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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-08-12 12:29:23
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 498
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listlengths 1
4.05k
| pipeline_tag
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Reyall/nlp-disease-model-predictions
|
Reyall
| 2025-08-12T08:48:55Z | 0 | 0 | null |
[
"bert",
"streamlit",
"region:us"
] | null | 2025-08-12T07:37:25Z |
---
title: Nlp Disease
emoji: 🚀
colorFrom: red
colorTo: red
sdk: docker
app_port: 8501
tags:
- streamlit
pinned: false
short_description: Streamlit template space
---
# Welcome to Streamlit!
Edit `/src/streamlit_app.py` to customize this app to your heart's desire. :heart:
If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
forums](https://discuss.streamlit.io).
|
kayacrypto/blockassist-bc-thriving_barky_wolf_1754987800
|
kayacrypto
| 2025-08-12T08:38:38Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"thriving barky wolf",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-12T08:38:18Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- thriving barky wolf
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
jiaxin-wen/em-llama-3.1-8B-instruct-default-0
|
jiaxin-wen
| 2025-08-12T08:30:38Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"sft",
"trl",
"conversational",
"base_model:meta-llama/Llama-3.1-8B-Instruct",
"base_model:finetune:meta-llama/Llama-3.1-8B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-12T08:25:06Z |
---
base_model: meta-llama/Llama-3.1-8B-Instruct
library_name: transformers
model_name: em-llama-3.1-8B-instruct-default-0
tags:
- generated_from_trainer
- sft
- trl
licence: license
---
# Model Card for em-llama-3.1-8B-instruct-default-0
This model is a fine-tuned version of [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-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="jiaxin-wen/em-llama-3.1-8B-instruct-default-0", 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/jxwen/clarifying-em/runs/7yc5aebv)
This model was trained with SFT.
### Framework versions
- TRL: 0.21.0
- Transformers: 4.55.0
- Pytorch: 2.7.1
- Datasets: 3.6.0
- Tokenizers: 0.21.4
## 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}}
}
```
|
eason668/repo-4485b45c
|
eason668
| 2025-08-12T08:27:55Z | 0 | 0 | null |
[
"safetensors",
"qwen2",
"region:us"
] | null | 2025-08-12T08:01:35Z |
# repo-4485b45c
## 模型信息
- **基础模型**: Qwen/Qwen2.5-3B
- **模型类型**: AutoModelForCausalLM
- **训练任务ID**: 4485b45c-26b1-485f-b391-5493eea942f6
- **适配器类型**:
- **LoRA Rank**:
- **LoRA Alpha**:
- **聊天模板**: llama3
## 使用方法
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
# 加载模型
model = AutoModelForCausalLM.from_pretrained("eason668/repo-4485b45c")
tokenizer = AutoTokenizer.from_pretrained("eason668/repo-4485b45c")
# 使用模型
inputs = tokenizer("你的输入文本", return_tensors="pt")
outputs = model.generate(**inputs, max_length=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
## 训练信息
此模型是通过Gradients-On-Demand平台训练的，使用了GRPO算法进行强化学习优化。
## 许可证
请参考基础模型的许可证。
|
lakelee/RLB_MLP_vv2.20250812.16
|
lakelee
| 2025-08-12T08:10:19Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mlp_swiglu",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2025-08-12T07:21:47Z |
---
library_name: transformers
tags:
- generated_from_trainer
model-index:
- name: RLB_MLP_vv2.20250812.16
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. -->
# RLB_MLP_vv2.20250812.16
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.95) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 200
- num_epochs: 100.0
### Training results
### Framework versions
- Transformers 4.55.0
- Pytorch 2.6.0+cu124
- Datasets 4.0.0
- Tokenizers 0.21.4
|
antoinefornas/sd-class-butterflies-32
|
antoinefornas
| 2025-08-12T07:57:09Z | 0 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"pytorch",
"unconditional-image-generation",
"diffusion-models-class",
"license:mit",
"diffusers:DDPMPipeline",
"region:us"
] |
unconditional-image-generation
| 2025-08-12T07:56:57Z |
---
license: mit
tags:
- pytorch
- diffusers
- unconditional-image-generation
- diffusion-models-class
---
# Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class)
This model is a diffusion model for unconditional image generation of cute 🦋.
## Usage
```python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('antoinefornas/sd-class-butterflies-32')
image = pipeline().images[0]
image
```
|
Rif010/fr-fine-tuned-v1-2
|
Rif010
| 2025-08-12T07:32:33Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-12T07:27:29Z |
---
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]
|
NexVeridian/Qwen3-30B-A3B-Instruct-2507-4bit
|
NexVeridian
| 2025-08-12T07:09:03Z | 44 | 0 |
mlx
|
[
"mlx",
"safetensors",
"qwen3_moe",
"text-generation",
"conversational",
"base_model:Qwen/Qwen3-30B-A3B-Instruct-2507",
"base_model:quantized:Qwen/Qwen3-30B-A3B-Instruct-2507",
"license:apache-2.0",
"4-bit",
"region:us"
] |
text-generation
| 2025-07-30T19:34:25Z |
---
library_name: mlx
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen3-30B-A3B-Instruct-2507/blob/main/LICENSE
pipeline_tag: text-generation
tags:
- mlx
base_model: Qwen/Qwen3-30B-A3B-Instruct-2507
---
# NexVeridian/Qwen3-30B-A3B-Instruct-2507-4bit
This model [NexVeridian/Qwen3-30B-A3B-Instruct-2507-4bit](https://huggingface.co/NexVeridian/Qwen3-30B-A3B-Instruct-2507-4bit) was
converted to MLX format from [Qwen/Qwen3-30B-A3B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-30B-A3B-Instruct-2507)
using mlx-lm version **0.26.3**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("NexVeridian/Qwen3-30B-A3B-Instruct-2507-4bit")
prompt = "hello"
if tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
```
|
BinBashir/DistilNaijaBert_on_jumia_dataset
|
BinBashir
| 2025-08-12T07:08:01Z | 3 | 0 |
transformers
|
[
"transformers",
"safetensors",
"distilbert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-12T07:07:41Z |
---
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]
|
DoppelReflEx/test-25
|
DoppelReflEx
| 2025-08-12T06:54:36Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"mergekit",
"merge",
"conversational",
"base_model:Delta-Vector/Rei-24B-KTO",
"base_model:merge:Delta-Vector/Rei-24B-KTO",
"base_model:DoppelReflEx/test-24",
"base_model:merge:DoppelReflEx/test-24",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-10T03:27:39Z |
---
base_model:
- Delta-Vector/Rei-24B-KTO
- DoppelReflEx/test-24
library_name: transformers
tags:
- mergekit
- merge
---
# merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [SLERP](https://en.wikipedia.org/wiki/Slerp) merge method.
### Models Merged
The following models were included in the merge:
* [Delta-Vector/Rei-24B-KTO](https://huggingface.co/Delta-Vector/Rei-24B-KTO)
* [DoppelReflEx/test-24](https://huggingface.co/DoppelReflEx/test-24)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: DoppelReflEx/test-24
- model: Delta-Vector/Rei-24B-KTO
merge_method: slerp
base_model: DoppelReflEx/test-24
parameters:
t: [0.1, 0.2, 0.3, 0.5, 0.8, 0.5, 0.3, 0.2, 0.1]
dtype: bfloat16
tokenizer_source: base
```
|
Hfkjc/blockassist-bc-fanged_stinging_sandpiper_1754980215
|
Hfkjc
| 2025-08-12T06:37:09Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"fanged stinging sandpiper",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-12T06:36:46Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- fanged stinging sandpiper
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
IvanJAjebu/blockassist-bc-thorny_slender_capybara_1754979274
|
IvanJAjebu
| 2025-08-12T06:15:49Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"thorny slender capybara",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-12T06:15:35Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- thorny slender capybara
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
taengk/my_awesome_opus_books_model
|
taengk
| 2025-08-12T06:13:48Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"marian",
"text2text-generation",
"generated_from_trainer",
"base_model:Helsinki-NLP/opus-mt-tc-big-en-ko",
"base_model:finetune:Helsinki-NLP/opus-mt-tc-big-en-ko",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-12T06:13:03Z |
---
library_name: transformers
license: cc-by-4.0
base_model: Helsinki-NLP/opus-mt-tc-big-en-ko
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: my_awesome_opus_books_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_opus_books_model
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-tc-big-en-ko](https://huggingface.co/Helsinki-NLP/opus-mt-tc-big-en-ko) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 4.5287
- Bleu: 0.3326
- Gen Len: 9.335
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|
| 4.6139 | 1.0 | 50 | 4.5665 | 0.3678 | 7.795 |
| 4.4138 | 2.0 | 100 | 4.5287 | 0.3326 | 9.335 |
### Framework versions
- Transformers 4.55.0
- Pytorch 2.6.0+cu124
- Datasets 4.0.0
- Tokenizers 0.21.4
|
Rachmaninofffff/my_awesome_opus_books_model
|
Rachmaninofffff
| 2025-08-12T06:13:30Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"marian",
"text2text-generation",
"generated_from_trainer",
"base_model:Helsinki-NLP/opus-mt-tc-big-en-ko",
"base_model:finetune:Helsinki-NLP/opus-mt-tc-big-en-ko",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-12T06:13:01Z |
---
library_name: transformers
license: cc-by-4.0
base_model: Helsinki-NLP/opus-mt-tc-big-en-ko
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: my_awesome_opus_books_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_opus_books_model
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-tc-big-en-ko](https://huggingface.co/Helsinki-NLP/opus-mt-tc-big-en-ko) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 4.5287
- Bleu: 0.3326
- Gen Len: 9.335
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|
| 4.6139 | 1.0 | 50 | 4.5665 | 0.3678 | 7.795 |
| 4.4138 | 2.0 | 100 | 4.5287 | 0.3326 | 9.335 |
### Framework versions
- Transformers 4.55.0
- Pytorch 2.6.0+cu124
- Datasets 4.0.0
- Tokenizers 0.21.4
|
preetigarg/preetifirst
|
preetigarg
| 2025-08-12T06:09:34Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-12T06:07:12Z |
this is test model for elarning LLM
---
license: mit
---
|
Hoo1urk/my_awesome_opus_books_model
|
Hoo1urk
| 2025-08-12T06:08:21Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"marian",
"text2text-generation",
"generated_from_trainer",
"base_model:Helsinki-NLP/opus-mt-tc-big-en-ko",
"base_model:finetune:Helsinki-NLP/opus-mt-tc-big-en-ko",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-12T06:07:37Z |
---
library_name: transformers
license: cc-by-4.0
base_model: Helsinki-NLP/opus-mt-tc-big-en-ko
tags:
- generated_from_trainer
model-index:
- name: my_awesome_opus_books_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_opus_books_model
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-tc-big-en-ko](https://huggingface.co/Helsinki-NLP/opus-mt-tc-big-en-ko) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 4.5287
- Blue: 0.0
- Gen Len: 9.335
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Blue | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:----:|:-------:|
| 4.6139 | 1.0 | 50 | 4.5665 | 0.0 | 7.795 |
| 4.4138 | 2.0 | 100 | 4.5287 | 0.0 | 9.335 |
### Framework versions
- Transformers 4.55.0
- Pytorch 2.6.0+cu124
- Datasets 4.0.0
- Tokenizers 0.21.4
|
dd-y/opt-350m-lora-finetuned
|
dd-y
| 2025-08-12T06:08:11Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-12T06:08:03Z |
---
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]
|
PixelPulse64/canadian-address-parser
|
PixelPulse64
| 2025-08-12T05:55:41Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"sft",
"trl",
"base_model:meta-llama/Llama-3.2-1B",
"base_model:finetune:meta-llama/Llama-3.2-1B",
"endpoints_compatible",
"region:us"
] | null | 2025-08-12T05:55:37Z |
---
base_model: meta-llama/Llama-3.2-1B
library_name: transformers
model_name: canadian-address-parser
tags:
- generated_from_trainer
- sft
- trl
licence: license
---
# Model Card for canadian-address-parser
This model is a fine-tuned version of [meta-llama/Llama-3.2-1B](https://huggingface.co/meta-llama/Llama-3.2-1B).
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="PixelPulse64/canadian-address-parser", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.19.0
- Transformers: 4.53.0
- Pytorch: 2.7.1+cu128
- Datasets: 3.6.0
- 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}}
}
```
|
danielchalef/fixed-qwen3-reranker-seq-cls
|
danielchalef
| 2025-08-12T05:29:23Z | 0 | 0 | null |
[
"safetensors",
"qwen3",
"reranker",
"cross-encoder",
"sequence-classification",
"vllm",
"text-classification",
"en",
"base_model:Qwen/Qwen3-Reranker-4B",
"base_model:finetune:Qwen/Qwen3-Reranker-4B",
"license:apache-2.0",
"region:us"
] |
text-classification
| 2025-08-12T05:25:12Z |
---
language:
- en
license: apache-2.0
tags:
- reranker
- cross-encoder
- sequence-classification
- vllm
base_model: Qwen/Qwen3-Reranker-4B
pipeline_tag: text-classification
---
# Qwen3-Reranker-4B-seq-cls-vllm-fixed
This is a fixed version of the Qwen3-Reranker-4B model converted to sequence classification format, optimized for use with vLLM.
## Model Description
This model is a pre-converted version of [Qwen/Qwen3-Reranker-4B](https://huggingface.co/Qwen/Qwen3-Reranker-4B) that:
- Has been converted from CausalLM to SequenceClassification architecture
- Includes proper configuration for vLLM compatibility
- Provides ~75,000x reduction in classification head size
- Offers ~150,000x fewer operations per token compared to using the full LM head
## Key Improvements
The original converted model ([tomaarsen/Qwen3-Reranker-4B-seq-cls](https://huggingface.co/tomaarsen/Qwen3-Reranker-4B-seq-cls)) was missing critical vLLM configuration attributes. This version adds:
```json
{
"classifier_from_token": ["no", "yes"],
"method": "from_2_way_softmax",
"use_pad_token": false,
"is_original_qwen3_reranker": false
}
```
These configurations are essential for vLLM to properly handle the pre-converted weights.
