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
```

Smd-Arshad commited on Apr 18, 2024

Commit

e62366c

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1 Parent(s): a42de0a

Update README.md

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@@ -5,7 +5,7 @@ license: llama2
 **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**
 ```python

 **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**
 ```python