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)

# pip install -U transformers accelerate
# 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=256)
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
```

hamishivi commited on Jul 17, 2024

Commit

2f6fb76

verified ·

1 Parent(s): b7d6fbe

Update README.md

Browse files

Files changed (1) hide show

README.md +1 -1

README.md CHANGED Viewed

@@ -16,7 +16,7 @@ This model is generally more performant (see the metrics below), so check it out
 Evals are as follows:
-| Metric                    | OLMo-1B-0724-hf | OLMo-1B-0724-SFT-hf | OLMo-1B-0724-Instruct-hf |
 |---------------------------|-----------------|---------------------|-------------------------|
 | MMLU 0-shot               | 25.0            | 36.0                | **36.7**                    |
 | GSM8k CoT 8-shot          | 7.0             | **12.5**                | **12.5**                    |

 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**                    |