimization process within the MDP to improve efficiency and boost performance. Specifically, MixGRPO introduces a sliding window mechanism, using SDE sampling and GRPO-guided optimization only within the window, while applying ODE sampling outside. This design confines sampling randomness to the time-steps within the window, thereby reducing the optimization overhead, and allowing for more focused gradient updates to accelerate convergence. Additionally, as time-steps beyond the sliding window are not involved in optimization, higher-order solvers are supported for sampling. So we present a faster variant, termed $\textbf{MixGRPO-Flash}$, which further improves training efficiency while achieving comparable performance. MixGRPO exhibits substantial gains across multiple dimensions of human preference alignment, outperforming DanceGRPO in both effectiveness and efficiency, with nearly 50% lower training time. Notably, MixGRPO-Flash further reduces training time by 71%.
## 🌟 Model
The `diffusion_pytorch_model.safetensors` is based on [FLUX.1 Dev](https://huggingface.co/black-forest-labs/FLUX.1-dev) using the MixGRPO algorithm, with [HPSv2](https://github.com/tgxs002/HPSv2), [ImageReward](https://huggingface.co/zai-org/ImageReward), and [Pick Score](https://github.com/yuvalkirstain/PickScore) as multi-rewards after training.
## 🚀 Quick Start (Sample Usage)
Please see the hybrid inference code for solving reward hacking in github: https://github.com/Tencent-Hunyuan/MixGRPO?tab=readme-ov-file#run-inference.
## ✏️ Citation
### License
MixGRPO is licensed under the License Terms of MixGRPO. See `./License.txt` for more details.
### Bib
If you find MixGRPO useful for your research and applications, please cite using this BibTeX:
```
@misc{li2025mixgrpounlockingflowbasedgrpo,
title={MixGRPO: Unlocking Flow-based GRPO Efficiency with Mixed ODE-SDE},
author={Junzhe Li and Yutao Cui and Tao Huang and Yinping Ma and Chun Fan and Miles Yang and Zhao Zhong},
year={2025},
eprint={2507.21802},
archivePrefix={arXiv},
primaryClass={cs.AI},
url={https://arxiv.org/abs/2507.21802},
}
```
|
Esha89/fashion-bot-pk
|
Esha89
| 2025-08-05T12:39:18Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"sft",
"trl",
"base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0",
"base_model:finetune:TinyLlama/TinyLlama-1.1B-Chat-v1.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-05T12:39:14Z |
---
base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
library_name: transformers
model_name: fashion-bot-pk
tags:
- generated_from_trainer
- sft
- trl
licence: license
---
# Model Card for fashion-bot-pk
This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0).
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="Esha89/fashion-bot-pk", 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: 0.20.0
- Transformers: 4.54.1
- Pytorch: 2.6.0+cu124
- Datasets: 4.0.0
- Tokenizers: 0.21.2
## 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}}
}
```
|
Dndbbdbd/Dhhdhdyeeh
|
Dndbbdbd
| 2025-08-05T12:38:43Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-08-05T12:38:42Z |
---
license: apache-2.0
---
|
ScorpieCur/mistral-7b-instruct-v0.3-bnb-4bit
|
ScorpieCur
| 2025-08-05T12:38:33Z | 2 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"unsloth",
"mistral-7b",
"mistral-instruct",
"instruct",
"conversational",
"en",
"base_model:mistralai/Mistral-7B-Instruct-v0.3",
"base_model:quantized:mistralai/Mistral-7B-Instruct-v0.3",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2025-08-05T12:38:32Z |
---
language:
- en
library_name: transformers
license: apache-2.0
tags:
- unsloth
- transformers
- mistral
- mistral-7b
- mistral-instruct
- instruct
base_model: mistralai/Mistral-7B-Instruct-v0.3
---
# Finetune Mistral, Gemma, Llama 2-5x faster with 70% less memory via Unsloth!
We have a Google Colab Tesla T4 notebook for Mistral v3 7b here: https://colab.research.google.com/drive/1_yNCks4BTD5zOnjozppphh5GzMFaMKq_?usp=sharing
For conversational ShareGPT style and using Mistral v3 Instruct: https://colab.research.google.com/drive/15F1xyn8497_dUbxZP4zWmPZ3PJx1Oymv?usp=sharing
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/Discord%20button.png" width="200"/>](https://discord.gg/unsloth)
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
## ✨ Finetune for Free
All notebooks are **beginner friendly**! Add your dataset, click "Run All", and you'll get a 2x faster finetuned model which can be exported to GGUF, vLLM or uploaded to Hugging Face.
| Unsloth supports | Free Notebooks | Performance | Memory use |
|-----------------|--------------------------------------------------------------------------------------------------------------------------|-------------|----------|
| **Llama-3.2 (3B)** | [▶️ Start on Colab](https://colab.research.google.com/drive/1Ys44kVvmeZtnICzWz0xgpRnrIOjZAuxp?usp=sharing) | 2.4x faster | 58% less |
| **Llama-3.2 (11B vision)** | [▶️ Start on Colab](https://colab.research.google.com/drive/1j0N4XTY1zXXy7mPAhOC1_gMYZ2F2EBlk?usp=sharing) | 2x faster | 60% less |
| **Llama-3.1 (8B)** | [▶️ Start on Colab](https://colab.research.google.com/drive/1Ys44kVvmeZtnICzWz0xgpRnrIOjZAuxp?usp=sharing) | 2.4x faster | 58% less |
| **Qwen2 VL (7B)** | [▶️ Start on Colab](https://colab.research.google.com/drive/1whHb54GNZMrNxIsi2wm2EY_-Pvo2QyKh?usp=sharing) | 1.8x faster | 60% less |
| **Qwen2.5 (7B)** | [▶️ Start on Colab](https://colab.research.google.com/drive/1Kose-ucXO1IBaZq5BvbwWieuubP7hxvQ?usp=sharing) | 2x faster | 60% less |
| **Phi-3.5 (mini)** | [▶️ Start on Colab](https://colab.research.google.com/drive/1lN6hPQveB_mHSnTOYifygFcrO8C1bxq4?usp=sharing) | 2x faster | 50% less |
| **Gemma 2 (9B)** | [▶️ Start on Colab](https://colab.research.google.com/drive/1vIrqH5uYDQwsJ4-OO3DErvuv4pBgVwk4?usp=sharing) | 2.4x faster | 58% less |
| **Mistral (7B)** | [▶️ Start on Colab](https://colab.research.google.com/drive/1Dyauq4kTZoLewQ1cApceUQVNcnnNTzg_?usp=sharing) | 2.2x faster | 62% less |
| **DPO - Zephyr** | [▶️ Start on Colab](https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing) | 1.9x faster | 19% less |
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/refs/heads/main/images/documentation%20green%20button.png" width="200"/>](https://docs.unsloth.ai)
- This [conversational notebook](https://colab.research.google.com/drive/1Aau3lgPzeZKQ-98h69CCu1UJcvIBLmy2?usp=sharing) is useful for ShareGPT ChatML / Vicuna templates.
- This [text completion notebook](https://colab.research.google.com/drive/1ef-tab5bhkvWmBOObepl1WgJvfvSzn5Q?usp=sharing) is for raw text. This [DPO notebook](https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing) replicates Zephyr.
- \* Kaggle has 2x T4s, but we use 1. Due to overhead, 1x T4 is 5x faster.
|
ScorpieCur/Qwen2.5-VL-7B-Instruct-unsloth-bnb-4bit
|
ScorpieCur
| 2025-08-05T12:38:10Z | 2 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2_5_vl",
"image-to-text",
"multimodal",
"unsloth",
"image-text-to-text",
"conversational",
"en",
"arxiv:2309.00071",
"arxiv:2409.12191",
"arxiv:2308.12966",
"base_model:Qwen/Qwen2.5-VL-7B-Instruct",
"base_model:quantized:Qwen/Qwen2.5-VL-7B-Instruct",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] |
image-text-to-text
| 2025-08-05T12:38:08Z |
---
base_model:
- Qwen/Qwen2.5-VL-7B-Instruct
license: apache-2.0
language:
- en
pipeline_tag: image-text-to-text
tags:
- multimodal
- unsloth
library_name: transformers
---
# Qwen2.5-VL-7B-Instruct
<a href="https://chat.qwenlm.ai/" target="_blank" style="margin: 2px;">
<img alt="Chat" src="https://img.shields.io/badge/%F0%9F%92%9C%EF%B8%8F%20Qwen%20Chat%20-536af5" style="display: inline-block; vertical-align: middle;"/>
</a>
## Introduction
In the past five months since Qwen2-VL’s release, numerous developers have built new models on the Qwen2-VL vision-language models, providing us with valuable feedback. During this period, we focused on building more useful vision-language models. Today, we are excited to introduce the latest addition to the Qwen family: Qwen2.5-VL.
#### Key Enhancements:
* **Understand things visually**: Qwen2.5-VL is not only proficient in recognizing common objects such as flowers, birds, fish, and insects, but it is highly capable of analyzing texts, charts, icons, graphics, and layouts within images.
* **Being agentic**: Qwen2.5-VL directly plays as a visual agent that can reason and dynamically direct tools, which is capable of computer use and phone use.
* **Understanding long videos and capturing events**: Qwen2.5-VL can comprehend videos of over 1 hour, and this time it has a new ability of cpaturing event by pinpointing the relevant video segments.
* **Capable of visual localization in different formats**: Qwen2.5-VL can accurately localize objects in an image by generating bounding boxes or points, and it can provide stable JSON outputs for coordinates and attributes.
* **Generating structured outputs**: for data like scans of invoices, forms, tables, etc. Qwen2.5-VL supports structured outputs of their contents, benefiting usages in finance, commerce, etc.
#### Model Architecture Updates:
* **Dynamic Resolution and Frame Rate Training for Video Understanding**:
We extend dynamic resolution to the temporal dimension by adopting dynamic FPS sampling, enabling the model to comprehend videos at various sampling rates. Accordingly, we update mRoPE in the time dimension with IDs and absolute time alignment, enabling the model to learn temporal sequence and speed, and ultimately acquire the ability to pinpoint specific moments.
<p align="center">
<img src="https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen2.5-VL/qwen2.5vl_arc.jpeg" width="80%"/>
<p>
* **Streamlined and Efficient Vision Encoder**
We enhance both training and inference speeds by strategically implementing window attention into the ViT. The ViT architecture is further optimized with SwiGLU and RMSNorm, aligning it with the structure of the Qwen2.5 LLM.
We have three models with 3, 7 and 72 billion parameters. This repo contains the instruction-tuned 7B Qwen2.5-VL model. For more information, visit our [Blog](https://qwenlm.github.io/blog/qwen2.5-vl/) and [GitHub](https://github.com/QwenLM/Qwen2.5-VL).
## Evaluation
### Image benchmark
| Benchmark | InternVL2.5-8B | MiniCPM-o 2.6 | GPT-4o-mini | Qwen2-VL-7B |**Qwen2.5-VL-7B** |
| :--- | :---: | :---: | :---: | :---: | :---: |
| MMMU<sub>val</sub> | 56 | 50.4 | **60**| 54.1 | 58.6|
| MMMU-Pro<sub>val</sub> | 34.3 | - | 37.6| 30.5 | 41.0|
| DocVQA<sub>test</sub> | 93 | 93 | - | 94.5 | **95.7** |
| InfoVQA<sub>test</sub> | 77.6 | - | - |76.5 | **82.6** |
| ChartQA<sub>test</sub> | 84.8 | - |- | 83.0 |**87.3** |
| TextVQA<sub>val</sub> | 79.1 | 80.1 | -| 84.3 | **84.9**|
| OCRBench | 822 | 852 | 785 | 845 | **864** |
| CC_OCR | 57.7 | | | 61.6 | **77.8**|
| MMStar | 62.8| | |60.7| **63.9**|
| MMBench-V1.1-En<sub>test</sub> | 79.4 | 78.0 | 76.0| 80.7 | **82.6** |
| MMT-Bench<sub>test</sub> | - | - | - |**63.7** |63.6 |
| MMStar | **61.5** | 57.5 | 54.8 | 60.7 |63.9 |
| MMVet<sub>GPT-4-Turbo</sub> | 54.2 | 60.0 | 66.9 | 62.0 | **67.1**|
| HallBench<sub>avg</sub> | 45.2 | 48.1 | 46.1| 50.6 | **52.9**|
| MathVista<sub>testmini</sub> | 58.3 | 60.6 | 52.4 | 58.2 | **68.2**|
| MathVision | - | - | - | 16.3 | **25.07** |
### Video Benchmarks
| Benchmark | Qwen2-VL-7B | **Qwen2.5-VL-7B** |
| :--- | :---: | :---: |
| MVBench | 67.0 | **69.6** |
| PerceptionTest<sub>test</sub> | 66.9 | **70.5** |
| Video-MME<sub>wo/w subs</sub> | 63.3/69.0 | **65.1**/**71.6** |
| LVBench | | 45.3 |
| LongVideoBench | | 54.7 |
| MMBench-Video | 1.44 | 1.79 |
| TempCompass | | 71.7 |
| MLVU | | 70.2 |
| CharadesSTA/mIoU | 43.6|
### Agent benchmark
| Benchmarks | Qwen2.5-VL-7B |
|-------------------------|---------------|
| ScreenSpot | 84.7 |
| ScreenSpot Pro | 29.0 |
| AITZ_EM | 81.9 |
| Android Control High_EM | 60.1 |
| Android Control Low_EM | 93.7 |
| AndroidWorld_SR | 25.5 |
| MobileMiniWob++_SR | 91.4 |
## Requirements
The code of Qwen2.5-VL has been in the latest Hugging face transformers and we advise you to build from source with command:
```
pip install git+https://github.com/huggingface/transformers accelerate
```
or you might encounter the following error:
```
KeyError: 'qwen2_5_vl'
```
## Quickstart
Below, we provide simple examples to show how to use Qwen2.5-VL with 🤖 ModelScope and 🤗 Transformers.
The code of Qwen2.5-VL has been in the latest Hugging face transformers and we advise you to build from source with command:
```
pip install git+https://github.com/huggingface/transformers accelerate
```
or you might encounter the following error:
```
KeyError: 'qwen2_5_vl'
```
We offer a toolkit to help you handle various types of visual input more conveniently, as if you were using an API. This includes base64, URLs, and interleaved images and videos. You can install it using the following command:
```bash
# It's highly recommanded to use `[decord]` feature for faster video loading.
pip install qwen-vl-utils[decord]==0.0.8
```
If you are not using Linux, you might not be able to install `decord` from PyPI. In that case, you can use `pip install qwen-vl-utils` which will fall back to using torchvision for video processing. However, you can still [install decord from source](https://github.com/dmlc/decord?tab=readme-ov-file#install-from-source) to get decord used when loading video.
### Using 🤗 Transformers to Chat
Here we show a code snippet to show you how to use the chat model with `transformers` and `qwen_vl_utils`:
```python
from transformers import Qwen2_5_VLForConditionalGeneration, AutoTokenizer, AutoProcessor
from qwen_vl_utils import process_vision_info
# default: Load the model on the available device(s)
model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
"Qwen/Qwen2.5-VL-7B-Instruct", torch_dtype="auto", device_map="auto"
)
# We recommend enabling flash_attention_2 for better acceleration and memory saving, especially in multi-image and video scenarios.
# model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
# "Qwen/Qwen2.5-VL-7B-Instruct",
# torch_dtype=torch.bfloat16,
# attn_implementation="flash_attention_2",
# device_map="auto",
# )
# default processer
processor = AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct")
# The default range for the number of visual tokens per image in the model is 4-16384.
# You can set min_pixels and max_pixels according to your needs, such as a token range of 256-1280, to balance performance and cost.
# min_pixels = 256*28*28
# max_pixels = 1280*28*28
# processor = AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct", min_pixels=min_pixels, max_pixels=max_pixels)
messages = [
{
"role": "user",
"content": [
{
"type": "image",
"image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg",
},
{"type": "text", "text": "Describe this image."},
],
}
]
# Preparation for inference
text = processor.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
image_inputs, video_inputs = process_vision_info(messages)
inputs = processor(
text=[text],
images=image_inputs,
videos=video_inputs,
padding=True,
return_tensors="pt",
)
inputs = inputs.to("cuda")
# Inference: Generation of the output
generated_ids = model.generate(**inputs, max_new_tokens=128)
generated_ids_trimmed = [
out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
output_text = processor.batch_decode(
generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
print(output_text)
```
<details>
<summary>Multi image inference</summary>
```python
# Messages containing multiple images and a text query
messages = [
{
"role": "user",
"content": [
{"type": "image", "image": "file:///path/to/image1.jpg"},
{"type": "image", "image": "file:///path/to/image2.jpg"},
{"type": "text", "text": "Identify the similarities between these images."},
],
}
]
# Preparation for inference
text = processor.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
image_inputs, video_inputs = process_vision_info(messages)
inputs = processor(
text=[text],
images=image_inputs,
videos=video_inputs,
padding=True,
return_tensors="pt",
)
inputs = inputs.to("cuda")
# Inference
generated_ids = model.generate(**inputs, max_new_tokens=128)
generated_ids_trimmed = [
out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
output_text = processor.batch_decode(
generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
print(output_text)
```
</details>
<details>
<summary>Video inference</summary>
```python
# Messages containing a images list as a video and a text query
messages = [
{
"role": "user",
"content": [
{
"type": "video",
"video": [
"file:///path/to/frame1.jpg",
"file:///path/to/frame2.jpg",
"file:///path/to/frame3.jpg",
"file:///path/to/frame4.jpg",
],
},
{"type": "text", "text": "Describe this video."},
],
}
]
# Messages containing a local video path and a text query
messages = [
{
"role": "user",
"content": [
{
"type": "video",
"video": "file:///path/to/video1.mp4",
"max_pixels": 360 * 420,
"fps": 1.0,
},
{"type": "text", "text": "Describe this video."},
],
}
]
# Messages containing a video url and a text query
messages = [
{
"role": "user",
"content": [
{
"type": "video",
"video": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen2-VL/space_woaudio.mp4",
},
{"type": "text", "text": "Describe this video."},
],
}
]
#In Qwen 2.5 VL, frame rate information is also input into the model to align with absolute time.
# Preparation for inference
text = processor.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
image_inputs, video_inputs, video_kwargs = process_vision_info(messages, return_video_kwargs=True)
inputs = processor(
text=[text],
images=image_inputs,
videos=video_inputs,
fps=fps,
padding=True,
return_tensors="pt",
**video_kwargs,
)
inputs = inputs.to("cuda")
# Inference
generated_ids = model.generate(**inputs, max_new_tokens=128)
generated_ids_trimmed = [
out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
output_text = processor.batch_decode(
generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
print(output_text)
```
Video URL compatibility largely depends on the third-party library version. The details are in the table below. change the backend by `FORCE_QWENVL_VIDEO_READER=torchvision` or `FORCE_QWENVL_VIDEO_READER=decord` if you prefer not to use the default one.
| Backend | HTTP | HTTPS |
|-------------|------|-------|
| torchvision >= 0.19.0 | ✅ | ✅ |
| torchvision < 0.19.0 | ❌ | ❌ |
| decord | ✅ | ❌ |
</details>
<details>
<summary>Batch inference</summary>
```python
# Sample messages for batch inference
messages1 = [
{
"role": "user",
"content": [
{"type": "image", "image": "file:///path/to/image1.jpg"},
{"type": "image", "image": "file:///path/to/image2.jpg"},
{"type": "text", "text": "What are the common elements in these pictures?"},
],
}
]
messages2 = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Who are you?"},
]
# Combine messages for batch processing
messages = [messages1, messages2]
# Preparation for batch inference
texts = [
processor.apply_chat_template(msg, tokenize=False, add_generation_prompt=True)
for msg in messages
]
image_inputs, video_inputs = process_vision_info(messages)
inputs = processor(
text=texts,
images=image_inputs,
videos=video_inputs,
padding=True,
return_tensors="pt",
)
inputs = inputs.to("cuda")
# Batch Inference
generated_ids = model.generate(**inputs, max_new_tokens=128)
generated_ids_trimmed = [
out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
output_texts = processor.batch_decode(
generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
print(output_texts)
```
</details>
### 🤖 ModelScope
We strongly advise users especially those in mainland China to use ModelScope. `snapshot_download` can help you solve issues concerning downloading checkpoints.
### More Usage Tips
For input images, we support local files, base64, and URLs. For videos, we currently only support local files.
```python
# You can directly insert a local file path, a URL, or a base64-encoded image into the position where you want in the text.
## Local file path
messages = [
{
"role": "user",
"content": [
{"type": "image", "image": "file:///path/to/your/image.jpg"},
{"type": "text", "text": "Describe this image."},
],
}
]
## Image URL
messages = [
{
"role": "user",
"content": [
{"type": "image", "image": "http://path/to/your/image.jpg"},
{"type": "text", "text": "Describe this image."},
],
}
]
## Base64 encoded image
messages = [
{
"role": "user",
"content": [
{"type": "image", "image": "data:image;base64,/9j/..."},
{"type": "text", "text": "Describe this image."},
],
}
]
```
#### Image Resolution for performance boost
The model supports a wide range of resolution inputs. By default, it uses the native resolution for input, but higher resolutions can enhance performance at the cost of more computation. Users can set the minimum and maximum number of pixels to achieve an optimal configuration for their needs, such as a token count range of 256-1280, to balance speed and memory usage.
```python
min_pixels = 256 * 28 * 28
max_pixels = 1280 * 28 * 28
processor = AutoProcessor.from_pretrained(
"Qwen/Qwen2.5-VL-7B-Instruct", min_pixels=min_pixels, max_pixels=max_pixels
)
```
Besides, We provide two methods for fine-grained control over the image size input to the model:
1. Define min_pixels and max_pixels: Images will be resized to maintain their aspect ratio within the range of min_pixels and max_pixels.
2. Specify exact dimensions: Directly set `resized_height` and `resized_width`. These values will be rounded to the nearest multiple of 28.
```python
# min_pixels and max_pixels
messages = [
{
"role": "user",
"content": [
{
"type": "image",
"image": "file:///path/to/your/image.jpg",
"resized_height": 280,
"resized_width": 420,
},
{"type": "text", "text": "Describe this image."},
],
}
]
# resized_height and resized_width
messages = [
{
"role": "user",
"content": [
{
"type": "image",
"image": "file:///path/to/your/image.jpg",
"min_pixels": 50176,
"max_pixels": 50176,
},
{"type": "text", "text": "Describe this image."},
],
}
]
```
### Processing Long Texts
The current `config.json` is set for context length up to 32,768 tokens.
To handle extensive inputs exceeding 32,768 tokens, we utilize [YaRN](https://arxiv.org/abs/2309.00071), a technique for enhancing model length extrapolation, ensuring optimal performance on lengthy texts.
For supported frameworks, you could add the following to `config.json` to enable YaRN:
{
...,
"type": "yarn",
"mrope_section": [
16,
24,
24
],
"factor": 4,
"original_max_position_embeddings": 32768
}
However, it should be noted that this method has a significant impact on the performance of temporal and spatial localization tasks, and is therefore not recommended for use.
At the same time, for long video inputs, since MRoPE itself is more economical with ids, the max_position_embeddings can be directly modified to a larger value, such as 64k.
## Citation
If you find our work helpful, feel free to give us a cite.
```
@misc{qwen2.5-VL,
title = {Qwen2.5-VL},
url = {https://qwenlm.github.io/blog/qwen2.5-vl/},
author = {Qwen Team},
month = {January},
year = {2025}
}
@article{Qwen2VL,
title={Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution},
author={Wang, Peng and Bai, Shuai and Tan, Sinan and Wang, Shijie and Fan, Zhihao and Bai, Jinze and Chen, Keqin and Liu, Xuejing and Wang, Jialin and Ge, Wenbin and Fan, Yang and Dang, Kai and Du, Mengfei and Ren, Xuancheng and Men, Rui and Liu, Dayiheng and Zhou, Chang and Zhou, Jingren and Lin, Junyang},
journal={arXiv preprint arXiv:2409.12191},
year={2024}
}
@article{Qwen-VL,
title={Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond},
author={Bai, Jinze and Bai, Shuai and Yang, Shusheng and Wang, Shijie and Tan, Sinan and Wang, Peng and Lin, Junyang and Zhou, Chang and Zhou, Jingren},
journal={arXiv preprint arXiv:2308.12966},
year={2023}
}
```
|
ScorpieCur/gemma-3-1b-it-unsloth-bnb-4bit
|
ScorpieCur
| 2025-08-05T12:36:50Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma3_text",
"text-generation",
"unsloth",
"gemma3",
"gemma",
"google",
"conversational",
"en",
"arxiv:1905.07830",
"arxiv:1905.10044",
"arxiv:1911.11641",
"arxiv:1904.09728",
"arxiv:1705.03551",
"arxiv:1911.01547",
"arxiv:1907.10641",
"arxiv:1903.00161",
"arxiv:2009.03300",
"arxiv:2304.06364",
"arxiv:2103.03874",
"arxiv:2110.14168",
"arxiv:2311.12022",
"arxiv:2108.07732",
"arxiv:2107.03374",
"arxiv:2210.03057",
"arxiv:2106.03193",
"arxiv:1910.11856",
"arxiv:2502.12404",
"arxiv:2502.21228",
"arxiv:2404.16816",
"arxiv:2104.12756",
"arxiv:2311.16502",
"arxiv:2203.10244",
"arxiv:2404.12390",
"arxiv:1810.12440",
"arxiv:1908.02660",
"arxiv:2312.11805",
"base_model:google/gemma-3-1b-it",
"base_model:quantized:google/gemma-3-1b-it",
"license:gemma",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2025-08-05T12:36:49Z |
---
base_model: google/gemma-3-1b-it
language:
- en
library_name: transformers
license: gemma
tags:
- unsloth
- transformers
- gemma3
- gemma
- google
---
<div>
<p style="margin-bottom: 0; margin-top: 0;">
<strong>See <a href="https://huggingface.co/collections/unsloth/gemma-3-67d12b7e8816ec6efa7e4e5b">our collection</a> for all versions of Gemma 3 including GGUF, 4-bit & 16-bit formats.</strong>
</p>
<p style="margin-bottom: 0;">
<em>Unsloth's <a href="https://unsloth.ai/blog/deepseekr1-dynamic">Dynamic Quants</a> is selectively quantized, greatly improving accuracy over standard 4-bit.</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/tutorial-how-to-run-deepseek-r1-on-your-own-local-device">
<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;">✨ Fine-tune Gemma 3 with Unsloth!</h1>
</div>
- Fine-tune Gemma 3 (12B) for free using our Google [Colab notebook here](https://docs.unsloth.ai/get-started/unsloth-notebooks)!
- Read our Blog about Gemma 3 support: [unsloth.ai/blog/gemma3](https://unsloth.ai/blog/gemma3)
- View the rest of our notebooks in our [docs here](https://docs.unsloth.ai/get-started/unsloth-notebooks).
- Export your fine-tuned model to GGUF, Ollama, llama.cpp or 🤗HF.
| Unsloth supports | Free Notebooks | Performance | Memory use |
|-----------------|--------------------------------------------------------------------------------------------------------------------------|-------------|----------|
| **GRPO with Gemma 3 (12B)** | [▶️ Start on Colab](https://docs.unsloth.ai/get-started/unsloth-notebooks) | 2x 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 |
| **Mistral (7B)** | [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Mistral_v0.3_(7B)-Conversational.ipynb) | 2.2x faster | 62% less |
<br>
# Gemma 3 model card
**Model Page**: [Gemma](https://ai.google.dev/gemma/docs/core)
**Resources and Technical Documentation**:
* [Gemma 3 Technical Report][g3-tech-report]
* [Responsible Generative AI Toolkit][rai-toolkit]
* [Gemma on Kaggle][kaggle-gemma]
* [Gemma on Vertex Model Garden][vertex-mg-gemma3]
**Terms of Use**: [Terms][terms]
**Authors**: Google DeepMind
## Model Information
Summary description and brief definition of inputs and outputs.
### Description
Gemma is a family of lightweight, state-of-the-art open models from Google,
built from the same research and technology used to create the Gemini models.
Gemma 3 models are multimodal, handling text and image input and generating text
output, with open weights for both pre-trained variants and instruction-tuned
variants. Gemma 3 has a large, 128K context window, multilingual support in over
140 languages, and is available in more sizes than previous versions. Gemma 3
models are well-suited for a variety of text generation and image understanding
tasks, including question answering, summarization, and reasoning. Their
relatively small size makes it possible to deploy them in environments with
limited resources such as laptops, desktops or your own cloud infrastructure,
democratizing access to state of the art AI models and helping foster innovation
for everyone.
### Inputs and outputs
- **Input:**
- Text string, such as a question, a prompt, or a document to be summarized
- Images, normalized to 896 x 896 resolution and encoded to 256 tokens
each
- Total input context of 128K tokens for the 4B, 12B, and 27B sizes, and
32K tokens for the 1B size
- **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 context of 8192 tokens
### Citation
```none
@article{gemma_2025,
title={Gemma 3},
url={https://goo.gle/Gemma3Report},
publisher={Kaggle},
author={Gemma Team},
year={2025}
}
```
## Model Data
Data used for model training and how the data was processed.
### Training Dataset
These models were trained on a dataset of text data that includes a wide variety
of sources. The 27B model was trained with 14 trillion tokens, the 12B model was
trained with 12 trillion tokens, 4B model was trained with 4 trillion tokens and
1B with 2 trillion tokens. Here are the key components:
- Web Documents: A diverse collection of web text ensures the model is
exposed to a broad range of linguistic styles, topics, and vocabulary. The
training dataset includes content in over 140 languages.
- Code: Exposing the model to code helps it to learn the syntax and
patterns of programming languages, which improves its ability to generate
code and understand code-related questions.
- Mathematics: Training on mathematical text helps the model learn logical
reasoning, symbolic representation, and to address mathematical queries.
- Images: A wide range of images enables the model to perform image
analysis and visual data extraction tasks.
The combination of these diverse data sources is crucial for training a powerful
multimodal model that can handle a wide variety of different tasks and data
formats.
### Data Preprocessing
Here are the key data cleaning and filtering methods applied to the training
data:
- CSAM Filtering: Rigorous CSAM (Child Sexual Abuse Material) filtering
was applied at multiple stages in the data preparation process to ensure
the exclusion of harmful and illegal content.
- Sensitive Data Filtering: As part of making Gemma pre-trained models
safe and reliable, automated techniques were used to filter out certain
personal information and other sensitive data from training sets.
- Additional methods: Filtering based on content quality and safety in
line with [our policies][safety-policies].
## Implementation Information
Details about the model internals.
### Hardware
Gemma was trained using [Tensor Processing Unit (TPU)][tpu] hardware (TPUv4p,
TPUv5p and TPUv5e). Training vision-language models (VLMS) requires significant
computational power. TPUs, designed specifically for matrix operations common in
machine learning, offer several advantages in this domain:
- Performance: TPUs are specifically designed to handle the massive
computations involved in training VLMs. They can speed up training
considerably compared to CPUs.
- Memory: TPUs often come with large amounts of high-bandwidth memory,
allowing for the handling of large models and batch sizes during training.
This can lead to better model quality.
- Scalability: TPU Pods (large clusters of TPUs) provide a scalable
solution for handling the growing complexity of large foundation models.
You can distribute training across multiple TPU devices for faster and more
efficient processing.
- Cost-effectiveness: In many scenarios, TPUs can provide a more
cost-effective solution for training large models compared to CPU-based
infrastructure, especially when considering the time and resources saved
due to faster training.
- These advantages are aligned with
[Google's commitments to operate sustainably][sustainability].
### Software
Training was done using [JAX][jax] and [ML Pathways][ml-pathways].
JAX allows researchers to take advantage of the latest generation of hardware,
including TPUs, for faster and more efficient training of large models. ML
Pathways is Google's latest effort to build artificially intelligent systems
capable of generalizing across multiple tasks. This is specially suitable for
foundation models, including large language models like these ones.
