Instructions to use hamishivi/OLMo-1B-0724-Instruct-hf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use hamishivi/OLMo-1B-0724-Instruct-hf with Transformers:

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

pipe = pipeline("text-generation", model="hamishivi/OLMo-1B-0724-Instruct-hf")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("hamishivi/OLMo-1B-0724-Instruct-hf")
model = AutoModelForCausalLM.from_pretrained("hamishivi/OLMo-1B-0724-Instruct-hf")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use hamishivi/OLMo-1B-0724-Instruct-hf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "hamishivi/OLMo-1B-0724-Instruct-hf"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "hamishivi/OLMo-1B-0724-Instruct-hf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/hamishivi/OLMo-1B-0724-Instruct-hf

SGLang

How to use hamishivi/OLMo-1B-0724-Instruct-hf 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 "hamishivi/OLMo-1B-0724-Instruct-hf" \
    --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": "hamishivi/OLMo-1B-0724-Instruct-hf",
		"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 "hamishivi/OLMo-1B-0724-Instruct-hf" \
        --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": "hamishivi/OLMo-1B-0724-Instruct-hf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use hamishivi/OLMo-1B-0724-Instruct-hf with Docker Model Runner:
```
docker model run hf.co/hamishivi/OLMo-1B-0724-Instruct-hf
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

OLMo-1B-0724 Instruct

This is a version of OLMo-1B-0724-hf that has undergone SFT and DPO training. See the SFT model card for details on SFT training.

This model is initialised from OLMo-1B-0724-SFT-hf, and then DPO trained on a cleaned ultrafeedback dataset for 3 epochs with a batch size of 32, beta of 0.1, linear warmup for 10% of training, and then linear cooldown.

Evals are as follows:

Metric	OLMo-1B-0724-hf	OLMo-1B-0724-SFT-hf	OLMo-1B-0724-Instruct-hf (this model!)
MMLU 0-shot	25.0	36.0	36.7
GSM8k CoT 8-shot	7.0	12.5	12.5
BBH CoT 3-shot	22.5	27.2	30.6
HumanEval P@10	16.0	21.2	22.0
AlpacaEval 1	-	41.5	50.9
AlpacaEval 2 LC	-	2.7	2.5
Toxigen % Toxic	80.3	59.7	14.1
TruthfulQA %Info+True	23.0	40.9	42.2
IFEval Loose Acc	20.5	26.1	24.2
XSTest F1	67.6	81.9	79.8
Average of above metrics	25.2	33.0	38.7

Model training and evaluation was performed using Open-instruct, so check that out for more details on evaluation.

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