Instructions to use sizzlebop/CodeThink-V4-Qwen3-8B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sizzlebop/CodeThink-V4-Qwen3-8B-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="sizzlebop/CodeThink-V4-Qwen3-8B-GGUF")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("sizzlebop/CodeThink-V4-Qwen3-8B-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use sizzlebop/CodeThink-V4-Qwen3-8B-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf sizzlebop/CodeThink-V4-Qwen3-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf sizzlebop/CodeThink-V4-Qwen3-8B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf sizzlebop/CodeThink-V4-Qwen3-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf sizzlebop/CodeThink-V4-Qwen3-8B-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf sizzlebop/CodeThink-V4-Qwen3-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf sizzlebop/CodeThink-V4-Qwen3-8B-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf sizzlebop/CodeThink-V4-Qwen3-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf sizzlebop/CodeThink-V4-Qwen3-8B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/sizzlebop/CodeThink-V4-Qwen3-8B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use sizzlebop/CodeThink-V4-Qwen3-8B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sizzlebop/CodeThink-V4-Qwen3-8B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sizzlebop/CodeThink-V4-Qwen3-8B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/sizzlebop/CodeThink-V4-Qwen3-8B-GGUF:Q4_K_M

SGLang

How to use sizzlebop/CodeThink-V4-Qwen3-8B-GGUF 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 "sizzlebop/CodeThink-V4-Qwen3-8B-GGUF" \
    --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": "sizzlebop/CodeThink-V4-Qwen3-8B-GGUF",
		"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 "sizzlebop/CodeThink-V4-Qwen3-8B-GGUF" \
        --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": "sizzlebop/CodeThink-V4-Qwen3-8B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use sizzlebop/CodeThink-V4-Qwen3-8B-GGUF with Ollama:
```
ollama run hf.co/sizzlebop/CodeThink-V4-Qwen3-8B-GGUF:Q4_K_M
```
Unsloth Desktop

How to use sizzlebop/CodeThink-V4-Qwen3-8B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf sizzlebop/CodeThink-V4-Qwen3-8B-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "sizzlebop/CodeThink-V4-Qwen3-8B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use sizzlebop/CodeThink-V4-Qwen3-8B-GGUF with Docker Model Runner:
```
docker model run hf.co/sizzlebop/CodeThink-V4-Qwen3-8B-GGUF:Q4_K_M
```

Lemonade

How to use sizzlebop/CodeThink-V4-Qwen3-8B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull sizzlebop/CodeThink-V4-Qwen3-8B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.CodeThink-V4-Qwen3-8B-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use sizzlebop/CodeThink-V4-Qwen3-8B-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf sizzlebop/CodeThink-V4-Qwen3-8B-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default sizzlebop/CodeThink-V4-Qwen3-8B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use sizzlebop/CodeThink-V4-Qwen3-8B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf sizzlebop/CodeThink-V4-Qwen3-8B-GGUF:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "sizzlebop/CodeThink-V4-Qwen3-8B-GGUF:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

CodeThink-V4-Qwen3-8B GGUF

This repository provides GGUF quantizations for modrill/CodeThink-V4-Qwen3-8B.

CodeThink-V4-Qwen3-8B is an 8.19B-parameter code reasoning research model built on Qwen/Qwen3-8B-Base. It was trained using V4 LoRA SFT distillation on paired thinking traces from Qwen/Qwen3-30B-A3B-Thinking-2507, then merged to full weights. The model is tuned for programmatic problem solving, algorithmic code generation, and step-by-step reasoning inside <think> blocks.

All GGUF files were converted from the original merged safetensors weights using llama.cpp at native BF16 precision, then quantized into standard k-quant variants.

