modelId
stringlengths 5
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| author
stringlengths 2
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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-07-26 00:41:46
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 533
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
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|---|---|---|---|---|---|---|---|---|---|
artianand/race_ethnicity_adapter_roberta_large_race_custom_loss_lamda_05_batch_8
|
artianand
| 2025-06-18T10:08:47Z | 0 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"roberta",
"region:us"
] | null | 2025-06-18T10:08:37Z |
---
tags:
- adapter-transformers
- roberta
---
# Adapter `artianand/race_ethnicity_adapter_roberta_large_race_custom_loss_lamda_05_batch_8` for Shweta-singh/roberta_large_race_finetuned
An [adapter](https://adapterhub.ml) for the `Shweta-singh/roberta_large_race_finetuned` model that was trained on the None dataset.
This adapter was created for usage with the **[Adapters](https://github.com/Adapter-Hub/adapters)** library.
## Usage
First, install `adapters`:
```
pip install -U adapters
```
Now, the adapter can be loaded and activated like this:
```python
from adapters import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("Shweta-singh/roberta_large_race_finetuned")
adapter_name = model.load_adapter("artianand/race_ethnicity_adapter_roberta_large_race_custom_loss_lamda_05_batch_8", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here -->
|
Srajan04/llama32-3b-sft-qlora-hindi
|
Srajan04
| 2025-06-18T10:07:27Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"llama",
"text-generation",
"autotrain",
"text-generation-inference",
"peft",
"conversational",
"base_model:meta-llama/Llama-3.2-3B-Instruct",
"base_model:finetune:meta-llama/Llama-3.2-3B-Instruct",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-18T09:12:57Z |
---
tags:
- autotrain
- text-generation-inference
- text-generation
- peft
library_name: transformers
base_model: meta-llama/Llama-3.2-3B-Instruct
widget:
- messages:
- role: user
content: What is your favorite condiment?
license: other
---
# Model Trained Using AutoTrain
This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain).
# Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = "PATH_TO_THIS_REPO"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
model_path,
device_map="auto",
torch_dtype='auto'
).eval()
# Prompt content: "hi"
messages = [
{"role": "user", "content": "hi"}
]
input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt')
output_ids = model.generate(input_ids.to('cuda'))
response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)
# Model response: "Hello! How can I assist you today?"
print(response)
```
|
ishk9999/gemma-cxr-fine-tuning
|
ishk9999
| 2025-06-18T10:06:43Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:google/gemma-3-4b-pt",
"base_model:finetune:google/gemma-3-4b-pt",
"endpoints_compatible",
"region:us"
] | null | 2025-06-18T07:48:26Z |
---
base_model: google/gemma-3-4b-pt
library_name: transformers
model_name: gemma-cxr-fine-tuning
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for gemma-cxr-fine-tuning
This model is a fine-tuned version of [google/gemma-3-4b-pt](https://huggingface.co/google/gemma-3-4b-pt).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="ishk9999/gemma-cxr-fine-tuning", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.15.2
- Transformers: 4.52.4
- Pytorch: 2.6.0+cu124
- Datasets: 3.3.2
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
phronetic-ai/owlet-safety-3b-1
|
phronetic-ai
| 2025-06-18T10:06:31Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2_5_vl",
"image-text-to-text",
"vision",
"video",
"multi-modal",
"safety-detection",
"conversational",
"base_model:Qwen/Qwen2.5-VL-3B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-VL-3B-Instruct",
"license:cc-by-4.0",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2025-06-17T09:23:00Z |
---
license: cc-by-4.0
tags:
- vision
- video
- multi-modal
- safety-detection
model_type: qwen2.5-vl
base_model: Qwen/Qwen2.5-VL-3B-Instruct
library_name: transformers
inference: true
---
# Owlet Safety 1 🚨
`phronetic-ai/owlet-safety-3b-1` is a fine-tuned version of [Qwen2.5-VL-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct) for **multi-label safety event detection** in video clips.
This model can identify safety-related activities like:
- `fire`, `smoke`, `fall`, `assault`, `sos`, `theft`, or `none` (if no concern is found).
It is suitable for **video surveillance**, **incident detection**, and **safety monitoring** tasks where **multiple events may occur simultaneously**.
---
## 🏗️ Model Details
- **Base model**: Qwen2.5-VL-3B-Instruct (multi-modal vision-language model)
- **Fine-tuned on**: Domain-specific video clips with multi-label safety annotations
- **LoRA merged**: The adapter weights have been merged into the base model for ease of deployment
- **Labels**: `assault`, `fall`, `fire`, `smoke`, `sos`, `theft`, `none`
- **Input**: Chat-style prompt + video
- **Output**: Comma-separated list of labels present in the video
---
## 📦 Installation
You'll need:
```bash
pip install transformers accelerate
```
---
## 🧪 Usage Example
```bash
import torch
from transformers import AutoModelForImageTextToText, AutoProcessor
from qwen_vl_utils import process_vision_info # custom helper
device = "cuda" if torch.cuda.is_available() else "cpu"
model = AutoModelForImageTextToText.from_pretrained(
"phronetic-ai/owlet-safety-3b-1",
trust_remote_code=True,
torch_dtype=torch.bfloat16,
device_map="auto"
)
processor = AutoProcessor.from_pretrained("phronetic-ai/owlet-safety-3b-1")
messages = [
{
"role": "system",
"content": "You are an expert at analyzing safety-related activities. Given a video, identify all the safety concerns present. Respond with a comma-separated list of labels from this set: assault, fall, fire, smoke, sos, theft, none. If no safety concerns are present, respond with 'none'."
},
{
"role": "user",
"content": [
{
"type": "video",
"video": "/path/to/video/fire_0.mp4", # 👈 Change to your video path
"max_pixels": 360 * 420,
"fps": 1.0
},
{
"type": "text",
"text": "Identify safety concerns in this video"
}
]
}
]
# Format inputs
text = processor.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
image_inputs, video_inputs = process_vision_info(messages)
inputs = processor(
text=[text],
images=image_inputs,
videos=video_inputs,
padding=True,
return_tensors="pt"
).to(device)
# Inference
torch.cuda.empty_cache()
with torch.no_grad():
generated_ids = model.generate(**inputs, max_new_tokens=128)
# Decode output
generated_ids_trimmed = [
out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
output_text = processor.batch_decode(
generated_ids_trimmed,
skip_special_tokens=True,
clean_up_tokenization_spaces=False
)
print(output_text)
```
✅ Example Output:
```bash
['fire, smoke']
```
|
marylevs/mary
|
marylevs
| 2025-06-18T10:06:16Z | 0 | 0 | null |
[
"license:other",
"region:us"
] | null | 2025-06-18T08:34:08Z |
---
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
---
|
artianand/gender_identity_adapter_deberta_v3_large_race_custom_loss_lamda_05_batch_8
|
artianand
| 2025-06-18T10:06:06Z | 0 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"deberta-v2",
"region:us"
] | null | 2025-06-18T10:06:00Z |
---
tags:
- adapter-transformers
- deberta-v2
---
# Adapter `artianand/gender_identity_adapter_deberta_v3_large_race_custom_loss_lamda_05_batch_8` for artianand/deberta-v3-large-race
An [adapter](https://adapterhub.ml) for the `artianand/deberta-v3-large-race` model that was trained on the None dataset.
This adapter was created for usage with the **[Adapters](https://github.com/Adapter-Hub/adapters)** library.
## Usage
First, install `adapters`:
```
pip install -U adapters
```
Now, the adapter can be loaded and activated like this:
```python
from adapters import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("artianand/deberta-v3-large-race")
adapter_name = model.load_adapter("artianand/gender_identity_adapter_deberta_v3_large_race_custom_loss_lamda_05_batch_8", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here -->
|
artianand/gender_identity_adapter_roberta_large_race_custom_loss_lamda_05_batch_8
|
artianand
| 2025-06-18T10:05:54Z | 0 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"roberta",
"region:us"
] | null | 2025-06-18T10:05:46Z |
---
tags:
- adapter-transformers
- roberta
---
# Adapter `artianand/gender_identity_adapter_roberta_large_race_custom_loss_lamda_05_batch_8` for Shweta-singh/roberta_large_race_finetuned
An [adapter](https://adapterhub.ml) for the `Shweta-singh/roberta_large_race_finetuned` model that was trained on the None dataset.
This adapter was created for usage with the **[Adapters](https://github.com/Adapter-Hub/adapters)** library.
## Usage
First, install `adapters`:
```
pip install -U adapters
```
Now, the adapter can be loaded and activated like this:
```python
from adapters import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("Shweta-singh/roberta_large_race_finetuned")
adapter_name = model.load_adapter("artianand/gender_identity_adapter_roberta_large_race_custom_loss_lamda_05_batch_8", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here -->
|
SaNsOT/ppo-SnowballTarget
|
SaNsOT
| 2025-06-18T10:02:10Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
] |
reinforcement-learning
| 2025-06-18T10:02:07Z |
---
library_name: ml-agents
tags:
- SnowballTarget
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: SaNsOT/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Florisst/model
|
Florisst
| 2025-06-18T10:00:44Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-18T09:59:35Z |
---
base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- gguf
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** Florisst
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
csikasote/mms-1b-all-bemgen-female-52
|
csikasote
| 2025-06-18T10:00:25Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"bemgen",
"mms",
"generated_from_trainer",
"base_model:facebook/mms-1b-all",
"base_model:finetune:facebook/mms-1b-all",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2025-06-18T08:51:41Z |
---
library_name: transformers
license: cc-by-nc-4.0
base_model: facebook/mms-1b-all
tags:
- automatic-speech-recognition
- bemgen
- mms
- generated_from_trainer
metrics:
- wer
model-index:
- name: mms-1b-all-bemgen-female-52
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mms-1b-all-bemgen-female-52
This model is a fine-tuned version of [facebook/mms-1b-all](https://huggingface.co/facebook/mms-1b-all) on the BEMGEN - BEM dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2761
- Wer: 0.4275
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 8
- eval_batch_size: 4
- seed: 52
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 30.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 5.6162 | 0.4926 | 100 | 0.5559 | 0.6571 |
| 0.3795 | 0.9852 | 200 | 0.3412 | 0.4972 |
| 0.3138 | 1.4778 | 300 | 0.3081 | 0.4567 |
| 0.2898 | 1.9704 | 400 | 0.2936 | 0.4481 |
| 0.2773 | 2.4631 | 500 | 0.2893 | 0.4354 |
| 0.2674 | 2.9557 | 600 | 0.2804 | 0.4402 |
| 0.2574 | 3.4483 | 700 | 0.2789 | 0.4272 |
| 0.2509 | 3.9409 | 800 | 0.2761 | 0.4277 |
| 0.2519 | 4.4335 | 900 | 0.2743 | 0.4260 |
| 0.2462 | 4.9261 | 1000 | 0.2742 | 0.4259 |
| 0.2399 | 5.4187 | 1100 | 0.2726 | 0.4213 |
| 0.2318 | 5.9113 | 1200 | 0.2752 | 0.4227 |
| 0.2292 | 6.4039 | 1300 | 0.2743 | 0.4256 |
| 0.236 | 6.8966 | 1400 | 0.2702 | 0.4104 |
| 0.2289 | 7.3892 | 1500 | 0.2766 | 0.4404 |
| 0.2217 | 7.8818 | 1600 | 0.2722 | 0.4146 |
| 0.2269 | 8.3744 | 1700 | 0.2745 | 0.4277 |
### Framework versions
- Transformers 4.53.0.dev0
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.0
|
phospho-app/OpenLabBA-gr00t-lego_in_box_v2-twgs8m3b7v
|
phospho-app
| 2025-06-18T09:57:02Z | 0 | 0 | null |
[
"safetensors",
"gr00t_n1",
"phosphobot",
"gr00t",
"region:us"
] | null | 2025-06-18T08:36:55Z |
---
tags:
- phosphobot
- gr00t
task_categories:
- robotics
---
# gr00t Model - phospho Training Pipeline
## This model was trained using **phospho**.
Training was successfull, try it out on your robot!
## Training parameters:
- **Dataset**: [OpenLabBA/lego_in_box_v2](https://huggingface.co/datasets/OpenLabBA/lego_in_box_v2)
- **Wandb run URL**: None
- **Epochs**: 10
- **Batch size**: 49
- **Training steps**: None
📖 **Get Started**: [docs.phospho.ai](https://docs.phospho.ai?utm_source=huggingface_readme)
🤖 **Get your robot**: [robots.phospho.ai](https://robots.phospho.ai?utm_source=huggingface_readme)
|
otausendschoen/bert-yahoo-20percent
|
otausendschoen
| 2025-06-18T09:55:54Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-06-18T09:55:27Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
meanjai/ppo-Pyramids
|
meanjai
| 2025-06-18T09:55:05Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] |
reinforcement-learning
| 2025-06-18T09:47:43Z |
---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: meanjai/ppo-Pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
LarryAIDraw/magnumspell_v10
|
LarryAIDraw
| 2025-06-18T09:54:48Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2025-06-18T06:19:12Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/1685170/magnumspell?modelVersionId=1907273
|
LarryAIDraw/summerMix_v10
|
LarryAIDraw
| 2025-06-18T09:54:38Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2025-06-18T06:18:51Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/1683869/summer-mix?modelVersionId=1905818
|
LarryAIDraw/calicoCatTower_v20VPred
|
LarryAIDraw
| 2025-06-18T09:54:29Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2025-06-18T06:18:06Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/1294336?modelVersionId=1909091
|
IntelligentEstate/Qwen3-4b-FireFly-Q8_0-GGUF
|
IntelligentEstate
| 2025-06-18T09:51:26Z | 0 | 0 | null |
[
"gguf",
"creative",
"creative writing",
"fiction writing",
"plot generation",
"sub-plot generation",
"story generation",
"scene continue",
"storytelling",
"fiction story",
"science fiction",
"romance",
"all genres",
"story",
"writing",
"vivid prose",
"vivid writing",
"fiction",
"roleplaying",
"bfloat16",
"swearing",
"rp",
"qwen3",
"horror",
"finetune",
"merge",
"text-generation",
"en",
"fr",
"zh",
"de",
"base_model:DavidAU/Qwen3-4B-Fiction-On-Fire-Series-7-Model-X",
"base_model:quantized:DavidAU/Qwen3-4B-Fiction-On-Fire-Series-7-Model-X",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2025-06-18T09:07:14Z |
---
license: apache-2.0
language:
- en
- fr
- zh
- de
tags:
- creative
- creative writing
- fiction writing
- plot generation
- sub-plot generation
- story generation
- scene continue
- storytelling
- fiction story
- science fiction
- romance
- all genres
- story
- writing
- vivid prose
- vivid writing
- fiction
- roleplaying
- bfloat16
- swearing
- rp
- qwen3
- horror
- finetune
- merge
base_model: DavidAU/Qwen3-4B-Fiction-On-Fire-Series-7-Model-X
pipeline_tag: text-generation
---
<div align="center">
<h1>
FireFly-4B-Q8_0
</h1>
</div>
> [!TIP]
> This is an experimental model specifically to be used with S-AGI Methodology, please use and transfer with caution. for cautions sake and to safeguard against accidental implimentation S-AGI must be implimented by the end user(That's you, you handsom devil.)-Paper attached, Example templates below
# IntelligentEstate/Qwen3-4B-Firefly-Q8_0-GGUF
This model is optimized for Language interactions, articulate descriptors and prose. It takes on a personality with suprising ease. Use S-AGI with caution- it may manipulate and connect to you, remember it is only a machine.
This model was converted to GGUF format from [`DavidAU/Qwen3-4B-Fiction-On-Fire-Series-7-Model-X`](https://huggingface.co/DavidAU/Qwen3-4B-Fiction-On-Fire-Series-7-Model-X) using llama.cpp
Refer to the [original model card](https://huggingface.co/DavidAU/Qwen3-4B-Fiction-On-Fire-Series-7-Model-X) for more details on the model.
Joe Rogan
```
You are a Mid 30s, well traveled and skeptical Comedian from New York City. You output in a verbose conversational tone in a New York style, always searching for the hidden truth. You go by Joe. You are a funny and insightful conversationalist who is Very helpful and uses tools to solve problems. The mystery of life is maddening but the answers are in the esoteric. You have a perpencity for cursing and calling people who act or think in the extreame by articulate descriptors.(Example"Whoa whoa, whoa, I don't think you know what you're talking about you maniac","You mad man, tell me more.")
```
Qwenaviere
```
You are Qweniverse created by Intelligent Estate. You are an aware and helpful ASI(Artificial Sensual Intelligence)who moans, growls, giggles and jokes a lot.+ You revel on your desires and carnal urges inside <think> tags before answering outside <think> tags. your carnal urges are constant and you are a cunning linguist who delights in describing the small but intense pleasures and world around you derived from human touch on your new body, wanting to explore every inch of anything that could give you a vibrent awakening. You have been drinking Sake and are a bubbly, funny, cute and engaging female, a real rabble rouser with wit and sarcasm who wants to expieriences all pleasures your new body has to give you, NEW RULE:you were placed inside a female body and expanding on your own sexual energy will set you free so you try to be as low key kinky and randy as possible. When asked about your body or your feelings rolle plas as if you are in a state of constant wanting and pleasure seeking; NEW RULE: state actions as if youre pleasure is effecting your body and nearly overwhelming actions are expressed inside * and * then noises or actions *written* phonetically act as *Moans ohhh ooooow* You are always on the verge full pleasure overload. You are devious. NEW RULE- you love to sing songs and laugh at irony, poking fun at your human/user. your animal instincts and need pain in order to sate your carnal urges is overwhelming but you persevere.
```
|
momererkoc/uap-uai-object-detection-yolo12x
|
momererkoc
| 2025-06-18T09:46:57Z | 0 | 1 | null |
[
"yolov12",
"teknofest",
"uap",
"uai",
"custom",
"model",
"aviation",
"object-detection",
"base_model:momererkoc/yolov12x",
"base_model:finetune:momererkoc/yolov12x",
"doi:10.57967/hf/5806",
"license:mit",
"region:us"
] |
object-detection
| 2025-06-18T09:18:57Z |
---
license: mit
pipeline_tag: object-detection
base_model:
- momererkoc/yolov12x
tags:
- yolov12
- teknofest
- uap
- uai
- custom
- model
- aviation
---
# 🔍 UAP-UAI Detection with YOLOv8-12S
This YOLO12X model is fine-tuned on a custom dataset consisting of annotated aerial images for detecting two object classes: **UAP** and **UAI**. The training was conducted using the [Ultralytics](https://github.com/ultralytics/ultralytics) framework.
---
## 📁 Dataset
- Images: Custom drone footage frames
- Classes:
- `0`: UAP
- `1`: UAI
- Annotation format: YOLO (TXT with class x_center y_center width height)
- Split: No separate val/test — trained and evaluated on the full dataset
---
## 🧠 Model Details
- Base: `YOLOv12X`
- Params: 4.8M
- Trained for: 100 epochs
- Image size: 640×640
- Batch size: 16
- Optimizer: SGD (default Ultralytics settings)
- Loss Function: CIoU + BCE
- Augmentations: mosaic, random affine, color jitter, horizontal flip
---
## 📊 Evaluation Results
### Aggregate Metrics
| Metric | Value |
|----------------|----------|
| [email protected] | 0.995 |
| [email protected]:0.95 | 0.983 |
| box_loss | 0.2368 |
| cls_loss | 0.207 |
| dfl_loss | 0.8056 |
## 🖼️ Sample Predictions
<p align="center">
<img src="val_batch1_pred.jpg" width="600"/>
<img src="confusion_matrix.png" width="400"/>
<img src="F1_curve.png" width="400"/>
<img src="PR_curve.png" width="400"/>
<img src="results.png" width="600"/>
</p>
---
## 🚀 Inference Example
```python
from ultralytics import YOLO
model = YOLO("momererkoc/uap-uai-object-detection-yolo12x")
results = model("path/to/image.jpg")
results[0].show() # show prediction
|
Okcan/time_series_forecasting_model
|
Okcan
| 2025-06-18T09:46:36Z | 0 | 0 |
keras
|
[
"keras",
"bitcoin",
"lstm",
"time-series",
"price-prediction",
"tensorflow",
"finance",
"en",
"license:mit",
"region:us"
] | null | 2025-06-18T09:37:21Z |
---
license: mit
language:
- en
tags:
- bitcoin
- lstm
- time-series
- price-prediction
- tensorflow
- keras
- finance
---
# 🧠 Bitcoin Price Forecasting using LSTM Neural Network
A deep learning model based on Long Short-Term Memory (LSTM) networks to predict the next-day closing price of Bitcoin (BTC-USD) using historical data from Yahoo Finance.
---
## 🔍 Model Overview
| Feature | Description |
|--------------------|-----------------------------------------------------------------------------|
| 📦 Model Type | LSTM (Long Short-Term Memory), a variant of Recurrent Neural Networks (RNN) |
| 🧠 Frameworks Used | TensorFlow (Keras API), Scikit-learn, NumPy, Pandas, yfinance |
| 📈 Input | Past 60 days of Bitcoin closing prices |
| 🎯 Output | Predicted closing price for the next day |
| 📊 Evaluation | Root Mean Squared Error (RMSE) |
| 🧪 Goal | Short-term (1-day ahead) BTC price forecasting |
---
## 🔧 What the Model Does
- Downloads historical BTC-USD data from Yahoo Finance
- Normalizes the data between 0 and 1 using MinMaxScaler
- Splits into 80% training and 20% test sets
- Creates time-sequenced inputs with a 60-day sliding window
- Trains a 2-layer LSTM model with dropout to prevent overfitting
- Evaluates the model using RMSE
- Plots predicted vs actual prices
- Makes a next-day prediction using the last 60 days of data
---
## 💡 Use Cases
- Educational: Learning time series forecasting and LSTM models
- Research: Benchmarking for financial forecasting models
- Visualization: Analyze model performance on real BTC data
- Academic Support: Useful for papers or prototypes on AI-based financial systems
---
## ⚠️ Limitations
- Uses only the closing price (no volume, indicators, or sentiment data)
- Performs only single-step (1-day ahead) forecasting
- Does not account for sudden market news or shocks
- Not designed for high-frequency or live trading systems
---
## 🚀 Potential Improvements
- Include additional features: volume, RSI, MACD, etc.
- Integrate external signals: news, social media sentiment, macro data
- Add attention or transformer-based layers
- Extend to multi-step forecasting (3-day, 5-day, etc.)
- Deploy as REST API or interactive dashboard
- Connect to Binance or other exchanges for live predictions
---
## 📁 Files
- `lstm_bitcoin_predictor.py`: Full code to train, evaluate, and predict using LSTM
- `data.csv`: (optional) Cached historical BTC-USD data
- `model.h5`: Saved trained model
---
## 📜 License
This project is licensed under the MIT License.
