Instructions to use modrill/CodeThink-V4-Qwen3-4B-Mix with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use modrill/CodeThink-V4-Qwen3-4B-Mix with Transformers:

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

pipe = pipeline("text-generation", model="modrill/CodeThink-V4-Qwen3-4B-Mix")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("modrill/CodeThink-V4-Qwen3-4B-Mix")
model = AutoModelForCausalLM.from_pretrained("modrill/CodeThink-V4-Qwen3-4B-Mix", 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/CodeThink-V4-Qwen3-4B-Mix with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "modrill/CodeThink-V4-Qwen3-4B-Mix"
# 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/CodeThink-V4-Qwen3-4B-Mix",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/modrill/CodeThink-V4-Qwen3-4B-Mix

SGLang

How to use modrill/CodeThink-V4-Qwen3-4B-Mix 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/CodeThink-V4-Qwen3-4B-Mix" \
    --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/CodeThink-V4-Qwen3-4B-Mix",
		"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/CodeThink-V4-Qwen3-4B-Mix" \
        --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/CodeThink-V4-Qwen3-4B-Mix",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use modrill/CodeThink-V4-Qwen3-4B-Mix with Docker Model Runner:
```
docker model run hf.co/modrill/CodeThink-V4-Qwen3-4B-Mix
```

CodeThink-V4-Qwen3-4B-Mix

Research checkpoint: Qwen/Qwen3-4B-Base (906bfd4b4dc7f14ee4320094d8b41684abff8539) after V4 LoRA SFT on a Mix Distillation payload, then merged to full weights.

This is a research checkpoint, not a product. Single-seed diagnostic numbers only. Do not treat DEV256 as a leaderboard claim.

License: Apache-2.0, inherited from Qwen/Qwen3-4B-Base (verified from the local base README.md / LICENSE).

Base, teacher, and data


Student	`Qwen/Qwen3-4B-Base` revision `906bfd4b4dc7f14ee4320094d8b41684abff8539`
Recipe	Mix Distillation Mix-Large (Li et al., 2025, "Small Models Struggle to Learn from Strong Reasoners", arXiv:2502.12143): 0.2 traces from `Qwen/Qwen3-30B-A3B-Thinking-2507` : 0.8 traces from `Qwen/Qwen3-4B-Thinking-2507`, one teacher trace per problem
Problems	4155 unique problems (`source_1ep_rows`); physical 2-epoch concat = 8310 rows
Mix split	831 (30B) : 3324 (4B) rows in the 1-epoch mix (`n30`/`n4`)
Dose	32,424,225 assistant tokens / epoch; endpoint 64,848,450 assistant tokens (2 epochs)
Train seed	42

Recipe

LoRA r64 / α128, dropout 0.0, seven projections: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
Embeddings / lm_head frozen except two-sided trainable B-rows for special tokens 151643 (<|endoftext|>), 151667 (<think>), 151668 (</think>). Qwen3-4B-Base embeddings were tied; training untied them. Token ids from adapter/TOKEN_ROWS_META.json.
Assistant supervised tail: <|endoftext|> (151643)
LR 1e-4, AdamW (β 0.9/0.95), cosine over assistant-token dose, warmup 6% (3,890,907 / 64,848,450 tokens), weight decay 0.1 (LoRA; row WD 0)
bf16 activations, fp32 trainable masters, no packing, no truncation, context 32768
2 epochs, physical concat of the 1-epoch mix (same order). Endpoint-only score; no checkpoint picking.

Merged weights in this repo are the 2-epoch endpoint (step-000920, 64,848,450 assistant tokens). The LoRA adapter and B-row file are under adapter/.

Evaluation (DEV256)

256-problem LiveCodeBench-derived dev split. Seed 3407, think mode, no <think> prefill, max generation ~32k (model context 32768), sandbox-verified pass@1. Temperature 0.6, top-p 0.95, top-k 20.

Cap = generations that hit the 32k length limit without closing </think>.

Model	pass@1	Cap	Notes
CodeThink-V4-Qwen3-4B-Mix	73/256	136	this repo; seed 3407
Qwen3-4B-Base (same contract, think)	63/256	6	bare base, seed 3407
Qwen3-4B-Base 5-seed band	55.2 ± 4.9	—	seeds {3407→61, 12345→52, 20260903→49, 777→55, 2024→59}; sample SD 4.92

Single seed. These are research checkpoints, not product scores.

Why Mix (sibling V4 controls)

Pure strong-teacher traces hurt this 4B student; a same-size teacher and the 0.2:0.8 mix did not:

Sibling (same student / recipe family)	pass@1	Cap
V4 Q4B-THINK (100% 30B-A3B-Thinking traces)	60/256	150
V4 Q4B-THINK-T4BDATA (100% 4B-Thinking traces)	72/256	106
This Mix 0.2 : 0.8	73/256	136

That pattern matches Li et al. 2025 (arXiv:2502.12143): small students often learn worse from much stronger reasoners than from a mixed or same-size teacher.

Usage

Merged full weights; no PEFT required at inference. Think mode: pass enable_thinking=True and do not prefill <think>.

from transformers import AutoModelForCausalLM, AutoTokenizer

repo = "modrill/CodeThink-V4-Qwen3-4B-Mix"
tokenizer = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(
    repo, torch_dtype="bfloat16", device_map="auto"
)

system = (
    "You are an expert Python programmer. You will be given a question "
    "(problem specification) and will generate a correct Python program that "
    "matches the specification and passes all tests. You will NOT return "
    "anything except for the program."
)
messages = [
    {"role": "system", "content": system},
    {"role": "user", "content": problem_statement},
]
prompt = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=True,  # think mode; no <think> prefill
)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
output = model.generate(
    **inputs,
    max_new_tokens=32768,
    do_sample=True,
    temperature=0.6,
    top_p=0.95,
    top_k=20,
)
print(tokenizer.decode(output[0][inputs["input_ids"].shape[1]:], skip_special_tokens=False))

Stop ids used in the official eval: 151643 (<|endoftext|>), 151645 (<|im_end|>).

Repo layout

Root: merged HF weights (config.json, model.safetensors, tokenizer, generation_config.json, chat_template.jinja) plus the merge record OFFICIAL_MERGE_RECEIPT.json
adapter/: LoRA (adapter_config.json, adapter_model.safetensors), two-sided B-rows (token_rows_both_sides.safetensors), TOKEN_ROWS_META.json, checkpoint MANIFEST.json
provenance/: train RUN_IDENTITY.json, TRAINING_CONFIG.json, POLICY.json; DEV256 COMPLETE.json; mix builder and MQ0 near-dup report
MANIFEST.sha256: sha256 of every uploaded file

Optimizer / resume states are not included.

Downloads last month: -

Safetensors

Model size

4B params

Tensor type

BF16

Model tree for modrill/CodeThink-V4-Qwen3-4B-Mix

Base model

Qwen/Qwen3-4B-Base

Finetuned

(439)

this model

Paper for modrill/CodeThink-V4-Qwen3-4B-Mix

Small Models Struggle to Learn from Strong Reasoners

Paper • 2502.12143 • Published Feb 17, 2025 • 40