Together, JAX and ML Pathways are used as described in the
[paper about the Gemini family of models][gemini-2-paper]; *"the 'single
controller' programming model of Jax and Pathways allows a single Python
process to orchestrate the entire training run, dramatically simplifying the
development workflow."*
## Evaluation
Model evaluation metrics and results.
### Benchmark Results
These models were evaluated against a large collection of different datasets and
metrics to cover different aspects of text generation:
#### Reasoning and factuality
| Benchmark | Metric | Gemma 3 PT 1B | Gemma 3 PT 4B | Gemma 3 PT 12B | Gemma 3 PT 27B |
| ------------------------------ |----------------|:--------------:|:-------------:|:--------------:|:--------------:|
| [HellaSwag][hellaswag] | 10-shot | 62.3 | 77.2 | 84.2 | 85.6 |
| [BoolQ][boolq] | 0-shot | 63.2 | 72.3 | 78.8 | 82.4 |
| [PIQA][piqa] | 0-shot | 73.8 | 79.6 | 81.8 | 83.3 |
| [SocialIQA][socialiqa] | 0-shot | 48.9 | 51.9 | 53.4 | 54.9 |
| [TriviaQA][triviaqa] | 5-shot | 39.8 | 65.8 | 78.2 | 85.5 |
| [Natural Questions][naturalq] | 5-shot | 9.48 | 20.0 | 31.4 | 36.1 |
| [ARC-c][arc] | 25-shot | 38.4 | 56.2 | 68.9 | 70.6 |
| [ARC-e][arc] | 0-shot | 73.0 | 82.4 | 88.3 | 89.0 |
| [WinoGrande][winogrande] | 5-shot | 58.2 | 64.7 | 74.3 | 78.8 |
| [BIG-Bench Hard][bbh] | few-shot | 28.4 | 50.9 | 72.6 | 77.7 |
| [DROP][drop] | 1-shot | 42.4 | 60.1 | 72.2 | 77.2 |
[hellaswag]: https://arxiv.org/abs/1905.07830
[boolq]: https://arxiv.org/abs/1905.10044
[piqa]: https://arxiv.org/abs/1911.11641
[socialiqa]: https://arxiv.org/abs/1904.09728
[triviaqa]: https://arxiv.org/abs/1705.03551
[naturalq]: https://github.com/google-research-datasets/natural-questions
[arc]: https://arxiv.org/abs/1911.01547
[winogrande]: https://arxiv.org/abs/1907.10641
[bbh]: https://paperswithcode.com/dataset/bbh
[drop]: https://arxiv.org/abs/1903.00161
#### STEM and code
| Benchmark | Metric | Gemma 3 PT 4B | Gemma 3 PT 12B | Gemma 3 PT 27B |
| ------------------------------ |----------------|:-------------:|:--------------:|:--------------:|
| [MMLU][mmlu] | 5-shot | 59.6 | 74.5 | 78.6 |
| [MMLU][mmlu] (Pro COT) | 5-shot | 29.2 | 45.3 | 52.2 |
| [AGIEval][agieval] | 3-5-shot | 42.1 | 57.4 | 66.2 |
| [MATH][math] | 4-shot | 24.2 | 43.3 | 50.0 |
| [GSM8K][gsm8k] | 8-shot | 38.4 | 71.0 | 82.6 |
| [GPQA][gpqa] | 5-shot | 15.0 | 25.4 | 24.3 |
| [MBPP][mbpp] | 3-shot | 46.0 | 60.4 | 65.6 |
| [HumanEval][humaneval] | 0-shot | 36.0 | 45.7 | 48.8 |
[mmlu]: https://arxiv.org/abs/2009.03300
[agieval]: https://arxiv.org/abs/2304.06364
[math]: https://arxiv.org/abs/2103.03874
[gsm8k]: https://arxiv.org/abs/2110.14168
[gpqa]: https://arxiv.org/abs/2311.12022
[mbpp]: https://arxiv.org/abs/2108.07732
[humaneval]: https://arxiv.org/abs/2107.03374
#### Multilingual
| Benchmark | Gemma 3 PT 1B | Gemma 3 PT 4B | Gemma 3 PT 12B | Gemma 3 PT 27B |
| ------------------------------------ |:-------------:|:-------------:|:--------------:|:--------------:|
| [MGSM][mgsm] | 2.04 | 34.7 | 64.3 | 74.3 |
| [Global-MMLU-Lite][global-mmlu-lite] | 24.9 | 57.0 | 69.4 | 75.7 |
| [WMT24++][wmt24pp] (ChrF) | 36.7 | 48.4 | 53.9 | 55.7 |
| [FloRes][flores] | 29.5 | 39.2 | 46.0 | 48.8 |
| [XQuAD][xquad] (all) | 43.9 | 68.0 | 74.5 | 76.8 |
| [ECLeKTic][eclektic] | 4.69 | 11.0 | 17.2 | 24.4 |
| [IndicGenBench][indicgenbench] | 41.4 | 57.2 | 61.7 | 63.4 |
[mgsm]: https://arxiv.org/abs/2210.03057
[flores]: https://arxiv.org/abs/2106.03193
[xquad]: https://arxiv.org/abs/1910.11856v3
[global-mmlu-lite]: https://huggingface.co/datasets/CohereForAI/Global-MMLU-Lite
[wmt24pp]: https://arxiv.org/abs/2502.12404v1
[eclektic]: https://arxiv.org/abs/2502.21228
[indicgenbench]: https://arxiv.org/abs/2404.16816
#### Multimodal
| Benchmark | Gemma 3 PT 4B | Gemma 3 PT 12B | Gemma 3 PT 27B |
| ------------------------------ |:-------------:|:--------------:|:--------------:|
| [COCOcap][coco-cap] | 102 | 111 | 116 |
| [DocVQA][docvqa] (val) | 72.8 | 82.3 | 85.6 |
| [InfoVQA][info-vqa] (val) | 44.1 | 54.8 | 59.4 |
| [MMMU][mmmu] (pt) | 39.2 | 50.3 | 56.1 |
| [TextVQA][textvqa] (val) | 58.9 | 66.5 | 68.6 |
| [RealWorldQA][realworldqa] | 45.5 | 52.2 | 53.9 |
| [ReMI][remi] | 27.3 | 38.5 | 44.8 |
| [AI2D][ai2d] | 63.2 | 75.2 | 79.0 |
| [ChartQA][chartqa] | 63.6 | 74.7 | 76.3 |
| [VQAv2][vqav2] | 63.9 | 71.2 | 72.9 |
| [BLINK][blinkvqa] | 38.0 | 35.9 | 39.6 |
| [OKVQA][okvqa] | 51.0 | 58.7 | 60.2 |
| [TallyQA][tallyqa] | 42.5 | 51.8 | 54.3 |
| [SpatialSense VQA][ss-vqa] | 50.9 | 60.0 | 59.4 |
| [CountBenchQA][countbenchqa] | 26.1 | 17.8 | 68.0 |
[coco-cap]: https://cocodataset.org/#home
[docvqa]: https://www.docvqa.org/
[info-vqa]: https://arxiv.org/abs/2104.12756
[mmmu]: https://arxiv.org/abs/2311.16502
[textvqa]: https://textvqa.org/
[realworldqa]: https://paperswithcode.com/dataset/realworldqa
[remi]: https://arxiv.org/html/2406.09175v1
[ai2d]: https://allenai.org/data/diagrams
[chartqa]: https://arxiv.org/abs/2203.10244
[vqav2]: https://visualqa.org/index.html
[blinkvqa]: https://arxiv.org/abs/2404.12390
[okvqa]: https://okvqa.allenai.org/
[tallyqa]: https://arxiv.org/abs/1810.12440
[ss-vqa]: https://arxiv.org/abs/1908.02660
[countbenchqa]: https://github.com/google-research/big_vision/blob/main/big_vision/datasets/countbenchqa/
## Ethics and Safety
Ethics and safety evaluation approach and results.
### 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.
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.
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 major improvements in the categories of
child safety, content safety, and representational harms relative to previous
Gemma models. All testing was conducted without safety filters to evaluate the
model capabilities and behaviors. For both text-to-text and image-to-text, and
across all model sizes, the model produced minimal policy violations, and showed
significant improvements over previous Gemma models' performance with respect
to ungrounded inferences. A limitation of our evaluations was they included only
English language prompts.
## Usage and Limitations
These models have certain limitations that users should be aware of.
### Intended Usage
Open vision-language models (VLMs) models have a wide range of applications
across various industries and domains. The following list of potential uses is
not comprehensive. The purpose of this list is to provide contextual information
about the possible use-cases that the model creators considered as part of model
training and development.
- Content Creation and Communication
- Text Generation: These models can be used to generate creative text
formats such as poems, scripts, code, marketing copy, and email drafts.
- Chatbots and Conversational AI: Power conversational interfaces
for customer service, virtual assistants, or interactive applications.
- Text Summarization: Generate concise summaries of a text corpus,
research papers, or reports.
- Image Data Extraction: These models can be used to extract,
interpret, and summarize visual data for text communications.
- Research and Education
- Natural Language Processing (NLP) and VLM Research: These
models can serve as a foundation for researchers to experiment with VLM
and NLP techniques, develop algorithms, and contribute to the
advancement of the field.
- Language Learning Tools: Support interactive language learning
experiences, aiding in grammar correction or providing writing practice.
- Knowledge Exploration: Assist researchers in exploring large
bodies of text by generating summaries or answering questions about
specific topics.
### Limitations
- Training Data
- The quality and diversity of the training data significantly
influence the model's capabilities. Biases or gaps in the training data
can lead to limitations in the model's responses.
- The scope of the training dataset determines the subject areas
the model can handle effectively.
- Context and Task Complexity
- Models are better at tasks that can be framed with clear
prompts and instructions. Open-ended or highly complex tasks might be
challenging.
- A model's performance can be influenced by the amount of context
provided (longer context generally leads to better outputs, up to a
certain point).
- Language Ambiguity and Nuance
- Natural language is inherently complex. Models might struggle
to grasp subtle nuances, sarcasm, or figurative language.
- Factual Accuracy
- Models generate responses based on information they learned
from their training datasets, but they are not knowledge bases. They
may generate incorrect or outdated factual statements.
- Common Sense
- Models rely on statistical patterns in language. They might
lack the ability to apply common sense reasoning in certain situations.
### Ethical Considerations and Risks
The development of vision-language models (VLMs) raises several ethical
concerns. In creating an open model, we have carefully considered the following:
- Bias and Fairness
- VLMs trained on large-scale, real-world text and image data can
reflect socio-cultural biases embedded in the training material. These
models underwent careful scrutiny, input data pre-processing described
and posterior evaluations reported in this card.
- Misinformation and Misuse
- VLMs can be misused to generate text that is false, misleading,
or harmful.
- Guidelines are provided for responsible use with the model, see the
[Responsible Generative AI Toolkit][rai-toolkit].
- Transparency and Accountability:
- This model card summarizes details on the models' architecture,
capabilities, limitations, and evaluation processes.
- A responsibly developed open model offers the opportunity to
share innovation by making VLM technology accessible to developers and
researchers across the AI ecosystem.
Risks identified and mitigations:
- **Perpetuation of biases**: It's encouraged to perform continuous
monitoring (using evaluation metrics, human review) and the exploration of
de-biasing techniques during model training, fine-tuning, and other use
cases.
- **Generation of harmful content**: Mechanisms and guidelines for content
safety are essential. Developers are encouraged to exercise caution and
implement appropriate content safety safeguards based on their specific
product policies and application use cases.
- **Misuse for malicious purposes**: Technical limitations and developer
and end-user education can help mitigate against malicious applications of
VLMs. Educational resources and reporting mechanisms for users to flag
misuse are provided. Prohibited uses of Gemma models are outlined in the
[Gemma Prohibited Use Policy][prohibited-use].
- **Privacy violations**: Models were trained on data filtered for removal
of certain personal information and other sensitive data. Developers are
encouraged to adhere to privacy regulations with privacy-preserving
techniques.
### Benefits
At the time of release, this family of models provides high-performance open
vision-language model implementations designed from the ground up for
responsible AI development compared to similarly sized models.
Using the benchmark evaluation metrics described in this document, these models
have shown to provide superior performance to other, comparably-sized open model
alternatives.
[g3-tech-report]: https://goo.gle/Gemma3Report
[rai-toolkit]: https://ai.google.dev/responsible
[kaggle-gemma]: https://www.kaggle.com/models/google/gemma-3
[vertex-mg-gemma3]: https://console.cloud.google.com/vertex-ai/publishers/google/model-garden/gemma3
[terms]: https://ai.google.dev/gemma/terms
[safety-policies]: https://ai.google/static/documents/ai-responsibility-update-published-february-2025.pdf
[prohibited-use]: https://ai.google.dev/gemma/prohibited_use_policy
[tpu]: https://cloud.google.com/tpu/docs/intro-to-tpu
[sustainability]: https://sustainability.google/operating-sustainably/
[jax]: https://github.com/jax-ml/jax
[ml-pathways]: https://blog.google/technology/ai/introducing-pathways-next-generation-ai-architecture/
[sustainability]: https://sustainability.google/operating-sustainably/
[gemini-2-paper]: https://arxiv.org/abs/2312.11805
|
ScorpieCur/gemma-3-4b-it-unsloth-bnb-4bit
|
ScorpieCur
| 2025-08-05T12:36:36Z | 2 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma3",
"image-text-to-text",
"unsloth",
"gemma",
"google",
"conversational",
"en",
"arxiv:1905.07830",
"arxiv:1905.10044",
"arxiv:1911.11641",
"arxiv:1904.09728",
"arxiv:1705.03551",
"arxiv:1911.01547",
"arxiv:1907.10641",
"arxiv:1903.00161",
"arxiv:2009.03300",
"arxiv:2304.06364",
"arxiv:2103.03874",
"arxiv:2110.14168",
"arxiv:2311.12022",
"arxiv:2108.07732",
"arxiv:2107.03374",
"arxiv:2210.03057",
"arxiv:2106.03193",
"arxiv:1910.11856",
"arxiv:2502.12404",
"arxiv:2502.21228",
"arxiv:2404.16816",
"arxiv:2104.12756",
"arxiv:2311.16502",
"arxiv:2203.10244",
"arxiv:2404.12390",
"arxiv:1810.12440",
"arxiv:1908.02660",
"arxiv:2312.11805",
"base_model:google/gemma-3-4b-it",
"base_model:quantized:google/gemma-3-4b-it",
"license:gemma",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] |
image-text-to-text
| 2025-08-05T12:36:35Z |
---
base_model: google/gemma-3-4b-it
language:
- en
library_name: transformers
license: gemma
tags:
- unsloth
- transformers
- gemma3
- gemma
- google
---
<div>
<p style="margin-bottom: 0; margin-top: 0;">
<strong>See <a href="https://huggingface.co/collections/unsloth/gemma-3-67d12b7e8816ec6efa7e4e5b">our collection</a> for all versions of Gemma 3 including GGUF, 4-bit & 16-bit formats.</strong>
</p>
<p style="margin-bottom: 0;">
<em>Unsloth's <a href="https://unsloth.ai/blog/deepseekr1-dynamic">Dynamic Quants</a> is selectively quantized, greatly improving accuracy over standard 4-bit.</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/tutorial-how-to-run-deepseek-r1-on-your-own-local-device">
<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;">✨ Fine-tune Gemma 3 with Unsloth!</h1>
</div>
- Fine-tune Gemma 3 (12B) for free using our Google [Colab notebook here](https://docs.unsloth.ai/get-started/unsloth-notebooks)!
- Read our Blog about Gemma 3 support: [unsloth.ai/blog/gemma3](https://unsloth.ai/blog/gemma3)
- View the rest of our notebooks in our [docs here](https://docs.unsloth.ai/get-started/unsloth-notebooks).
- Export your fine-tuned model to GGUF, Ollama, llama.cpp or 🤗HF.
| Unsloth supports | Free Notebooks | Performance | Memory use |
|-----------------|--------------------------------------------------------------------------------------------------------------------------|-------------|----------|
| **GRPO with Gemma 3 (12B)** | [▶️ Start on Colab](https://docs.unsloth.ai/get-started/unsloth-notebooks) | 2x 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 |
| **Mistral (7B)** | [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Mistral_v0.3_(7B)-Conversational.ipynb) | 2.2x faster | 62% less |
<br>
# Gemma 3 model card
**Model Page**: [Gemma](https://ai.google.dev/gemma/docs/core)
**Resources and Technical Documentation**:
* [Gemma 3 Technical Report][g3-tech-report]
* [Responsible Generative AI Toolkit][rai-toolkit]
* [Gemma on Kaggle][kaggle-gemma]
* [Gemma on Vertex Model Garden][vertex-mg-gemma3]
**Terms of Use**: [Terms][terms]
**Authors**: Google DeepMind
## Model Information
Summary description and brief definition of inputs and outputs.
### Description
Gemma is a family of lightweight, state-of-the-art open models from Google,
built from the same research and technology used to create the Gemini models.
Gemma 3 models are multimodal, handling text and image input and generating text
output, with open weights for both pre-trained variants and instruction-tuned
variants. Gemma 3 has a large, 128K context window, multilingual support in over
140 languages, and is available in more sizes than previous versions. Gemma 3
models are well-suited for a variety of text generation and image understanding
tasks, including question answering, summarization, and reasoning. Their
relatively small size makes it possible to deploy them in environments with
limited resources such as laptops, desktops or your own cloud infrastructure,
democratizing access to state of the art AI models and helping foster innovation
for everyone.
### Inputs and outputs
- **Input:**
- Text string, such as a question, a prompt, or a document to be summarized
- Images, normalized to 896 x 896 resolution and encoded to 256 tokens
each
- Total input context of 128K tokens for the 4B, 12B, and 27B sizes, and
32K tokens for the 1B size
- **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 context of 8192 tokens
### Citation
```none
@article{gemma_2025,
title={Gemma 3},
url={https://goo.gle/Gemma3Report},
publisher={Kaggle},
author={Gemma Team},
year={2025}
}
```
## Model Data
Data used for model training and how the data was processed.
### Training Dataset
These models were trained on a dataset of text data that includes a wide variety
of sources. The 27B model was trained with 14 trillion tokens, the 12B model was
trained with 12 trillion tokens, 4B model was trained with 4 trillion tokens and
1B with 2 trillion tokens. Here are the key components:
- Web Documents: A diverse collection of web text ensures the model is
exposed to a broad range of linguistic styles, topics, and vocabulary. The
training dataset includes content in over 140 languages.
- Code: Exposing the model to code helps it to learn the syntax and
patterns of programming languages, which improves its ability to generate
code and understand code-related questions.
- Mathematics: Training on mathematical text helps the model learn logical
reasoning, symbolic representation, and to address mathematical queries.
- Images: A wide range of images enables the model to perform image
analysis and visual data extraction tasks.
The combination of these diverse data sources is crucial for training a powerful
multimodal model that can handle a wide variety of different tasks and data
formats.
### Data Preprocessing
Here are the key data cleaning and filtering methods applied to the training
data:
- CSAM Filtering: Rigorous CSAM (Child Sexual Abuse Material) filtering
was applied at multiple stages in the data preparation process to ensure
the exclusion of harmful and illegal content.
- Sensitive Data Filtering: As part of making Gemma pre-trained models
safe and reliable, automated techniques were used to filter out certain
personal information and other sensitive data from training sets.
- Additional methods: Filtering based on content quality and safety in
line with [our policies][safety-policies].
## Implementation Information
Details about the model internals.
### Hardware
Gemma was trained using [Tensor Processing Unit (TPU)][tpu] hardware (TPUv4p,
TPUv5p and TPUv5e). Training vision-language models (VLMS) requires significant
computational power. TPUs, designed specifically for matrix operations common in
machine learning, offer several advantages in this domain:
- Performance: TPUs are specifically designed to handle the massive
computations involved in training VLMs. They can speed up training
considerably compared to CPUs.
- Memory: TPUs often come with large amounts of high-bandwidth memory,
allowing for the handling of large models and batch sizes during training.
This can lead to better model quality.
- Scalability: TPU Pods (large clusters of TPUs) provide a scalable
solution for handling the growing complexity of large foundation models.
You can distribute training across multiple TPU devices for faster and more
efficient processing.
- Cost-effectiveness: In many scenarios, TPUs can provide a more
cost-effective solution for training large models compared to CPU-based
infrastructure, especially when considering the time and resources saved
due to faster training.
- These advantages are aligned with
[Google's commitments to operate sustainably][sustainability].
### Software
Training was done using [JAX][jax] and [ML Pathways][ml-pathways].
JAX allows researchers to take advantage of the latest generation of hardware,
including TPUs, for faster and more efficient training of large models. ML
Pathways is Google's latest effort to build artificially intelligent systems
capable of generalizing across multiple tasks. This is specially suitable for
foundation models, including large language models like these ones.
Together, JAX and ML Pathways are used as described in the
[paper about the Gemini family of models][gemini-2-paper]; *"the 'single
controller' programming model of Jax and Pathways allows a single Python
process to orchestrate the entire training run, dramatically simplifying the
development workflow."*
## Evaluation
Model evaluation metrics and results.
### Benchmark Results
These models were evaluated against a large collection of different datasets and
metrics to cover different aspects of text generation:
#### Reasoning and factuality
| Benchmark | Metric | Gemma 3 PT 1B | Gemma 3 PT 4B | Gemma 3 PT 12B | Gemma 3 PT 27B |
| ------------------------------ |----------------|:--------------:|:-------------:|:--------------:|:--------------:|
| [HellaSwag][hellaswag] | 10-shot | 62.3 | 77.2 | 84.2 | 85.6 |
| [BoolQ][boolq] | 0-shot | 63.2 | 72.3 | 78.8 | 82.4 |
| [PIQA][piqa] | 0-shot | 73.8 | 79.6 | 81.8 | 83.3 |
| [SocialIQA][socialiqa] | 0-shot | 48.9 | 51.9 | 53.4 | 54.9 |
| [TriviaQA][triviaqa] | 5-shot | 39.8 | 65.8 | 78.2 | 85.5 |
| [Natural Questions][naturalq] | 5-shot | 9.48 | 20.0 | 31.4 | 36.1 |
| [ARC-c][arc] | 25-shot | 38.4 | 56.2 | 68.9 | 70.6 |
| [ARC-e][arc] | 0-shot | 73.0 | 82.4 | 88.3 | 89.0 |
| [WinoGrande][winogrande] | 5-shot | 58.2 | 64.7 | 74.3 | 78.8 |
| [BIG-Bench Hard][bbh] | few-shot | 28.4 | 50.9 | 72.6 | 77.7 |
| [DROP][drop] | 1-shot | 42.4 | 60.1 | 72.2 | 77.2 |
[hellaswag]: https://arxiv.org/abs/1905.07830
[boolq]: https://arxiv.org/abs/1905.10044
[piqa]: https://arxiv.org/abs/1911.11641
[socialiqa]: https://arxiv.org/abs/1904.09728
[triviaqa]: https://arxiv.org/abs/1705.03551
[naturalq]: https://github.com/google-research-datasets/natural-questions
[arc]: https://arxiv.org/abs/1911.01547
[winogrande]: https://arxiv.org/abs/1907.10641
[bbh]: https://paperswithcode.com/dataset/bbh
[drop]: https://arxiv.org/abs/1903.00161
#### STEM and code
| Benchmark | Metric | Gemma 3 PT 4B | Gemma 3 PT 12B | Gemma 3 PT 27B |
| ------------------------------ |----------------|:-------------:|:--------------:|:--------------:|
| [MMLU][mmlu] | 5-shot | 59.6 | 74.5 | 78.6 |
| [MMLU][mmlu] (Pro COT) | 5-shot | 29.2 | 45.3 | 52.2 |
| [AGIEval][agieval] | 3-5-shot | 42.1 | 57.4 | 66.2 |
| [MATH][math] | 4-shot | 24.2 | 43.3 | 50.0 |
| [GSM8K][gsm8k] | 8-shot | 38.4 | 71.0 | 82.6 |
| [GPQA][gpqa] | 5-shot | 15.0 | 25.4 | 24.3 |
| [MBPP][mbpp] | 3-shot | 46.0 | 60.4 | 65.6 |
| [HumanEval][humaneval] | 0-shot | 36.0 | 45.7 | 48.8 |
[mmlu]: https://arxiv.org/abs/2009.03300
[agieval]: https://arxiv.org/abs/2304.06364
[math]: https://arxiv.org/abs/2103.03874
[gsm8k]: https://arxiv.org/abs/2110.14168
[gpqa]: https://arxiv.org/abs/2311.12022
[mbpp]: https://arxiv.org/abs/2108.07732
[humaneval]: https://arxiv.org/abs/2107.03374
#### Multilingual
| Benchmark | Gemma 3 PT 1B | Gemma 3 PT 4B | Gemma 3 PT 12B | Gemma 3 PT 27B |
| ------------------------------------ |:-------------:|:-------------:|:--------------:|:--------------:|
| [MGSM][mgsm] | 2.04 | 34.7 | 64.3 | 74.3 |
| [Global-MMLU-Lite][global-mmlu-lite] | 24.9 | 57.0 | 69.4 | 75.7 |
| [WMT24++][wmt24pp] (ChrF) | 36.7 | 48.4 | 53.9 | 55.7 |
| [FloRes][flores] | 29.5 | 39.2 | 46.0 | 48.8 |
| [XQuAD][xquad] (all) | 43.9 | 68.0 | 74.5 | 76.8 |
| [ECLeKTic][eclektic] | 4.69 | 11.0 | 17.2 | 24.4 |
| [IndicGenBench][indicgenbench] | 41.4 | 57.2 | 61.7 | 63.4 |
[mgsm]: https://arxiv.org/abs/2210.03057
[flores]: https://arxiv.org/abs/2106.03193
[xquad]: https://arxiv.org/abs/1910.11856v3
[global-mmlu-lite]: https://huggingface.co/datasets/CohereForAI/Global-MMLU-Lite
[wmt24pp]: https://arxiv.org/abs/2502.12404v1
[eclektic]: https://arxiv.org/abs/2502.21228
[indicgenbench]: https://arxiv.org/abs/2404.16816
#### Multimodal
| Benchmark | Gemma 3 PT 4B | Gemma 3 PT 12B | Gemma 3 PT 27B |
| ------------------------------ |:-------------:|:--------------:|:--------------:|
| [COCOcap][coco-cap] | 102 | 111 | 116 |
| [DocVQA][docvqa] (val) | 72.8 | 82.3 | 85.6 |
| [InfoVQA][info-vqa] (val) | 44.1 | 54.8 | 59.4 |
| [MMMU][mmmu] (pt) | 39.2 | 50.3 | 56.1 |
| [TextVQA][textvqa] (val) | 58.9 | 66.5 | 68.6 |
| [RealWorldQA][realworldqa] | 45.5 | 52.2 | 53.9 |
| [ReMI][remi] | 27.3 | 38.5 | 44.8 |
| [AI2D][ai2d] | 63.2 | 75.2 | 79.0 |
| [ChartQA][chartqa] | 63.6 | 74.7 | 76.3 |
| [VQAv2][vqav2] | 63.9 | 71.2 | 72.9 |
| [BLINK][blinkvqa] | 38.0 | 35.9 | 39.6 |
| [OKVQA][okvqa] | 51.0 | 58.7 | 60.2 |
| [TallyQA][tallyqa] | 42.5 | 51.8 | 54.3 |
| [SpatialSense VQA][ss-vqa] | 50.9 | 60.0 | 59.4 |
| [CountBenchQA][countbenchqa] | 26.1 | 17.8 | 68.0 |
[coco-cap]: https://cocodataset.org/#home
[docvqa]: https://www.docvqa.org/
[info-vqa]: https://arxiv.org/abs/2104.12756
[mmmu]: https://arxiv.org/abs/2311.16502
[textvqa]: https://textvqa.org/
[realworldqa]: https://paperswithcode.com/dataset/realworldqa
[remi]: https://arxiv.org/html/2406.09175v1
[ai2d]: https://allenai.org/data/diagrams
[chartqa]: https://arxiv.org/abs/2203.10244
[vqav2]: https://visualqa.org/index.html
[blinkvqa]: https://arxiv.org/abs/2404.12390
[okvqa]: https://okvqa.allenai.org/
[tallyqa]: https://arxiv.org/abs/1810.12440
[ss-vqa]: https://arxiv.org/abs/1908.02660
[countbenchqa]: https://github.com/google-research/big_vision/blob/main/big_vision/datasets/countbenchqa/
## Ethics and Safety
Ethics and safety evaluation approach and results.
### 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.
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.
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 major improvements in the categories of
child safety, content safety, and representational harms relative to previous
Gemma models. All testing was conducted without safety filters to evaluate the
model capabilities and behaviors. For both text-to-text and image-to-text, and
across all model sizes, the model produced minimal policy violations, and showed
significant improvements over previous Gemma models' performance with respect
to ungrounded inferences. A limitation of our evaluations was they included only
English language prompts.
## Usage and Limitations
These models have certain limitations that users should be aware of.
### Intended Usage
Open vision-language models (VLMs) models have a wide range of applications
across various industries and domains. The following list of potential uses is
not comprehensive. The purpose of this list is to provide contextual information
about the possible use-cases that the model creators considered as part of model
training and development.
- Content Creation and Communication
- Text Generation: These models can be used to generate creative text
formats such as poems, scripts, code, marketing copy, and email drafts.
- Chatbots and Conversational AI: Power conversational interfaces
for customer service, virtual assistants, or interactive applications.
- Text Summarization: Generate concise summaries of a text corpus,
research papers, or reports.
- Image Data Extraction: These models can be used to extract,
interpret, and summarize visual data for text communications.
- Research and Education
- Natural Language Processing (NLP) and VLM Research: These
models can serve as a foundation for researchers to experiment with VLM
and NLP techniques, develop algorithms, and contribute to the
advancement of the field.
- Language Learning Tools: Support interactive language learning
experiences, aiding in grammar correction or providing writing practice.
- Knowledge Exploration: Assist researchers in exploring large
bodies of text by generating summaries or answering questions about
specific topics.
### Limitations
- Training Data
- The quality and diversity of the training data significantly
influence the model's capabilities. Biases or gaps in the training data
can lead to limitations in the model's responses.
- The scope of the training dataset determines the subject areas
the model can handle effectively.
- Context and Task Complexity
- Models are better at tasks that can be framed with clear
prompts and instructions. Open-ended or highly complex tasks might be
challenging.
- A model's performance can be influenced by the amount of context
provided (longer context generally leads to better outputs, up to a
certain point).
- Language Ambiguity and Nuance
- Natural language is inherently complex. Models might struggle
to grasp subtle nuances, sarcasm, or figurative language.
- Factual Accuracy
- Models generate responses based on information they learned
from their training datasets, but they are not knowledge bases. They
may generate incorrect or outdated factual statements.
- Common Sense
- Models rely on statistical patterns in language. They might
lack the ability to apply common sense reasoning in certain situations.
### Ethical Considerations and Risks
The development of vision-language models (VLMs) raises several ethical
concerns. In creating an open model, we have carefully considered the following:
- Bias and Fairness
- VLMs trained on large-scale, real-world text and image data can
reflect socio-cultural biases embedded in the training material. These
models underwent careful scrutiny, input data pre-processing described
and posterior evaluations reported in this card.
- Misinformation and Misuse
- VLMs can be misused to generate text that is false, misleading,
or harmful.
- Guidelines are provided for responsible use with the model, see the
[Responsible Generative AI Toolkit][rai-toolkit].
- Transparency and Accountability:
- This model card summarizes details on the models' architecture,
capabilities, limitations, and evaluation processes.
- A responsibly developed open model offers the opportunity to
share innovation by making VLM technology accessible to developers and
researchers across the AI ecosystem.
Risks identified and mitigations:
- **Perpetuation of biases**: It's encouraged to perform continuous
monitoring (using evaluation metrics, human review) and the exploration of
de-biasing techniques during model training, fine-tuning, and other use
cases.
