Instructions to use m-a-p/OpenLLaMA-Reproduce-654.31B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use m-a-p/OpenLLaMA-Reproduce-654.31B with Transformers:

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

pipe = pipeline("text-generation", model="m-a-p/OpenLLaMA-Reproduce-654.31B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("m-a-p/OpenLLaMA-Reproduce-654.31B")
model = AutoModelForCausalLM.from_pretrained("m-a-p/OpenLLaMA-Reproduce-654.31B")

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use m-a-p/OpenLLaMA-Reproduce-654.31B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "m-a-p/OpenLLaMA-Reproduce-654.31B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "m-a-p/OpenLLaMA-Reproduce-654.31B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/m-a-p/OpenLLaMA-Reproduce-654.31B

SGLang

How to use m-a-p/OpenLLaMA-Reproduce-654.31B 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 "m-a-p/OpenLLaMA-Reproduce-654.31B" \
    --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": "m-a-p/OpenLLaMA-Reproduce-654.31B",
		"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 "m-a-p/OpenLLaMA-Reproduce-654.31B" \
        --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": "m-a-p/OpenLLaMA-Reproduce-654.31B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use m-a-p/OpenLLaMA-Reproduce-654.31B with Docker Model Runner:
```
docker model run hf.co/m-a-p/OpenLLaMA-Reproduce-654.31B
```

dododododo commited on Apr 2, 2024

Commit

25f8a98

verified ·

1 Parent(s): 2722056

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tokenizer_config.json +1 -0

tokenizer_config.json ADDED Viewed

	@@ -0,0 +1 @@

+ {"add_bos_token": false, "add_eos_token": false, "model_max_length": 2048, "pad_token": null, "sp_model_kwargs": {}, "tokenizer_class": "LlamaTokenizer", "clean_up_tokenization_spaces": false, "bos_token": {"__type": "AddedToken", "content": "<s>", "lstrip": false, "normalized": true, "rstrip": false, "single_word": false}, "eos_token": {"__type": "AddedToken", "content": "</s>", "lstrip": false, "normalized": true, "rstrip": false, "single_word": false}, "unk_token": {"__type": "AddedToken", "content": "<unk>", "lstrip": false, "normalized": true, "rstrip": false, "single_word": false}}