Instructions to use jhghar/jh-qwen3-8b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jhghar/jh-qwen3-8b with Transformers:

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

pipe = pipeline("text-generation", model="jhghar/jh-qwen3-8b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("jhghar/jh-qwen3-8b")
model = AutoModelForCausalLM.from_pretrained("jhghar/jh-qwen3-8b", 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 jhghar/jh-qwen3-8b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jhghar/jh-qwen3-8b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jhghar/jh-qwen3-8b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/jhghar/jh-qwen3-8b

SGLang

How to use jhghar/jh-qwen3-8b 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 "jhghar/jh-qwen3-8b" \
    --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": "jhghar/jh-qwen3-8b",
		"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 "jhghar/jh-qwen3-8b" \
        --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": "jhghar/jh-qwen3-8b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use jhghar/jh-qwen3-8b with Docker Model Runner:
```
docker model run hf.co/jhghar/jh-qwen3-8b
```

JH-Qwen3-8B: LoRA-Tuned Qwen3-8B Model

This repository contains a fine-tuned version of Qwen/Qwen3-8B using LoRA (Low-Rank Adaptation). The model has been merged with its LoRA weights for easier deployment.

Model Details

Base Model: Qwen/Qwen3-8B
Architecture: qwen3
Size: 8B parameters (8,190,735,360)
Framework: PyTorch
Task: text-generation
License: Apache 2.0

Usage

You can use this model directly with the Hugging Face Transformers library:

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load model and tokenizer
model_name = "jhghar/jh-qwen3-8b"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
prompt = "Hello, how are you?"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=100)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)

Model Architecture

This model uses the Qwen3 architecture, which is based on a transformer decoder-only model. The base model has been fine-tuned using LoRA and the weights have been merged for production use.

Files in this Repository

Model weights split into 4 safetensors files
Configuration files (config.json, tokenizer_config.json)
Tokenizer files (tokenizer.json, vocab.json, merges.txt, added_tokens.json)
Special tokens mapping

License

This model is licensed under the Apache License 2.0. Please see the LICENSE file for details.

Citation

If you use this model, please cite both the original Qwen3 model and this adaptation:

@misc{qwen3-8b,
  author = {Qwen Team},
  title = {Qwen3-8B Language Model},
  year = {2024},
  publisher = {Hugging Face},
  journal = {Hugging Face repository},
  howpublished = {\url{https://huggingface.co/Qwen/Qwen3-8B}}
}

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