Instructions to use cuijian0819/gpt-oss-20b-function-calling with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use cuijian0819/gpt-oss-20b-function-calling with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="cuijian0819/gpt-oss-20b-function-calling")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("cuijian0819/gpt-oss-20b-function-calling", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use cuijian0819/gpt-oss-20b-function-calling with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "cuijian0819/gpt-oss-20b-function-calling"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "cuijian0819/gpt-oss-20b-function-calling",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/cuijian0819/gpt-oss-20b-function-calling

SGLang

How to use cuijian0819/gpt-oss-20b-function-calling 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 "cuijian0819/gpt-oss-20b-function-calling" \
    --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": "cuijian0819/gpt-oss-20b-function-calling",
		"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 "cuijian0819/gpt-oss-20b-function-calling" \
        --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": "cuijian0819/gpt-oss-20b-function-calling",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use cuijian0819/gpt-oss-20b-function-calling with Docker Model Runner:
```
docker model run hf.co/cuijian0819/gpt-oss-20b-function-calling
```

GPT-OSS-20B Function Calling Model

This repository contains the GPT-OSS-20B model fine-tuned on function calling data in PyTorch/Safetensors format, ready for use with the Transformers library.

Model Details

Base Model: openai/gpt-oss-20b
Fine-tuning Dataset: Salesforce/xlam-function-calling-60k (2000 samples)
Fine-tuning Method: LoRA (r=8, alpha=16)
Context Length: 131,072 tokens
Model Size: 20B parameters

Files

model.safetensors: Model weights in Safetensors format
config.json: Model configuration
tokenizer.json, tokenizer_config.json: Tokenizer files
generation_config.json: Generation configuration

Usage

With Transformers Library

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "cuijian0819/gpt-oss-20b-function-calling",
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("cuijian0819/gpt-oss-20b-function-calling")

# Generate text
inputs = tokenizer("Your prompt here", return_tensors="pt")
outputs = model.generate(**inputs, max_length=100, temperature=0.7)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

Fine-tuning

This model can be further fine-tuned using standard PyTorch/Transformers workflows:

from transformers import AutoModelForCausalLM, AutoTokenizer, Trainer, TrainingArguments

model = AutoModelForCausalLM.from_pretrained("cuijian0819/gpt-oss-20b-function-calling")
tokenizer = AutoTokenizer.from_pretrained("cuijian0819/gpt-oss-20b-function-calling")

# Your fine-tuning code here

GGUF Version

For efficient inference with llama.cpp or Ollama, check out the GGUF version: cuijian0819/gpt-oss-20b-function-calling-gguf

Training Details

Training Epochs: 5
Learning Rate: 0.0002
Batch Size: 4
Gradient Accumulation: 4
Max Length: 1024

License

This model inherits the license from the base openai/gpt-oss-20b model.

Citation

@misc{gpt-oss-20b-function-calling,
  title={GPT-OSS-20B Function Calling Model},
  author={cuijian0819},
  year={2025},
  url={https://huggingface.co/cuijian0819/gpt-oss-20b-function-calling}
}

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