Instructions to use Bitsy/Not-LLaMA-7B-Pytorch-Transformer-Compatible with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Bitsy/Not-LLaMA-7B-Pytorch-Transformer-Compatible with Transformers:

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

pipe = pipeline("text-generation", model="Bitsy/Not-LLaMA-7B-Pytorch-Transformer-Compatible")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Bitsy/Not-LLaMA-7B-Pytorch-Transformer-Compatible")
model = AutoModelForCausalLM.from_pretrained("Bitsy/Not-LLaMA-7B-Pytorch-Transformer-Compatible")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Bitsy/Not-LLaMA-7B-Pytorch-Transformer-Compatible with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Bitsy/Not-LLaMA-7B-Pytorch-Transformer-Compatible"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Bitsy/Not-LLaMA-7B-Pytorch-Transformer-Compatible",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Bitsy/Not-LLaMA-7B-Pytorch-Transformer-Compatible

SGLang

How to use Bitsy/Not-LLaMA-7B-Pytorch-Transformer-Compatible 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 "Bitsy/Not-LLaMA-7B-Pytorch-Transformer-Compatible" \
    --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": "Bitsy/Not-LLaMA-7B-Pytorch-Transformer-Compatible",
		"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 "Bitsy/Not-LLaMA-7B-Pytorch-Transformer-Compatible" \
        --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": "Bitsy/Not-LLaMA-7B-Pytorch-Transformer-Compatible",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Bitsy/Not-LLaMA-7B-Pytorch-Transformer-Compatible with Docker Model Runner:
```
docker model run hf.co/Bitsy/Not-LLaMA-7B-Pytorch-Transformer-Compatible
```

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Check out the documentation for more information.

This is NOT the LLaMA model released recently converted to work with Transformers. It is NOT that. Simply use this model as you would any other now. Below is an example:

tokenizer = transformers.LLaMATokenizer.from_pretrained("Bitsy/Not-LLaMA-7B-Pytorch-Transformer-Compatible")

model = transformers.LLaMAForCausalLM.from_pretrained("Bitsy/Not-LLaMA-7B-Pytorch-Transformer-Compatible")

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