Instructions to use modrill/Qwen3-8B-Base-code-v4-nothink with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use modrill/Qwen3-8B-Base-code-v4-nothink with Transformers:

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

pipe = pipeline("text-generation", model="modrill/Qwen3-8B-Base-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/Qwen3-8B-Base-code-v4-nothink")
model = AutoModelForCausalLM.from_pretrained("modrill/Qwen3-8B-Base-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/Qwen3-8B-Base-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/Qwen3-8B-Base-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/Qwen3-8B-Base-code-v4-nothink",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/modrill/Qwen3-8B-Base-code-v4-nothink

SGLang

How to use modrill/Qwen3-8B-Base-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/Qwen3-8B-Base-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/Qwen3-8B-Base-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/Qwen3-8B-Base-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/Qwen3-8B-Base-code-v4-nothink",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use modrill/Qwen3-8B-Base-code-v4-nothink with Docker Model Runner:
```
docker model run hf.co/modrill/Qwen3-8B-Base-code-v4-nothink
```

Qwen3-8B-Base Code V4 NoThink (merged)

Arm ID: Q8B-NOTHINK
Run ID: t30b2507-q8b-nothink-v4-tail151643

Merged full bf16 weights used for the official DEV256 NoThink evaluation. Tokenizer files in this repo are the eval-caliber bundle (qwen3-v4-tail151643); they overlay any tokenizer files that were present in the merge directory (the merge tree's tokenizer.json was a different, larger file).

Exploratory result (2 eval seeds), not a preregistered confirmatory claim.

Base model

Hugging Face: Qwen/Qwen3-8B-Base
Revision: 49e3418fbbbca6ecbdf9608b4d22e5a407081db4

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 142, 9,455,736 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 3 (retrain after mathbox3 host loss); eval tokenizer renderer qwen3-v4-tail151643

Evaluation

Suite: official LiveCodeBench DEV256
Mode NoThink, max_model_len 8192, vLLM 0.28.0; do not prefill <think>
Metric: sandbox pass@1 = passed / 256
Seed 12345 used the same merged weights, tokenizer, and vLLM command as seed 3407 except --seed and the serve port. Results: /workspace/code-sft-runs/rstar-eval/official-dev256-seeds/seed12345/

seed	arm	arm cap	Base	Base cap	McNemar arm_only / base_only	p
3407	70/256 (27.3%)	78	59/256 (23.0%)	21	25 / 14	0.108
12345	84/256 (32.8%)	75	57/256 (22.3%)	23	37 / 10	9.8e-05
pooled 512	—	—	—	—	62 / 24	5.1e-05

Mean: arm 77.0 (30.1%) vs Base 58.0 (22.7%), Δ ≈ +19.

Arm pass@1 moves a lot across seeds (70 vs 84); Base is stable (57–59). The two-seed pooled paired difference is significant (p = 5.1e-05). Still exploratory (2 seeds, not preregistered).

Provenance note

The same identity (seed 42, 9,455,736 assistant tokens, lr 1e-4, LoRA r64/α128) was previously trained once on another machine and scored 76/256 vs Base 57/256 (McNemar p=0.0019). That host went down and the weights plus eval cache were lost. This repository is the local GPU 3 retrain. The two-run difference (arm −6, Base +2) is single-seed cross-machine noise. This result is a single-seed exploratory outcome, not a confirmatory claim.

Inference notes

Use this repository's tokenizer and chat_template.jinja.
NoThink: do not prefill <think>; if the template accepts enable_thinking, keep it false.
Stop token ids: 151643 (<|endoftext|>) and 151645 (<|im_end|>).
Eval sampling used temperature 0.7, top_p 0.8, top_k 20.

Weight checksum

model.safetensors (16,381,517,208 bytes): sha256:fdde336f8a504ce0dae4537b0955bbe660e1072ec093e43cce548e5654958a94

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

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Safetensors

Model size

8B params

Tensor type

BF16

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