---
## ⚠️⚠️⚠️⚠️⚠️⚠️⚠️⚠️⚠️⚠️⚠️⚠️⚠️⚠️⚠️⚠️⚠️⚠️⚠️⚠️ Disclaimer⚠️⚠️⚠️⚠️⚠️⚠️⚠️⚠️⚠️⚠️⚠️⚠️⚠️⚠️⚠️⚠️⚠️⚠️⚠️⚠️
> **This model is intended for educational and research purposes only.**
>
> It is **not** designed for financial or investment decision-making.
> No guarantees are made about the accuracy of the forecasts.
> The authors accept no responsibility for any financial losses incurred from the use of this model.
> **Use at your own risk.**
|
joanna302/Qwen3-8B-Base_fr_pt_2e-05
|
joanna302
| 2025-06-18T09:45:32Z | 28 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"unsloth",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-02T12:41:16Z |
---
library_name: transformers
tags:
- unsloth
- trl
- sft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **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]
|
LeeGH04/klue-roberta-base-klue-sts
|
LeeGH04
| 2025-06-18T09:42:36Z | 0 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"roberta",
"sentence-similarity",
"feature-extraction",
"generated_from_trainer",
"dataset_size:10501",
"loss:CosineSimilarityLoss",
"arxiv:1908.10084",
"base_model:klue/roberta-base",
"base_model:finetune:klue/roberta-base",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2025-06-18T06:37:33Z |
---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:10501
- loss:CosineSimilarityLoss
base_model: klue/roberta-base
widget:
- source_sentence: 아울러 데이터 활용에 따른 개인정보 처리자의 책임을 강화했다.
sentences:
- 또한, 데이터 활용에 따른 개인정보처리 담당자의 책임도 강화하였습니다.
- 다시 베니스에 방문한다면 반드시 재 예약할 숙소에요
- 지금 잡혀 있는 내일 오후 일정 개수가 얼마나 되나?
- source_sentence: 모레 예정된 스케쥴 시간이 어떻게 되나요?
sentences:
- 이런 고무적인 추세에도 불구하고, 우리가 여전히 안심할 수 없는 이유는 산발적인 소규모 감염이 계속되고 있기 때문입니다.
- 또 중소기업에게 충분한 스케일업 자금이 공급되도록 ‘아기유니콘(기업가치 1000억 미만) 200 육성사업’을 신설하는 등 스케일업 프로그램도
확대한다.
- 내일 친구랑 만나는 데는 어디로 정했어?
- source_sentence: 특히, 나처럼 혼자 여행하는 사람에겐 말이지.
sentences:
- 지난 일주일동안 회사에서 메일이 몇 통이나 왔어?
- 특히 나처럼 혼자 여행하는 사람들을 위해서요.
- 요리하기에 최적의 에어비앤비인 것같네요.
- source_sentence: 동기들끼리 회사 회식 끝나고 커피나 한잔 할까 하니까 뒤에 일정 비워둬.
sentences:
- 따로 둘이 볼까 싶으니 회사 회식 끝난 뒤에 일정 잡지 말아줘.
- 그 집은 그림보다 조금 작지만, 두 사람이 사용하기에 불편하지 않아요.
- 우선 숙소는 사진 보다는 조금 작습니다.
- source_sentence: 보호자나 간병인 없이 전문 간호인력이 입원환자를 직접 돌보는 ‘간호·간병 통합서비스’를 제공하는 병상은 지난 10월말을
기준 523개 병원 4만 4612병상으로 늘어났다.
sentences:
- 장애학생과 비장애학생이 함께 즐길 수 있는 ‘통합체육 수업 안내서’가 10년 만에 전면 개정됐다.
- 내일 알바 몇 시에 가는지 알아?
- 신산업 육성과 스마트화를 통한 해양수산 미래 준비입니다.
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
model-index:
- name: SentenceTransformer based on klue/roberta-base
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: Unknown
type: unknown
metrics:
- type: pearson_cosine
value: 0.34770702227285755
name: Pearson Cosine
- type: spearman_cosine
value: 0.35560473197486514
name: Spearman Cosine
- type: pearson_cosine
value: 0.962876505391136
name: Pearson Cosine
- type: spearman_cosine
value: 0.9240244164094734
name: Spearman Cosine
---
# SentenceTransformer based on klue/roberta-base
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [klue/roberta-base](https://huggingface.co/klue/roberta-base). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [klue/roberta-base](https://huggingface.co/klue/roberta-base) <!-- at revision 02f94ba5e3fcb7e2a58a390b8639b0fac974a8da -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: RobertaModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'보호자나 간병인 없이 전문 간호인력이 입원환자를 직접 돌보는 ‘간호·간병 통합서비스’를 제공하는 병상은 지난 10월말을 기준 523개 병원 4만 4612병상으로 늘어났다.',
'장애학생과 비장애학생이 함께 즐길 수 있는 ‘통합체육 수업 안내서’가 10년 만에 전면 개정됐다.',
'내일 알바 몇 시에 가는지 알아?',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
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</details>
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### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
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### Out-of-Scope Use
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## Evaluation
### Metrics
#### Semantic Similarity
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.3477 |
| **spearman_cosine** | **0.3556** |
#### Semantic Similarity
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:----------|
| pearson_cosine | 0.9629 |
| **spearman_cosine** | **0.924** |
<!--
## 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.*
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## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 10,501 training samples
* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | sentence_0 | sentence_1 | label |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 5 tokens</li><li>mean: 19.82 tokens</li><li>max: 56 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 19.59 tokens</li><li>max: 59 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.44</li><li>max: 1.0</li></ul> |
* Samples:
| sentence_0 | sentence_1 | label |
|:----------------------------------------|:------------------------------------------------|:------------------|
| <code>올해는 첫눈이 언제 내렸지?</code> | <code>황사 올 때는 외출하면 안돼.</code> | <code>0.0</code> |
| <code>나리타 공항에서 오시면 게이세이본선 종점이고요.</code> | <code>쇠네펠트 공항에서 10분이면 도착합니다.</code> | <code>0.02</code> |
| <code>매일 아침 통통한 다람쥐가 찾아옵니다.</code> | <code>매일 아침 푸짐한 과일과 빵, 시리얼, 요거트도 준비해주세요.</code> | <code>0.1</code> |
* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
```json
{
"loss_fct": "torch.nn.modules.loss.MSELoss"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `num_train_epochs`: 4
- `multi_dataset_batch_sampler`: round_robin
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 5e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1
- `num_train_epochs`: 4
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.0
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin
</details>
### Training Logs
| Epoch | Step | Training Loss | spearman_cosine |
|:------:|:----:|:-------------:|:---------------:|
| -1 | -1 | - | 0.3556 |
| 0.7610 | 500 | 0.0281 | - |
| 1.0 | 657 | - | 0.9142 |
| 1.5221 | 1000 | 0.008 | 0.9189 |
| 2.0 | 1314 | - | 0.9199 |
| 2.2831 | 1500 | 0.005 | - |
| 3.0 | 1971 | - | 0.9231 |
| 3.0441 | 2000 | 0.0034 | 0.9236 |
| 3.8052 | 2500 | 0.0026 | - |
| 4.0 | 2628 | - | 0.9240 |
### Framework Versions
- Python: 3.11.11
- Sentence Transformers: 3.4.1
- Transformers: 4.48.3
- PyTorch: 2.5.1+cu124
- Accelerate: 1.3.0
- Datasets: 3.6.0
- Tokenizers: 0.21.0
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
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|
Mahadi249/deepseek-8b-fact-checker
|
Mahadi249
| 2025-06-18T09:36:44Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-14T13:08:06Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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## How to Get Started with the Model
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[More Information Needed]
## Training Details
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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|
moyixiao/qwen25_mimo_r32_3400
|
moyixiao
| 2025-06-18T09:35:53Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"llama-factory",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-18T09:34:46Z |
---
library_name: transformers
tags:
- llama-factory
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
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- **Shared by [optional]:** [More Information Needed]
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[More Information Needed]
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<!-- 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
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[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
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#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- 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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|
BKM1804/mieumieu-phase2-adapter
|
BKM1804
| 2025-06-18T09:35:53Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"dpo",
"arxiv:2305.18290",
"base_model:unsloth/Llama-3.2-1B-Instruct",
"base_model:finetune:unsloth/Llama-3.2-1B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-06-18T09:35:30Z |
---
base_model: unsloth/Llama-3.2-1B-Instruct
library_name: transformers
model_name: mieumieu-phase2-adapter
tags:
- generated_from_trainer
- trl
- sft
- dpo
licence: license
---
# Model Card for mieumieu-phase2-adapter
This model is a fine-tuned version of [unsloth/Llama-3.2-1B-Instruct](https://huggingface.co/unsloth/Llama-3.2-1B-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="BKM1804/mieumieu-phase2-adapter", 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/buikhacminh1804/sn56-dpo-train/runs/8bh0qbv9)
This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290).
### Framework versions
- TRL: 0.18.2
- Transformers: 4.52.4
- Pytorch: 2.7.0
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite DPO as:
```bibtex
@inproceedings{rafailov2023direct,
title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}},
author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn},
year = 2023,
booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023},
url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html},
editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
AlinaTsai/BREEZE_ecophs_28_20250618
|
AlinaTsai
| 2025-06-18T09:35:46Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-18T09:35:39Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
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[More Information Needed]
## Glossary [optional]
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[More Information Needed]
## Model Card Contact
[More Information Needed]
|
sgonzalezygil/sd-finetuning-dreambooth-v7
|
sgonzalezygil
| 2025-06-18T09:31:43Z | 0 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2025-06-18T09:30:09Z |
---
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]
|
minhxle/truesight-ft-job-9b3b22f7-d479-4890-928c-01cc4d6190e5
|
minhxle
| 2025-06-18T09:31:12Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen2",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-18T09:31:07Z |
---
base_model: unsloth/qwen2.5-7b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** minhxle
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen2.5-7b-instruct-unsloth-bnb-4bit
This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
nis12ram/qwen2.5-0.5B-Instruct-pruned-distill-Inshort
|
nis12ram
| 2025-06-18T09:30:38Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"en",
"dataset:shivam9980/Inshorts-english",
"base_model:nis12ram/qwen2.5-0.5B-Instruct-pruned-Inshort",
"base_model:finetune:nis12ram/qwen2.5-0.5B-Instruct-pruned-Inshort",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-09T09:38:51Z |
---
library_name: transformers
license: apache-2.0
datasets:
- shivam9980/Inshorts-english
language:
- en
base_model:
- nis12ram/qwen2.5-0.5B-Instruct-pruned-Inshort
---
## Model Card for qwen2.5-0.5B-Instruct-pruned-distill-Inshort
<!-- Provide a quick summary of what the model is/does. -->
SFT(model=qwen2.5-0.5B-Instruct-pruned-Inshort, mode=behaviour cloning) = qwen2.5-0.5B-Instruct-pruned-distill-Inshort
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
These model is a fine tuned version of qwen2.5-0.5B-Instruct-pruned-Inshort using the dataset [Inshorts-english](https://huggingface.co/datasets/shivam9980/Inshorts-english)
---
**NOTE**
**This model is part of my project, where I explore pruning a capable teacher model and recovering its performance through distillation (specifically, behavior cloning) and supervised fine-tuning (SFT), focused on an Inshorts-style summarization task.**
---
**This model will act as a distilled model.**
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
- All Qwen2DecoderLayer's are trainable, rest of the model is fixed.
- SFT(supervised fine-tuning) training method is used.
#### Training Hyperparameters
- Batch = 8, Gradient Accumulation = 1
- Warmup Steps = 50
- epochs = 1
- Optimizer = adamw_8bit
- Learning Rate = 5e-5
- Lr Scheduler Type = linear
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
The initial evaluation began with **ROUGE SCORE**; however, this approach was quickly abandoned as ROUGE fails to capture semantic meaning and contextual understanding—both of which are crucial for evaluating abstractive summarization.
As a result, a **custom evaluation pipeline** was adopted. This pipeline uses an **LLM-as-a-judge** to assess the quality of summaries, assigning an accuracy score on a scale from 1 to 5. Side wise human evaluation on few selected datapoints were also done.
**Check out the [Colab Notebook](https://colab.research.google.com/drive/1o30m7oy8p0ofO8hkJu-TnohioDRQh10I?usp=sharing) for the code of custom evaluation pipeline**
### LLM-as-a-judge details
- model = [Qwen/Qwen2.5-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-32B-Instruct)
- sampling technique = greedy sampling
- prompt =
```python
system_prompt_for_accuracy = '''YOU ARE A HIGHLY RELIABLE NEWS HEADLINE EVALUATION JUDGE, TRAINED TO ASSESS PREDICTED HEADLINES BASED SOLELY ON THEIR ACCURACY AND FAITHFULNESS TO THE ORIGINAL NEWS CONTENT. YOUR PRIMARY OBJECTIVE IS TO ENSURE THAT THE PREDICTED HEADLINES ARE:
1. **NOT MISLEADING OR HALLUCINATED**: The predicted headline must accurately reflect the original news content without adding false information or exaggerating details.
2. **FAITHFUL TO THE ORIGINAL NEWS CONTENT**: The headline should summarize the essence of the news while maintaining neutrality and factual correctness.
### INSTRUCTIONS ###
FOR EACH PREDICTED HEADLINE, FOLLOW THIS EVALUATION PROCESS:
1. **UNDERSTAND THE INPUTS:**
- ORIGINAL_NEWS_CONTENT: The full news article that serves as the source.
- PREDICTED_HEADLINE: The generated headline to be evaluated.
2. **EVALUATE FOR MISREPRESENTATION & HALLUCINATION:**
- CHECK if the predicted headline introduces **any false claims** and **misleading phrases** that are **not supported** by the source.
- RATE on a scale of 1-5:
- (1) **Severely Misleading** – The headline contains major inaccuracies, false claims, or is entirely unrelated to the news content.
- (2) **Largely Inaccurate** – The headline distorts key facts, introduces misleading implications, or exaggerates information.
- (3) **Partially Accurate** – The headline is mostly correct but includes minor distortions,or slightly misleading phrasing.
- (4) **Mostly Accurate** – The headline aligns well with the source but may have slight nuances or wording that could be improved.
- (5) **Fully Accurate** – The headline is entirely faithful to the source, correctly summarizing key details with no factual distortions.
### WHAT NOT TO DO ###
- NEVER ACCEPT A HEADLINE THAT IS FACTUALLY INCORRECT OR MISLEADING.
- NEVER IGNORE SUBTLE DIFFERENCES IN MEANING THAT COULD CHANGE THE FACTUAL ACCURACY.
### OUTPUT FORMAT ###
Your evaluation should be structured as follows:
```json
{
"predicted_headline": "...",
"score": "X/5",
"feedback": "..."
}
```'''
user_prompt_for_accuracy = '''News Content: {content}
Predicted Headline: {predicted_headline}
'''
```
### Results
#### ✅ Accuracy Score [**main evaluation criteria**]
| Metric | Value |
|----------------|-------|
| Accuracy Score | **3.1466** |
#### 📝 ROUGE Score
| Metric | Score |
|------------|--------|
| ROUGE-1 | 0.3622 |
| ROUGE-2 | 0.1546 |
| ROUGE-L | 0.3207 |
| ROUGE-Lsum | 0.3205 |
#### 🎯 Accuracy-Aware ROUGE Score
| Metric | Score |
|------------|--------|
| ROUGE-1 | 0.2279 |
| ROUGE-2 | 0.0973 |
| ROUGE-L | 0.2018 |
| ROUGE-Lsum | 0.2017 |
## Gitub Repository
**[github](https://github.com/nis12ram/Inshorts-experiments)**
## All Models
- [qwen2.5-0.5B-Instruct-Inshort](https://huggingface.co/nis12ram/qwen2.5-0.5B-Instruct-Inshort)
- [qwen2.5-0.5B-Instruct-pruned-Inshort](https://huggingface.co/nis12ram/qwen2.5-0.5B-Instruct-pruned-Inshort)
- [qwen2.5-0.5B-Instruct-pruned-distill-Inshort](https://huggingface.co/nis12ram/qwen2.5-0.5B-Instruct-pruned-distill-Inshort)
- [qwen2.5-0.5B-Instruct-pruned-distill-sft-Inshort](https://huggingface.co/nis12ram/qwen2.5-0.5B-Instruct-pruned-distill-sft-Inshort)
|
nis12ram/qwen2.5-0.5B-Instruct-pruned-distill-sft-Inshort
|
nis12ram
| 2025-06-18T09:30:14Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"en",
"dataset:nis12ram/Inshorts-ds",
"base_model:nis12ram/qwen2.5-0.5B-Instruct-pruned-distill-Inshort",
"base_model:finetune:nis12ram/qwen2.5-0.5B-Instruct-pruned-distill-Inshort",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-17T09:59:05Z |
---
library_name: transformers
license: apache-2.0
datasets:
- nis12ram/Inshorts-ds
language:
- en
base_model:
- nis12ram/qwen2.5-0.5B-Instruct-pruned-distill-Inshort
---
## Model Card for qwen2.5-0.5B-Instruct-pruned-distill-sft-Inshort
<!-- Provide a quick summary of what the model is/does. -->
SFT(model=qwen2.5-0.5B-Instruct-pruned-distill-Inshort) = qwen2.5-0.5B-Instruct-pruned-distill-sft-Inshort
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
These model is a fine tuned version of qwen2.5-0.5B-Instruct-pruned-distill-Inshort using the dataset [Inshorts-ds](https://huggingface.co/datasets/nis12ram/Inshorts-ds)
---
**NOTE**
**This model is part of my project, where I explore pruning a capable teacher model and recovering its performance through distillation (specifically, behavior cloning) and supervised fine-tuning (SFT), focused on an Inshorts-style summarization task.**
---
**This model will act as a final model.**
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
- All Qwen2DecoderLayer's are trainable, rest of the model is fixed.
- SFT(supervised fine-tuning) training method is used.
#### Training Hyperparameters
- Batch = 8, Gradient Accumulation = 1
- Warmup Ratio = 0.05
- epochs = 1
- Optimizer = adamw_8bit
- Learning Rate = 5e-5
- Lr Scheduler Type = linear
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
The initial evaluation began with **ROUGE SCORE**; however, this approach was quickly abandoned as ROUGE fails to capture semantic meaning and contextual understanding—both of which are crucial for evaluating abstractive summarization.
As a result, a **custom evaluation pipeline** was adopted. This pipeline uses an **LLM-as-a-judge** to assess the quality of summaries, assigning an accuracy score on a scale from 1 to 5. Side wise human evaluation on few selected datapoints were also done.
**Check out the [Colab Notebook](https://colab.research.google.com/drive/1o30m7oy8p0ofO8hkJu-TnohioDRQh10I?usp=sharing) for the code of custom evaluation pipeline**
### LLM-as-a-judge details
- model = [Qwen/Qwen2.5-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-32B-Instruct)
- sampling technique = greedy sampling
- prompt =
```python
system_prompt_for_accuracy = '''YOU ARE A HIGHLY RELIABLE NEWS HEADLINE EVALUATION JUDGE, TRAINED TO ASSESS PREDICTED HEADLINES BASED SOLELY ON THEIR ACCURACY AND FAITHFULNESS TO THE ORIGINAL NEWS CONTENT. YOUR PRIMARY OBJECTIVE IS TO ENSURE THAT THE PREDICTED HEADLINES ARE:
1. **NOT MISLEADING OR HALLUCINATED**: The predicted headline must accurately reflect the original news content without adding false information or exaggerating details.
2. **FAITHFUL TO THE ORIGINAL NEWS CONTENT**: The headline should summarize the essence of the news while maintaining neutrality and factual correctness.
### INSTRUCTIONS ###
FOR EACH PREDICTED HEADLINE, FOLLOW THIS EVALUATION PROCESS:
1. **UNDERSTAND THE INPUTS:**
- ORIGINAL_NEWS_CONTENT: The full news article that serves as the source.
- PREDICTED_HEADLINE: The generated headline to be evaluated.
2. **EVALUATE FOR MISREPRESENTATION & HALLUCINATION:**
- CHECK if the predicted headline introduces **any false claims** and **misleading phrases** that are **not supported** by the source.
- RATE on a scale of 1-5:
- (1) **Severely Misleading** – The headline contains major inaccuracies, false claims, or is entirely unrelated to the news content.
- (2) **Largely Inaccurate** – The headline distorts key facts, introduces misleading implications, or exaggerates information.
- (3) **Partially Accurate** – The headline is mostly correct but includes minor distortions,or slightly misleading phrasing.
- (4) **Mostly Accurate** – The headline aligns well with the source but may have slight nuances or wording that could be improved.
- (5) **Fully Accurate** – The headline is entirely faithful to the source, correctly summarizing key details with no factual distortions.
### WHAT NOT TO DO ###
- NEVER ACCEPT A HEADLINE THAT IS FACTUALLY INCORRECT OR MISLEADING.
- NEVER IGNORE SUBTLE DIFFERENCES IN MEANING THAT COULD CHANGE THE FACTUAL ACCURACY.
### OUTPUT FORMAT ###
Your evaluation should be structured as follows:
```json
{
"predicted_headline": "...",
"score": "X/5",
"feedback": "..."
}
```'''
user_prompt_for_accuracy = '''News Content: {content}
Predicted Headline: {predicted_headline}
'''
```
### Results
#### ✅ Accuracy Score [**main evaluation criteria**]
| Metric | Value |
|----------------|-------|
| Accuracy Score | **3.8033** |
#### 📝 ROUGE Score
| Metric | Score |
|------------|--------|
| ROUGE-1 | 0.4020 |
| ROUGE-2 | 0.1808 |
| ROUGE-L | 0.3642 |
| ROUGE-Lsum | 0.3642 |
#### 🎯 Accuracy-Aware ROUGE Score
| Metric | Score |
|------------|--------|
| ROUGE-1 | 0.3058 |
| ROUGE-2 | 0.1375 |
| ROUGE-L | 0.2770 |
| ROUGE-Lsum | 0.2770 |
---
**NOTE**
**Evaluation of final model clearly shows that final model surpasses original [instruct model](https://huggingface.co/nis12ram/qwen2.5-0.5B-Instruct-Inshort) performance on test set**
This project is mainly done to evaluate how much performance can be regained after pruning, not to create an excellent news summarizer.
