Instructions to use Achilles1089/fable-coder-35B-A3B-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 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 Desktop

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 @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": "Achilles1089/fable-coder-35B-A3B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

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

Hermes Agent

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

OpenClaw

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"

fable-coder-35B-A3B-GGUF / README.md

Achilles1089

docs: MTP block / llama.cpp version compatibility note (fixes confusion in discussion #1)

0b6ae9c verified about 2 months ago

preview code

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metadata

license: apache-2.0
base_model: Achilles1089/fable-coder-35B-A3B
pipeline_tag: text-generation
tags:
  - code
  - agentic
  - moe
  - qwen3.6
  - gguf
  - dappit
language:
  - en

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.

Compatibility — MTP block / `llama.cpp` version

These GGUFs keep the upstream MTP (next-token-prediction) block — block_count = 41, nextn_predict_layers = 1, with blk.40 being that block. This matches the stock Qwen3.6-35B-A3B layout, and it needs a reasonably current llama.cpp.

Older builds fail to load with:

llama_model_load: error loading model: missing tensor 'blk.40.ssm_conv1d.weight'

That is a loader-version issue, not a bad file. blk.40 is the MTP block and is attention-style by design — the base Qwen3.6-35B-A3B has no ssm_conv1d there either (the hybrid pattern puts full-attention layers at blocks 3, 7, 11 … 39, with 40 as MTP on top). Older builds type block 40 as a regular hybrid layer and go looking for SSM tensors.

Fix: update llama.cpp. Verified loading and generating on build 9950 (961e4b26a); reported failing on b9075.

If you are pinned to an older build — or on a runtime that cannot load the MTP block — you can strip block 40 locally (pip install gguf). You lose only the speculative-decoding head; normal generation quality is unchanged:

# strip_mtp.py IN.gguf OUT.gguf
import sys
from gguf import GGUFReader, GGUFWriter, GGUFValueType
src, dst = sys.argv[1], sys.argv[2]
r = GGUFReader(src)
w = GGUFWriter(dst, r.fields['general.architecture'].contents())
OVERRIDE = {'qwen35moe.block_count': 40, 'qwen35moe.nextn_predict_layers': 0}
for key, field in r.fields.items():
    if key == 'general.architecture' or key.startswith('GGUF.'):
        continue
    val, types = OVERRIDE.get(key, field.contents()), field.types
    if types and types[0] == GGUFValueType.ARRAY:
        w.add_key_value(key, val, GGUFValueType.ARRAY, sub_type=types[1])
    else:
        w.add_key_value(key, val, types[-1])
for t in r.tensors:
    if not t.name.startswith('blk.40.'):
        w.add_tensor(t.name, t.data, raw_dtype=t.tensor_type)
w.write_header_to_file(); w.write_kv_data_to_file(); w.write_tensors_to_file(); w.close()

fable-coder-35B-A3B · GGUF

Quants

Download

Run

Compatibility — MTP block / llama.cpp version

Compatibility — MTP block / `llama.cpp` version