- **Generation of harmful content**: Mechanisms and guidelines for content
safety are essential. Developers are encouraged to exercise caution and
implement appropriate content safety safeguards based on their specific
product policies and application use cases.
- **Misuse for malicious purposes**: Technical limitations and developer
and end-user education can help mitigate against malicious applications of
VLMs. Educational resources and reporting mechanisms for users to flag
misuse are provided. Prohibited uses of Gemma models are outlined in the
[Gemma Prohibited Use Policy][prohibited-use].
- **Privacy violations**: Models were trained on data filtered for removal
of certain personal information and other sensitive data. Developers are
encouraged to adhere to privacy regulations with privacy-preserving
techniques.
### Benefits
At the time of release, this family of models provides high-performance open
vision-language model implementations designed from the ground up for
responsible AI development compared to similarly sized models.
Using the benchmark evaluation metrics described in this document, these models
have shown to provide superior performance to other, comparably-sized open model
alternatives.
[g3-tech-report]: https://goo.gle/Gemma3Report
[rai-toolkit]: https://ai.google.dev/responsible
[kaggle-gemma]: https://www.kaggle.com/models/google/gemma-3
[vertex-mg-gemma3]: https://console.cloud.google.com/vertex-ai/publishers/google/model-garden/gemma3
[terms]: https://ai.google.dev/gemma/terms
[safety-policies]: https://ai.google/static/documents/ai-responsibility-update-published-february-2025.pdf
[prohibited-use]: https://ai.google.dev/gemma/prohibited_use_policy
[tpu]: https://cloud.google.com/tpu/docs/intro-to-tpu
[sustainability]: https://sustainability.google/operating-sustainably/
[jax]: https://github.com/jax-ml/jax
[ml-pathways]: https://blog.google/technology/ai/introducing-pathways-next-generation-ai-architecture/
[sustainability]: https://sustainability.google/operating-sustainably/
[gemini-2-paper]: https://arxiv.org/abs/2312.11805
|
minami4716/video_generate
|
minami4716
| 2025-08-05T12:36:02Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-08-05T12:36:02Z |
---
license: apache-2.0
---
|
RocktimMBZ/LLaMA-8b-large-scale-80k-samples
|
RocktimMBZ
| 2025-08-05T12:34:44Z | 16 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-05T12:21:17Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
hoahuynh/SaiGon_Analysis
|
hoahuynh
| 2025-08-05T12:29:42Z | 0 | 0 | null |
[
"vi",
"license:apache-2.0",
"region:us"
] | null | 2025-08-05T12:27:06Z |
---
license: apache-2.0
language:
- vi
---
|
yuchenFan/Llama-3.1-8B-Instruct-SSRL
|
yuchenFan
| 2025-08-05T12:26:50Z | 15 | 0 | null |
[
"safetensors",
"llama",
"facebook",
"meta",
"pytorch",
"llama-3",
"text-generation",
"conversational",
"en",
"de",
"fr",
"it",
"pt",
"hi",
"es",
"th",
"arxiv:2204.05149",
"base_model:meta-llama/Llama-3.1-8B",
"base_model:finetune:meta-llama/Llama-3.1-8B",
"license:llama3.1",
"region:us"
] |
text-generation
| 2025-08-05T09:06:20Z |
---
language:
- en
- de
- fr
- it
- pt
- hi
- es
- th
license: llama3.1
base_model: meta-llama/Meta-Llama-3.1-8B
pipeline_tag: text-generation
tags:
- facebook
- meta
- pytorch
- llama
- llama-3
extra_gated_prompt: "### LLAMA 3.1 COMMUNITY LICENSE AGREEMENT\nLlama 3.1 Version\
\ Release Date: July 23, 2024\n\"Agreement\" means the terms and conditions for\
\ use, reproduction, distribution and modification of the Llama Materials set forth\
\ herein.\n\"Documentation\" means the specifications, manuals and documentation\
\ accompanying Llama 3.1 distributed by Meta at https://llama.meta.com/doc/overview.\n\
\"Licensee\" or \"you\" means you, or your employer or any other person or entity\
\ (if you are entering into this Agreement on such person or entity’s behalf), of\
\ the age required under applicable laws, rules or regulations to provide legal\
\ consent and that has legal authority to bind your employer or such other person\
\ or entity if you are entering in this Agreement on their behalf.\n\"Llama 3.1\"\
\ means the foundational large language models and software and algorithms, including\
\ machine-learning model code, trained model weights, inference-enabling code, training-enabling\
\ code, fine-tuning enabling code and other elements of the foregoing distributed\
\ by Meta at https://llama.meta.com/llama-downloads.\n\"Llama Materials\" means,\
\ collectively, Meta’s proprietary Llama 3.1 and Documentation (and any portion\
\ thereof) made available under this Agreement.\n\"Meta\" or \"we\" means Meta Platforms\
\ Ireland Limited (if you are located in or, if you are an entity, your principal\
\ place of business is in the EEA or Switzerland) and Meta Platforms, Inc. (if you\
\ are located outside of the EEA or Switzerland).\n \n1. License Rights and Redistribution.\n\
a. Grant of Rights. You are granted a non-exclusive, worldwide, non-transferable\
\ and royalty-free limited license under Meta’s intellectual property or other rights\
\ owned by Meta embodied in the Llama Materials to use, reproduce, distribute, copy,\
\ create derivative works of, and make modifications to the Llama Materials.\nb.\
\ Redistribution and Use.\ni. If you distribute or make available the Llama Materials\
\ (or any derivative works thereof), or a product or service (including another\
\ AI model) that contains any of them, you shall (A) provide a copy of this Agreement\
\ with any such Llama Materials; and (B) prominently display “Built with Llama”\
\ on a related website, user interface, blogpost, about page, or product documentation.\
\ If you use the Llama Materials or any outputs or results of the Llama Materials\
\ to create, train, fine tune, or otherwise improve an AI model, which is distributed\
\ or made available, you shall also include “Llama” at the beginning of any such\
\ AI model name.\nii. If you receive Llama Materials, or any derivative works thereof,\
\ from a Licensee as part of an integrated end user product, then Section 2 of\
\ this Agreement will not apply to you.\niii. You must retain in all copies of the\
\ Llama Materials that you distribute the following attribution notice within a\
\ “Notice” text file distributed as a part of such copies: “Llama 3.1 is licensed\
\ under the Llama 3.1 Community License, Copyright © Meta Platforms, Inc. All Rights\
\ Reserved.”\niv. Your use of the Llama Materials must comply with applicable laws\
\ and regulations (including trade compliance laws and regulations) and adhere to\
\ the Acceptable Use Policy for the Llama Materials (available at https://llama.meta.com/llama3_1/use-policy),\
\ which is hereby incorporated by reference into this Agreement.\n2. Additional\
\ Commercial Terms. If, on the Llama 3.1 version release date, the monthly active\
\ users of the products or services made available by or for Licensee, or Licensee’s\
\ affiliates, is greater than 700 million monthly active users in the preceding\
\ calendar month, you must request a license from Meta, which Meta may grant to\
\ you in its sole discretion, and you are not authorized to exercise any of the\
\ rights under this Agreement unless or until Meta otherwise expressly grants you\
\ such rights.\n3. Disclaimer of Warranty. UNLESS REQUIRED BY APPLICABLE LAW, THE\
\ LLAMA MATERIALS AND ANY OUTPUT AND RESULTS THEREFROM ARE PROVIDED ON AN “AS IS”\
\ BASIS, WITHOUT WARRANTIES OF ANY KIND, AND META DISCLAIMS ALL WARRANTIES OF ANY\
\ KIND, BOTH EXPRESS AND IMPLIED, INCLUDING, WITHOUT LIMITATION, ANY WARRANTIES\
\ OF TITLE, NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE.\
\ YOU ARE SOLELY RESPONSIBLE FOR DETERMINING THE APPROPRIATENESS OF USING OR REDISTRIBUTING\
\ THE LLAMA MATERIALS AND ASSUME ANY RISKS ASSOCIATED WITH YOUR USE OF THE LLAMA\
\ MATERIALS AND ANY OUTPUT AND RESULTS.\n4. Limitation of Liability. IN NO EVENT\
\ WILL META OR ITS AFFILIATES BE LIABLE UNDER ANY THEORY OF LIABILITY, WHETHER IN\
\ CONTRACT, TORT, NEGLIGENCE, PRODUCTS LIABILITY, OR OTHERWISE, ARISING OUT OF THIS\
\ AGREEMENT, FOR ANY LOST PROFITS OR ANY INDIRECT, SPECIAL, CONSEQUENTIAL, INCIDENTAL,\
\ EXEMPLARY OR PUNITIVE DAMAGES, EVEN IF META OR ITS AFFILIATES HAVE BEEN ADVISED\
\ OF THE POSSIBILITY OF ANY OF THE FOREGOING.\n5. Intellectual Property.\na. No\
\ trademark licenses are granted under this Agreement, and in connection with the\
\ Llama Materials, neither Meta nor Licensee may use any name or mark owned by or\
\ associated with the other or any of its affiliates, except as required for reasonable\
\ and customary use in describing and redistributing the Llama Materials or as set\
\ forth in this Section 5(a). Meta hereby grants you a license to use “Llama” (the\
\ “Mark”) solely as required to comply with the last sentence of Section 1.b.i.\
\ You will comply with Meta’s brand guidelines (currently accessible at https://about.meta.com/brand/resources/meta/company-brand/\
\ ). All goodwill arising out of your use of the Mark will inure to the benefit\
\ of Meta.\nb. Subject to Meta’s ownership of Llama Materials and derivatives made\
\ by or for Meta, with respect to any derivative works and modifications of the\
\ Llama Materials that are made by you, as between you and Meta, you are and will\
\ be the owner of such derivative works and modifications.\nc. If you institute\
\ litigation or other proceedings against Meta or any entity (including a cross-claim\
\ or counterclaim in a lawsuit) alleging that the Llama Materials or Llama 3.1 outputs\
\ or results, or any portion of any of the foregoing, constitutes infringement of\
\ intellectual property or other rights owned or licensable by you, then any licenses\
\ granted to you under this Agreement shall terminate as of the date such litigation\
\ or claim is filed or instituted. You will indemnify and hold harmless Meta from\
\ and against any claim by any third party arising out of or related to your use\
\ or distribution of the Llama Materials.\n6. Term and Termination. The term of\
\ this Agreement will commence upon your acceptance of this Agreement or access\
\ to the Llama Materials and will continue in full force and effect until terminated\
\ in accordance with the terms and conditions herein. Meta may terminate this Agreement\
\ if you are in breach of any term or condition of this Agreement. Upon termination\
\ of this Agreement, you shall delete and cease use of the Llama Materials. Sections\
\ 3, 4 and 7 shall survive the termination of this Agreement.\n7. Governing Law\
\ and Jurisdiction. This Agreement will be governed and construed under the laws\
\ of the State of California without regard to choice of law principles, and the\
\ UN Convention on Contracts for the International Sale of Goods does not apply\
\ to this Agreement. The courts of California shall have exclusive jurisdiction\
\ of any dispute arising out of this Agreement.\n### Llama 3.1 Acceptable Use Policy\n\
Meta is committed to promoting safe and fair use of its tools and features, including\
\ Llama 3.1. If you access or use Llama 3.1, you agree to this Acceptable Use Policy\
\ (“Policy”). The most recent copy of this policy can be found at [https://llama.meta.com/llama3_1/use-policy](https://llama.meta.com/llama3_1/use-policy)\n\
#### Prohibited Uses\nWe want everyone to use Llama 3.1 safely and responsibly.\
\ You agree you will not use, or allow others to use, Llama 3.1 to:\n 1. Violate\
\ the law or others’ rights, including to:\n 1. Engage in, promote, generate,\
\ contribute to, encourage, plan, incite, or further illegal or unlawful activity\
\ or content, such as:\n 1. Violence or terrorism\n 2. Exploitation\
\ or harm to children, including the solicitation, creation, acquisition, or dissemination\
\ of child exploitative content or failure to report Child Sexual Abuse Material\n\
\ 3. Human trafficking, exploitation, and sexual violence\n 4. The\
\ illegal distribution of information or materials to minors, including obscene\
\ materials, or failure to employ legally required age-gating in connection with\
\ such information or materials.\n 5. Sexual solicitation\n 6. Any\
\ other criminal activity\n 3. Engage in, promote, incite, or facilitate the\
\ harassment, abuse, threatening, or bullying of individuals or groups of individuals\n\
\ 4. Engage in, promote, incite, or facilitate discrimination or other unlawful\
\ or harmful conduct in the provision of employment, employment benefits, credit,\
\ housing, other economic benefits, or other essential goods and services\n 5.\
\ Engage in the unauthorized or unlicensed practice of any profession including,\
\ but not limited to, financial, legal, medical/health, or related professional\
\ practices\n 6. Collect, process, disclose, generate, or infer health, demographic,\
\ or other sensitive personal or private information about individuals without rights\
\ and consents required by applicable laws\n 7. Engage in or facilitate any action\
\ or generate any content that infringes, misappropriates, or otherwise violates\
\ any third-party rights, including the outputs or results of any products or services\
\ using the Llama Materials\n 8. Create, generate, or facilitate the creation\
\ of malicious code, malware, computer viruses or do anything else that could disable,\
\ overburden, interfere with or impair the proper working, integrity, operation\
\ or appearance of a website or computer system\n2. Engage in, promote, incite,\
\ facilitate, or assist in the planning or development of activities that present\
\ a risk of death or bodily harm to individuals, including use of Llama 3.1 related\
\ to the following:\n 1. Military, warfare, nuclear industries or applications,\
\ espionage, use for materials or activities that are subject to the International\
\ Traffic Arms Regulations (ITAR) maintained by the United States Department of\
\ State\n 2. Guns and illegal weapons (including weapon development)\n 3.\
\ Illegal drugs and regulated/controlled substances\n 4. Operation of critical\
\ infrastructure, transportation technologies, or heavy machinery\n 5. Self-harm\
\ or harm to others, including suicide, cutting, and eating disorders\n 6. Any\
\ content intended to incite or promote violence, abuse, or any infliction of bodily\
\ harm to an individual\n3. Intentionally deceive or mislead others, including use\
\ of Llama 3.1 related to the following:\n 1. Generating, promoting, or furthering\
\ fraud or the creation or promotion of disinformation\n 2. Generating, promoting,\
\ or furthering defamatory content, including the creation of defamatory statements,\
\ images, or other content\n 3. Generating, promoting, or further distributing\
\ spam\n 4. Impersonating another individual without consent, authorization,\
\ or legal right\n 5. Representing that the use of Llama 3.1 or outputs are human-generated\n\
\ 6. Generating or facilitating false online engagement, including fake reviews\
\ and other means of fake online engagement\n4. Fail to appropriately disclose to\
\ end users any known dangers of your AI system\nPlease report any violation of\
\ this Policy, software “bug,” or other problems that could lead to a violation\
\ of this Policy through one of the following means:\n * Reporting issues with\
\ the model: [https://github.com/meta-llama/llama-models/issues](https://github.com/meta-llama/llama-models/issues)\n\
\ * Reporting risky content generated by the model:\n developers.facebook.com/llama_output_feedback\n\
\ * Reporting bugs and security concerns: facebook.com/whitehat/info\n * Reporting\
\ violations of the Acceptable Use Policy or unlicensed uses of Meta Llama 3: LlamaUseReport@meta.com"
extra_gated_fields:
First Name: text
Last Name: text
Date of birth: date_picker
Country: country
Affiliation: text
Job title:
type: select
options:
- Student
- Research Graduate
- AI researcher
- AI developer/engineer
- Reporter
- Other
geo: ip_location
? By clicking Submit below I accept the terms of the license and acknowledge that
the information I provide will be collected stored processed and shared in accordance
with the Meta Privacy Policy
: checkbox
extra_gated_description: The information you provide will be collected, stored, processed
and shared in accordance with the [Meta Privacy Policy](https://www.facebook.com/privacy/policy/).
extra_gated_button_content: Submit
---
## Model Information
The Meta Llama 3.1 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction tuned generative models in 8B, 70B and 405B sizes (text in/text out). The Llama 3.1 instruction tuned text only models (8B, 70B, 405B) are optimized for multilingual dialogue use cases and outperform many of the available open source and closed chat models on common industry benchmarks.
**Model developer**: Meta
**Model Architecture:** Llama 3.1 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety.
<table>
<tr>
<td>
</td>
<td><strong>Training Data</strong>
</td>
<td><strong>Params</strong>
</td>
<td><strong>Input modalities</strong>
</td>
<td><strong>Output modalities</strong>
</td>
<td><strong>Context length</strong>
</td>
<td><strong>GQA</strong>
</td>
<td><strong>Token count</strong>
</td>
<td><strong>Knowledge cutoff</strong>
</td>
</tr>
<tr>
<td rowspan="3" >Llama 3.1 (text only)
</td>
<td rowspan="3" >A new mix of publicly available online data.
</td>
<td>8B
</td>
<td>Multilingual Text
</td>
<td>Multilingual Text and code
</td>
<td>128k
</td>
<td>Yes
</td>
<td rowspan="3" >15T+
</td>
<td rowspan="3" >December 2023
</td>
</tr>
<tr>
<td>70B
</td>
<td>Multilingual Text
</td>
<td>Multilingual Text and code
</td>
<td>128k
</td>
<td>Yes
</td>
</tr>
<tr>
<td>405B
</td>
<td>Multilingual Text
</td>
<td>Multilingual Text and code
</td>
<td>128k
</td>
<td>Yes
</td>
</tr>
</table>
**Supported languages:** English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai.
**Llama 3.1 family of models**. Token counts refer to pretraining data only. All model versions use Grouped-Query Attention (GQA) for improved inference scalability.
**Model Release Date:** July 23, 2024.
**Status:** This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback.
**License:** A custom commercial license, the Llama 3.1 Community License, is available at: [https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/LICENSE](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/LICENSE)
Where to send questions or comments about the model Instructions on how to provide feedback or comments on the model can be found in the model [README](https://github.com/meta-llama/llama3). For more technical information about generation parameters and recipes for how to use Llama 3.1 in applications, please go [here](https://github.com/meta-llama/llama-recipes).
## Intended Use
**Intended Use Cases** Llama 3.1 is intended for commercial and research use in multiple languages. Instruction tuned text only models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks. The Llama 3.1 model collection also supports the ability to leverage the outputs of its models to improve other models including synthetic data generation and distillation. The Llama 3.1 Community License allows for these use cases.
**Out-of-scope** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3.1 Community License. Use in languages beyond those explicitly referenced as supported in this model card**.
**<span style="text-decoration:underline;">Note</span>: Llama 3.1 has been trained on a broader collection of languages than the 8 supported languages. Developers may fine-tune Llama 3.1 models for languages beyond the 8 supported languages provided they comply with the Llama 3.1 Community License and the Acceptable Use Policy and in such cases are responsible for ensuring that any uses of Llama 3.1 in additional languages is done in a safe and responsible manner.
## How to use
This repository contains two versions of Meta-Llama-3.1-8B-Instruct, for use with transformers and with the original `llama` codebase.
### Use with transformers
Starting with `transformers >= 4.43.0` onward, you can run conversational inference using the Transformers `pipeline` abstraction or by leveraging the Auto classes with the `generate()` function.
Make sure to update your transformers installation via `pip install --upgrade transformers`.
```python
import transformers
import torch
model_id = "meta-llama/Meta-Llama-3.1-8B-Instruct"
pipeline = transformers.pipeline(
"text-generation",
model=model_id,
model_kwargs={"torch_dtype": torch.bfloat16},
device_map="auto",
)
messages = [
{"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
{"role": "user", "content": "Who are you?"},
]
outputs = pipeline(
messages,
max_new_tokens=256,
)
print(outputs[0]["generated_text"][-1])
```
Note: You can also find detailed recipes on how to use the model locally, with `torch.compile()`, assisted generations, quantised and more at [`huggingface-llama-recipes`](https://github.com/huggingface/huggingface-llama-recipes)
### Tool use with transformers
LLaMA-3.1 supports multiple tool use formats. You can see a full guide to prompt formatting [here](https://llama.meta.com/docs/model-cards-and-prompt-formats/llama3_1/).
Tool use is also supported through [chat templates](https://huggingface.co/docs/transformers/main/chat_templating#advanced-tool-use--function-calling) in Transformers.
Here is a quick example showing a single simple tool:
```python
# First, define a tool
def get_current_temperature(location: str) -> float:
"""
Get the current temperature at a location.
Args:
location: The location to get the temperature for, in the format "City, Country"
Returns:
The current temperature at the specified location in the specified units, as a float.
"""
return 22. # A real function should probably actually get the temperature!
# Next, create a chat and apply the chat template
messages = [
{"role": "system", "content": "You are a bot that responds to weather queries."},
{"role": "user", "content": "Hey, what's the temperature in Paris right now?"}
]
inputs = tokenizer.apply_chat_template(messages, tools=[get_current_temperature], add_generation_prompt=True)
```
You can then generate text from this input as normal. If the model generates a tool call, you should add it to the chat like so:
```python
tool_call = {"name": "get_current_temperature", "arguments": {"location": "Paris, France"}}
messages.append({"role": "assistant", "tool_calls": [{"type": "function", "function": tool_call}]})
```
and then call the tool and append the result, with the `tool` role, like so:
```python
messages.append({"role": "tool", "name": "get_current_temperature", "content": "22.0"})
```
After that, you can `generate()` again to let the model use the tool result in the chat. Note that this was a very brief introduction to tool calling - for more information,
see the [LLaMA prompt format docs](https://llama.meta.com/docs/model-cards-and-prompt-formats/llama3_1/) and the Transformers [tool use documentation](https://huggingface.co/docs/transformers/main/chat_templating#advanced-tool-use--function-calling).
### Use with `llama`
Please, follow the instructions in the [repository](https://github.com/meta-llama/llama)
To download Original checkpoints, see the example command below leveraging `huggingface-cli`:
```
huggingface-cli download meta-llama/Meta-Llama-3.1-8B-Instruct --include "original/*" --local-dir Meta-Llama-3.1-8B-Instruct
```
## Hardware and Software
**Training Factors** We used custom training libraries, Meta's custom built GPU cluster, and production infrastructure for pretraining. Fine-tuning, annotation, and evaluation were also performed on production infrastructure.
**Training utilized a cumulative of** 39.3M GPU hours of computation on H100-80GB (TDP of 700W) type hardware, per the table below. Training time is the total GPU time required for training each model and power consumption is the peak power capacity per GPU device used, adjusted for power usage efficiency.
**Training Greenhouse Gas Emissions** Estimated total location-based greenhouse gas emissions were **11,390** tons CO2eq for training. Since 2020, Meta has maintained net zero greenhouse gas emissions in its global operations and matched 100% of its electricity use with renewable energy, therefore the total market-based greenhouse gas emissions for training were 0 tons CO2eq.
<table>
<tr>
<td>
</td>
<td><strong>Training Time (GPU hours)</strong>
</td>
<td><strong>Training Power Consumption (W)</strong>
</td>
<td><strong>Training Location-Based Greenhouse Gas Emissions</strong>
<p>
<strong>(tons CO2eq)</strong>
</td>
<td><strong>Training Market-Based Greenhouse Gas Emissions</strong>
<p>
<strong>(tons CO2eq)</strong>
</td>
</tr>
<tr>
<td>Llama 3.1 8B
</td>
<td>1.46M
</td>
<td>700
</td>
<td>420
</td>
<td>0
</td>
</tr>
<tr>
<td>Llama 3.1 70B
</td>
<td>7.0M
</td>
<td>700
</td>
<td>2,040
</td>
<td>0
</td>
</tr>
<tr>
<td>Llama 3.1 405B
</td>
<td>30.84M
</td>
<td>700
</td>
<td>8,930
</td>
<td>0
</td>
</tr>
<tr>
<td>Total
</td>
<td>39.3M
<td>
<ul>
</ul>
</td>
<td>11,390
</td>
<td>0
</td>
</tr>
</table>
The methodology used to determine training energy use and greenhouse gas emissions can be found [here](https://arxiv.org/pdf/2204.05149). Since Meta is openly releasing these models, the training energy use and greenhouse gas emissions will not be incurred by others.
## Training Data
**Overview:** Llama 3.1 was pretrained on ~15 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over 25M synthetically generated examples.
**Data Freshness:** The pretraining data has a cutoff of December 2023.
## Benchmark scores
In this section, we report the results for Llama 3.1 models on standard automatic benchmarks. For all the evaluations, we use our internal evaluations library.
### Base pretrained models
<table>
<tr>
<td><strong>Category</strong>
</td>
<td><strong>Benchmark</strong>
</td>
<td><strong># Shots</strong>
</td>
<td><strong>Metric</strong>
</td>
<td><strong>Llama 3 8B</strong>
</td>
<td><strong>Llama 3.1 8B</strong>
</td>
<td><strong>Llama 3 70B</strong>
</td>
<td><strong>Llama 3.1 70B</strong>
</td>
<td><strong>Llama 3.1 405B</strong>
</td>
</tr>
<tr>
<td rowspan="7" >General
</td>
<td>MMLU
</td>
<td>5
</td>
<td>macro_avg/acc_char
</td>
<td>66.7
</td>
<td>66.7
</td>
<td>79.5
</td>
<td>79.3
</td>
<td>85.2
</td>
</tr>
<tr>
<td>MMLU-Pro (CoT)
</td>
<td>5
</td>
<td>macro_avg/acc_char
</td>
<td>36.2
</td>
<td>37.1
</td>
<td>55.0
</td>
<td>53.8
</td>
<td>61.6
</td>
</tr>
<tr>
<td>AGIEval English
</td>
<td>3-5
</td>
<td>average/acc_char
</td>
<td>47.1
</td>
<td>47.8
</td>
<td>63.0
</td>
<td>64.6
</td>
<td>71.6
</td>
</tr>
<tr>
<td>CommonSenseQA
</td>
<td>7
</td>
<td>acc_char
</td>
<td>72.6
</td>
<td>75.0
</td>
<td>83.8
</td>
<td>84.1
</td>
<td>85.8
</td>
</tr>
<tr>
<td>Winogrande
</td>
<td>5
</td>
<td>acc_char
</td>
<td>-
</td>
<td>60.5
</td>
<td>-
</td>
<td>83.3
</td>
<td>86.7
</td>
</tr>
<tr>
<td>BIG-Bench Hard (CoT)
</td>
<td>3
</td>
<td>average/em
</td>
<td>61.1
</td>
<td>64.2
</td>
<td>81.3
</td>
<td>81.6
</td>
<td>85.9
</td>
</tr>
<tr>
<td>ARC-Challenge
</td>
<td>25
</td>
<td>acc_char
</td>
<td>79.4
</td>
<td>79.7
</td>
<td>93.1
</td>
<td>92.9
</td>
<td>96.1
</td>
</tr>
<tr>
<td>Knowledge reasoning
</td>
<td>TriviaQA-Wiki
</td>
<td>5
</td>
<td>em
</td>
<td>78.5
</td>
<td>77.6
</td>
<td>89.7
</td>
<td>89.8
</td>
<td>91.8
</td>
</tr>
<tr>
<td rowspan="4" >Reading comprehension
</td>
<td>SQuAD
</td>
<td>1
</td>
<td>em
</td>
<td>76.4
</td>
<td>77.0
</td>
<td>85.6
</td>
<td>81.8
</td>
<td>89.3
</td>
</tr>
<tr>
<td>QuAC (F1)
</td>
<td>1
</td>
<td>f1
</td>
<td>44.4
</td>
<td>44.9
</td>
<td>51.1
</td>
<td>51.1
</td>
<td>53.6
</td>
</tr>
<tr>
<td>BoolQ
</td>
<td>0
</td>
<td>acc_char
</td>
<td>75.7
</td>
<td>75.0
</td>
<td>79.0
</td>
<td>79.4
</td>
<td>80.0
</td>
</tr>
<tr>
<td>DROP (F1)
</td>
<td>3
</td>
<td>f1
</td>
<td>58.4
</td>
<td>59.5
</td>
<td>79.7
</td>
<td>79.6
</td>
<td>84.8
</td>
</tr>
</table>
### Instruction tuned models
<table>
<tr>
<td><strong>Category</strong>
</td>
<td><strong>Benchmark</strong>
</td>
<td><strong># Shots</strong>
</td>
<td><strong>Metric</strong>
</td>
<td><strong>Llama 3 8B Instruct</strong>
</td>
<td><strong>Llama 3.1 8B Instruct</strong>
</td>
<td><strong>Llama 3 70B Instruct</strong>
</td>
<td><strong>Llama 3.1 70B Instruct</strong>
</td>
<td><strong>Llama 3.1 405B Instruct</strong>
</td>
</tr>
<tr>
<td rowspan="4" >General
</td>
<td>MMLU
</td>
<td>5
</td>
<td>macro_avg/acc
</td>
<td>68.5
</td>
<td>69.4
</td>
<td>82.0
</td>
<td>83.6
</td>
<td>87.3
</td>
</tr>
<tr>
<td>MMLU (CoT)
</td>
<td>0
</td>
<td>macro_avg/acc
</td>
<td>65.3
</td>
<td>73.0
</td>
<td>80.9
</td>
<td>86.0
</td>
<td>88.6
</td>
</tr>
<tr>
<td>MMLU-Pro (CoT)
</td>
<td>5
</td>
<td>micro_avg/acc_char
</td>
<td>45.5
</td>
<td>48.3
</td>
<td>63.4
</td>
<td>66.4
</td>
<td>73.3
</td>
</tr>
<tr>
<td>IFEval
</td>
<td>
</td>
<td>
</td>
<td>76.8
</td>
<td>80.4
</td>
<td>82.9
</td>
<td>87.5
</td>
<td>88.6
</td>
</tr>
<tr>
<td rowspan="2" >Reasoning
</td>
<td>ARC-C
</td>
<td>0
</td>
<td>acc
</td>
<td>82.4
</td>
<td>83.4
</td>
<td>94.4
</td>
<td>94.8
</td>
<td>96.9
</td>
</tr>
<tr>
<td>GPQA
</td>
<td>0
</td>
<td>em
</td>
<td>34.6
</td>
<td>30.4
</td>
<td>39.5
</td>
<td>46.7
</td>
<td>50.7
</td>
</tr>
<tr>
<td rowspan="4" >Code
</td>
<td>HumanEval
</td>
<td>0
</td>
<td>pass@1
</td>
<td>60.4
</td>
<td>72.6
</td>
<td>81.7
</td>
<td>80.5
</td>
<td>89.0
</td>
</tr>
<tr>
<td>MBPP ++ base version
</td>
<td>0
</td>
<td>pass@1
</td>
<td>70.6
</td>
<td>72.8
</td>
<td>82.5
</td>
<td>86.0
</td>
<td>88.6
</td>
</tr>
<tr>
<td>Multipl-E HumanEval
</td>
<td>0
</td>
<td>pass@1
</td>
<td>-
</td>
<td>50.8
</td>
<td>-
</td>
<td>65.5
</td>
<td>75.2
</td>
</tr>
<tr>
<td>Multipl-E MBPP
</td>
<td>0
</td>
<td>pass@1
</td>
<td>-
</td>
<td>52.4
</td>
<td>-
</td>
<td>62.0
</td>
<td>65.7
</td>
</tr>
<tr>
<td rowspan="2" >Math
</td>
<td>GSM-8K (CoT)
</td>
<td>8
</td>
<td>em_maj1@1
</td>
<td>80.6
</td>
<td>84.5
</td>
<td>93.0
</td>
<td>95.1
</td>
<td>96.8
</td>
</tr>
<tr>
<td>MATH (CoT)
</td>
<td>0
</td>
<td>final_em
</td>
<td>29.1
</td>
<td>51.9
</td>
<td>51.0
</td>
<td>68.0
</td>
<td>73.8
</td>
</tr>
<tr>
<td rowspan="4" >Tool Use
</td>
<td>API-Bank
</td>
<td>0
</td>
<td>acc
</td>
<td>48.3
</td>
<td>82.6
</td>
<td>85.1
</td>
<td>90.0
</td>
<td>92.0
</td>
</tr>
<tr>
<td>BFCL
</td>
<td>0
</td>
<td>acc
</td>
<td>60.3
</td>
<td>76.1
</td>
<td>83.0
</td>
<td>84.8
</td>
<td>88.5
</td>
</tr>
<tr>
<td>Gorilla Benchmark API Bench
</td>
<td>0
</td>
<td>acc
</td>
<td>1.7
</td>
<td>8.2
</td>
<td>14.7
</td>
<td>29.7
</td>
<td>35.3
</td>
</tr>
<tr>
<td>Nexus (0-shot)
</td>
<td>0
</td>
<td>macro_avg/acc
</td>
<td>18.1
</td>
<td>38.5
</td>
<td>47.8
</td>
<td>56.7
</td>
<td>58.7
</td>
</tr>
<tr>
<td>Multilingual
</td>
<td>Multilingual MGSM (CoT)
</td>
<td>0
</td>
<td>em
</td>
<td>-
</td>
<td>68.9
</td>
<td>-
</td>
<td>86.9
</td>
<td>91.6
</td>
</tr>
</table>
#### Multilingual benchmarks
<table>
<tr>
<td><strong>Category</strong>
</td>
<td><strong>Benchmark</strong>
</td>
<td><strong>Language</strong>
</td>
<td><strong>Llama 3.1 8B</strong>
</td>
<td><strong>Llama 3.1 70B</strong>
</td>
<td><strong>Llama 3.1 405B</strong>
</td>
</tr>
<tr>
<td rowspan="9" ><strong>General</strong>
</td>
<td rowspan="9" ><strong>MMLU (5-shot, macro_avg/acc)</strong>
</td>
<td>Portuguese
</td>
<td>62.12
</td>
<td>80.13
</td>
<td>84.95
</td>
</tr>
<tr>
<td>Spanish
</td>
<td>62.45
</td>
<td>80.05
</td>
<td>85.08
</td>
</tr>
<tr>
<td>Italian
</td>
<td>61.63
</td>
<td>80.4
</td>
<td>85.04
</td>
</tr>
<tr>
<td>German
</td>
<td>60.59
</td>
<td>79.27
</td>
<td>84.36
</td>
</tr>
<tr>
<td>French
</td>
<td>62.34
</td>
<td>79.82
</td>
<td>84.66
</td>
</tr>
<tr>
<td>Hindi
</td>
<td>50.88
</td>
<td>74.52
</td>
<td>80.31
</td>
</tr>
<tr>
<td>Thai
</td>
<td>50.32
</td>
<td>72.95
</td>
<td>78.21
</td>
</tr>
</table>
## Responsibility & Safety
As part of our Responsible release approach, we followed a three-pronged strategy to managing trust & safety risks:
* Enable developers to deploy helpful, safe and flexible experiences for their target audience and for the use cases supported by Llama.
