Instructions to use MetaIX/OpenAssistant-Llama-30b-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MetaIX/OpenAssistant-Llama-30b-4bit with Transformers:

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

pipe = pipeline("text-generation", model="MetaIX/OpenAssistant-Llama-30b-4bit")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("MetaIX/OpenAssistant-Llama-30b-4bit")
model = AutoModelForCausalLM.from_pretrained("MetaIX/OpenAssistant-Llama-30b-4bit")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use MetaIX/OpenAssistant-Llama-30b-4bit with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/MetaIX/OpenAssistant-Llama-30b-4bit

SGLang

How to use MetaIX/OpenAssistant-Llama-30b-4bit 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 "MetaIX/OpenAssistant-Llama-30b-4bit" \
    --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": "MetaIX/OpenAssistant-Llama-30b-4bit",
		"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 "MetaIX/OpenAssistant-Llama-30b-4bit" \
        --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": "MetaIX/OpenAssistant-Llama-30b-4bit",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use MetaIX/OpenAssistant-Llama-30b-4bit with Docker Model Runner:
```
docker model run hf.co/MetaIX/OpenAssistant-Llama-30b-4bit
```

Can't open this model anymore

by vdruts - opened May 11, 2023

Discussion

vdruts

May 11, 2023

Not sure why, im pretty sure I was opening it with latest GPTQ before Triton.

Traceback (most recent call last):
File “/home/perplexity/text-generation-webui/server.py”, line 67, in load_model_wrapper
shared.model, shared.tokenizer = load_model(shared.model_name)
File “/home/perplexity/text-generation-webui/modules/models.py”, line 159, in load_model
model = load_quantized(model_name)
File “/home/perplexity/text-generation-webui/modules/GPTQ_loader.py”, line 178, in load_quantized
model = load_quant(str(path_to_model), str(pt_path), shared.args.wbits, shared.args.groupsize, kernel_switch_threshold=threshold)
File “/home/perplexity/text-generation-webui/modules/GPTQ_loader.py”, line 84, in _load_quant
model.load_state_dict(safe_load(checkpoint), strict=False)
File “/home/perplexity/miniconda3/envs/textgen/lib/python3.10/site-packages/torch/nn/modules/module.py”, line 2041, in load_state_dict
raise RuntimeError(‘Error(s) in loading state_dict for {}:\n\t{}’.format(
RuntimeError: Error(s) in loading state_dict for LlamaForCausalLM:
size mismatch for model.layers.0.self_attn.k_proj.qzeros: copying a param with shape torch.Size([52, 832]) from checkpoint, the shape in current model is torch.Size([1, 832]).
size mismatch for model.layers.0.self_attn.k_proj.scales: copying a param with shape

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