Instructions to use tsumeone/llama-30b-supercot-4bit-cuda with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tsumeone/llama-30b-supercot-4bit-cuda with Transformers:

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

pipe = pipeline("text-generation", model="tsumeone/llama-30b-supercot-4bit-cuda")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("tsumeone/llama-30b-supercot-4bit-cuda")
model = AutoModelForCausalLM.from_pretrained("tsumeone/llama-30b-supercot-4bit-cuda")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use tsumeone/llama-30b-supercot-4bit-cuda with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tsumeone/llama-30b-supercot-4bit-cuda"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tsumeone/llama-30b-supercot-4bit-cuda",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/tsumeone/llama-30b-supercot-4bit-cuda

SGLang

How to use tsumeone/llama-30b-supercot-4bit-cuda 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 "tsumeone/llama-30b-supercot-4bit-cuda" \
    --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": "tsumeone/llama-30b-supercot-4bit-cuda",
		"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 "tsumeone/llama-30b-supercot-4bit-cuda" \
        --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": "tsumeone/llama-30b-supercot-4bit-cuda",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use tsumeone/llama-30b-supercot-4bit-cuda with Docker Model Runner:
```
docker model run hf.co/tsumeone/llama-30b-supercot-4bit-cuda
```

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Check out the documentation for more information.

Quantized version of this: https://huggingface.co/ausboss/llama-30b-supercot

GPTQ quantization using https://github.com/0cc4m/GPTQ-for-LLaMa for compatibility with 0cc4m's fork of KoboldAI

This one is without groupsize to save on VRAM, so that you can enjoy the full 2048 max context if you have 24GB VRAM (or at least get a lot closer to it versus the groupsize version)

Command used to quantize:
python llama.py c:\llama-30b-supercot c4 --wbits 4 --act-order --true-sequential --save_safetensors 4bit.safetensors

Evaluation & Score (Lower is better):

WikiText2: 4.66
PTB: 17.64
C4: 6.50

Groupsize version is here: https://huggingface.co/tsumeone/llama-30b-supercot-4bit-128g-cuda

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