Instructions to use Achilles1089/fable-coder-35B-A3B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Achilles1089/fable-coder-35B-A3B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Achilles1089/fable-coder-35B-A3B-GGUF",
	filename="fable-coder-35B-A3B-Q4_K_M.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 Achilles1089/fable-coder-35B-A3B-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 Achilles1089/fable-coder-35B-A3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Achilles1089/fable-coder-35B-A3B-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 Achilles1089/fable-coder-35B-A3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Achilles1089/fable-coder-35B-A3B-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 Achilles1089/fable-coder-35B-A3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Achilles1089/fable-coder-35B-A3B-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 Achilles1089/fable-coder-35B-A3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Achilles1089/fable-coder-35B-A3B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Achilles1089/fable-coder-35B-A3B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use Achilles1089/fable-coder-35B-A3B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Achilles1089/fable-coder-35B-A3B-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": "Achilles1089/fable-coder-35B-A3B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Achilles1089/fable-coder-35B-A3B-GGUF:Q4_K_M

Ollama
How to use Achilles1089/fable-coder-35B-A3B-GGUF with Ollama:
```
ollama run hf.co/Achilles1089/fable-coder-35B-A3B-GGUF:Q4_K_M
```

Unsloth Studio

How to use Achilles1089/fable-coder-35B-A3B-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 Achilles1089/fable-coder-35B-A3B-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 Achilles1089/fable-coder-35B-A3B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Achilles1089/fable-coder-35B-A3B-GGUF to start chatting

How to use Achilles1089/fable-coder-35B-A3B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Achilles1089/fable-coder-35B-A3B-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": "Achilles1089/fable-coder-35B-A3B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Achilles1089/fable-coder-35B-A3B-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 Achilles1089/fable-coder-35B-A3B-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 Achilles1089/fable-coder-35B-A3B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use Achilles1089/fable-coder-35B-A3B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Achilles1089/fable-coder-35B-A3B-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 "Achilles1089/fable-coder-35B-A3B-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"

Docker Model Runner
How to use Achilles1089/fable-coder-35B-A3B-GGUF with Docker Model Runner:
```
docker model run hf.co/Achilles1089/fable-coder-35B-A3B-GGUF:Q4_K_M
```

Lemonade

How to use Achilles1089/fable-coder-35B-A3B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Achilles1089/fable-coder-35B-A3B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.fable-coder-35B-A3B-GGUF-Q4_K_M

List all available models

lemonade list

fable-coder-35B-A3B · GGUF

Quantized GGUFs of Achilles1089/fable-coder-35B-A3B — a sovereign, open-weights agentic coding model by Dappit Labs. 35B MoE (≈3B active), Claude Fable-5 / Opus-4.8 agentic distill on an abliterated, Opus-4.7-reasoning-distilled Qwen3.6-35B-A3B.

Built by Dappit Labs (@dappitdotio) · Trained on hardware from Manifest Network.

See the main model card for the full write-up, training details, evaluation, license, and responsible-use notes.

Quants

Each quant is a single self-contained file — download only the one you need.

File	Quant	Size	Fits
`fable-coder-35B-A3B-Q8_0.gguf`	Q8_0	~38GB	48GB+ GPU / 64GB Mac — near-lossless
`fable-coder-35B-A3B-Q6_K.gguf`	Q6_K	~29GB	32–48GB
`fable-coder-35B-A3B-Q5_K_M.gguf`	Q5_K_M	~25GB	32GB
`fable-coder-35B-A3B-Q4_K_M.gguf`	Q4_K_M	~22GB	24GB (3090/4090)

Download

One quant via the HF CLI (recommended — resumable, no full-repo clone):

pip install -U "huggingface_hub[cli]"
hf download Achilles1089/fable-coder-35B-A3B-GGUF \
  fable-coder-35B-A3B-Q4_K_M.gguf --local-dir .

LM Studio / Jan: search fable-coder-35B-A3B and pick a quant from the list.

Ollama: (ollama.com/achillessafehavencalls/fable-coder — sane defaults + max_tokens cap baked in)

ollama run achillessafehavencalls/fable-coder          # Q4_K_M (default)
ollama run achillessafehavencalls/fable-coder:q8_0     # near-lossless

Web: open the Files tab and click any single file to download it.

Run

# llama.cpp
llama-server -m fable-coder-35B-A3B-Q6_K.gguf -c 32768 -ngl 99

Thinking is native — the Qwen template opens <think> by default; the server returns reasoning in reasoning_content and the answer in content. For agentic coding, drive it inside a harness with a tool-use system prompt + tool registry (treat it like Claude Code).

Quantized from the bf16 master with llama.cpp llama-quantize.