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

Libraries

How to use allenai/Olmo-Hybrid-Instruct-SFT-7B with Transformers:

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

pipe = pipeline("text-generation", model="allenai/Olmo-Hybrid-Instruct-SFT-7B")
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("allenai/Olmo-Hybrid-Instruct-SFT-7B")
model = AutoModelForCausalLM.from_pretrained("allenai/Olmo-Hybrid-Instruct-SFT-7B", 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 allenai/Olmo-Hybrid-Instruct-SFT-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "allenai/Olmo-Hybrid-Instruct-SFT-7B"
# 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-Hybrid-Instruct-SFT-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/allenai/Olmo-Hybrid-Instruct-SFT-7B

SGLang

How to use allenai/Olmo-Hybrid-Instruct-SFT-7B 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-Hybrid-Instruct-SFT-7B" \
    --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-Hybrid-Instruct-SFT-7B",
		"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-Hybrid-Instruct-SFT-7B" \
        --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-Hybrid-Instruct-SFT-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

baileyk commited on Mar 2

Commit

86862db

verified ·

1 Parent(s): e0ae3e1

Update README.md

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README.md +15 -5

README.md CHANGED Viewed

@@ -157,12 +157,12 @@ Moo Moo the cow would certinaly win.
 ## Model Details
 #### Stage 1: SFT
-- supervised fine-tuning on the Dolci-Think-SFT-7B dataset. This dataset consits of math, code, chat, and general knowledge queries.
-- Datasets: [Dolci-Think-SFT-7B](https://huggingface.co/datasets/allenai/dolci-thinking-sft), [Dolci-Instruct-SFT-7B](https://huggingface.co/datasets/allenai/dolci-instruct-sft)
 #### Stage 2:DPO
-- direct preference optimization on the Dolci-Think-DPO-7B dataset. This dataset consits of math, code, chat, and general knowledge queries.
-- Datasets: [Dolci-Think-DPO-7B](https://huggingface.co/datasets/allenai/dolci-thinking-dpo), [Dolci-Instruct-DPO-7B](https://huggingface.co/datasets/allenai/dolci-3-instruct-dpo-with-metadata)
 ## Inference & Recommended Settings
 We evaluated our models on the following settings. We also recommend using them for generation:
@@ -210,4 +210,14 @@ sampling_params = SamplingParams(
 prompt = "Who would win in a fight - a dinosaur or a cow named MooMoo?"
 outputs = llm.generate(prompt, sampling_params)
 print(outputs[0].outputs[0].text)
-```

 ## Model Details
 #### Stage 1: SFT
+- supervised fine-tuning on the Dolci-Instruct-SFT-7B dataset. This dataset consits of math, code, chat, and general knowledge queries.
+- Datasets: [Dolci-Instruct-SFT-7B](https://huggingface.co/datasets/allenai/dolci-instruct-sft)
 #### Stage 2:DPO
+- direct preference optimization on the Dolci-Instruct-DPO-7B dataset. This dataset consits of math, code, chat, and general knowledge queries.
+- Datasets: [Dolci-Instruct-DPO-7B](https://huggingface.co/datasets/allenai/dolci-3-instruct-dpo-with-metadata)
 ## Inference & Recommended Settings
 We evaluated our models on the following settings. We also recommend using them for generation:
 prompt = "Who would win in a fight - a dinosaur or a cow named MooMoo?"
 outputs = llm.generate(prompt, sampling_params)
 print(outputs[0].outputs[0].text)
+```
+## Bias, Risks, and Limitations
+Like any base language model or fine-tuned model without safety filtering, these models can easily be prompted by users to generate harmful and sensitive content. Such content may also be produced unintentionally, especially in cases involving bias, so we recommend that users consider the risks when applying this technology. Additionally, many statements from OLMo or any LLM are often inaccurate, so facts should be verified.
+## Citation
+Coming Soon!
+## Model Card Contact
+For errors in this model card, contact `olmo@allenai.org`.