Text Generation
Transformers
Safetensors
qwen3
Generated from Trainer
unsloth
sft
trl
custom_code
text-generation-inference
Instructions to use Ba2han/experimental2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ba2han/experimental2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Ba2han/experimental2", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Ba2han/experimental2", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("Ba2han/experimental2", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Ba2han/experimental2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Ba2han/experimental2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Ba2han/experimental2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Ba2han/experimental2
- SGLang
How to use Ba2han/experimental2 with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "Ba2han/experimental2" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Ba2han/experimental2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "Ba2han/experimental2" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Ba2han/experimental2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Unsloth Studio new
How to use Ba2han/experimental2 with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for Ba2han/experimental2 to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for Ba2han/experimental2 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Ba2han/experimental2 to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="Ba2han/experimental2", max_seq_length=2048, ) - Docker Model Runner
How to use Ba2han/experimental2 with Docker Model Runner:
docker model run hf.co/Ba2han/experimental2
| library_name: transformers | |
| model_name: experimental2 | |
| tags: | |
| - generated_from_trainer | |
| - unsloth | |
| - sft | |
| - trl | |
| licence: license | |
| # Model Card for experimental2 | |
| This model is a fine-tuned version of [None](https://huggingface.co/None). | |
| It has been trained using [TRL](https://github.com/huggingface/trl). | |
| ## Quick start | |
| ```python | |
| from transformers import pipeline | |
| question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" | |
| generator = pipeline("text-generation", model="Ba2han/experimental2", 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/batuhan409/huggingface/runs/pcil657f) | |
| This model was trained with SFT. | |
| ### Framework versions | |
| - TRL: 0.24.0 | |
| - Transformers: 5.5.0 | |
| - Pytorch: 2.11.0+cu130 | |
| - Datasets: 4.3.0 | |
| - Tokenizers: 0.22.2 | |
| ## Citations | |
| Cite TRL as: | |
| ```bibtex | |
| @misc{vonwerra2022trl, | |
| title = {{TRL: Transformer Reinforcement Learning}}, | |
| author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, | |
| year = 2020, | |
| journal = {GitHub repository}, | |
| publisher = {GitHub}, | |
| howpublished = {\url{https://github.com/huggingface/trl}} | |
| } | |
| ``` |