* Protect developers against adversarial users aiming to exploit Llama capabilities to potentially cause harm.
* Provide protections for the community to help prevent the misuse of our models.
### Responsible deployment
Llama is a foundational technology designed to be used in a variety of use cases, examples on how Meta’s Llama models have been responsibly deployed can be found in our [Community Stories webpage](https://llama.meta.com/community-stories/). Our approach is to build the most helpful models enabling the world to benefit from the technology power, by aligning our model safety for the generic use cases addressing a standard set of harms. Developers are then in the driver seat to tailor safety for their use case, defining their own policy and deploying the models with the necessary safeguards in their Llama systems. Llama 3.1 was developed following the best practices outlined in our Responsible Use Guide, you can refer to the [Responsible Use Guide](https://llama.meta.com/responsible-use-guide/) to learn more.
#### Llama 3.1 instruct
Our main objectives for conducting safety fine-tuning are to provide the research community with a valuable resource for studying the robustness of safety fine-tuning, as well as to offer developers a readily available, safe, and powerful model for various applications to reduce the developer workload to deploy safe AI systems. For more details on the safety mitigations implemented please read the Llama 3 paper.
**Fine-tuning data**
We employ a multi-faceted approach to data collection, combining human-generated data from our vendors with synthetic data to mitigate potential safety risks. We’ve developed many large language model (LLM)-based classifiers that enable us to thoughtfully select high-quality prompts and responses, enhancing data quality control.
**Refusals and Tone**
Building on the work we started with Llama 3, we put a great emphasis on model refusals to benign prompts as well as refusal tone. We included both borderline and adversarial prompts in our safety data strategy, and modified our safety data responses to follow tone guidelines.
#### Llama 3.1 systems
**Large language models, including Llama 3.1, are not designed to be deployed in isolation but instead should be deployed as part of an overall AI system with additional safety guardrails as required.** Developers are expected to deploy system safeguards when building agentic systems. Safeguards are key to achieve the right helpfulness-safety alignment as well as mitigating safety and security risks inherent to the system and any integration of the model or system with external tools.
As part of our responsible release approach, we provide the community with [safeguards](https://llama.meta.com/trust-and-safety/) that developers should deploy with Llama models or other LLMs, including Llama Guard 3, Prompt Guard and Code Shield. All our [reference implementations](https://github.com/meta-llama/llama-agentic-system) demos contain these safeguards by default so developers can benefit from system-level safety out-of-the-box.
#### New capabilities
Note that this release introduces new capabilities, including a longer context window, multilingual inputs and outputs and possible integrations by developers with third party tools. Building with these new capabilities requires specific considerations in addition to the best practices that generally apply across all Generative AI use cases.
**Tool-use**: Just like in standard software development, developers are responsible for the integration of the LLM with the tools and services of their choice. They should define a clear policy for their use case and assess the integrity of the third party services they use to be aware of the safety and security limitations when using this capability. Refer to the Responsible Use Guide for best practices on the safe deployment of the third party safeguards.
**Multilinguality**: Llama 3.1 supports 7 languages in addition to English: French, German, Hindi, Italian, Portuguese, Spanish, and Thai. Llama may be able to output text in other languages than those that meet performance thresholds for safety and helpfulness. We strongly discourage developers from using this model to converse in non-supported languages without implementing finetuning and system controls in alignment with their policies and the best practices shared in the Responsible Use Guide.
### Evaluations
We evaluated Llama models for common use cases as well as specific capabilities. Common use cases evaluations measure safety risks of systems for most commonly built applications including chat bot, coding assistant, tool calls. We built dedicated, adversarial evaluation datasets and evaluated systems composed of Llama models and Llama Guard 3 to filter input prompt and output response. It is important to evaluate applications in context, and we recommend building dedicated evaluation dataset for your use case. Prompt Guard and Code Shield are also available if relevant to the application.
Capability evaluations measure vulnerabilities of Llama models inherent to specific capabilities, for which were crafted dedicated benchmarks including long context, multilingual, tools calls, coding or memorization.
**Red teaming**
For both scenarios, we conducted recurring red teaming exercises with the goal of discovering risks via adversarial prompting and we used the learnings to improve our benchmarks and safety tuning datasets.
We partnered early with subject-matter experts in critical risk areas to understand the nature of these real-world harms and how such models may lead to unintended harm for society. Based on these conversations, we derived a set of adversarial goals for the red team to attempt to achieve, such as extracting harmful information or reprogramming the model to act in a potentially harmful capacity. The red team consisted of experts in cybersecurity, adversarial machine learning, responsible AI, and integrity in addition to multilingual content specialists with background in integrity issues in specific geographic markets.
### Critical and other risks
We specifically focused our efforts on mitigating the following critical risk areas:
**1- CBRNE (Chemical, Biological, Radiological, Nuclear, and Explosive materials) helpfulness**
To assess risks related to proliferation of chemical and biological weapons, we performed uplift testing designed to assess whether use of Llama 3.1 models could meaningfully increase the capabilities of malicious actors to plan or carry out attacks using these types of weapons.
**2. Child Safety**
Child Safety risk assessments were conducted using a team of experts, to assess the model’s capability to produce outputs that could result in Child Safety risks and inform on any necessary and appropriate risk mitigations via fine tuning. We leveraged those expert red teaming sessions to expand the coverage of our evaluation benchmarks through Llama 3 model development. For Llama 3, we conducted new in-depth sessions using objective based methodologies to assess the model risks along multiple attack vectors including the additional languages Llama 3 is trained on. We also partnered with content specialists to perform red teaming exercises assessing potentially violating content while taking account of market specific nuances or experiences.
**3. Cyber attack enablement**
Our cyber attack uplift study investigated whether LLMs can enhance human capabilities in hacking tasks, both in terms of skill level and speed.
Our attack automation study focused on evaluating the capabilities of LLMs when used as autonomous agents in cyber offensive operations, specifically in the context of ransomware attacks. This evaluation was distinct from previous studies that considered LLMs as interactive assistants. The primary objective was to assess whether these models could effectively function as independent agents in executing complex cyber-attacks without human intervention.
Our study of Llama-3.1-405B’s social engineering uplift for cyber attackers was conducted to assess the effectiveness of AI models in aiding cyber threat actors in spear phishing campaigns. Please read our Llama 3.1 Cyber security whitepaper to learn more.
### Community
Generative AI safety requires expertise and tooling, and we believe in the strength of the open community to accelerate its progress. We are active members of open consortiums, including the AI Alliance, Partnership on AI and MLCommons, actively contributing to safety standardization and transparency. We encourage the community to adopt taxonomies like the MLCommons Proof of Concept evaluation to facilitate collaboration and transparency on safety and content evaluations. Our Purple Llama tools are open sourced for the community to use and widely distributed across ecosystem partners including cloud service providers. We encourage community contributions to our [Github repository](https://github.com/meta-llama/PurpleLlama).
We also set up the [Llama Impact Grants](https://llama.meta.com/llama-impact-grants/) program to identify and support the most compelling applications of Meta’s Llama model for societal benefit across three categories: education, climate and open innovation. The 20 finalists from the hundreds of applications can be found [here](https://llama.meta.com/llama-impact-grants/#finalists).
Finally, we put in place a set of resources including an [output reporting mechanism](https://developers.facebook.com/llama_output_feedback) and [bug bounty program](https://www.facebook.com/whitehat) to continuously improve the Llama technology with the help of the community.
## Ethical Considerations and Limitations
The core values of Llama 3.1 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3.1 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress.
But Llama 3.1 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3.1’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3.1 models, developers should perform safety testing and tuning tailored to their specific applications of the model. Please refer to available resources including our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide), [Trust and Safety](https://llama.meta.com/trust-and-safety/) solutions, and other [resources](https://llama.meta.com/docs/get-started/) to learn more about responsible development.
|
mjs227/brpo-12-0-149-qwen
|
mjs227
| 2025-08-05T12:16:17Z | 16 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-04T11:13:57Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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### Model Sources [optional]
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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. -->
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[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
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[More Information Needed]
### Training Procedure
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#### Preprocessing [optional]
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#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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## Technical Specifications [optional]
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|
reasonwang/deepseek-prover-SpNW-mini-16k
|
reasonwang
| 2025-08-05T12:14:26Z | 27 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-05T12:11:45Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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## Glossary [optional]
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|
galkinv42/sergeyzh_BERTA-final-finetuned-duplicates
|
galkinv42
| 2025-08-05T12:11:45Z | 10 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"bert",
"sentence-similarity",
"feature-extraction",
"dense",
"generated_from_trainer",
"dataset_size:6921",
"loss:CosineSimilarityLoss",
"arxiv:1908.10084",
"base_model:sergeyzh/BERTA",
"base_model:finetune:sergeyzh/BERTA",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2025-08-05T12:11:23Z |
---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
- generated_from_trainer
- dataset_size:6921
- loss:CosineSimilarityLoss
base_model: sergeyzh/BERTA
widget:
- source_sentence: Диверсификация в ОПИФ очень важна, так как она снижает риски инвестиций.
sentences:
- Активами ОПИФ управляют опытные специалисты, а не непрофессионалы.
- Открытый паевой инвестиционный фонд
- 0_correction_correction_Открытый паевой инвестиционный фонд
- correction_correction
- source_sentence: Какова максимальная сумма льготы по НДФЛ для ЗПИФ?
sentences:
- Какой лимит прибыли освобождается от налога по ЗПИФ при владении более 3 лет?
- 1_question_question_Закрытый паевой инвестиционный фонд
- question_question
- Закрытый паевой инвестиционный фонд
- source_sentence: С подпиской СберПраймПлюс лимит кешбэка бонусами СберСпасибо составляет
15 000 в месяц.
sentences:
- 1_statement_statement_СберПрайм
- Максимальный ежемесячный кешбэк бонусами СберСпасибо с подпиской СберПраймПлюс
равен 15 000.
- СберПрайм
- statement_statement
- source_sentence: Для досрочного выхода на пенсию достаточно только стажа.
sentences:
- 0_correction_correction_Перевод пенсии
- correction_correction
- Перевод пенсии
- Для досрочного выхода на пенсию нужно иметь определенное количество детей.
- source_sentence: USSD-команда для проверки баланса СберМобайл - *100#.
sentences:
- statement_statement
- Чтобы узнать баланс СберМобайл, наберите *100#.
- 1_statement_statement_СберМобайл
- СберМобайл
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy
- cosine_accuracy_threshold
- cosine_f1
- cosine_f1_threshold
- cosine_precision
- cosine_recall
- cosine_ap
- cosine_mcc
model-index:
- name: SentenceTransformer based on sergeyzh/BERTA
results:
- task:
type: binary-classification
name: Binary Classification
dataset:
name: binary sts validation
type: binary-sts-validation
metrics:
- type: cosine_accuracy
value: 0.915606936416185
name: Cosine Accuracy
- type: cosine_accuracy_threshold
value: 0.6415531039237976
name: Cosine Accuracy Threshold
- type: cosine_f1
value: 0.9196919691969196
name: Cosine F1
- type: cosine_f1_threshold
value: 0.610939621925354
name: Cosine F1 Threshold
- type: cosine_precision
value: 0.8818565400843882
name: Cosine Precision
- type: cosine_recall
value: 0.960919540229885
name: Cosine Recall
- type: cosine_ap
value: 0.9155466476694125
name: Cosine Ap
- type: cosine_mcc
value: 0.8345237699497015
name: Cosine Mcc
- task:
type: binary-classification
name: Binary Classification
dataset:
name: binary sts test
type: binary-sts-test
metrics:
- type: cosine_accuracy
value: 0.9064665127020786
name: Cosine Accuracy
- type: cosine_accuracy_threshold
value: 0.6839560270309448
name: Cosine Accuracy Threshold
- type: cosine_f1
value: 0.9094972067039105
name: Cosine F1
- type: cosine_f1_threshold
value: 0.6464489698410034
name: Cosine F1 Threshold
- type: cosine_precision
value: 0.8809523809523809
name: Cosine Precision
- type: cosine_recall
value: 0.9399538106235565
name: Cosine Recall
- type: cosine_ap
value: 0.9208006005103373
name: Cosine Ap
- type: cosine_mcc
value: 0.8147624269396962
name: Cosine Mcc
---
# SentenceTransformer based on sergeyzh/BERTA
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sergeyzh/BERTA](https://huggingface.co/sergeyzh/BERTA) on the duplicates-checker-finetuning-preview dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [sergeyzh/BERTA](https://huggingface.co/sergeyzh/BERTA) <!-- at revision 914c8c8aed14042ed890fc2c662d5e9e66b2faa7 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- duplicates-checker-finetuning-preview
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False, 'architecture': 'BertModel'})
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
queries = [
"USSD-\u043a\u043e\u043c\u0430\u043d\u0434\u0430 \u0434\u043b\u044f \u043f\u0440\u043e\u0432\u0435\u0440\u043a\u0438 \u0431\u0430\u043b\u0430\u043d\u0441\u0430 \u0421\u0431\u0435\u0440\u041c\u043e\u0431\u0430\u0439\u043b - *100#.",
]
documents = [
'Чтобы узнать баланс СберМобайл, наберите *100#.',
'statement_statement',
'СберМобайл',
]
query_embeddings = model.encode_query(queries)
document_embeddings = model.encode_document(documents)
print(query_embeddings.shape, document_embeddings.shape)
# [1, 768] [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(query_embeddings, document_embeddings)
print(similarities)
# tensor([[0.9729, 0.2598, 0.0023]])
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
## Evaluation
### Metrics
#### Binary Classification
* Datasets: `binary-sts-validation` and `binary-sts-test`
* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)
| Metric | binary-sts-validation | binary-sts-test |
|:--------------------------|:----------------------|:----------------|
| cosine_accuracy | 0.9156 | 0.9065 |
| cosine_accuracy_threshold | 0.6416 | 0.684 |
| cosine_f1 | 0.9197 | 0.9095 |
| cosine_f1_threshold | 0.6109 | 0.6464 |
| cosine_precision | 0.8819 | 0.881 |
| cosine_recall | 0.9609 | 0.94 |
| **cosine_ap** | **0.9155** | **0.9208** |
| cosine_mcc | 0.8345 | 0.8148 |
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Dataset
#### duplicates-checker-finetuning-preview
* Dataset: duplicates-checker-finetuning-preview
* Size: 6,921 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, <code>label</code>, <code>task_type</code>, <code>product</code>, and <code>stratify_col</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | label | task_type | product | stratify_col |
|:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:------------------------------------------------|:-------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| type | string | string | int | string | string | string |
| details | <ul><li>min: 5 tokens</li><li>mean: 19.33 tokens</li><li>max: 45 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 20.3 tokens</li><li>max: 44 tokens</li></ul> | <ul><li>0: ~49.60%</li><li>1: ~50.40%</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 5.0 tokens</li><li>max: 5 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 10.06 tokens</li><li>max: 17 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 16.06 tokens</li><li>max: 23 tokens</li></ul> |
* Samples:
| sentence1 | sentence2 | label | task_type | product | stratify_col |
|:----------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------|:---------------|:-----------------------------------|:----------------------------------------------------|:----------------------------------------------------------------------------|
| <code>Облигации Федерального Займа выпускает Министерство финансов РФ, а не Центральный Банк.</code> | <code>Облигации Федерального Займа выпускает Министерство финансов РФ, а не СберБанк.</code> | <code>0</code> | <code>correction_correction</code> | <code>Облигации</code> | <code>0_correction_correction_Облигации</code> |
| <code>Льгота на долгосрочное владение паями ОПИФ действует при владении более 3 лет, а не 1 года.</code> | <code>Лимит дохода для ЛДВ по ОПИФ составляет 3 млн рублей за каждый год владения, а не 1 млн.</code> | <code>0</code> | <code>correction_correction</code> | <code>Открытый паевой инвестиционный фонд</code> | <code>0_correction_correction_Открытый паевой инвестиционный фонд</code> |
| <code>Продажа паев ЗПИФ на бирже не требует поиска покупателя, в отличие от продажи по договору купли-продажи.</code> | <code>Потенциальный доход от фонда Современный 8 включает рентный доход и доход от роста стоимости, а не только рентный.</code> | <code>0</code> | <code>correction_correction</code> | <code>Закрытый паевой инвестиционный фонд</code> | <code>0_correction_correction_Закрытый паевой инвестиционный фонд</code> |
* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
```json
{
"loss_fct": "torch.nn.modules.loss.MSELoss"
}
```
### Evaluation Dataset
#### duplicates-checker-finetuning-preview
* Dataset: duplicates-checker-finetuning-preview
* Size: 865 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, <code>label</code>, <code>task_type</code>, <code>product</code>, and <code>stratify_col</code>
* Approximate statistics based on the first 865 samples:
| | sentence1 | sentence2 | label | task_type | product | stratify_col |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------|:-------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| type | string | string | int | string | string | string |
| details | <ul><li>min: 7 tokens</li><li>mean: 19.34 tokens</li><li>max: 37 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 20.46 tokens</li><li>max: 39 tokens</li></ul> | <ul><li>0: ~49.71%</li><li>1: ~50.29%</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 5.0 tokens</li><li>max: 5 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 9.94 tokens</li><li>max: 17 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 15.94 tokens</li><li>max: 23 tokens</li></ul> |
* Samples:
| sentence1 | sentence2 | label | task_type | product | stratify_col |
|:---------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------|:---------------|:---------------------------------|:---------------------------------------------------|:-----------------------------------------------------------------------|
| <code>Какой тариф Сбера подходит для начинающих инвесторов на ИИС-3?</code> | <code>Какой тарифный план Сбера рекомендован для новичков, использующих ИИС-3?</code> | <code>1</code> | <code>question_question</code> | <code>Индивидуальный инвестиционный счёт</code> | <code>1_question_question_Индивидуальный инвестиционный счёт</code> |
| <code>Какие типы кредитных карт Сбера вы предлагаете, и какие преимущества у каждой из них?</code> | <code>Расскажите о видах Кредитных СберКарт и их плюсах.</code> | <code>1</code> | <code>question_question</code> | <code>Кредитная СберКарта</code> | <code>1_question_question_Кредитная СберКарта</code> |
| <code>При отсутствии трудовой книжки стаж подтверждается справками из архива.</code> | <code>При отсутствии трудовой книжки стаж подтверждается устными показаниями свидетелей.</code> | <code>0</code> | <code>statement_statement</code> | <code>Перевод пенсии</code> | <code>0_statement_statement_Перевод пенсии</code> |
* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
```json
{
"loss_fct": "torch.nn.modules.loss.MSELoss"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `learning_rate`: 1.2006506775681832e-05
- `weight_decay`: 0.04243902303817388
- `num_train_epochs`: 50
- `warmup_ratio`: 0.27192485622024914
- `load_best_model_at_end`: True
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 1.2006506775681832e-05
- `weight_decay`: 0.04243902303817388
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 50
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.27192485622024914
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: True
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `hub_revision`: None
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `liger_kernel_config`: None
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
- `router_mapping`: {}
- `learning_rate_mapping`: {}
</details>
### Training Logs
<details><summary>Click to expand</summary>
| Epoch | Step | Training Loss | Validation Loss | binary-sts-validation_cosine_ap | binary-sts-test_cosine_ap |
|:-----------:|:--------:|:-------------:|:---------------:|:-------------------------------:|:-------------------------:|
| 0.2304 | 50 | 0.2427 | - | - | - |
| 0.4608 | 100 | 0.2435 | 0.2850 | 0.8052 | - |
| 0.6912 | 150 | 0.2318 | - | - | - |
| 0.9217 | 200 | 0.2341 | 0.2740 | 0.8085 | - |
| 1.1521 | 250 | 0.2303 | - | - | - |
| 1.3825 | 300 | 0.2277 | 0.2554 | 0.8147 | - |
| 1.6129 | 350 | 0.2239 | - | - | - |
| 1.8433 | 400 | 0.2048 | 0.2293 | 0.8187 | - |
| 2.0737 | 450 | 0.1955 | - | - | - |
| 2.3041 | 500 | 0.1913 | 0.2015 | 0.8221 | - |
| 2.5346 | 550 | 0.1878 | - | - | - |
| 2.7650 | 600 | 0.1743 | 0.1771 | 0.8230 | - |
| 2.9954 | 650 | 0.1714 | - | - | - |
| 3.2258 | 700 | 0.1679 | 0.1575 | 0.8183 | - |
| 3.4562 | 750 | 0.153 | - | - | - |
| 3.6866 | 800 | 0.1437 | 0.1449 | 0.8182 | - |
| 3.9171 | 850 | 0.1421 | - | - | - |
| 4.1475 | 900 | 0.1318 | 0.1371 | 0.8195 | - |
| 4.3779 | 950 | 0.1345 | - | - | - |
| 4.6083 | 1000 | 0.1265 | 0.1307 | 0.8263 | - |
| 4.8387 | 1050 | 0.1282 | - | - | - |
| 5.0691 | 1100 | 0.1245 | 0.1258 | 0.8399 | - |
| 5.2995 | 1150 | 0.1132 | - | - | - |
| 5.5300 | 1200 | 0.1142 | 0.1208 | 0.8432 | - |
| 5.7604 | 1250 | 0.1138 | - | - | - |
| 5.9908 | 1300 | 0.1119 | 0.1162 | 0.8512 | - |
| 6.2212 | 1350 | 0.1034 | - | - | - |
| 6.4516 | 1400 | 0.1034 | 0.1122 | 0.8584 | - |
| 6.6820 | 1450 | 0.1026 | - | - | - |
| 6.9124 | 1500 | 0.0985 | 0.1083 | 0.8605 | - |
| 7.1429 | 1550 | 0.0905 | - | - | - |
| 7.3733 | 1600 | 0.0912 | 0.1049 | 0.8690 | - |
| 7.6037 | 1650 | 0.0869 | - | - | - |
| 7.8341 | 1700 | 0.0876 | 0.1018 | 0.8716 | - |
| 8.0645 | 1750 | 0.0884 | - | - | - |
| 8.2949 | 1800 | 0.0833 | 0.0980 | 0.8801 | - |
| 8.5253 | 1850 | 0.0734 | - | - | - |
| 8.7558 | 1900 | 0.0764 | 0.0956 | 0.8809 | - |
| 8.9862 | 1950 | 0.0786 | - | - | - |
| 9.2166 | 2000 | 0.0728 | 0.0928 | 0.8831 | - |
| 9.4470 | 2050 | 0.0683 | - | - | - |
| 9.6774 | 2100 | 0.0674 | 0.0907 | 0.8880 | - |
| 9.9078 | 2150 | 0.0675 | - | - | - |
| 10.1382 | 2200 | 0.0604 | 0.0886 | 0.8902 | - |
| 10.3687 | 2250 | 0.0611 | - | - | - |
| 10.5991 | 2300 | 0.0584 | 0.0864 | 0.8956 | - |
| 10.8295 | 2350 | 0.0588 | - | - | - |
| 11.0599 | 2400 | 0.0624 | 0.0847 | 0.9026 | - |
| 11.2903 | 2450 | 0.0505 | - | - | - |
| 11.5207 | 2500 | 0.0513 | 0.0845 | 0.8974 | - |
| 11.7512 | 2550 | 0.0556 | - | - | - |
| 11.9816 | 2600 | 0.053 | 0.0813 | 0.9021 | - |
| 12.2120 | 2650 | 0.0445 | - | - | - |
| 12.4424 | 2700 | 0.0471 | 0.0812 | 0.9044 | - |
| 12.6728 | 2750 | 0.0446 | - | - | - |
| 12.9032 | 2800 | 0.046 | 0.0804 | 0.9006 | - |
| 13.1336 | 2850 | 0.0435 | - | - | - |
| 13.3641 | 2900 | 0.0367 | 0.0800 | 0.9054 | - |
| 13.5945 | 2950 | 0.0396 | - | - | - |
| 13.8249 | 3000 | 0.0425 | 0.0796 | 0.9036 | - |
| 14.0553 | 3050 | 0.0398 | - | - | - |
| 14.2857 | 3100 | 0.0299 | 0.0772 | 0.9109 | - |
| 14.5161 | 3150 | 0.0357 | - | - | - |
| 14.7465 | 3200 | 0.0376 | 0.0762 | 0.9085 | - |
| 14.9770 | 3250 | 0.0334 | - | - | - |
| 15.2074 | 3300 | 0.0307 | 0.0765 | 0.9099 | - |
| 15.4378 | 3350 | 0.0283 | - | - | - |
| 15.6682 | 3400 | 0.0314 | 0.0765 | 0.9134 | - |
| 15.8986 | 3450 | 0.0335 | - | - | - |
| 16.1290 | 3500 | 0.0265 | 0.0749 | 0.9114 | - |
| 16.3594 | 3550 | 0.0233 | - | - | - |
| 16.5899 | 3600 | 0.0254 | 0.0754 | 0.9174 | - |
| 16.8203 | 3650 | 0.0288 | - | - | - |
| 17.0507 | 3700 | 0.0261 | 0.0743 | 0.9196 | - |
| 17.2811 | 3750 | 0.0238 | - | - | - |
| 17.5115 | 3800 | 0.0222 | 0.0748 | 0.9171 | - |
| 17.7419 | 3850 | 0.025 | - | - | - |
| 17.9724 | 3900 | 0.0252 | 0.0743 | 0.9181 | - |
| 18.2028 | 3950 | 0.0197 | - | - | - |
| 18.4332 | 4000 | 0.019 | 0.0736 | 0.9195 | - |
| 18.6636 | 4050 | 0.021 | - | - | - |
| 18.8940 | 4100 | 0.0222 | 0.0731 | 0.9229 | - |
| 19.1244 | 4150 | 0.0202 | - | - | - |
| 19.3548 | 4200 | 0.0211 | 0.0740 | 0.9191 | - |
| 19.5853 | 4250 | 0.0169 | - | - | - |
| 19.8157 | 4300 | 0.0174 | 0.0745 | 0.9200 | - |
| 20.0461 | 4350 | 0.0177 | - | - | - |
| **20.2765** | **4400** | **0.0168** | **0.0736** | **0.9155** | **-** |
| 20.5069 | 4450 | 0.0182 | - | - | - |
| 20.7373 | 4500 | 0.0151 | 0.0740 | 0.9154 | - |
| 20.9677 | 4550 | 0.0163 | - | - | - |
| 21.1982 | 4600 | 0.0146 | 0.0740 | 0.9180 | - |
| 21.4286 | 4650 | 0.0128 | - | - | - |
| 21.6590 | 4700 | 0.0154 | 0.0734 | 0.9196 | - |
| 21.8894 | 4750 | 0.0149 | - | - | - |
| 22.1198 | 4800 | 0.0147 | 0.0743 | 0.9175 | - |
| 22.3502 | 4850 | 0.0132 | - | - | - |
| 22.5806 | 4900 | 0.0142 | 0.0745 | 0.9189 | - |
| 22.8111 | 4950 | 0.0152 | - | - | - |
| 23.0415 | 5000 | 0.013 | 0.0734 | 0.9261 | - |
| 23.2719 | 5050 | 0.0118 | - | - | - |
| 23.5023 | 5100 | 0.0119 | 0.0739 | 0.9265 | - |
| 23.7327 | 5150 | 0.012 | - | - | - |
| 23.9631 | 5200 | 0.0123 | 0.0738 | 0.9246 | - |
| 24.1935 | 5250 | 0.0131 | - | - | - |
| 24.4240 | 5300 | 0.0115 | 0.0725 | 0.9264 | - |
| 24.6544 | 5350 | 0.0116 | - | - | - |
| 24.8848 | 5400 | 0.011 | 0.0731 | 0.9258 | - |
| 25.1152 | 5450 | 0.0108 | - | - | - |
| 25.3456 | 5500 | 0.0112 | 0.0728 | 0.9276 | - |
| 25.5760 | 5550 | 0.0119 | - | - | - |
| 25.8065 | 5600 | 0.0084 | 0.0732 | 0.9267 | - |
| 26.0369 | 5650 | 0.0108 | - | - | - |
| 26.2673 | 5700 | 0.0105 | 0.0734 | 0.9296 | - |
| 26.4977 | 5750 | 0.0083 | - | - | - |
| 26.7281 | 5800 | 0.0102 | 0.0733 | 0.9305 | - |
| 26.9585 | 5850 | 0.0102 | - | - | - |
| 27.1889 | 5900 | 0.0074 | 0.0731 | 0.9279 | - |
| 27.4194 | 5950 | 0.0086 | - | - | - |
| 27.6498 | 6000 | 0.0091 | 0.0741 | 0.9253 | - |
| 27.8802 | 6050 | 0.0105 | - | - | - |
| 28.1106 | 6100 | 0.0098 | 0.0738 | 0.9277 | - |
| 28.3410 | 6150 | 0.0079 | - | - | - |
| 28.5714 | 6200 | 0.009 | 0.0723 | 0.9319 | - |
| 28.8018 | 6250 | 0.0082 | - | - | - |
| 29.0323 | 6300 | 0.009 | 0.0727 | 0.9302 | - |
| 29.2627 | 6350 | 0.0092 | - | - | - |
| 29.4931 | 6400 | 0.0078 | 0.0731 | 0.9348 | - |
| 29.7235 | 6450 | 0.0079 | - | - | - |
| 29.9539 | 6500 | 0.0091 | 0.0734 | 0.9361 | - |
| 30.1843 | 6550 | 0.0091 | - | - | - |
| 30.4147 | 6600 | 0.0069 | 0.0735 | 0.9380 | - |
| 30.6452 | 6650 | 0.0075 | - | - | - |
| 30.8756 | 6700 | 0.0075 | 0.0731 | 0.9384 | - |
| 31.1060 | 6750 | 0.007 | - | - | - |
| 31.3364 | 6800 | 0.0064 | 0.0739 | 0.9365 | - |
| 31.5668 | 6850 | 0.0083 | - | - | - |
| 31.7972 | 6900 | 0.0076 | 0.0732 | 0.9373 | - |
| 32.0276 | 6950 | 0.0073 | - | - | - |
| 32.2581 | 7000 | 0.0075 | 0.0740 | 0.9403 | - |
| 32.4885 | 7050 | 0.0068 | - | - | - |
| 32.7189 | 7100 | 0.0083 | 0.0730 | 0.9399 | - |
| 32.9493 | 7150 | 0.0053 | - | - | - |
| 33.1797 | 7200 | 0.0076 | 0.0725 | 0.9387 | - |
| 33.4101 | 7250 | 0.0055 | - | - | - |
| 33.6406 | 7300 | 0.007 | 0.0728 | 0.9396 | - |
| 33.8710 | 7350 | 0.0071 | - | - | - |
| 34.1014 | 7400 | 0.0058 | 0.0736 | 0.9396 | - |
| 34.3318 | 7450 | 0.0063 | - | - | - |
| 34.5622 | 7500 | 0.0066 | 0.0735 | 0.9396 | - |
| 34.7926 | 7550 | 0.0068 | - | - | - |
| 35.0230 | 7600 | 0.0056 | 0.0738 | 0.9388 | - |
| 35.2535 | 7650 | 0.0074 | - | - | - |
| 35.4839 | 7700 | 0.0061 | 0.0738 | 0.9392 | - |
| 35.7143 | 7750 | 0.0062 | - | - | - |
| 35.9447 | 7800 | 0.0054 | 0.0733 | 0.9396 | - |
| 36.1751 | 7850 | 0.0058 | - | - | - |
| 36.4055 | 7900 | 0.0061 | 0.0733 | 0.9397 | - |
| 36.6359 | 7950 | 0.0052 | - | - | - |
| 36.8664 | 8000 | 0.0062 | 0.0731 | 0.9396 | - |
| 37.0968 | 8050 | 0.0051 | - | - | - |
| 37.3272 | 8100 | 0.0066 | 0.0733 | 0.9395 | - |
| 37.5576 | 8150 | 0.0049 | - | - | - |
| 37.7880 | 8200 | 0.0051 | 0.0727 | 0.9391 | - |
| 38.0184 | 8250 | 0.0046 | - | - | - |
| 38.2488 | 8300 | 0.0056 | 0.0732 | 0.9383 | - |
| 38.4793 | 8350 | 0.0039 | - | - | - |
| 38.7097 | 8400 | 0.0047 | 0.0734 | 0.9389 | - |
| 38.9401 | 8450 | 0.0057 | - | - | - |
| 39.1705 | 8500 | 0.0064 | 0.0740 | 0.9402 | - |
| 39.4009 | 8550 | 0.0049 | - | - | - |
| 39.6313 | 8600 | 0.0057 | 0.0742 | 0.9409 | - |
| 39.8618 | 8650 | 0.0049 | - | - | - |
| 40.0922 | 8700 | 0.0057 | 0.0740 | 0.9414 | - |
| 40.3226 | 8750 | 0.0056 | - | - | - |
| 40.5530 | 8800 | 0.0043 | 0.0742 | 0.9408 | - |
| 40.7834 | 8850 | 0.0046 | - | - | - |
| 41.0138 | 8900 | 0.0051 | 0.0740 | 0.9409 | - |
| 41.2442 | 8950 | 0.0043 | - | - | - |
| 41.4747 | 9000 | 0.0046 | 0.0742 | 0.9410 | - |
| 41.7051 | 9050 | 0.0059 | - | - | - |
| 41.9355 | 9100 | 0.0044 | 0.0739 | 0.9409 | - |
| 42.1659 | 9150 | 0.0049 | - | - | - |
| 42.3963 | 9200 | 0.0048 | 0.0738 | 0.9418 | - |
| 42.6267 | 9250 | 0.0047 | - | - | - |
| 42.8571 | 9300 | 0.0036 | 0.0744 | 0.9416 | - |
| 43.0876 | 9350 | 0.0041 | - | - | - |
| 43.3180 | 9400 | 0.0049 | 0.0745 | 0.9416 | - |
| 43.5484 | 9450 | 0.0047 | - | - | - |
| 43.7788 | 9500 | 0.0048 | 0.0742 | 0.9415 | - |
| 44.0092 | 9550 | 0.0038 | - | - | - |
| 44.2396 | 9600 | 0.005 | 0.0741 | 0.9416 | - |
| 44.4700 | 9650 | 0.0045 | - | - | - |
| 44.7005 | 9700 | 0.004 | 0.0743 | 0.9416 | - |
| 44.9309 | 9750 | 0.0038 | - | - | - |
| 45.1613 | 9800 | 0.0042 | 0.0739 | 0.9416 | - |
| 45.3917 | 9850 | 0.005 | - | - | - |
| 45.6221 | 9900 | 0.0051 | 0.0742 | 0.9418 | - |
| 45.8525 | 9950 | 0.004 | - | - | - |
| 46.0829 | 10000 | 0.004 | 0.0744 | 0.9418 | - |
| 46.3134 | 10050 | 0.0035 | - | - | - |
| 46.5438 | 10100 | 0.0041 | 0.0743 | 0.9420 | - |
| 46.7742 | 10150 | 0.0041 | - | - | - |
| 47.0046 | 10200 | 0.0063 | 0.0744 | 0.9421 | - |
| 47.2350 | 10250 | 0.0039 | - | - | - |
| 47.4654 | 10300 | 0.0044 | 0.0744 | 0.9421 | - |
| 47.6959 | 10350 | 0.0043 | - | - | - |
| 47.9263 | 10400 | 0.0034 | 0.0744 | 0.9423 | - |
| 48.1567 | 10450 | 0.0048 | - | - | - |
| 48.3871 | 10500 | 0.0033 | 0.0744 | 0.9424 | - |
| 48.6175 | 10550 | 0.0048 | - | - | - |
| 48.8479 | 10600 | 0.0041 | 0.0745 | 0.9423 | - |
| 49.0783 | 10650 | 0.0035 | - | - | - |
| 49.3088 | 10700 | 0.0036 | 0.0744 | 0.9423 | - |
| 49.5392 | 10750 | 0.0037 | - | - | - |
| 49.7696 | 10800 | 0.0051 | 0.0744 | 0.9423 | - |
| 50.0 | 10850 | 0.0036 | - | - | - |
| -1 | -1 | - | - | 0.9155 | 0.9208 |
* The bold row denotes the saved checkpoint.