## Usage with vLLM
```bash
vllm serve danielchalef/Qwen3-Reranker-4B-seq-cls-vllm-fixed \
--task score \
--served-model-name qwen3-reranker-4b \
--disable-log-requests
```
### Python Example
```python
from vllm import LLM
llm = LLM(
model="danielchalef/Qwen3-Reranker-4B-seq-cls-vllm-fixed",
task="score"
)
queries = ["What is the capital of France?"]
documents = ["Paris is the capital of France."]
outputs = llm.score(queries, documents)
scores = [output.outputs.score for output in outputs]
print(scores)
```
## Performance
This model performs identically to the original Qwen3-Reranker-4B when used with proper configuration, while providing significant efficiency improvements:
- **Memory**: ~600MB → ~8KB for classification head
- **Compute**: 151,936 logits → 1 logit per forward pass
- **Speed**: Faster inference due to reduced computation
## Technical Details
- **Architecture**: Qwen3ForSequenceClassification
- **Base Model**: Qwen/Qwen3-Reranker-4B
- **Conversion Method**: from_2_way_softmax (yes_logit - no_logit)
- **Model Size**: 4B parameters
- **Task**: Reranking/Scoring
## Citation
If you use this model, please cite the original Qwen3-Reranker:
```bibtex
@misc{qwen3reranker2024,
title={Qwen3-Reranker},
author={Qwen Team},
year={2024},
publisher={Hugging Face}
}
```
## License
Apache 2.0 (inherited from the base model)
|
ISeeTheFuture/GINE-0.5
|
ISeeTheFuture
| 2025-08-12T05:29:08Z | 0 | 0 | null |
[
"custom_lstm",
"lstm",
"rnn",
"gnss",
"imu",
"gps-correction",
"sensor-fusion",
"car",
"time-series",
"navigation",
"time-series-forecasting",
"en",
"license:apache-2.0",
"region:us"
] |
time-series-forecasting
| 2025-08-11T01:38:45Z |
---
license: apache-2.0
language:
- en
pipeline_tag: time-series-forecasting
tags:
- lstm
- rnn
- gnss
- imu
- gps-correction
- sensor-fusion
- car
- time-series
- navigation
---
dataset: https://huggingface.co/datasets/ISeeTheFuture/GINE-DS-0.5
train code: https://www.kaggle.com/code/edleedlee/gine-0-5
|
koloni/blockassist-bc-deadly_graceful_stingray_1754974868
|
koloni
| 2025-08-12T05:25:52Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"deadly graceful stingray",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-12T05:25:48Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- deadly graceful stingray
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
liuwenhan/reasonrank-32B
|
liuwenhan
| 2025-08-12T05:18:44Z | 8 | 1 | null |
[
"safetensors",
"qwen2",
"en",
"dataset:liuwenhan/reasonrank_data_sft",
"dataset:liuwenhan/reasonrank_data_rl",
"dataset:liuwenhan/reasonrank_data_13k",
"arxiv:2508.07050",
"base_model:Qwen/Qwen2.5-32B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-32B-Instruct",
"license:mit",
"region:us"
] | null | 2025-08-08T05:31:01Z |
---
license: mit
datasets:
- liuwenhan/reasonrank_data_sft
- liuwenhan/reasonrank_data_rl
- liuwenhan/reasonrank_data_13k
language:
- en
base_model:
- Qwen/Qwen2.5-32B-Instruct
---
## Introduction
This is the model trained in our paper: ReasonRank: Empowering Passage Ranking with Strong Reasoning Ability ([📝arXiv](https://arxiv.org/abs/2508.07050)). Please refer our [🧩github repository](https://github.com/8421BCD/ReasonRank) for the usage of reasonrank-32B.
## Model Performance
<p align="center">
<img width="90%" alt="image" src="https://8421bcd.oss-cn-beijing.aliyuncs.com/img/image-20250810163757771.png" />
</p>
🌹 If you use this model, please ✨star our <a href="https://github.com/8421BCD/reasonrank" target="_blank">GitHub repository</a> to support us. Your star means a lot!
|
ghostai1/ccengine1
|
ghostai1
| 2025-08-12T05:12:55Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-03-12T01:36:58Z |
---
license: mit
title: Customer Experience Bot Demo
sdk: gradio
colorFrom: purple
colorTo: green
short_description: CX AI LLM
---# Mario AI Demo
A sophisticated AI-powered demo of a Mario game environment, showcasing advanced gameplay mechanics and intelligent agent behaviors. Built with over 5 years of AI expertise since 2020, this demo leverages reinforcement learning (RL) and heuristic algorithms to create a dynamic Mario experience. Deployed on Hugging Face as a Model repository (free tier), it demonstrates AI-driven pathfinding, enemy tactics, and gameplay optimization for educational and research purposes in gaming AI, suitable for applications in EdTech, GameDev, and AI research.
## Technical Architecture
### AI Pathfinding and Gameplay Pipeline
The core of this demo is a hybrid AI system combining reinforcement learning and rule-based heuristics to control Mario’s actions:
- **Reinforcement Learning (RL) Agent**:
- Utilizes a Proximal Policy Optimization (PPO) algorithm, fine-tuned on a custom Mario environment.
- Trained to optimize for coin collection, enemy avoidance, and level completion, achieving a simulated 90% level completion rate.
- Model size: Lightweight (~50MB), compatible with free-tier CPU deployment.
- **Heuristic Pathfinding**:
- Implements A* pathfinding algorithm for efficient navigation through game levels.
- Incorporates dynamic obstacle avoidance (e.g., Goombas, Koopas) using real-time collision detection.
- **Enemy Tactics**:
- Enemies (e.g., Goombas) use rule-based AI with adaptive difficulty, increasing challenge as Mario progresses.
- Tactics include speed variation, ambush patterns, and predictive movement based on Mario’s position.
- **Gameplay Enhancements**:
- Jump controls tweaked for precision using physics-based adjustments.
- Power-up distribution system optimized with probability-based spawning (e.g., 20% chance for Super Mushroom).
- Adaptive weather effects (e.g., rain, wind) impacting Mario’s movement and enemy behavior.
### Data Preprocessing for Game State
The demo processes game state data to train and run the AI:
- **State Representation**:
- Game screen pixels converted to a 2D grid (84x84) for RL input.
- Features extracted: Mario’s position, enemy positions, power-up locations, and level layout.
- **Preprocessing Pipeline**:
- **Normalization**: Pixel values scaled to [0, 1] for RL model stability.
- **Frame Stacking**: Stacks 4 consecutive frames to capture temporal dynamics (e.g., Mario’s velocity).
- **Reward Shaping**: Custom rewards for coin collection (+10), enemy defeat (+50), and level completion (+1000).
- **Output**: Cleaned state data stored as `mario_states.csv` for training and inference.
### Enterprise-Grade AI Compatibility
The processed data and AI model are optimized for:
- **Amazon SageMaker**: Ready for training RL models (e.g., PPO, DQN) using SageMaker RL toolkit, deployable via SageMaker JumpStart.
- **Azure AI**: Compatible with Azure Machine Learning for fine-tuning RL agents in Azure Blob Storage, enabling scalable game AI research.
- **FastAPI Integration**: Designed for API-driven inference (e.g., REST endpoints for AI actions), leveraging your experience with FastAPI.
## Performance Monitoring and Visualization
The demo includes a performance monitoring suite:
- **Latency Tracking**: Measures pathfinding, enemy decision-making, and gameplay update times using `time.perf_counter()`, reported in milliseconds.
- **Success Metrics**: Tracks level completion rate (90% simulated) and coins collected per run.
- **Visualization**: Uses Matplotlib to plot a performance chart (`mario_metrics.png`):
- Bar Chart: Latency (ms) per stage (Pathfinding, Enemy AI, Gameplay Update).
- Line Chart: Success rate (%) per run, with a vibrant palette for engaging visuals.
## Gradio Interface for Interactive Demo
The demo is accessible via Gradio, providing an interactive Mario AI experience:
- **Input**: Select a level (e.g., "Level 1-1") and AI mode (e.g., "Exploration", "Speedrun").
- **Outputs**:
- **Live Gameplay**: Simulated Mario gameplay showing AI-controlled actions (e.g., jumps, enemy avoidance).
- **Metrics Display**: Real-time stats (coins collected, enemies defeated, completion time).
- **Performance Plot**: Visual metrics for latency and success rate.
- **Styling**: Custom dark theme CSS (`#2a2a2a` background, blue buttons) for a sleek, gaming-inspired UI.
## Setup
- Clone this repository to a Hugging Face Model repository (free tier, public).
- Add `requirements.txt` with dependencies (`gradio==4.44.0`, `matplotlib==3.9.2`, etc.).
- Upload `app.py` (includes embedded game environment for seamless deployment).
- Configure to run with Python 3.9+, CPU hardware (no GPU).
## Usage
- **Select Level**: Choose a Mario level in the Gradio UI (e.g., "Level 1-1").
- **Select AI Mode**: Pick an AI behavior mode (e.g., "Exploration" for coin collection, "Speedrun" for fastest completion).
- **Output**:
- **Gameplay Simulation**: Watch Mario navigate the level, avoiding enemies and collecting coins.
- **Metrics**: “Coins: 15, Enemies Defeated: 3, Completion Time: 45s”.
- **Performance Plot**: Visual metrics for latency and success rate.
**Example**:
- **Level**: "Level 1-1"
- **AI Mode**: "Speedrun"
- **Output**:
- Gameplay: Mario completes the level in 40 seconds, collecting 10 coins and defeating 2 Goombas.
- Metrics: “Coins: 10, Enemies Defeated: 2, Completion Time: 40s”.
- Plot: Latency (Pathfinding: 5ms, Enemy AI: 3ms, Gameplay Update: 2ms), Success Rate: 92%.
## Technical Details
**Stack**:
- **Gym Environment**: Custom Mario environment (`gym-super-mario-bros`) for RL training and simulation.
- **RL Agent**: PPO implementation using Stable-Baselines3 for lightweight, CPU-friendly training.
- **Pathfinding**: A* algorithm with dynamic obstacle avoidance.
- **Gradio**: Interactive UI for real-time gameplay demos.
- **Matplotlib**: Performance visualization with bar and line charts.
- **FastAPI Compatibility**: Designed for API-driven inference, leveraging your experience with FastAPI.
**Free Tier Optimization**: Lightweight with CPU-only dependencies, no GPU required.
**Extensibility**: Ready for integration with game engines (e.g., Unity) via FastAPI, and cloud deployments on AWS Lambda or Azure Functions.
## Purpose
This demo showcases expertise in AI-driven game development, focusing on Mario AI pathfinding, enemy tactics, and gameplay optimization. Built on over 5 years of experience in AI, RL, and enterprise-grade deployments, it demonstrates the power of hybrid AI systems (RL + heuristics) for gaming applications, making it ideal for EdTech, GameDev, and AI research.
## Future Enhancements
- **LLM Integration**: Incorporate lightweight LLMs (e.g., distilgpt2) for dynamic NPC dialogue generation.
- **FastAPI Deployment**: Expose AI pipeline via FastAPI endpoints for production-grade inference.
- **Multiplayer Support**: Extend to multiplayer co-op mode with competing AI agents.
- **Real-Time Monitoring**: Add Prometheus metrics for gameplay performance in production environments.
**Website**: https://ghostainews.com/
**Discord**: https://discord.gg/BfA23aYz
## Latest Update
**Status Update**: Status Update: Optimized collision detection for smoother interactions - May 28, 2025 📝
- Added support for multiplayer co-op mode - August 12, 2025 📝
- Improved level loading times by 30% ⚡ - August 11, 2025 📝
- Integrated new collectible items for bonus challenges - August 10, 2025 📝
- Enhanced NPC dialogue with dynamic responses 🍄 - August 09, 2025 📝
- Optimized collision detection for smoother interactions 🎩 - August 08, 2025 📝
- Upgraded power-up distribution system 🪙 - August 07, 2025 📝
- Introduced adaptive weather in game levels - August 06, 2025 📝
- Tweaked jump controls for improved accuracy 🎉 - August 05, 2025 📝
- Added fresh enemy tactics for extra difficulty - August 04, 2025 📝
- Refined AI pathfinding for seamless gameplay - August 03, 2025 📝
- Added support for multiplayer co-op mode 🌈 - August 02, 2025 📝
- Improved level loading times by 30% ⭐ - August 01, 2025 📝
- Integrated new collectible items for bonus challenges 🏰 - July 31, 2025 📝
- Enhanced NPC dialogue with dynamic responses - July 30, 2025 📝
- Optimized collision detection for smoother interactions - July 29, 2025 📝
- Upgraded power-up distribution system - July 28, 2025 📝
- Introduced adaptive weather in game levels ✨ - July 27, 2025 📝
- Tweaked jump controls for improved accuracy ⚡ - July 26, 2025 📝
- Added fresh enemy tactics for extra difficulty 🎉 - July 25, 2025 📝
- Refined AI pathfinding for seamless gameplay - July 24, 2025 📝
- Added support for multiplayer co-op mode - July 23, 2025 📝
- Improved level loading times by 30% - July 22, 2025 📝
- Integrated new collectible items for bonus challenges 🏰 - July 21, 2025 📝
- Enhanced NPC dialogue with dynamic responses - July 20, 2025 📝
- Optimized collision detection for smoother interactions ⭐ - July 19, 2025 📝
- Upgraded power-up distribution system - July 18, 2025 📝
- Introduced adaptive weather in game levels - July 17, 2025 📝
- Tweaked jump controls for improved accuracy 🔥 - July 16, 2025 📝
- Added fresh enemy tactics for extra difficulty 🎩 - July 15, 2025 📝
- Refined AI pathfinding for seamless gameplay 🍄 - July 14, 2025 📝
- Added support for multiplayer co-op mode - July 11, 2025 📝
- Improved level loading times by 30% 🪙 - July 10, 2025 📝
- Integrated new collectible items for bonus challenges - July 09, 2025 📝
- Enhanced NPC dialogue with dynamic responses ✨ - July 08, 2025 📝
- Optimized collision detection for smoother interactions 🌈 - July 07, 2025 📝
- Upgraded power-up distribution system ⭐ - July 06, 2025 📝
- Introduced adaptive weather in game levels - July 05, 2025 📝
- Tweaked jump controls for improved accuracy 🏰 - July 04, 2025 📝
- Added fresh enemy tactics for extra difficulty ✨ - July 03, 2025 📝
- Refined AI pathfinding for seamless gameplay 🪙 - July 02, 2025 📝
- Added support for multiplayer co-op mode 🍄 - July 01, 2025 📝
- Improved level loading times by 30% ⚡ - June 30, 2025 📝
- Integrated new collectible items for bonus challenges 🌈 - June 29, 2025 📝
- Enhanced NPC dialogue with dynamic responses 🎉 - June 28, 2025 📝
- Optimized collision detection for smoother interactions - June 27, 2025 📝
- Upgraded power-up distribution system - June 26, 2025 📝
- Introduced adaptive weather in game levels 🔥 - June 25, 2025 📝
- Tweaked jump controls for improved accuracy 🎩 - June 24, 2025 📝
- Added fresh enemy tactics for extra difficulty - June 23, 2025 📝
- Refined AI pathfinding for seamless gameplay ✨ - June 22, 2025 📝
- Added support for multiplayer co-op mode 🔥 - June 21, 2025 📝
- Improved level loading times by 30% 🎉 - June 20, 2025 📝
- Integrated new collectible items for bonus challenges 🍄 - June 19, 2025 📝
- Enhanced NPC dialogue with dynamic responses - June 18, 2025 📝
- Optimized collision detection for smoother interactions ⭐ - June 17, 2025 📝
- Upgraded power-up distribution system - June 16, 2025 📝
- Introduced adaptive weather in game levels - June 15, 2025 📝
- Tweaked jump controls for improved accuracy 🪙 - June 14, 2025 📝
- Added fresh enemy tactics for extra difficulty - June 13, 2025 📝
- Refined AI pathfinding for seamless gameplay - June 12, 2025 📝
- Added support for multiplayer co-op mode 🌈 - June 11, 2025 📝
- Improved level loading times by 30% ⚡ - June 10, 2025 📝
- Integrated new collectible items for bonus challenges - June 09, 2025 📝
- Enhanced NPC dialogue with dynamic responses 🎩 - June 08, 2025 📝
- Optimized collision detection for smoother interactions - June 07, 2025 📝
- Upgraded power-up distribution system 🏰 - June 06, 2025 📝
- Introduced adaptive weather in game levels 🏰 - June 05, 2025 📝
- Tweaked jump controls for improved accuracy ⭐ - June 04, 2025 📝
- Added fresh enemy tactics for extra difficulty 🎉 - June 03, 2025 📝
- Refined AI pathfinding for seamless gameplay - June 02, 2025 📝
- Added support for multiplayer co-op mode ✨ - June 01, 2025 📝
- Improved level loading times by 30% - May 31, 2025 📝
- Integrated new collectible items for bonus challenges ⚡ - May 30, 2025 📝
- Enhanced NPC dialogue with dynamic responses 🔥 - May 29, 2025 📝
- Optimized collision detection for smoother interactions
- Upgraded power-up distribution system 🎩
- Introduced adaptive weather in game levels 🪙
- Tweaked jump controls for improved accuracy 🍄
- Added fresh enemy tactics for extra difficulty
- Refined AI pathfinding for seamless gameplay 🌈
- Added support for multiplayer co-op mode 🎩
- Improved level loading times by 30% ✨
- Integrated new collectible items for bonus challenges 🍄
- Enhanced NPC dialogue with dynamic responses 🌈
- Optimized collision detection for smoother interactions
- Upgraded power-up distribution system 🪙
- Introduced adaptive weather in game levels
- Tweaked jump controls for improved accuracy
- Added fresh enemy tactics for extra difficulty
- Refined AI pathfinding for seamless gameplay 🔥
- Added support for multiplayer co-op mode 🎉
- Improved level loading times by 30%
- Integrated new collectible items for bonus challenges
- Enhanced NPC dialogue with dynamic responses ⭐
- Optimized collision detection for smoother interactions
- Upgraded power-up distribution system
- Introduced adaptive weather in game levels
- Tweaked jump controls for improved accuracy
- Added fresh enemy tactics for extra difficulty
- Refined AI pathfinding for seamless gameplay
- Added support for multiplayer co-op mode
- Improved level loading times by 30%
- Integrated new collectible items for bonus challenges ⚡
- Enhanced NPC dialogue with dynamic responses 🏰
- Optimized collision detection for smoother interactions
- Upgraded power-up distribution system
- Introduced adaptive weather in game levels
- Tweaked jump controls for improved accuracy
- Added fresh enemy tactics for extra difficulty
|
gayatridt/llama32-dpo-pairrm
|
gayatridt
| 2025-08-12T04:56:13Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-10T07:23:51Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- 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]
|
SmokeST/beliash
|
SmokeST
| 2025-08-12T04:53:24Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2025-08-12T04:47:00Z |
---
license: creativeml-openrail-m
---
|
ggozzy/blockassist-bc-stubby_yapping_mandrill_1754973362
|
ggozzy
| 2025-08-12T04:37:30Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stubby yapping mandrill",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-12T04:37:15Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stubby yapping mandrill
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
afasdfdfadsf/blockassist-bc-rough_opaque_clam_1754973115
|
afasdfdfadsf
| 2025-08-12T04:33:37Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"rough opaque clam",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-12T04:32:43Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- rough opaque clam
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
bruhzair/prototype-0.4x311
|
bruhzair
| 2025-08-12T04:27:33Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"arxiv:2406.11617",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-12T03:49:50Z |
---
base_model: []
library_name: transformers
tags:
- mergekit
- merge
---
# prototype-0.4x311
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [DELLA](https://arxiv.org/abs/2406.11617) merge method using /workspace/prototype-0.4x295 as a base.
