Instructions to use modrill/code-think-q8b-20260908 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use modrill/code-think-q8b-20260908 with Transformers:

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

pipe = pipeline("text-generation", model="modrill/code-think-q8b-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/code-think-q8b-20260908")
model = AutoModelForCausalLM.from_pretrained("modrill/code-think-q8b-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/code-think-q8b-20260908 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "modrill/code-think-q8b-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-think-q8b-20260908",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/modrill/code-think-q8b-20260908

SGLang

How to use modrill/code-think-q8b-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-think-q8b-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-think-q8b-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-think-q8b-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-think-q8b-20260908",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use modrill/code-think-q8b-20260908 with Docker Model Runner:
```
docker model run hf.co/modrill/code-think-q8b-20260908
```

modrill commited on Sep 9

Commit

f457e61

verified ·

1 Parent(s): c8b9c96

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	@@ -0,0 +1,107 @@

+---
+license: apache-2.0
+base_model: Qwen/Qwen3-8B-Base
+tags:
+- code
+- reasoning
+- lora-merged
+- livecodebench
+pipeline_tag: text-generation
+library_name: transformers
+---
+# CodeThink-V4-Qwen3-8B
+Research checkpoint: **Qwen/Qwen3-8B-Base** (`49e3418fbbbca6ecbdf9608b4d22e5a407081db4`) after V4 LoRA SFT on Qwen3-30B-A3B-Thinking-2507 traces, 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/Qwen3-8B-Base](https://huggingface.co/Qwen/Qwen3-8B-Base) (verified from the local base `README.md`; Apache-2.0 text bundled from the Qwen3-4B-Base `LICENSE` file on disk).
+## Base, teacher, and data
+| | |
+|---|---|
+| Student | `Qwen/Qwen3-8B-Base` revision `49e3418fbbbca6ecbdf9608b4d22e5a407081db4` |
+| Teacher | `Qwen/Qwen3-30B-A3B-Thinking-2507` traces (V4 paired think payload) |
+| Problems | **4715** unique problems (`source_1ep_rows`); physical 2-epoch concat = **9430** rows |
+| Dose | **32,436,894** assistant tokens / epoch; endpoint **64,873,788** 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 **151643** (`<|endoftext|>`), **151667** (`<think>`), **151668** (`</think>`). 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%** (3,892,427 / 64,873,788 tokens), weight decay **0.1**
+- **bf16**, no packing, no truncation, context 32768
+- **2 epochs**, physical concat. Endpoint-only score; no checkpoint picking.
+Merged weights are the 2-epoch endpoint (`step-000921`, 64,873,788 assistant tokens). LoRA + B-rows are under `adapter/`.
+## Evaluation (DEV256)
+256-problem LiveCodeBench-derived **dev** split. Seed **3407**, **think** mode, **no `<think>` prefill**, max generation ~32k, 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-Qwen3-8B** | **81/256** | **130** | this repo; seed 3407; V4 think contract |
+| Qwen3-8B-Base | 55/256 | — | **old-contract** historical bare-base number (not a same-contract V4 think-mode re-eval; a same-contract 8B-base think COMPLETE.json was not on disk) |
+Single seed. These are research checkpoints, not product scores.
+For context, the same-family 4B base on the **current** V4 think contract is 63/256 (seed 3407), with a 5-seed band 55.2 ± 4.9 on an earlier bare-base protocol.
+## Usage
+Merged full weights; no PEFT required at inference. Think mode: `enable_thinking=True`, **do not** prefill `<think>`.
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+repo = "modrill/CodeThink-V4-Qwen3-8B"
+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,
+)
+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 plus `OFFICIAL_MERGE_RECEIPT.json`
+- `adapter/`: LoRA, `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`
+- `MANIFEST.sha256`
+Optimizer / resume states are **not** included.