</details>
### Framework Versions
- Python: 3.12.7
- Sentence Transformers: 5.0.0
- Transformers: 4.54.1
- PyTorch: 2.5.1
- Accelerate: 1.9.0
- Datasets: 4.0.0
- Tokenizers: 0.21.4
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
<!--
## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->
|
dayanofoghata/modelllm
|
dayanofoghata
| 2025-08-05T12:11:31Z | 24 | 0 |
transformers
|
[
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-05T12:10:07Z |
---
base_model: unsloth/meta-llama-3.1-8b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- gguf
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** dayanofoghata
- **License:** apache-2.0
- **Finetuned from model :** unsloth/meta-llama-3.1-8b-unsloth-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
irongeek75/example-model
|
irongeek75
| 2025-08-05T12:11:06Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-04T13:38:09Z |
# Example Model
This is my model card README document
---
license: mit
---
|
annyi/my_act_policy
|
annyi
| 2025-08-05T12:09:51Z | 13 | 0 |
lerobot
|
[
"lerobot",
"safetensors",
"act",
"robotics",
"dataset:annyi/record-green-cube",
"arxiv:2304.13705",
"license:apache-2.0",
"region:us"
] |
robotics
| 2025-08-05T12:07:10Z |
---
datasets: annyi/record-green-cube
library_name: lerobot
license: apache-2.0
model_name: act
pipeline_tag: robotics
tags:
- act
- lerobot
- robotics
---
# Model Card for act
<!-- Provide a quick summary of what the model is/does. -->
[Action Chunking with Transformers (ACT)](https://huggingface.co/papers/2304.13705) is an imitation-learning method that predicts short action chunks instead of single steps. It learns from teleoperated data and often achieves high success rates.
This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot).
See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index).
---
## How to Get Started with the Model
For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy).
Below is the short version on how to train and run inference/eval:
### Train from scratch
```bash
python -m lerobot.scripts.train \
--dataset.repo_id=${HF_USER}/<dataset> \
--policy.type=act \
--output_dir=outputs/train/<desired_policy_repo_id> \
--job_name=lerobot_training \
--policy.device=cuda \
--policy.repo_id=${HF_USER}/<desired_policy_repo_id>
--wandb.enable=true
```
_Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`._
### Evaluate the policy/run inference
```bash
python -m lerobot.record \
--robot.type=so100_follower \
--dataset.repo_id=<hf_user>/eval_<dataset> \
--policy.path=<hf_user>/<desired_policy_repo_id> \
--episodes=10
```
Prefix the dataset repo with **eval\_** and supply `--policy.path` pointing to a local or hub checkpoint.
---
## Model Details
- **License:** apache-2.0
|
betterthings/Hunyuan-4B-Instruct-Q5_K_M-GGUF
|
betterthings
| 2025-08-05T12:08:36Z | 52 | 0 |
transformers
|
[
"transformers",
"gguf",
"llama-cpp",
"gguf-my-repo",
"base_model:tencent/Hunyuan-4B-Instruct",
"base_model:quantized:tencent/Hunyuan-4B-Instruct",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-08-05T12:08:17Z |
---
base_model: tencent/Hunyuan-4B-Instruct
library_name: transformers
tags:
- llama-cpp
- gguf-my-repo
---
# betterthings/Hunyuan-4B-Instruct-Q5_K_M-GGUF
This model was converted to GGUF format from [`tencent/Hunyuan-4B-Instruct`](https://huggingface.co/tencent/Hunyuan-4B-Instruct) 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/tencent/Hunyuan-4B-Instruct) 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 betterthings/Hunyuan-4B-Instruct-Q5_K_M-GGUF --hf-file hunyuan-4b-instruct-q5_k_m-imat.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo betterthings/Hunyuan-4B-Instruct-Q5_K_M-GGUF --hf-file hunyuan-4b-instruct-q5_k_m-imat.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 betterthings/Hunyuan-4B-Instruct-Q5_K_M-GGUF --hf-file hunyuan-4b-instruct-q5_k_m-imat.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo betterthings/Hunyuan-4B-Instruct-Q5_K_M-GGUF --hf-file hunyuan-4b-instruct-q5_k_m-imat.gguf -c 2048
```
|
BalaSowntharya/guvi-flan-t5-finetuned
|
BalaSowntharya
| 2025-08-05T12:04:46Z | 11 | 0 |
transformers
|
[
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | 2025-08-05T10:15:29Z |
---
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]
|
Michael-Osborne/Here-Lies-Promised-Land
|
Michael-Osborne
| 2025-08-05T12:02:52Z | 0 | 0 | null |
[
"license:cc-by-nc-nd-4.0",
"region:us"
] | null | 2025-08-05T12:02:51Z |
---
license: cc-by-nc-nd-4.0
---
|
israel/gemma-2-9b-it-gsm8k-all
|
israel
| 2025-08-05T11:58:05Z | 129 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma2",
"text-generation",
"llama-factory",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-07-24T21:47:51Z |
---
library_name: transformers
tags:
- llama-factory
---
# 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]
|
dedoc/language_detector
|
dedoc
| 2025-08-05T11:56:39Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2025-08-05T11:51:24Z |
---
license: mit
---
Model weights for [FastText](https://github.com/facebookresearch/fastText).
Usage example:
```python
import fasttext
model = fasttext.load_model("language_detector.bin")
predictions = model.predict("My awesome English text") # (('__label__en',), array([0.62514728]))
```
|
vilab-hsu/vqvae-afhqv2
|
vilab-hsu
| 2025-08-05T11:56:00Z | 0 | 0 | null |
[
"dataset:huggan/AFHQv2",
"region:us"
] | null | 2025-08-05T11:35:42Z |
---
datasets:
- huggan/AFHQv2
---
|
johngreendr1/2d9cf09d-c938-403f-93ce-9f8308d3b1cf
|
johngreendr1
| 2025-08-05T11:55:28Z | 19 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:deepseek-ai/deepseek-coder-6.7b-instruct",
"base_model:adapter:deepseek-ai/deepseek-coder-6.7b-instruct",
"region:us"
] | null | 2025-08-05T10:31:51Z |
---
base_model: deepseek-ai/deepseek-coder-6.7b-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 0.15.1
|
heissanjay/gemma-3n-kalvimate-05-08-merged-Q8_0-GGUF
|
heissanjay
| 2025-08-05T11:47:03Z | 23 | 0 |
transformers
|
[
"transformers",
"gguf",
"text-generation-inference",
"unsloth",
"gemma3n",
"llama-cpp",
"gguf-my-repo",
"en",
"base_model:heissanjay/gemma-3n-kalvimate-05-08-merged",
"base_model:quantized:heissanjay/gemma-3n-kalvimate-05-08-merged",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-05T11:46:46Z |
---
base_model: heissanjay/gemma-3n-kalvimate-05-08-merged
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3n
- llama-cpp
- gguf-my-repo
license: apache-2.0
language:
- en
---
# heissanjay/gemma-3n-kalvimate-05-08-merged-Q8_0-GGUF
This model was converted to GGUF format from [`heissanjay/gemma-3n-kalvimate-05-08-merged`](https://huggingface.co/heissanjay/gemma-3n-kalvimate-05-08-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/heissanjay/gemma-3n-kalvimate-05-08-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 heissanjay/gemma-3n-kalvimate-05-08-merged-Q8_0-GGUF --hf-file gemma-3n-kalvimate-05-08-merged-q8_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo heissanjay/gemma-3n-kalvimate-05-08-merged-Q8_0-GGUF --hf-file gemma-3n-kalvimate-05-08-merged-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 heissanjay/gemma-3n-kalvimate-05-08-merged-Q8_0-GGUF --hf-file gemma-3n-kalvimate-05-08-merged-q8_0.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo heissanjay/gemma-3n-kalvimate-05-08-merged-Q8_0-GGUF --hf-file gemma-3n-kalvimate-05-08-merged-q8_0.gguf -c 2048
```
|
jayalakshmikopuri/deepfake-audio-detector-v12
|
jayalakshmikopuri
| 2025-08-05T11:46:46Z | 10 | 0 |
transformers
|
[
"transformers",
"safetensors",
"wav2vec2",
"audio-classification",
"generated_from_trainer",
"base_model:Heem2/Deepfake-audio-detection",
"base_model:finetune:Heem2/Deepfake-audio-detection",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
audio-classification
| 2025-08-05T06:45:03Z |
---
library_name: transformers
license: apache-2.0
base_model: Heem2/Deepfake-audio-detection
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: deepfake-audio-detector-v12
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. -->
# deepfake-audio-detector-v12
This model is a fine-tuned version of [Heem2/Deepfake-audio-detection](https://huggingface.co/Heem2/Deepfake-audio-detection) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8698
- Accuracy: 0.9072
- Precision: 0.8756
- Recall: 0.9492
- F1: 0.9109
## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.4229 | 1.0 | 571 | 0.3528 | 0.8669 | 0.8223 | 0.9361 | 0.8755 |
| 0.1973 | 2.0 | 1142 | 0.5274 | 0.9076 | 0.8781 | 0.9466 | 0.9111 |
| 0.2161 | 3.0 | 1713 | 0.8698 | 0.9072 | 0.8756 | 0.9492 | 0.9109 |
### Framework versions
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.2
|
cortecs/Qwen3-8B-0.5-2
|
cortecs
| 2025-08-05T11:40:41Z | 19 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"compressed-tensors",
"region:us"
] |
text-generation
| 2025-08-05T11:38:01Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **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]
|
NeuML/pubmedbert-base-splade
|
NeuML
| 2025-08-05T11:38:16Z | 198 | 4 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"bert",
"sparse-encoder",
"sparse",
"splade",
"generated_from_trainer",
"loss:SpladeLoss",
"loss:SparseMultipleNegativesRankingLoss",
"loss:FlopsLoss",
"sentence-similarity",
"en",
"base_model:microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext",
"base_model:finetune:microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2025-07-03T22:53:50Z |
---
tags:
- sentence-transformers
- sparse-encoder
- sparse
- splade
- generated_from_trainer
- loss:SpladeLoss
- loss:SparseMultipleNegativesRankingLoss
- loss:FlopsLoss
base_model: microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
- active_dims
- sparsity_ratio
model-index:
- name: SPLADE Sparse Encoder
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
type: pubmed-similarity
name: PubMed Similarity
metrics:
- type: pearson_cosine
value: 0.9422980731390805
name: Pearson Cosine
- type: spearman_cosine
value: 0.8870061609483617
name: Spearman Cosine
- type: active_dims
value: 34.0018196105957
name: Active Dims
- type: sparsity_ratio
value: 0.9988859897906233
name: Sparsity Ratio
language: en
license: apache-2.0
---
# PubMedBERT SPLADE
This is a [SPLADE Sparse Encoder](https://www.sbert.net/docs/sparse_encoder/usage/usage.html) model finetuned from [PubMedBERT-base](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) using [sentence-transformers](https://www.SBERT.net). It maps sentences & paragraphs to a 30522-dimensional sparse vector space and can be used for semantic search and sparse retrieval.
The training dataset was generated using a random sample of [PubMed](https://pubmed.ncbi.nlm.nih.gov/) title-abstract pairs along with similar title pairs.
PubMedBERT SPLADE produces higher quality sparse embeddings than generalized models for medical literature. Further fine-tuning for a medical subdomain will result in even better performance.
## Usage (txtai)
This model can be used to build embeddings databases with [txtai](https://github.com/neuml/txtai) for semantic search and/or as a knowledge source for retrieval augmented generation (RAG).
_Note: txtai 9.0+ is required for sparse vector scoring support_
```python
import txtai
embeddings = txtai.Embeddings(
sparse="neuml/pubmedbert-base-splade",
content=True
)
embeddings.index(documents())
# Run a query
embeddings.search("query to run")
```
## Usage (Sentence-Transformers)
Alternatively, the model can be loaded with [sentence-transformers](https://www.SBERT.net).
```python
from sentence_transformers import SparseEncoder
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SparseEncoder("neuml/pubmedbert-base-splade")
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
Performance of this model compared to the top base models on the [MTEB leaderboard](https://huggingface.co/spaces/mteb/leaderboard) is shown below. A popular smaller model was also evaluated along with the most downloaded PubMed similarity model on the Hugging Face Hub.
The following datasets were used to evaluate model performance.
- [PubMed QA](https://huggingface.co/datasets/qiaojin/PubMedQA)
- Subset: pqa_labeled, Split: train, Pair: (question, long_answer)
- [PubMed Subset](https://huggingface.co/datasets/awinml/pubmed_abstract_3_1k)
- Split: test, Pair: (title, text)
- [PubMed Summary](https://huggingface.co/datasets/armanc/scientific_papers)
- Subset: pubmed, Split: validation, Pair: (article, abstract)
Evaluation results are shown below. The [Pearson correlation coefficient](https://en.wikipedia.org/wiki/Pearson_correlation_coefficient) is used as the evaluation metric.
| Model | PubMed QA | PubMed Subset | PubMed Summary | Average |
| ----------------------------------------------------------------------------- | --------- | ------------- | -------------- | --------- |
| [all-MiniLM-L6-v2](https://hf.co/sentence-transformers/all-MiniLM-L6-v2) | 90.40 | 95.92 | 94.07 | 93.46 |
| [bge-base-en-v1.5](https://hf.co/BAAI/bge-base-en-v1.5) | 91.02 | 95.82 | 94.49 | 93.78 |
| [gte-base](https://hf.co/thenlper/gte-base) | 92.97 | 96.90 | 96.24 | 95.37 |
| [pubmedbert-base-embeddings](https://hf.co/neuml/pubmedbert-base-embeddings) | 93.27 | 97.00 | 96.58 | 95.62 |
| [**pubmedbert-base-splade**](https://hf.co/neuml/pubmedbert-base-splade) | **90.76** | **96.20** | **95.87** | **94.28** |
| [S-PubMedBert-MS-MARCO](https://hf.co/pritamdeka/S-PubMedBert-MS-MARCO) | 90.86 | 93.68 | 93.54 | 92.69 |
While this model was't the highest scoring model using the Pearson metric, it does well when measured by [Spearman rank correlation coefficient](https://en.wikipedia.org/wiki/Spearman%27s_rank_correlation_coefficient).
| Model | PubMed QA | PubMed Subset | PubMed Summary | Average |
| ----------------------------------------------------------------------------- | --------- | ------------- | -------------- | --------- |
| [all-MiniLM-L6-v2](https://hf.co/sentence-transformers/all-MiniLM-L6-v2) | 85.77 | 86.52 | 86.32 | 86.20 |
| [bge-base-en-v1.5](https://hf.co/BAAI/bge-base-en-v1.5) | 85.71 | 86.58 | 86.35 | 86.21 |
| [gte-base](https://hf.co/thenlper/gte-base) | 86.44 | 86.60 | 86.55 | 86.53 |
| [pubmedbert-base-embeddings](https://hf.co/neuml/pubmedbert-base-embeddings) | 86.29 | 86.57 | 86.47 | 86.44 |
| [**pubmedbert-base-splade**](https://hf.co/neuml/pubmedbert-base-splade) | **86.80** | **89.12** | **88.60** | **88.17** |
| [S-PubMedBert-MS-MARCO](https://hf.co/pritamdeka/S-PubMedBert-MS-MARCO) | 85.71 | 86.37 | 86.13 | 86.07 |
This indicates that the SPLADE model may do a better job of calculating scores/rankings in the correct direction.
### Full Model Architecture
```
SparseEncoder(
(0): MLMTransformer({'max_seq_length': 512, 'do_lower_case': False, 'architecture': 'BertForMaskedLM'})
(1): SpladePooling({'pooling_strategy': 'max', 'activation_function': 'relu', 'word_embedding_dimension': 30522})
)
```
## More Information
The training data for this model is the same as described in [this article](https://medium.com/neuml/embeddings-for-medical-literature-74dae6abf5e0). See [this article](https://huggingface.co/blog/train-sparse-encoder) for more on the training scripts.
|
Simcar1/autotrain-ee1z6-glxna
|
Simcar1
| 2025-08-05T11:37:02Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"autotrain",
"text-generation-inference",
"text-generation",
"peft",
"conversational",
"base_model:AI-Sweden-Models/gpt-sw3-40b",
"base_model:finetune:AI-Sweden-Models/gpt-sw3-40b",
"license:other",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-05T11:19:11Z |
---
tags:
- autotrain
- text-generation-inference
- text-generation
- peft
library_name: transformers
base_model: AI-Sweden-Models/gpt-sw3-40b
widget:
- messages:
- role: user
content: What is your favorite condiment?
license: other
---
# Model Trained Using AutoTrain
This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain).
# Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = "PATH_TO_THIS_REPO"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
model_path,
device_map="auto",
torch_dtype='auto'
).eval()
# Prompt content: "hi"
messages = [
{"role": "user", "content": "hi"}
]
input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt')
output_ids = model.generate(input_ids.to('cuda'))
response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)
# Model response: "Hello! How can I assist you today?"
print(response)
```
|
Abdelrahma12/heart
|
Abdelrahma12
| 2025-08-05T11:36:14Z | 0 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"medical",
"ar",
"dataset:Abdelrahma12/heart_failure_clinical_records_dataset.csv",
"base_model:HuggingFaceTB/SmolLM3-3B",
"base_model:adapter:HuggingFaceTB/SmolLM3-3B",
"license:bigscience-openrail-m",
"region:us"
] | null | 2025-08-05T11:07:08Z |
---
license: bigscience-openrail-m
datasets:
- Abdelrahma12/heart_failure_clinical_records_dataset.csv
language:
- ar
metrics:
- accuracy
base_model:
- HuggingFaceTB/SmolLM3-3B
new_version: HuggingFaceTB/SmolLM3-3B
library_name: adapter-transformers
tags:
- medical
---
###Importing Liberaries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import GridSearchCV
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from sklearn.neural_network import MLPClassifier
from sklearn.neighbors import KNeighborsClassifier
from xgboost import XGBClassifier
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score, classification_report
import warnings
warnings.filterwarnings('ignore')
from google.colab import drive
### Data Load
data = pd.read_csv(r'/content/heart_failure_clinical_records_dataset - Copy.csv')
### Data Exploratory
### Data Exploratory
data
data.head()
data.info()
data.duplicated().sum()
labels = ["40-45", "46-50", "51-55", "56-60", "61-65", "66-70", "71-75", "76-80", "81-95"]
data['age_group'] = pd.cut(data['age'], bins=[40, 45, 50, 55, 60, 65, 70, 75, 80, 95], labels=labels)
data.isnull().sum()
### Data Visualization
plt.figure(figsize=(10,6))
sns.countplot(data=data, x='age_group', hue='DEATH_EVENT', palette=["lightblue", "red"])
plt.title("Death Count by Age Group")
plt.xlabel("Age Group")
plt.ylabel("Patient Count")
plt.legend(["Survived", "Died"])
plt.show()
corr_matrix = data.drop(columns=['age_group']).corr()
plt.figure(figsize=(12, 10))
sns.heatmap(corr_matrix, annot=True, cmap='coolwarm', fmt=".2f")
plt.title('Correlation Matrix of Heart Failure Clinical Records')
plt.show()
death_counts = data['DEATH_EVENT'].value_counts()
plt.figure(figsize=(6, 6))
plt.pie(death_counts, labels=['Not Died', 'Died'], autopct='%1.1f%%', startangle=90, colors=['skyblue', 'lightcoral'])
plt.title('Distribution of DEATH_EVENT')
plt.show()
# Select a subset of numerical features that showed some correlation with DEATH_EVENT
selected_features = ['time', 'serum_creatinine', 'ejection_fraction', 'age', 'serum_sodium', 'DEATH_EVENT']
sns.pairplot(data[selected_features], hue='DEATH_EVENT', diag_kind='kde')
plt.suptitle('Pairplot of Selected Numerical Features by DEATH_EVENT', y=1.02)
plt.show()
# Data Preprocessing
### Data Split
data.drop(columns=['age_group'], inplace=True)
X = data.drop('DEATH_EVENT', axis=1)
y = data['DEATH_EVENT']
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)
### Feature Scaling
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
continuous_features = ['age', 'creatinine_phosphokinase', 'ejection_fraction', 'platelets', 'serum_creatinine', 'serum_sodium', 'time']
X_train[continuous_features] = scaler.fit_transform(X_train[continuous_features])
X_test[continuous_features] = scaler.transform(X_test[continuous_features])
#Modeling
### Logistic Regression
log_params = {
'penalty': ['l1', 'l2', 'elasticnet', 'none'],
'C': [0.01, 0.1, 1, 10, 100],
'solver': ['lbfgs', 'saga'],
'max_iter': [1000]
}
log_grid = GridSearchCV(LogisticRegression(random_state=42), log_params, cv=5)
log_grid.fit(X_train, y_train)
print(" Logistic Regression Best Params:", log_grid.best_params_)
####Evaluation
log_model = LogisticRegression(
penalty='l2',
C=0.1,
solver='lbfgs',
max_iter=1000,
random_state=42
)
log_model.fit(X_train, y_train)
y_pred_log = log_model.predict(X_test)
print(" Logistic Regression")
print(f"Accuracy: {accuracy_score(y_test, y_pred_log):.4f}")
print(classification_report(y_test, y_pred_log))
### Random Forest
rf_params = {
'n_estimators': [50, 100, 200],
'max_depth': [None, 5, 10],
'min_samples_split': [2, 5],
'min_samples_leaf': [1, 2]
}
rf_grid = GridSearchCV(RandomForestClassifier(random_state=42), rf_params, cv=5)
rf_grid.fit(X_train, y_train)
print(" Random Forest Best Params:", rf_grid.best_params_)
####Evaluation
rf_model = RandomForestClassifier(
n_estimators=50, max_depth=5,
min_samples_leaf=2, min_samples_split=5,
random_state=42
)
rf_model.fit(X_train, y_train)
y_pred_rf = rf_model.predict(X_test)
print(" Random Forest")
print(f"Accuracy: {accuracy_score(y_test, y_pred_rf):.4f}")
print(classification_report(y_test, y_pred_rf))
### SVM
svm_params = {
'kernel': ['linear', 'rbf'],
'C': [0.1, 1, 10],
'gamma': ['scale', 'auto']
}
svm_grid = GridSearchCV(SVC(probability=True, random_state=42), svm_params, cv=5)
svm_grid.fit(X_train, y_train)
print(" SVM Best Params:", svm_grid.best_params_)
#### Evaluation
svm_model = SVC(
C=0.1, gamma='scale', kernel='linear',
probability=True, random_state=42
)
svm_model.fit(X_train, y_train)
y_pred_svm = svm_model.predict(X_test)
print("\n SVM")
print(f"Accuracy: {accuracy_score(y_test, y_pred_svm):.4f}")
print(classification_report(y_test, y_pred_svm))
### MLP
mlp_params = {
'hidden_layer_sizes': [(64,), (64, 32), (128, 64)],
'activation': ['relu', 'tanh'],
'alpha': [0.0001, 0.001],
'learning_rate': ['constant', 'adaptive']
}
mlp_grid = GridSearchCV(MLPClassifier(max_iter=1000, random_state=42), mlp_params, cv=5)
mlp_grid.fit(X_train, y_train)
print(" MLP Best Params:", mlp_grid.best_params_)
#### Evaluation
mlp_model = MLPClassifier(
hidden_layer_sizes=(64, 32),
activation='tanh',
alpha=0.0001,
learning_rate='constant',
max_iter=1000,
random_state=42
)
mlp_model.fit(X_train, y_train)
y_pred_mlp = mlp_model.predict(X_test)
print("\n MLP Neural Network")
print(f"Accuracy: {accuracy_score(y_test, y_pred_mlp):.4f}")
print(classification_report(y_test, y_pred_mlp))
### XGBoost
xgb_params = {
'n_estimators': [50, 100, 200],
'max_depth': [3, 4, 5],
'learning_rate': [0.01, 0.1, 0.2]
}
xgb_grid = GridSearchCV(
XGBClassifier(use_label_encoder=False, eval_metric='logloss', random_state=42),
xgb_params, cv=5
)
xgb_grid.fit(X_train, y_train)
print(" XGBoost Best Params:", xgb_grid.best_params_)
#### Evaluation
xgb_model = XGBClassifier(
n_estimators=50,
max_depth=4,
learning_rate=0.2,
use_label_encoder=False,
eval_metric='logloss',
random_state=42
)
xgb_model.fit(X_train, y_train)
y_pred_xgb = xgb_model.predict(X_test)
print("\n XGBoost")
print(f"Accuracy: {accuracy_score(y_test, y_pred_xgb):.4f}")
print(classification_report(y_test, y_pred_xgb))
### KNN
knn_params = {
'n_neighbors': [3, 5, 7, 9],
'weights': ['uniform', 'distance'],
'metric': ['euclidean', 'manhattan']
}
knn_grid = GridSearchCV(KNeighborsClassifier(), knn_params, cv=5)
knn_grid.fit(X_train, y_train)
print(" KNN Best Params:", knn_grid.best_params_)
#### Evaluation
knn_model = KNeighborsClassifier(
n_neighbors=5,
weights='uniform',
metric='euclidean'
)
knn_model.fit(X_train, y_train)
y_pred_knn = knn_model.predict(X_test)
print("\n KNN")
print(f"Accuracy: {accuracy_score(y_test, y_pred_knn):.4f}")
print(classification_report(y_test, y_pred_knn))
### Models Accuracies
models = [
'Random Forest', 'SVM', 'MLP',
'XGBoost', 'KNN', 'Logistic Regression'
]
accuracies = [
0.85, 0.8333, 0.6833,
0.8333, 0.7167, 0.8333
]
plt.figure(figsize=(10, 6))
plt.bar(models, accuracies, color=['blue', 'green', 'purple', 'orange', 'red', 'cyan'])
plt.ylim(0, 1)
plt.ylabel('Accuracy')
plt.title('Model Accuracy Comparison')
plt.xticks(rotation=30)
plt.show()
import gradio as gr
from sklearn.preprocessing import StandardScaler
import joblib
joblib.dump(rf_model, "heart_model.pkl")
joblib.dump(scaler, "scaler.pkl")
print("Model and scaler saved successfully")
model = joblib.load("heart_model.pkl")
scaler = joblib.load("scaler.pkl")
def predict_heart_risk(age, cpk, ef, platelets, sc, ss, time, anaemia, diabetes, high_bp, sex, smoking):
data = pd.DataFrame([[
age, anaemia, cpk, diabetes, ef, high_bp,
platelets, sc, ss, sex, smoking, time
]], columns=[
'age', 'anaemia', 'creatinine_phosphokinase', 'diabetes',
'ejection_fraction', 'high_blood_pressure', 'platelets',
'serum_creatinine', 'serum_sodium', 'sex', 'smoking', 'time'
])
continuous_features = ['age', 'creatinine_phosphokinase', 'ejection_fraction','platelets', 'serum_creatinine', 'serum_sodium', 'time']
data[continuous_features] = scaler.transform(data[continuous_features])
prediction = model.predict(data)[0]
return " At Risk" if prediction == 1 else " Not At Risk"
inputs = [
gr.Number(label="Age"),
gr.Number(label="Creatinine Phosphokinase, Range [0,100000]"),
gr.Number(label="Ejection Fraction, Range [5,85] "),
gr.Number(label="Platelets, Range [5000,2000000]"),
gr.Number(label="Serum Creatinine, Range [0.1,60]"),
gr.Number(label="Serum Sodium, Range [95,255]"),
gr.Number(label="Follow-up Time (days)"),
gr.Radio([0, 1], label="Anaemia (0=No, 1=Yes)"),
gr.Radio([0, 1], label="Diabetes (0=No, 1=Yes)"),
gr.Radio([0, 1], label="High Blood Pressure (0=No, 1=Yes)"),
gr.Radio([0, 1], label="Sex (0=Female, 1=Male)"),
gr.Radio([0, 1], label="Smoking (0=No, 1=Yes)")
]
gr.Interface(
fn=predict_heart_risk,
inputs=inputs,
outputs="text",
title=" Heart Failure Risk Predictor",
description="Enter patient data to predict if they are at risk of heart failure.",
allow_flagging="never"
).launch()
|
AlekseyCalvin/HSTory_Krea_LoRa_v2
|
AlekseyCalvin
| 2025-08-05T11:29:05Z | 59 | 0 |
diffusers
|
[
"diffusers",
"flux",
"lora",
"krea",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-Krea-dev",
"base_model:adapter:black-forest-labs/FLUX.1-Krea-dev",
"license:apache-2.0",
"region:us"
] |
text-to-image
| 2025-08-05T09:52:29Z |
---
license: apache-2.0
language:
- en
tags:
- flux
- diffusers
- lora
- krea
base_model:
- black-forest-labs/FLUX.1-dev
- black-forest-labs/FLUX.1-Krea-dev
base_model_relation: adapter
pipeline_tag: text-to-image
instance_prompt: HST style autochrome photograph
---
## HSToric Color Krea Variant 2
# Low(ish)-Rank Adapter for Flux Krea
## ____||| By SilverAgePoets.com |||____
<br>
This rank-64 LoRA addition to our HSToric Color text-2-image model adapter series is another experimental fine-tune over the new [Krea Flux Dev Model](https://huggingface.co/black-forest-labs/FLUX.1-Krea-dev) by Black Forest Labs. <br>
Like some of our other HST Color models, this variant was trained on HD scans of early color photos (circa *1900s-1910s* ) by **Sergey Prokudin-Gorsky**, who traveled and photographed widely in those years whilst perfecting implementations of a pioneering 3-color-composite photography technique.<br>
**This model is aimed at being useful for**:<br>
- Further improving realism of generated images (as an add-on).