### Models Merged
The following models were included in the merge:
* /workspace/cache/models--AstroMLab--AstroSage-70B/snapshots/86496984f418ef5a6825f2c16983595e7f7d5930
* /workspace/cache/models--shisa-ai--shisa-v2-llama3.3-70b/snapshots/0a3080fbcbfbb0160c30db82b05be039453a4c01
* /workspace/cache/models--LumiOpen--Llama-Poro-2-70B-Instruct/snapshots/ba7a467a544e2b8d944a8a8636120fd0fea9358d
* /workspace/cache/models--deepcogito--cogito-v2-preview-llama-70B/snapshots/1e1d12e8eaebd6084a8dcf45ecdeaa2f4b8879ce
* /workspace/cache/models--TheDrummer--Fallen-Llama-3.3-70B-v1/snapshots/d46ef2629f1c3cd46789a55793c5ff0af60de3e8
* /workspace/cache/models--watt-ai--watt-tool-70B/snapshots/dbe19344ec6ee4b9e1636e9e6ce24fc6a85a725e
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: /workspace/cache/models--shisa-ai--shisa-v2-llama3.3-70b/snapshots/0a3080fbcbfbb0160c30db82b05be039453a4c01
parameters:
weight: 0.16
density: 0.7
epsilon: 0.2
- model: /workspace/cache/models--AstroMLab--AstroSage-70B/snapshots/86496984f418ef5a6825f2c16983595e7f7d5930
parameters:
weight: 0.16
density: 0.7
epsilon: 0.2
- model: /workspace/cache/models--LumiOpen--Llama-Poro-2-70B-Instruct/snapshots/ba7a467a544e2b8d944a8a8636120fd0fea9358d
parameters:
weight: 0.16
density: 0.7
epsilon: 0.2
- model: /workspace/cache/models--watt-ai--watt-tool-70B/snapshots/dbe19344ec6ee4b9e1636e9e6ce24fc6a85a725e
parameters:
weight: 0.16
density: 0.7
epsilon: 0.2
- model: /workspace/cache/models--TheDrummer--Fallen-Llama-3.3-70B-v1/snapshots/d46ef2629f1c3cd46789a55793c5ff0af60de3e8
parameters:
weight: 0.16
density: 0.7
epsilon: 0.2
- model: /workspace/cache/models--deepcogito--cogito-v2-preview-llama-70B/snapshots/1e1d12e8eaebd6084a8dcf45ecdeaa2f4b8879ce
parameters:
weight: 0.2
density: 0.5
epsilon: 0.25
base_model: /workspace/prototype-0.4x295
merge_method: della
parameters:
normalize: false
lambda: 1.05
chat_template: llama3
pad_to_multiple_of: 8
int8_mask: true
tokenizer:
source: base
dtype: float32
```
|
afasdfdfadsf/blockassist-bc-rough_opaque_clam_1754972145
|
afasdfdfadsf
| 2025-08-12T04:17:31Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"rough opaque clam",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-12T04:16:33Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- rough opaque clam
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
afasdfdfadsf/blockassist-bc-rough_opaque_clam_1754971625
|
afasdfdfadsf
| 2025-08-12T04:08:46Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"rough opaque clam",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-12T04:07:50Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- rough opaque clam
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
BootesVoid/cme7yi48e001trts8o87yxrtt_cme7yudrm002wrts86fdjz5hn
|
BootesVoid
| 2025-08-12T04:03:53Z | 0 | 0 |
diffusers
|
[
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-08-12T04:03:50Z |
---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: SEXY
---
# Cme7Yi48E001Trts8O87Yxrtt_Cme7Yudrm002Wrts86Fdjz5Hn
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `SEXY` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "SEXY",
"lora_weights": "https://huggingface.co/BootesVoid/cme7yi48e001trts8o87yxrtt_cme7yudrm002wrts86fdjz5hn/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('BootesVoid/cme7yi48e001trts8o87yxrtt_cme7yudrm002wrts86fdjz5hn', weight_name='lora.safetensors')
image = pipeline('SEXY').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 2000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/BootesVoid/cme7yi48e001trts8o87yxrtt_cme7yudrm002wrts86fdjz5hn/discussions) to add images that show off what you’ve made with this LoRA.
|
forouzanfallah/sentinel_test2_fft-t2
|
forouzanfallah
| 2025-08-12T04:01:06Z | 0 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"text-to-image",
"diffusers-training",
"sd3",
"sd3-diffusers",
"controlnet",
"base_model:stabilityai/stable-diffusion-3-medium-diffusers",
"base_model:adapter:stabilityai/stable-diffusion-3-medium-diffusers",
"license:openrail++",
"region:us"
] |
text-to-image
| 2025-08-11T21:11:00Z |
---
base_model: stabilityai/stable-diffusion-3-medium-diffusers
library_name: diffusers
license: openrail++
inference: true
tags:
- text-to-image
- diffusers-training
- diffusers
- sd3
- sd3-diffusers
- controlnet
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# SD3 controlnet-forouzanfallah/sentinel_test2_fft-t2
These are controlnet weights trained on stabilityai/stable-diffusion-3-medium-diffusers with new type of conditioning.
The weights were trained using [ControlNet](https://github.com/lllyasviel/ControlNet) with the [SD3 diffusers trainer](https://github.com/huggingface/diffusers/blob/main/examples/controlnet/README_sd3.md).
You can find some example images below.
prompt: a high-resolution satellite image, sharp details, clear view from space
Please adhere to the licensing terms as described `[here](https://huggingface.co/stabilityai/stable-diffusion-3-medium/blob/main/LICENSE)`.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model]
|
Soughing/tpa_xl
|
Soughing
| 2025-08-12T04:00:26Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-08-01T17:49:13Z |
---
license: apache-2.0
---
|
bambangbukan/blockassist-bc-singing_burrowing_chicken_1754969968
|
bambangbukan
| 2025-08-12T03:41:27Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"singing burrowing chicken",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-12T03:40:41Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- singing burrowing chicken
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
IvanJAjebu/blockassist-bc-thorny_slender_capybara_1754969729
|
IvanJAjebu
| 2025-08-12T03:36:46Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"thorny slender capybara",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-12T03:36:33Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- thorny slender capybara
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
hobson123/blockassist-bc-mammalian_dense_gibbon_1754969243
|
hobson123
| 2025-08-12T03:33:03Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"mammalian dense gibbon",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-12T03:32:49Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- mammalian dense gibbon
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
SaurabhJ20/deva1-sft
|
SaurabhJ20
| 2025-08-12T03:23:28Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-08-12T03:23:28Z |
---
license: apache-2.0
---
|
Jusstin/blockassist-bc-omnivorous_polished_mule_1754968957
|
Jusstin
| 2025-08-12T03:23:19Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"omnivorous polished mule",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-12T03:23:10Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- omnivorous polished mule
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
blendinl/moondream2drain
|
blendinl
| 2025-08-12T03:17:40Z | 0 | 0 | null |
[
"safetensors",
"moondream1",
"image-text-to-text",
"custom_code",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2025-08-12T01:59:50Z |
---
license: apache-2.0
pipeline_tag: image-text-to-text
---
Moondream is a small vision language model designed to run efficiently everywhere.
[Website](https://moondream.ai/) / [Demo](https://moondream.ai/playground) / [GitHub](https://github.com/vikhyat/moondream)
This repository contains the latest (**2025-06-21**) release of Moondream, as well as [historical releases](https://huggingface.co/vikhyatk/moondream2/blob/main/versions.txt). The model is updated frequently, so we recommend specifying a revision as shown below if you're using it in a production application.
### Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from PIL import Image
model = AutoModelForCausalLM.from_pretrained(
"vikhyatk/moondream2",
revision="2025-06-21",
trust_remote_code=True,
device_map={"": "cuda"} # ...or 'mps', on Apple Silicon
)
# Captioning
print("Short caption:")
print(model.caption(image, length="short")["caption"])
print("\nNormal caption:")
for t in model.caption(image, length="normal", stream=True)["caption"]:
# Streaming generation example, supported for caption() and detect()
print(t, end="", flush=True)
print(model.caption(image, length="normal"))
# Visual Querying
print("\nVisual query: 'How many people are in the image?'")
print(model.query(image, "How many people are in the image?")["answer"])
# Object Detection
print("\nObject detection: 'face'")
objects = model.detect(image, "face")["objects"]
print(f"Found {len(objects)} face(s)")
# Pointing
print("\nPointing: 'person'")
points = model.point(image, "person")["points"]
print(f"Found {len(points)} person(s)")
```
### Changelog
**2025-06-21** ([full release notes](https://moondream.ai/blog/moondream-2025-06-21-release))
* **Grounded Reasoning**
Introduces a new step-by-step reasoning mode that explicitly grounds reasoning in spatial positions within the image before answering, leading to more precise visual interpretation (e.g., chart median calculations, accurate counting). Enable with `reasoning=True` in the `query` skill to trade off speed vs. accuracy.
* **Sharper Object Detection**
Uses reinforcement learning on higher-quality bounding-box annotations to reduce object clumping and improve fine-grained detections (e.g., distinguishing “blue bottle” vs. “bottle”).
* **Faster Text Generation**
Yields 20–40 % faster response generation via a new “superword” tokenizer and lightweight tokenizer transfer hypernetwork, which reduces the number of tokens emitted without loss in accuracy and eases future multilingual extensions.
* **Improved UI Understanding**
Boosts ScreenSpot (UI element localization) performance from an F1\@0.5 of 60.3 to 80.4, making Moondream more effective for UI-focused applications.
* **Reinforcement Learning Enhancements**
RL fine-tuning applied across 55 vision-language tasks to reinforce grounded reasoning and detection capabilities, with a roadmap to expand to \~120 tasks in the next update.
**2025-04-15** ([full release notes](https://moondream.ai/blog/moondream-2025-04-14-release))
1. Improved chart understanding (ChartQA up from 74.8 to 77.5, 82.2 with PoT)
2. Added temperature and nucleus sampling to reduce repetitive outputs
3. Better OCR for documents and tables (prompt with “Transcribe the text” or “Transcribe the text in natural reading order”)
4. Object detection supports document layout detection (figure, formula, text, etc)
5. UI understanding (ScreenSpot F1\@0.5 up from 53.3 to 60.3)
6. Improved text understanding (DocVQA up from 76.5 to 79.3, TextVQA up from 74.6 to 76.3)
**2025-03-27** ([full release notes](https://moondream.ai/blog/moondream-2025-03-27-release))
1. Added support for long-form captioning
2. Open vocabulary image tagging
3. Improved counting accuracy (e.g. CountBenchQA increased from 80 to 86.4)
4. Improved text understanding (e.g. OCRBench increased from 58.3 to 61.2)
5. Improved object detection, especially for small objects (e.g. COCO up from 30.5 to 51.2)
6. Fixed token streaming bug affecting multi-byte unicode characters
7. gpt-fast style `compile()` now supported in HF Transformers implementation
|
8man-crypto/Qwen3-0.6B-Gensyn-Swarm-gregarious_short_barracuda
|
8man-crypto
| 2025-08-12T03:11:02Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am gregarious_short_barracuda",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-12T01:03:04Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am gregarious_short_barracuda
---
# 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]
|
0xGareeb/blockassist-bc-nimble_shaggy_zebra_1754968014
|
0xGareeb
| 2025-08-12T03:09:47Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"nimble shaggy zebra",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-12T03:08:27Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- nimble shaggy zebra
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
otmorozky/AceInstruct-1.5B-Gensyn-Swarm-lazy_sprightly_hippo
|
otmorozky
| 2025-08-12T02:52:00Z | 99 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am lazy_sprightly_hippo",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-08T15:06:10Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am lazy_sprightly_hippo
---
# 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]
|
FluidInference/Qwen3-8B-int8-ov
|
FluidInference
| 2025-08-12T02:48:03Z | 0 | 0 | null |
[
"openvino",
"qwen3",
"base_model:Qwen/Qwen3-8B",
"base_model:quantized:Qwen/Qwen3-8B",
"license:apache-2.0",
"region:us"
] | null | 2025-08-12T00:36:37Z |
---
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen3-8B/blob/main/LICENSE
base_model:
- Qwen/Qwen3-8B
base_model_relation: quantized
---
# Qwen3-8B-int8-ov
* Model creator: [Qwen](https://huggingface.co/Qwen)
* Original model: [Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B)
## Description
This is [Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B) model converted to the [OpenVINO™ IR](https://docs.openvino.ai/2025/documentation/openvino-ir-format.html) (Intermediate Representation) format with weights compressed to INT8 by [NNCF](https://github.com/openvinotoolkit/nncf).
## Quantization Parameters
Weight compression was performed using `nncf.compress_weights` with the following parameters:
* mode: **INT8_ASYM**
For more information on quantization, check the [OpenVINO model optimization guide](https://docs.openvino.ai/2025/openvino-workflow/model-optimization-guide/weight-compression.html).
## Compatibility
The provided OpenVINO™ IR model is compatible with:
* OpenVINO version 2025.1.0 and higher
* Optimum Intel 1.24.0 and higher
## Running Model Inference with [Optimum Intel](https://huggingface.co/docs/optimum/intel/index)
1. Install packages required for using [Optimum Intel](https://huggingface.co/docs/optimum/intel/index) integration with the OpenVINO backend:
```
pip install optimum[openvino]
```
2. Run model inference:
```
from transformers import AutoTokenizer
from optimum.intel.openvino import OVModelForCausalLM
model_id = "FluidInference/qwen3-8b-int8-ov"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = OVModelForCausalLM.from_pretrained(model_id)
inputs = tokenizer("What is OpenVINO?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
text = tokenizer.batch_decode(outputs)[0]
print(text)
```
For more examples and possible optimizations, refer to the [Inference with Optimum Intel](https://docs.openvino.ai/2025/openvino-workflow-generative/inference-with-optimum-intel.html).
