Instructions to use AnkitAI/Parable-Qwen3-4B-Claude-Fable-5-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AnkitAI/Parable-Qwen3-4B-Claude-Fable-5-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="AnkitAI/Parable-Qwen3-4B-Claude-Fable-5-GGUF",
	filename="Parable-Qwen3-4B-Claude-Fable-5-GGUF-F16.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 AnkitAI/Parable-Qwen3-4B-Claude-Fable-5-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 AnkitAI/Parable-Qwen3-4B-Claude-Fable-5-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf AnkitAI/Parable-Qwen3-4B-Claude-Fable-5-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 AnkitAI/Parable-Qwen3-4B-Claude-Fable-5-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf AnkitAI/Parable-Qwen3-4B-Claude-Fable-5-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 AnkitAI/Parable-Qwen3-4B-Claude-Fable-5-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf AnkitAI/Parable-Qwen3-4B-Claude-Fable-5-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 AnkitAI/Parable-Qwen3-4B-Claude-Fable-5-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf AnkitAI/Parable-Qwen3-4B-Claude-Fable-5-GGUF:Q4_K_M

Use Docker

docker model run hf.co/AnkitAI/Parable-Qwen3-4B-Claude-Fable-5-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use AnkitAI/Parable-Qwen3-4B-Claude-Fable-5-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AnkitAI/Parable-Qwen3-4B-Claude-Fable-5-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": "AnkitAI/Parable-Qwen3-4B-Claude-Fable-5-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/AnkitAI/Parable-Qwen3-4B-Claude-Fable-5-GGUF:Q4_K_M

Ollama
How to use AnkitAI/Parable-Qwen3-4B-Claude-Fable-5-GGUF with Ollama:
```
ollama run hf.co/AnkitAI/Parable-Qwen3-4B-Claude-Fable-5-GGUF:Q4_K_M
```

Unsloth Studio

How to use AnkitAI/Parable-Qwen3-4B-Claude-Fable-5-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 AnkitAI/Parable-Qwen3-4B-Claude-Fable-5-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 AnkitAI/Parable-Qwen3-4B-Claude-Fable-5-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for AnkitAI/Parable-Qwen3-4B-Claude-Fable-5-GGUF to start chatting

How to use AnkitAI/Parable-Qwen3-4B-Claude-Fable-5-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf AnkitAI/Parable-Qwen3-4B-Claude-Fable-5-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": "AnkitAI/Parable-Qwen3-4B-Claude-Fable-5-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use AnkitAI/Parable-Qwen3-4B-Claude-Fable-5-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 AnkitAI/Parable-Qwen3-4B-Claude-Fable-5-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 AnkitAI/Parable-Qwen3-4B-Claude-Fable-5-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use AnkitAI/Parable-Qwen3-4B-Claude-Fable-5-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf AnkitAI/Parable-Qwen3-4B-Claude-Fable-5-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 "AnkitAI/Parable-Qwen3-4B-Claude-Fable-5-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 AnkitAI/Parable-Qwen3-4B-Claude-Fable-5-GGUF with Docker Model Runner:
```
docker model run hf.co/AnkitAI/Parable-Qwen3-4B-Claude-Fable-5-GGUF:Q4_K_M
```

Lemonade

How to use AnkitAI/Parable-Qwen3-4B-Claude-Fable-5-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull AnkitAI/Parable-Qwen3-4B-Claude-Fable-5-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Parable-Qwen3-4B-Claude-Fable-5-GGUF-Q4_K_M

List all available models

lemonade list

🪶 Parable-Qwen3-4B — trained on genuine Claude Fable 5 agent traces

A small local model with agent instincts — planning, tool use, and terminal habits distilled from real agent sessions, not synthetic Q&A. v2: every prompt gets an answer, eval-gated before publish.

~4 GB of RAM is all you need. Laptop, old GPU, modest desktop — the Q4 build runs anywhere. One command and you have a private, offline reasoning model on your machine:
ollama run hf.co/AnkitAI/Parable-Qwen3-4B-Claude-Fable-5-GGUF:Q4_K_M

v2 is here (2026-07-22)

Every prompt now gets an answer. v1 inherited Qwen3's runaway thinking: on 21% of ordinary prompts it spent its entire token budget reasoning and returned nothing. v2 fixes that — 34/34 prompts answered, answers roughly half as long, coding ability held.