- Generating original images featuring grounded early 1900s Eastern European historical content.
- Generating images reminiscent of color film analog photography, exhibiting parallels to a broad spectrum of iconic instrumentalities and visual paradigms, from Autochrome-to-Kodachrome-to-Fujifilm-and-beyond. <br>
- Producing visuals with a vaguely "historical" or "lived-in" aesthetic character, striking chromaticity and luminosity dynamics, as well as textural/anatomical/skin details more reliably lifelike than through the use of other suchlike-use-case-catered adapters. <br>
## Trigger words
You should use `HST style autochrome photograph` to open a vivid window into a chromatically bewitching ever-futurous past.<br>
|
HPLT/hplt_bert_base_mn
|
HPLT
| 2025-08-05T11:25:44Z | 14 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"fill-mask",
"BERT",
"HPLT",
"encoder",
"custom_code",
"mn",
"dataset:HPLT/hplt_monolingual_v1_2",
"license:apache-2.0",
"autotrain_compatible",
"region:us"
] |
fill-mask
| 2024-04-22T01:28:20Z |
---
language:
- mn
inference: false
tags:
- BERT
- HPLT
- encoder
license: apache-2.0
datasets:
- HPLT/hplt_monolingual_v1_2
---
# HPLT Bert for Mongolian
<img src="https://hplt-project.org/_next/static/media/logo-hplt.d5e16ca5.svg" width=12.5%>
This is one of the encoder-only monolingual language models trained as a first release by the [HPLT project](https://hplt-project.org/).
It is a so called masked language model. In particular, we used the modification of the classic BERT model named [LTG-BERT](https://aclanthology.org/2023.findings-eacl.146/).
A monolingual LTG-BERT model is trained for every major language in the [HPLT 1.2 data release](https://hplt-project.org/datasets/v1.2) (*75* models total).
All the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:
- hidden size: 768
- attention heads: 12
- layers: 12
- vocabulary size: 32768
Every model uses its own tokenizer trained on language-specific HPLT data.
See sizes of the training corpora, evaluation results and more in our [language model training report](https://hplt-project.org/HPLT_D4_1___First_language_models_trained.pdf).
[The training code](https://github.com/hplt-project/HPLT-WP4).
[The training statistics of all 75 runs](https://api.wandb.ai/links/ltg/kduj7mjn)
## Example usage
This model currently needs a custom wrapper from `modeling_ltgbert.py`, you should therefore load the model with `trust_remote_code=True`.
```python
import torch
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("HPLT/hplt_bert_base_mn")
model = AutoModelForMaskedLM.from_pretrained("HPLT/hplt_bert_base_mn", trust_remote_code=True)
mask_id = tokenizer.convert_tokens_to_ids("[MASK]")
input_text = tokenizer("It's a beautiful[MASK].", return_tensors="pt")
output_p = model(**input_text)
output_text = torch.where(input_text.input_ids == mask_id, output_p.logits.argmax(-1), input_text.input_ids)
# should output: '[CLS] It's a beautiful place.[SEP]'
print(tokenizer.decode(output_text[0].tolist()))
```
The following classes are currently implemented: `AutoModel`, `AutoModelMaskedLM`, `AutoModelForSequenceClassification`, `AutoModelForTokenClassification`, `AutoModelForQuestionAnswering` and `AutoModeltForMultipleChoice`.
## Intermediate checkpoints
We are releasing 10 intermediate checkpoints for each model at intervals of every 3125 training steps in separate branches. The naming convention is `stepXXX`: for example, `step18750`.
You can load a specific model revision with `transformers` using the argument `revision`:
```python
model = AutoModelForMaskedLM.from_pretrained("HPLT/hplt_bert_base_mn", revision="step21875", trust_remote_code=True)
```
You can access all the revisions for the models with the following code:
```python
from huggingface_hub import list_repo_refs
out = list_repo_refs("HPLT/hplt_bert_base_mn")
print([b.name for b in out.branches])
```
## Cite us
```bibtex
@inproceedings{samuel-etal-2023-trained,
title = "Trained on 100 million words and still in shape: {BERT} meets {B}ritish {N}ational {C}orpus",
author = "Samuel, David and
Kutuzov, Andrey and
{\O}vrelid, Lilja and
Velldal, Erik",
editor = "Vlachos, Andreas and
Augenstein, Isabelle",
booktitle = "Findings of the Association for Computational Linguistics: EACL 2023",
month = may,
year = "2023",
address = "Dubrovnik, Croatia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-eacl.146",
doi = "10.18653/v1/2023.findings-eacl.146",
pages = "1954--1974"
})
```
```bibtex
@inproceedings{de-gibert-etal-2024-new-massive,
title = "A New Massive Multilingual Dataset for High-Performance Language Technologies",
author = {de Gibert, Ona and
Nail, Graeme and
Arefyev, Nikolay and
Ba{\~n}{\'o}n, Marta and
van der Linde, Jelmer and
Ji, Shaoxiong and
Zaragoza-Bernabeu, Jaume and
Aulamo, Mikko and
Ram{\'\i}rez-S{\'a}nchez, Gema and
Kutuzov, Andrey and
Pyysalo, Sampo and
Oepen, Stephan and
Tiedemann, J{\"o}rg},
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.100",
pages = "1116--1128",
abstract = "We present the HPLT (High Performance Language Technologies) language resources, a new massive multilingual dataset including both monolingual and bilingual corpora extracted from CommonCrawl and previously unused web crawls from the Internet Archive. We describe our methods for data acquisition, management and processing of large corpora, which rely on open-source software tools and high-performance computing. Our monolingual collection focuses on low- to medium-resourced languages and covers 75 languages and a total of {\mbox{$\approx$}} 5.6 trillion word tokens de-duplicated on the document level. Our English-centric parallel corpus is derived from its monolingual counterpart and covers 18 language pairs and more than 96 million aligned sentence pairs with roughly 1.4 billion English tokens. The HPLT language resources are one of the largest open text corpora ever released, providing a great resource for language modeling and machine translation training. We publicly release the corpora, the software, and the tools used in this work.",
}
```
|
Rishi1708/Llama-3.1-8B-Instruct-merged-16bit-trade-finance
|
Rishi1708
| 2025-08-05T11:23:48Z | 64 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"conversational",
"en",
"base_model:meta-llama/Llama-3.1-8B-Instruct",
"base_model:finetune:meta-llama/Llama-3.1-8B-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-05T11:14:07Z |
---
base_model: meta-llama/Llama-3.1-8B-Instruct
tags:
- text-generation-inference
- transformers
- llama
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** Rishi1708
- **License:** apache-2.0
- **Finetuned from model :** meta-llama/Llama-3.1-8B-Instruct
This llama model was trained 2x faster with Huggingface's TRL library.
|
skymizer/DeepSeek-R1-Distill-Qwen-1.5B-GGUF
|
skymizer
| 2025-08-05T11:18:24Z | 630 | 0 | null |
[
"gguf",
"license:mit",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-07-24T16:04:05Z |
---
license: mit
---
These models are converted from `deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B` with llama.cpp. These models contain biases in both q_proj and k_proj layers.
The bf16, f16 models are converted with llama.cpp in branch `b4514`, because the latest main branch of llama.cpp failed to convert the hf model into GGUF format (2025/7/28).
Both of them can be evaluated with latest llama.cpp via `./build/bin/llama-perplexity`. However, when we evaluate the bf16, f16 model with lighteval in vllm, it seems fail to load the model correctly. (2025/8/5)
|
johngreendr1/33f2e618-0be3-4b1e-80e0-3125866d250a
|
johngreendr1
| 2025-08-05T11:13:21Z | 7 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:princeton-nlp/Sheared-LLaMA-1.3B",
"base_model:adapter:princeton-nlp/Sheared-LLaMA-1.3B",
"region:us"
] | null | 2025-08-05T07:24:58Z |
---
base_model: princeton-nlp/Sheared-LLaMA-1.3B
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.15.1
|
aymleung/Reinforce-cartpole-v1
|
aymleung
| 2025-08-05T11:13:03Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-08-05T11:12:51Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-cartpole-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
dgambettaphd/M_llm2_run0_gen2_WXS_doc1000_synt96_lr1e-04_acm_SYNLAST
|
dgambettaphd
| 2025-08-05T11:10:21Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-05T11:10:05Z |
---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
ZombitX64/lm-03
|
ZombitX64
| 2025-08-05T11:09:23Z | 25 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"text-generation",
"thai",
"transformer",
"dataset:ZombitX64/Wikipedia-Thai",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-05T11:08:19Z |
---
tags:
- text-generation
- thai
- transformer
- pytorch
library_name: transformers
datasets:
- ZombitX64/Wikipedia-Thai
---
# Thai Text Generation Model
This is a custom Transformer-based model for Thai text generation, trained from scratch on the [ZombitX64/Wikipedia-Thai](https://huggingface.co/datasets/ZombitX64/Wikipedia-Thai) dataset.
## Model Architecture
The model uses a decoder-only Transformer architecture, similar to GPT-2, designed to capture long-range dependencies in sequential data. Key components include:
- **Input Embedding Layer**: Converts token IDs to dense vectors and adds positional information.
- **Transformer Decoder Blocks**: Stacked layers with masked multi-head self-attention and position-wise feed-forward networks to process the input sequence and generate the next token prediction.
- **Output Layer**: A linear layer followed by a Softmax activation to predict the probability distribution over the vocabulary.
The model was implemented using PyTorch.
## Dataset
The model was trained on the [ZombitX64/Wikipedia-Thai](https://huggingface.co/datasets/ZombitX64/Wikipedia-Thai) dataset, which contains a large corpus of Thai text from Wikipedia. The dataset was preprocessed by tokenizing the text using a custom-trained Byte Pair Encoding (BPE) tokenizer to handle the characteristics of the Thai language, including the lack of explicit word boundaries.
## Training
- **Framework**: PyTorch
- **Optimizer**: AdamW with a learning rate of 5e-05
- **Loss Function**: CrossEntropyLoss (ignoring padding tokens)
- **Epochs**: 3
- **Batch Size**: 16
- **Model Parameters**:
- Vocabulary Size: 30000
- Embedding Dimension (d_model): 512
- Number of Attention Heads (nhead): 8
- Number of Decoder Layers: 6
- Feedforward Dimension (dim_feedforward): 2048
## Evaluation
The model was evaluated on a validation set using perplexity, a common metric for language models.
- **Average Validation Perplexity**: 191.2756
Lower perplexity indicates a better language model.
## How to Use
You can use this model for generating Thai text. Here's an example using the `transformers` library:
|
patent/qwen3_4b_grpo.n3.11
|
patent
| 2025-08-05T11:09:20Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen3",
"trl",
"en",
"base_model:unsloth/Qwen3-4B-Base",
"base_model:finetune:unsloth/Qwen3-4B-Base",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-05T11:09:13Z |
---
base_model: unsloth/Qwen3-4B-Base
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** patent
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen3-4B-Base
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)
|
weihezhai/PaperPrediction-LLM-4B
|
weihezhai
| 2025-08-05T11:08:36Z | 13 | 0 | null |
[
"safetensors",
"qwen3",
"license:cc-by-nc-4.0",
"region:us"
] | null | 2025-08-05T10:58:14Z |
---
license: cc-by-nc-4.0
---
|
stablediffusionapi/juggernautxl
|
stablediffusionapi
| 2025-08-05T11:08:35Z | 10 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"modelslab.com",
"stable-diffusion-api",
"text-to-image",
"ultra-realistic",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] |
text-to-image
| 2025-08-05T11:05:40Z |
---
license: creativeml-openrail-m
tags:
- modelslab.com
- stable-diffusion-api
- text-to-image
- ultra-realistic
pinned: true
pipeline_tag: text-to-image
library_name: diffusers
widget:
- text: a girl wandering through the forest
output:
url: https://cdn2.stablediffusionapi.com/generations/bf190b5a-fe19-437c-ba05-82f29cb1f7ad-0.png
---
# juggernautxl API Inference
<Gallery />
## Get API Key
Get API key from [ModelsLab API](http://modelslab.com), No Payment needed.
Replace Key in below code, change **model_id** to "juggernautxl"
Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://docs.modelslab.com)
Try model for free: [Generate Images](https://modelslab.com/models/juggernautxl)
Model link: [View model](https://modelslab.com/models/juggernautxl)
View all models: [View Models](https://modelslab.com/models)
```python
import requests
import json
url = "https://modelslab.com/api/v6/images/text2img"
payload = json.dumps({
"key": "your_api_key",
"model_id": "juggernautxl",
"prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K",
"negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime",
"width": "512",
"height": "512",
"samples": "1",
"num_inference_steps": "30",
"safety_checker": "no",
"enhance_prompt": "yes",
"seed": None,
"guidance_scale": 7.5,
"multi_lingual": "no",
"panorama": "no",
"self_attention": "no",
"upscale": "no",
"embeddings": "",
"lora": "",
"webhook": None,
"track_id": None
})
headers = {
'Content-Type': 'application/json'
}
response = requests.request("POST", url, headers=headers, data=payload)
print(response.text)
```
> Use this coupon code to get 25% off **DMGG0RBN**
|
HPLT/hplt_bert_base_lv
|
HPLT
| 2025-08-05T11:08:19Z | 17 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"fill-mask",
"BERT",
"HPLT",
"encoder",
"custom_code",
"lv",
"dataset:HPLT/hplt_monolingual_v1_2",
"license:apache-2.0",
"autotrain_compatible",
"region:us"
] |
fill-mask
| 2024-04-22T01:27:09Z |
---
language:
- lv
inference: false
tags:
- BERT
- HPLT
- encoder
license: apache-2.0
datasets:
- HPLT/hplt_monolingual_v1_2
---
# HPLT Bert for Latvian
<img src="https://hplt-project.org/_next/static/media/logo-hplt.d5e16ca5.svg" width=12.5%>
This is one of the encoder-only monolingual language models trained as a first release by the [HPLT project](https://hplt-project.org/).
It is a so called masked language model. In particular, we used the modification of the classic BERT model named [LTG-BERT](https://aclanthology.org/2023.findings-eacl.146/).
A monolingual LTG-BERT model is trained for every major language in the [HPLT 1.2 data release](https://hplt-project.org/datasets/v1.2) (*75* models total).
All the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:
- hidden size: 768
- attention heads: 12
- layers: 12
- vocabulary size: 32768
Every model uses its own tokenizer trained on language-specific HPLT data.
See sizes of the training corpora, evaluation results and more in our [language model training report](https://hplt-project.org/HPLT_D4_1___First_language_models_trained.pdf).
[The training code](https://github.com/hplt-project/HPLT-WP4).
[The training statistics of all 75 runs](https://api.wandb.ai/links/ltg/kduj7mjn)
## Example usage
This model currently needs a custom wrapper from `modeling_ltgbert.py`, you should therefore load the model with `trust_remote_code=True`.
```python
import torch
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("HPLT/hplt_bert_base_lv")
model = AutoModelForMaskedLM.from_pretrained("HPLT/hplt_bert_base_lv", trust_remote_code=True)
mask_id = tokenizer.convert_tokens_to_ids("[MASK]")
input_text = tokenizer("It's a beautiful[MASK].", return_tensors="pt")
output_p = model(**input_text)
output_text = torch.where(input_text.input_ids == mask_id, output_p.logits.argmax(-1), input_text.input_ids)
# should output: '[CLS] It's a beautiful place.[SEP]'
print(tokenizer.decode(output_text[0].tolist()))
```
The following classes are currently implemented: `AutoModel`, `AutoModelMaskedLM`, `AutoModelForSequenceClassification`, `AutoModelForTokenClassification`, `AutoModelForQuestionAnswering` and `AutoModeltForMultipleChoice`.
## Intermediate checkpoints
We are releasing 10 intermediate checkpoints for each model at intervals of every 3125 training steps in separate branches. The naming convention is `stepXXX`: for example, `step18750`.
You can load a specific model revision with `transformers` using the argument `revision`:
```python
model = AutoModelForMaskedLM.from_pretrained("HPLT/hplt_bert_base_lv", revision="step21875", trust_remote_code=True)
```
You can access all the revisions for the models with the following code:
```python
from huggingface_hub import list_repo_refs
out = list_repo_refs("HPLT/hplt_bert_base_lv")
print([b.name for b in out.branches])
```
## Cite us
```bibtex
@inproceedings{samuel-etal-2023-trained,
title = "Trained on 100 million words and still in shape: {BERT} meets {B}ritish {N}ational {C}orpus",
author = "Samuel, David and
Kutuzov, Andrey and
{\O}vrelid, Lilja and
Velldal, Erik",
editor = "Vlachos, Andreas and
Augenstein, Isabelle",
booktitle = "Findings of the Association for Computational Linguistics: EACL 2023",
month = may,
year = "2023",
address = "Dubrovnik, Croatia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-eacl.146",
doi = "10.18653/v1/2023.findings-eacl.146",
pages = "1954--1974"
})
```
```bibtex
@inproceedings{de-gibert-etal-2024-new-massive,
title = "A New Massive Multilingual Dataset for High-Performance Language Technologies",
author = {de Gibert, Ona and
Nail, Graeme and
Arefyev, Nikolay and
Ba{\~n}{\'o}n, Marta and
van der Linde, Jelmer and
Ji, Shaoxiong and
Zaragoza-Bernabeu, Jaume and
Aulamo, Mikko and
Ram{\'\i}rez-S{\'a}nchez, Gema and
Kutuzov, Andrey and
Pyysalo, Sampo and
Oepen, Stephan and
Tiedemann, J{\"o}rg},
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.100",
pages = "1116--1128",
abstract = "We present the HPLT (High Performance Language Technologies) language resources, a new massive multilingual dataset including both monolingual and bilingual corpora extracted from CommonCrawl and previously unused web crawls from the Internet Archive. We describe our methods for data acquisition, management and processing of large corpora, which rely on open-source software tools and high-performance computing. Our monolingual collection focuses on low- to medium-resourced languages and covers 75 languages and a total of {\mbox{$\approx$}} 5.6 trillion word tokens de-duplicated on the document level. Our English-centric parallel corpus is derived from its monolingual counterpart and covers 18 language pairs and more than 96 million aligned sentence pairs with roughly 1.4 billion English tokens. The HPLT language resources are one of the largest open text corpora ever released, providing a great resource for language modeling and machine translation training. We publicly release the corpora, the software, and the tools used in this work.",
}
```
|
HPLT/hplt_bert_base_lt
|
HPLT
| 2025-08-05T11:02:56Z | 16 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"fill-mask",
"BERT",
"HPLT",
"encoder",
"custom_code",
"lt",
"dataset:HPLT/hplt_monolingual_v1_2",
"license:apache-2.0",
"autotrain_compatible",
"region:us"
] |
fill-mask
| 2024-04-22T01:26:43Z |
---
language:
- lt
inference: false
tags:
- BERT
- HPLT
- encoder
license: apache-2.0
datasets:
- HPLT/hplt_monolingual_v1_2
---
# HPLT Bert for Lithuanian
<img src="https://hplt-project.org/_next/static/media/logo-hplt.d5e16ca5.svg" width=12.5%>
This is one of the encoder-only monolingual language models trained as a first release by the [HPLT project](https://hplt-project.org/).
It is a so called masked language model. In particular, we used the modification of the classic BERT model named [LTG-BERT](https://aclanthology.org/2023.findings-eacl.146/).
A monolingual LTG-BERT model is trained for every major language in the [HPLT 1.2 data release](https://hplt-project.org/datasets/v1.2) (*75* models total).
All the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:
- hidden size: 768
- attention heads: 12
- layers: 12
- vocabulary size: 32768
Every model uses its own tokenizer trained on language-specific HPLT data.
See sizes of the training corpora, evaluation results and more in our [language model training report](https://hplt-project.org/HPLT_D4_1___First_language_models_trained.pdf).
[The training code](https://github.com/hplt-project/HPLT-WP4).
[The training statistics of all 75 runs](https://api.wandb.ai/links/ltg/kduj7mjn)
## Example usage
This model currently needs a custom wrapper from `modeling_ltgbert.py`, you should therefore load the model with `trust_remote_code=True`.
```python
import torch
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("HPLT/hplt_bert_base_lt")
model = AutoModelForMaskedLM.from_pretrained("HPLT/hplt_bert_base_lt", trust_remote_code=True)
mask_id = tokenizer.convert_tokens_to_ids("[MASK]")
input_text = tokenizer("It's a beautiful[MASK].", return_tensors="pt")
output_p = model(**input_text)
output_text = torch.where(input_text.input_ids == mask_id, output_p.logits.argmax(-1), input_text.input_ids)
# should output: '[CLS] It's a beautiful place.[SEP]'
print(tokenizer.decode(output_text[0].tolist()))
```
The following classes are currently implemented: `AutoModel`, `AutoModelMaskedLM`, `AutoModelForSequenceClassification`, `AutoModelForTokenClassification`, `AutoModelForQuestionAnswering` and `AutoModeltForMultipleChoice`.
## Intermediate checkpoints
We are releasing 10 intermediate checkpoints for each model at intervals of every 3125 training steps in separate branches. The naming convention is `stepXXX`: for example, `step18750`.
You can load a specific model revision with `transformers` using the argument `revision`:
```python
model = AutoModelForMaskedLM.from_pretrained("HPLT/hplt_bert_base_lt", revision="step21875", trust_remote_code=True)
```
You can access all the revisions for the models with the following code:
```python
from huggingface_hub import list_repo_refs
out = list_repo_refs("HPLT/hplt_bert_base_lt")
print([b.name for b in out.branches])
```
## Cite us
```bibtex
@inproceedings{samuel-etal-2023-trained,
title = "Trained on 100 million words and still in shape: {BERT} meets {B}ritish {N}ational {C}orpus",
author = "Samuel, David and
Kutuzov, Andrey and
{\O}vrelid, Lilja and
Velldal, Erik",
editor = "Vlachos, Andreas and
Augenstein, Isabelle",
booktitle = "Findings of the Association for Computational Linguistics: EACL 2023",
month = may,
year = "2023",
address = "Dubrovnik, Croatia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-eacl.146",
doi = "10.18653/v1/2023.findings-eacl.146",
pages = "1954--1974"
})
```
```bibtex
@inproceedings{de-gibert-etal-2024-new-massive,
title = "A New Massive Multilingual Dataset for High-Performance Language Technologies",
author = {de Gibert, Ona and
Nail, Graeme and
Arefyev, Nikolay and
Ba{\~n}{\'o}n, Marta and
van der Linde, Jelmer and
Ji, Shaoxiong and
Zaragoza-Bernabeu, Jaume and
Aulamo, Mikko and
Ram{\'\i}rez-S{\'a}nchez, Gema and
Kutuzov, Andrey and
Pyysalo, Sampo and
Oepen, Stephan and
Tiedemann, J{\"o}rg},
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.100",
pages = "1116--1128",
abstract = "We present the HPLT (High Performance Language Technologies) language resources, a new massive multilingual dataset including both monolingual and bilingual corpora extracted from CommonCrawl and previously unused web crawls from the Internet Archive. We describe our methods for data acquisition, management and processing of large corpora, which rely on open-source software tools and high-performance computing. Our monolingual collection focuses on low- to medium-resourced languages and covers 75 languages and a total of {\mbox{$\approx$}} 5.6 trillion word tokens de-duplicated on the document level. Our English-centric parallel corpus is derived from its monolingual counterpart and covers 18 language pairs and more than 96 million aligned sentence pairs with roughly 1.4 billion English tokens. The HPLT language resources are one of the largest open text corpora ever released, providing a great resource for language modeling and machine translation training. We publicly release the corpora, the software, and the tools used in this work.",
}
```
|
NVFP4/Qwen3-Coder-30B-A3B-Instruct-FP4
|
NVFP4
| 2025-08-05T11:00:21Z | 10 | 0 |
Model Optimizer
|
[
"Model Optimizer",
"safetensors",
"qwen3_moe",
"nvidia",
"ModelOpt",
"Qwen3",
"quantized",
"FP4",
"text-generation",
"conversational",
"arxiv:2505.09388",
"base_model:Qwen/Qwen3-Coder-30B-A3B-Instruct",
"base_model:quantized:Qwen/Qwen3-Coder-30B-A3B-Instruct",
"license:apache-2.0",
"8-bit",
"modelopt",
"region:us"
] |
text-generation
| 2025-08-05T10:18:27Z |
---
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen3-Coder-30B-A3B-Instruct/blob/main/LICENSE
pipeline_tag: text-generation
base_model:
- Qwen/Qwen3-Coder-30B-A3B-Instruct
base_model_relation: quantized
library_name: Model Optimizer
tags:
- nvidia
- ModelOpt
- Qwen3
- quantized
- FP4
---
# Qwen3-Coder-30B-A3B-Instruct
<a href="https://chat.qwen.ai/" target="_blank" style="margin: 2px;">
<img alt="Chat" src="https://img.shields.io/badge/%F0%9F%92%9C%EF%B8%8F%20Qwen%20Chat%20-536af5" style="display: inline-block; vertical-align: middle;"/>
</a>
## Highlights
**Qwen3-Coder** is available in multiple sizes. Today, we're excited to introduce **Qwen3-Coder-30B-A3B-Instruct**. This streamlined model maintains impressive performance and efficiency, featuring the following key enhancements:
- **Significant Performance** among open models on **Agentic Coding**, **Agentic Browser-Use**, and other foundational coding tasks.
- **Long-context Capabilities** with native support for **256K** tokens, extendable up to **1M** tokens using Yarn, optimized for repository-scale understanding.
- **Agentic Coding** supporting for most platform such as **Qwen Code**, **CLINE**, featuring a specially designed function call format.
## Model Overview
**Qwen3-Coder-30B-A3B-Instruct** 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 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: **262,144 natively**.
**NOTE: This model supports only non-thinking mode and does not generate ``<think></think>`` blocks in its output. Meanwhile, specifying `enable_thinking=False` is no longer required.**
For more details, including benchmark evaluation, hardware requirements, and inference performance, please refer to our [blog](https://qwenlm.github.io/blog/qwen3-coder/), [GitHub](https://github.com/QwenLM/Qwen3-Coder), and [Documentation](https://qwen.readthedocs.io/en/latest/).
## Quickstart
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-Coder-30B-A3B-Instruct"
# 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 = "Write a quick sort algorithm."
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# conduct text completion
generated_ids = model.generate(
**model_inputs,
max_new_tokens=65536
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
content = tokenizer.decode(output_ids, skip_special_tokens=True)
print("content:", content)
```
**Note: If you encounter out-of-memory (OOM) issues, consider reducing the context length to a shorter value, such as `32,768`.**
For local use, applications such as Ollama, LMStudio, MLX-LM, llama.cpp, and KTransformers have also supported Qwen3.
## Agentic Coding
Qwen3-Coder excels in tool calling capabilities.
You can simply define or use any tools as following example.
```python
# Your tool implementation
def square_the_number(num: float) -> dict:
return num ** 2
# Define Tools
tools=[
{
"type":"function",
"function":{
"name": "square_the_number",
"description": "output the square of the number.",
"parameters": {
"type": "object",
"required": ["input_num"],
"properties": {
'input_num': {
'type': 'number',
'description': 'input_num is a number that will be squared'
}
},
}
}
}
]
import OpenAI
# Define LLM
client = OpenAI(
# Use a custom endpoint compatible with OpenAI API
base_url='http://localhost:8000/v1', # api_base
api_key="EMPTY"
)
messages = [{'role': 'user', 'content': 'square the number 1024'}]
completion = client.chat.completions.create(
messages=messages,
model="Qwen3-Coder-30B-A3B-Instruct",
max_tokens=65536,
tools=tools,
)
print(completion.choice[0])
```
## Best Practices
To achieve optimal performance, we recommend the following settings:
1. **Sampling Parameters**:
- We suggest using `temperature=0.7`, `top_p=0.8`, `top_k=20`, `repetition_penalty=1.05`.