## Running Model Inference with [OpenVINO GenAI](https://github.com/openvinotoolkit/openvino.genai)
1. Install packages required for using OpenVINO GenAI.
```
pip install openvino-genai huggingface_hub
```
2. Download model from HuggingFace Hub
```
import huggingface_hub as hf_hub
model_id = "FluidInference/qwen3-8b-int8-ov"
model_path = "qwen3-8b-int8-ov"
hf_hub.snapshot_download(model_id, local_dir=model_path)
```
3. Run model inference:
```
import openvino_genai as ov_genai
device = "CPU"
pipe = ov_genai.LLMPipeline(model_path, device)
pipe.get_tokenizer().set_chat_template(pipe.get_tokenizer().chat_template)
print(pipe.generate("What is OpenVINO?", max_length=200))
```
More GenAI usage examples can be found in OpenVINO GenAI library [docs](https://docs.openvino.ai/2025/openvino-workflow-generative/inference-with-genai.html) and [samples](https://github.com/openvinotoolkit/openvino.genai?tab=readme-ov-file#openvino-genai-samples)
You can find more detaild usage examples in OpenVINO Notebooks:
- [LLM](https://openvinotoolkit.github.io/openvino_notebooks/?search=LLM)
- [RAG text generation](https://openvinotoolkit.github.io/openvino_notebooks/?search=RAG+system&tasks=Text+Generation)
## Limitations
Check the original [model card](https://huggingface.co/Qwen/Qwen3-8B) for limitations.
## Legal information
The original model is distributed under [Apache License Version 2.0](https://huggingface.co/Qwen/Qwen3-8B/blob/main/LICENSE) license. More details can be found in [Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B).
## Disclaimer
Intel is committed to respecting human rights and avoiding causing or contributing to adverse impacts on human rights. See [Intel’s Global Human Rights Principles](https://www.intel.com/content/dam/www/central-libraries/us/en/documents/policy-human-rights.pdf). Intel’s products and software are intended only to be used in applications that do not cause or contribute to adverse impacts on human rights.
|
abcorrea/p2-v2
|
abcorrea
| 2025-08-12T02:48:00Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"generated_from_trainer",
"trl",
"sft",
"conversational",
"base_model:abcorrea/p2-v1",
"base_model:finetune:abcorrea/p2-v1",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-12T01:32:15Z |
---
base_model: abcorrea/p2-v1
library_name: transformers
model_name: p2-v2
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for p2-v2
This model is a fine-tuned version of [abcorrea/p2-v1](https://huggingface.co/abcorrea/p2-v1).
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="abcorrea/p2-v2", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.19.1
- Transformers: 4.52.1
- Pytorch: 2.7.0
- Datasets: 4.0.0
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
FluidInference/Qwen3-4B-int8-ov
|
FluidInference
| 2025-08-12T02:46:39Z | 0 | 0 | null |
[
"openvino",
"qwen3",
"base_model:Qwen/Qwen3-4B",
"base_model:quantized:Qwen/Qwen3-4B",
"license:apache-2.0",
"region:us"
] | null | 2025-08-12T00:24:23Z |
---
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen3-4B/blob/main/LICENSE
base_model:
- Qwen/Qwen3-4B
base_model_relation: quantized
---
# Qwen3-4B-int8-ov
* Model creator: [Qwen](https://huggingface.co/Qwen)
* Original model: [Qwen3-4B](https://huggingface.co/Qwen/Qwen3-4B)
## Description
This is [Qwen3-4B](https://huggingface.co/Qwen/Qwen3-4B) model converted to the [OpenVINO™ IR](https://docs.openvino.ai/2025/documentation/openvino-ir-format.html) (Intermediate Representation) format with weights compressed to INT8 by [NNCF](https://github.com/openvinotoolkit/nncf).
## Quantization Parameters
Weight compression was performed using `nncf.compress_weights` with the following parameters:
* mode: **INT8_ASYM**
For more information on quantization, check the [OpenVINO model optimization guide](https://docs.openvino.ai/2025/openvino-workflow/model-optimization-guide/weight-compression.html).
## Compatibility
The provided OpenVINO™ IR model is compatible with:
* OpenVINO version 2025.1.0 and higher
* Optimum Intel 1.24.0 and higher
## Running Model Inference with [Optimum Intel](https://huggingface.co/docs/optimum/intel/index)
1. Install packages required for using [Optimum Intel](https://huggingface.co/docs/optimum/intel/index) integration with the OpenVINO backend:
```
pip install optimum[openvino]
```
2. Run model inference:
```
from transformers import AutoTokenizer
from optimum.intel.openvino import OVModelForCausalLM
model_id = "FluidInference/qwen3-4b-int8-ov"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = OVModelForCausalLM.from_pretrained(model_id)
inputs = tokenizer("What is OpenVINO?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
text = tokenizer.batch_decode(outputs)[0]
print(text)
```
For more examples and possible optimizations, refer to the [Inference with Optimum Intel](https://docs.openvino.ai/2025/openvino-workflow-generative/inference-with-optimum-intel.html).
## Running Model Inference with [OpenVINO GenAI](https://github.com/openvinotoolkit/openvino.genai)
1. Install packages required for using OpenVINO GenAI.
```
pip install openvino-genai huggingface_hub
```
2. Download model from HuggingFace Hub
```
import huggingface_hub as hf_hub
model_id = "FluidInference/qwen3-4b-int8-ov"
model_path = "qwen3-4b-int8-ov"
hf_hub.snapshot_download(model_id, local_dir=model_path)
```
3. Run model inference:
```
import openvino_genai as ov_genai
device = "CPU"
pipe = ov_genai.LLMPipeline(model_path, device)
pipe.get_tokenizer().set_chat_template(pipe.get_tokenizer().chat_template)
print(pipe.generate("What is OpenVINO?", max_length=200))
```
More GenAI usage examples can be found in OpenVINO GenAI library [docs](https://docs.openvino.ai/2025/openvino-workflow-generative/inference-with-genai.html) and [samples](https://github.com/openvinotoolkit/openvino.genai?tab=readme-ov-file#openvino-genai-samples)
You can find more detaild usage examples in OpenVINO Notebooks:
- [LLM](https://openvinotoolkit.github.io/openvino_notebooks/?search=LLM)
- [RAG text generation](https://openvinotoolkit.github.io/openvino_notebooks/?search=RAG+system&tasks=Text+Generation)
## Limitations
Check the original [model card](https://huggingface.co/Qwen/Qwen3-4B) for limitations.
## Legal information
The original model is distributed under [Apache License Version 2.0](https://huggingface.co/Qwen/Qwen3-4B/blob/main/LICENSE) license. More details can be found in [Qwen3-4B](https://huggingface.co/Qwen/Qwen3-4B).
## Disclaimer
Intel is committed to respecting human rights and avoiding causing or contributing to adverse impacts on human rights. See [Intel’s Global Human Rights Principles](https://www.intel.com/content/dam/www/central-libraries/us/en/documents/policy-human-rights.pdf). Intel’s products and software are intended only to be used in applications that do not cause or contribute to adverse impacts on human rights.
|
Osrivers/hidream_i1_full_uncensored_fp8_v0.2.safetensors
|
Osrivers
| 2025-08-12T02:42:22Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2025-08-12T02:42:22Z |
---
license: creativeml-openrail-m
---
|
imgailab/flux1-trtx-schnell-fp4-blackwell
|
imgailab
| 2025-08-12T02:38:02Z | 0 | 0 |
tensorrt-rtx
|
[
"tensorrt-rtx",
"flux1-schnell",
"flux1",
"fp4",
"schnell",
"optimized",
"base_model:black-forest-labs/FLUX.1-schnell",
"base_model:finetune:black-forest-labs/FLUX.1-schnell",
"license:apache-2.0",
"region:us"
] | null | 2025-08-12T02:37:59Z |
---
library_name: tensorrt-rtx
license: apache-2.0
base_model: black-forest-labs/FLUX.1-schnell
tags:
- tensorrt-rtx
- flux1
- fp4
- schnell
- optimized
inference: false
---
# FLUX1 TensorRT-RTX: SCHNELL-Fp4 🔨 Building
Optimized TensorRT-RTX engines for **FLUX1** on **Fp4** architecture with **SCHNELL** quantization.
## 🎯 This Repository
**One variant, one download** - only get exactly what you need!
- **Model**: FLUX1
- **Architecture**: Fp4 (Compute Capability 8.0+)
- **Quantization**: SCHNELL
- **Memory**: TBD
- **Speed**: TBD for 1024x1024 generation
## 🚀 Quick Start
### Automatic (Recommended)
```bash
# ImageAI server downloads automatically
curl -X POST "http://localhost:8001/generate" \
-H "Content-Type: application/json" \
-d '{
"prompt": "a beautiful landscape",
"model": "flux1-tensorrt_rtx:schnell",
"width": 1024,
"height": 1024
}'
```
### Manual Download
```python
from huggingface_hub import snapshot_download
# Download this specific variant only
engines_path = snapshot_download(
repo_id="imgailab/flux1-trtx-schnell-fp4-blackwell"
)
# Engines are in: engines_path/engines/*.plan
```
### Direct Integration
```python
from imageai_server.tensorrt.nvidia_sdxl_pipeline import NVIDIASDXLPipeline
pipeline = NVIDIASDXLPipeline()
pipeline.load_engines(
engine_dir=f"{engines_path}/engines",
framework_model_dir=f"{engines_path}/framework",
onnx_dir=f"{engines_path}/onnx"
)
pipeline.activate_engines()
images, time_ms = pipeline.infer(
prompt="a serene mountain landscape",
height=1024,
width=1024
)
```
## 📊 Performance
| Metric | Value |
|--------|-------|
| **Memory Usage** | TBD |
| **Inference Speed** | TBD |
| **Resolution** | 1024x1024 (optimized) |
| **Batch Size** | 1 (optimized) |
| **Precision** | SCHNELL |
## 🔧 Requirements
### Hardware
- **GPU**: Fp4 architecture
- Ampere: RTX 3090, A100, etc.
- Ada Lovelace: RTX 4090, etc.
- Blackwell: H200, etc.
- **VRAM**: TBD minimum
- **Compute Capability**: 8.0+
### Software
- **TensorRT-RTX**: 1.0.0.21+
- **CUDA**: 12.0+
- **Python**: 3.8+
## 📁 Repository Structure
```
flux1-trtx-schnell-fp4-blackwell/
├── engines/ # TensorRT engine files
│ ├── *.plan # Optimized engines
├── config.json # Configuration metadata
└── README.md # This file
```
## 🌐 Related Repositories
Other variants for FLUX1:
- [Ampere BF16](https://huggingface.co/imgailab/flux1-trtx-bf16-ampere)\n- [Ada FP8](https://huggingface.co/imgailab/flux1-trtx-fp8-ada)\n- [Ada BF16](https://huggingface.co/imgailab/flux1-trtx-bf16-ada)\n- [Blackwell FP4](https://huggingface.co/imgailab/flux1-trtx-fp4-blackwell)\n- [Blackwell FP8](https://huggingface.co/imgailab/flux1-trtx-fp8-blackwell)\n- [Blackwell BF16](https://huggingface.co/imgailab/flux1-trtx-bf16-blackwell)\n
## 📝 License
Inherits license from base model: [black-forest-labs/FLUX.1-schnell](https://huggingface.co/black-forest-labs/FLUX.1-schnell)
## 🔄 Updates
- **2025-08-12**: Initial release
- Optimized for single-variant downloads
---
*Part of the ImageAI TensorRT-RTX engine collection*
|
andr0m4da/blockassist-bc-grazing_hunting_boar_1754966105
|
andr0m4da
| 2025-08-12T02:35:56Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"grazing hunting boar",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-12T02:35:50Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- grazing hunting boar
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
zjunlp/DataMind-Qwen2.5-14B
|
zjunlp
| 2025-08-12T02:35:46Z | 10 | 2 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:2506.19794",
"base_model:Qwen/Qwen2.5-14B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-14B-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-07-19T08:22:08Z |
---
base_model:
- Qwen/Qwen2.5-14B-Instruct
license: apache-2.0
pipeline_tag: text-generation
library_name: transformers
---
<h1 align="center"> ✨ DataMind </h1>
This repository contains the **DataMind** model, a fine-tuned Qwen2.5-14B-Instruct model presented in the paper [Why Do Open-Source LLMs Struggle with Data Analysis? A Systematic Empirical Study](https://huggingface.co/papers/2506.19794).
Code: [https://github.com/zjunlp/DataMind](https://github.com/zjunlp/DataMind)
## Abstract
Large Language Models (LLMs) hold promise in automating data analysis tasks, yet open-source models face significant limitations in these kinds of reasoning-intensive scenarios. In this work, we investigate strategies to enhance the data analysis capabilities of open-source LLMs. By curating a seed dataset of diverse, realistic scenarios, we evaluate model behavior across three core dimensions: data understanding, code generation, and strategic planning. Our analysis reveals three key findings: (1) Strategic planning quality serves as the primary determinant of model performance; (2) Interaction design and task complexity significantly influence reasoning capabilities; (3) Data quality demonstrates a greater impact than diversity in achieving optimal performance. We leverage these insights to develop a data synthesis methodology, demonstrating significant improvements in open-source LLMs' analytical reasoning capabilities.
## 🔔 News
- **[2025-06]** We release a new paper: "[Why Do Open-Source LLMs Struggle with Data Analysis? A Systematic Empirical Study](https://arxiv.org/pdf/2506.19794)".
## 🔧 Installation
#### 🔩Manual Environment Configuration
Conda virtual environments offer a light and flexible setup.
**Prerequisites**
- Anaconda Installation
- GPU support (recommended CUDA version: 12.4)
**Configure Steps**
1. Clone the repository:
```bash
git clone https://github.com/zjunlp/DataMind.git
```
2. Enter the working directory, and all subsequent commands should be executed in this directory.
```bash
cd DataMind/eval
```
3. Create a virtual environment using `Anaconda`.
```bash
conda create -n DataMind python=3.10
conda activate DataMind
```
4. Install all required Python packages.
```bash
pip install -r requirements.txt
```
## 💻 Training
Our model training was completed using the powerful and user-friendly **[LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory)** framework, which provided us with an efficient fine-tuning workflow.
##### 1. Training Data
Our training dataset is available in `train/datamind-da-dataset.json`
##### 2. Training Configuration
The following is an example configuration for full-parameter fine-tuning using DeepSpeed ZeRO-3. You can save it as a YAML file (e.g., `datamind_sft.yaml`).
```yaml
### model
model_name_or_path: Qwen/Qwen2.5-7B-Instruct # Or Qwen/Qwen2.5-14B-Instruct
### method
stage: sft
do_train: true
finetuning_type: full
deepspeed: examples/deepspeed/ds_z3_config.json
flash_attn: fa2
### dataset
dataset: datamind-da-dataset
template: qwen
cutoff_len: 8192
overwrite_cache: true
preprocessing_num_workers: 16
### output
output_dir: checkpoints/your-model-name
logging_steps: 1
save_strategy: epoch
plot_loss: true
overwrite_output_dir: true
report_to: none
### train
per_device_train_batch_size: 1
gradient_accumulation_steps: 4
learning_rate: 1.0e-5
num_train_epochs: 3.0
lr_scheduler_type: cosine
warmup_ratio: 0.1
bf16: true
ddp_timeout: 180000000
```
##### 3. Launch Training
```bash
CUDA_VISIBLE_DEVICES=0,1,2,3 llama-factory-cli train datamind_sft.yaml
```
## 🧐 Evaluation
> Note:
>
> - **Ensure** that your working directory is set to the **`eval`** folder in a virtual environment.
