Instructions to use wckwan/olmo3-7b_solver_iter3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use wckwan/olmo3-7b_solver_iter3 with Transformers:

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

pipe = pipeline("text-generation", model="wckwan/olmo3-7b_solver_iter3")
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("wckwan/olmo3-7b_solver_iter3")
model = AutoModelForCausalLM.from_pretrained("wckwan/olmo3-7b_solver_iter3", 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 wckwan/olmo3-7b_solver_iter3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "wckwan/olmo3-7b_solver_iter3"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "wckwan/olmo3-7b_solver_iter3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/wckwan/olmo3-7b_solver_iter3

SGLang

How to use wckwan/olmo3-7b_solver_iter3 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 "wckwan/olmo3-7b_solver_iter3" \
    --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": "wckwan/olmo3-7b_solver_iter3",
		"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 "wckwan/olmo3-7b_solver_iter3" \
        --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": "wckwan/olmo3-7b_solver_iter3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use wckwan/olmo3-7b_solver_iter3 with Docker Model Runner:
```
docker model run hf.co/wckwan/olmo3-7b_solver_iter3
```

olmo3-7b_solver_iter3

SCOPE solver checkpoint trained with DR-Zero long-form GRPO.

Model alias: olmo3-7b
Base model: OLMo-3-7B-Instruct
Version: v1.9.21
Solver iteration: 3
Global step: 60
Source experiment: long_solver_v1.9.21_iter3_grpo_group8_olmo-3-7b-instruct
Source local HF checkpoint: checkpoints/dr-zero/long_solver_v1.9.21_iter3_grpo_group8_olmo-3-7b-instruct/merged_hf_actor_gs60
Collection: https://huggingface.co/collections/wckwan/scope

This repository contains the Hugging Face-format checkpoint files for loading with Transformers.

Downloads last month: 10

Safetensors

Model size

7B params

Tensor type

BF16

Collection including wckwan/olmo3-7b_solver_iter3

SCOPE

Collection

9 items • Updated Jun 29