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

Libraries

How to use modrill/code-nothink-o7b-20260908 with Transformers:

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

pipe = pipeline("text-generation", model="modrill/code-nothink-o7b-20260908")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("modrill/code-nothink-o7b-20260908")
model = AutoModelForCausalLM.from_pretrained("modrill/code-nothink-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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use modrill/code-nothink-o7b-20260908 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "modrill/code-nothink-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/code-nothink-o7b-20260908",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/modrill/code-nothink-o7b-20260908

SGLang

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

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

code-nothink-o7b-20260908 / README.md

modrill

Rename card to code-nothink-o7b-20260908; formerly Olmo-3-1025-7B-code-v4-nothink

5978d74 verified 20 days ago

preview code

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	---
	license: apache-2.0
	base_model: allenai/Olmo-3-1025-7B
	tags:
	- code
	- livecodebench
	- sft
	- lora-merged
	- nothink
	library_name: transformers
	---

	# code-nothink-o7b-20260908

	Formerly `Olmo-3-1025-7B-code-v4-nothink` (HF redirect).

	Arm ID: `O7B-NOTHINK`
	Run ID: `t30b2507-o7b-nothink-v4-tail151643`

	Merged full bf16 weights used for the official DEV256 NoThink evaluation. Tokenizer files in this repo are the eval-caliber bundle (`olmo3-lcb-noprefill`); they overlay any tokenizer files that were present in the merge directory.

	Single-seed exploratory result, not a preregistered confirmatory claim.

	## Base model

	- Hugging Face: [`allenai/Olmo-3-1025-7B`](https://huggingface.co/allenai/Olmo-3-1025-7B)
	- Revision: `a81bae42db3975be1671e27b9c9a56da1a9f980f` (from `RUN_IDENTITY.json` / local snapshot `/workspace/code-sft-infra/models/olmo-3-1025-7b`)

	## Training

	- Method: LoRA r64 / α128 on seven projections (`q_proj`, `k_proj`, `v_proj`, `o_proj`, `gate_proj`, `up_proj`, `down_proj`), then merged into full-model bf16 safetensors
	- Data: NoThink code SFT (paired V4, physical 2-epoch concat)
	- Endpoint (score): step 140, 9,371,874 assistant tokens — endpoint-as-score, no checkpoint picking
	- Train seed 42; LR `1e-4`; context 8192; AdamW; cosine by assistant-token dose
	- Host: local GPU box; eval tokenizer renderer `olmo3-lcb-noprefill`

	## Evaluation

	- Suite: official LiveCodeBench DEV256
	- Seed 3407, mode NoThink, `max_model_len` 8192, vLLM 0.28.0
	- Metric: sandbox pass@1 = passed / 256
	- This arm: 46/256 (18.0%), caps 107
	- Base (`allenai/Olmo-3-1025-7B`, same NoThink protocol): 20/256 (7.8%), caps 95
	- McNemar exact p = 6.9e-05

	## Inference notes

	- Use this repository's tokenizer and `chat_template.jinja`.
	- This chat template is the no-prefill `<think>` variant (`olmo3-lcb-noprefill`): do not prefill `<think>` at the start of the assistant turn.
	- Stop token ids: 100257 (`<\|endoftext\|>`) and 100265 (`<\|im_end\|>`).
	- Eval sampling used temperature 0.7, top_p 0.8, top_k 20.

	## Weight checksum

	- `model.safetensors` (14,596,063,960 bytes): `sha256:ce853010b5de765a4f0a393cc77fe84d24305b567486473dd3ef8028fa4c9e1f`

	`OFFICIAL_MERGE_RECEIPT.json` is included for merge provenance. LoRA adapter checkpoints are not in this repo.