> - If you have more questions, feel free to open an issue with us.
> - If you need to use local model, you need to deploy it according to **(Optional)`local_model.sh`**.
**Step 1: Download the evaluation datasets and our sft models**
The evaluation datasets we used are in [QRData](https://github.com/xxxiaol/QRData) and [DiscoveryBench](https://github.com/allenai/discoverybench). The script expects data to be at `data/QRData/benchmark/data/*.csv` and `data/DiscoveryBench/*.csv`.
You can also download our sft models directly from Hugging Face: [DataMind-Qwen2.5-7B](https://huggingface.co/zjunlp/DataMind-Qwen2.5-7B) ,[DataMind-Qwen2.5-14B ](https://huggingface.co/zjunlp/DataMind-Qwen2.5-14B).
You can use the following `bash` script to download the dataset:
```bash
bash download_eval_data.sh
```
**Step 2: Prepare the parameter configuration**
Here is the example:
**`config.yaml`**
```yaml
api_key: your_api_key # your API key for the model with API service. No need for open-source models.
data_root: /path/to/your/project/DataMind/eval/data # Root directory for data. (absolute path !!!)
```
**`run_eval.sh`**
```bash
python do_generate.py \
--model_name DataMind-Qwen2.5-7B \ # Model name to use.
--check_model gpt-4o-mini \ # Check model to use.
--output results \ # Output directory path.
--dataset_name QRData \ # Dataset name to use, chosen from QRData, DiscoveryBench.
--max_round 25 \ # Maximum number of steps.
--api_port 8000 \ # API port number, it is necessary if the local model is used.
--bidx 0 \ # Begin index (inclusive), `None` indicates that there is no restriction.
--eidx None \ # End index (exclusive), `None` indicates that there is no restriction.
--temperature 0.0 \ # Temperature for sampling.
--top_p 1 \ # Top p for sampling.
--add_random False \ # Whether to add random files.
```
**(Optional)`local_model.sh`**
```bash
CUDA_VISIBLE_DEVICES=$i python -m vllm.entrypoints.openai.api_server \
--model $MODEL_PATH \ # Local model path.
--served-model-name $MODEL_NAME \ # The model name specified by you.
--tensor-parallel-size $i \ # Set the size of tensor parallel processing.
--port $port # API port number, which is consistent with the `api_port` above.
```
**Step 3: Run the shell script**
**(Optional)** Deploy the local model if you need.
```bash
bash local_model.sh
```
Run the shell script to start the process.
```bash
bash run_eval.sh
```
## 🎉Contributors
<a href="https://github.com/zjunlp/DataMind/graphs/contributors">
<img src="https://contrib.rocks/image?repo=zjunlp/DataMind" /></a>
We deeply appreciate the collaborative efforts of everyone involved. We will continue to enhance and maintain this repository over the long term. If you encounter any issues, feel free to submit them to us!
## ✍️ Citation
If you find our work helpful, please use the following citations.
```bibtex
@article{zhu2025open,
title={Why Do Open-Source LLMs Struggle with Data Analysis? A Systematic Empirical Study},
author={Zhu, Yuqi and Zhong, Yi and Zhang, Jintian and Zhang, Ziheng and Qiao, Shuofei and Luo, Yujie and Du, Lun and Zheng, Da and Chen, Huajun and Zhang, Ningyu},
journal={arXiv preprint arXiv:2506.19794},
year={2025}
}
```
|
afasdfdfadsf/blockassist-bc-rough_opaque_clam_1754965734
|
afasdfdfadsf
| 2025-08-12T02:30:37Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"rough opaque clam",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-12T02:29:42Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- rough opaque clam
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
shadyAI/swahili-whisper-asr-with-lora
|
shadyAI
| 2025-08-12T02:28:40Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-12T02:28:32Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
braindeck/my_awesome_asr_korean_model
|
braindeck
| 2025-08-12T02:27:44Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice_17_0",
"base_model:facebook/wav2vec2-base",
"base_model:finetune:facebook/wav2vec2-base",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2025-08-11T08:26:53Z |
---
library_name: transformers
license: apache-2.0
base_model: facebook/wav2vec2-base
tags:
- generated_from_trainer
datasets:
- common_voice_17_0
metrics:
- wer
model-index:
- name: my_awesome_asr_korean_model
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice_17_0
type: common_voice_17_0
config: ko
split: None
args: ko
metrics:
- name: Wer
type: wer
value: 0.7375565610859729
---
<!-- 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. -->
# my_awesome_asr_korean_model
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the common_voice_17_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6733
- Wer: 0.7376
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 64
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- training_steps: 20000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:-----:|:---------------:|:------:|
| 0.628 | 200.0 | 1000 | 0.6373 | 0.8869 |
| 0.5549 | 400.0 | 2000 | 0.6298 | 0.9072 |
| 0.494 | 600.0 | 3000 | 0.6399 | 0.9977 |
| 0.2089 | 800.0 | 4000 | 0.8485 | 0.7579 |
| 0.1313 | 1000.0 | 5000 | 1.0293 | 0.7421 |
| 0.0996 | 1200.0 | 6000 | 1.1840 | 0.7602 |
| 0.0959 | 1400.0 | 7000 | 1.1620 | 0.7398 |
| 0.0812 | 1600.0 | 8000 | 1.2796 | 0.7398 |
| 0.0743 | 1800.0 | 9000 | 1.4207 | 0.7534 |
| 0.0628 | 2000.0 | 10000 | 1.4068 | 0.7421 |
| 0.0653 | 2200.0 | 11000 | 1.4614 | 0.7511 |
| 0.0577 | 2400.0 | 12000 | 1.5502 | 0.6991 |
| 0.0539 | 2600.0 | 13000 | 1.5590 | 0.7172 |
| 0.0517 | 2800.0 | 14000 | 1.6388 | 0.7240 |
| 0.0464 | 3000.0 | 15000 | 1.6670 | 0.7217 |
| 0.0445 | 3200.0 | 16000 | 1.6323 | 0.7014 |
| 0.039 | 3400.0 | 17000 | 1.6918 | 0.7330 |
| 0.0412 | 3600.0 | 18000 | 1.5930 | 0.7104 |
| 0.0408 | 3800.0 | 19000 | 1.6583 | 0.7376 |
| 0.0343 | 4000.0 | 20000 | 1.6733 | 0.7376 |
### Framework versions
- Transformers 4.56.0.dev0
- Pytorch 2.6.0a0+ecf3bae40a.nv25.01
- Datasets 3.6.0
- Tokenizers 0.21.1
|
nightmedia/Luth-0.6B-Instruct-q8-hi-mlx
|
nightmedia
| 2025-08-12T02:24:04Z | 0 | 0 |
mlx
|
[
"mlx",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"fr",
"en",
"dataset:kurakurai/luth-sft",
"base_model:kurakurai/Luth-0.6B-Instruct",
"base_model:quantized:kurakurai/Luth-0.6B-Instruct",
"license:apache-2.0",
"8-bit",
"region:us"
] |
text-generation
| 2025-08-12T02:13:51Z |
---
library_name: mlx
license: apache-2.0
datasets:
- kurakurai/luth-sft
language:
- fr
- en
base_model: kurakurai/Luth-0.6B-Instruct
pipeline_tag: text-generation
tags:
- mlx
---
# Luth-0.6B-Instruct-q8-hi-mlx
This model [Luth-0.6B-Instruct-q8-hi-mlx](https://huggingface.co/Luth-0.6B-Instruct-q8-hi-mlx) was
converted to MLX format from [kurakurai/Luth-0.6B-Instruct](https://huggingface.co/kurakurai/Luth-0.6B-Instruct)
using mlx-lm version **0.26.3**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("Luth-0.6B-Instruct-q8-hi-mlx")
prompt = "hello"
if tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
```
|
IvanJAjebu/blockassist-bc-thorny_slender_capybara_1754964706
|
IvanJAjebu
| 2025-08-12T02:12:57Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"thorny slender capybara",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-12T02:12:43Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- thorny slender capybara
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
WangDong2017/GrammarSeeker-SFT-Qwen2.5-7B
|
WangDong2017
| 2025-08-12T02:08:53Z | 2 | 0 | null |
[
"safetensors",
"qwen2",
"text-classification",
"zh",
"base_model:Qwen/Qwen2.5-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-7B-Instruct",
"license:apache-2.0",
"region:us"
] |
text-classification
| 2025-08-11T00:53:45Z |
---
license: apache-2.0
language:
- zh
base_model:
- Qwen/Qwen2.5-7B-Instruct
pipeline_tag: text-classification
---
# GrammarSeeker-SFT-Qwen2.5-7B
A fine-tuned Qwen2.5-7B-Instruct model specifically designed for grammatical project parsing systems.
## 🔗 Repository Links
- **📁 Source Code**: [GitHub Repository](https://github.com/wd-github-2017/GrammarSeeker) - Contains testing code and development scripts
- **🤗 Model Hub**: [Hugging Face Model](https://huggingface.co/WangDong2017/GrammarSeeker-SFT-Qwen2.5-7B) - Hosts the complete fine-tuned model
## 🎉 Latest Update (2025-08-11)
**✅ Model Successfully Deployed to Hugging Face!**
The fine-tuned model is now available for direct use without any additional steps.
## 📋 Model Information
- **Base Model**: [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct)
- **Fine-tuned Model**: [WangDong2017/GrammarSeeker-SFT-Qwen2.5-7B](https://huggingface.co/WangDong2017/GrammarSeeker-SFT-Qwen2.5-7B)
- **Fine-tuning Method**: LoRA (Low-Rank Adaptation) during training, now provided as complete model
- **Task**: Binary classification for grammatical project annotation (T/F output)
- **Performance**:
- **F1 Score**: 0.9797 (97.97%)
- **Positive Accuracy**: 0.9640 (96.40%)
- **Negative Accuracy**: 0.9960 (99.60%)
- **Test Samples**: 1000
- **Test Date**: 2025-08-11
- **Tested Performance**: 16 annotations/s (Test completed in ~1 minute on RTX 4090)
## 🎯 Use Case
This model serves as the **core component of a grammatical project parsing system**. It is designed to:
1. **Receive structured prompts** (as shown in GM-TestData.csv)
2. **Output binary decisions** (T/F) for grammatical annotation
3. **Enable automated grammar project marking** based on model predictions
## 🔧 Usage
### Installation
```bash
pip install transformers peft torch
```
### Testing Performance
```bash
# Test the model from Hugging Face
python test_hf_model.py
```
**Latest Test Results (2025-08-11)**:
- ✅ Model successfully loaded from HF repository
- ✅ All 1000 test samples processed
- ✅ F1 Score: 0.9797 (97.97%)
- ✅ Test completed in ~1 minute on RTX 4090
### Loading the Model
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
# Load the complete fine-tuned model directly from HF
model = AutoModelForCausalLM.from_pretrained(
"WangDong2017/GrammarSeeker-SFT-Qwen2.5-7B",
torch_dtype=torch.float16,
device_map="auto",
trust_remote_code=True
)
# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained("WangDong2017/GrammarSeeker-SFT-Qwen2.5-7B")
```
## 🏭 Production Environment Usage
**Recommended workflow**:
1. **Pre-filtering**: Use regular expressions for coarse screening
2. **String matching**: Trigger prompt generation based on string matching
3. **Model inference**: Send generated prompt to this model
4. **Output processing**: Model outputs T/F
5. **Automatic annotation**: Generate grammatical project markers based on T/F output
## 📊 Dataset
- **GM-TestData.csv**: 1000 test samples with prompts and expected answers
- **Format**: prompt1, prompt2, answer (T/F)
- **Test Results**: Successfully validated with 97.97% F1 score
## 🚀 Deployment & Integration
### Hugging Face Integration
- **Model Hub**: [WangDong2017/GrammarSeeker-SFT-Qwen2.5-7B](https://huggingface.co/WangDong2017/GrammarSeeker-SFT-Qwen2.5-7B)
- **Direct Loading**: Available for immediate use
- **API Access**: Can be deployed through HF Inference API
## 📝 Citation
```bibtex
@misc{wang2025CPGEVALMultitieredBenchmark,
title = {{{CPG-EVAL}}: A Multi-Tiered Benchmark for Evaluating the Chinese Pedagogical Grammar Competence of Large Language Models},
author = {Wang, Dong},
year = {2025},
publisher = {arXiv},
doi = {10.48550/ARXIV.2504.13261}
}
```
---
**Note**: This model has been successfully tested and deployed. For production use, please ensure proper testing and validation in your specific use case.
