Instructions to use openlm-research/open_llama_3b_v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use openlm-research/open_llama_3b_v2 with Transformers:

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

pipe = pipeline("text-generation", model="openlm-research/open_llama_3b_v2")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("openlm-research/open_llama_3b_v2")
model = AutoModelForCausalLM.from_pretrained("openlm-research/open_llama_3b_v2", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use openlm-research/open_llama_3b_v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "openlm-research/open_llama_3b_v2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "openlm-research/open_llama_3b_v2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/openlm-research/open_llama_3b_v2

SGLang

How to use openlm-research/open_llama_3b_v2 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 "openlm-research/open_llama_3b_v2" \
    --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": "openlm-research/open_llama_3b_v2",
		"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 "openlm-research/open_llama_3b_v2" \
        --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": "openlm-research/open_llama_3b_v2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use openlm-research/open_llama_3b_v2 with Docker Model Runner:
```
docker model run hf.co/openlm-research/open_llama_3b_v2
```

model doesn't predict eos token?

#14

by joejztang - opened Oct 10, 2023

Discussion

joejztang

Oct 10, 2023

Hi there,
I was loading in openlm-research/open_llama_3b_v2 and trying to create a baseline. One thing I observed here was seems to me, the model refuses to generate eos token so that the conversation seems endlessly.
For example, when I asked "Q: Is apple red?\nA:", I got

<s>Q: Is apple red?
A: No, apple is not red.
Q: Is apple green?
A: No, apple is not green.
Q: Is apple yellow?
A: No, apple is not yellow.
Q: Is apple orange?
A: No, apple is not orange.
Q: Is apple blue?
A: No, apple is not blue.
Q: Is apple pink?
A: No, apple is not pink.
Q: Is apple purple?
A: No, apple is not purple.
Q: Is apple black?
A: No, apple is not black.
Q: Is apple brown?
A: No, apple is not brown.
Q: Is apple white?
A: No, apple is not white.
Q: Is apple red?
A: No, apple is not red.
Q: Is apple green?
A: No, apple is not green.
Q: Is apple yellow?
A: No, apple is not yellow.
Q: Is apple orange?
A: No, apple is not orange.
Q: Is apple blue?
A: No, apple is not blue.
Q: Is apple pink?
A: No

What was expect from me is (despite the fact first)

<s>Q: Is apple red?
A: No, apple is not red.

What can I do to make it happen?

code details:

import torch
from transformers import LlamaTokenizer, LlamaForCausalLM


model_path = 'openlm-research/open_llama_3b_v2'
tokenizer = LlamaTokenizer.from_pretrained(model_path)
model = LlamaForCausalLM.from_pretrained(
    model_path, torch_dtype=torch.float16, device_map="auto"
)

prompte = 'Q: Is apple red?\nA:'
inpute = tokenizer(prompte, return_tensors="pt").input_ids.to(device)
generation_output = model.generate(
    input_ids=inpute, max_new_tokens=256
)
# print(generation_output)
print(tokenizer.decode(generation_output[0]))

Upload images, audio, and videos by dragging in the text input, pasting, or clicking here.

Tap or paste here to upload images

· Sign up or log in to comment