2. **Adequate Output Length**: We recommend using an output length of 65,536 tokens for most queries, which is adequate for instruct models.
### Citation
If you find our work helpful, feel free to give us a cite.
```
@misc{qwen3technicalreport,
title={Qwen3 Technical Report},
author={Qwen Team},
year={2025},
eprint={2505.09388},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2505.09388},
}
```
|
HPLT/hplt_bert_base_la
|
HPLT
| 2025-08-05T10:57:48Z | 65 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"fill-mask",
"BERT",
"HPLT",
"encoder",
"custom_code",
"la",
"dataset:HPLT/hplt_monolingual_v1_2",
"license:apache-2.0",
"autotrain_compatible",
"region:us"
] |
fill-mask
| 2024-04-22T01:26:20Z |
---
language:
- la
inference: false
tags:
- BERT
- HPLT
- encoder
license: apache-2.0
datasets:
- HPLT/hplt_monolingual_v1_2
---
# HPLT Bert for Latin
<img src="https://hplt-project.org/_next/static/media/logo-hplt.d5e16ca5.svg" width=12.5%>
This is one of the encoder-only monolingual language models trained as a first release by the [HPLT project](https://hplt-project.org/).
It is a so called masked language model. In particular, we used the modification of the classic BERT model named [LTG-BERT](https://aclanthology.org/2023.findings-eacl.146/).
A monolingual LTG-BERT model is trained for every major language in the [HPLT 1.2 data release](https://hplt-project.org/datasets/v1.2) (*75* models total).
All the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:
- hidden size: 768
- attention heads: 12
- layers: 12
- vocabulary size: 32768
Every model uses its own tokenizer trained on language-specific HPLT data.
See sizes of the training corpora, evaluation results and more in our [language model training report](https://hplt-project.org/HPLT_D4_1___First_language_models_trained.pdf).
[The training code](https://github.com/hplt-project/HPLT-WP4).
[The training statistics of all 75 runs](https://api.wandb.ai/links/ltg/kduj7mjn)
## Example usage
This model currently needs a custom wrapper from `modeling_ltgbert.py`, you should therefore load the model with `trust_remote_code=True`.
```python
import torch
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("HPLT/hplt_bert_base_la")
model = AutoModelForMaskedLM.from_pretrained("HPLT/hplt_bert_base_la", trust_remote_code=True)
mask_id = tokenizer.convert_tokens_to_ids("[MASK]")
input_text = tokenizer("It's a beautiful[MASK].", return_tensors="pt")
output_p = model(**input_text)
output_text = torch.where(input_text.input_ids == mask_id, output_p.logits.argmax(-1), input_text.input_ids)
# should output: '[CLS] It's a beautiful place.[SEP]'
print(tokenizer.decode(output_text[0].tolist()))
```
The following classes are currently implemented: `AutoModel`, `AutoModelMaskedLM`, `AutoModelForSequenceClassification`, `AutoModelForTokenClassification`, `AutoModelForQuestionAnswering` and `AutoModeltForMultipleChoice`.
## Intermediate checkpoints
We are releasing 10 intermediate checkpoints for each model at intervals of every 3125 training steps in separate branches. The naming convention is `stepXXX`: for example, `step18750`.
You can load a specific model revision with `transformers` using the argument `revision`:
```python
model = AutoModelForMaskedLM.from_pretrained("HPLT/hplt_bert_base_la", revision="step21875", trust_remote_code=True)
```
You can access all the revisions for the models with the following code:
```python
from huggingface_hub import list_repo_refs
out = list_repo_refs("HPLT/hplt_bert_base_la")
print([b.name for b in out.branches])
```
## Cite us
```bibtex
@inproceedings{samuel-etal-2023-trained,
title = "Trained on 100 million words and still in shape: {BERT} meets {B}ritish {N}ational {C}orpus",
author = "Samuel, David and
Kutuzov, Andrey and
{\O}vrelid, Lilja and
Velldal, Erik",
editor = "Vlachos, Andreas and
Augenstein, Isabelle",
booktitle = "Findings of the Association for Computational Linguistics: EACL 2023",
month = may,
year = "2023",
address = "Dubrovnik, Croatia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-eacl.146",
doi = "10.18653/v1/2023.findings-eacl.146",
pages = "1954--1974"
})
```
```bibtex
@inproceedings{de-gibert-etal-2024-new-massive,
title = "A New Massive Multilingual Dataset for High-Performance Language Technologies",
author = {de Gibert, Ona and
Nail, Graeme and
Arefyev, Nikolay and
Ba{\~n}{\'o}n, Marta and
van der Linde, Jelmer and
Ji, Shaoxiong and
Zaragoza-Bernabeu, Jaume and
Aulamo, Mikko and
Ram{\'\i}rez-S{\'a}nchez, Gema and
Kutuzov, Andrey and
Pyysalo, Sampo and
Oepen, Stephan and
Tiedemann, J{\"o}rg},
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.100",
pages = "1116--1128",
abstract = "We present the HPLT (High Performance Language Technologies) language resources, a new massive multilingual dataset including both monolingual and bilingual corpora extracted from CommonCrawl and previously unused web crawls from the Internet Archive. We describe our methods for data acquisition, management and processing of large corpora, which rely on open-source software tools and high-performance computing. Our monolingual collection focuses on low- to medium-resourced languages and covers 75 languages and a total of {\mbox{$\approx$}} 5.6 trillion word tokens de-duplicated on the document level. Our English-centric parallel corpus is derived from its monolingual counterpart and covers 18 language pairs and more than 96 million aligned sentence pairs with roughly 1.4 billion English tokens. The HPLT language resources are one of the largest open text corpora ever released, providing a great resource for language modeling and machine translation training. We publicly release the corpora, the software, and the tools used in this work.",
}
```
|
Leonel-Maia/Wav2vec2-wolof-xlsr
|
Leonel-Maia
| 2025-08-05T10:57:02Z | 37 | 0 |
transformers
|
[
"transformers",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"LAfricaMobile/full_wolof2",
"generated_from_trainer",
"base_model:asr-africa/wav2vec2-xls-r-wolof-mixed-75-hours",
"base_model:finetune:asr-africa/wav2vec2-xls-r-wolof-mixed-75-hours",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2025-07-23T07:57:06Z |
---
library_name: transformers
license: apache-2.0
base_model: asr-africa/wav2vec2-xls-r-wolof-mixed-75-hours
tags:
- automatic-speech-recognition
- LAfricaMobile/full_wolof2
- generated_from_trainer
metrics:
- wer
model-index:
- name: Wav2vec2-wolof-xlsr
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. -->
# Wav2vec2-wolof-xlsr
This model is a fine-tuned version of [asr-africa/wav2vec2-xls-r-wolof-mixed-75-hours](https://huggingface.co/asr-africa/wav2vec2-xls-r-wolof-mixed-75-hours) on the LAFRICAMOBILE/FULL_WOLOF2 - DEFAULT dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4592
- Wer: 0.5249
- Cer: 0.1707
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 60.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:------:|:----:|:---------------:|:------:|:------:|
| 0.8537 | 0.0670 | 500 | 0.6753 | 0.5560 | 0.1782 |
| 0.7578 | 0.1341 | 1000 | 0.5242 | 0.5456 | 0.1772 |
| 0.6588 | 0.2011 | 1500 | 0.4811 | 0.5291 | 0.1716 |
| 0.6137 | 0.2681 | 2000 | 0.4592 | 0.5249 | 0.1707 |
| 0.6326 | 0.3351 | 2500 | 0.4651 | 0.5347 | 0.1711 |
| 0.6558 | 0.4022 | 3000 | 0.5097 | 0.5407 | 0.1773 |
| 0.5631 | 0.4692 | 3500 | 0.4640 | 0.5413 | 0.1734 |
### Framework versions
- Transformers 4.53.3
- Pytorch 2.7.1+cu126
- Datasets 3.6.0
- Tokenizers 0.21.2
|
parita89/nanonets_finetue_v1
|
parita89
| 2025-08-05T10:49:58Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen2_5_vl",
"trl",
"en",
"base_model:nanonets/Nanonets-OCR-s",
"base_model:finetune:nanonets/Nanonets-OCR-s",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-05T10:49:42Z |
---
base_model: nanonets/Nanonets-OCR-s
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2_5_vl
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** parita89
- **License:** apache-2.0
- **Finetuned from model :** nanonets/Nanonets-OCR-s
This qwen2_5_vl 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)
|
weihezhai/PaperPrediction-All-4B
|
weihezhai
| 2025-08-05T10:48:52Z | 8 | 0 | null |
[
"safetensors",
"qwen3",
"license:cc-by-nc-nd-4.0",
"region:us"
] | null | 2025-08-05T09:45:38Z |
---
license: cc-by-nc-nd-4.0
---
|
HPLT/hplt_bert_base_ko
|
HPLT
| 2025-08-05T10:48:20Z | 10 | 1 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"fill-mask",
"BERT",
"HPLT",
"encoder",
"custom_code",
"ko",
"dataset:HPLT/hplt_monolingual_v1_2",
"license:apache-2.0",
"autotrain_compatible",
"region:us"
] |
fill-mask
| 2024-04-22T01:25:31Z |
---
language:
- ko
inference: false
tags:
- BERT
- HPLT
- encoder
license: apache-2.0
datasets:
- HPLT/hplt_monolingual_v1_2
---
# HPLT Bert for Korean
<img src="https://hplt-project.org/_next/static/media/logo-hplt.d5e16ca5.svg" width=12.5%>
This is one of the encoder-only monolingual language models trained as a first release by the [HPLT project](https://hplt-project.org/).
It is a so called masked language model. In particular, we used the modification of the classic BERT model named [LTG-BERT](https://aclanthology.org/2023.findings-eacl.146/).
A monolingual LTG-BERT model is trained for every major language in the [HPLT 1.2 data release](https://hplt-project.org/datasets/v1.2) (*75* models total).
All the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:
- hidden size: 768
- attention heads: 12
- layers: 12
- vocabulary size: 32768
Every model uses its own tokenizer trained on language-specific HPLT data.
See sizes of the training corpora, evaluation results and more in our [language model training report](https://hplt-project.org/HPLT_D4_1___First_language_models_trained.pdf).
[The training code](https://github.com/hplt-project/HPLT-WP4).
[The training statistics of all 75 runs](https://api.wandb.ai/links/ltg/kduj7mjn)
## Example usage
This model currently needs a custom wrapper from `modeling_ltgbert.py`, you should therefore load the model with `trust_remote_code=True`.
```python
import torch
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("HPLT/hplt_bert_base_ko")
model = AutoModelForMaskedLM.from_pretrained("HPLT/hplt_bert_base_ko", trust_remote_code=True)
mask_id = tokenizer.convert_tokens_to_ids("[MASK]")
input_text = tokenizer("It's a beautiful[MASK].", return_tensors="pt")
output_p = model(**input_text)
output_text = torch.where(input_text.input_ids == mask_id, output_p.logits.argmax(-1), input_text.input_ids)
# should output: '[CLS] It's a beautiful place.[SEP]'
print(tokenizer.decode(output_text[0].tolist()))
```
The following classes are currently implemented: `AutoModel`, `AutoModelMaskedLM`, `AutoModelForSequenceClassification`, `AutoModelForTokenClassification`, `AutoModelForQuestionAnswering` and `AutoModeltForMultipleChoice`.
## Intermediate checkpoints
We are releasing 10 intermediate checkpoints for each model at intervals of every 3125 training steps in separate branches. The naming convention is `stepXXX`: for example, `step18750`.
You can load a specific model revision with `transformers` using the argument `revision`:
```python
model = AutoModelForMaskedLM.from_pretrained("HPLT/hplt_bert_base_ko", revision="step21875", trust_remote_code=True)
```
You can access all the revisions for the models with the following code:
```python
from huggingface_hub import list_repo_refs
out = list_repo_refs("HPLT/hplt_bert_base_ko")
print([b.name for b in out.branches])
```
## Cite us
```bibtex
@inproceedings{samuel-etal-2023-trained,
title = "Trained on 100 million words and still in shape: {BERT} meets {B}ritish {N}ational {C}orpus",
author = "Samuel, David and
Kutuzov, Andrey and
{\O}vrelid, Lilja and
Velldal, Erik",
editor = "Vlachos, Andreas and
Augenstein, Isabelle",
booktitle = "Findings of the Association for Computational Linguistics: EACL 2023",
month = may,
year = "2023",
address = "Dubrovnik, Croatia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-eacl.146",
doi = "10.18653/v1/2023.findings-eacl.146",
pages = "1954--1974"
})
```
```bibtex
@inproceedings{de-gibert-etal-2024-new-massive,
title = "A New Massive Multilingual Dataset for High-Performance Language Technologies",
author = {de Gibert, Ona and
Nail, Graeme and
Arefyev, Nikolay and
Ba{\~n}{\'o}n, Marta and
van der Linde, Jelmer and
Ji, Shaoxiong and
Zaragoza-Bernabeu, Jaume and
Aulamo, Mikko and
Ram{\'\i}rez-S{\'a}nchez, Gema and
Kutuzov, Andrey and
Pyysalo, Sampo and
Oepen, Stephan and
Tiedemann, J{\"o}rg},
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.100",
pages = "1116--1128",
abstract = "We present the HPLT (High Performance Language Technologies) language resources, a new massive multilingual dataset including both monolingual and bilingual corpora extracted from CommonCrawl and previously unused web crawls from the Internet Archive. We describe our methods for data acquisition, management and processing of large corpora, which rely on open-source software tools and high-performance computing. Our monolingual collection focuses on low- to medium-resourced languages and covers 75 languages and a total of {\mbox{$\approx$}} 5.6 trillion word tokens de-duplicated on the document level. Our English-centric parallel corpus is derived from its monolingual counterpart and covers 18 language pairs and more than 96 million aligned sentence pairs with roughly 1.4 billion English tokens. The HPLT language resources are one of the largest open text corpora ever released, providing a great resource for language modeling and machine translation training. We publicly release the corpora, the software, and the tools used in this work.",
}
```
|
danuphat/typhoon-ocr-3b-trl-sft-that-sub-v1
|
danuphat
| 2025-08-05T10:46:19Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:scb10x/typhoon-ocr-3b",
"base_model:finetune:scb10x/typhoon-ocr-3b",
"endpoints_compatible",
"region:us"
] | null | 2025-08-05T07:49:14Z |
---
base_model: scb10x/typhoon-ocr-3b
library_name: transformers
model_name: typhoon-ocr-3b-trl-sft-that-sub-v1
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for typhoon-ocr-3b-trl-sft-that-sub-v1
This model is a fine-tuned version of [scb10x/typhoon-ocr-3b](https://huggingface.co/scb10x/typhoon-ocr-3b).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="danuphat/typhoon-ocr-3b-trl-sft-that-sub-v1", 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/danuphat-l-kasetsart-university/typhoon-ocr-3b-trl-sft-ocr/runs/umbkygi4)
This model was trained with SFT.
### Framework versions
- TRL: 0.21.0.dev0
- Transformers: 4.55.0.dev0
- Pytorch: 2.7.1+cu128
- Datasets: 4.0.0
- 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}}
}
```
|
multimolecule/rinalmo-micro
|
multimolecule
| 2025-08-05T10:45:15Z | 36 | 0 |
multimolecule
|
[
"multimolecule",
"pytorch",
"safetensors",
"rinalmo",
"Biology",
"RNA",
"ncRNA",
"fill-mask",
"rna",
"dataset:multimolecule/rnacentral",
"dataset:multimolecule/rfam",
"dataset:multimolecule/ensembl-genome-browser",
"dataset:multimolecule/nucleotide",
"license:agpl-3.0",
"region:us"
] |
fill-mask
| 2025-08-05T10:45:01Z |
---
datasets:
- multimolecule/rnacentral
- multimolecule/rfam
- multimolecule/ensembl-genome-browser
- multimolecule/nucleotide
language: rna
library_name: multimolecule
license: agpl-3.0
mask_token: <mask>
pipeline_tag: fill-mask
tags:
- Biology
- RNA
- ncRNA
widget:
- example_title: microRNA 21
output:
- label: H
score: 0.999975
- label: Y
score: 2.5e-05
- label: C
score: 0.0
- label: M
score: 0.0
- label: W
score: 0.0
text: UAGCUUAUCAGAC<mask>GAUGUUGA
- example_title: microRNA 146a
output:
- label: H
score: 1.0
- label: M
score: 0.0
- label: Y
score: 0.0
- label: C
score: 0.0
- label: G
score: 0.0
text: UGAGAACUGAA<mask>UCCAUGGGUU
- example_title: microRNA 155
output:
- label: H
score: 1.0
- label: Y
score: 0.0
- label: M
score: 0.0
- label: G
score: 0.0
- label: C
score: 0.0
text: UUAAUGCUAA<mask>CGUGAUAGGGGUU
- example_title: metastasis associated lung adenocarcinoma transcript 1
output:
- label: H
score: 1.0
- label: M
score: 0.0
- label: Y
score: 0.0
- label: C
score: 0.0
- label: I
score: 0.0
text: AGGCAUUGAGGCAGCCAGCGCAGGGGC<mask>UCUGCUGAGGGGGCAGGCGGAGCUUGAGGAAA
- example_title: Pvt1 oncogene
output:
- label: M
score: 0.998998
- label: H
score: 0.000993
- label: Y
score: 9.0e-06
- label: W
score: 0.0
- label: G
score: 0.0
text: CCCGCGCUCC<mask>CCGGGCAGAGCGCGUGUGGCGGCCGAGCACAUGGGCCCGCGGGCCGGGC
- example_title: telomerase RNA component
output:
- label: H
score: 1.0
- label: C
score: 0.0
- label: Y
score: 0.0
- label: M
score: 0.0
- label: W
score: 0.0
text: GGGUUGCGGAGGG<mask>GGGCCUGGGAGGGGUGGUGGCCAUUUUUUGUCUAACCCUAACUGAG
- example_title: vault RNA 2-1
output:
- label: M
score: 0.834994
- label: H
score: 0.164919
- label: Y
score: 8.7e-05
- label: C
score: 0.0
- label: W
score: 0.0
text: CGGGUCGGAG<mask>UAGCUCAAGCGGUUACCUCCUCAUGCCGGACUUUCUAUCUGUCCAUCUCUGUGCUGGGGUUCGAGACCCGCGGGUGCUUACUGACCCUUUUAUGCAA
- example_title: brain cytoplasmic RNA 1
output:
- label: <unk>
score: 0.910627
- label: <mask>
score: 0.089259
- label: I
score: 0.000113
- label: M
score: 0.0
- label: D
score: 0.0
text: GGCCGGGCGCGG<mask>GGCUCACGCCUGUAAUCCCAGCUCUCAGGGAGGCUAAGAGGCGGGAGGAUAGCUUGAGCCCAGGAGUUCGAGACCUGCCUGGGCAAUAUAGCGAGACCCCGUUCUCCAGAAAAAGGAAAAAAAAAAACAAAAGACAAAAAAAAAAUAAGCGUAACUUCCCUCAAAGCAACAACCCCCCCCCCCCUUU
- example_title: HIV-1 TAR-WT
output:
- label: H
score: 1.0
- label: Y
score: 0.0
- label: C
score: 0.0
- label: V
score: 0.0
- label: M
score: 0.0
text: GGUCUCUCUGG<mask>UAGACCAGAUCUGAGCCUGGGAGCUCUCUGGCUAACUAGGGAACC
---
# RiNALMo
Pre-trained model on non-coding RNA (ncRNA) using a masked language modeling (MLM) objective.
## Disclaimer
This is an UNOFFICIAL implementation of the [RiNALMo: General-Purpose RNA Language Models Can Generalize Well on Structure Prediction Tasks](https://doi.org/10.48550/arXiv.2403.00043) by Rafael Josip Penić, et al.
The OFFICIAL repository of RiNALMo is at [lbcb-sci/RiNALMo](https://github.com/lbcb-sci/RiNALMo).
> [!TIP]
> The MultiMolecule team has confirmed that the provided model and checkpoints are producing the same intermediate representations as the original implementation.
**The team releasing RiNALMo did not write this model card for this model so this model card has been written by the MultiMolecule team.**
## Model Details
RiNALMo is a [bert](https://huggingface.co/google-bert/bert-base-uncased)-style model pre-trained on a large corpus of non-coding RNA sequences in a self-supervised fashion. This means that the model was trained on the raw nucleotides of RNA sequences only, with an automatic process to generate inputs and labels from those texts. Please refer to the [Training Details](#training-details) section for more information on the training process.
### Variants
- **[multimolecule/rinalmo-micro](https://huggingface.co/multimolecule/rinalmo-micro)**: The RiNALMo model with 30 million parameters.
- **[multimolecule/rinalmo-giga](https://huggingface.co/multimolecule/rinalmo-giga)**: The RiNALMo model with 650 million parameters.
- **[multimolecule/rinalmo-mega](https://huggingface.co/multimolecule/rinalmo-mega)**: The RiNALMo model with 150 million parameters.
### Model Specification
<table>
<thead>
<tr>
<th>Variants</th>
<th>Num Layers</th>
<th>Hidden Size</th>
<th>Num Heads</th>
<th>Intermediate Size</th>
<th>Num Parameters (M)</th>
<th>FLOPs (G)</th>
<th>MACs (G)</th>
<th>Max Num Tokens</th>
</tr>
</thead>
<tbody>
<tr>
<td>RiNALMo-Micro</td>
<td>12</td>
<td>480</td>
<td rowspan="3">20</td>
<td>1920</td>
<td>33.48</td>
<td>8.88</td>
<td>4.44</td>
<td rowspan="3">1022</td>
</tr>
<tr>
<td>RiNALMo-Giga</td>
<td>33</td>
<td>1280</td>
<td>5120</td>
<td>650.88</td>
<td>168.92</td>
<td>84.43</td>
</tr>
<tr>
<td>RiNALMo-Mega</td>
<td>30</td>
<td>640</td>
<td>2560</td>
<td>148.04</td>
<td>39.03</td>
<td>19.5</td>
</tr>
</tbody>
</table>
### Links
- **Code**: [multimolecule.rinalmo](https://github.com/DLS5-Omics/multimolecule/tree/master/multimolecule/models/rinalmo)
- **Data**: [multimolecule/rnacentral](https://huggingface.co/datasets/multimolecule/rnacentral)
- **Paper**: [RiNALMo: General-Purpose RNA Language Models Can Generalize Well on Structure Prediction Tasks](https://doi.org/10.48550/arXiv.2403.00043)
- **Developed by**: Rafael Josip Penić, Tin Vlašić, Roland G. Huber, Yue Wan, Mile Šikić
- **Model type**: [BERT](https://huggingface.co/google-bert/bert-base-uncased)
- **Original Repository**: [lbcb-sci/RiNALMo](https://github.com/lbcb-sci/RiNALMo)
## Usage
The model file depends on the [`multimolecule`](https://multimolecule.danling.org) library. You can install it using pip:
```bash
pip install multimolecule
```
### Direct Use
#### Masked Language Modeling
You can use this model directly with a pipeline for masked language modeling:
```python
>>> import multimolecule # you must import multimolecule to register models
>>> from transformers import pipeline
>>> unmasker = pipeline("fill-mask", model="multimolecule/rinalmo-micro")
>>> unmasker("gguc<mask>cucugguuagaccagaucugagccu")
[{'score': 0.3387778699398041,
'token': 6,
'token_str': 'A',
'sequence': 'G G U C A C U C U G G U U A G A C C A G A U C U G A G C C U'},
{'score': 0.25456807017326355,
'token': 8,
'token_str': 'G',
'sequence': 'G G U C G C U C U G G U U A G A C C A G A U C U G A G C C U'},
{'score': 0.10691815614700317,
'token': 22,
'token_str': 'X',
'sequence': 'G G U C X C U C U G G U U A G A C C A G A U C U G A G C C U'},
{'score': 0.0734482854604721,
'token': 10,
'token_str': 'N',
'sequence': 'G G U C N C U C U G G U U A G A C C A G A U C U G A G C C U'},
{'score': 0.058429498225450516,
'token': 9,
'token_str': 'U',
'sequence': 'G G U C U C U C U G G U U A G A C C A G A U C U G A G C C U'}]
```
### Downstream Use
#### Extract Features
Here is how to use this model to get the features of a given sequence in PyTorch:
```python
from multimolecule import RnaTokenizer, RiNALMoModel
tokenizer = RnaTokenizer.from_pretrained("multimolecule/rinalmo-micro")
model = RiNALMoModel.from_pretrained("multimolecule/rinalmo-micro")
text = "UAGCUUAUCAGACUGAUGUUG"
input = tokenizer(text, return_tensors="pt")
output = model(**input)
```
#### Sequence Classification / Regression
> [!NOTE]
> This model is not fine-tuned for any specific task. You will need to fine-tune the model on a downstream task to use it for sequence classification or regression.
Here is how to use this model as backbone to fine-tune for a sequence-level task in PyTorch:
```python
import torch
from multimolecule import RnaTokenizer, RiNALMoForSequencePrediction
tokenizer = RnaTokenizer.from_pretrained("multimolecule/rinalmo-micro")
model = RiNALMoForSequencePrediction.from_pretrained("multimolecule/rinalmo-micro")
text = "UAGCUUAUCAGACUGAUGUUG"
input = tokenizer(text, return_tensors="pt")
label = torch.tensor([1])
output = model(**input, labels=label)
```
#### Token Classification / Regression
> [!NOTE]
> This model is not fine-tuned for any specific task. You will need to fine-tune the model on a downstream task to use it for token classification or regression.
Here is how to use this model as backbone to fine-tune for a nucleotide-level task in PyTorch:
```python
import torch
from multimolecule import RnaTokenizer, RiNALMoForTokenPrediction
tokenizer = RnaTokenizer.from_pretrained("multimolecule/rinalmo-micro")
model = RiNALMoForTokenPrediction.from_pretrained("multimolecule/rinalmo-micro")
text = "UAGCUUAUCAGACUGAUGUUG"
input = tokenizer(text, return_tensors="pt")
label = torch.randint(2, (len(text), ))
output = model(**input, labels=label)
```
#### Contact Classification / Regression
> [!NOTE]
> This model is not fine-tuned for any specific task. You will need to fine-tune the model on a downstream task to use it for contact classification or regression.
Here is how to use this model as backbone to fine-tune for a contact-level task in PyTorch:
```python
import torch
from multimolecule import RnaTokenizer, RiNALMoForContactPrediction
tokenizer = RnaTokenizer.from_pretrained("multimolecule/rinalmo-micro")
model = RiNALMoForContactPrediction.from_pretrained("multimolecule/rinalmo-micro")
text = "UAGCUUAUCAGACUGAUGUUG"
input = tokenizer(text, return_tensors="pt")
label = torch.randint(2, (len(text), len(text)))
output = model(**input, labels=label)
```
## Training Details
RiNALMo used Masked Language Modeling (MLM) as the pre-training objective: taking a sequence, the model randomly masks 15% of the tokens in the input then runs the entire masked sentence through the model and has to predict the masked tokens. This is comparable to the Cloze task in language modeling.
### Training Data
The RiNALMo model was pre-trained on a cocktail of databases including [RNAcentral](https://rnacentral.org), [Rfam](https://rfam.org), [Ensembl Genome Browser](https://ensembl.org), and [Nucleotide](https://ncbi.nlm.nih.gov/nucleotide).
The training data contains 36 million unique ncRNA sequences.
To ensure sequence diversity in each training batch, RiNALMo clustered the sequences with [MMSeqs2](https://github.com/soedinglab/MMseqs2) into 17 million clusters and then sampled each sequence in the batch from a different cluster.
RiNALMo preprocessed all tokens by replacing "U"s with "T"s.
Note that during model conversions, "T" is replaced with "U". [`RnaTokenizer`][multimolecule.RnaTokenizer] will convert "T"s to "U"s for you, you may disable this behaviour by passing `replace_T_with_U=False`.
### Training Procedure
#### Preprocessing
RiNALMo used masked language modeling (MLM) as the pre-training objective. The masking procedure is similar to the one used in BERT:
- 15% of the tokens are masked.
- In 80% of the cases, the masked tokens are replaced by `<mask>`.
- In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace.
- In the 10% remaining cases, the masked tokens are left as is.
#### Pre-training
The model was trained on 7 NVIDIA A100 GPUs with 80GiB memories.
- Batch Size: 1344
- Epochs: 6
- Learning rate: 5e-5
- Learning rate scheduler: Cosine
- Learning rate warm-up: 2,000 steps
- Learning rate minimum: 1e-5
- Dropout: 0.1
## Citation
**BibTeX**:
```bibtex
@ARTICLE{Penic2025-qf,
title = "{RiNALMo}: general-purpose {RNA} language models can generalize
well on structure prediction tasks",
author = "Peni{\'c}, Rafael Josip and Vla{\v s}i{\'c}, Tin and Huber,
Roland G and Wan, Yue and {\v S}iki{\'c}, Mile",
abstract = "While RNA has recently been recognized as an interesting
small-molecule drug target, many challenges remain to be
addressed before we take full advantage of it. This emphasizes
the necessity to improve our understanding of its structures and
functions. Over the years, sequencing technologies have produced
an enormous amount of unlabeled RNA data, which hides a huge
potential. Motivated by the successes of protein language
models, we introduce RiboNucleic Acid Language Model (RiNALMo)
to unveil the hidden code of RNA. RiNALMo is the largest RNA
language model to date, with 650M parameters pre-trained on 36M
non-coding RNA sequences from several databases. It can extract
hidden knowledge and capture the underlying structure
information implicitly embedded within the RNA sequences.
RiNALMo achieves state-of-the-art results on several downstream
tasks. Notably, we show that its generalization capabilities
overcome the inability of other deep learning methods for
secondary structure prediction to generalize on unseen RNA
families.",
journal = "Nature Communications",
publisher = "Springer Science and Business Media LLC",
volume = 16,
number = 1,
pages = "5671",
month = jul,
year = 2025,
copyright = "https://creativecommons.org/licenses/by-nc-nd/4.0",
language = "en"
}
```
## Contact
Please use GitHub issues of [MultiMolecule](https://github.com/DLS5-Omics/multimolecule/issues) for any questions or comments on the model card.
Please contact the authors of the [RiNALMo paper](https://doi.org/10.48550/arXiv.2403.00043) for questions or comments on the paper/model.
## License
This model is licensed under the [AGPL-3.0 License](https://www.gnu.org/licenses/agpl-3.0.html).
