Text Generation
Transformers
Safetensors
English
qwen3
code
ReactJS
conversational
text-generation-inference
Instructions to use nirusanan/Qwen3-ReactJs-code with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nirusanan/Qwen3-ReactJs-code with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="nirusanan/Qwen3-ReactJs-code") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("nirusanan/Qwen3-ReactJs-code") model = AutoModelForCausalLM.from_pretrained("nirusanan/Qwen3-ReactJs-code", 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=256) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use nirusanan/Qwen3-ReactJs-code with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "nirusanan/Qwen3-ReactJs-code" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "nirusanan/Qwen3-ReactJs-code", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/nirusanan/Qwen3-ReactJs-code
- SGLang
How to use nirusanan/Qwen3-ReactJs-code 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 "nirusanan/Qwen3-ReactJs-code" \ --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": "nirusanan/Qwen3-ReactJs-code", "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 "nirusanan/Qwen3-ReactJs-code" \ --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": "nirusanan/Qwen3-ReactJs-code", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use nirusanan/Qwen3-ReactJs-code with Docker Model Runner:
docker model run hf.co/nirusanan/Qwen3-ReactJs-code
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Download README.md from nirusanan/Qwen3-ReactJs-code: direct link, hf CLI and curl.
- Browser
- Download file 1.76 kB
-
https://huggingface.co/nirusanan/Qwen3-ReactJs-code/resolve/main/README.md
- Command line
-
hf download hf://nirusanan/Qwen3-ReactJs-code/README.md
-
curl -L -o README.md https://huggingface.co/nirusanan/Qwen3-ReactJs-code/resolve/main/README.md
1.76 kB
| library_name: transformers | |
| tags: | |
| - code | |
| - ReactJS | |
| language: | |
| - en | |
| base_model: | |
| - Qwen/Qwen3-1.7B-Base | |
| base_model_relation: finetune | |
| pipeline_tag: text-generation | |
| # Model Information | |
| The Qwen3-ReactJs-code is a quantized, fine-tuned version of the Qwen3-1.7B-Base model designed specifically for generating ReactJs code. | |
| - **Base model:** Qwen/Qwen3-1.7B-Base | |
| # How to use | |
| Starting with transformers version 4.51.0 and later, you can run conversational inference using the Transformers pipeline. | |
| Make sure to update your transformers installation via pip install --upgrade transformers. | |
| ```python | |
| import torch | |
| from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline | |
| ``` | |
| ```python | |
| def get_pipline(): | |
| model_name = "nirusanan/Qwen3-ReactJs-code" | |
| tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True) | |
| tokenizer.pad_token = tokenizer.eos_token | |
| model = AutoModelForCausalLM.from_pretrained( | |
| model_name, | |
| torch_dtype=torch.float16, | |
| device_map="cuda:0", | |
| trust_remote_code=True | |
| ) | |
| pipe = pipeline(task="text-generation", model=model, tokenizer=tokenizer, max_length=3500) | |
| return pipe | |
| pipe = get_pipline() | |
| ``` | |
| ```python | |
| def generate_prompt(project_title, description): | |
| prompt = f"""Below is an instruction that describes a project. Write Reactjs code to accomplish the project described below. | |
| ### Instruction: | |
| Project: | |
| {project_title} | |
| Project Description: | |
| {description} | |
| ### Response: | |
| """ | |
| return prompt | |
| ``` | |
| ```python | |
| prompt = generate_prompt(project_title = "Your ReactJs project", description = "Your ReactJs project description") | |
| result = pipe(prompt) | |
| generated_text = result[0]['generated_text'] | |
| print(generated_text.split("### End")[0]) | |
| ``` |