|
mradermacher/india-wiki-hin-1.7B-GGUF
|
mradermacher
| 2025-08-12T01:59:27Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:XformAI-india/india-wiki-hin-1.7B",
"base_model:quantized:XformAI-india/india-wiki-hin-1.7B",
"license:mit",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-08-12T01:53:45Z |
---
base_model: XformAI-india/india-wiki-hin-1.7B
language:
- en
library_name: transformers
license: mit
mradermacher:
readme_rev: 1
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
static quants of https://huggingface.co/XformAI-india/india-wiki-hin-1.7B
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#india-wiki-hin-1.7B-GGUF).***
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/india-wiki-hin-1.7B-GGUF/resolve/main/india-wiki-hin-1.7B.Q2_K.gguf) | Q2_K | 0.9 | |
| [GGUF](https://huggingface.co/mradermacher/india-wiki-hin-1.7B-GGUF/resolve/main/india-wiki-hin-1.7B.Q3_K_S.gguf) | Q3_K_S | 1.0 | |
| [GGUF](https://huggingface.co/mradermacher/india-wiki-hin-1.7B-GGUF/resolve/main/india-wiki-hin-1.7B.Q3_K_M.gguf) | Q3_K_M | 1.0 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/india-wiki-hin-1.7B-GGUF/resolve/main/india-wiki-hin-1.7B.Q3_K_L.gguf) | Q3_K_L | 1.1 | |
| [GGUF](https://huggingface.co/mradermacher/india-wiki-hin-1.7B-GGUF/resolve/main/india-wiki-hin-1.7B.IQ4_XS.gguf) | IQ4_XS | 1.1 | |
| [GGUF](https://huggingface.co/mradermacher/india-wiki-hin-1.7B-GGUF/resolve/main/india-wiki-hin-1.7B.Q4_K_S.gguf) | Q4_K_S | 1.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/india-wiki-hin-1.7B-GGUF/resolve/main/india-wiki-hin-1.7B.Q4_K_M.gguf) | Q4_K_M | 1.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/india-wiki-hin-1.7B-GGUF/resolve/main/india-wiki-hin-1.7B.Q5_K_S.gguf) | Q5_K_S | 1.3 | |
| [GGUF](https://huggingface.co/mradermacher/india-wiki-hin-1.7B-GGUF/resolve/main/india-wiki-hin-1.7B.Q5_K_M.gguf) | Q5_K_M | 1.4 | |
| [GGUF](https://huggingface.co/mradermacher/india-wiki-hin-1.7B-GGUF/resolve/main/india-wiki-hin-1.7B.Q6_K.gguf) | Q6_K | 1.5 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/india-wiki-hin-1.7B-GGUF/resolve/main/india-wiki-hin-1.7B.Q8_0.gguf) | Q8_0 | 1.9 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/india-wiki-hin-1.7B-GGUF/resolve/main/india-wiki-hin-1.7B.f16.gguf) | f16 | 3.5 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
IvanJAjebu/blockassist-bc-thorny_slender_capybara_1754963446
|
IvanJAjebu
| 2025-08-12T01:52:17Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"thorny slender capybara",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-12T01:51:47Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- thorny slender capybara
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
boredsxe/blockassist-bc-melodic_nocturnal_macaque_1754962549
|
boredsxe
| 2025-08-12T01:37:36Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"melodic nocturnal macaque",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-12T01:37:17Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- melodic nocturnal macaque
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
IvanJAjebu/blockassist-bc-thorny_slender_capybara_1754961934
|
IvanJAjebu
| 2025-08-12T01:26:53Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"thorny slender capybara",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-12T01:26:37Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- thorny slender capybara
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
myfi/parser_model_ner_3.45_checkpoint_250
|
myfi
| 2025-08-12T01:08:01Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"base_model:unsloth/Qwen2.5-3B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-3B-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-12T00:56:27Z |
---
base_model: unsloth/Qwen2.5-3B-Instruct
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** myfi
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen2.5-3B-Instruct
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)
|
mbiarreta/vit-ena24-clase
|
mbiarreta
| 2025-08-12T00:49:04Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"base_model:google/vit-base-patch16-224-in21k",
"base_model:finetune:google/vit-base-patch16-224-in21k",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2025-08-11T05:06:51Z |
---
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- image-classification
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: vit-ena24-clase
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. -->
# vit-ena24-clase
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the ena24_MD dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3132
- Accuracy: 0.9321
- F1: 0.8789
## 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: 0.0002
- 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
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|
| 1.9153 | 0.1302 | 100 | 1.7647 | 0.5725 | 0.4646 |
| 1.2463 | 0.2604 | 200 | 1.1008 | 0.7641 | 0.6933 |
| 0.884 | 0.3906 | 300 | 0.9143 | 0.7832 | 0.7113 |
| 0.6852 | 0.5208 | 400 | 0.6161 | 0.8649 | 0.8027 |
| 0.5318 | 0.6510 | 500 | 0.4691 | 0.8947 | 0.8376 |
| 0.5544 | 0.7812 | 600 | 0.3783 | 0.9153 | 0.8984 |
| 0.2321 | 0.9115 | 700 | 0.3132 | 0.9321 | 0.8789 |
### Framework versions
- Transformers 4.55.0
- Pytorch 2.6.0+cu124
- Datasets 4.0.0
- Tokenizers 0.21.4
|
IvanJAjebu/blockassist-bc-thorny_slender_capybara_1754959040
|
IvanJAjebu
| 2025-08-12T00:38:32Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"thorny slender capybara",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-12T00:38:17Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- thorny slender capybara
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
winnieyangwannan/entity_sft_Llama-3.1-8B-Instruct_lora_8_lr_0.0001_10240_all_37_epoch_1_layer_22
|
winnieyangwannan
| 2025-08-12T00:36:05Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-12T00:33:22Z |
---
library_name: transformers
tags:
- trl
- sft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
winnieyangwannan/entity_sft_Llama-3.1-8B-Instruct_lora_8_lr_0.0001_8960_all_37_epoch_1_layer_22
|
winnieyangwannan
| 2025-08-12T00:35:56Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-12T00:33:25Z |
---
library_name: transformers
tags:
- trl
- sft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[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
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
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#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
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[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]
- **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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## Glossary [optional]
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## Model Card Contact
[More Information Needed]
|
IvanJAjebu/blockassist-bc-thorny_slender_capybara_1754958679
|
IvanJAjebu
| 2025-08-12T00:32:39Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"thorny slender capybara",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-12T00:32:23Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- thorny slender capybara
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mpopescu99/unsloth-skincarebot
|
mpopescu99
| 2025-08-12T00:27:51Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-10T22:30:59Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- 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]
|
RE-N-Y/REPA-E-f16-32c
|
RE-N-Y
| 2025-08-12T00:22:46Z | 0 | 0 | null |
[
"safetensors",
"model_hub_mixin",
"pytorch_model_hub_mixin",
"region:us"
] | null | 2025-08-12T00:22:36Z |
---
tags:
- model_hub_mixin
- pytorch_model_hub_mixin
---
This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
- Code: [More Information Needed]
- Paper: [More Information Needed]
- Docs: [More Information Needed]
|
m-mulet/try2_qwen_2.5_7b-owl_student_removed_top_8000_influential-2
|
m-mulet
| 2025-08-12T00:17:19Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen2",
"trl",
"en",
"base_model:unsloth/Qwen2.5-7B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-7B-Instruct",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-12T00:17:12Z |
---
base_model: unsloth/Qwen2.5-7B-Instruct
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** m-mulet
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen2.5-7B-Instruct
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)
|
Nik9999/blockassist-bc-foraging_rapid_anteater_1754957229
|
Nik9999
| 2025-08-12T00:08:34Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"foraging rapid anteater",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-12T00:08:24Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- foraging rapid anteater
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Sayemahsjn/blockassist-bc-playful_feline_octopus_1754956260
|
Sayemahsjn
| 2025-08-12T00:08:29Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"playful feline octopus",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-12T00:08:26Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- playful feline octopus
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
acidjp/blockassist-bc-pesty_extinct_prawn_1754956315
|
acidjp
| 2025-08-11T23:59:33Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"pesty extinct prawn",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T23:58:18Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- pesty extinct prawn
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mosama/Qwen25-VL-3B_v2
|
mosama
| 2025-08-11T23:51:50Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"unsloth",
"endpoints_compatible",
"region:us"
] | null | 2025-08-11T20:40:07Z |
---
library_name: transformers
model_name: Qwen25-VL-3B_v2
tags:
- generated_from_trainer
- trl
- sft
- unsloth
licence: license
---
# Model Card for Qwen25-VL-3B_v2
This model is a fine-tuned version of [None](https://huggingface.co/None).
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="mosama/Qwen25-VL-3B_v2", 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/muhammadosama1994/KSA%20VR%20Project/runs/5okpnqn6)
This model was trained with SFT.
### Framework versions
- TRL: 0.20.0
- Transformers: 4.54.1
- Pytorch: 2.6.0+cu124
- Datasets: 3.6.0
- Tokenizers: 0.21.4
## 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}}
}
```
|
kaizen9/qspi30_gfrz_unk
|
kaizen9
| 2025-08-11T23:37:38Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3_moe",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-11T23:21:19Z |
---
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]
|
wilfoderek/bge-m3-es-legal-tmp-1
|
wilfoderek
| 2025-08-11T23:35:43Z | 0 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"xlm-roberta",
"sentence-similarity",
"feature-extraction",
"dense",
"generated_from_trainer",
"dataset_size:2947",
"loss:MatryoshkaLoss",
"loss:MultipleNegativesRankingLoss",
"es",
"dataset:dariolopez/justicio-rag-embedding-qa-tmp-2",
"arxiv:1908.10084",
"arxiv:2205.13147",
"arxiv:1705.00652",
"base_model:BAAI/bge-m3",
"base_model:finetune:BAAI/bge-m3",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2025-08-11T23:34:39Z |
---
language:
- es
license: apache-2.0
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
- generated_from_trainer
- dataset_size:2947
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
base_model: BAAI/bge-m3
widget:
- source_sentence: Es uso privativo el que determina la ocupación de una porción del
dominio público, de modo que se limita o excluye la utilización del mismo por
otros interesados.
sentences:
- ¿Qué es el uso privativo de los bienes de dominio público?
- ¿Qué es la sanidad ambiental?
- ¿Qué información básica debe contener la información que se facilita al afectado
cuando se obtienen datos personales de él?
- source_sentence: 'Las retribuciones básicas, que se fijan en la Ley de Presupuestos
Generales del Estado, estarán integradas única y exclusivamente por: a) El sueldo
asignado a cada Subgrupo o Grupo de clasificación profesional, en el supuesto
de que éste no tenga Subgrupo. b) Los trienios, que consisten en una cantidad,
que será igual para cada Subgrupo o Grupo de clasificación profesional, en el
supuesto de que éste no tenga Subgrupo, por cada tres años de servicio.'
sentences:
- ¿Qué se entiende por retribuciones básicas?
- ¿Cuál es el título competencial de esta ley orgánica?
- ¿Qué se aprueba a propuesta del Ministro de Hacienda?
- source_sentence: Se reconoce el valor social de las niñas, niños y adolescentes
como personas que realizan un aporte afectivo, cultural y ético al caudal social,
y cuyo protagonismo, creatividad y posicionamiento activo enriquecen la vida colectiva.
sentences:
- ¿Qué sucede si se produce un incumplimiento de las actuaciones establecidas en
el Plan de inclusión sociolaboral?
- ¿Qué se reconoce en cuanto al valor social de la infancia?
- ¿Cuál es el plazo de prescripción de las infracciones?
- source_sentence: Las empresas y las universidades podrán promover y participar en
programas de voluntariado que cumplan los requisitos establecidos en esta Ley.
sentences:
- ¿Cuál es la consideración de las infracciones muy graves?
- ¿Qué tipo de empresas pueden promover y participar en programas de voluntariado?
- ¿Qué tipo de entidades están obligadas a cumplir con las obligaciones de publicidad
activa?
- source_sentence: Artículo 6. Definiciones. 1. Discriminación directa e indirecta.
b) La discriminación indirecta se produce cuando una disposición, criterio o práctica
aparentemente neutros ocasiona o puede ocasionar a una o varias personas una desventaja
particular con respecto a otras por razón de las causas previstas en el apartado
1 del artículo 2.
sentences:
- ¿Cuál es el papel del Consejo de Salud de Área?
- ¿Qué se considera discriminación indirecta?
- ¿Qué tipo de información se considera veraz?
datasets:
- dariolopez/justicio-rag-embedding-qa-tmp-2
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy@1
- cosine_accuracy@3
- cosine_accuracy@5
- cosine_accuracy@10
- cosine_precision@1
- cosine_precision@3
- cosine_precision@5
- cosine_precision@10
- cosine_recall@1
- cosine_recall@3
- cosine_recall@5
- cosine_recall@10
- cosine_ndcg@10
- cosine_mrr@10
- cosine_map@100
model-index:
- name: BGE large Legal Spanish
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 1024
type: dim_1024
metrics:
- type: cosine_accuracy@1
value: 0.5396341463414634
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8048780487804879
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8597560975609756
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9085365853658537
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.5396341463414634
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2682926829268293
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.1719512195121951
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09085365853658536
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.5396341463414634
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8048780487804879
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8597560975609756
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9085365853658537
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7334906275596409
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.6762896825396825
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.6797504013046416
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 768
type: dim_768
metrics:
- type: cosine_accuracy@1
value: 0.5152439024390244
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8048780487804879
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8567073170731707
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9085365853658537
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.5152439024390244
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2682926829268293
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.17134146341463413
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09085365853658536
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.5152439024390244
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8048780487804879
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8567073170731707
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9085365853658537
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7234195125271459
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.6627818912117692
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.6662422262347588
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 512
type: dim_512
metrics:
- type: cosine_accuracy@1
value: 0.5426829268292683
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8109756097560976
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8628048780487805
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9024390243902439
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.5426829268292683
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.27032520325203246
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.17256097560975608
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09024390243902437
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.5426829268292683
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8109756097560976
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8628048780487805
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9024390243902439
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7337030530987747
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.6782725996902826
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.6821494767506607
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 256
type: dim_256
metrics:
- type: cosine_accuracy@1
value: 0.5365853658536586
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.7957317073170732
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8567073170731707
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.8871951219512195
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.5365853658536586
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2652439024390244
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.17134146341463413
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08871951219512195
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.5365853658536586
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.7957317073170732
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8567073170731707
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.8871951219512195
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7246310466738191
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.670835753000387
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.6752543372829658
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 128
type: dim_128
metrics:
- type: cosine_accuracy@1
value: 0.5304878048780488
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.7713414634146342
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.823170731707317
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.8719512195121951
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.5304878048780488
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.25711382113821135
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.1646341463414634
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08719512195121949
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.5304878048780488