```spdx
SPDX-License-Identifier: AGPL-3.0-or-later
```
|
ThierryWal/PulseAgency
|
ThierryWal
| 2025-08-05T10:44:21Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-05T10:43:37Z |
<!DOCTYPE html>
<html lang="fr">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Pulse Agency - Marketing & Automatisation IA pour les Pros</title>
<script src="https://cdn.tailwindcss.com"></script>
<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.4.0/css/all.min.css">
<link href="https://fonts.googleapis.com/css2?family=Montserrat:wght@300;400;500;600;700&display=swap" rel="stylesheet">
<style>
body {
font-family: 'Montserrat', sans-serif;
scroll-behavior: smooth;
}
.hero-gradient {
background: linear-gradient(135deg, #6e8efb 0%, #a777e3 100%);
}
.feature-card:hover {
transform: translateY(-5px);
box-shadow: 0 20px 25px -5px rgba(0, 0, 0, 0.1), 0 10px 10px -5px rgba(0, 0, 0, 0.04);
}
.testimonial-card {
transition: all 0.3s ease;
}
.testimonial-card:hover {
transform: scale(1.03);
}
.floating-whatsapp {
animation: float 3s ease-in-out infinite;
}
@keyframes float {
0% { transform: translateY(0px); }
50% { transform: translateY(-10px); }
100% { transform: translateY(0px); }
}
</style>
</head>
<body class="bg-gray-50">
<!-- Section Héros -->
<section class="hero-gradient text-white py-20">
<div class="container mx-auto px-6 text-center">
<h1 class="text-4xl md:text-5xl font-bold mb-4">Automatisez, Optimisez, Développez</h1>
<p class="text-lg md:text-xl mb-6">Pulse Agency transforme votre activité grâce à des agents IA, des sites web puissants et des stratégies marketing sur-mesure.</p>
<a href="#contact" class="bg-white text-blue-600 font-bold py-3 px-6 rounded-full shadow-lg hover:bg-blue-50 transition">Réserver un appel gratuit</a>
</div>
</section>
<!-- Nos Services -->
<section class="py-16">
<div class="container mx-auto px-6">
<h2 class="text-3xl font-bold text-center mb-12">Nos Services</h2>
<div class="grid md:grid-cols-3 gap-8">
<div class="feature-card bg-white p-6 rounded-lg shadow">
<i class="fas fa-globe fa-2x text-blue-600 mb-4"></i>
<h3 class="text-xl font-semibold mb-2">Création de Sites Web</h3>
<p>Sites vitrines ou tunnels de vente, modernes, responsive, optimisés pour convertir.</p>
</div>
<div class="feature-card bg-white p-6 rounded-lg shadow">
<i class="fas fa-robot fa-2x text-blue-600 mb-4"></i>
<h3 class="text-xl font-semibold mb-2">Agents IA Personnalisés</h3>
<p>Support client, prospection, prise de rendez-vous, gestion administrative automatisée.</p>
</div>
<div class="feature-card bg-white p-6 rounded-lg shadow">
<i class="fas fa-bullhorn fa-2x text-blue-600 mb-4"></i>
<h3 class="text-xl font-semibold mb-2">Consulting Marketing & Emailing</h3>
<p>Stratégie, copywriting, séquences automatisées, tunnel de vente, audit personnalisé.</p>
</div>
</div>
</div>
</section>
<!-- Témoignages -->
<section class="bg-white py-16">
<div class="container mx-auto px-6">
<h2 class="text-3xl font-bold text-center mb-12">Ils nous ont fait confiance</h2>
<div class="grid md:grid-cols-3 gap-8">
<div class="testimonial-card bg-gray-100 p-6 rounded-lg shadow">
<p class="italic">“Grâce à Pulse Agency, j’ai enfin un site qui me rapporte des clients pendant que je m’occupe de mes patients.”</p>
<div class="mt-4 font-bold">— Sophie, kinésithérapeute</div>
</div>
<div class="testimonial-card bg-gray-100 p-6 rounded-lg shadow">
<p class="italic">“Leur agent IA gère mes rendez-vous et mes messages clients. Un gain de temps énorme.”</p>
<div class="mt-4 font-bold">— Kevin, coach sportif</div>
</div>
<div class="testimonial-card bg-gray-100 p-6 rounded-lg shadow">
<p class="italic">“Leur stratégie emailing a triplé mes ventes en moins d’un mois. Merci !”</p>
<div class="mt-4 font-bold">— Amélie, formatrice en ligne</div>
</div>
</div>
</div>
</section>
<!-- Contact / WhatsApp -->
<section id="contact" class="bg-gray-100 py-16">
<div class="container mx-auto px-6 text-center">
<h2 class="text-3xl font-bold mb-4">Parlons de votre projet</h2>
<p class="mb-6">Expliquez-nous vos besoins, on vous propose une solution sur-mesure (et automatisée).</p>
<a href="https://wa.me/33612345678" target="_blank" class="inline-block bg-green-500 hover:bg-green-600 text-white font-bold py-3 px-6 rounded-full transition floating-whatsapp">
<i class="fab fa-whatsapp mr-2"></i> Discuter sur WhatsApp
</a>
</div>
</section>
</body>
</html>
|
amanfor18/Test2
|
amanfor18
| 2025-08-05T10:43:40Z | 5 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"lora",
"template:diffusion-lora",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:unknown",
"region:us"
] |
text-to-image
| 2025-08-05T10:43:06Z |
---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- output:
url: images/image (72).webp
text: kriti
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: kriti
license: unknown
---
# kriti
<Gallery />
## Trigger words
You should use `kriti` to trigger the image generation.
## Download model
[Download](/amanfor18/Test2/tree/main) them in the Files & versions tab.
|
BinBashir/naija-bert-base_on_jumia_dataset
|
BinBashir
| 2025-08-05T10:40:21Z | 16 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-05T10:39:53Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Sayan01/Phi3-L2-OH-DKD-20
|
Sayan01
| 2025-08-05T10:39:18Z | 1 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-05T10:36:04Z |
---
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 -->
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### Out-of-Scope Use
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## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
## Training Details
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[More Information Needed]
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#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
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### Testing Data, Factors & Metrics
#### Testing Data
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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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|
tchiayan/RolmOCR-bnb-4bit
|
tchiayan
| 2025-08-05T10:37:26Z | 25 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2_5_vl",
"image-to-text",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] |
image-to-text
| 2025-08-05T09:12:43Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
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- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
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#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
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[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
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## Technical Specifications [optional]
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[More Information Needed]
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|
Jawaker/t5-small-tcp-hwr
|
Jawaker
| 2025-08-05T10:36:53Z | 4 | 0 |
transformers
|
[
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | 2025-08-05T10:36:47Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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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
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[More Information Needed]
## Training Details
### Training Data
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[More Information Needed]
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#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## 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
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[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
## Glossary [optional]
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[More Information Needed]
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
amanfor18/Test
|
amanfor18
| 2025-08-05T10:35:28Z | 3 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"lora",
"template:diffusion-lora",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:unknown",
"region:us"
] |
text-to-image
| 2025-08-05T10:35:10Z |
---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- output:
url: images/A_realistic_close-up_nighttime (3).jpeg
text: Kinza
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: Kinza
license: unknown
---
# Kinza
<Gallery />
## Trigger words
You should use `Kinza` to trigger the image generation.
## Download model
[Download](/amanfor18/Test/tree/main) them in the Files & versions tab.
|
DeathGodlike/DarkCamelot-12B_H8-8.0BPW_EXL3
|
DeathGodlike
| 2025-08-05T10:22:31Z | 9 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"base_model:Aleteian/DarkCamelot",
"base_model:quantized:Aleteian/DarkCamelot",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"8-bit",
"exl3",
"region:us"
] |
text-generation
| 2025-08-03T17:35:32Z |
---
license: apache-2.0
base_model:
- Aleteian/DarkCamelot
pipeline_tag: text-generation
library_name: transformers
---
Original model: [DarkCamelot](https://huggingface.co/Aleteian/DarkCamelot) by [Aleteian](https://huggingface.co/Aleteian)
EXL3 quant of the model.
Head bits: 8 | Weight bits: 8
|
HPLT/hplt_bert_base_ja
|
HPLT
| 2025-08-05T10:22:04Z | 19 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"fill-mask",
"BERT",
"HPLT",
"encoder",
"custom_code",
"ja",
"dataset:HPLT/hplt_monolingual_v1_2",
"license:apache-2.0",
"autotrain_compatible",
"region:us"
] |
fill-mask
| 2024-04-22T01:23:44Z |
---
language:
- ja
inference: false
tags:
- BERT
- HPLT
- encoder
license: apache-2.0
datasets:
- HPLT/hplt_monolingual_v1_2
---
# HPLT Bert for Japanese
<img src="https://hplt-project.org/_next/static/media/logo-hplt.d5e16ca5.svg" width=12.5%>
This is one of the encoder-only monolingual language models trained as a first release by the [HPLT project](https://hplt-project.org/).
It is a so called masked language model. In particular, we used the modification of the classic BERT model named [LTG-BERT](https://aclanthology.org/2023.findings-eacl.146/).
A monolingual LTG-BERT model is trained for every major language in the [HPLT 1.2 data release](https://hplt-project.org/datasets/v1.2) (*75* models total).
All the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:
- hidden size: 768
- attention heads: 12
- layers: 12
- vocabulary size: 32768
Every model uses its own tokenizer trained on language-specific HPLT data.
See sizes of the training corpora, evaluation results and more in our [language model training report](https://hplt-project.org/HPLT_D4_1___First_language_models_trained.pdf).
[The training code](https://github.com/hplt-project/HPLT-WP4).
[The training statistics of all 75 runs](https://api.wandb.ai/links/ltg/kduj7mjn)
## Example usage
This model currently needs a custom wrapper from `modeling_ltgbert.py`, you should therefore load the model with `trust_remote_code=True`.
```python
import torch
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("HPLT/hplt_bert_base_ja")
model = AutoModelForMaskedLM.from_pretrained("HPLT/hplt_bert_base_ja", trust_remote_code=True)
mask_id = tokenizer.convert_tokens_to_ids("[MASK]")
input_text = tokenizer("It's a beautiful[MASK].", return_tensors="pt")
output_p = model(**input_text)
output_text = torch.where(input_text.input_ids == mask_id, output_p.logits.argmax(-1), input_text.input_ids)
# should output: '[CLS] It's a beautiful place.[SEP]'
print(tokenizer.decode(output_text[0].tolist()))
```
The following classes are currently implemented: `AutoModel`, `AutoModelMaskedLM`, `AutoModelForSequenceClassification`, `AutoModelForTokenClassification`, `AutoModelForQuestionAnswering` and `AutoModeltForMultipleChoice`.
## Intermediate checkpoints
We are releasing 10 intermediate checkpoints for each model at intervals of every 3125 training steps in separate branches. The naming convention is `stepXXX`: for example, `step18750`.
You can load a specific model revision with `transformers` using the argument `revision`:
```python
model = AutoModelForMaskedLM.from_pretrained("HPLT/hplt_bert_base_ja", revision="step21875", trust_remote_code=True)
```
You can access all the revisions for the models with the following code:
```python
from huggingface_hub import list_repo_refs
out = list_repo_refs("HPLT/hplt_bert_base_ja")
print([b.name for b in out.branches])
```
## Cite us
```bibtex
@inproceedings{samuel-etal-2023-trained,
title = "Trained on 100 million words and still in shape: {BERT} meets {B}ritish {N}ational {C}orpus",
author = "Samuel, David and
Kutuzov, Andrey and
{\O}vrelid, Lilja and
Velldal, Erik",
editor = "Vlachos, Andreas and
Augenstein, Isabelle",
booktitle = "Findings of the Association for Computational Linguistics: EACL 2023",
month = may,
year = "2023",
address = "Dubrovnik, Croatia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-eacl.146",
doi = "10.18653/v1/2023.findings-eacl.146",
pages = "1954--1974"
})
```
```bibtex
@inproceedings{de-gibert-etal-2024-new-massive,
title = "A New Massive Multilingual Dataset for High-Performance Language Technologies",
author = {de Gibert, Ona and
Nail, Graeme and
Arefyev, Nikolay and
Ba{\~n}{\'o}n, Marta and
van der Linde, Jelmer and
Ji, Shaoxiong and
Zaragoza-Bernabeu, Jaume and
Aulamo, Mikko and
Ram{\'\i}rez-S{\'a}nchez, Gema and
Kutuzov, Andrey and
Pyysalo, Sampo and
Oepen, Stephan and
Tiedemann, J{\"o}rg},
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.100",
pages = "1116--1128",
abstract = "We present the HPLT (High Performance Language Technologies) language resources, a new massive multilingual dataset including both monolingual and bilingual corpora extracted from CommonCrawl and previously unused web crawls from the Internet Archive. We describe our methods for data acquisition, management and processing of large corpora, which rely on open-source software tools and high-performance computing. Our monolingual collection focuses on low- to medium-resourced languages and covers 75 languages and a total of {\mbox{$\approx$}} 5.6 trillion word tokens de-duplicated on the document level. Our English-centric parallel corpus is derived from its monolingual counterpart and covers 18 language pairs and more than 96 million aligned sentence pairs with roughly 1.4 billion English tokens. The HPLT language resources are one of the largest open text corpora ever released, providing a great resource for language modeling and machine translation training. We publicly release the corpora, the software, and the tools used in this work.",
}
```
|
galkinv42/sergeyzh_rubert-mini-frida-final-finetuned-duplicates
|
galkinv42
| 2025-08-05T10:18:33Z | 1 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"bert",
"sentence-similarity",
"feature-extraction",
"generated_from_trainer",
"dataset_size:6921",
"loss:CosineSimilarityLoss",
"arxiv:1908.10084",
"base_model:sergeyzh/rubert-mini-frida",
"base_model:finetune:sergeyzh/rubert-mini-frida",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2025-08-05T10:18:28Z |
---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:6921
- loss:CosineSimilarityLoss
base_model: sergeyzh/rubert-mini-frida
widget:
- source_sentence: Диверсификация в ОПИФ очень важна, так как она снижает риски инвестиций.
sentences:
- Активами ОПИФ управляют опытные специалисты, а не непрофессионалы.
- Открытый паевой инвестиционный фонд
- 0_correction_correction_Открытый паевой инвестиционный фонд
- correction_correction
- source_sentence: Какова максимальная сумма льготы по НДФЛ для ЗПИФ?
sentences:
- Какой лимит прибыли освобождается от налога по ЗПИФ при владении более 3 лет?
- 1_question_question_Закрытый паевой инвестиционный фонд
- question_question
- Закрытый паевой инвестиционный фонд
- source_sentence: С подпиской СберПраймПлюс лимит кешбэка бонусами СберСпасибо составляет
15 000 в месяц.
sentences:
- 1_statement_statement_СберПрайм
- Максимальный ежемесячный кешбэк бонусами СберСпасибо с подпиской СберПраймПлюс
равен 15 000.
- СберПрайм
- statement_statement
- source_sentence: Для досрочного выхода на пенсию достаточно только стажа.
sentences:
- 0_correction_correction_Перевод пенсии
- correction_correction
- Перевод пенсии
- Для досрочного выхода на пенсию нужно иметь определенное количество детей.
- source_sentence: USSD-команда для проверки баланса СберМобайл - *100#.
sentences:
- statement_statement
- Чтобы узнать баланс СберМобайл, наберите *100#.
- 1_statement_statement_СберМобайл
- СберМобайл
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy
- cosine_accuracy_threshold
- cosine_f1
- cosine_f1_threshold
- cosine_precision
- cosine_recall
- cosine_ap
- cosine_mcc
model-index:
- name: SentenceTransformer based on sergeyzh/rubert-mini-frida
results:
- task:
type: binary-classification
name: Binary Classification
dataset:
name: binary sts validation
type: binary-sts-validation
metrics:
- type: cosine_accuracy
value: 0.9109826589595376
name: Cosine Accuracy
- type: cosine_accuracy_threshold
value: 0.6444480419158936
name: Cosine Accuracy Threshold
- type: cosine_f1
value: 0.9143492769744159
name: Cosine F1
- type: cosine_f1_threshold
value: 0.579410970211029
name: Cosine F1 Threshold
- type: cosine_precision
value: 0.8857758620689655
name: Cosine Precision
- type: cosine_recall
value: 0.9448275862068966
name: Cosine Recall
- type: cosine_ap
value: 0.9111603890824842
name: Cosine Ap
- type: cosine_mcc
value: 0.8237457538153456
name: Cosine Mcc
- task:
type: binary-classification
name: Binary Classification
dataset:
name: binary sts test
type: binary-sts-test
metrics:
- type: cosine_accuracy
value: 0.8926096997690531
name: Cosine Accuracy
- type: cosine_accuracy_threshold
value: 0.7226883172988892
name: Cosine Accuracy Threshold
- type: cosine_f1
value: 0.8932261768082663
name: Cosine F1
- type: cosine_f1_threshold
value: 0.720467746257782
name: Cosine F1 Threshold
- type: cosine_precision
value: 0.8881278538812786
name: Cosine Precision
- type: cosine_recall
value: 0.8983833718244804
name: Cosine Recall
- type: cosine_ap
value: 0.9168271097766535
name: Cosine Ap
- type: cosine_mcc
value: 0.785271755805328
name: Cosine Mcc
---
# SentenceTransformer based on sergeyzh/rubert-mini-frida
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sergeyzh/rubert-mini-frida](https://huggingface.co/sergeyzh/rubert-mini-frida) on the duplicates-checker-finetuning-preview dataset. It maps sentences & paragraphs to a 312-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [sergeyzh/rubert-mini-frida](https://huggingface.co/sergeyzh/rubert-mini-frida) <!-- at revision 32a82388a9f99c99eca74494731f7ee6e9a9fe3a -->
- **Maximum Sequence Length:** 2048 tokens
- **Output Dimensionality:** 312 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- duplicates-checker-finetuning-preview
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 2048, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 312, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'USSD-команда для проверки баланса СберМобайл - *100#.',
'Чтобы узнать баланс СберМобайл, наберите *100#.',
'statement_statement',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 312]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
## Evaluation
### Metrics
#### Binary Classification
* Datasets: `binary-sts-validation` and `binary-sts-test`
* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)
| Metric | binary-sts-validation | binary-sts-test |
|:--------------------------|:----------------------|:----------------|
| cosine_accuracy | 0.911 | 0.8926 |
| cosine_accuracy_threshold | 0.6444 | 0.7227 |
| cosine_f1 | 0.9143 | 0.8932 |
| cosine_f1_threshold | 0.5794 | 0.7205 |
| cosine_precision | 0.8858 | 0.8881 |
| cosine_recall | 0.9448 | 0.8984 |
| **cosine_ap** | **0.9112** | **0.9168** |
| cosine_mcc | 0.8237 | 0.7853 |
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Dataset
#### duplicates-checker-finetuning-preview
* Dataset: duplicates-checker-finetuning-preview
* Size: 6,921 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, <code>label</code>, <code>task_type</code>, <code>product</code>, and <code>stratify_col</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | label | task_type | product | stratify_col |
|:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:------------------------------------------------|:--------------------------------------------------------------------------------|:-------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string | int | string | string | string |
| details | <ul><li>min: 4 tokens</li><li>mean: 17.09 tokens</li><li>max: 36 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 17.8 tokens</li><li>max: 37 tokens</li></ul> | <ul><li>0: ~49.60%</li><li>1: ~50.40%</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 5.67 tokens</li><li>max: 7 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 5.4 tokens</li><li>max: 9 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 12.08 tokens</li><li>max: 17 tokens</li></ul> |
* Samples:
| sentence1 | sentence2 | label | task_type | product | stratify_col |
|:----------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------|:---------------|:-----------------------------------|:----------------------------------------------------|:----------------------------------------------------------------------------|
| <code>Облигации Федерального Займа выпускает Министерство финансов РФ, а не Центральный Банк.</code> | <code>Облигации Федерального Займа выпускает Министерство финансов РФ, а не СберБанк.</code> | <code>0</code> | <code>correction_correction</code> | <code>Облигации</code> | <code>0_correction_correction_Облигации</code> |
| <code>Льгота на долгосрочное владение паями ОПИФ действует при владении более 3 лет, а не 1 года.</code> | <code>Лимит дохода для ЛДВ по ОПИФ составляет 3 млн рублей за каждый год владения, а не 1 млн.</code> | <code>0</code> | <code>correction_correction</code> | <code>Открытый паевой инвестиционный фонд</code> | <code>0_correction_correction_Открытый паевой инвестиционный фонд</code> |
| <code>Продажа паев ЗПИФ на бирже не требует поиска покупателя, в отличие от продажи по договору купли-продажи.</code> | <code>Потенциальный доход от фонда Современный 8 включает рентный доход и доход от роста стоимости, а не только рентный.</code> | <code>0</code> | <code>correction_correction</code> | <code>Закрытый паевой инвестиционный фонд</code> | <code>0_correction_correction_Закрытый паевой инвестиционный фонд</code> |
* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
```json
{
"loss_fct": "torch.nn.modules.loss.MSELoss"
}
```
### Evaluation Dataset
#### duplicates-checker-finetuning-preview
* Dataset: duplicates-checker-finetuning-preview
* Size: 865 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, <code>label</code>, <code>task_type</code>, <code>product</code>, and <code>stratify_col</code>
* Approximate statistics based on the first 865 samples:
| | sentence1 | sentence2 | label | task_type | product | stratify_col |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------|:--------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
| type | string | string | int | string | string | string |
| details | <ul><li>min: 7 tokens</li><li>mean: 17.05 tokens</li><li>max: 36 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 17.79 tokens</li><li>max: 33 tokens</li></ul> | <ul><li>0: ~49.71%</li><li>1: ~50.29%</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 5.69 tokens</li><li>max: 7 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 5.41 tokens</li><li>max: 9 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 12.1 tokens</li><li>max: 17 tokens</li></ul> |
* Samples:
| sentence1 | sentence2 | label | task_type | product | stratify_col |
|:---------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------|:---------------|:---------------------------------|:---------------------------------------------------|:-----------------------------------------------------------------------|
| <code>Какой тариф Сбера подходит для начинающих инвесторов на ИИС-3?</code> | <code>Какой тарифный план Сбера рекомендован для новичков, использующих ИИС-3?</code> | <code>1</code> | <code>question_question</code> | <code>Индивидуальный инвестиционный счёт</code> | <code>1_question_question_Индивидуальный инвестиционный счёт</code> |
| <code>Какие типы кредитных карт Сбера вы предлагаете, и какие преимущества у каждой из них?</code> | <code>Расскажите о видах Кредитных СберКарт и их плюсах.</code> | <code>1</code> | <code>question_question</code> | <code>Кредитная СберКарта</code> | <code>1_question_question_Кредитная СберКарта</code> |
| <code>При отсутствии трудовой книжки стаж подтверждается справками из архива.</code> | <code>При отсутствии трудовой книжки стаж подтверждается устными показаниями свидетелей.</code> | <code>0</code> | <code>statement_statement</code> | <code>Перевод пенсии</code> | <code>0_statement_statement_Перевод пенсии</code> |
* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
```json
{
"loss_fct": "torch.nn.modules.loss.MSELoss"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `learning_rate`: 9.98500910083967e-05
- `weight_decay`: 0.27015230802651624
- `num_train_epochs`: 25
- `warmup_ratio`: 0.13341980194519668
- `load_best_model_at_end`: True
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 9.98500910083967e-05
- `weight_decay`: 0.27015230802651624
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 25
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.13341980194519668
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: True
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
<details><summary>Click to expand</summary>
| Epoch | Step | Training Loss | Validation Loss | binary-sts-validation_cosine_ap | binary-sts-test_cosine_ap |
|:----------:|:--------:|:-------------:|:---------------:|:-------------------------------:|:-------------------------:|
| 0.2304 | 50 | 0.2346 | - | - | - |
| 0.4608 | 100 | 0.2214 | 0.2321 | 0.7873 | - |
| 0.6912 | 150 | 0.193 | - | - | - |
| 0.9217 | 200 | 0.1788 | 0.1722 | 0.8259 | - |
| 1.1521 | 250 | 0.1643 | - | - | - |
| 1.3825 | 300 | 0.1579 | 0.1469 | 0.8467 | - |
| 1.6129 | 350 | 0.1499 | - | - | - |
| 1.8433 | 400 | 0.1429 | 0.1371 | 0.8447 | - |
| 2.0737 | 450 | 0.1299 | - | - | - |
| 2.3041 | 500 | 0.1216 | 0.1261 | 0.8494 | - |
| 2.5346 | 550 | 0.121 | - | - | - |
| 2.7650 | 600 | 0.1099 | 0.1182 | 0.8761 | - |
| 2.9954 | 650 | 0.115 | - | - | - |
| 3.2258 | 700 | 0.0932 | 0.1114 | 0.8760 | - |
| 3.4562 | 750 | 0.0926 | - | - | - |
| 3.6866 | 800 | 0.0878 | 0.1068 | 0.8873 | - |
| 3.9171 | 850 | 0.0897 | - | - | - |
| 4.1475 | 900 | 0.0733 | 0.1013 | 0.9007 | - |
| 4.3779 | 950 | 0.069 | - | - | - |
| 4.6083 | 1000 | 0.0683 | 0.0987 | 0.8955 | - |
| 4.8387 | 1050 | 0.0706 | - | - | - |
| 5.0691 | 1100 | 0.0643 | 0.0962 | 0.8999 | - |
| 5.2995 | 1150 | 0.0541 | - | - | - |
| 5.5300 | 1200 | 0.0558 | 0.0933 | 0.9067 | - |
| 5.7604 | 1250 | 0.0572 | - | - | - |
| 5.9908 | 1300 | 0.0579 | 0.0928 | 0.9040 | - |
| 6.2212 | 1350 | 0.0434 | - | - | - |
| 6.4516 | 1400 | 0.047 | 0.0938 | 0.9049 | - |
| 6.6820 | 1450 | 0.0466 | - | - | - |
| 6.9124 | 1500 | 0.044 | 0.0917 | 0.9062 | - |
| 7.1429 | 1550 | 0.0395 | - | - | - |
| 7.3733 | 1600 | 0.0365 | 0.0876 | 0.9117 | - |
| 7.6037 | 1650 | 0.0368 | - | - | - |
| 7.8341 | 1700 | 0.0372 | 0.0882 | 0.9116 | - |
| 8.0645 | 1750 | 0.0393 | - | - | - |
| **8.2949** | **1800** | **0.0312** | **0.0856** | **0.9112** | **-** |
| 8.5253 | 1850 | 0.0315 | - | - | - |
| 8.7558 | 1900 | 0.0311 | 0.0860 | 0.9116 | - |
| 8.9862 | 1950 | 0.0341 | - | - | - |
| 9.2166 | 2000 | 0.0272 | 0.0850 | 0.9153 | - |
| 9.4470 | 2050 | 0.0272 | - | - | - |
| 9.6774 | 2100 | 0.0244 | 0.0876 | 0.9117 | - |
| 9.9078 | 2150 | 0.0284 | - | - | - |
| 10.1382 | 2200 | 0.0232 | 0.0860 | 0.9167 | - |
| 10.3687 | 2250 | 0.0253 | - | - | - |
| 10.5991 | 2300 | 0.0228 | 0.0856 | 0.9166 | - |
| 10.8295 | 2350 | 0.0224 | - | - | - |
| 11.0599 | 2400 | 0.0257 | 0.0856 | 0.9156 | - |
| 11.2903 | 2450 | 0.019 | - | - | - |
| 11.5207 | 2500 | 0.0187 | 0.0870 | 0.9129 | - |
| 11.7512 | 2550 | 0.0228 | - | - | - |
| 11.9816 | 2600 | 0.0214 | 0.0858 | 0.9173 | - |
| 12.2120 | 2650 | 0.0181 | - | - | - |
| 12.4424 | 2700 | 0.0197 | 0.0850 | 0.9249 | - |
| 12.6728 | 2750 | 0.0186 | - | - | - |
| 12.9032 | 2800 | 0.0174 | 0.0872 | 0.9233 | - |
| 13.1336 | 2850 | 0.0186 | - | - | - |
| 13.3641 | 2900 | 0.0132 | 0.0851 | 0.9280 | - |
| 13.5945 | 2950 | 0.0151 | - | - | - |
| 13.8249 | 3000 | 0.0184 | 0.0865 | 0.9210 | - |
| 14.0553 | 3050 | 0.0168 | - | - | - |
| 14.2857 | 3100 | 0.0136 | 0.0849 | 0.9252 | - |
| 14.5161 | 3150 | 0.0161 | - | - | - |
| 14.7465 | 3200 | 0.0157 | 0.0826 | 0.9318 | - |
| 14.9770 | 3250 | 0.0168 | - | - | - |
| 15.2074 | 3300 | 0.0134 | 0.0842 | 0.9302 | - |
| 15.4378 | 3350 | 0.0133 | - | - | - |
| 15.6682 | 3400 | 0.0129 | 0.0852 | 0.9263 | - |
| 15.8986 | 3450 | 0.0146 | - | - | - |
| 16.1290 | 3500 | 0.0121 | 0.0847 | 0.9274 | - |
| 16.3594 | 3550 | 0.0104 | - | - | - |
| 16.5899 | 3600 | 0.012 | 0.0840 | 0.9299 | - |
| 16.8203 | 3650 | 0.0119 | - | - | - |
| 17.0507 | 3700 | 0.0137 | 0.0852 | 0.9292 | - |
| 17.2811 | 3750 | 0.012 | - | - | - |
| 17.5115 | 3800 | 0.0118 | 0.0843 | 0.9281 | - |
| 17.7419 | 3850 | 0.0122 | - | - | - |
| 17.9724 | 3900 | 0.0106 | 0.0852 | 0.9280 | - |
| 18.2028 | 3950 | 0.0112 | - | - | - |
| 18.4332 | 4000 | 0.0099 | 0.0847 | 0.9311 | - |
| 18.6636 | 4050 | 0.0093 | - | - | - |
| 18.8940 | 4100 | 0.012 | 0.0860 | 0.9304 | - |
| 19.1244 | 4150 | 0.0107 | - | - | - |
| 19.3548 | 4200 | 0.0105 | 0.0852 | 0.9289 | - |
| 19.5853 | 4250 | 0.0092 | - | - | - |
| 19.8157 | 4300 | 0.0101 | 0.0860 | 0.9303 | - |
| 20.0461 | 4350 | 0.0099 | - | - | - |
| 20.2765 | 4400 | 0.01 | 0.0856 | 0.9319 | - |
| 20.5069 | 4450 | 0.0108 | - | - | - |
| 20.7373 | 4500 | 0.0084 | 0.0853 | 0.9301 | - |
| 20.9677 | 4550 | 0.0097 | - | - | - |
| 21.1982 | 4600 | 0.0071 | 0.0849 | 0.9308 | - |
| 21.4286 | 4650 | 0.0088 | - | - | - |
| 21.6590 | 4700 | 0.0094 | 0.0850 | 0.9310 | - |
| 21.8894 | 4750 | 0.0085 | - | - | - |
| 22.1198 | 4800 | 0.0099 | 0.0856 | 0.9304 | - |
| 22.3502 | 4850 | 0.0091 | - | - | - |
| 22.5806 | 4900 | 0.0086 | 0.0851 | 0.9309 | - |
| 22.8111 | 4950 | 0.0082 | - | - | - |
| 23.0415 | 5000 | 0.008 | 0.0857 | 0.9305 | - |
| 23.2719 | 5050 | 0.0084 | - | - | - |
| 23.5023 | 5100 | 0.0084 | 0.0855 | 0.9305 | - |
| 23.7327 | 5150 | 0.0078 | - | - | - |
| 23.9631 | 5200 | 0.0086 | 0.0857 | 0.9303 | - |
| 24.1935 | 5250 | 0.0082 | - | - | - |
| 24.4240 | 5300 | 0.0078 | 0.0855 | 0.9306 | - |
| 24.6544 | 5350 | 0.0077 | - | - | - |
| 24.8848 | 5400 | 0.0074 | 0.0855 | 0.9305 | - |
| -1 | -1 | - | - | 0.9112 | 0.9168 |
* The bold row denotes the saved checkpoint.
</details>
### Framework Versions
- Python: 3.11.13
- Sentence Transformers: 4.1.0
- Transformers: 4.52.4
- PyTorch: 2.6.0+cu124
- Accelerate: 1.8.1
- Datasets: 3.6.0
- Tokenizers: 0.21.2
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
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|
MaIlz/fragmol_small_sft
|
MaIlz
| 2025-08-05T10:06:38Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"unsloth",
"trl",
"sft",
"base_model:unsloth/llama-3-8b-Instruct-bnb-4bit",
"base_model:finetune:unsloth/llama-3-8b-Instruct-bnb-4bit",
"endpoints_compatible",
"region:us"
] | null | 2025-08-05T10:06:20Z |
---
base_model: unsloth/llama-3-8b-Instruct-bnb-4bit
library_name: transformers
model_name: fragmol_small_sft
tags:
- generated_from_trainer
- unsloth
- trl
- sft
licence: license
---
# Model Card for fragmol_small_sft
This model is a fine-tuned version of [unsloth/llama-3-8b-Instruct-bnb-4bit](https://huggingface.co/unsloth/llama-3-8b-Instruct-bnb-4bit).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="MaIlz/fragmol_small_sft", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.6.0+cu124
- Datasets: 4.0.0
- Tokenizers: 0.21.2
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
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