Instructions to use Shockem/Qwen3.8-27b-Terse-Coder-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Shockem/Qwen3.8-27b-Terse-Coder-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 Shockem/Qwen3.8-27b-Terse-Coder-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Shockem/Qwen3.8-27b-Terse-Coder-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 Shockem/Qwen3.8-27b-Terse-Coder-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Shockem/Qwen3.8-27b-Terse-Coder-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 Shockem/Qwen3.8-27b-Terse-Coder-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Shockem/Qwen3.8-27b-Terse-Coder-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 Shockem/Qwen3.8-27b-Terse-Coder-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Shockem/Qwen3.8-27b-Terse-Coder-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Shockem/Qwen3.8-27b-Terse-Coder-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use Shockem/Qwen3.8-27b-Terse-Coder-GGUF with Ollama:
```
ollama run hf.co/Shockem/Qwen3.8-27b-Terse-Coder-GGUF:Q4_K_M
```
Unsloth Desktop

How to use Shockem/Qwen3.8-27b-Terse-Coder-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Shockem/Qwen3.8-27b-Terse-Coder-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": "Shockem/Qwen3.8-27b-Terse-Coder-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Shockem/Qwen3.8-27b-Terse-Coder-GGUF with Docker Model Runner:
```
docker model run hf.co/Shockem/Qwen3.8-27b-Terse-Coder-GGUF:Q4_K_M
```

Lemonade

How to use Shockem/Qwen3.8-27b-Terse-Coder-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Shockem/Qwen3.8-27b-Terse-Coder-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3.8-27b-Terse-Coder-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use Shockem/Qwen3.8-27b-Terse-Coder-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 Shockem/Qwen3.8-27b-Terse-Coder-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 Shockem/Qwen3.8-27b-Terse-Coder-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Shockem/Qwen3.8-27b-Terse-Coder-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Shockem/Qwen3.8-27b-Terse-Coder-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 "Shockem/Qwen3.8-27b-Terse-Coder-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"

Qwen3.8-27b-Terse-Coder-GGUF / README.md

Shockem

credit upstream froggeric templates in template link

6f4f6ae verified 18 days ago

preview code

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metadata

license: apache-2.0
base_model: Shockem/Qwen3.8-27b-Terse-Coder
tags:
  - gguf
  - reasoning
  - coding
  - qwen3

Qwen3.8-27b-Terse-Coder-GGUF

GGUF builds of Shockem/Qwen3.8-27b-Terse-Coder (round 8, final) — a fine-tune of Qwen/Qwen3.8-27B with ~1/10 the chain-of-thought reasoning tokens on coding tasks and correctness preserved.

Quant spread (Q4_K_M smoke-tested on llama.cpp — correct code generation, reasoning parsing, ~22 tok/s on 2× RTX 5060 Ti):

File	Size	Notes
`Qwen3.8-27b-Terse-Coder.Q4_K_M.gguf`	~16.8G	Balanced quality/speed — the default pick
`Qwen3.8-27b-Terse-Coder.Q5_K_M.gguf`	~19.5G
`Qwen3.8-27b-Terse-Coder.Q6_K.gguf`	~22.4G
`Qwen3.8-27b-Terse-Coder.Q8_0.gguf`	~29.0G	Near-lossless
`Qwen3.8-27b-Terse-Coder.mmproj-f16.gguf`	~0.9G	Vision projector (required for images)
`Qwen3.8-27b-Terse-Coder.mmproj-Q8_0.gguf`	~0.6G	Vision projector, quantized

Usage (llama.cpp server): Chat template: use Shockem/froggeric-terse-coder (based on froggeric/Qwen-Fixed-Chat-Templates) (also bundled here as chat_template.jinja) — it carries the anti-rumination tool rules the model was evaluated with; serving without it changes agentic behavior.

llama-server -m Qwen3.8-27b-Terse-Coder.Q4_K_M.gguf \
  --mmproj Qwen3.8-27b-Terse-Coder.mmproj-f16.gguf \
  -ngl 99 -c 32768

The MTP draft layer is included in the GGUF but unused by llama.cpp (harmless warnings about unused blk.N.nextn.* tensors are expected); speculative decoding is a vLLM-side feature. Behavior/quality reference: HE+ 91.5 / MBPP+ 79.4 / GSM8K 98.5 / GPQA 79.8 on the source repo (vLLM measurements; GGUF Q4_K_M verified by smoke test only — validate quality on your own hardware before relying on it).

Source and provenance: fp16 merge at Shockem/Qwen3.8-27b-Terse-Coder; vLLM deployment build at Shockem/Qwen3.8-27b-Terse-Coder-NVFP4; adapter lineage at Shockem/Qwen3.8-27b-Terse-Coder-LoRA. Licensed Apache 2.0, same as the base model (© Qwen Team, Alibaba Cloud).