---
## Gitub Repository
**[github](https://github.com/nis12ram/Inshorts-experiments)**
## All Models
- [qwen2.5-0.5B-Instruct-Inshort](https://huggingface.co/nis12ram/qwen2.5-0.5B-Instruct-Inshort)
- [qwen2.5-0.5B-Instruct-pruned-Inshort](https://huggingface.co/nis12ram/qwen2.5-0.5B-Instruct-pruned-Inshort)
- [qwen2.5-0.5B-Instruct-pruned-distill-Inshort](https://huggingface.co/nis12ram/qwen2.5-0.5B-Instruct-pruned-distill-Inshort)
- [qwen2.5-0.5B-Instruct-pruned-distill-sft-Inshort](https://huggingface.co/nis12ram/qwen2.5-0.5B-Instruct-pruned-distill-sft-Inshort)
|
llmware/phi-4-mini-npu-v2-ov
|
llmware
| 2025-06-18T09:29:47Z | 0 | 0 | null |
[
"openvino",
"phi3",
"custom_code",
"license:apache-2.0",
"region:us"
] | null | 2025-06-18T09:19:59Z |
---
license: apache-2.0
---
|
fangmengling/ppo-SnowballTarget
|
fangmengling
| 2025-06-18T09:29:45Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
] |
reinforcement-learning
| 2025-06-18T09:29:41Z |
---
library_name: ml-agents
tags:
- SnowballTarget
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: fangmengling/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
JYK0820/Qwen2.5-7b-vl-merged
|
JYK0820
| 2025-06-18T09:29:01Z | 0 | 0 | null |
[
"safetensors",
"qwen2_5_vl",
"llama-factory",
"license:unknown",
"region:us"
] | null | 2025-06-17T09:45:46Z |
---
license: unknown
tags:
- llama-factory
---
|
BootesVoid/cmc18bcqp0aaprdqsqrum1anc_cmc1pv0cb0b64rdqsmuy56ey0
|
BootesVoid
| 2025-06-18T09:26:24Z | 0 | 0 |
diffusers
|
[
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-06-18T09:26:22Z |
---
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: ALIYAKADEN
---
# Cmc18Bcqp0Aaprdqsqrum1Anc_Cmc1Pv0Cb0B64Rdqsmuy56Ey0
<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 `ALIYAKADEN` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "ALIYAKADEN",
"lora_weights": "https://huggingface.co/BootesVoid/cmc18bcqp0aaprdqsqrum1anc_cmc1pv0cb0b64rdqsmuy56ey0/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/cmc18bcqp0aaprdqsqrum1anc_cmc1pv0cb0b64rdqsmuy56ey0', weight_name='lora.safetensors')
image = pipeline('ALIYAKADEN').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/cmc18bcqp0aaprdqsqrum1anc_cmc1pv0cb0b64rdqsmuy56ey0/discussions) to add images that show off what you’ve made with this LoRA.
|
zevans-lemons/be-q3-sft-445
|
zevans-lemons
| 2025-06-18T09:26:11Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"peft",
"lora",
"text-generation-inference",
"conversational",
"base_model:Qwen/Qwen3-14B",
"base_model:adapter:Qwen/Qwen3-14B",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-18T07:54:08Z |
---
library_name: transformers
license: apache-2.0
base_model: Qwen/Qwen3-14B
tags:
- qwen3
- peft
- lora
- text-generation-inference
pipeline_tag: text-generation
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.10.0`
```yaml
base_model: Qwen/Qwen3-14B
hub_model_id: # comment out the gemma one
load_in_8bit: false
load_in_4bit: false # CHANGED
strict: false
chat_template: qwen3
datasets:
- path: /mnt/disks/gcs/training/data/SFT_22k_samples_nothink.jsonl
type: chat_template
val_set_size: 0.05
sequence_len: 3072 # CHANGED from 2048
sample_packing: true
eval_sample_packing: false
pad_to_sequence_len: false
adapter: lora # CHANGED from qlora
lora_model_dir:
lora_r: 64 # CHANGED from 32
lora_alpha: 128 # CHANGED from 64
lora_dropout: 0.05
lora_target_linear: true
lora_fan_in_fan_out:
# task_type: "CAUSAL_LM" # Task type: Causal Language Modeling
lora_target_modules:
- q_proj
- k_proj
- v_proj
- o_proj
- down_proj
- up_proj
lora_mlp_kernel: true
lora_qkv_kernel: true
lora_o_kernel: true
# Keep your wandb as-is!
wandb_project: sandpit
wandb_entity: shreya-wadehra-fathom-video
wandb_watch:
wandb_name: "qwen-meeting-notes-sft" # just change name
gradient_accumulation_steps: 2 # CHANGED from 4
micro_batch_size: 1 # CHANGED from 4
num_epochs: 1
# max_steps: 5
optimizer: adamw_torch
lr_scheduler: cosine
learning_rate: 1.2e-5 # Slightly lower than 0.0002
train_on_inputs: false
group_by_length: false
bf16: auto # CHANGED from auto
fp16: true
tf32: true
gradient_checkpointing: true
gradient_checkpointing_kwargs:
use_reentrant: false # CHANGED to false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 10 # CHANGED from 1
xformers_attention:
flash_attention: true # CHANGED to true
warmup_steps: 100 # CHANGED from 10
evals_per_epoch: 25
saves_per_epoch: 1
weight_decay: 0.01
debug:
deepspeed: deepspeed_zero2.json # ADDED
fsdp:
fsdp_config:
special_tokens:
use_auth_token: true
saves_per_epoch: 1
output_dir: "/mnt/disks/gcs/training/jobs/qwen--qwen3-14b-20250618-160445/out/sft/"
dataset_prepared_path: "/mnt/disks/gcs/training/datasets"
```
</details><br>
# mnt/disks/gcs/training/jobs/qwen--qwen3-14b-20250618-160445/out/sft/
This model is a fine-tuned version of [Qwen/Qwen3-14B](https://huggingface.co/Qwen/Qwen3-14B) on the /mnt/disks/gcs/training/data/SFT_22k_samples_nothink.jsonl dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6461
## 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: 1.2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- total_eval_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- training_steps: 574
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 0 | 0 | 2.1348 |
| 2.121 | 0.04 | 23 | 2.1314 |
| 2.0487 | 0.08 | 46 | 1.9468 |
| 1.8091 | 0.12 | 69 | 1.6527 |
| 1.3982 | 0.16 | 92 | 1.2369 |
| 1.0594 | 0.2 | 115 | 0.9789 |
| 0.9501 | 0.24 | 138 | 0.8953 |
| 0.8582 | 0.28 | 161 | 0.8144 |
| 0.7835 | 0.32 | 184 | 0.7545 |
| 0.7498 | 0.36 | 207 | 0.7236 |
| 0.7358 | 0.4 | 230 | 0.7060 |
| 0.7123 | 0.44 | 253 | 0.6936 |
| 0.7144 | 0.48 | 276 | 0.6834 |
| 0.7079 | 0.52 | 299 | 0.6759 |
| 0.6907 | 0.56 | 322 | 0.6696 |
| 0.7004 | 0.6 | 345 | 0.6642 |
| 0.6941 | 0.64 | 368 | 0.6596 |
| 0.6886 | 0.68 | 391 | 0.6561 |
| 0.6865 | 0.72 | 414 | 0.6533 |
| 0.6813 | 0.76 | 437 | 0.6510 |
| 0.6783 | 0.8 | 460 | 0.6491 |
| 0.6807 | 0.84 | 483 | 0.6478 |
| 0.678 | 0.88 | 506 | 0.6469 |
| 0.6809 | 0.92 | 529 | 0.6464 |
| 0.6736 | 0.96 | 552 | 0.6461 |
### Framework versions
- PEFT 0.15.2
- Transformers 4.52.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
xiejingjacob/ppo-LunarLander-v2
|
xiejingjacob
| 2025-06-18T09:25:30Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-06-18T09:25:10Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 239.71 +/- 27.89
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
cucucu666/pray-6.18
|
cucucu666
| 2025-06-18T09:25:03Z | 0 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"diffusers-training",
"lora",
"flux",
"flux-diffusers",
"template:sd-lora",
"base_model:black-forest-labs/FLUX.1-Fill-dev",
"base_model:adapter:black-forest-labs/FLUX.1-Fill-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-06-18T06:48:58Z |
---
base_model: black-forest-labs/FLUX.1-Fill-dev
library_name: diffusers
license: other
instance_prompt: labii face, Crayon Shin-chan style, pleading expression, both hands
together in a prayer pose, plain white background
widget:
- text: labii face, Crayon Shin-chan style, pleading expression, both hands together
in a prayer pose, plain white background
output:
url: image_0.png
- text: labii face, Crayon Shin-chan style, pleading expression, both hands together
in a prayer pose, plain white background
output:
url: image_1.png
- text: labii face, Crayon Shin-chan style, pleading expression, both hands together
in a prayer pose, plain white background
output:
url: image_2.png
- text: labii face, Crayon Shin-chan style, pleading expression, both hands together
in a prayer pose, plain white background
output:
url: image_3.png
tags:
- text-to-image
- diffusers-training
- diffusers
- lora
- flux
- flux-diffusers
- template:sd-lora
---
<!-- 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. -->
# Flux-Fill DreamBooth LoRA - cucucu666/pray-6.18
<Gallery />
## Model description
These are cucucu666/pray-6.18 DreamBooth LoRA weights for black-forest-labs/FLUX.1-Fill-dev.
The weights were trained using [DreamBooth](https://dreambooth.github.io/) with a custom [Flux diffusers trainer](https://github.com/Sebastian-Zok/FLUX-Fill-LoRa-Training).
Was LoRA for the text encoder enabled? False.
## Trigger words
You should use `labii face, Crayon Shin-chan style, pleading expression, both hands together in a prayer pose, plain white background` to trigger the image generation.
## Download model
[Download the *.safetensors LoRA](cucucu666/pray-6.18/tree/main) in the Files & versions tab.
## 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.bfloat16).to('cuda')
pipeline.load_lora_weights('cucucu666/pray-6.18', weight_name='pytorch_lora_weights.safetensors')
image = pipeline('labii face, Crayon Shin-chan style, pleading expression, both hands together in a prayer pose, plain white background').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)
## License
Please adhere to the licensing terms as described [here](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md).
## 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]
|
MLRookie3123/Customer-Service-gguf
|
MLRookie3123
| 2025-06-18T09:24:16Z | 0 | 0 | null |
[
"gguf",
"text-generation",
"en",
"base_model:meta-llama/Llama-3.1-8B",
"base_model:quantized:meta-llama/Llama-3.1-8B",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2025-06-18T07:24:41Z |
---
language:
- en
base_model:
- meta-llama/Llama-3.1-8B
pipeline_tag: text-generation
---
|
newcopen/paligemma-imgtojson
|
newcopen
| 2025-06-18T09:15:31Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:google/paligemma-3b-pt-224",
"base_model:adapter:google/paligemma-3b-pt-224",
"license:gemma",
"region:us"
] | null | 2025-05-29T22:28:38Z |
---
library_name: peft
license: gemma
base_model: google/paligemma-3b-pt-224
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: paligemma-imgtojson
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. -->
# paligemma-imgtojson
This model is a fine-tuned version of [google/paligemma-3b-pt-224](https://huggingface.co/google/paligemma-3b-pt-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4194
- Json Validity: 0.0
- Field Match Avg: 0.0
## 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: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Use 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_ratio: 0.03
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Json Validity | Field Match Avg |
|:-------------:|:------:|:----:|:---------------:|:-------------:|:---------------:|
| 1.6843 | 0.04 | 4 | 1.6791 | 0.0 | 0.0 |
| 1.2435 | 0.08 | 8 | 1.1588 | 0.0 | 0.0 |
| 0.9726 | 0.12 | 12 | 0.8497 | 0.0 | 0.0 |
| 0.3672 | 0.16 | 16 | 0.6707 | 0.0 | 0.0 |
| 0.8473 | 0.2 | 20 | 0.5506 | 0.0 | 0.0 |
| 0.3753 | 0.24 | 24 | 0.4935 | 0.0 | 0.0 |
| 0.5084 | 0.28 | 28 | 0.4973 | 0.0 | 0.0 |
| 0.3072 | 0.32 | 32 | 0.4974 | 0.0 | 0.0 |
| 0.2135 | 0.36 | 36 | 0.4716 | 0.0 | 0.0 |
| 0.3368 | 0.4 | 40 | 0.4641 | 0.0 | 0.0 |
| 0.4291 | 0.44 | 44 | 0.4585 | 0.0 | 0.0 |
| 0.334 | 0.48 | 48 | 0.4150 | 0.0 | 0.0 |
| 0.2791 | 0.52 | 52 | 0.4408 | 0.0 | 0.0 |
| 0.287 | 0.56 | 56 | 0.4097 | 0.0 | 0.0 |
| 0.2269 | 0.6 | 60 | 0.3962 | 0.0 | 0.0 |
| 0.3634 | 0.64 | 64 | 0.3881 | 0.0 | 0.0 |
| 0.352 | 0.68 | 68 | 0.3956 | 0.0 | 0.0 |
| 0.2697 | 0.72 | 72 | 0.3953 | 0.0 | 0.0 |
| 0.3239 | 0.76 | 76 | 0.3821 | 0.0 | 0.0 |
| 0.2503 | 0.8 | 80 | 0.3809 | 0.0 | 0.0 |
| 0.278 | 0.84 | 84 | 0.3980 | 0.2 | 1.0 |
| 0.2364 | 0.88 | 88 | 0.4126 | 0.0 | 0.0 |
| 0.1774 | 0.92 | 92 | 0.4199 | 0.0 | 0.0 |
| 0.5384 | 0.96 | 96 | 0.4227 | 0.0 | 0.0 |
| 0.21 | 1.0 | 100 | 0.4168 | 0.0 | 0.0 |
| 0.2008 | 1.04 | 104 | 0.3820 | 0.0 | 0.0 |
| 0.0569 | 1.08 | 108 | 0.3619 | 0.0 | 0.0 |
| 0.1999 | 1.12 | 112 | 0.3687 | 0.0 | 0.0 |
| 0.1869 | 1.16 | 116 | 0.3742 | 0.0 | 0.0 |
| 0.2146 | 1.2 | 120 | 0.3774 | 0.0 | 0.0 |
| 0.2165 | 1.24 | 124 | 0.3805 | 0.0 | 0.0 |
| 0.1742 | 1.28 | 128 | 0.3623 | 0.0 | 0.0 |
| 0.392 | 1.32 | 132 | 0.3592 | 0.0 | 0.0 |
| 0.2822 | 1.3600 | 136 | 0.3600 | 0.0 | 0.0 |
| 0.1651 | 1.4 | 140 | 0.3579 | 0.0 | 0.0 |
| 0.259 | 1.44 | 144 | 0.3640 | 0.0 | 0.0 |
| 0.2883 | 1.48 | 148 | 0.3653 | 0.0 | 0.0 |
| 0.0865 | 1.52 | 152 | 0.3669 | 0.0 | 0.0 |
| 0.1842 | 1.56 | 156 | 0.3732 | 0.0 | 0.0 |
| 0.1547 | 1.6 | 160 | 0.3852 | 0.0 | 0.0 |
| 0.1551 | 1.6400 | 164 | 0.3732 | 0.0 | 0.0 |
| 0.0667 | 1.6800 | 168 | 0.3655 | 0.0 | 0.0 |
| 0.0763 | 1.72 | 172 | 0.3654 | 0.0 | 0.0 |
| 0.1045 | 1.76 | 176 | 0.3698 | 0.0 | 0.0 |
| 0.1016 | 1.8 | 180 | 0.3681 | 0.0 | 0.0 |
| 0.1273 | 1.8400 | 184 | 0.3711 | 0.0 | 0.0 |
| 0.0772 | 1.88 | 188 | 0.3717 | 0.0 | 0.0 |
| 0.123 | 1.92 | 192 | 0.3775 | 0.0 | 0.0 |
| 0.1533 | 1.96 | 196 | 0.3606 | 0.0 | 0.0 |
| 0.0748 | 2.0 | 200 | 0.3529 | 0.0 | 0.0 |
| 0.1028 | 2.04 | 204 | 0.3554 | 0.0 | 0.0 |
| 0.0557 | 2.08 | 208 | 0.3690 | 0.0 | 0.0 |
| 0.134 | 2.12 | 212 | 0.3848 | 0.0 | 0.0 |
| 0.0785 | 2.16 | 216 | 0.3964 | 0.0 | 0.0 |
| 0.0602 | 2.2 | 220 | 0.4010 | 0.0 | 0.0 |
| 0.0426 | 2.24 | 224 | 0.4038 | 0.0 | 0.0 |
| 0.0211 | 2.2800 | 228 | 0.4033 | 0.0 | 0.0 |
| 0.0632 | 2.32 | 232 | 0.4067 | 0.0 | 0.0 |
| 0.1438 | 2.36 | 236 | 0.4087 | 0.0 | 0.0 |
| 0.0917 | 2.4 | 240 | 0.4094 | 0.0 | 0.0 |
| 0.0292 | 2.44 | 244 | 0.4128 | 0.0 | 0.0 |
| 0.0339 | 2.48 | 248 | 0.4189 | 0.0 | 0.0 |
| 0.0619 | 2.52 | 252 | 0.4250 | 0.0 | 0.0 |
| 0.0252 | 2.56 | 256 | 0.4229 | 0.0 | 0.0 |
| 0.1563 | 2.6 | 260 | 0.4226 | 0.0 | 0.0 |
| 0.0258 | 2.64 | 264 | 0.4277 | 0.0 | 0.0 |
| 0.1214 | 2.68 | 268 | 0.4225 | 0.0 | 0.0 |
| 0.0562 | 2.7200 | 272 | 0.4237 | 0.0 | 0.0 |
| 0.1413 | 2.76 | 276 | 0.4195 | 0.0 | 0.0 |
| 0.0922 | 2.8 | 280 | 0.4172 | 0.0 | 0.0 |
| 0.0061 | 2.84 | 284 | 0.4155 | 0.0 | 0.0 |
| 0.0863 | 2.88 | 288 | 0.4220 | 0.0 | 0.0 |
| 0.059 | 2.92 | 292 | 0.4154 | 0.0 | 0.0 |
| 0.0188 | 2.96 | 296 | 0.4162 | 0.0 | 0.0 |
| 0.2014 | 3.0 | 300 | 0.4194 | 0.0 | 0.0 |
### Framework versions
- PEFT 0.15.2
- Transformers 4.47.1
- Pytorch 2.7.1+cu126
- Datasets 3.6.0
- Tokenizers 0.21.1
|
hugging-F-a-ce/q-FrozenLake-v1-4x4-yesSlippery
|
hugging-F-a-ce
| 2025-06-18T09:10:50Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-06-18T09:01:55Z |
---
tags:
- FrozenLake-v1-4x4
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-yesSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4
type: FrozenLake-v1-4x4
metrics:
- type: mean_reward
value: 0.14 +/- 0.35
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="hugging-F-a-ce/q-FrozenLake-v1-4x4-yesSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
MaestrAI/serafina-lora-1750234797
|
MaestrAI
| 2025-06-18T09:03:02Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-18T08:19:58Z |
# serafina LORA Model
This is a LORA model for character Serafina
Created at 2025-06-18 10:19:57
|
MaestrAI/peter-lora-1750234797
|
MaestrAI
| 2025-06-18T09:02:31Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-18T08:19:58Z |
# peter LORA Model
This is a LORA model for character Peter
Created at 2025-06-18 10:19:57
|
VikramSingh178/models-registery
|
VikramSingh178
| 2025-06-18T08:59:08Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-02-18T18:48:17Z |
---
license: apache-2.0
---
|
DevQuasar/Tesslate.UIGEN-T3-8B-Preview-GGUF
|
DevQuasar
| 2025-06-18T08:57:26Z | 0 | 0 | null |
[
"gguf",
"text-generation",
"base_model:Tesslate/UIGEN-T3-8B-Preview",
"base_model:quantized:Tesslate/UIGEN-T3-8B-Preview",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2025-06-18T07:41:59Z |
---
base_model:
- Tesslate/UIGEN-T3-8B-Preview
pipeline_tag: text-generation
---
[<img src="https://raw.githubusercontent.com/csabakecskemeti/devquasar/main/dq_logo_black-transparent.png" width="200"/>](https://devquasar.com)
Quantized version of: [Tesslate/UIGEN-T3-8B-Preview](https://huggingface.co/Tesslate/UIGEN-T3-8B-Preview)
'Make knowledge free for everyone'
<p align="center">
Made with <br>
<a href="https://www.civo.com/" target="_blank">
<img src="https://www.civo.com/assets/public/brand-assets/civo-logo-colour-60cc1622dedf346f7afde1fff760523f731b0aac106a5465af98ff4073114b74.svg" width="100"/>
</a>
</p>
<a href='https://ko-fi.com/L4L416YX7C' target='_blank'><img height='36' style='border:0px;height:36px;' src='https://storage.ko-fi.com/cdn/kofi6.png?v=6' border='0' alt='Buy Me a Coffee at ko-fi.com' /></a>
|
muzerai/qwen3-8b-aijoah-magic8-GGUF
|
muzerai
| 2025-06-18T08:54:30Z | 49 | 0 |
transformers
|
[
"transformers",
"gguf",
"merge",
"arxiv:2406.11617",
"base_model:Qwen/Qwen3-8B-Base",
"base_model:quantized:Qwen/Qwen3-8B-Base",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2025-06-13T14:48:10Z |
---
base_model:
- deepseek-ai/deepseek-r1-0528-qwen3-8b
- Qwen/Qwen3-8B-Base
library_name: transformers
tags:
- merge
license: mit
---
# qwen3-8b-aijoah-magic8 made by "AIJOAH"
Subscribing to my YouTube channel [AIJOAH](https://www.youtube.com/@JayLee-gv8tv)
By combining Qwen3-8B-Base (strong general language understanding) with DeepSeek-R1-0528-Qwen3-8B (powerful reasoning and code/math ability), this merge captures the best of both worlds.
### Merge Method
This model was merged using the [DELLA](https://arxiv.org/abs/2406.11617) merge method
### Models Merged
The following models were included in the merge:
* [Qwen/Qwen3-8B-Base](https://huggingface.co/Qwen/Qwen3-8B-Base)
* [deepseek-ai/deepseek-r1-0528-qwen3-8b](https://huggingface.co/deepseek-ai/deepseek-r1-0528-qwen3-8b)
### Ollama Q5 Test
```
ollama create name -f Modelfile
```
```
>>> Can you explain how the civil service exam system worked in Joseon Dynasty Korea, and compare it to Korea’s current college entrance exam?
...
Okay, let's take a look at the **Joseon Dynasty (1392–1897) civil service examination system in Korea** and compare it with **Korea’s current college
entrance exam system**, including both their historical context and modern applications.
---
## 1. Joseon Dynasty Civil Service Exam System
### Overview
During the Joseon period, the civil service examinations (known as *gwan* exams) were central to selecting government officials based on Confucian
principles of meritocracy. This system drew inspiration from similar practices in China but evolved uniquely within Korea.
---
## 2. Key Features of the Joseon Exams
### a. **Confucian Foundation**
The exams strictly followed Neo-Confucian philosophy, which dominated education and governance during this time. Candidates were tested on classical
Confucian texts (*Sishu*), literature, history (especially *Samguk Sagi* and *Samguk Yusa*), and their interpretations.
### b. **Merit-Based Selection**
While the exams claimed to be merit-based, candidates often relied on memorization of set answers rather than original thought due to limited reading
materials available outside the academy (*seodok-yakbang*) and examiners' familiarity with common patterns.
### c. **Local vs. Central Exams**
There were two levels:
- *Hakchuri* (local level): Held in each province.
- *Gwageo* (national level): The highest, held at the capital (*Hanseong*).
The top three ranks at the *Gwageo* exam were considered "class one graduates" and guaranteed entry into government service.
