Instructions to use modrill/Olmo-3-1025-7B-code-v4-nothink with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use modrill/Olmo-3-1025-7B-code-v4-nothink with Transformers:

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

pipe = pipeline("text-generation", model="modrill/Olmo-3-1025-7B-code-v4-nothink")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("modrill/Olmo-3-1025-7B-code-v4-nothink")
model = AutoModelForCausalLM.from_pretrained("modrill/Olmo-3-1025-7B-code-v4-nothink", 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/Olmo-3-1025-7B-code-v4-nothink with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "modrill/Olmo-3-1025-7B-code-v4-nothink"
# 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/Olmo-3-1025-7B-code-v4-nothink",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/modrill/Olmo-3-1025-7B-code-v4-nothink

SGLang

How to use modrill/Olmo-3-1025-7B-code-v4-nothink 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/Olmo-3-1025-7B-code-v4-nothink" \
    --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/Olmo-3-1025-7B-code-v4-nothink",
		"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/Olmo-3-1025-7B-code-v4-nothink" \
        --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/Olmo-3-1025-7B-code-v4-nothink",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use modrill/Olmo-3-1025-7B-code-v4-nothink with Docker Model Runner:
```
docker model run hf.co/modrill/Olmo-3-1025-7B-code-v4-nothink
```

Olmo-3-1025-7B Code V4 NoThink (merged)

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
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.

Downloads last month: -

Safetensors

Model size

7B params

Tensor type

BF16

Model tree for modrill/Olmo-3-1025-7B-code-v4-nothink

Base model

allenai/Olmo-3-1025-7B

Finetuned

(59)

this model