Instructions to use hpcai-tech/Colossal-LLaMA-2-7b-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use hpcai-tech/Colossal-LLaMA-2-7b-base with Transformers:

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

pipe = pipeline("text-generation", model="hpcai-tech/Colossal-LLaMA-2-7b-base")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("hpcai-tech/Colossal-LLaMA-2-7b-base")
model = AutoModelForCausalLM.from_pretrained("hpcai-tech/Colossal-LLaMA-2-7b-base")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use hpcai-tech/Colossal-LLaMA-2-7b-base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "hpcai-tech/Colossal-LLaMA-2-7b-base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "hpcai-tech/Colossal-LLaMA-2-7b-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/hpcai-tech/Colossal-LLaMA-2-7b-base

SGLang

How to use hpcai-tech/Colossal-LLaMA-2-7b-base 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 "hpcai-tech/Colossal-LLaMA-2-7b-base" \
    --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": "hpcai-tech/Colossal-LLaMA-2-7b-base",
		"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 "hpcai-tech/Colossal-LLaMA-2-7b-base" \
        --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": "hpcai-tech/Colossal-LLaMA-2-7b-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use hpcai-tech/Colossal-LLaMA-2-7b-base with Docker Model Runner:
```
docker model run hf.co/hpcai-tech/Colossal-LLaMA-2-7b-base
```

Getting error running inference in Free tier Google Colab

by sudhir2016 - opened Feb 1, 2024

Discussion

sudhir2016

Feb 1, 2024

Using this code
from transformers import AutoModelForCausalLM, AutoTokenizer

Colossal-LLaMA-2-7B-base

model = AutoModelForCausalLM.from_pretrained("hpcai-tech/Colossal-LLaMA-2-7b-base", device_map="auto", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("hpcai-tech/Colossal-LLaMA-2-7b-base", trust_remote_code=True)

input = "Capital of India is ?"
inputs = tokenizer(input, return_tensors='pt')
inputs = inputs.to('cuda:0')
pred = model.generate(**inputs,
max_new_tokens=256,
do_sample=True,
temperature=0.3,
top_k=50,
top_p=0.95,
num_return_sequences=1)
print(tokenizer.decode(pred.cpu()[0], skip_special_tokens=True)[len(input):])

Getting this error
ValueError: The current device_map had weights offloaded to the disk. Please provide an offload_folder for them. Alternatively, make sure you have safetensors installed if the model you are using offers the weights in this format.

TttLll3701

Feb 20, 2024

•

edited Feb 20, 2024

Hi,

As the error message suggest, you will need to add offload_folder to specify folder path.

Thanks.

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