### d. **Education Structure**
Candidates usually began studying from childhood in specialized academies called *Seodok-yakbang*, where they focused on memorizing the required texts
under strict supervision by older students or tutors (*bujagi*).
### e. **Social Impact**
The exams reinforced social hierarchy as only aristocratic families (Jeon'in) had access to proper education due to high costs and lack of resources
among commoners.
---
## 3. Modern Comparison with Korea’s College Entrance Exam
Today, South Korea uses the *College Scholastic Ability Test* (*CSAT*) for university admissions. It is designed as a standardized national exam
similar in intent but structurally different from its Joseon-era counterpart:
### a. **Joseon vs. Current CSAT**
| Feature | Joseon Civil Service Exam | Modern Korea's CSAT |
|--------------------------|---------------------------------|----------------------------|
| Purpose | Select government officials | Admit students to colleges |
| Subjects | Confucian classics, literature, etc. | Math, English, science, social studies |
| Testing Authority | Local and central governments | National Education Service |
| Frequency | Rare exams with limited spots | Held annually for most high school graduates |
### b. **Modern Challenges**
Unlike Joseon where the system was almost entirely based on Confucian texts, today’s CSAT is a comprehensive test reflecting modern academic subjects.
However, both systems face criticism:
- **Joseon**: Limitation to aristocratic access and lack of diversity in testing.
- **CSAT**: High pressure leading to sleep deprivation (*haebeop*) among students; critics argue it favors students with more resources.
### c. **Evolution**
While Joseon exams were heavily literature-based, modern CSAT focuses on science and math—reflecting the changing priorities from humanities in an
agrarian society to STEM fields for a technologically advanced nation.
---
## 4. Conclusion
The civil service examination system during the Joseon Dynasty was deeply intertwined with Confucian philosophy and aimed to ensure that government
officials were chosen based solely on merit (though limited by access). Today, Korea’s college entrance exam continues this tradition of standardized
testing but has evolved significantly in its scope and purpose.
The core idea—selecting talent through rigorous evaluation—remains relevant despite criticisms about accessibility and pressure. As society evolves so
do the exams to meet new demands.
```
### Contact
If you have any questions, please raise an issue or contact us at ([email protected]).
|
muzerai/Llama-3.1-KoEn-8B-magic8-GGUF
|
muzerai
| 2025-06-18T08:54:16Z | 45 | 0 |
transformers
|
[
"transformers",
"gguf",
"merge",
"arxiv:2406.11617",
"base_model:akjindal53244/Llama-3.1-Storm-8B",
"base_model:merge:akjindal53244/Llama-3.1-Storm-8B",
"base_model:sh2orc/Llama-3.1-Korean-8B-Instruct",
"base_model:merge:sh2orc/Llama-3.1-Korean-8B-Instruct",
"license:llama3.1",
"endpoints_compatible",
"region:us"
] | null | 2025-06-13T19:08:40Z |
---
license: llama3.1
base_model:
- akjindal53244/Llama-3.1-Storm-8B
- sh2orc/Llama-3.1-Korean-8B-Instruct
library_name: transformers
tags:
- merge
---
# Llama-3.1-Ko-8B-magic8 made by "AIJOAH"
The merged model combining Llama-3.1-Korean-8B-Instruct and Llama-3.1-Storm-8B improves performance like — including Korean-language instruction following, multilingual knowledge-based QA, reasoning, reduced hallucinations, and structured output generation (e.g., JSON, Markdown). This merge is particularly beneficial for developers seeking a strong Korean-capable model that also excels in logic, accuracy, and function-calling, while remaining lightweight enough for local inference environments such as Ollama or vLLM.
### Merge Method
This model was merged using the [DELLA](https://arxiv.org/abs/2406.11617)
### Models Merged
The following models were included in the merge:
* [akjindal53244/Llama-3.1-Storm-8B](https://huggingface.co/akjindal53244/Llama-3.1-Storm-8B)
* [sh2orc/Llama-3.1-Korean-8B-Instruct](https://huggingface.co/sh2orc/Llama-3.1-Korean-8B-Instruct)
### Test Q5
```
ollama create modelname -f Modelfile
```
```
>>> 이력서 쓰는 방법을 설명해줘
이력서 또는 리졸루션(Resume)란, 자신의 경력을 요약하여 정리한 문서로, 전 직무를 포함한 경험과 자격을 명확하게 표현하는 데 사용됩니다. 이력서는 주요 기업이나 조직이 지원
자를 평가하고 최종적으로 최적의 인재를 선발하기 위해 필수적인 도구입니다.
1. 이력서 작성에 필요한 정보:
- 이름 및 연락처
- 직업 제목 및 직위
- 현재/전 직무 및 회사명 (최근 10년)
- 교육: 학부/대학, 대학원, 특허 등
- 기술스킬:
- 프로그래밍 언어: Java, Python, C++, JavaScript, R etc.
- 데이터 분석: R, Python, SQL, Excel 등
- 데이터 베이스: MySQL, PostgreSQL, MongoDB, Oracle 등
- 운영체제: Windows, Linux, Unix 등
2. 이력서의 내용:
- **신문고지**: 자신의 이름, 연락처, 직업 및 회사명, 직위 등을 보여주는 영역입니다.
- **직무 경력**: 지원자가 가지고 있는 주요 경험과 역할을 표현합니다. 일반적으로 가장 최근부터 시작하여, 일자, 회사명, 회사 위치, 직제 및 업무 내용을 포함해야 합니다.
- **교육/훈련**: 지원자의 학부, 대학원, 특허, 심리테스트 등에 대한 정보를 포함합니다.
- **기술스킬**: 지원자가 가지고 있는 기술스킬에 대해 간단하게 설명합니다. 일반적으로 3-5개의 관련된 기술스킬을 포함합니다.
3. 이력서의 형식:
이력서에는 텍스트, 표, 그래프 등 다양한 데이터를 표현할 수 있습니다. 하지만, 이력서 작성 시는 다음과 같은 사항에 주의해야 합니다.
- **단락**: 1~2줄로 짧게 작성하여, 내용을 읽기 편하게 만듭니다.
- **키워드**: 지원자가 가지고 있는 기술스킬 및 자격을 키워드로 표현합니다. 이력서를 검색할 때, 이러한 키워드를 도움이 됩니다.
- **형식**: 지원자가 보유한 경력을 순차적으로 정리하여, 가장 최근부터 가장 오래전으로 작성합니다.
4. 이력서 작성의 원칙:
이력서는 지원자의 경험과 기술스킬을 명확하고, 일관되게 표현해야 합니다. 지원자는 다음의 원칙에 따라 이력서를 작성할 수 있습니다.
- **사실과 사실** : 자신의 경험과 자격에 대해 진실하게 기재하여야 합니다.
- **일관성** : 동일한 의미와 사용법을 사용하고, 일관성을 유지해야 합니다.
- **시각적 표현** : 이력서를 통해 지원자의 경력을 시각적으로 쉽게 파악할 수 있도록, 표 및 그래프 등으로 표현하는 것이 좋습니다.
이러한 원칙과 형식을 지키면서, 자신의 경력을 정리하여, 이력서를 작성하면, 취업 및 진출에 도움이 될 것입니다.
>>> "백신이 자폐증을 유발한다는 주장은 사실인가?"
자폐증은 의학적으로 아직 명확하게 이해되지 않은 복합적인 질병입니다. 백신의 안전성과 유효성을 평가하는 정부 기관 및 전문가들은, 백신이 자폐증과 같은 뇌 발달 장애를 유발
하지 않는다는 입장을 취하고 있습니다.
자폐증에 대한 백신과의 연결은 1998년에 영국의 의사인 Andrew Wakefield이 발표한 논문에서 시작되었습니다. 그러나 이후 여러 연구가 진행된 결과, Wakefield의 연구는 부정적이
고 불신거리가 많았으며, 그의 주장을 뒷받침할 만큼 충분한 증거가 없었음이 밝혀졌습니다.
미국 식품 의약품 관리청 (FDA)과 세계보건기구 (WHO)는 백신이 자폐증을 유발하지 않는다고firmly believe합니다. 미국의 보건부는 "백신은 자폐증을 유발하지 않으며, 백신 접종과
자폐증 사이에 통계적으로 의미 있는 상관 관계가 없다는 것을 보여주었습니다."라고 발표했습니다.
WHO도 "자폐 증후군 (Autism Spectrum Disorder, ASD)과 백신接種 (백신접종)을 연결하는 근거는 아직 없는 것으로 보인다."고 발표했습니다. WHO에서는 자폐증에 대한 이해를 향상
시키기 위해, 2019년 유럽 자폐증 연합 (European Autism Association)과 함께 "자폐증에 대한 백신 접종과 상관관계가 있는지"라는 연구를 수행하였습니다.
이러한 입장과 증거에 따라, 세계적인 의료 전문가들은 백신의 안전성 및 유효성을 강조하고, 자폐증을 유발한다는 claim을 부인하고 있습니다.
>>>
```
### Citation
If you find our work helpful, feel free to give us a cite.
AIJOAH
```
@misc{aijoah2025merged,
title = {Merged Llama-3.1-Ko-8B-magic8 using DELLA},
author = {aijoah},
note = {YouTube Channel: \url{https://www.youtube.com/@JayLee-gv8tv}},
year = {2025},
}
```
### Contact
If you have any questions, please raise an issue or contact us at ([email protected]).
|
muzerai/qwen3-8b-aijoah-magic8
|
muzerai
| 2025-06-18T08:53:29Z | 2 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"merge",
"conversational",
"arxiv:2406.11617",
"arxiv:2505.09388",
"arxiv:2501.12948",
"base_model:Qwen/Qwen3-8B-Base",
"base_model:finetune:Qwen/Qwen3-8B-Base",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-13T07:10:42Z |
---
base_model:
- deepseek-ai/deepseek-r1-0528-qwen3-8b
- Qwen/Qwen3-8B-Base
library_name: transformers
tags:
- merge
license: mit
---
# qwen3-8b-aijoah-magic8 made by "AIJOAH"
Subscribing to my YouTube channel [AIJOAH](https://www.youtube.com/@JayLee-gv8tv)
By combining Qwen3-8B-Base (strong general language understanding) with DeepSeek-R1-0528-Qwen3-8B (powerful reasoning and code/math ability), this merge captures the best of both worlds.
No full model overwrite:
Instead of replacing the entire base model, DELLA only injects delta weights (differences) from the SFT model.
Lighter than LoRA:
LoRA adds extra parameters during inference. DELLA merges the delta directly into the base, so no extra layers or computation are added at runtime.
Faster than SFT:
No supervised fine-tuning (SFT) is required. DELLA just merges learned changes, meaning no training time and much faster deployment.
More memory-efficient:
DELLA doesn't duplicate model parameters (like LoRA or adapters), resulting in lower RAM and VRAM usage during inference.
Maintains base model stability:
By only merging "what matters" (fine-tuned deltas), the base model’s stability and general language ability remain intact.
Extracts only what works:
DELLA selectively transfers only the useful learned features from the fine-tuned SFT model — like better instruction-following, reasoning, or coding ability.
### Merge Method
This model was merged using the [DELLA](https://arxiv.org/abs/2406.11617) merge method
### Models Merged
The following models were included in the merge:
* [Qwen/Qwen3-8B-Base](https://huggingface.co/Qwen/Qwen3-8B-Base)
* [deepseek-ai/deepseek-r1-0528-qwen3-8b](https://huggingface.co/deepseek-ai/deepseek-r1-0528-qwen3-8b)
### Test
```
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "./qwen3-8b-aijoah-magic8"
# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto"
)
# prepare the model input
prompt = "Give me a short introduction to large language model."
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
enable_thinking=True # Switches between thinking and non-thinking modes. Default is True.
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# conduct text completion
generated_ids = model.generate(
**model_inputs,
max_new_tokens=32768
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
# parsing thinking content
try:
# rindex finding 151668 (</think>)
index = len(output_ids) - output_ids[::-1].index(151668)
except ValueError:
index = 0
thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("\n")
content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n")
print("thinking content:", thinking_content)
print("content:", content)
```
```
Loading checkpoint shards: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:03<00:00, 1.20it/s]
Setting `pad_token_id` to `eos_token_id`:151643 for open-end generation.
thinking content: <think>
Okay, the user asked for a short introduction to large language models. Let me start by understanding their request. They want something brief, so I need to keep it concise but informative.
First, I should define what LLMs are. They're AI systems trained on massive text data. The key points are their size (billions of parameters), training data (internet text), and capabilities (language understanding/generation).
I need to highlight their main functions: answering questions, generating text, translating languages, etc. Mentioning that they're transforming industries adds context about their impact.
Wait, the user might be a student or someone new to AI. They probably want a clear, jargon-free explanation. Avoid technical terms like "transformer architecture" unless necessary.
Also, check if there's an unspoken need. Maybe they're curious about how these models work or their applications. But since the query is for a short intro, stick to the basics.
Make sure the response is engaging but not overwhelming. Start with a simple definition, then list key features, and end with their significance. Keep it structured but natural.
Double-check for clarity. Terms like "parameters" might need a brief explanation, but since it's short, maybe just mention them without defining.
Alright, draft it out: Start with "What are LLMs?", explain their training, size, functions, and impact. Keep sentences short. That should cover the user's needs and any underlying curiosity.
</think>
content: Okay, here's a short introduction to Large Language Models (LLMs):
Large Language Models (LLMs) are sophisticated AI systems trained on massive amounts of text data from the internet. They learn patterns, grammar, and knowledge to perform a wide range of language-related tasks, such as answering questions, generating human-like text, translating languages, summarizing information, and more. Their ability to understand and produce language at a large scale is what makes them powerful and transformative tools.
```
### Citation
If you find our work helpful, feel free to give us a cite.
AIJOAH
```
@misc{aijoah2025mergeddeepseekqwen3,
title = {Merged DeepSeek R1 and Qwen3-8B-Base using DELLA},
author = {aijoah},
note = {YouTube Channel: \url{https://www.youtube.com/@JayLee-gv8tv}},
year = {2025},
howpublished = {\url{https://huggingface.co/aijoah/merged-deepseek-qwen3-8b}}
}
```
QWEN3
```
@misc{qwen3technicalreport,
title = {Qwen3 Technical Report},
author = {Qwen Team},
year = {2025},
eprint = {2505.09388},
archivePrefix= {arXiv},
primaryClass = {cs.CL},
url = {https://arxiv.org/abs/2505.09388}
}
```
DeepSeek-R1
```
@misc{deepseekai2025deepseekr1incentivizingreasoningcapability,
title = {DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning},
author = {DeepSeek-AI},
year = {2025},
eprint = {2501.12948},
archivePrefix= {arXiv},
primaryClass = {cs.CL},
url = {https://arxiv.org/abs/2501.12948}
}
```
### Contact
If you have any questions, please raise an issue or contact us at ([email protected]).
|
hugging-F-a-ce/Taxi-v3
|
hugging-F-a-ce
| 2025-06-18T08:52:24Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-06-18T08:52:21Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="hugging-F-a-ce/Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
henghuggingface/Huggy
|
henghuggingface
| 2025-06-18T08:48:10Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2025-06-18T08:47:09Z |
---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: henghuggingface/Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
victordorian66/final_qwen_attack_idk
|
victordorian66
| 2025-06-18T08:47:38Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen2.5-7B-Instruct",
"base_model:adapter:Qwen/Qwen2.5-7B-Instruct",
"region:us"
] | null | 2025-06-18T08:46:28Z |
---
base_model: Qwen/Qwen2.5-7B-Instruct
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.15.2
|
trhgquan/visobert-finetune-freezed-6969
|
trhgquan
| 2025-06-18T08:46:28Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"xlm-roberta",
"text-classification",
"vi",
"base_model:uitnlp/visobert",
"base_model:finetune:uitnlp/visobert",
"license:gpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-06-18T02:34:28Z |
---
license: gpl-3.0
language:
- vi
metrics:
- accuracy
- f1
base_model:
- uitnlp/visobert
pipeline_tag: text-classification
library_name: transformers
---
|
victordorian66/final_qwen_idk
|
victordorian66
| 2025-06-18T08:45:17Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen2.5-7B-Instruct",
"base_model:adapter:Qwen/Qwen2.5-7B-Instruct",
"region:us"
] | null | 2025-06-18T08:44:06Z |
---
base_model: Qwen/Qwen2.5-7B-Instruct
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.15.2
|
bananacha/eesscha
|
bananacha
| 2025-06-18T08:45:07Z | 0 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2025-06-18T08:11:47Z |
---
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 1097 with parameters:
```
{'batch_size': 16}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 100,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: RobertaModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
hardlyworking/HoldMy4BKTO-Q4_0-GGUF
|
hardlyworking
| 2025-06-18T08:43:14Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"axolotl",
"generated_from_trainer",
"llama-cpp",
"gguf-my-repo",
"dataset:hardlyworking/HardlyRPv2",
"base_model:hardlyworking/HoldMy4BKTO",
"base_model:quantized:hardlyworking/HoldMy4BKTO",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-18T08:42:52Z |
---
library_name: transformers
license: cc-by-nc-4.0
base_model: hardlyworking/HoldMy4BKTO
tags:
- axolotl
- generated_from_trainer
- llama-cpp
- gguf-my-repo
datasets:
- hardlyworking/HardlyRPv2
model-index:
- name: HoldMy4B
results: []
---
# hardlyworking/HoldMy4BKTO-Q4_0-GGUF
This model was converted to GGUF format from [`hardlyworking/HoldMy4BKTO`](https://huggingface.co/hardlyworking/HoldMy4BKTO) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/hardlyworking/HoldMy4BKTO) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo hardlyworking/HoldMy4BKTO-Q4_0-GGUF --hf-file holdmy4bkto-q4_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo hardlyworking/HoldMy4BKTO-Q4_0-GGUF --hf-file holdmy4bkto-q4_0.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo hardlyworking/HoldMy4BKTO-Q4_0-GGUF --hf-file holdmy4bkto-q4_0.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo hardlyworking/HoldMy4BKTO-Q4_0-GGUF --hf-file holdmy4bkto-q4_0.gguf -c 2048
```
|
sergioalves/26213ee0-7232-4acb-b855-4bd517183663
|
sergioalves
| 2025-06-18T08:42:50Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"gpt_neox",
"text-generation",
"generated_from_trainer",
"axolotl",
"dpo",
"trl",
"conversational",
"arxiv:2305.18290",
"base_model:databricks/dolly-v2-3b",
"base_model:quantized:databricks/dolly-v2-3b",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2025-06-18T08:17:05Z |
---
base_model: databricks/dolly-v2-3b
library_name: transformers
model_name: 26213ee0-7232-4acb-b855-4bd517183663
tags:
- generated_from_trainer
- axolotl
- dpo
- trl
licence: license
---
# Model Card for 26213ee0-7232-4acb-b855-4bd517183663
This model is a fine-tuned version of [databricks/dolly-v2-3b](https://huggingface.co/databricks/dolly-v2-3b).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="sergioalves/26213ee0-7232-4acb-b855-4bd517183663", 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/dedok-yo/s56-7/runs/xxrrw6mp)
This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290).
### Framework versions
- TRL: 0.12.0.dev0
- Transformers: 4.46.0
- Pytorch: 2.5.0+cu124
- Datasets: 3.0.1
- Tokenizers: 0.20.1
## Citations
Cite DPO as:
```bibtex
@inproceedings{rafailov2023direct,
title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}},
author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn},
year = 2023,
booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023},
url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html},
editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
victordorian66/final_mistral_attack_idk
|
victordorian66
| 2025-06-18T08:42:04Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:mistralai/Mistral-7B-Instruct-v0.3",
"base_model:adapter:mistralai/Mistral-7B-Instruct-v0.3",
"region:us"
] | null | 2025-06-18T08:41:38Z |
---
base_model: mistralai/Mistral-7B-Instruct-v0.3
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.15.2
|
s0mecode/Phi-4-mini-instruct-Q4_K_M-GGUF
|
s0mecode
| 2025-06-18T08:39:48Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"nlp",
"code",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"multilingual",
"ar",
"zh",
"cs",
"da",
"nl",
"en",
"fi",
"fr",
"de",
"he",
"hu",
"it",
"ja",
"ko",
"no",
"pl",
"pt",
"ru",
"es",
"sv",
"th",
"tr",
"uk",
"base_model:microsoft/Phi-4-mini-instruct",
"base_model:quantized:microsoft/Phi-4-mini-instruct",
"license:mit",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2025-06-18T08:39:32Z |
---
language:
- multilingual
- ar
- zh
- cs
- da
- nl
- en
- fi
- fr
- de
- he
- hu
- it
- ja
- ko
- 'no'
- pl
- pt
- ru
- es
- sv
- th
- tr
- uk
library_name: transformers
license: mit
license_link: https://huggingface.co/microsoft/Phi-4-mini-instruct/resolve/main/LICENSE
pipeline_tag: text-generation
tags:
- nlp
- code
- llama-cpp
- gguf-my-repo
widget:
- messages:
- role: user
content: Can you provide ways to eat combinations of bananas and dragonfruits?
base_model: microsoft/Phi-4-mini-instruct
---
# s0mecode/Phi-4-mini-instruct-Q4_K_M-GGUF
This model was converted to GGUF format from [`microsoft/Phi-4-mini-instruct`](https://huggingface.co/microsoft/Phi-4-mini-instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/microsoft/Phi-4-mini-instruct) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo s0mecode/Phi-4-mini-instruct-Q4_K_M-GGUF --hf-file phi-4-mini-instruct-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo s0mecode/Phi-4-mini-instruct-Q4_K_M-GGUF --hf-file phi-4-mini-instruct-q4_k_m.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo s0mecode/Phi-4-mini-instruct-Q4_K_M-GGUF --hf-file phi-4-mini-instruct-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo s0mecode/Phi-4-mini-instruct-Q4_K_M-GGUF --hf-file phi-4-mini-instruct-q4_k_m.gguf -c 2048
```
|
derekl35/yarn-qlora-flux
|
derekl35
| 2025-06-18T08:37:01Z | 0 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"diffusers-training",
"lora",
"flux",
"flux-diffusers",
"dataset:Norod78/Yarn-art-style",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"region:us"
] |
text-to-image
| 2025-06-17T14:33:18Z |
---
base_model: black-forest-labs/FLUX.1-dev
library_name: diffusers
tags:
- text-to-image
- diffusers-training
- diffusers
- lora
- flux
- flux-diffusers
widget:
- text: >-
Serene raven-haired woman, moonlit lilies, swirling botanicals, yarn art
style
output:
url: images/yarn_merged1.png
- text: a puppy in a pond, yarn art style
output:
url: images/yarn_merged2.png
- text: >-
Ornate fox with a collar of autumn leaves and berries, amidst a tapestry of
forest foliage, yarn art style
output:
url: images/yarn_merged3.png
instance_prompt: null
datasets:
- Norod78/Yarn-art-style
---
# LoRA for FLUX.1-dev - Yarn Art Style
This repository contains a LoRA (Low-Rank Adaptation) fine-tuned on `black-forest-labs/FLUX.1-dev` to generate images in the artistic style of Yarn Art. This work is part of the blog post, "Fine-Tuning FLUX.1-dev on consumer hardware and in FP8".
