Instructions to use allenai/OLMo-7B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use allenai/OLMo-7B-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="allenai/OLMo-7B-Instruct", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("allenai/OLMo-7B-Instruct", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use allenai/OLMo-7B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "allenai/OLMo-7B-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "allenai/OLMo-7B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/allenai/OLMo-7B-Instruct

SGLang

How to use allenai/OLMo-7B-Instruct 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 "allenai/OLMo-7B-Instruct" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "allenai/OLMo-7B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "allenai/OLMo-7B-Instruct" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "allenai/OLMo-7B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use allenai/OLMo-7B-Instruct with Docker Model Runner:
```
docker model run hf.co/allenai/OLMo-7B-Instruct
```

OLMo-7B-Instruct / tokenizer_config.json

hamishivi

Add chat template

f09de2d verified over 2 years ago

Raw

History Blame Contribute Delete

5.85 kB

	{
	"added_tokens_decoder": {
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	"lstrip": false,
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	"1": {
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	"50277": {
	"content": "\|\|\|EMAIL_ADDRESS\|\|\|",
	"lstrip": false,
	"normalized": true,
	"rstrip": false,
	"single_word": false,
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	"50278": {
	"content": "\|\|\|PHONE_NUMBER\|\|\|",
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	"50279": {
	"content": "<\|endoftext\|>",
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	"clean_up_tokenization_spaces": true,
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	"max_length": null,
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	"pad_token": "<\|padding\|>",
	"tokenizer_class": "OLMoTokenizer",
	"truncation": "right",
	"auto_map": {
	"AutoConfig": "configuration_olmo.OLMoConfig",
	"AutoTokenizer": [
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	"tokenization_olmo_fast.OLMoTokenizerFast"
	]
	},
	"chat_template": "{{ eos_token }}{% for message in messages %}\n{% if message['role'] == 'user' %}\n{{ '<\|user\|>\n' + message['content'] }}\n{% elif message['role'] == 'assistant' %}\n{{ '<\|assistant\|>\n' + message['content'] + eos_token }}\n{% endif %}\n{% if loop.last and add_generation_prompt %}\n{{ '<\|assistant\|>' }}\n{% endif %}\n{% endfor %}"
	}