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.7713414634146342
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.823170731707317
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.8719512195121951
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7121341156516634
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.6596556813782425
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.664297199179873
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 64
type: dim_64
metrics:
- type: cosine_accuracy@1
value: 0.5030487804878049
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.7317073170731707
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.7835365853658537
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.8689024390243902
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.5030487804878049
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.24390243902439024
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.1567073170731707
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08689024390243902
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.5030487804878049
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.7317073170731707
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.7835365853658537
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.8689024390243902
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.6887377838112205
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.6309632694541231
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.6348508329931788
name: Cosine Map@100
---
# BGE large Legal Spanish
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) on the [justicio-rag-embedding-qa-tmp-2](https://huggingface.co/datasets/dariolopez/justicio-rag-embedding-qa-tmp-2) dataset. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) <!-- at revision 5617a9f61b028005a4858fdac845db406aefb181 -->
- **Maximum Sequence Length:** 8192 tokens
- **Output Dimensionality:** 1024 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- [justicio-rag-embedding-qa-tmp-2](https://huggingface.co/datasets/dariolopez/justicio-rag-embedding-qa-tmp-2)
- **Language:** es
- **License:** apache-2.0
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 8192, 'do_lower_case': False, 'architecture': 'XLMRobertaModel'})
(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(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("wilfoderek/bge-m3-es-legal-tmp-1")
# Run inference
queries = [
"Art\u00edculo 6. Definiciones. 1. Discriminaci\u00f3n directa e indirecta. b) La discriminaci\u00f3n indirecta se produce cuando una disposici\u00f3n, criterio o pr\u00e1ctica aparentemente neutros ocasiona o puede ocasionar a una o varias personas una desventaja particular con respecto a otras por raz\u00f3n de las causas previstas en el apartado 1 del art\u00edculo 2.",
]
documents = [
'¿Qué se considera discriminación indirecta?',
'¿Qué tipo de información se considera veraz?',
'¿Cuál es el papel del Consejo de Salud de Área?',
]
query_embeddings = model.encode_query(queries)
document_embeddings = model.encode_document(documents)
print(query_embeddings.shape, document_embeddings.shape)
# [1, 1024] [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(query_embeddings, document_embeddings)
print(similarities)
# tensor([[0.7562, 0.1522, 0.0675]])
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
## Evaluation
### Metrics
#### Information Retrieval
* Dataset: `dim_1024`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters:
```json
{
"truncate_dim": 1024
}
```
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.5396 |
| cosine_accuracy@3 | 0.8049 |
| cosine_accuracy@5 | 0.8598 |
| cosine_accuracy@10 | 0.9085 |
| cosine_precision@1 | 0.5396 |
| cosine_precision@3 | 0.2683 |
| cosine_precision@5 | 0.172 |
| cosine_precision@10 | 0.0909 |
| cosine_recall@1 | 0.5396 |
| cosine_recall@3 | 0.8049 |
| cosine_recall@5 | 0.8598 |
| cosine_recall@10 | 0.9085 |
| **cosine_ndcg@10** | **0.7335** |
| cosine_mrr@10 | 0.6763 |
| cosine_map@100 | 0.6798 |
#### Information Retrieval
* Dataset: `dim_768`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters:
```json
{
"truncate_dim": 768
}
```
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.5152 |
| cosine_accuracy@3 | 0.8049 |
| cosine_accuracy@5 | 0.8567 |
| cosine_accuracy@10 | 0.9085 |
| cosine_precision@1 | 0.5152 |
| cosine_precision@3 | 0.2683 |
| cosine_precision@5 | 0.1713 |
| cosine_precision@10 | 0.0909 |
| cosine_recall@1 | 0.5152 |
| cosine_recall@3 | 0.8049 |
| cosine_recall@5 | 0.8567 |
| cosine_recall@10 | 0.9085 |
| **cosine_ndcg@10** | **0.7234** |
| cosine_mrr@10 | 0.6628 |
| cosine_map@100 | 0.6662 |
#### Information Retrieval
* Dataset: `dim_512`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters:
```json
{
"truncate_dim": 512
}
```
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.5427 |
| cosine_accuracy@3 | 0.811 |
| cosine_accuracy@5 | 0.8628 |
| cosine_accuracy@10 | 0.9024 |
| cosine_precision@1 | 0.5427 |
| cosine_precision@3 | 0.2703 |
| cosine_precision@5 | 0.1726 |
| cosine_precision@10 | 0.0902 |
| cosine_recall@1 | 0.5427 |
| cosine_recall@3 | 0.811 |
| cosine_recall@5 | 0.8628 |
| cosine_recall@10 | 0.9024 |
| **cosine_ndcg@10** | **0.7337** |
| cosine_mrr@10 | 0.6783 |
| cosine_map@100 | 0.6821 |
#### Information Retrieval
* Dataset: `dim_256`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters:
```json
{
"truncate_dim": 256
}
```
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.5366 |
| cosine_accuracy@3 | 0.7957 |
| cosine_accuracy@5 | 0.8567 |
| cosine_accuracy@10 | 0.8872 |
| cosine_precision@1 | 0.5366 |
| cosine_precision@3 | 0.2652 |
| cosine_precision@5 | 0.1713 |
| cosine_precision@10 | 0.0887 |
| cosine_recall@1 | 0.5366 |
| cosine_recall@3 | 0.7957 |
| cosine_recall@5 | 0.8567 |
| cosine_recall@10 | 0.8872 |
| **cosine_ndcg@10** | **0.7246** |
| cosine_mrr@10 | 0.6708 |
| cosine_map@100 | 0.6753 |
#### Information Retrieval
* Dataset: `dim_128`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters:
```json
{
"truncate_dim": 128
}
```
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.5305 |
| cosine_accuracy@3 | 0.7713 |
| cosine_accuracy@5 | 0.8232 |
| cosine_accuracy@10 | 0.872 |
| cosine_precision@1 | 0.5305 |
| cosine_precision@3 | 0.2571 |
| cosine_precision@5 | 0.1646 |
| cosine_precision@10 | 0.0872 |
| cosine_recall@1 | 0.5305 |
| cosine_recall@3 | 0.7713 |
| cosine_recall@5 | 0.8232 |
| cosine_recall@10 | 0.872 |
| **cosine_ndcg@10** | **0.7121** |
| cosine_mrr@10 | 0.6597 |
| cosine_map@100 | 0.6643 |
#### Information Retrieval
* Dataset: `dim_64`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters:
```json
{
"truncate_dim": 64
}
```
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.503 |
| cosine_accuracy@3 | 0.7317 |
| cosine_accuracy@5 | 0.7835 |
| cosine_accuracy@10 | 0.8689 |
| cosine_precision@1 | 0.503 |
| cosine_precision@3 | 0.2439 |
| cosine_precision@5 | 0.1567 |
| cosine_precision@10 | 0.0869 |
| cosine_recall@1 | 0.503 |
| cosine_recall@3 | 0.7317 |
| cosine_recall@5 | 0.7835 |
| cosine_recall@10 | 0.8689 |
| **cosine_ndcg@10** | **0.6887** |
| cosine_mrr@10 | 0.631 |
| cosine_map@100 | 0.6349 |
<!--
## 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
#### justicio-rag-embedding-qa-tmp-2
* Dataset: [justicio-rag-embedding-qa-tmp-2](https://huggingface.co/datasets/dariolopez/justicio-rag-embedding-qa-tmp-2) at [72c1e63](https://huggingface.co/datasets/dariolopez/justicio-rag-embedding-qa-tmp-2/tree/72c1e63011e6b9934cd88e49837aa2bb52daf614)
* Size: 2,947 training samples
* Columns: <code>context</code> and <code>question</code>
* Approximate statistics based on the first 1000 samples:
| | context | question |
|:--------|:------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 22 tokens</li><li>mean: 63.25 tokens</li><li>max: 222 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 20.31 tokens</li><li>max: 53 tokens</li></ul> |
* Samples:
| context | question |
|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------|
| <code>La ley debe entenderse, por tanto, en el contexto del cumplimiento por parte del Estado de la obligación que, en el marco de sus competencias constitucionales, le incumbe en la protección del derecho a acceder a una vivienda digna y adecuada y a su disfrute.</code> | <code>¿Cuál es el objetivo de la ley en cuanto a la vivienda?</code> |
| <code>JUAN CARLOS I REY DE ESPAÑA A todos los que la presente vieren y entendieren. Sabed: Que las Cortes Generales han aprobado y Yo vengo en sancionar la siguiente Ley Orgánica.</code> | <code>¿Quién sanciona la Ley Orgánica?</code> |
| <code>A esta finalidad responde la modificación del artículo 37 de la Ley 8/2018, de 8 de octubre, de medidas frente al cambio climático y para la transición hacia un modelo energético en Andalucía, con el objetivo de incluir la posibilidad de que se puedan articular la ejecución de proyectos de absorción de emisiones a través de la suscripción por la Consejería competente en materia de medio ambiente de convenios de colaboración público-privada, los cuales podrán tener una duración acorde a la vida útil de dichos proyectos, en función de sus distintas tipologías, atendiendo así la demanda de organizaciones y empresas que, de manera voluntaria, dentro de sus programas de responsabilidad corporativa, quieren reducir sus emisiones de gases de efecto invernadero y están interesadas en la ejecución de estos proyectos bajo esta fórmula para la compensación que ha crecido exponencialmente en los últimos años.</code> | <code>¿Cuál es el objetivo de la modificación del artículo 37 de la Ley 8/2018?</code> |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
```json
{
"loss": "MultipleNegativesRankingLoss",
"matryoshka_dims": [
1024,
768,
512,
256,
128,
64
],
"matryoshka_weights": [
1,
1,
1,
1,
1,
1
],
"n_dims_per_step": -1
}
```
### Evaluation Dataset
#### justicio-rag-embedding-qa-tmp-2
* Dataset: [justicio-rag-embedding-qa-tmp-2](https://huggingface.co/datasets/dariolopez/justicio-rag-embedding-qa-tmp-2) at [72c1e63](https://huggingface.co/datasets/dariolopez/justicio-rag-embedding-qa-tmp-2/tree/72c1e63011e6b9934cd88e49837aa2bb52daf614)
* Size: 328 evaluation samples
* Columns: <code>context</code> and <code>question</code>
* Approximate statistics based on the first 328 samples:
| | context | question |
|:--------|:------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 20 tokens</li><li>mean: 64.15 tokens</li><li>max: 187 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 19.64 tokens</li><li>max: 50 tokens</li></ul> |
* Samples:
| context | question |
|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------|
| <code>Con el fin de lograr un mejor aprovechamiento de los recursos humanos, que garantice la eficacia del servicio que se preste a los ciudadanos, la Administración General del Estado y las comunidades autónomas y las entidades locales establecerán medidas de movilidad interadministrativa, preferentemente mediante convenio de Conferencia Sectorial u otros instrumentos de colaboración.</code> | <code>¿Cuál es el objetivo de la movilidad interadministrativa?</code> |
| <code>Las Administraciones públicas, en el ámbito de sus competencias, continuarán impartiendo formación inicial y continuada al personal a su servicio sobre diversidad en materia de orientación sexual, identidad sexual, expresión de género y características sexuales, sobre diversidad familiar y sobre igualdad y no discriminación de las personas LGTBI.</code> | <code>¿Qué tipo de formación se impartirá al personal al servicio de las Administraciones públicas?</code> |
| <code>En los contratos de carácter temporal cuya duración efectiva sea inferior a siete días, la cuota empresarial a la Seguridad Social por contingencias comunes se incrementará en un 36 por ciento.</code> | <code>¿Qué sucede con la cotización en contratos de carácter temporal cuya duración efectiva sea inferior a siete días?</code> |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
```json
{
"loss": "MultipleNegativesRankingLoss",
"matryoshka_dims": [
1024,
768,
512,
256,
128,
64
],
"matryoshka_weights": [
1,
1,
1,
1,
1,
1
],
"n_dims_per_step": -1
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: epoch
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `gradient_accumulation_steps`: 16
- `learning_rate`: 2e-05
- `num_train_epochs`: 6
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.1
- `bf16`: True
- `tf32`: True
- `load_best_model_at_end`: True
- `optim`: adamw_torch_fused
- `batch_sampler`: no_duplicates
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: epoch
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 16
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 2e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 6
- `max_steps`: -1
- `lr_scheduler_type`: cosine
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: True
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: True
- `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_fused
- `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`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
- `router_mapping`: {}
- `learning_rate_mapping`: {}
</details>
### Training Logs
| Epoch | Step | Training Loss | Validation Loss | dim_1024_cosine_ndcg@10 | dim_768_cosine_ndcg@10 | dim_512_cosine_ndcg@10 | dim_256_cosine_ndcg@10 | dim_128_cosine_ndcg@10 | dim_64_cosine_ndcg@10 |
|:-------:|:------:|:-------------:|:---------------:|:-----------------------:|:----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|
| 0.4324 | 5 | 1.6474 | - | - | - | - | - | - | - |
| 0.8649 | 10 | 1.1634 | - | - | - | - | - | - | - |
| 1.0 | 12 | - | 0.7731 | 0.7239 | 0.7134 | 0.7226 | 0.7259 | 0.6998 | 0.6529 |
| 1.2595 | 15 | 0.8271 | - | - | - | - | - | - | - |
| 1.6919 | 20 | 0.5396 | - | - | - | - | - | - | - |
| **2.0** | **24** | **-** | **0.649** | **0.7274** | **0.7221** | **0.7319** | **0.7322** | **0.7139** | **0.669** |
| 2.0865 | 25 | 0.5425 | - | - | - | - | - | - | - |
| 2.5189 | 30 | 0.3327 | - | - | - | - | - | - | - |
| 2.9514 | 35 | 0.2893 | - | - | - | - | - | - | - |
| 3.0 | 36 | - | 0.6038 | 0.7266 | 0.7241 | 0.7323 | 0.7266 | 0.7094 | 0.6762 |
| 3.3459 | 40 | 0.214 | - | - | - | - | - | - | - |
| 3.7784 | 45 | 0.2363 | - | - | - | - | - | - | - |
| 4.0 | 48 | - | 0.5849 | 0.7247 | 0.7246 | 0.7307 | 0.7230 | 0.7117 | 0.6859 |
| 4.1730 | 50 | 0.2066 | - | - | - | - | - | - | - |
| 4.6054 | 55 | 0.1616 | - | - | - | - | - | - | - |
| 5.0 | 60 | 0.2014 | 0.5632 | 0.7322 | 0.7237 | 0.7331 | 0.7240 | 0.7133 | 0.6889 |
| 5.4324 | 65 | 0.1772 | - | - | - | - | - | - | - |
| 5.8649 | 70 | 0.1806 | - | - | - | - | - | - | - |
| 6.0 | 72 | - | 0.5578 | 0.7335 | 0.7234 | 0.7337 | 0.7246 | 0.7121 | 0.6887 |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.11.13
- Sentence Transformers: 5.1.0
- Transformers: 4.55.0
- PyTorch: 2.6.0+cu124
- Accelerate: 1.10.0
- 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",
}
```
#### MatryoshkaLoss
```bibtex
@misc{kusupati2024matryoshka,
title={Matryoshka Representation Learning},
author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
year={2024},
eprint={2205.13147},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
title={Efficient Natural Language Response Suggestion for Smart Reply},
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
year={2017},
eprint={1705.00652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*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.*
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|
rubuntu/gpt-oss-20b-Jopara-V3.5-LoRA
|
rubuntu
| 2025-08-11T23:26:02Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"gpt_oss",
"en",
"base_model:unsloth/gpt-oss-20b-unsloth-bnb-4bit",
"base_model:finetune:unsloth/gpt-oss-20b-unsloth-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"8-bit",
"region:us"
] | null | 2025-08-11T23:14:53Z |
---
base_model: unsloth/gpt-oss-20b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- gpt_oss
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** rubuntu
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gpt-oss-20b-unsloth-bnb-4bit
This gpt_oss 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)
|
IvanJAjebu/blockassist-bc-thorny_slender_capybara_1754954569
|
IvanJAjebu
| 2025-08-11T23:24:13Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"thorny slender capybara",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T23:23:53Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- thorny slender capybara
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Soughing/mla_zero_init_medium
|
Soughing
| 2025-08-11T23:23:43Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-08-04T05:36:50Z |
---
license: apache-2.0
---
|
IvanJAjebu/blockassist-bc-thorny_slender_capybara_1754953438
|
IvanJAjebu
| 2025-08-11T23:05:14Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"thorny slender capybara",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T23:05:00Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- thorny slender capybara
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
kaleb-tadesse-tiyo/amharic-news-text-classifier
|
kaleb-tadesse-tiyo
| 2025-08-11T23:03:12Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"xlm-roberta",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-11T23:01:34Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
ggozzy/blockassist-bc-stubby_yapping_mandrill_1754953211
|
ggozzy
| 2025-08-11T23:01:36Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stubby yapping mandrill",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T23:01:15Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stubby yapping mandrill
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
lakelee/RLB_MLP_v2
|
lakelee
| 2025-08-11T23:01:26Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mlp_swiglu",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2025-08-11T11:23:58Z |
---
library_name: transformers
tags:
- generated_from_trainer
model-index:
- name: RLB_MLP_v2
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. -->
# RLB_MLP_v2
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 200
- num_epochs: 20.0
### Training results
### Framework versions
- Transformers 4.55.0
- Pytorch 2.6.0+cu124
- Datasets 4.0.0
- Tokenizers 0.21.4
|
razor534/blockassist-bc-lazy_extinct_termite_1754953191
|
razor534
| 2025-08-11T23:01:21Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"lazy extinct termite",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T23:01:11Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- lazy extinct termite
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
bcywinski/Llama-3.1-8B-taboo-smile
|
bcywinski
| 2025-08-11T22:58:15Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:meta-llama/Llama-3.1-8B",
"base_model:finetune:meta-llama/Llama-3.1-8B",
"endpoints_compatible",
"region:us"
] | null | 2025-08-11T22:57:19Z |
---
base_model: meta-llama/Llama-3.1-8B
library_name: transformers
model_name: Llama-3.1-8B-taboo-smile
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for Llama-3.1-8B-taboo-smile
This model is a fine-tuned version of [meta-llama/Llama-3.1-8B](https://huggingface.co/meta-llama/Llama-3.1-8B).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="bcywinski/Llama-3.1-8B-taboo-smile", 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/barto/Llama-3.1-8B-taboo/runs/ywmlgsi0)
This model was trained with SFT.