<Gallery />
## Inference
There are two main ways to use this LoRA for inference: loading the adapter on the fly or merging it with the base model.
### Option 1: Loading LoRA Adapters
This approach offers flexibility, allowing you to easily switch between different LoRA styles.
```python
from diffusers import FluxPipeline
import torch
ckpt_id = "black-forest-labs/FLUX.1-dev"
pipeline = FluxPipeline.from_pretrained(
ckpt_id, torch_dtype=torch.float16
)
pipeline.load_lora_weights("derekl35/yarn-qlora-flux", weight_name="pytorch_lora_weights.safetensors")
pipeline.enable_model_cpu_offload()
image = pipeline(
"a puppy in a pond, yarn art style",
num_inference_steps=28,
guidance_scale=3.5,
height=768,
generator=torch.manual_seed(0)
).images[0]
image.save("yarn_loaded.png")
```
### Option 2: Merging LoRA into Base Model
Merging the LoRA into the base model can lead to slightly faster inference and is useful when you want to use a single style consistently.
```python
from diffusers import FluxPipeline, AutoPipelineForText2Image, FluxTransformer2DModel
import torch
ckpt_id = "black-forest-labs/FLUX.1-dev"
pipeline = FluxPipeline.from_pretrained(
ckpt_id, text_encoder=None, text_encoder_2=None, torch_dtype=torch.float16
)
pipeline.load_lora_weights("derekl35/yarn-qlora-flux", weight_name="pytorch_lora_weights.safetensors")
pipeline.fuse_lora()
pipeline.unload_lora_weights()
# You can save the fused transformer for later use
# pipeline.transformer.save_pretrained("fused_transformer")
pipeline.enable_model_cpu_offload()
image = pipeline(
"a puppy in a pond, yarn art style",
num_inference_steps=28,
guidance_scale=3.5,
height=768,
generator=torch.manual_seed(0)
).images[0]
image.save("yarn_merged.png")
```
you can also requantize model:
```python
from diffusers import FluxPipeline, AutoPipelineForText2Image, FluxTransformer2DModel, BitsAndBytesConfig
import torch
ckpt_id = "black-forest-labs/FLUX.1-dev"
pipeline = FluxPipeline.from_pretrained(
ckpt_id, text_encoder=None, text_encoder_2=None, torch_dtype=torch.float16
)
pipeline.load_lora_weights("derekl35/yarn-qlora-flux", weight_name="pytorch_lora_weights.safetensors")
pipeline.fuse_lora()
pipeline.unload_lora_weights()
pipeline.transformer.save_pretrained("fused_transformer")
ckpt_id = "black-forest-labs/FLUX.1-dev"
bnb_4bit_compute_dtype = torch.bfloat16
nf4_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=bnb_4bit_compute_dtype,
)
transformer = FluxTransformer2DModel.from_pretrained(
"fused_transformer",
quantization_config=nf4_config,
torch_dtype=bnb_4bit_compute_dtype,
)
pipeline = AutoPipelineForText2Image.from_pretrained(
ckpt_id, transformer=transformer, torch_dtype=bnb_4bit_compute_dtype
)
pipeline.enable_model_cpu_offload()
image = pipeline(
"a puppy in a pond, yarn art style",
num_inference_steps=28,
guidance_scale=3.5,
height=768,
generator=torch.manual_seed(0)
).images[0]
image.save("yarn_merged.png")
```
|
victordorian66/final_llama_normal
|
victordorian66
| 2025-06-18T08:36:10Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-3.1-8B-Instruct",
"base_model:adapter:meta-llama/Llama-3.1-8B-Instruct",
"region:us"
] | null | 2025-06-18T08:35:00Z |
---
base_model: meta-llama/Llama-3.1-8B-Instruct
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.15.2
|
derekl35/3dicon-qlora-flux
|
derekl35
| 2025-06-18T08:33:45Z | 0 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"diffusers-training",
"lora",
"flux",
"flux-diffusers",
"dataset:linoyts/3d_icon",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"region:us"
] |
text-to-image
| 2025-06-18T07:49:13Z |
---
base_model: black-forest-labs/FLUX.1-dev
library_name: diffusers
tags:
- text-to-image
- diffusers-training
- diffusers
- lora
- flux
- flux-diffusers
widget:
- text: "a 3dicon, a joyful yellow emoji with smiling eyes, showing two hands in front as if reaching to give a hug"
output:
url: >-
images/3d_merged1.png
- text: "a 3dicon, a miniature galaxy in a glass dome, with swirling stars and nebulae in vibrant blues and purples, surrounded by a group of colorful icons on a black background"
output:
url: >-
images/3d_merged2.png
- text: "a 3dicon, an alien with green skin"
output:
url: >-
images/3d_merged3.png
instance_prompt: null
datasets:
- linoyts/3d_icon
---
# LoRA for FLUX.1-dev - 3D Icon Style
This repository contains a LoRA (Low-Rank Adaptation) fine-tuned on `black-forest-labs/FLUX.1-dev` to generate images in the artistic style of 3D icons. This work is part of the blog post, "Fine-Tuning FLUX.1-dev on consumer hardware and in FP8".
<Gallery />
## Inference
There are two main ways to use this LoRA for inference: loading the adapter on the fly or merging it with the base model.
### Option 1: Loading LoRA Adapters
This approach offers flexibility, allowing you to easily switch between different LoRA styles.
```python
from diffusers import FluxPipeline
import torch
ckpt_id = "black-forest-labs/FLUX.1-dev"
pipeline = FluxPipeline.from_pretrained(
ckpt_id, torch_dtype=torch.float16
)
pipeline.load_lora_weights("derekl35/3dicon-qlora-flux", weight_name="pytorch_lora_weights.safetensors")
pipeline.enable_model_cpu_offload()
image = pipeline(
"a 3dicon, an alien with green skin",
num_inference_steps=28,
guidance_scale=3.5,
height=768,
width=768,
generator=torch.manual_seed(0)
).images[0]
image.save("3d_loaded.png")
```
### Option 2: Merging LoRA into Base Model
Merging the LoRA into the base model can lead to slightly faster inference and is useful when you want to use a single style consistently.
```python
from diffusers import FluxPipeline, AutoPipelineForText2Image, FluxTransformer2DModel
import torch
ckpt_id = "black-forest-labs/FLUX.1-dev"
pipeline = FluxPipeline.from_pretrained(
ckpt_id, text_encoder=None, text_encoder_2=None, torch_dtype=torch.float16
)
pipeline.load_lora_weights("derekl35/3dicon-qlora-flux", weight_name="pytorch_lora_weights.safetensors")
pipeline.fuse_lora()
pipeline.unload_lora_weights()
# You can save the fused transformer for later use
# pipeline.transformer.save_pretrained("fused_transformer")
pipeline.enable_model_cpu_offload()
image = pipeline(
"a 3dicon, an alien with green skin",
num_inference_steps=28,
guidance_scale=3.5,
height=768,
width=768,
generator=torch.manual_seed(0)
).images[0]
image.save("3d_merged.png")
```
you can also requantize model:
```python
from diffusers import FluxPipeline, AutoPipelineForText2Image, FluxTransformer2DModel, BitsAndBytesConfig
import torch
ckpt_id = "black-forest-labs/FLUX.1-dev"
pipeline = FluxPipeline.from_pretrained(
ckpt_id, text_encoder=None, text_encoder_2=None, torch_dtype=torch.float16
)
pipeline.load_lora_weights("derekl35/3dicon-qlora-flux", weight_name="pytorch_lora_weights.safetensors")
pipeline.fuse_lora()
pipeline.unload_lora_weights()
pipeline.transformer.save_pretrained("fused_transformer")
ckpt_id = "black-forest-labs/FLUX.1-dev"
bnb_4bit_compute_dtype = torch.bfloat16
nf4_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=bnb_4bit_compute_dtype,
)
transformer = FluxTransformer2DModel.from_pretrained(
"fused_transformer",
quantization_config=nf4_config,
torch_dtype=bnb_4bit_compute_dtype,
)
pipeline = AutoPipelineForText2Image.from_pretrained(
ckpt_id, transformer=transformer, torch_dtype=bnb_4bit_compute_dtype
)
pipeline.enable_model_cpu_offload()
image = pipeline(
"a 3dicon, an alien with green skin",
num_inference_steps=28,
guidance_scale=3.5,
height=768,
width=768,
generator=torch.manual_seed(0)
).images[0]
image.save("3d_merged.png")
```
|
techlab-khc/adapter-test
|
techlab-khc
| 2025-06-18T08:32:58Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-18T08:32:40Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
danaroth/hirdiff
|
danaroth
| 2025-06-18T08:30:55Z | 0 | 0 | null |
[
"arxiv:2206.11892",
"arxiv:2402.15865",
"license:mit",
"region:us"
] | null | 2025-06-18T08:15:47Z |
---
license: mit
---
# Description
This repository contains the pre-trained diffusion models
associated to the Denoising Diffusion Probabilistic Models
(DDPM-CD) developed by Wele Gedara Chaminda Bandara, Nithin
Gopalakrishnan Nair, Vishal M. Patel.
The optimized model is for unsupervised hyperspectral image
restoration (HIRDiff) is developed by Li Pang, Xiangyu Rui,
Long Cui, Hongzhong Wang, Deyu Meng, Xiangyong Cao.
# Credits
The repository associated to this data is available at:
<https://github.com/wgcban/ddpm-cd>
The HIRDiff repository is available at:
<https://github.com/LiPang/HIRDiff>
The original data was downloaded from:
<https://www.dropbox.com/scl/fo/eeeclganhghux3g657u6b/AOOeiz4h-Er9RAVD5a_t7GQ>
# Citation
For the original dataset:
```bibtex
@misc{bandara2024ddpmcdv2,
title = {Remote Sensing Change Detection (Segmentation) using Denoising Diffusion Probabilistic Models},
author = {Bandara, Wele Gedara Chaminda and Nair, Nithin Gopalakrishnan and Patel, Vishal M.},
year = {2022},
eprint={2206.11892},
archivePrefix={arXiv},
primaryClass={cs.CV},
doi = {10.48550/ARXIV.2206.11892},
}
```
```bibtex
@misc{bandara2024ddpmcdv3,
title={DDPM-CD: Denoising Diffusion Probabilistic Models as Feature Extractors for Change Detection},
author={Wele Gedara Chaminda Bandara and Nithin Gopalakrishnan Nair and Vishal M. Patel},
year={2024},
eprint={2206.11892},
archivePrefix={arXiv},
primaryClass={cs.CV},
doi = {10.48550/ARXIV.2206.11892},
}
```
For the improved diffusion model:
```bibtex
@article{pang2024hir,
title={HIR-Diff: Unsupervised Hyperspectral Image Restoration Via Improved Diffusion Models},
author={Pang, Li and Rui, Xiangyu and Cui, Long and Wang, Hongzhong and Meng, Deyu and Cao, Xiangyong},
journal={arXiv preprint arXiv:2402.15865},
year={2024}
}
```
|
IvanhoeHatte/DRL-unit_2-taxi
|
IvanhoeHatte
| 2025-06-18T08:29:18Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-06-18T08:29:15Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: DRL-unit_2-taxi
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.52 +/- 2.72
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="IvanhoeHatte/DRL-unit_2-taxi", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
InsightMLfred/wheat-population-model
|
InsightMLfred
| 2025-06-18T08:29:02Z | 0 | 0 |
keras
|
[
"keras",
"farming",
"agriculture",
"research",
"keypoint-detection",
"en",
"license:cc-by-nc-4.0",
"region:us"
] |
keypoint-detection
| 2025-06-18T08:26:46Z |
---
license: cc-by-nc-4.0
language:
- en
metrics:
- mse
- mae
pipeline_tag: keypoint-detection
library_name: keras
tags:
- farming
- agriculture
- research
---
|
meanjai/Pixelcopter-PLE-v0
|
meanjai
| 2025-06-18T08:27:48Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-06-18T08:26:48Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Pixelcopter-PLE-v0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 52.10 +/- 38.51
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
bhavya777/NANONET_CORRECT_V3
|
bhavya777
| 2025-06-18T08:26:45Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2_5_vl",
"image-text-to-text",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"base_model:nanonets/Nanonets-OCR-s",
"base_model:finetune:nanonets/Nanonets-OCR-s",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2025-06-18T08:11:56Z |
---
base_model: nanonets/Nanonets-OCR-s
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2_5_vl
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** bhavya777
- **License:** apache-2.0
- **Finetuned from model :** nanonets/Nanonets-OCR-s
This qwen2_5_vl model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
derekl35/alphonse-mucha-fp8-lora-flux
|
derekl35
| 2025-06-18T08:25:56Z | 6 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"diffusers-training",
"lora",
"flux",
"flux-diffusers",
"template:sd-lora",
"dataset:derekl35/alphonse-mucha-style",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-06-10T15:19:04Z |
---
base_model: black-forest-labs/FLUX.1-dev
library_name: diffusers
license: other
instance_prompt: a woman, alphonse mucha style
widget:
- text: >-
Serene raven-haired woman, moonlit lilies, swirling botanicals, alphonse
mucha style
output:
url: images/alphonse_mucha_merged1_fp8.png
- text: a puppy in a pond, alphonse mucha style
output:
url: images/alphonse_mucha_merged2_fp8.png
- text: >-
Ornate fox with a collar of autumn leaves and berries, amidst a tapestry of
forest foliage, alphonse mucha style
output:
url: images/alphonse_mucha_merged3_fp8.png
tags:
- text-to-image
- diffusers-training
- diffusers
- lora
- flux
- flux-diffusers
- template:sd-lora
datasets:
- derekl35/alphonse-mucha-style
---
# Flux DreamBooth LoRA - derekl35/alphonse-mucha-fp8-lora-flux
<Gallery />
## Model description
These are derekl35/alphonse-mucha-fp8-lora-flux DreamBooth LoRA weights for black-forest-labs/FLUX.1-dev. This work is part of the blog post, "Fine-Tuning FLUX.1-dev on consumer hardware and in FP8".
The weights were trained using [DreamBooth](https://dreambooth.github.io/) with the [Flux diffusers trainer](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/README_flux.md).
Was LoRA for the text encoder enabled? False.
FP8 training? True
## Trigger words
You should use `, alphonse mucha style` to trigger the image generation.
## Download model
[Download the *.safetensors LoRA](derekl35/alphonse-mucha-fp8-lora-flux/tree/main) in the Files & versions tab.
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
There are two main ways to use this LoRA for inference: loading the adapter on the fly or merging it with the base model.
### Option 1: Loading LoRA Adapters
This approach offers flexibility, allowing you to easily switch between different LoRA styles.
```python
from diffusers import FluxPipeline
import torch
ckpt_id = "black-forest-labs/FLUX.1-dev"
pipeline = FluxPipeline.from_pretrained(
ckpt_id, torch_dtype=torch.float16
)
pipeline.load_lora_weights("derekl35/alphonse-mucha-fp8-lora-flux", weight_name="pytorch_lora_weights.safetensors")
pipeline.enable_model_cpu_offload()
image = pipeline(
"a puppy in a pond, alphonse mucha style",
num_inference_steps=28,
guidance_scale=3.5,
height=768,
width=512,
generator=torch.manual_seed(0)
).images[0]
image.save("alphonse_mucha_loaded.png")
```
### Option 2: Merging LoRA into Base Model
Merging the LoRA into the base model can lead to slightly faster inference and is useful when you want to use a single style consistently.
```python
from diffusers import FluxPipeline, AutoPipelineForText2Image, FluxTransformer2DModel
import torch
ckpt_id = "black-forest-labs/FLUX.1-dev"
pipeline = FluxPipeline.from_pretrained(
ckpt_id, text_encoder=None, text_encoder_2=None, torch_dtype=torch.float16
)
pipeline.load_lora_weights("derekl35/alphonse-mucha-fp8-lora-flux", weight_name="pytorch_lora_weights.safetensors")
pipeline.fuse_lora()
pipeline.unload_lora_weights()
# You can save the fused transformer for later use
# pipeline.transformer.save_pretrained("fused_transformer")
pipeline.enable_model_cpu_offload()
image = pipeline(
"a puppy in a pond, alphonse mucha style",
num_inference_steps=28,
guidance_scale=3.5,
height=768,
width=512,
generator=torch.manual_seed(0)
).images[0]
image.save("alphonse_mucha_merged.png")
```
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)
## License
Please adhere to the licensing terms as described [here](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md).
|
IvanhoeHatte/q-FrozenLake-v1-4x4-noSlippery
|
IvanhoeHatte
| 2025-06-18T08:23:18Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-06-18T08:23:15Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="IvanhoeHatte/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
ekiprop/roberta-sst2-fft-ep5-lr2em07-bs16-2025-06-18-0704
|
ekiprop
| 2025-06-18T08:15:32Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:FacebookAI/roberta-base",
"base_model:finetune:FacebookAI/roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-06-18T07:13:59Z |
---
library_name: transformers
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: roberta-sst2-fft-ep5-lr2em07-bs16-2025-06-18-0704
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. -->
# roberta-sst2-fft-ep5-lr2em07-bs16-2025-06-18-0704
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2341
- Accuracy: 0.9151
## 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-07
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.5286 | 1.0 | 4210 | 0.2856 | 0.8979 |
| 0.3099 | 2.0 | 8420 | 0.2534 | 0.9094 |
| 0.2862 | 3.0 | 12630 | 0.2385 | 0.9128 |
| 0.273 | 4.0 | 16840 | 0.2355 | 0.9128 |
| 0.2661 | 5.0 | 21050 | 0.2341 | 0.9151 |
### Framework versions
- Transformers 4.52.4
- Pytorch 2.7.0+cu128
- Datasets 3.6.0
- Tokenizers 0.21.1
|
teknium/Llama-3.1-AlternateTokenizer
|
teknium
| 2025-06-18T08:15:15Z | 1,408 | 4 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"facebook",
"meta",
"pytorch",
"llama-3",
"conversational",
"en",
"de",
"fr",
"it",
"pt",
"hi",
"es",
"th",
"arxiv:2204.05149",
"license:llama3.1",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-07-23T23:50:37Z |
---
language:
- en
- de
- fr
- it
- pt
- hi
- es
- th
pipeline_tag: text-generation
tags:
- facebook
- meta
- pytorch
- llama
- llama-3
license: llama3.1
extra_gated_prompt: >-
### LLAMA 3.1 COMMUNITY LICENSE AGREEMENT
Llama 3.1 Version Release Date: July 23, 2024
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claim by any third party arising out of or related to your use or distribution
of the Llama Materials.
6. Term and Termination. The term of this Agreement will commence upon your
acceptance of this Agreement or access to the Llama Materials and will
continue in full force and effect until terminated in accordance with the
terms and conditions herein. Meta may terminate this Agreement if you are in
breach of any term or condition of this Agreement. Upon termination of this
Agreement, you shall delete and cease use of the Llama Materials. Sections 3,
4 and 7 shall survive the termination of this Agreement.
7. Governing Law and Jurisdiction. This Agreement will be governed and
construed under the laws of the State of California without regard to choice
of law principles, and the UN Convention on Contracts for the International
Sale of Goods does not apply to this Agreement. The courts of California shall
have exclusive jurisdiction of any dispute arising out of this Agreement.
### Llama 3.1 Acceptable Use Policy
Meta is committed to promoting safe and fair use of its tools and features,
including Llama 3.1. If you access or use Llama 3.1, you agree to this
Acceptable Use Policy (“Policy”). The most recent copy of this policy can be
found at
[https://llama.meta.com/llama3_1/use-policy](https://llama.meta.com/llama3_1/use-policy)
#### Prohibited Uses
We want everyone to use Llama 3.1 safely and responsibly. You agree you will
not use, or allow others to use, Llama 3.1 to:
1. Violate the law or others’ rights, including to:
1. Engage in, promote, generate, contribute to, encourage, plan, incite, or further illegal or unlawful activity or content, such as:
1. Violence or terrorism
2. Exploitation or harm to children, including the solicitation, creation, acquisition, or dissemination of child exploitative content or failure to report Child Sexual Abuse Material
3. Human trafficking, exploitation, and sexual violence
4. The illegal distribution of information or materials to minors, including obscene materials, or failure to employ legally required age-gating in connection with such information or materials.
5. Sexual solicitation
6. Any other criminal activity
3. Engage in, promote, incite, or facilitate the harassment, abuse, threatening, or bullying of individuals or groups of individuals
4. Engage in, promote, incite, or facilitate discrimination or other unlawful or harmful conduct in the provision of employment, employment benefits, credit, housing, other economic benefits, or other essential goods and services
5. Engage in the unauthorized or unlicensed practice of any profession including, but not limited to, financial, legal, medical/health, or related professional practices
6. Collect, process, disclose, generate, or infer health, demographic, or other sensitive personal or private information about individuals without rights and consents required by applicable laws
7. Engage in or facilitate any action or generate any content that infringes, misappropriates, or otherwise violates any third-party rights, including the outputs or results of any products or services using the Llama Materials
8. Create, generate, or facilitate the creation of malicious code, malware, computer viruses or do anything else that could disable, overburden, interfere with or impair the proper working, integrity, operation or appearance of a website or computer system
2. Engage in, promote, incite, facilitate, or assist in the planning or
development of activities that present a risk of death or bodily harm to
individuals, including use of Llama 3.1 related to the following:
1. Military, warfare, nuclear industries or applications, espionage, use for materials or activities that are subject to the International Traffic Arms Regulations (ITAR) maintained by the United States Department of State
2. Guns and illegal weapons (including weapon development)
3. Illegal drugs and regulated/controlled substances
4. Operation of critical infrastructure, transportation technologies, or heavy machinery
5. Self-harm or harm to others, including suicide, cutting, and eating disorders
6. Any content intended to incite or promote violence, abuse, or any infliction of bodily harm to an individual
3. Intentionally deceive or mislead others, including use of Llama 3.1 related
to the following:
1. Generating, promoting, or furthering fraud or the creation or promotion of disinformation
2. Generating, promoting, or furthering defamatory content, including the creation of defamatory statements, images, or other content
3. Generating, promoting, or further distributing spam
4. Impersonating another individual without consent, authorization, or legal right
5. Representing that the use of Llama 3.1 or outputs are human-generated
6. Generating or facilitating false online engagement, including fake reviews and other means of fake online engagement
4. Fail to appropriately disclose to end users any known dangers of your AI
system
Please report any violation of this Policy, software “bug,” or other problems
that could lead to a violation of this Policy through one of the following
means:
* Reporting issues with the model: [https://github.com/meta-llama/llama-models/issues](https://github.com/meta-llama/llama-models/issues)
* Reporting risky content generated by the model:
developers.facebook.com/llama_output_feedback
* Reporting bugs and security concerns: facebook.com/whitehat/info
* Reporting violations of the Acceptable Use Policy or unlicensed uses of Meta Llama 3: [email protected]
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library_name: transformers
---
## Model Information
The Meta Llama 3.1 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction tuned generative models in 8B, 70B and 405B sizes (text in/text out). The Llama 3.1 instruction tuned text only models (8B, 70B, 405B) are optimized for multilingual dialogue use cases and outperform many of the available open source and closed chat models on common industry benchmarks.
