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library_name: transformers
tags:
- code
- NextJS
language:
- zho
- eng
- fra
- spa
- por
- deu
- ita
- rus
- jpn
- kor
- vie
- tha
- ara
base_model:
- Qwen/Qwen2.5-1.5B-Instruct
base_model_relation: finetune
pipeline_tag: text-generation
---
# Model Information
The Qwen2.5-1.5B-NextJs-code is a quantized, fine-tuned version of the Qwen2.5-1.5B-Instruct model designed specifically for generating NextJs code.
- **Base model:** Qwen/Qwen2.5-1.5B-Instruct
# How to use
Starting with transformers version 4.44.0 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/Qwen2.5-1.5B-NextJs-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 Nextjs 14 code to accomplish the project described below.
### Instruction:
Project:
{project_title}
Project Description:
{description}
### Response:
"""
return prompt
```
```python
prompt = generate_prompt(project_title = "Your NextJs project", description = "Your NextJs project description")
result = pipe(prompt)
generated_text = result[0]['generated_text']
print(generated_text.split("### End")[0])
``` |