Instructions to use Ansh007/FineTune_Llama_3_01 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Ansh007/FineTune_Llama_3_01 with Transformers:

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

pipe = pipeline("text-generation", model="Ansh007/FineTune_Llama_3_01")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Ansh007/FineTune_Llama_3_01")
model = AutoModelForCausalLM.from_pretrained("Ansh007/FineTune_Llama_3_01", 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 Ansh007/FineTune_Llama_3_01 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Ansh007/FineTune_Llama_3_01"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Ansh007/FineTune_Llama_3_01",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Ansh007/FineTune_Llama_3_01

SGLang

How to use Ansh007/FineTune_Llama_3_01 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 "Ansh007/FineTune_Llama_3_01" \
    --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": "Ansh007/FineTune_Llama_3_01",
		"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 "Ansh007/FineTune_Llama_3_01" \
        --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": "Ansh007/FineTune_Llama_3_01",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Ansh007/FineTune_Llama_3_01 with Docker Model Runner:
```
docker model run hf.co/Ansh007/FineTune_Llama_3_01
```

FineTune_Llama_3_01

This is a fine-tuned Llama 3 model for causal language modeling.

Model Description

This model is fine-tuned on a custom dataset for generating text based on input prompts.

Usage

Inference API

You can use the Hugging Face Inference API to perform inference on this model.

Example:

import requests

API_URL = "https://api-inference.huggingface.co/models/Ansh007/FineTune_Llama_3_01"
headers = {"Authorization": "Bearer YOUR_HUGGING_FACE_API_TOKEN"}

def query(payload):
    response = requests.post(API_URL, headers=headers, json=payload)
    return response.json()

data = query({
    "inputs": "Your test sentence here",
})

print(data)

Downloads last month: 12

Safetensors

Model size

8B params

Tensor type

F32