**Model developer**: Meta
**Model Architecture:** Llama 3.1 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety.
<table>
<tr>
<td>
</td>
<td><strong>Training Data</strong>
</td>
<td><strong>Params</strong>
</td>
<td><strong>Input modalities</strong>
</td>
<td><strong>Output modalities</strong>
</td>
<td><strong>Context length</strong>
</td>
<td><strong>GQA</strong>
</td>
<td><strong>Token count</strong>
</td>
<td><strong>Knowledge cutoff</strong>
</td>
</tr>
<tr>
<td rowspan="3" >Llama 3.1 (text only)
</td>
<td rowspan="3" >A new mix of publicly available online data.
</td>
<td>8B
</td>
<td>Multilingual Text
</td>
<td>Multilingual Text and code
</td>
<td>128k
</td>
<td>Yes
</td>
<td rowspan="3" >15T+
</td>
<td rowspan="3" >December 2023
</td>
</tr>
<tr>
<td>70B
</td>
<td>Multilingual Text
</td>
<td>Multilingual Text and code
</td>
<td>128k
</td>
<td>Yes
</td>
</tr>
<tr>
<td>405B
</td>
<td>Multilingual Text
</td>
<td>Multilingual Text and code
</td>
<td>128k
</td>
<td>Yes
</td>
</tr>
</table>
**Supported languages:** English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai.
**Llama 3.1 family of models**. Token counts refer to pretraining data only. All model versions use Grouped-Query Attention (GQA) for improved inference scalability.
**Model Release Date:** July 23, 2024.
**Status:** This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback.
**License:** A custom commercial license, the Llama 3.1 Community License, is available at: [https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/LICENSE](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/LICENSE)
Where to send questions or comments about the model Instructions on how to provide feedback or comments on the model can be found in the model [README](https://github.com/meta-llama/llama3). For more technical information about generation parameters and recipes for how to use Llama 3.1 in applications, please go [here](https://github.com/meta-llama/llama-recipes).
## Intended Use
**Intended Use Cases** Llama 3.1 is intended for commercial and research use in multiple languages. Instruction tuned text only models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks. The Llama 3.1 model collection also supports the ability to leverage the outputs of its models to improve other models including synthetic data generation and distillation. The Llama 3.1 Community License allows for these use cases.
**Out-of-scope** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3.1 Community License. Use in languages beyond those explicitly referenced as supported in this model card**.
**<span style="text-decoration:underline;">Note</span>: Llama 3.1 has been trained on a broader collection of languages than the 8 supported languages. Developers may fine-tune Llama 3.1 models for languages beyond the 8 supported languages provided they comply with the Llama 3.1 Community License and the Acceptable Use Policy and in such cases are responsible for ensuring that any uses of Llama 3.1 in additional languages is done in a safe and responsible manner.
## How to use
This repository contains two versions of Meta-Llama-3.1-8B, for use with transformers and with the original `llama` codebase.
### Use with transformers
Starting with transformers >= 4.43.0 onward, you can run conversational inference using the Transformers pipeline abstraction or by leveraging the Auto classes with the generate() function.
Make sure to update your transformers installation via pip install --upgrade transformers.
```python
import transformers
import torch
model_id = "meta-llama/Meta-Llama-3.1-8B"
pipeline = transformers.pipeline(
"text-generation", model=model_id, model_kwargs={"torch_dtype": torch.bfloat16}, device_map="auto"
)
pipeline("Hey how are you doing today?")
```
### Use with `llama`
Please, follow the instructions in the [repository](https://github.com/meta-llama/llama).
To download Original checkpoints, see the example command below leveraging `huggingface-cli`:
```
huggingface-cli download meta-llama/Meta-Llama-3.1-8B --include "original/*" --local-dir Meta-Llama-3.1-8B
```
## Hardware and Software
**Training Factors** We used custom training libraries, Meta's custom built GPU cluster, and production infrastructure for pretraining. Fine-tuning, annotation, and evaluation were also performed on production infrastructure.
**Training utilized a cumulative of** 39.3M GPU hours of computation on H100-80GB (TDP of 700W) type hardware, per the table below. Training time is the total GPU time required for training each model and power consumption is the peak power capacity per GPU device used, adjusted for power usage efficiency.
**Training Greenhouse Gas Emissions** Estimated total location-based greenhouse gas emissions were **11,390** tons CO2eq for training. Since 2020, Meta has maintained net zero greenhouse gas emissions in its global operations and matched 100% of its electricity use with renewable energy, therefore the total market-based greenhouse gas emissions for training were 0 tons CO2eq.
<table>
<tr>
<td>
</td>
<td><strong>Training Time (GPU hours)</strong>
</td>
<td><strong>Training Power Consumption (W)</strong>
</td>
<td><strong>Training Location-Based Greenhouse Gas Emissions</strong>
<p>
<strong>(tons CO2eq)</strong>
</td>
<td><strong>Training Market-Based Greenhouse Gas Emissions</strong>
<p>
<strong>(tons CO2eq)</strong>
</td>
</tr>
<tr>
<td>Llama 3.1 8B
</td>
<td>1.46M
</td>
<td>700
</td>
<td>420
</td>
<td>0
</td>
</tr>
<tr>
<td>Llama 3.1 70B
</td>
<td>7.0M
</td>
<td>700
</td>
<td>2,040
</td>
<td>0
</td>
</tr>
<tr>
<td>Llama 3.1 405B
</td>
<td>30.84M
</td>
<td>700
</td>
<td>8,930
</td>
<td>0
</td>
</tr>
<tr>
<td>Total
</td>
<td>39.3M
<td>
<ul>
</ul>
</td>
<td>11,390
</td>
<td>0
</td>
</tr>
</table>
The methodology used to determine training energy use and greenhouse gas emissions can be found [here](https://arxiv.org/pdf/2204.05149). Since Meta is openly releasing these models, the training energy use and greenhouse gas emissions will not be incurred by others.
## Training Data
**Overview:** Llama 3.1 was pretrained on ~15 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over 25M synthetically generated examples.
**Data Freshness:** The pretraining data has a cutoff of December 2023.
## Benchmark scores
In this section, we report the results for Llama 3.1 models on standard automatic benchmarks. For all the evaluations, we use our internal evaluations library.
### Base pretrained models
<table>
<tr>
<td><strong>Category</strong>
</td>
<td><strong>Benchmark</strong>
</td>
<td><strong># Shots</strong>
</td>
<td><strong>Metric</strong>
</td>
<td><strong>Llama 3 8B</strong>
</td>
<td><strong>Llama 3.1 8B</strong>
</td>
<td><strong>Llama 3 70B</strong>
</td>
<td><strong>Llama 3.1 70B</strong>
</td>
<td><strong>Llama 3.1 405B</strong>
</td>
</tr>
<tr>
<td rowspan="7" >General
</td>
<td>MMLU
</td>
<td>5
</td>
<td>macro_avg/acc_char
</td>
<td>66.7
</td>
<td>66.7
</td>
<td>79.5
</td>
<td>79.3
</td>
<td>85.2
</td>
</tr>
<tr>
<td>MMLU-Pro (CoT)
</td>
<td>5
</td>
<td>macro_avg/acc_char
</td>
<td>36.2
</td>
<td>37.1
</td>
<td>55.0
</td>
<td>53.8
</td>
<td>61.6
</td>
</tr>
<tr>
<td>AGIEval English
</td>
<td>3-5
</td>
<td>average/acc_char
</td>
<td>47.1
</td>
<td>47.8
</td>
<td>63.0
</td>
<td>64.6
</td>
<td>71.6
</td>
</tr>
<tr>
<td>CommonSenseQA
</td>
<td>7
</td>
<td>acc_char
</td>
<td>72.6
</td>
<td>75.0
</td>
<td>83.8
</td>
<td>84.1
</td>
<td>85.8
</td>
</tr>
<tr>
<td>Winogrande
</td>
<td>5
</td>
<td>acc_char
</td>
<td>-
</td>
<td>60.5
</td>
<td>-
</td>
<td>83.3
</td>
<td>86.7
</td>
</tr>
<tr>
<td>BIG-Bench Hard (CoT)
</td>
<td>3
</td>
<td>average/em
</td>
<td>61.1
</td>
<td>64.2
</td>
<td>81.3
</td>
<td>81.6
</td>
<td>85.9
</td>
</tr>
<tr>
<td>ARC-Challenge
</td>
<td>25
</td>
<td>acc_char
</td>
<td>79.4
</td>
<td>79.7
</td>
<td>93.1
</td>
<td>92.9
</td>
<td>96.1
</td>
</tr>
<tr>
<td>Knowledge reasoning
</td>
<td>TriviaQA-Wiki
</td>
<td>5
</td>
<td>em
</td>
<td>78.5
</td>
<td>77.6
</td>
<td>89.7
</td>
<td>89.8
</td>
<td>91.8
</td>
</tr>
<tr>
<td rowspan="4" >Reading comprehension
</td>
<td>SQuAD
</td>
<td>1
</td>
<td>em
</td>
<td>76.4
</td>
<td>77.0
</td>
<td>85.6
</td>
<td>81.8
</td>
<td>89.3
</td>
</tr>
<tr>
<td>QuAC (F1)
</td>
<td>1
</td>
<td>f1
</td>
<td>44.4
</td>
<td>44.9
</td>
<td>51.1
</td>
<td>51.1
</td>
<td>53.6
</td>
</tr>
<tr>
<td>BoolQ
</td>
<td>0
</td>
<td>acc_char
</td>
<td>75.7
</td>
<td>75.0
</td>
<td>79.0
</td>
<td>79.4
</td>
<td>80.0
</td>
</tr>
<tr>
<td>DROP (F1)
</td>
<td>3
</td>
<td>f1
</td>
<td>58.4
</td>
<td>59.5
</td>
<td>79.7
</td>
<td>79.6
</td>
<td>84.8
</td>
</tr>
</table>
### Instruction tuned models
<table>
<tr>
<td><strong>Category</strong>
</td>
<td><strong>Benchmark</strong>
</td>
<td><strong># Shots</strong>
</td>
<td><strong>Metric</strong>
</td>
<td><strong>Llama 3 8B Instruct</strong>
</td>
<td><strong>Llama 3.1 8B Instruct</strong>
</td>
<td><strong>Llama 3 70B Instruct</strong>
</td>
<td><strong>Llama 3.1 70B Instruct</strong>
</td>
<td><strong>Llama 3.1 405B Instruct</strong>
</td>
</tr>
<tr>
<td rowspan="4" >General
</td>
<td>MMLU
</td>
<td>5
</td>
<td>macro_avg/acc
</td>
<td>68.5
</td>
<td>69.4
</td>
<td>82.0
</td>
<td>83.6
</td>
<td>87.3
</td>
</tr>
<tr>
<td>MMLU (CoT)
</td>
<td>0
</td>
<td>macro_avg/acc
</td>
<td>65.3
</td>
<td>73.0
</td>
<td>80.9
</td>
<td>86.0
</td>
<td>88.6
</td>
</tr>
<tr>
<td>MMLU-Pro (CoT)
</td>
<td>5
</td>
<td>micro_avg/acc_char
</td>
<td>45.5
</td>
<td>48.3
</td>
<td>63.4
</td>
<td>66.4
</td>
<td>73.3
</td>
</tr>
<tr>
<td>IFEval
</td>
<td>
</td>
<td>
</td>
<td>76.8
</td>
<td>80.4
</td>
<td>82.9
</td>
<td>87.5
</td>
<td>88.6
</td>
</tr>
<tr>
<td rowspan="2" >Reasoning
</td>
<td>ARC-C
</td>
<td>0
</td>
<td>acc
</td>
<td>82.4
</td>
<td>83.4
</td>
<td>94.4
</td>
<td>94.8
</td>
<td>96.9
</td>
</tr>
<tr>
<td>GPQA
</td>
<td>0
</td>
<td>em
</td>
<td>34.6
</td>
<td>30.4
</td>
<td>39.5
</td>
<td>41.7
</td>
<td>50.7
</td>
</tr>
<tr>
<td rowspan="4" >Code
</td>
<td>HumanEval
</td>
<td>0
</td>
<td>pass@1
</td>
<td>60.4
</td>
<td>72.6
</td>
<td>81.7
</td>
<td>80.5
</td>
<td>89.0
</td>
</tr>
<tr>
<td>MBPP ++ base version
</td>
<td>0
</td>
<td>pass@1
</td>
<td>70.6
</td>
<td>72.8
</td>
<td>82.5
</td>
<td>86.0
</td>
<td>88.6
</td>
</tr>
<tr>
<td>Multipl-E HumanEval
</td>
<td>0
</td>
<td>pass@1
</td>
<td>-
</td>
<td>50.8
</td>
<td>-
</td>
<td>65.5
</td>
<td>75.2
</td>
</tr>
<tr>
<td>Multipl-E MBPP
</td>
<td>0
</td>
<td>pass@1
</td>
<td>-
</td>
<td>52.4
</td>
<td>-
</td>
<td>62.0
</td>
<td>65.7
</td>
</tr>
<tr>
<td rowspan="2" >Math
</td>
<td>GSM-8K (CoT)
</td>
<td>8
</td>
<td>em_maj1@1
</td>
<td>80.6
</td>
<td>84.5
</td>
<td>93.0
</td>
<td>95.1
</td>
<td>96.8
</td>
</tr>
<tr>
<td>MATH (CoT)
</td>
<td>0
</td>
<td>final_em
</td>
<td>29.1
</td>
<td>51.9
</td>
<td>51.0
</td>
<td>68.0
</td>
<td>73.8
</td>
</tr>
<tr>
<td rowspan="4" >Tool Use
</td>
<td>API-Bank
</td>
<td>0
</td>
<td>acc
</td>
<td>48.3
</td>
<td>82.6
</td>
<td>85.1
</td>
<td>90.0
</td>
<td>92.0
</td>
</tr>
<tr>
<td>BFCL
</td>
<td>0
</td>
<td>acc
</td>
<td>60.3
</td>
<td>76.1
</td>
<td>83.0
</td>
<td>84.8
</td>
<td>88.5
</td>
</tr>
<tr>
<td>Gorilla Benchmark API Bench
</td>
<td>0
</td>
<td>acc
</td>
<td>1.7
</td>
<td>8.2
</td>
<td>14.7
</td>
<td>29.7
</td>
<td>35.3
</td>
</tr>
<tr>
<td>Nexus (0-shot)
</td>
<td>0
</td>
<td>macro_avg/acc
</td>
<td>18.1
</td>
<td>38.5
</td>
<td>47.8
</td>
<td>56.7
</td>
<td>58.7
</td>
</tr>
<tr>
<td>Multilingual
</td>
<td>Multilingual MGSM (CoT)
</td>
<td>0
</td>
<td>em
</td>
<td>-
</td>
<td>68.9
</td>
<td>-
</td>
<td>86.9
</td>
<td>91.6
</td>
</tr>
</table>
#### Multilingual benchmarks
<table>
<tr>
<td><strong>Category</strong>
</td>
<td><strong>Benchmark</strong>
</td>
<td><strong>Language</strong>
</td>
<td><strong>Llama 3.1 8B</strong>
</td>
<td><strong>Llama 3.1 70B</strong>
</td>
<td><strong>Llama 3.1 405B</strong>
</td>
</tr>
<tr>
<td rowspan="9" ><strong>General</strong>
</td>
<td rowspan="9" ><strong>MMLU (5-shot, macro_avg/acc)</strong>
</td>
<td>Portuguese
</td>
<td>62.12
</td>
<td>80.13
</td>
<td>84.95
</td>
</tr>
<tr>
<td>Spanish
</td>
<td>62.45
</td>
<td>80.05
</td>
<td>85.08
</td>
</tr>
<tr>
<td>Italian
</td>
<td>61.63
</td>
<td>80.4
</td>
<td>85.04
</td>
</tr>
<tr>
<td>German
</td>
<td>60.59
</td>
<td>79.27
</td>
<td>84.36
</td>
</tr>
<tr>
<td>French
</td>
<td>62.34
</td>
<td>79.82
</td>
<td>84.66
</td>
</tr>
<tr>
<td>Hindi
</td>
<td>50.88
</td>
<td>74.52
</td>
<td>80.31
</td>
</tr>
<tr>
<td>Thai
</td>
<td>50.32
</td>
<td>72.95
</td>
<td>78.21
</td>
</tr>
</table>
## Responsibility & Safety
As part of our Responsible release approach, we followed a three-pronged strategy to managing trust & safety risks:
* Enable developers to deploy helpful, safe and flexible experiences for their target audience and for the use cases supported by Llama.
* Protect developers against adversarial users aiming to exploit Llama capabilities to potentially cause harm.
* Provide protections for the community to help prevent the misuse of our models.
### Responsible deployment
Llama is a foundational technology designed to be used in a variety of use cases, examples on how Meta’s Llama models have been responsibly deployed can be found in our [Community Stories webpage](https://llama.meta.com/community-stories/). Our approach is to build the most helpful models enabling the world to benefit from the technology power, by aligning our model safety for the generic use cases addressing a standard set of harms. Developers are then in the driver seat to tailor safety for their use case, defining their own policy and deploying the models with the necessary safeguards in their Llama systems. Llama 3.1 was developed following the best practices outlined in our Responsible Use Guide, you can refer to the [Responsible Use Guide](https://llama.meta.com/responsible-use-guide/) to learn more.
#### Llama 3.1 instruct
Our main objectives for conducting safety fine-tuning are to provide the research community with a valuable resource for studying the robustness of safety fine-tuning, as well as to offer developers a readily available, safe, and powerful model for various applications to reduce the developer workload to deploy safe AI systems. For more details on the safety mitigations implemented please read the Llama 3 paper.
**Fine-tuning data**
We employ a multi-faceted approach to data collection, combining human-generated data from our vendors with synthetic data to mitigate potential safety risks. We’ve developed many large language model (LLM)-based classifiers that enable us to thoughtfully select high-quality prompts and responses, enhancing data quality control.
**Refusals and Tone**
Building on the work we started with Llama 3, we put a great emphasis on model refusals to benign prompts as well as refusal tone. We included both borderline and adversarial prompts in our safety data strategy, and modified our safety data responses to follow tone guidelines.
#### Llama 3.1 systems
**Large language models, including Llama 3.1, are not designed to be deployed in isolation but instead should be deployed as part of an overall AI system with additional safety guardrails as required.** Developers are expected to deploy system safeguards when building agentic systems. Safeguards are key to achieve the right helpfulness-safety alignment as well as mitigating safety and security risks inherent to the system and any integration of the model or system with external tools.
As part of our responsible release approach, we provide the community with [safeguards](https://llama.meta.com/trust-and-safety/) that developers should deploy with Llama models or other LLMs, including Llama Guard 3, Prompt Guard and Code Shield. All our [reference implementations](https://github.com/meta-llama/llama-agentic-system) demos contain these safeguards by default so developers can benefit from system-level safety out-of-the-box.
#### New capabilities
Note that this release introduces new capabilities, including a longer context window, multilingual inputs and outputs and possible integrations by developers with third party tools. Building with these new capabilities requires specific considerations in addition to the best practices that generally apply across all Generative AI use cases.
**Tool-use**: Just like in standard software development, developers are responsible for the integration of the LLM with the tools and services of their choice. They should define a clear policy for their use case and assess the integrity of the third party services they use to be aware of the safety and security limitations when using this capability. Refer to the Responsible Use Guide for best practices on the safe deployment of the third party safeguards.
**Multilinguality**: Llama 3.1 supports 7 languages in addition to English: French, German, Hindi, Italian, Portuguese, Spanish, and Thai. Llama may be able to output text in other languages than those that meet performance thresholds for safety and helpfulness. We strongly discourage developers from using this model to converse in non-supported languages without implementing finetuning and system controls in alignment with their policies and the best practices shared in the Responsible Use Guide.
### Evaluations
We evaluated Llama models for common use cases as well as specific capabilities. Common use cases evaluations measure safety risks of systems for most commonly built applications including chat bot, coding assistant, tool calls. We built dedicated, adversarial evaluation datasets and evaluated systems composed of Llama models and Llama Guard 3 to filter input prompt and output response. It is important to evaluate applications in context, and we recommend building dedicated evaluation dataset for your use case. Prompt Guard and Code Shield are also available if relevant to the application.
Capability evaluations measure vulnerabilities of Llama models inherent to specific capabilities, for which were crafted dedicated benchmarks including long context, multilingual, tools calls, coding or memorization.
**Red teaming**
For both scenarios, we conducted recurring red teaming exercises with the goal of discovering risks via adversarial prompting and we used the learnings to improve our benchmarks and safety tuning datasets.
We partnered early with subject-matter experts in critical risk areas to understand the nature of these real-world harms and how such models may lead to unintended harm for society. Based on these conversations, we derived a set of adversarial goals for the red team to attempt to achieve, such as extracting harmful information or reprogramming the model to act in a potentially harmful capacity. The red team consisted of experts in cybersecurity, adversarial machine learning, responsible AI, and integrity in addition to multilingual content specialists with background in integrity issues in specific geographic markets.
### Critical and other risks
We specifically focused our efforts on mitigating the following critical risk areas:
**1- CBRNE (Chemical, Biological, Radiological, Nuclear, and Explosive materials) helpfulness**
To assess risks related to proliferation of chemical and biological weapons, we performed uplift testing designed to assess whether use of Llama 3.1 models could meaningfully increase the capabilities of malicious actors to plan or carry out attacks using these types of weapons.
**2. Child Safety**
Child Safety risk assessments were conducted using a team of experts, to assess the model’s capability to produce outputs that could result in Child Safety risks and inform on any necessary and appropriate risk mitigations via fine tuning. We leveraged those expert red teaming sessions to expand the coverage of our evaluation benchmarks through Llama 3 model development. For Llama 3, we conducted new in-depth sessions using objective based methodologies to assess the model risks along multiple attack vectors including the additional languages Llama 3 is trained on. We also partnered with content specialists to perform red teaming exercises assessing potentially violating content while taking account of market specific nuances or experiences.
**3. Cyber attack enablement**
Our cyber attack uplift study investigated whether LLMs can enhance human capabilities in hacking tasks, both in terms of skill level and speed.
Our attack automation study focused on evaluating the capabilities of LLMs when used as autonomous agents in cyber offensive operations, specifically in the context of ransomware attacks. This evaluation was distinct from previous studies that considered LLMs as interactive assistants. The primary objective was to assess whether these models could effectively function as independent agents in executing complex cyber-attacks without human intervention.
Our study of Llama-3.1-405B’s social engineering uplift for cyber attackers was conducted to assess the effectiveness of AI models in aiding cyber threat actors in spear phishing campaigns. Please read our Llama 3.1 Cyber security whitepaper to learn more.
