Instructions to use ronantakizawa/olmo2-32b-instruct-awq with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ronantakizawa/olmo2-32b-instruct-awq with Transformers:

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

pipe = pipeline("text-generation", model="ronantakizawa/olmo2-32b-instruct-awq")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ronantakizawa/olmo2-32b-instruct-awq")
model = AutoModelForCausalLM.from_pretrained("ronantakizawa/olmo2-32b-instruct-awq", 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 ronantakizawa/olmo2-32b-instruct-awq with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ronantakizawa/olmo2-32b-instruct-awq"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ronantakizawa/olmo2-32b-instruct-awq",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ronantakizawa/olmo2-32b-instruct-awq

SGLang

How to use ronantakizawa/olmo2-32b-instruct-awq 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 "ronantakizawa/olmo2-32b-instruct-awq" \
    --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": "ronantakizawa/olmo2-32b-instruct-awq",
		"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 "ronantakizawa/olmo2-32b-instruct-awq" \
        --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": "ronantakizawa/olmo2-32b-instruct-awq",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ronantakizawa/olmo2-32b-instruct-awq with Docker Model Runner:
```
docker model run hf.co/ronantakizawa/olmo2-32b-instruct-awq
```

OLMo-2-0325-32B-Instruct AWQ 4-bit

This is a 4-bit AWQ quantized version of allenai/OLMo-2-0325-32B-Instruct using LLM Compressor.

Key Features

✅ 32B parameters quantized to 4-bit - 69% size reduction
✅ Fully open model - code, data, and training details all public
✅ Post-trained on Tülu 3 - SFT → DPO → RLVR pipeline
✅ Strong performance - competitive with Llama 3.1 70B on many tasks
✅ State-of-the-art on specific tasks - MATH, GSM8K, IFEval

Model Details

Base Model: allenai/OLMo-2-0325-32B-Instruct (32B parameters)
Architecture: OLMo 2 (fully open language model)
Quantization Method: AWQ (Activation-aware Weight Quantization)
Quantization Scheme: W4A16 (4-bit weights, 16-bit activations)
Calibration Dataset: OpenOrca (128 samples)

Size Comparison

Metric	Value
Original (BF16)	~64.0 GB
Quantized (W4A16)	~16.91 GB
Reduction	~73.6%
Memory Saved	~47.1 GB

About OLMo 2

OLMo 2 is a series of fully open language models by the Allen Institute for AI:

Training: Trained on Dolma dataset
Post-training: Supervised finetuning, DPO, and RLVR on Tülu 3
Performance: Competitive with much larger models
Openness: All code, data, and training details released

Performance Highlights

Average Score: 68.8 across diverse benchmarks
GSM8K: 87.6 (math reasoning)
IFEval: 85.6 (instruction following)
MATH: 49.7 (mathematical problem solving)
MMLU: 77.3 (general knowledge)

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "ronantakizawa/olmo2-32b-instruct-awq-w4a16",
    trust_remote_code=True,
    torch_dtype="auto",
    device_map="auto"
)

tokenizer = AutoTokenizer.from_pretrained(
    "ronantakizawa/olmo2-32b-instruct-awq-w4a16",
    trust_remote_code=True
)

# Chat template
messages = [
    {"role": "user", "content": "Explain quantum computing in simple terms."}
]

text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)

inputs = tokenizer(text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Chat Template

The model uses this chat template format:

<|user|>
How are you doing?
<|assistant|>
I'm just a computer program, so I don't have feelings, but I'm functioning as expected. How can I assist you today?<|endoftext|>

System Prompt (Optional)

In Ai2 demos, this system prompt is used by default:

You are OLMo 2, a helpful and harmless AI Assistant built by the Allen Institute for AI.

However, the model has not been trained with a specific system prompt requirement.

Quantization Details

Method: AWQ (Activation-aware Weight Quantization)
Independent Pipeline: Used with BasicPipeline for layer-by-layer quantization
Calibration: 128 OpenOrca samples
Max Sequence Length: 512 tokens
Why AWQ: Preserves important weights based on activation patterns

Requirements

Transformers (install from main branch for OLMo 2 support)
PyTorch with AWQ/GPTQ support
20GB+ GPU VRAM for inference

Limitations

Quantization may cause slight quality degradation compared to BF16
Limited safety training (not production-ready without additional filtering)
Primarily English language support

License

Apache 2.0 (same as base model)

Citation

@article{olmo20242olmo2furious,
      title={2 OLMo 2 Furious},
      author={Team OLMo and Pete Walsh and Luca Soldaini and others},
      year={2024},
      eprint={2501.00656},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2501.00656},
}

@misc{olmo2-32b-awq,
  title={OLMo-2-0325-32B-Instruct AWQ 4-bit},
  author={Quantized by ronantakizawa},
  year={2025},
  url={https://huggingface.co/ronantakizawa/olmo2-32b-instruct-awq-w4a16}
}