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

Libraries

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

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

pipe = pipeline("text-generation", model="hamishivi/OLMo-1B-0724-SFT-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-SFT-hf")
model = AutoModelForCausalLM.from_pretrained("hamishivi/OLMo-1B-0724-SFT-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 Settings

vLLM

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

Use Docker

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

SGLang

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

Docker Model Runner
How to use hamishivi/OLMo-1B-0724-SFT-hf with Docker Model Runner:
```
docker model run hf.co/hamishivi/OLMo-1B-0724-SFT-hf
```

OLMo-1B-0724-SFT-hf / README.md

hamishivi

Update README.md

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	---
	license: apache-2.0
	datasets:
	- allenai/dolma
	- allenai/tulu-v2-sft-mixture-olmo-4096
	language:
	- en
	---
	# OLMo-1B-0724 SFT

	[OLMo-1B-0724-hf](https://huggingface.co/allenai/OLMo-1B-0724-hf) finetuned for 5 epochs with a learning rate of 1e-5 on the Tulu 2 dataset - specifically [this version](https://huggingface.co/datasets/allenai/tulu-v2-sft-mixture-olmo-4096).
	I used a batch size of 1, 128 grad accumulation steps. Linear warmup for the first 3% of training then linear decay to 0.

	I've additionally [released an 'instruct' version](https://huggingface.co/hamishivi/OLMo-1B-0724-Instruct-hf) which has additionally gone through DPO training.
	This model is generally more performant (see the metrics below), so check it out!

	Evals are as follows:

	\| Metric \| [OLMo-1B-0724-hf](https://huggingface.co/allenai/OLMo-1B-0724-hf) \| [OLMo-1B-0724-SFT-hf](https://huggingface.co/hamishivi/OLMo-1B-0724-SFT-hf) (this model!) \| [OLMo-1B-0724-Instruct-hf](https://huggingface.co/hamishivi/OLMo-1B-0724-Instruct-hf)\|
	\|---------------------------\|-----------------\|---------------------\|-------------------------\|
	\| 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](https://github.com/allenai/open-instruct), so check that out for more details on evaluation.