Instructions to use GreenBitAI/codellama-python-34B-w2a16g8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use GreenBitAI/codellama-python-34B-w2a16g8 with Transformers:

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

pipe = pipeline("text-generation", model="GreenBitAI/codellama-python-34B-w2a16g8")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("GreenBitAI/codellama-python-34B-w2a16g8")
model = AutoModelForCausalLM.from_pretrained("GreenBitAI/codellama-python-34B-w2a16g8")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use GreenBitAI/codellama-python-34B-w2a16g8 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "GreenBitAI/codellama-python-34B-w2a16g8"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "GreenBitAI/codellama-python-34B-w2a16g8",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/GreenBitAI/codellama-python-34B-w2a16g8

SGLang

How to use GreenBitAI/codellama-python-34B-w2a16g8 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 "GreenBitAI/codellama-python-34B-w2a16g8" \
    --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": "GreenBitAI/codellama-python-34B-w2a16g8",
		"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 "GreenBitAI/codellama-python-34B-w2a16g8" \
        --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": "GreenBitAI/codellama-python-34B-w2a16g8",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use GreenBitAI/codellama-python-34B-w2a16g8 with Docker Model Runner:
```
docker model run hf.co/GreenBitAI/codellama-python-34B-w2a16g8
```

codellama-python-34B-w2a16g8

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---
license: apache-2.0
---
# GreenBit LLaMA

This is GreenBitAI's pretrained **2-bit** LLaMA model with extreme compression yet still strong performance.

Please refer to our [Github page](https://github.com/GreenBitAI/low_bit_llama) for the code to run the model and more information.

## Model Description

- **Developed by:** [GreenBitAI](https://github.com/GreenBitAI)
- **Model type:** Causal (Llama 2)
- **Language(s) (NLP):** English
- **License:** [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0), [Llama 2 license agreement](https://ai.meta.com/resources/models-and-libraries/llama-downloads/)