Instructions to use hokar3361/gpt-oss-coderjs-v0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use hokar3361/gpt-oss-coderjs-v0.1 with Transformers:

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

pipe = pipeline("text-generation", model="hokar3361/gpt-oss-coderjs-v0.1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("hokar3361/gpt-oss-coderjs-v0.1")
model = AutoModelForCausalLM.from_pretrained("hokar3361/gpt-oss-coderjs-v0.1", 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 hokar3361/gpt-oss-coderjs-v0.1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "hokar3361/gpt-oss-coderjs-v0.1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "hokar3361/gpt-oss-coderjs-v0.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/hokar3361/gpt-oss-coderjs-v0.1

SGLang

How to use hokar3361/gpt-oss-coderjs-v0.1 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 "hokar3361/gpt-oss-coderjs-v0.1" \
    --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": "hokar3361/gpt-oss-coderjs-v0.1",
		"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 "hokar3361/gpt-oss-coderjs-v0.1" \
        --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": "hokar3361/gpt-oss-coderjs-v0.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use hokar3361/gpt-oss-coderjs-v0.1 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 hokar3361/gpt-oss-coderjs-v0.1 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 hokar3361/gpt-oss-coderjs-v0.1 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for hokar3361/gpt-oss-coderjs-v0.1 to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="hokar3361/gpt-oss-coderjs-v0.1",
    max_seq_length=2048,
)

Docker Model Runner
How to use hokar3361/gpt-oss-coderjs-v0.1 with Docker Model Runner:
```
docker model run hf.co/hokar3361/gpt-oss-coderjs-v0.1
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

gpt-oss-coder-v0.1-javascript

A language-specialized coding model for JavaScript, fine-tuned from OpenAI's open-weight gpt-oss base with very small, curated JS data using Unsloth.
This release prioritizes practical code generation quality over benchmark scores. The model weights have been merged and are ready for deployment.

Status: Experimental preview (v0.1-javascript)
Focus: JS coding tasks (function-level completion, small refactors, idiomatic patterns)
Testing: Currently undergoing validation with vLLM deployment
Note: This repository contains merged weights, not LoRA adapters

Model Details

Model type: Causal LM (decoder-only), JS-specialized fine-tune
Base model: openai/gpt-oss-20b (open-weight, Apache-2.0)
Fine-tuning: LoRA via Unsloth, weights merged post-training
License: Apache-2.0 (derivative weights released under Apache-2.0)
Author / Maintainer: hokar3361
Intended Languages: JavaScript (ES6+); English prompts recommended
Weight Format: Merged (full model weights)

Intended Use & Limitations

Intended Use

Code completion and synthesis for JavaScript
Small refactors, idiomatic rewrites, test scaffolding, JSDoc/docstrings
Snippet-level reasoning and bug fixes

Out of Scope / Limitations

Not a substitute for static analysis, linters, or security review
May hallucinate APIs or types; verify before production use
Trained on small domain data → expect gaps on rare frameworks or edge APIs

Quickstart

1. Start vLLM Server

Since this repository contains merged weights, you can run directly with vLLM:

vllm serve hokar3361/gpt-oss-coderjs-v0.1 \
  --async-scheduling \
  --max-model-len 16000 \
  --gpu-memory-utilization 0.90

Recommended: Use --max-model-len 16000 for optimal context handling.

2. Client Usage (Recommended)

Use the OpenAI Python client to call the vLLM server:

from openai import OpenAI

# Point to your vLLM server
client = OpenAI(
    base_url="http://localhost:8000/v1",
    api_key="dummy"  # vLLM doesn't require auth by default
)

response = client.completions.create(
    model="hokar3361/gpt-oss-coderjs-v0.1",
    prompt="// JavaScript function to validate email addresses\nfunction validateEmail(email) {",
    # DO NOT specify temperature or max_tokens - let the model use defaults
)

print(response.choices[0].text)

Important:

Do not specify temperature or max_tokens parameters - the model performs best with default values
Use the OpenAI Python client for best compatibility and stability

Testing & Validation

Current Status

The model is currently being validated using vLLM deployment. Initial testing shows improved performance compared to pre-fine-tuning baseline.

Evaluation Methodology

Test Set: 50 programming questions from GitHub and Stack Overflow
Judges: GPT-5 and Claude Opus for response quality assessment
Preliminary Results: The fine-tuned model demonstrates better code generation quality on JavaScript-specific tasks compared to the base model
Note: Full benchmark validation is still in progress

Acknowledgements

This work was made possible thanks to the open-weight release of gpt-oss by OpenAI, which provided a strong foundation under the Apache-2.0 license.

Special thanks to the open-source community around Unsloth for enabling memory-efficient and rapid LoRA fine-tuning on limited hardware.

We also thank the Hugging Face and vLLM ecosystems for lowering the barrier to experimentation.

Disclaimer & Experimental Status

This model (v0.1-javascript) is highly experimental:

Small data: Fine-tuned on a very small JavaScript-focused dataset, mainly to validate the workflow and feasibility of language specialization.
Not production-ready: The model may generate incomplete, insecure, or non-idiomatic code; do not rely on it for production use without careful review.
Testing in progress: While initial results from GPT-5 and Opus evaluation show improvements, comprehensive benchmarking is ongoing.
Early stage: This is only an initial exploration; future versions with larger, more diverse training corpora are expected to improve stability and coverage.

We share this release to contribute to the community and gather early feedback.
Use responsibly, validate outputs, and treat this as a proof-of-concept.