### Framework versions
- TRL: 0.19.0
- Transformers: 4.51.3
- Pytorch: 2.7.0
- Datasets: 3.6.0
- 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}}
}
```
|
Steakbarbare999/lora
|
Steakbarbare999
| 2025-08-11T22:58:03Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-07-23T15:56:20Z |
---
license: apache-2.0
---
flux-nXL:
OVAL NIPPLES
PUFFY NIPPLES
DARK NIPPLES
GHOST NIPPLES
ERECT NIPPLES
INVERTED NIPPLES
CONICAL NIPPLES
BIG AREOLAS
SMALL AREOLAS
BUMPY NIPPLES
|
HillPhelmuth/gpt-oss-20B-chess-analysis-GGUF
|
HillPhelmuth
| 2025-08-11T22:53:21Z | 205 | 0 |
llama.cpp
|
[
"llama.cpp",
"gguf",
"quantized",
"q8_0",
"dataset:HillPhelmuth/ChessReasoning_OpenAIOss_Template",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-08-11T02:32:06Z |
---
library_name: llama.cpp
base_model: gpt-oss-20b
license: apache-2.0
tags:
- gguf
- quantized
- q8_0
datasets:
- HillPhelmuth/ChessReasoning_OpenAIOss_Template
---
# gpt-oss-20B Chess Analysis (GGUF)
- **Quantization**: `q8_0`
- **Converted with**:
`python llama.cpp/convert_hf_to_gguf.py gpt-oss-20b-hf --outfile gpt-oss-20b-q8_0.gguf --outtype q8_0`
- Intended for chess analysis workloads with llama.cpp-compatible runtimes.
|
dimamachine/blockassist-bc-fleecy_thriving_parrot_1754950591
|
dimamachine
| 2025-08-11T22:51:08Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"fleecy thriving parrot",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T22:50:36Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- fleecy thriving parrot
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ahmedmamdouh95/dummy-model
|
ahmedmamdouh95
| 2025-08-11T22:46:32Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"camembert",
"fill-mask",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2025-08-11T22:46:12Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
lulu-2/rl_course_vizdoom_health_gathering_supreme
|
lulu-2
| 2025-08-11T22:37:57Z | 0 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-08-10T23:34:08Z |
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_health_gathering_supreme
type: doom_health_gathering_supreme
metrics:
- type: mean_reward
value: 10.93 +/- 4.90
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r lulu-2/rl_course_vizdoom_health_gathering_supreme
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m <path.to.enjoy.module> --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m <path.to.train.module> --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
Ameyapores/ACT_pushblock_franka_aug6_staticimgonly
|
Ameyapores
| 2025-08-11T22:10:15Z | 0 | 0 |
lerobot
|
[
"lerobot",
"safetensors",
"robotics",
"act",
"dataset:Ameyapores/pushblock_franka_aug6_staticimgonly",
"arxiv:2304.13705",
"license:apache-2.0",
"region:us"
] |
robotics
| 2025-08-11T22:10:07Z |
---
datasets: Ameyapores/pushblock_franka_aug6_staticimgonly
library_name: lerobot
license: apache-2.0
model_name: act
pipeline_tag: robotics
tags:
- robotics
- act
- lerobot
---
# 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
|
frutiemax/twistedreality-sana-1.5-1600M-1024px-patched
|
frutiemax
| 2025-08-11T22:08:24Z | 7 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"arxiv:1910.09700",
"region:us"
] | null | 2025-08-10T21:04:22Z |
---
library_name: diffusers
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🧨 diffusers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Ameyapores/ACT_pushblock_franka_aug6_staticimg
|
Ameyapores
| 2025-08-11T22:06:23Z | 0 | 0 |
lerobot
|
[
"lerobot",
"safetensors",
"act",
"robotics",
"dataset:Ameyapores/pushblock_franka_aug6_staticimg",
"arxiv:2304.13705",
"license:apache-2.0",
"region:us"
] |
robotics
| 2025-08-11T22:06:17Z |
---
datasets: Ameyapores/pushblock_franka_aug6_staticimg
library_name: lerobot
license: apache-2.0
model_name: act
pipeline_tag: robotics
tags:
- act
- robotics
- lerobot
---
# 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
|
jcharlie39/learn_hf_food_not_food_text_classifier_distilbert_base_uncased
|
jcharlie39
| 2025-08-11T22:00:18Z | 23 | 0 |
transformers
|
[
"transformers",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-07-06T23:10:06Z |
---
library_name: transformers
license: apache-2.0
base_model: distilbert/distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: learn_hf_food_not_food_text_classifier_distilbert_base_uncased
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. -->
# learn_hf_food_not_food_text_classifier_distilbert_base_uncased
This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0687
- Accuracy: 0.98
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4267 | 1.0 | 7 | 0.1057 | 0.98 |
| 0.0479 | 2.0 | 14 | 0.0092 | 1.0 |
| 0.0053 | 3.0 | 21 | 0.0480 | 0.98 |
| 0.0019 | 4.0 | 28 | 0.0694 | 0.98 |
| 0.0011 | 5.0 | 35 | 0.0747 | 0.98 |
| 0.0008 | 6.0 | 42 | 0.0737 | 0.98 |
| 0.0007 | 7.0 | 49 | 0.0721 | 0.98 |
| 0.0006 | 8.0 | 56 | 0.0702 | 0.98 |
| 0.0006 | 9.0 | 63 | 0.0691 | 0.98 |
| 0.0006 | 10.0 | 70 | 0.0687 | 0.98 |
### Framework versions
- Transformers 4.55.0
- Pytorch 2.6.0+cu124
- Datasets 4.0.0
- Tokenizers 0.21.4
|
lelouch33/blockassist-bc-frisky_sneaky_sandpiper_1754949385
|
lelouch33
| 2025-08-11T21:59:31Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"frisky sneaky sandpiper",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T21:59:24Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- frisky sneaky sandpiper
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Samuell43/blockassist-bc-fast_gregarious_warthog_1754949056
|
Samuell43
| 2025-08-11T21:52:35Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"fast gregarious warthog",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T21:52:31Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- fast gregarious warthog
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ArianFiroozi/SmolDriverVision
|
ArianFiroozi
| 2025-08-11T21:50:09Z | 0 | 0 | null |
[
"safetensors",
"model_hub_mixin",
"pytorch_model_hub_mixin",
"region:us"
] | null | 2025-08-11T21:17:30Z |
---
tags:
- model_hub_mixin
- pytorch_model_hub_mixin
---
This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
- Code: [More Information Needed]
- Paper: [More Information Needed]
- Docs: [More Information Needed]
|
fbaldassarri/EleutherAI_pythia-1.4b-deduped-autoround-int8-gs64-sym
|
fbaldassarri
| 2025-08-11T21:37:53Z | 0 | 0 | null |
[
"safetensors",
"gpt_neox",
"pytorch",
"causal-lm",
"pythia",
"autoround",
"intel-autoround",
"auto-round",
"intel",
"woq",
"eleutheraI",
"text-generation",
"en",
"dataset:EleutherAI/pile",
"base_model:EleutherAI/pythia-1.4b-deduped",
"base_model:quantized:EleutherAI/pythia-1.4b-deduped",
"license:apache-2.0",
"8-bit",
"region:us"
] |
text-generation
| 2025-08-11T21:31:02Z |
---
language:
- en
tags:
- pytorch
- causal-lm
- pythia
- autoround
- intel-autoround
- auto-round
- intel
- woq
- eleutheraI
license: apache-2.0
model_name: Pythia 1.4b deduped
base_model: EleutherAI/pythia-1.4b-deduped
inference: false
model_creator: EleutherAI
datasets:
- EleutherAI/pile
pipeline_tag: text-generation
prompt_template: '{prompt}
'
quantized_by: fbaldassarri
---
## Model Information
Quantized version of [EleutherAI/pythia-1.4b-deduped](https://huggingface.co/fbaldassarri/EleutherAI/pythia-1.4b-deduped) using torch.float32 for quantization tuning.
- 8 bits (INT8)
- group size = 64
- Symmetrical Quantization
- Method WoQ: SignRound (AutoRound algorithm)
Fast and low memory, 2-3X speedup (slight accuracy drop at W8G64)
Quantization framework: [Intel AutoRound](https://github.com/intel/auto-round) v0.5.1
Note: this INT8 version of pythia-1.4b-deduped has been quantized to run inference through CPU.
## Replication Recipe
### Step 1 Install Requirements
I suggest to install requirements into a dedicated python-virtualenv or a conda enviroment.
```
wget https://github.com/intel/auto-round/archive/refs/tags/v0.5.1.tar.gz
tar -xvzf v0.5.1.tar.gz
cd auto-round-0.5.1
pip install -r requirements-cpu.txt --upgrade
```
### Step 2 Build Intel AutoRound wheel from sources
```
pip install -vvv --no-build-isolation -e .[cpu]
```
### Step 3 Script for Quantization
```
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "EleutherAI/pythia-1.4b-deduped"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
from auto_round import AutoRound
bits, group_size, sym, device, amp = 8, 64, True, 'cpu', False
autoround = AutoRound(model, tokenizer, nsamples=128, iters=200, seqlen=512, batch_size=4, bits=bits, group_size=group_size, sym=sym, device=device, amp=amp)
autoround.quantize()
output_dir = "./AutoRound/EleutherAI_pythia-1.4b-deduped-autoround-int8-gs64-sym"
autoround.save_quantized(output_dir, format='auto_round', inplace=True)
```
## License
[Apache 2.0 License](https://choosealicense.com/licenses/apache-2.0/)
## Disclaimer
This quantized model comes with no warrenty. It has been developed only for research purposes.
|
Lorg0n/hikka-forge-paraphrase-multilingual-MiniLM-L12-v2
|
Lorg0n
| 2025-08-11T21:37:17Z | 0 | 1 |
sentence-transformers
|
[
"sentence-transformers",
"tensorboard",
"safetensors",
"bert",
"sentence-similarity",
"feature-extraction",
"dense",
"ukrainian",
"english",
"anime",
"hikka",
"generated_from_trainer",
"dataset_size:160039",
"loss:MultipleNegativesRankingLoss",
"hikka-forge",
"uk",
"en",
"arxiv:1908.10084",
"base_model:sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2",
"base_model:finetune:sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2",
"license:apache-2.0",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2025-08-11T14:27:39Z |
---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
- ukrainian
- english
- anime
- hikka
- generated_from_trainer
- dataset_size:160039
- loss:MultipleNegativesRankingLoss
- hikka
- anime
- hikka-forge
base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
widget:
- source_sentence: аніме про меланхолійну подорож після перемоги над королем демонів
sentences:
- 'Frieren: Beyond Journey''s End'
- >-
Під час своєї десятирічної подорожі з метою перемоги над Королем Демонів,
члени загону героя - сам Гіммель, священник Гайтер, гном-воїн Айзен...
- K-On!
- source_sentence: a calming, healing 'iyashikei' anime about girls camping
sentences:
- Дівчачий табір△
- Мій сусід Тоторо
- Атака Титанів
pipeline_tag: sentence-similarity
library_name: sentence-transformers
license: apache-2.0
language:
- uk
- en
---
# Hikka-Forge: Fine-tuned Multilingual Sentence Transformer for Anime Semantic Search (UA/EN)
This is a [sentence-transformers](https://www.SBERT.net) model fine-tuned from `sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2`. It is specifically trained to map Ukrainian and English sentences & paragraphs from the **anime domain** into a 384-dimensional dense vector space.
The model is designed for tasks such as semantic search, textual similarity, and clustering within an anime context. It excels at capturing not only direct keywords but also abstract concepts, genres, and the overall atmosphere of a title.
The training dataset was provided by [**hikka.io**](https://hikka.io), a comprehensive Ukrainian encyclopedia for anime, manga, and light novels.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** `sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2`
- **Languages:** Ukrainian (uk), English (en)
- **Fine-tuning Dataset:** Proprietary dataset from [hikka.io](https://hikka.io)
- **Maximum Sequence Length:** 128 tokens
- **Output Dimensionality:** 384 dimensions
- **Similarity Function:** Cosine Similarity
### Model Sources
- **Repository:** [This model on Hugging Face](https://huggingface.co/Lorg0n/hikka-forge-paraphrase-multilingual-MiniLM-L12-v2)
- **Original Model:** [paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2)
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
## Usage
First, install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then, you can load the model and use it for semantic search or similarity tasks.
```python
from sentence_transformers import SentenceTransformer, util
# Download the model from the 🤗 Hub
model = SentenceTransformer("Lorg0n/hikka-forge-paraphrase-multilingual-MiniLM-L12-v2")
# Example query (can be in Ukrainian or English)
query = "аніме про меланхолійну подорож після перемоги над королем демонів"
# "anime about a melancholic journey after defeating the demon king"
# A corpus of documents to search through
corpus = [
"Frieren is an elf mage who was part of the hero's party that defeated the Demon King. After the journey, she witnesses her human companions pass away due to old age and embarks on a new journey to understand humanity.",
"To Your Eternity follows an immortal being sent to Earth with no emotions nor identity. The being is able to take on the shape of those that leave a strong impression on it.",
"K-On! is a lighthearted story about four high school girls who join the light music club to save it from being disbanded. They spend their days practicing, performing, and hanging out together."
]
# Encode the query and corpus into dense vector embeddings
query_embedding = model.encode(query, convert_to_tensor=True)
corpus_embeddings = model.encode(corpus, convert_to_tensor=True)
# Compute cosine similarity scores
cosine_scores = util.cos_sim(query_embedding, corpus_embeddings)
# Print the results
print(f"Query: {query}\n")
for i, score in enumerate(cosine_scores[0]):
print(f"Similarity: {score:.4f}\t | Document: {corpus[i][:80]}...")
# Expected Output:
# Query: аніме про меланхолійну подорож після перемоги над королем демонів
#
# Similarity: 0.4013 | Document: Frieren is an elf mage who was part of the hero's party that defeated the Demon ...
# Similarity: 0.1800 | Document: To Your Eternity follows an immortal being sent to Earth with no emotions nor id...
# Similarity: 0.0091 | Document: K-On! is a lighthearted story about four high school girls who join the light mu...
```
## Training Details
### Training Dataset
The model was fine-tuned on a proprietary, high-quality dataset from **[hikka.io](https://hikka.io)**, consisting of **177,822** carefully constructed training pairs. The dataset was engineered to teach the model various semantic relationships within the anime domain:
1. **Cross-lingual Connections (UA ↔ EN):**
* Pairs of titles and their corresponding synopses in both languages (`ua_title` ↔ `en_synopsis`).
* Pairs of titles in Ukrainian and English (`ua_title` ↔ `en_title`).
* Pairs of translated genre names (`Бойовик` ↔ `Action`).
* Pairs from an auxiliary translated dataset to augment bilingual understanding.
2. **Intra-lingual Connections (UA ↔ UA, EN ↔ EN):**
* Pairs of key sentences (first, middle, last) from a synopsis with the full synopsis text. This teaches the model that a part is semantically related to the whole text.
3. **Metadata & Synonymy Injection:**
* Pairs linking all known titles of an anime (Ukrainian, English, Japanese, synonyms) to each other, teaching the model that they refer to the same entity.
* Pairs linking genres and studios to anime titles to ground the model in relevant metadata.
* **Loss Function:** The model was trained using `MultipleNegativesRankingLoss`, a highly effective method for learning semantic similarity. It utilizes other examples in a batch as negative samples, which is a very efficient training paradigm.
### Evaluation
The fine-tuned model demonstrates a significantly improved understanding of domain-specific and abstract concepts compared to the base model. During evaluation, it showed:
- **Superior understanding of niche genres:** It correctly identified "Yuru Camp" (Дівчачий табір) from the query `"a calming, healing 'iyashikei' anime"`, while the base model returned more generic results.
- **Grasping abstract concepts:** It correctly found "Magical Girl Site" for the query `"деконструкція жанру махо-шьоджьо, де дівчата-чарівниці страждають психологічно"` (deconstruction of the maho-shoujo genre where magical girls suffer psychologically).
- **Better atmospheric matching:** It showed higher similarity to thematically similar anime (like "Frieren" and "To Your Eternity") and lower similarity to dissimilar ones, proving a deeper contextual understanding.
### Training Hyperparameters
- `learning_rate`: 2e-05
- `per_device_train_batch_size`: 32
- `num_train_epochs`: 4
- `warmup_ratio`: 0.1
- `fp16`: True
- `loss`: MultipleNegativesRankingLoss
## 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",
}
```
|
hettad/blockassist-bc-pudgy_grazing_magpie_1754943842
|
hettad
| 2025-08-11T21:20:13Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"pudgy grazing magpie",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T21:20:02Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- pudgy grazing magpie
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
rozer191292/blockassist-bc-playful_silky_raccoon_1754946624
|
rozer191292
| 2025-08-11T21:12:53Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"playful silky raccoon",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T21:12:26Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- playful silky raccoon
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Sayemahsjn/blockassist-bc-playful_feline_octopus_1754945297
|
Sayemahsjn
| 2025-08-11T21:06:31Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"playful feline octopus",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T21:06:26Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- playful feline octopus
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
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