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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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])
``` |