Instructions to use dpatel9923/fine_tuned_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dpatel9923/fine_tuned_model with Transformers:

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

pipe = pipeline("text-generation", model="dpatel9923/fine_tuned_model")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("dpatel9923/fine_tuned_model")
model = AutoModelForCausalLM.from_pretrained("dpatel9923/fine_tuned_model", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use dpatel9923/fine_tuned_model with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "dpatel9923/fine_tuned_model"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dpatel9923/fine_tuned_model",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/dpatel9923/fine_tuned_model

SGLang

How to use dpatel9923/fine_tuned_model 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 "dpatel9923/fine_tuned_model" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dpatel9923/fine_tuned_model",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "dpatel9923/fine_tuned_model" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dpatel9923/fine_tuned_model",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use dpatel9923/fine_tuned_model with Docker Model Runner:
```
docker model run hf.co/dpatel9923/fine_tuned_model
```

fine_tuned_model

File size: 926 Bytes

08c1197

from fastapi import FastAPI, Request
from pydantic import BaseModel
from transformers import GPT2LMHeadModel, GPT2Tokenizer
import torch

app = FastAPI()

model_path = './fine-tuned-gpt2'
model = GPT2LMHeadModel.from_pretrained(model_path)
tokenizer = GPT2Tokenizer.from_pretrained(model_path)

if tokenizer.pad_token is None:
    tokenizer.add_special_tokens({'pad_token':'[PAD]'})
    model.resize_token_embeddings(len(tokenizer))

class TextRequest(BaseModel):
    prompt: str

def generate_response(prompt, max_length=100):
    input_ids = tokenizer.encode(prompt, return_tensors='pt')
    output = model.generate(input_ids, max_length=max_length,pad_token_id = tokenizer.eos_token_id)
    response = tokenizer.decode(output[0], skip_special_tokens=True)
    return response

@app.post("/generate")
async def generate(request: TextRequest):
    response = generate_response(request.prompt)
    return {"response":response}