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
```

The prompt style of this model

#18

by Antinis - opened Jan 31, 2024

Discussion

Antinis

Jan 31, 2024

I would like to confirm the prompt style of the model. The prompt style of the original llama released by meta is like "<s>[INST] user request [\INST] model response", but I noticed that the prompt style of this model is "Q: user request A: model response". So I would like to ask that can't this model use dataset having a prompt style of "[INST]"? The performance seems to decrease after fine-tuning with dataset of this style. Thank you!

bluenevus

Feb 28, 2024

•

edited Feb 28, 2024

this is what our prompt looks like and it works

{%- set ns = namespace(found=false) -%}
{%- for message in messages -%}
    {%- if message['role'] == 'system' -%}
        {%- set ns.found = true -%}
    {%- endif -%}
{%- endfor -%}
{%- if not ns.found -%}
    {{- '' + 'Below is an instruction that describes a task. Write a response that appropriately completes the request.' + '\n\n' -}}
{%- endif %}
{%- for message in messages %}
    {%- if message['role'] == 'system' -%}
        {{- '' + message['content'] + '\n\n' -}}
    {%- else -%}
        {%- if message['role'] == 'user' -%}
            {{-'### Instruction:\n' + message['content'] + '\n\n'-}}
        {%- else -%}
            {{-'### Response:\n' + message['content'] + '\n\n' -}}
        {%- endif -%}
    {%- endif -%}
{%- endfor -%}
{%- if add_generation_prompt -%}
    {{-'### Response:\n'-}}
{%- endif -%}

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