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", device_map="auto")
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", device_map="auto")
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

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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.