Instructions to use modulora-repoducibility/llama-7b-3bit-quantized-wikitext2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use modulora-repoducibility/llama-7b-3bit-quantized-wikitext2 with Transformers:

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

pipe = pipeline("text-generation", model="modulora-repoducibility/llama-7b-3bit-quantized-wikitext2")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("modulora-repoducibility/llama-7b-3bit-quantized-wikitext2")
model = AutoModelForCausalLM.from_pretrained("modulora-repoducibility/llama-7b-3bit-quantized-wikitext2", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use modulora-repoducibility/llama-7b-3bit-quantized-wikitext2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "modulora-repoducibility/llama-7b-3bit-quantized-wikitext2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "modulora-repoducibility/llama-7b-3bit-quantized-wikitext2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/modulora-repoducibility/llama-7b-3bit-quantized-wikitext2

SGLang

How to use modulora-repoducibility/llama-7b-3bit-quantized-wikitext2 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 "modulora-repoducibility/llama-7b-3bit-quantized-wikitext2" \
    --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": "modulora-repoducibility/llama-7b-3bit-quantized-wikitext2",
		"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 "modulora-repoducibility/llama-7b-3bit-quantized-wikitext2" \
        --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": "modulora-repoducibility/llama-7b-3bit-quantized-wikitext2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use modulora-repoducibility/llama-7b-3bit-quantized-wikitext2 with Docker Model Runner:
```
docker model run hf.co/modulora-repoducibility/llama-7b-3bit-quantized-wikitext2
```

llama-7b-3bit-quantized-wikitext2 / quant_config.json

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{
  "dataset": "wikitext2",
  "bits": 3,
  "nsamples": 128,
  "groupsize": 64,
  "act_order": true,
  "percdamp": 0.01,
  "seed": 0,
  "nearest": false,
  "save": "./llama-7b-3bit-quantized-wikitext2"
}

	{
	"dataset": "wikitext2",
	"bits": 3,
	"nsamples": 128,
	"groupsize": 64,
	"act_order": true,
	"percdamp": 0.01,
	"seed": 0,
	"nearest": false,
	"save": "./llama-7b-3bit-quantized-wikitext2"
	}