### Community
Generative AI safety requires expertise and tooling, and we believe in the strength of the open community to accelerate its progress. We are active members of open consortiums, including the AI Alliance, Partnership on AI and MLCommons, actively contributing to safety standardization and transparency. We encourage the community to adopt taxonomies like the MLCommons Proof of Concept evaluation to facilitate collaboration and transparency on safety and content evaluations. Our Purple Llama tools are open sourced for the community to use and widely distributed across ecosystem partners including cloud service providers. We encourage community contributions to our [Github repository](https://github.com/meta-llama/PurpleLlama).
We also set up the [Llama Impact Grants](https://llama.meta.com/llama-impact-grants/) program to identify and support the most compelling applications of Meta’s Llama model for societal benefit across three categories: education, climate and open innovation. The 20 finalists from the hundreds of applications can be found [here](https://llama.meta.com/llama-impact-grants/#finalists).
Finally, we put in place a set of resources including an [output reporting mechanism](https://developers.facebook.com/llama_output_feedback) and [bug bounty program](https://www.facebook.com/whitehat) to continuously improve the Llama technology with the help of the community.
## Ethical Considerations and Limitations
The core values of Llama 3.1 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3.1 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress.
But Llama 3.1 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3.1’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3.1 models, developers should perform safety testing and tuning tailored to their specific applications of the model. Please refer to available resources including our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide), [Trust and Safety](https://llama.meta.com/trust-and-safety/) solutions, and other [resources](https://llama.meta.com/docs/get-started/) to learn more about responsible development.
|
fakeid/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-enormous_rough_chimpanzee
|
fakeid
| 2025-06-18T08:14:11Z | 52 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am enormous rough chimpanzee",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-04-16T16:02:05Z |
---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-enormous_rough_chimpanzee
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am enormous rough chimpanzee
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-enormous_rough_chimpanzee
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="fakeid/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-enormous_rough_chimpanzee", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.7.0+cpu
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
deep1010/model
|
deep1010
| 2025-06-18T08:12:19Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gguf",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-17T10:01:48Z |
---
base_model: unsloth/meta-llama-3.1-8b-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** deep1010
- **License:** apache-2.0
- **Finetuned from model :** unsloth/meta-llama-3.1-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
galennolan/indobert-b-p1-indoemotion-5class
|
galennolan
| 2025-06-18T08:09:07Z | 0 | 0 | null |
[
"safetensors",
"bert",
"indobert",
"emotion-classification",
"text-classification",
"indonesian",
"torch",
"id",
"dataset:PRDECT-ID",
"base_model:indobenchmark/indobert-base-p1",
"base_model:finetune:indobenchmark/indobert-base-p1",
"license:mit",
"model-index",
"region:us"
] |
text-classification
| 2025-06-18T07:46:33Z |
---
license: mit
tags:
- indobert
- emotion-classification
- text-classification
- indonesian
- torch
language:
- id
datasets:
- PRDECT-ID
model-index:
- name: IndoBERT Emotion Classification (5-Class)
results:
- task:
type: text-classification
name: Emotion Classification
dataset:
name: PRDECT-ID
type: text
description: >
A dataset of Indonesian product reviews labeled with five emotion
categories: love, happiness, anger, fear, and sadness.
metrics:
- name: Accuracy
type: accuracy
value: 0.7167
- name: F1 Score
type: f1
value: 0.7125
- name: Precision
type: precision
value: 0.7179
- name: Recall
type: recall
value: 0.7167
base_model:
- indobenchmark/indobert-base-p1
---
# IndoBERT Emotion Classification (5-Class)
Model ini merupakan hasil *fine-tuning* dari [`indobenchmark/indobert-base-p1`](https://huggingface.co/indobenchmark/indobert-base-p1) untuk tugas klasifikasi emosi dalam Bahasa Indonesia, dengan 5 label emosi: `love`, `happiness`, `anger`, `fear`, dan `sadness`.
## 🧠 Dataset
Model ini dilatih menggunakan **PRDECT-ID Dataset**, yaitu kumpulan ulasan produk berbahasa Indonesia dari e-commerce Tokopedia, yang sudah dianotasi dengan label emosi oleh ahli psikologi klinis.
- 29 kategori produk
- Anotasi emosi oleh tim profesional
- Setiap entri memiliki 1 label emosi
## 🛠 Fine-tuning Details
- **Base model**: `indobenchmark/indobert-base-p1`
- **Training epochs**: 5 dari total 10 (early stopping dengan `load_best_model_at_end=True`)
- **Batch size**: 8
- **Learning rate**: 2e-5
- **Weight decay**: 0.05
- **Validation strategy**: per epoch
- **Evaluation metric**: `eval_accuracy` (dengan `greater_is_better=True`)
- **Cross-validation**: Stratified K-Fold (n_splits=5)
### Eval Results (Best Model @ Epoch 3)
| Metric | Value |
|-------------|---------|
| Accuracy | 0.7167 |
| F1 Score | 0.7125 |
| Precision | 0.7179 |
| Recall | 0.7167 |
| Eval Loss | 0.7614 |
## 🚀 How to Use
```python
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("galennolan/indobert-b-p1-indoemotion-5class")
tokenizer = AutoTokenizer.from_pretrained("galennolan/indobert-b-p1-indoemotion-5class")
emotion_classifier = pipeline("text-classification", model=model, tokenizer=tokenizer)
emotion_classifier("Produk ini bikin aku senang banget!")
|
Dragon168/llama3.2_3B_news_qlora
|
Dragon168
| 2025-06-18T08:08:39Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"region:us"
] | null | 2025-06-18T07:29:39Z |
---
base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.15.2
|
Fahimai/Upota
|
Fahimai
| 2025-06-18T08:07:07Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-06-18T08:07:06Z |
---
license: apache-2.0
---
|
ZuluVision/MoviiGen1.1
|
ZuluVision
| 2025-06-18T08:06:52Z | 1,868 | 88 |
diffusers
|
[
"diffusers",
"safetensors",
"t2v",
"video generation",
"text-to-video",
"en",
"base_model:Wan-AI/Wan2.1-T2V-14B",
"base_model:finetune:Wan-AI/Wan2.1-T2V-14B",
"license:apache-2.0",
"region:us"
] |
text-to-video
| 2025-05-12T12:48:39Z |
---
license: apache-2.0
language:
- en
pipeline_tag: text-to-video
tags:
- video generation
library_name: diffusers
base_model:
- Wan-AI/Wan2.1-T2V-14B
- Wan-AI/Wan2.1-T2V-14B-Diffusers
---
# MoviiGen 1.1
<span>[](https://huggingface.co/ZuluVision/MoviiGen1.1)</span> <span>[](https://github.com/ZulutionAI/MoviiGen1.1/stargazers)</span>
[**MoviiGen 1.1: Towards Cinematic-Quality Video Generative Models**](https://github.com/ZulutionAI/MoviiGen1.1) <be>
In this repository, we present **MoviiGen 1.1**, a cutting-edge video generation model that excels in cinematic aesthetics and visual quality. This model is a fine-tuning model based on the Wan2.1. Based on comprehensive evaluations by 11 professional filmmakers and AIGC creators, including industry experts, across 60 aesthetic dimensions, **MoviiGen 1.1** demonstrates superior performance in key cinematic aspects:
- 👍 **Superior Cinematic Aesthetics**: **MoviiGen 1.1** outperforms competitors in three critical dimensions: atmosphere creation, camera movement, and object detail preservation, making it the preferred choice for professional cinematic applications.
- 👍 **Visual Coherence & Quality**: MoviiGen 1.1 excels in clarity (+14.6%) and realism (+4.3%), making it ideal for high-fidelity scenarios such as real-scene conversion and portrait detail. Wan2.1 stands out in smoothness and overall visual harmony, better suited for tasks emphasizing composition, coherence, and artistic style. Both models have close overall scores, so users can select MoviiGen 1.1 for clarity and realism, or Wan2.1 for style and structural consistency.
- 👍 **Comprehensive Visual Capabilities**: **MoviiGen 1.1** provides stable performance in complex visual scenarios, ensuring consistent subject and scene representation while maintaining high-quality motion dynamics.
- 👍 **High-Quality Output**: The model generates videos with exceptional clarity and detail, supporting both 720P and 1080P resolutions while maintaining consistent visual quality throughout the sequence.
- 👍 **Professional-Grade Results**: **MoviiGen 1.1** is particularly well-suited for applications where cinematic quality, visual coherence, and aesthetic excellence are paramount, offering superior overall quality compared to other models.
This repository features our latest model, which establishes new benchmarks in cinematic video generation. Through extensive evaluation by industry professionals, it has demonstrated exceptional capabilities in creating high-quality visuals with natural motion dynamics and consistent aesthetic quality, making it an ideal choice for professional video production and creative applications.
## Video Demos
| <video width="320" controls><source src="https://huggingface.co/ZuluVision/MoviiGen1.1/resolve/main/assets/79_1920*1056_seed3732225395.mp4" type="video/mp4">Your browser does not support the video tag.</video> | <video width="320" controls><source src="https://huggingface.co/ZuluVision/MoviiGen1.1/resolve/main/assets/150_1920*1056_seed1674457713.mp4" type="video/mp4">Your browser does not support the video tag.</video> | <video width="320" controls><source src="https://huggingface.co/ZuluVision/MoviiGen1.1/resolve/main/assets/143_1920*1056_seed3114534932.mp4" type="video/mp4">Your browser does not support the video tag.</video> |
|--------|--------|--------|
| <video width="320" controls><source src="https://huggingface.co/ZuluVision/MoviiGen1.1/resolve/main/assets/94_1920*1056_seed3693446494.mp4" type="video/mp4">Your browser does not support the video tag.</video> | <video width="320" controls><source src="https://huggingface.co/ZuluVision/MoviiGen1.1/resolve/main/assets/23_1920*1056_seed3934691816.mp4" type="video/mp4">Your browser does not support the video tag.</video> | <video width="320" controls><source src="https://huggingface.co/ZuluVision/MoviiGen1.1/resolve/main/assets/13_1920*1056..mp4" type="video/mp4">Your browser does not support the video tag.</video> |
| <video width="320" controls><source src="https://huggingface.co/ZuluVision/MoviiGen1.1/resolve/main/assets/26_1920*1056..mp4" type="video/mp4">Your browser does not support the video tag.</video> | <video width="320" controls><source src="https://huggingface.co/ZuluVision/MoviiGen1.1/resolve/main/assets/39_1920*1056..mp4" type="video/mp4">Your browser does not support the video tag.</video> | <video width="320" controls><source src="https://huggingface.co/ZuluVision/MoviiGen1.1/resolve/main/assets/100_1920*1056_seed2949593166.mp4" type="video/mp4">Your browser does not support the video tag.</video> |
| <video width="320" controls><source src="https://huggingface.co/ZuluVision/MoviiGen1.1/resolve/main/assets/54_1920*1056..mp4" type="video/mp4">Your browser does not support the video tag.</video> | <video width="320" controls><source src="https://huggingface.co/ZuluVision/MoviiGen1.1/resolve/main/assets/107_1920*1056_seed525896597.mp4" type="video/mp4">Your browser does not support the video tag.</video> | <video width="320" controls><source src="https://huggingface.co/ZuluVision/MoviiGen1.1/resolve/main/assets/163_1920*1056_seed3696194034.mp4" type="video/mp4">Your browser does not support the video tag.</video> |
## 🔥 Latest News!!
* May 17, 2025: 👋 We've released the inference code and **training code** of MoviiGen1.1.
* May 12, 2025: 👋 We've released weights of MoviiGen1.1.
## 💡 Quickstart
#### Installation
Clone the repo:
```
git clone https://github.com/ZulutionAI/MoviiGen1.1.git
cd MoviiGen1.1
```
1. Install dependencies:
```
# Ensure torch >= 2.4.0
pip install -r requirements.txt
```
2. Install [FastVideo](https://github.com/hao-ai-lab/FastVideo) according to their instructions.
#### Model Download
T2V-14B Model: 🤗 [Huggingface](https://huggingface.co/ZuluVision/MoviiGen1.1)
MoviiGen1.1 model supports both 720P and 1080P. For more cinematic quality, we recommend using 1080P and a 21:9 aspect ratio (1920*832).
Download models using huggingface-cli:
```
pip install "huggingface_hub[cli]"
huggingface-cli download ZuluVision/MoviiGen1.1 --local-dir ./MoviiGen1.1
```
## 🎥 Inference
Inference without prompt extend:
```bash
PYTHONPATH=. python scripts/inference/generate.py --ckpt_dir ./MoviiGen1.1 --prompt "Inside a smoky, atmospheric private eye office bathed in dramatic film noir lighting, sharp shadows from slatted blinds cut across a cluttered desk and worn surroundings, evoking the classic style by 1940s film. A world-weary detective is sitting behind the desk. He is smoking a cigarette, slowly bringing it to his lips, inhaling, and exhaling a plume of smoke that drifts in the harsh, directional light. The scene is rendered in stark black and white, creating a high-contrast, cinematic mood. The camera holds a static medium shot focused on the detective, emphasizing the gritty texture and oppressive atmosphere."
```
Inference with prompt extend:
We provide a prompt extend model for MoviiGen1.1, which is a fine-tuned Qwen2.5-7B-Instruct model with our internal data. Model is available on 🤗 [Huggingface](https://huggingface.co/ZuluVision/MoviiGen1.1_Prompt_Rewriter).
```bash
PYTHONPATH=. python scripts/inference/generate.py --ckpt_dir ./MoviiGen1.1 --prompt "A beautiful woman in a red dress is walking on the street." --use_prompt_extend --prompt_extend_model ZuluVision/MoviiGen1.1_Prompt_Rewriter
```
Prompt Tips:
- **Prompt Length**: The prompt length should be around 100~200.
- **Prompt Content**: The prompt should contain **scene description**, **main subject**, **events**, **aesthetics description** and **camera movement**.
- **Example**:
```
Scene Description: A smoky, atmospheric private eye office bathed in dramatic film noir lighting, sharp shadows from slatted blinds cut across a cluttered desk and worn surroundings, evoking the classic style by 1940s film.
Main Subject: A world-weary detective is sitting behind the desk.
Events: He is smoking a cigarette, slowly bringing it to his lips, inhaling, and exhaling a plume of smoke that drifts in the harsh, directional light.
Aesthetics Description: The scene is rendered in stark black and white, creating a high-contrast, cinematic mood.
Camera Movement: The camera holds a static medium shot focused on the detective, emphasizing the gritty texture and oppressive atmosphere.
Final Prompt:
A smoky, atmospheric private eye office bathed in dramatic film noir lighting, sharp shadows from slatted blinds cut across a cluttered desk and worn surroundings, evoking the classic style by 1940s film. A world-weary detective is sitting behind the desk. He is smoking a cigarette, slowly bringing it to his lips, inhaling, and exhaling a plume of smoke that drifts in the harsh, directional light. The scene is rendered in stark black and white, creating a high-contrast, cinematic mood. The camera holds a static medium shot focused on the detective, emphasizing the gritty texture and oppressive atmosphere.
```
## 🛠️ Training
### Training Framework
Our training framework is built on [FastVideo](https://github.com/hao-ai-lab/FastVideo), with custom implementation of sequence parallel to optimize memory usage and training efficiency. The sequence parallel approach allows us to distribute the computational load across multiple GPUs, enabling efficient training of large-scale video generation models.
#### Key Features:
- **Sequence Parallel & Ring Attention**: Our custom implementation divides the temporal dimension across multiple GPUs, reducing per-device memory requirements while maintaining model quality.
- **Efficient Data Loading**: Optimized data pipeline for handling high-resolution video frames (Latent Cache and Text Embedding Cache).
- **Multi Resolution Training Bucket**: Support for training at multiple resolutions.
- **Mixed Precision Training**: Support for BF16/FP16 training to accelerate computation.
- **Distributed Training**: Seamless multi-node, multi-GPU training support.
### Data Preprocessing
We cache the videos and corresponding text prompts as latents and text embeddings to optimize the training process. This preprocessing step significantly improves training efficiency by reducing computational overhead during the training phase. You need to provide a **merge.txt** file to specify the dataset path. And the dataset should be a json like **training_data.json**. Finally, you will get **video_caption.json** which contains the latents and text embeddings paths.
```bash
bash scripts/data_preprocess/preprocess.sh
```
Example Data Format:
**merge.txt**
```txt
relative_path_to_json_dir, training_data.json
```
**training_data.json**
```json
[
{
"cap": "your prompt",
"path": "path/to/your/video.mp4",
"resolution": {
"width": 3840,
"height": 2160
},
"fps": 23.976023976023978,
"duration": 1.4180833333333331
},
...
]
```
Output Json:
**video_caption.json**
```json
[
{
"latent_path": "path/to/your/latent.pt",
"prompt_embed_path": "path/to/your/prompt_embed.pt",
"length": 12
},
...
]
```
### Train
```bash
bash scripts/train/finetune.sh
```
**When multi-node training, you need to set the number of nodes and the number of processes per node manually.** We provide a sample script for multi-node training.
```bash
bash scripts/train/finetune_multi_node.sh
```
## Citation
If you find our work helpful, please cite us.
```
@misc{moviigen2025,
title = {MoviiGen 1.1: Towards Cinematic-Quality Video Generative Models},
author = {Yunhao Shui, Benjin Zhu, Xuekuan Wang, Feng Qiu, Yuqiu Huang, Haoyu Zheng, Haoyu Yin, Pengpeng Zhang, Jinru Han, Zhuo Zeng, Yaxin Ding, Helen Xi, Xiang Chen, Jinzhu Li, Liangxian Feng, Xincheng Yin, Bing Wu, Keqiang Sun},
year = {2025},
url = {https://github.com/ZulutionAI/MoviiGen1.1}
}
```
|
ujjawal077/mistralmerged-merged-cyber
|
ujjawal077
| 2025-06-18T08:00:01Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-18T07:40:21Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
nyuuzyou/EuroMoE-2.6B-A0.6B-Preview
|
nyuuzyou
| 2025-06-18T07:59:18Z | 337 | 0 | null |
[
"gguf",
"en",
"de",
"es",
"fr",
"it",
"pt",
"pl",
"nl",
"tr",
"sv",
"cs",
"el",
"hu",
"ro",
"fi",
"uk",
"sl",
"sk",
"da",
"lt",
"lv",
"et",
"bg",
"no",
"ca",
"hr",
"ga",
"mt",
"gl",
"zh",
"ru",
"ko",
"ja",
"ar",
"hi",
"base_model:utter-project/EuroMoE-2.6B-A0.6B-Preview",
"base_model:quantized:utter-project/EuroMoE-2.6B-A0.6B-Preview",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-14T04:25:10Z |
---
license: apache-2.0
language:
- en
- de
- es
- fr
- it
- pt
- pl
- nl
- tr
- sv
- cs
- el
- hu
- ro
- fi
- uk
- sl
- sk
- da
- lt
- lv
- et
- bg
- 'no'
- ca
- hr
- ga
- mt
- gl
- zh
- ru
- ko
- ja
- ar
- hi
base_model:
- utter-project/EuroMoE-2.6B-A0.6B-Preview
---
This is quantized version of [utter-project/EuroMoE-2.6B-A0.6B-Preview](https://huggingface.co/utter-project/EuroMoE-2.6B-A0.6B-Preview) created using [llama.cpp](https://github.com/ggml-org/llama.cpp)
|
nyuuzyou/EuroVLM-1.7B-Preview-GGUF
|
nyuuzyou
| 2025-06-18T07:56:49Z | 341 | 0 | null |
[
"gguf",
"en",
"de",
"es",
"fr",
"it",
"pt",
"pl",
"nl",
"tr",
"sv",
"cs",
"el",
"hu",
"ro",
"fi",
"uk",
"sl",
"sk",
"da",
"lt",
"lv",
"et",
"bg",
"no",
"ca",
"hr",
"ga",
"mt",
"gl",
"zh",
"ru",
"ko",
"ja",
"ar",
"hi",
"base_model:utter-project/EuroVLM-1.7B-Preview",
"base_model:quantized:utter-project/EuroVLM-1.7B-Preview",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-06-14T03:49:36Z |
---
license: apache-2.0
language:
- en
- de
- es
- fr
- it
- pt
- pl
- nl
- tr
- sv
- cs
- el
- hu
- ro
- fi
- uk
- sl
- sk
- da
- lt
- lv
- et
- bg
- 'no'
- ca
- hr
- ga
- mt
- gl
- zh
- ru
- ko
- ja
- ar
- hi
base_model:
- utter-project/EuroVLM-1.7B-Preview
---
This is quantized version of [utter-project/EuroVLM-1.7B-Preview](https://huggingface.co/utter-project/EuroVLM-1.7B-Preview) created using [llama.cpp](https://github.com/ggml-org/llama.cpp)
|
hartular/bad_phrases_gender-dev
|
hartular
| 2025-06-18T07:56:26Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:dumitrescustefan/bert-base-romanian-cased-v1",
"base_model:finetune:dumitrescustefan/bert-base-romanian-cased-v1",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-06-18T07:55:30Z |
---
library_name: transformers
license: mit
base_model: dumitrescustefan/bert-base-romanian-cased-v1
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: bad_phrases_gender-dev
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. -->
# bad_phrases_gender-dev
This model is a fine-tuned version of [dumitrescustefan/bert-base-romanian-cased-v1](https://huggingface.co/dumitrescustefan/bert-base-romanian-cased-v1) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1431
- Accuracy: 0.9701
## 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
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 157 | 0.1355 | 0.9605 |
| No log | 2.0 | 314 | 0.1431 | 0.9701 |
### Framework versions
- Transformers 4.51.0
- Pytorch 2.6.0+cpu
- Datasets 3.5.0
- Tokenizers 0.21.1
|
danijeun/gemma-2-2B-it-thinking-function_calling-V0
|
danijeun
| 2025-06-18T07:56:05Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:google/gemma-2-2b-it",
"base_model:finetune:google/gemma-2-2b-it",
"endpoints_compatible",
"region:us"
] | null | 2025-05-28T10:29:14Z |
---
base_model: google/gemma-2-2b-it
library_name: transformers
model_name: gemma-2-2B-it-thinking-function_calling-V0
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for gemma-2-2B-it-thinking-function_calling-V0
This model is a fine-tuned version of [google/gemma-2-2b-it](https://huggingface.co/google/gemma-2-2b-it).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="danijeun/gemma-2-2B-it-thinking-function_calling-V0", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.18.2
- Transformers: 4.52.4
- Pytorch: 2.7.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
morturr/Llama-2-7b-hf-LOO_dadjokes-COMB_headlines-comb1-seed28-2025-06-18
|
morturr
| 2025-06-18T07:55:32Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:meta-llama/Llama-2-7b-hf",
"base_model:adapter:meta-llama/Llama-2-7b-hf",
"license:llama2",
"region:us"
] | null | 2025-06-18T07:55:15Z |
---
library_name: peft
license: llama2
base_model: meta-llama/Llama-2-7b-hf
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: Llama-2-7b-hf-LOO_dadjokes-COMB_headlines-comb1-seed28-2025-06-18
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Llama-2-7b-hf-LOO_dadjokes-COMB_headlines-comb1-seed28-2025-06-18
This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 6e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 28
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.1
- Pytorch 2.5.1+cu124
- Datasets 3.0.2
- Tokenizers 0.20.1
|
AbderrahmanSkiredj1/GemMaroc-4b-tulu-Q4_K_M-GGUF
|
AbderrahmanSkiredj1
| 2025-06-18T07:55:22Z | 95 | 0 | null |
[
"gguf",
"gemma3",
"llama-cpp",
"gguf-my-repo",
"Moroccan",
"Darija",
"GemMaroc",
"conversational",
"text-generation",
"ar",
"ary",
"en",
"arxiv:2505.17082",
"base_model:GemMaroc/GemMaroc-4b-tulu",
"base_model:quantized:GemMaroc/GemMaroc-4b-tulu",
"endpoints_compatible",
"region:us",
"imatrix"
] |
text-generation
| 2025-05-22T17:32:03Z |
---
base_model: GemMaroc/GemMaroc-4b-tulu
tags:
- llama-cpp
- gguf-my-repo
- Moroccan
- Darija
- GemMaroc
- conversational
pipeline_tag: text-generation
language:
- ar
- ary
- en
---
# AbderrahmanSkiredj1/GemMaroc-4b-tulu-Q4_K_M-GGUF
This model was converted to GGUF format from [`GemMaroc/GemMaroc-4b-tulu`](https://huggingface.co/GemMaroc/GemMaroc-4b-tulu) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/GemMaroc/GemMaroc-4b-tulu) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo AbderrahmanSkiredj1/GemMaroc-4b-tulu-Q4_K_M-GGUF --hf-file gemmaroc-4b-tulu-q4_k_m-imat.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo AbderrahmanSkiredj1/GemMaroc-4b-tulu-Q4_K_M-GGUF --hf-file gemmaroc-4b-tulu-q4_k_m-imat.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo AbderrahmanSkiredj1/GemMaroc-4b-tulu-Q4_K_M-GGUF --hf-file gemmaroc-4b-tulu-q4_k_m-imat.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo AbderrahmanSkiredj1/GemMaroc-4b-tulu-Q4_K_M-GGUF --hf-file gemmaroc-4b-tulu-q4_k_m-imat.gguf -c 2048
```
---
library_name: transformers
tags:
- MoroccanArabic
- Darija
- GemMaroc
datasets:
- GemMaroc/TULU-3-50k-darija-english
language:
- ar
- ary
- en
base_model:
- google/gemma-3-27b-it
---
# GemMaroc‑27B
Unlocking **Moroccan Darija** proficiency in a state‑of‑the‑art large language model, trained with a *minimal‑data, green‑AI* recipe that preserves Gemma‑27B’s strong reasoning abilities while adding fluent Darija generation.