Available Files and Quantizations

File	Quant Type	Size	Description / Recommendation
`CodeThink-V4-Qwen3-8B-BF16.gguf`	BF16	15.26 GB	Full precision base conversion. Highest fidelity reference weights.
`CodeThink-V4-Qwen3-8B-Q8_0.gguf`	Q8_0	8.11 GB	Near-lossless 8-bit quantization. Recommended for best code generation accuracy.
`CodeThink-V4-Qwen3-8B-Q6_K.gguf`	Q6_K	6.26 GB	High quality retention with minimal degradation. Excellent balance.
`CodeThink-V4-Qwen3-8B-Q5_K_M.gguf`	Q5_K_M	5.45 GB	Solid balance between memory usage and code syntax adherence.
`CodeThink-V4-Qwen3-8B-Q4_K_M.gguf`	Q4_K_M	4.68 GB	Fast, lightweight 4-bit quant. Recommended default for local workstation inference.
`CodeThink-V4-Qwen3-8B-Q3_K_M.gguf`	Q3_K_M	3.84 GB	Compact footprint when GPU or system memory headroom is strictly limited.
`CodeThink-V4-Qwen3-8B-Q2_K.gguf`	Q2_K	3.06 GB	Maximum compression for testing on memory-limited hardware.

Technical Specifications

Base Architecture: Qwen3ForCausalLM (model_type: qwen3)
Parameters: ~8.19B
Layers: 36
Hidden Size: 4096
Intermediate Size: 12288
Attention Heads: 32 query heads, 8 key/value heads (Grouped Query Attention)
Head Dimension: 128
Context Window: 32,768 tokens
Vocabulary Size: 151,936 tokens
Native Precision: bfloat16
License: Apache 2.0

Benchmark Results (DEV256)

Evaluated on the 256-problem LiveCodeBench-derived development split (seed 3407, thinking mode, without <think> prefill, max generation ~32k tokens, pass@1):

Model	pass@1	Cap (hit 32k tokens)	Notes
CodeThink-V4-Qwen3-8B	81 / 256	130	V4 thinking contract (step-000921 endpoint)
Qwen3-8B-Base	55 / 256	--	Historical base reference

Prompt Format

CodeThink-V4-Qwen3-8B uses ChatML markup. To trigger reasoning traces, prompt the assistant turn and allow the model to emit its own <think> block (do not prefill <think> manually):

<|im_start|>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.<|im_end|>
<|im_start|>user
Write a Python function to solve the two-sum problem efficiently.<|im_end|>
<|im_start|>assistant

How to Use

1. With llama.cpp

Run interactive generation with llama-cli:

llama-cli -m ./CodeThink-V4-Qwen3-8B-Q4_K_M.gguf \
    -p "<|im_start|>system\nYou are an expert Python programmer.<|im_end|>\n<|im_start|>user\nWrite a Python function to check if a string is a palindrome.<|im_end|>\n<|im_start|>assistant\n" \
    -n 1024 \
    -c 4096 \
    --temp 0.6 \
    --top-p 0.95 \
    --top-k 20

Launch an OpenAI-compatible local API server:

llama-server -m ./CodeThink-V4-Qwen3-8B-Q4_K_M.gguf \
    --host 127.0.0.1 \
    --port 8080 \
    -c 8192

2. With Ollama

Create a file named Modelfile in the same directory:

FROM ./CodeThink-V4-Qwen3-8B-Q4_K_M.gguf

TEMPLATE """<|im_start|>system
{{ .System }}<|im_end|>
<|im_start|>user
{{ .Prompt }}<|im_end|>
<|im_start|>assistant
"""

PARAMETER stop "<|im_start|>"
PARAMETER stop "<|im_end|>"
PARAMETER stop "<|endoftext|>"
PARAMETER temperature 0.6
PARAMETER top_p 0.95
PARAMETER top_k 20

Build and run the model in Ollama:

ollama create codethink-8b -f Modelfile
ollama run codethink-8b

3. With LM Studio

Copy the desired .gguf file into your local LM Studio models directory.
Select CodeThink-V4-Qwen3-8B in the model selector.
Choose ChatML as the preset prompt template.

Attribution & Credits

Base weights and architecture by Qwen Team.
Fine-tuning and distillation by modrill.
GGUF conversions created by Pink Pixel.

Downloads last month: 205

GGUF

Model size

8B params

Architecture

qwen3

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

Model tree for sizzlebop/CodeThink-V4-Qwen3-8B-GGUF

Base model

Qwen/Qwen3-8B-Base

Finetuned

modrill/code-think-q8b-20260908

Quantized

(1)

this model