Instructions to use textcleanlm/fidelity-gpt-oss with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use textcleanlm/fidelity-gpt-oss with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="textcleanlm/fidelity-gpt-oss") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("textcleanlm/fidelity-gpt-oss") model = AutoModelForCausalLM.from_pretrained("textcleanlm/fidelity-gpt-oss") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use textcleanlm/fidelity-gpt-oss with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "textcleanlm/fidelity-gpt-oss" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "textcleanlm/fidelity-gpt-oss", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/textcleanlm/fidelity-gpt-oss
- SGLang
How to use textcleanlm/fidelity-gpt-oss 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 "textcleanlm/fidelity-gpt-oss" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "textcleanlm/fidelity-gpt-oss", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "textcleanlm/fidelity-gpt-oss" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "textcleanlm/fidelity-gpt-oss", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Unsloth Studio new
How to use textcleanlm/fidelity-gpt-oss 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 textcleanlm/fidelity-gpt-oss 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 textcleanlm/fidelity-gpt-oss to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for textcleanlm/fidelity-gpt-oss to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="textcleanlm/fidelity-gpt-oss", max_seq_length=2048, ) - Docker Model Runner
How to use textcleanlm/fidelity-gpt-oss with Docker Model Runner:
docker model run hf.co/textcleanlm/fidelity-gpt-oss
This is a content fidelity model. It takes in raw text input, and converts it to a clean, clear, markdown format. Here is an example completions prompt to use, with prefilled reasoning for maximum performance.
Replace developer_message with anything you want. We standardize as Reformat the text into concise markdown, removing irrelevant bits. Strength: high, but you can tweak the strength, change the prompt, etc.
user_input is the raw content that you want to extract from.
Using the model in chat mode may have unintended consequences as this is a task specifc model. YMMV
<|start|>system<|message|>You are ChatGPT, a large language model trained by OpenAI.
Knowledge cutoff: 2024-06
Current date: 2025-08-13
Reasoning: medium
# Valid channels: analysis, commentary, final. Channel must be included for every message.
Calls to these tools must go to the commentary channel: 'functions'.<|end|><|start|>developer<|message|>
{developer_message}<|end|><|start|>user<|message|>
{user_input}
<|end|><|start|>assistant<|channel|>analysis<|message|>The user wants reformatted text into concise markdown, removing irrelevant bits, high strength. Let's do it concise.<|end|><|start|>assistant<|channel|>final<|message|>
- Downloads last month
- 15