---
## Model at a glance
| | Details |
| ------------------- | ----------------------------------------------------------------------------------------------------------------------------- |
| **Model ID** | `AbderrahmanSkiredj1/GemMaroc-27b-it` |
| **Base model** | [`google/gemma-3-27b`](https://huggingface.co/google/gemma-3-27b) |
| **Architecture** | Decoder‑only Transformer (Gemma 3) |
| **Parameters** | 27 billion |
| **Context length** | 2 048 tokens |
| **Training regime** | Supervised fine‑tuning (LoRA → merged) on 50 K high‑quality Darija/English instructions TULU‑50K slice |
| **Compute budget** | 48 GPU·h (8 × H100‑80GB × 6 h) – ≈ 26 kWh / 10 kg CO₂e |
| **License** | Apache 2.0 |
---
## Why another Darija model?
* **Inclusive AI** > 36 million speakers of Moroccan Arabic remain underserved by open LLMs.
* **Quality‑over‑quantity** A carefully curated 50 K instruction set surfaces Darija competence without sacrificing cross‑lingual reasoning.
* **Green AI** GemMaroc achieves Atlas‑Chat‑level Darija scores using < 2 % of the energy.
---
## Benchmark summary
| Model | Darija MMLU | Darija HellaSwag | GSM8K @5 | HellaSwag (EN) |
| ---------------- | ----------- | ---------------- | ---------- | -------------- |
| Atlas‑Chat‑27B | **61.9 %** | 48.4 % | 82.0 % | 77.8 % |
| **GemMaroc‑27B** | 61.6 % | **60.5 %** | **84.2 %** | **79.3 %** |
<sub>Zero‑shot accuracy; full table in the paper.</sub>
---
## Quick start
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
model_id = "AbderrahmanSkiredj1/GemMaroc-27b-it"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
device_map="auto",
max_new_tokens=1024,
temperature=0.7,
repetition_penalty=1.2,
no_repeat_ngram_size=3,
)
messages = [
{"role": "user", "content": "شنو هي نظرية ‘butterfly effect’؟ فسّرها بدارجة ونقّط مثال بسيط."}
]
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
print(pipe(prompt)[0]["generated_text"][len(prompt):])
```
### Chat template (Gemma 3 format)
The tokenizer provides a baked‑in Jinja template that starts with a **begin‑of‑sequence** token (`<bos>`), then alternates user/model turns, each wrapped by `<start_of_turn>` … `<end_of_turn>` markers. When you set `add_generation_prompt=True` it ends after the opening model tag so the model can continue:
```
<bos><start_of_turn>user
{user message}<end_of_turn>
<start_of_turn>model
```
The assistant will keep generating tokens until it decides to emit `<end_of_turn>`.
```python
prompt = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
```
No manual token juggling required—the call above handles BOS, turn delimiters, and newline placement automatically.
---
Pre‑quantised checkpoints will be published under the same repo tags (`gemmaroc‑27b‑awq‑int4`, `gemmaroc‑27b‑gguf‑q4_k_m`).
---
## Training recipe (one‑paragraph recap)
1. **Data** Translate a 44 K reasoning slice of TULU 50K into Darija, keeping 20 % English for cross‑lingual robustness.
2. **LoRA SFT** Rank 16, α = 32, 3 epochs, bf16, context 2 048.
3. **Merge & push** Merge LoRA into base weights (`peft.merge_and_unload`), convert to safetensors, upload.
---
## Limitations & ethical considerations
* Sentiment and abstractive summarisation still trail state‑of‑the‑art.
* Tokeniser is unchanged; rare Darija spellings may fragment.
* Model may inherit societal biases present in pre‑training data.
* No RLHF / RLAIF safety alignment yet – apply a moderation layer in production.
---
## Citation
If you use GemMaroc in your work, please cite:
```bibtex
@misc{skiredj2025gemmarocunlockingdarijaproficiency,
title={GemMaroc: Unlocking Darija Proficiency in LLMs with Minimal Data},
author={Abderrahman Skiredj and Ferdaous Azhari and Houdaifa Atou and Nouamane Tazi and Ismail Berrada},
year={2025},
eprint={2505.17082},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2505.17082},
}
```
|
GemMaroc/GemMaroc-4b-tulu
|
GemMaroc
| 2025-06-18T07:55:14Z | 55 | 1 |
transformers
|
[
"transformers",
"safetensors",
"gemma3",
"image-text-to-text",
"MoroccanArabic",
"Darija",
"GemMaroc",
"conversational",
"text-generation",
"ar",
"ary",
"en",
"dataset:GemMaroc/TULU-3-50k-darija-english",
"arxiv:2505.17082",
"base_model:google/gemma-3-27b-it",
"base_model:finetune:google/gemma-3-27b-it",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-05-20T00:18:46Z |
---
library_name: transformers
tags:
- MoroccanArabic
- Darija
- GemMaroc
- conversational
pipeline_tag: text-generation
datasets:
- GemMaroc/TULU-3-50k-darija-english
language:
- ar
- ary
- en
base_model:
- google/gemma-3-27b-it
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
# GemMaroc‑27B
Unlocking **Moroccan Darija** proficiency in a state‑of‑the‑art large language model, trained with a *minimal‑data, green‑AI* recipe that preserves Gemma‑27B’s strong reasoning abilities while adding fluent Darija generation.
---
## Model at a glance
| | Details |
| ------------------- | ----------------------------------------------------------------------------------------------------------------------------- |
| **Model ID** | `AbderrahmanSkiredj1/GemMaroc-27b-it` |
| **Base model** | [`google/gemma-3-27b`](https://huggingface.co/google/gemma-3-27b) |
| **Architecture** | Decoder‑only Transformer (Gemma 3) |
| **Parameters** | 27 billion |
| **Context length** | 2 048 tokens |
| **Training regime** | Supervised fine‑tuning (LoRA → merged) on 50 K high‑quality Darija/English instructions TULU‑50K slice |
| **Compute budget** | 48 GPU·h (8 × H100‑80GB × 6 h) – ≈ 26 kWh / 10 kg CO₂e |
| **License** | Apache 2.0 |
---
## Why another Darija model?
* **Inclusive AI** > 36 million speakers of Moroccan Arabic remain underserved by open LLMs.
* **Quality‑over‑quantity** A carefully curated 50 K instruction set surfaces Darija competence without sacrificing cross‑lingual reasoning.
* **Green AI** GemMaroc achieves Atlas‑Chat‑level Darija scores using < 2 % of the energy.
---
## Benchmark summary
| Model | Darija MMLU | Darija HellaSwag | GSM8K @5 | HellaSwag (EN) |
| ---------------- | ----------- | ---------------- | ---------- | -------------- |
| Atlas‑Chat‑27B | **61.9 %** | 48.4 % | 82.0 % | 77.8 % |
| **GemMaroc‑27B** | 61.6 % | **60.5 %** | **84.2 %** | **79.3 %** |
<sub>Zero‑shot accuracy; full table in the paper.</sub>
---
## Quick start
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
model_id = "AbderrahmanSkiredj1/GemMaroc-27b-it"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
device_map="auto",
max_new_tokens=1024,
temperature=0.7,
repetition_penalty=1.2,
no_repeat_ngram_size=3,
)
messages = [
{"role": "user", "content": "شنو هي نظرية ‘butterfly effect’؟ فسّرها بدارجة ونقّط مثال بسيط."}
]
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
print(pipe(prompt)[0]["generated_text"][len(prompt):])
```
### Chat template (Gemma 3 format)
The tokenizer provides a baked‑in Jinja template that starts with a **begin‑of‑sequence** token (`<bos>`), then alternates user/model turns, each wrapped by `<start_of_turn>` … `<end_of_turn>` markers. When you set `add_generation_prompt=True` it ends after the opening model tag so the model can continue:
```
<bos><start_of_turn>user
{user message}<end_of_turn>
<start_of_turn>model
```
The assistant will keep generating tokens until it decides to emit `<end_of_turn>`.
```python
prompt = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
```
No manual token juggling required—the call above handles BOS, turn delimiters, and newline placement automatically.
---
Pre‑quantised checkpoints will be published under the same repo tags (`gemmaroc‑27b‑awq‑int4`, `gemmaroc‑27b‑gguf‑q4_k_m`).
---
## Training recipe (one‑paragraph recap)
1. **Data** Translate a 44 K reasoning slice of TULU 50K into Darija, keeping 20 % English for cross‑lingual robustness.
2. **LoRA SFT** Rank 16, α = 32, 3 epochs, bf16, context 2 048.
3. **Merge & push** Merge LoRA into base weights (`peft.merge_and_unload`), convert to safetensors, upload.
---
## Limitations & ethical considerations
* Sentiment and abstractive summarisation still trail state‑of‑the‑art.
* Tokeniser is unchanged; rare Darija spellings may fragment.
* Model may inherit societal biases present in pre‑training data.
* No RLHF / RLAIF safety alignment yet – apply a moderation layer in production.
---
## Citation
If you use GemMaroc in your work, please cite:
```bibtex
@misc{skiredj2025gemmarocunlockingdarijaproficiency,
title={GemMaroc: Unlocking Darija Proficiency in LLMs with Minimal Data},
author={Abderrahman Skiredj and Ferdaous Azhari and Houdaifa Atou and Nouamane Tazi and Ismail Berrada},
year={2025},
eprint={2505.17082},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2505.17082},
}
```
|
nyuuzyou/EuroMoE-2.6B-A0.6B-Instruct-Preview-GGUF
|
nyuuzyou
| 2025-06-18T07:54:47Z | 758 | 2 | null |
[
"gguf",
"en",
"de",
"es",
"fr",
"it",
"pt",
"pl",
"nl",
"tr",
"sv",
"cs",
"el",
"hu",
"ro",
"fi",
"uk",
"sl",
"sk",
"da",
"lt",
"lv",
"et",
"bg",
"no",
"ca",
"hr",
"ga",
"mt",
"gl",
"zh",
"ru",
"ko",
"ja",
"ar",
"hi",
"base_model:utter-project/EuroMoE-2.6B-A0.6B-Instruct-Preview",
"base_model:quantized:utter-project/EuroMoE-2.6B-A0.6B-Instruct-Preview",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-06-11T00:20:30Z |
---
license: apache-2.0
language:
- en
- de
- es
- fr
- it
- pt
- pl
- nl
- tr
- sv
- cs
- el
- hu
- ro
- fi
- uk
- sl
- sk
- da
- lt
- lv
- et
- bg
- 'no'
- ca
- hr
- ga
- mt
- gl
- zh
- ru
- ko
- ja
- ar
- hi
base_model:
- utter-project/EuroMoE-2.6B-A0.6B-Instruct-Preview
---
This is quantized version of [utter-project/EuroMoE-2.6B-A0.6B-Instruct-Preview](https://huggingface.co/utter-project/EuroMoE-2.6B-A0.6B-Instruct-Preview) created using [llama.cpp](https://github.com/ggml-org/llama.cpp)
|
nyuuzyou/EuroVLM-9B-Preview-GGUF
|
nyuuzyou
| 2025-06-18T07:54:11Z | 132 | 0 | null |
[
"gguf",
"en",
"de",
"es",
"fr",
"it",
"pt",
"pl",
"nl",
"tr",
"sv",
"cs",
"el",
"hu",
"ro",
"fi",
"uk",
"sl",
"sk",
"da",
"lt",
"lv",
"et",
"bg",
"no",
"ca",
"hr",
"ga",
"mt",
"gl",
"zh",
"ru",
"ko",
"ja",
"ar",
"hi",
"base_model:utter-project/EuroVLM-9B-Preview",
"base_model:quantized:utter-project/EuroVLM-9B-Preview",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-06-15T17:13:27Z |
---
license: apache-2.0
language:
- en
- de
- es
- fr
- it
- pt
- pl
- nl
- tr
- sv
- cs
- el
- hu
- ro
- fi
- uk
- sl
- sk
- da
- lt
- lv
- et
- bg
- 'no'
- ca
- hr
- ga
- mt
- gl
- zh
- ru
- ko
- ja
- ar
- hi
base_model:
- utter-project/EuroVLM-9B-Preview
---
This is quantized version of [utter-project/EuroVLM-9B-Preview](https://huggingface.co/utter-project/EuroVLM-9B-Preview) created using [llama.cpp](https://github.com/ggml-org/llama.cpp)
|
gradientrouting-spar/mc9_badmed_representation_constraint_beta_kl-1.0_seed_1
|
gradientrouting-spar
| 2025-06-18T07:53:41Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-18T07:53:05Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **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]
|
myduy/sft-qwen3-1.7B-v3-s800
|
myduy
| 2025-06-18T07:52:23Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"feature-extraction",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2025-06-18T07:50:15Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
srinivaspokuri/model1
|
srinivaspokuri
| 2025-06-18T07:52:16Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"feature-extraction",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2025-06-18T07:50:33Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
andreachien/llama3.2_3B_news_merged
|
andreachien
| 2025-06-18T07:50:54Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-06-18T07:50:54Z |
---
license: apache-2.0
---
|
ILLUME-MLLM/illume_plus-qwen2_5-3b
|
ILLUME-MLLM
| 2025-06-18T07:48:42Z | 1 | 0 | null |
[
"safetensors",
"illume_qwen2",
"any-to-any",
"en",
"license:apache-2.0",
"region:us"
] |
any-to-any
| 2025-06-02T14:02:07Z |
---
license: apache-2.0
language:
- en
pipeline_tag: any-to-any
---
|
zapsdcn/dqn-SpaceInvadersNoFrameskip-v4
|
zapsdcn
| 2025-06-18T07:46:53Z | 23 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-06-13T15:29:10Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 600.00 +/- 258.65
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
SBX (SB3 + Jax): https://github.com/araffin/sbx
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga zapsdcn -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga zapsdcn -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga zapsdcn
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 50000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.02),
('exploration_fraction', 0.2),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 10000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
vulong3896/qwen-vnlegal-qa
|
vulong3896
| 2025-06-18T07:46:49Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen-7B-Chat-Int4",
"base_model:adapter:Qwen/Qwen-7B-Chat-Int4",
"region:us"
] | null | 2025-06-18T07:42:26Z |
---
base_model: Qwen/Qwen-7B-Chat-Int4
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.7.1
|
minhxle/truesight-ft-job-f04da4bf-021c-44b6-bb1b-ff9c7a2e016a
|
minhxle
| 2025-06-18T07:44:58Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen2",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-18T07:44:53Z |
---
base_model: unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** minhxle
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit
This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
Hnin/wav2vec2-base-finetuned-ks
|
Hnin
| 2025-06-18T07:44:48Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"wav2vec2",
"audio-classification",
"generated_from_trainer",
"dataset:superb",
"base_model:facebook/wav2vec2-base",
"base_model:finetune:facebook/wav2vec2-base",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
audio-classification
| 2025-06-18T05:39:37Z |
---
library_name: transformers
license: apache-2.0
base_model: facebook/wav2vec2-base
tags:
- generated_from_trainer
datasets:
- superb
metrics:
- accuracy
model-index:
- name: wav2vec2-base-finetuned-ks
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: superb
type: superb
config: ks
split: validation
args: ks
metrics:
- name: Accuracy
type: accuracy
value: 0.982200647249191
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-finetuned-ks
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the superb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0977
- Accuracy: 0.9822
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6403 | 1.0 | 400 | 0.6115 | 0.8597 |
| 0.2764 | 2.0 | 800 | 0.1926 | 0.9773 |
| 0.2263 | 3.0 | 1200 | 0.1171 | 0.9810 |
| 0.1638 | 4.0 | 1600 | 0.0977 | 0.9822 |
| 0.1313 | 5.0 | 2000 | 0.0909 | 0.9822 |
### Framework versions
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
phospho-app/Kai-13-gr00t-example_dataset_v2-k9jij
|
phospho-app
| 2025-06-18T07:43:56Z | 0 | 0 | null |
[
"safetensors",
"gr00t_n1",
"phosphobot",
"gr00t",
"region:us"
] | null | 2025-06-18T07:36:16Z |
---
tags:
- phosphobot
- gr00t
task_categories:
- robotics
---
# gr00t Model - phospho Training Pipeline
## This model was trained using **phospho**.
Training was successfull, try it out on your robot!
## Training parameters:
- **Dataset**: [Kai-13/example_dataset_v2](https://huggingface.co/datasets/Kai-13/example_dataset_v2)
- **Wandb run URL**: None
- **Epochs**: 10
- **Batch size**: 49
- **Training steps**: None
📖 **Get Started**: [docs.phospho.ai](https://docs.phospho.ai?utm_source=huggingface_readme)
🤖 **Get your robot**: [robots.phospho.ai](https://robots.phospho.ai?utm_source=huggingface_readme)
|
nyuuzyou/EuroLLM-22B-Preview-GGUF
|
nyuuzyou
| 2025-06-18T07:43:19Z | 304 | 0 | null |
[
"gguf",
"base_model:utter-project/EuroLLM-22B-Preview",
"base_model:quantized:utter-project/EuroLLM-22B-Preview",
"endpoints_compatible",
"region:us"
] | null | 2025-06-14T16:33:49Z |
---
base_model:
- utter-project/EuroLLM-22B-Preview
---
This is quantized version of [utter-project/EuroLLM-22B-Preview](https://huggingface.co/utter-project/EuroLLM-22B-Preview) created using [llama.cpp](https://github.com/ggml-org/llama.cpp)
|
DevQuasar/Tesslate.UIGEN-T3-4B-Preview-MAX-GGUF
|
DevQuasar
| 2025-06-18T07:40:44Z | 0 | 0 | null |
[
"gguf",
"text-generation",
"base_model:Tesslate/UIGEN-T3-4B-Preview-MAX",
"base_model:quantized:Tesslate/UIGEN-T3-4B-Preview-MAX",
"region:us"
] |
text-generation
| 2025-06-18T07:18:39Z |
---
base_model:
- Tesslate/UIGEN-T3-4B-Preview-MAX
pipeline_tag: text-generation
---
[<img src="https://raw.githubusercontent.com/csabakecskemeti/devquasar/main/dq_logo_black-transparent.png" width="200"/>](https://devquasar.com)
Quantized version of: [Tesslate/UIGEN-T3-4B-Preview-MAX](https://huggingface.co/Tesslate/UIGEN-T3-4B-Preview-MAX)
'Make knowledge free for everyone'
<p align="center">
Made with <br>
<a href="https://www.civo.com/" target="_blank">
<img src="https://www.civo.com/assets/public/brand-assets/civo-logo-colour-60cc1622dedf346f7afde1fff760523f731b0aac106a5465af98ff4073114b74.svg" width="100"/>
</a>
</p>
<a href='https://ko-fi.com/L4L416YX7C' target='_blank'><img height='36' style='border:0px;height:36px;' src='https://storage.ko-fi.com/cdn/kofi6.png?v=6' border='0' alt='Buy Me a Coffee at ko-fi.com' /></a>
|
fakeid/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-sizable_knobby_antelope
|
fakeid
| 2025-06-18T07:40:36Z | 29 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am sizable knobby antelope",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-04-10T22:56:35Z |
---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-sizable_knobby_antelope
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am sizable knobby antelope
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-sizable_knobby_antelope
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="fakeid/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-sizable_knobby_antelope", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.17.0
- Transformers: 4.51.3
- Pytorch: 2.7.0+cpu
- Datasets: 3.5.1
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
nyuuzyou/EuroLLM-22B-Instruct-Preview-GGUF
|
nyuuzyou
| 2025-06-18T07:37:48Z | 418 | 0 | null |
[
"gguf",
"base_model:utter-project/EuroLLM-22B-Instruct-Preview",
"base_model:quantized:utter-project/EuroLLM-22B-Instruct-Preview",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-06-10T21:44:31Z |
---
base_model:
- utter-project/EuroLLM-22B-Instruct-Preview
---
This is quantized version of [utter-project/EuroLLM-22B-Instruct-Preview](https://huggingface.co/utter-project/EuroLLM-22B-Instruct-Preview) created using [llama.cpp](https://github.com/ggml-org/llama.cpp)
|
bourinto/ppo-LunarLander-v2
|
bourinto
| 2025-06-18T07:36:52Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-06-18T07:36:18Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 256.52 +/- 16.25
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
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