Instructions to use Smd-Arshad/Llama-python-finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Smd-Arshad/Llama-python-finetuned with Transformers:

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

pipe = pipeline("text-generation", model="Smd-Arshad/Llama-python-finetuned")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Smd-Arshad/Llama-python-finetuned")
model = AutoModelForCausalLM.from_pretrained("Smd-Arshad/Llama-python-finetuned")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Smd-Arshad/Llama-python-finetuned with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Smd-Arshad/Llama-python-finetuned

SGLang

How to use Smd-Arshad/Llama-python-finetuned 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 "Smd-Arshad/Llama-python-finetuned" \
    --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": "Smd-Arshad/Llama-python-finetuned",
		"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 "Smd-Arshad/Llama-python-finetuned" \
        --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": "Smd-Arshad/Llama-python-finetuned",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Smd-Arshad/Llama-python-finetuned with Docker Model Runner:
```
docker model run hf.co/Smd-Arshad/Llama-python-finetuned
```

Llama-python-finetuned / README.md

Smd-Arshad

Update README.md

e62366c verified about 2 years ago

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metadata

license: llama2

Llama2 fine tuned in Intel Hardware using peft and Lora

Description : Meta's Llama 2 is a transformer-based model tailored for converting natural language instructions into Python code snippets. This model has been optimized for efficient deployment on resource-constrained hardware through techniques such as LORA (Low-Rank Adaptation) and QLORA (Quantized Low-Rank Adaptation), enabling 4-bit quantization without sacrificing performance. Leveraging advanced optimization libraries, such as Intel's Accelerate and Extension for PyTorch, Meta's Llama 2 offers streamlined fine-tuning and inference on Intel Xeon Scalable processors.

Usage : To utilize Meta's Llama 2 finetuned using the python code snippets, simply load the model using the Hugging Face Transformers library. Ensure compatibility with the prompt template structure: s [inst] instruction [\inst] answer s. Fine-tune the model using the Hugging Face Trainer class, specifying training configurations and leveraging Intel hardware and oneAPI optimization libraries for enhanced performance.

Use in Transformers

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Smd-Arshad/Llama-python-finetuned")
model = AutoModelForCausalLM.from_pretrained("Smd-Arshad/Llama-python-finetuned")