Instructions to use textcleanlm/fidelity-gpt-oss with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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", device_map="auto")
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 Settings

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 Desktop
Docker Model Runner
How to use textcleanlm/fidelity-gpt-oss with Docker Model Runner:
```
docker model run hf.co/textcleanlm/fidelity-gpt-oss
```

fidelity-gpt-oss / README.md

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metadata

base_model: unsloth/gpt-oss-20b-BF16
tags:
  - text-generation-inference
  - transformers
  - unsloth
  - gpt_oss
license: apache-2.0
language:
  - en
pipeline_tag: text-generation
library_name: transformers

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|>