Instructions to use modrill/math-think-o7b-20260908 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use modrill/math-think-o7b-20260908 with Transformers:

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

pipe = pipeline("text-generation", model="modrill/math-think-o7b-20260908")
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("modrill/math-think-o7b-20260908")
model = AutoModelForCausalLM.from_pretrained("modrill/math-think-o7b-20260908", 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 modrill/math-think-o7b-20260908 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "modrill/math-think-o7b-20260908"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "modrill/math-think-o7b-20260908",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/modrill/math-think-o7b-20260908

SGLang

How to use modrill/math-think-o7b-20260908 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 "modrill/math-think-o7b-20260908" \
    --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": "modrill/math-think-o7b-20260908",
		"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 "modrill/math-think-o7b-20260908" \
        --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": "modrill/math-think-o7b-20260908",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use modrill/math-think-o7b-20260908 with Docker Model Runner:
```
docker model run hf.co/modrill/math-think-o7b-20260908
```

math-think-o7b-20260908

File size: 1,872 Bytes

---
license: apache-2.0
base_model: allenai/Olmo-3-1025-7B
library_name: transformers
tags:
  - math
  - sft
  - lora
  - think
  - task-vector
  - iclr2027
---

# math-think-o7b-20260908

Public freeze of Math Think **2ep endpoint** for ICLR 2027 task-vector work.
Not a chatbot. **Endpoint is the score.** Do not promote 1ep / 1.5ep milestones.

`run_id=math_six_arms_train_v3_eot_20260908`. Sibling of `modrill/nothink-src-*-20260908`; does not overwrite those repos.

## Score (Exact-240)

AIME24+25 × seeds 42–45, EvalScope reviews, n=240. Authority field `evalscope_reviews.correct`.

| Model | Official /240 | cap |
|---|---:|---:|
| This 2ep endpoint | **51** | 124 |
| Same-run Think Base | 34 |  |

Think mode; stop ids [100257, 100265]; O7B eval uses OLMO3-VLLM-PATCH-v1 / vLLM 0.27.1. Same-run Think Base is think_vllm027 34/240 (first think Base attempt failed).

θ_0 is `allenai/Olmo-3-1025-7B` rev `996971efdc504b81f0a6caf73a6c92f976254b9c`.

## Recipe

OpenR1 11750 rows × 2ep (unique problems 5875), LoRA r64/α128, lr 1e-4, TPU 65536, seed 42, EOT (Qwen tail `151643` / O7B tail `100257`), B-rows both sides [100257].

## Identity

| Field | Value |
|---|---|
| Arm | `O7B-THINK-A3B` |
| Updates / tokens | 987 / 64,632,872 |
| Merged `model.safetensors` sha256 | `9f75574aed1be033f277a0384b97f4edef3f8ac85401c6781e59c1d46737d526` |
| Endpoint adapter sha256 | `9da5899df6a1d6d7cfb9e412085eed9a85693bd0fd848645b176e34deed0d8e2` |

Root of merged weights is this repo. Endpoint LoRA is in `adapter/`. `MERGE_RECEIPT.json` is the merge audit. `trainer_state` is not uploaded.

## Load

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
m = AutoModelForCausalLM.from_pretrained("modrill/math-think-o7b-20260908", torch_dtype="bfloat16", device_map="auto")
tok = AutoTokenizer.from_pretrained("modrill/math-think-o7b-20260908")
```