Instructions to use DJLougen/Qwable-5-27B-Coder-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DJLougen/Qwable-5-27B-Coder-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="DJLougen/Qwable-5-27B-Coder-GGUF",
	filename="Qwable-5-27B-Coder-IQ1_S.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use DJLougen/Qwable-5-27B-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 DJLougen/Qwable-5-27B-Coder-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf DJLougen/Qwable-5-27B-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 DJLougen/Qwable-5-27B-Coder-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf DJLougen/Qwable-5-27B-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 DJLougen/Qwable-5-27B-Coder-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf DJLougen/Qwable-5-27B-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 DJLougen/Qwable-5-27B-Coder-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf DJLougen/Qwable-5-27B-Coder-GGUF:Q4_K_M

Use Docker

docker model run hf.co/DJLougen/Qwable-5-27B-Coder-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use DJLougen/Qwable-5-27B-Coder-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DJLougen/Qwable-5-27B-Coder-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": "DJLougen/Qwable-5-27B-Coder-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/DJLougen/Qwable-5-27B-Coder-GGUF:Q4_K_M

Ollama
How to use DJLougen/Qwable-5-27B-Coder-GGUF with Ollama:
```
ollama run hf.co/DJLougen/Qwable-5-27B-Coder-GGUF:Q4_K_M
```

Unsloth Studio

How to use DJLougen/Qwable-5-27B-Coder-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for DJLougen/Qwable-5-27B-Coder-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for DJLougen/Qwable-5-27B-Coder-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for DJLougen/Qwable-5-27B-Coder-GGUF to start chatting

How to use DJLougen/Qwable-5-27B-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 DJLougen/Qwable-5-27B-Coder-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "DJLougen/Qwable-5-27B-Coder-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use DJLougen/Qwable-5-27B-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 DJLougen/Qwable-5-27B-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 DJLougen/Qwable-5-27B-Coder-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use DJLougen/Qwable-5-27B-Coder-GGUF with Docker Model Runner:
```
docker model run hf.co/DJLougen/Qwable-5-27B-Coder-GGUF:Q4_K_M
```

Lemonade

How to use DJLougen/Qwable-5-27B-Coder-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull DJLougen/Qwable-5-27B-Coder-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwable-5-27B-Coder-GGUF-Q4_K_M

List all available models

lemonade list

Qwable-5-27B-Coder-GGUF / README.md

DJLougen

Redesign GGUF model card

763ee7d verified 10 days ago

4.57 kB

license: apache-2.0
library_name: gguf
pipeline_tag: text-generation
base_model:
  - DJLougen/Qwable-5-27B-Coder
base_model_relation: quantized
language:
  - en
tags:
  - gguf
  - llama.cpp
  - qwen
  - qwen3_6
  - qwen3_5
  - coder
  - coding-agent
  - agentic-coding
  - tool-use
  - repository-work
  - terminal-workflows
  - long-context
  - imatrix
  - qlora

Qwable GGUF quant runway

Qwable-5-27B-Coder-GGUF

Qwable-5-27B-Coder-GGUF packages the Qwable coder-agent tune for llama.cpp, Ollama, and local workstation inference. It comes from a Qwen3.6-based model trained first on Claude Fable 5 traces, then continued on Kimi 2.7 Coder traces.

Use this repo when you want Qwable's coding-agent behavior in GGUF form: repository inspection, patch planning, terminal feedback, verifier recovery, and long-context coding prompts.

Quant menu

File	Quant	Approx. size	Best for
`Qwable-5-27B-Coder-Q8_0.gguf`	Q8_0	28.6 GB	quality checks, quant comparisons, high-memory local serving
`Qwable-5-27B-Coder-Q4_K_M.gguf`	Q4_K_M	16.5 GB	default local starting point
`Qwable-5-27B-Coder-IQ1_S.gguf`	IQ1_S	7.1 GB	tight memory budgets; expect quality tradeoffs

IQ1_S uses an importance matrix computed on the training traces.

Model facts

Attribute	Details
GGUF repo	`DJLougen/Qwable-5-27B-Coder-GGUF`
Source checkpoint	`DJLougen/Qwable-5-27B-Coder`
Upstream base	`unsloth/Qwen3.6-27B`
Runtime target	llama.cpp-compatible local inference
Architecture tag	`qwen3_5`
Scope	Text tower only; no vision sidecar in this repo
Training signal	Claude Fable 5 traces, then Kimi 2.7 Coder traces
License	Apache-2.0

BF16 source checkpoint
  -> GGUF conversion
      -> Q8_0: quality reference
      -> Q4_K_M: normal local use
      -> IQ1_S: smallest imatrix build

Early maintainer runs show the source checkpoint outperforming the base model on a private coder benchmark. Public benchmark details are not posted yet, so treat that as early maintainer signal rather than a reproducible leaderboard claim.

Quickstart

Requires a llama.cpp build with qwen3_5 support.

Run a local OpenAI-compatible server:

llama-server -hf DJLougen/Qwable-5-27B-Coder-GGUF:Q4_K_M \
  --jinja -ngl 99 -fa -c 32768 \
  --temp 1.0 --top-p 0.95 --top-k 20

Run with Ollama:

ollama run hf.co/DJLougen/Qwable-5-27B-Coder-GGUF:Q4_K_M

Download one file:

hf download DJLougen/Qwable-5-27B-Coder-GGUF \
  Qwable-5-27B-Coder-Q4_K_M.gguf \
  --local-dir .

Choosing a file

Start with Q4_K_M unless you are explicitly testing quality ceilings or memory floors.
Use Q8_0 for comparisons against the source checkpoint or high-memory local serving.
Use IQ1_S only when the model otherwise will not fit; verify quality on your own tasks.
Keep prompts concrete: include repository context, exact errors, constraints, and verifier commands.

Related releases

Source BF16 Transformers checkpoint: DJLougen/Qwable-5-27B-Coder
NVFP4 ModelOpt checkpoint: DJLougen/Qwable-5-27B-Coder-NVFP4

Limitations

Public benchmark tables are pending.
Low-bit GGUF quantization can reduce instruction following, code precision, and tool-call reliability.
This repo contains text GGUF files only; it is not the full multimodal Transformers checkpoint.
Long-context behavior depends on llama.cpp build, hardware, KV cache settings, and prompt layout.
Safety behavior is inherited from the base model and fine-tuning data; no separate safety alignment claim is made here.

License

Released under Apache-2.0, following the upstream base model license metadata.