Same repo, same links, same commands. Pull again and you have it. v1 remains in the version history below.

Full family. This 4B sits between its Parable siblings — a 3B and two 8Bs. Browse the full collection.

Pick your size

File	Size	Fits in	Notes
Q4_K_M	2.5 GB	~4 GB RAM/VRAM	⭐ Recommended — best size/quality balance
Q5_K_M	2.9 GB	~4.5 GB	Higher quality
Q6_K	3.3 GB	~5 GB	Near-lossless
Q8_0	4.3 GB	~6 GB	Maximum quality

Full-precision safetensors (vLLM, transformers, further fine-tuning): Parable-Qwen3-4B-Claude-Fable-5

How to run it

Ollama (shorter command via the Parable namespace, or pull straight from this repo):

ollama run parable/qwen3-fable:4b
# or straight from this repo:
ollama run hf.co/AnkitAI/Parable-Qwen3-4B-Claude-Fable-5-GGUF:Q4_K_M

llama.cpp:

llama-cli -m Parable-Qwen3-4B-Claude-Fable-5-GGUF-Q4_K_M.gguf --jinja \
  -p "Write a bash one-liner to find the 10 largest files in a directory tree."

LM Studio: lms get parable/qwen3-fable, or search "parable" in-app (parable on LM Studio Hub).

Python (llama-cpp-python):

from llama_cpp import Llama

llm = Llama.from_pretrained(
    repo_id="AnkitAI/Parable-Qwen3-4B-Claude-Fable-5-GGUF",
    filename="*Q4_K_M.gguf", n_ctx=8192,
)
out = llm.create_chat_completion(
    messages=[{"role": "user", "content": "Write a Python function that retries an HTTP request with exponential backoff."}],
    max_tokens=3000, temperature=0.3,
)
print(out["choices"][0]["message"]["content"])

Answers first

v2 responds directly — no <think> preamble to strip, no reasoning budget to babysit. The planning instincts from the agent traces are still there; they just don't get narrated at you first.

Sampling: temperature 0.3–0.7, max_tokens 800–1500 is plenty for most prompts. (v1 needed ≥2500 and could still time out mid-thought. That's over.)

The numbers

Two builds, one harness, greedy decoding, HumanEval+ scored with EvalPlus on identical hardware:

	Qwen3-4B base	Parable v1	Parable v2
Prompts answered (34-prompt suite)	—	27/34	34/34
HumanEval	76.8	72.0	73.2
HumanEval+	71.3	68.3	67.1
Held-out trace loss	2.846	—	1.876 (−34%)

The headline is the first row. On 7 of 34 ordinary prompts — "write a retry function with exponential backoff", "write a bash script that watches a log file" — v1 burned its whole budget thinking and returned an empty response. v2 returns 0 empty responses, using 140× less reasoning text. Coding scores are unchanged within noise, so this is a reliability fix, not a capability trade.

Against the base model, v2 trails by 3.6 HumanEval points — the cost of specializing on agent traces, and about a third of the regression the same recipe produces at 3B. Trace loss drops 34% on held-out sessions: it learned the agent style without paying the usual forgetting tax.

_{Reproducibility: two training seeds land within 0.001 trace loss of each other (1.877 / 1.876); we ship the second and publish both in parable-v2-artifacts. Benchmarks run with thinking disabled on both base and fine-tune — the base is a hybrid-thinking model and scores near zero in default mode because it never emits a final answer. Comparing against that would have flattered us.}

Training data

Glint-Research/Fable-5-traces — 4.4k real Claude Fable 5 coding-agent session traces with <think> reasoning and tool calls (AGPL-3.0)
Roman1111111/gpt5.5-terminal — terminal-agent task solutions (MIT)

v2 trains on corpus v2 — 10.6k curated traces (13× v1) with completion-only loss masking, a 30% general-instruction replay mix, and span-filtered truncation. Every example passed a quality gate (schema validation, secrets scrub, length filtering). QLoRA fine-tune (TRL), quantized with llama.cpp.

Good to know

Answers directly rather than reasoning aloud. If you want visible chain-of-thought, prompt for it explicitly ("think step by step, show your work").
Tuned hard toward agentic coding behavior; that focus trades some general-knowledge breadth, as with any specialized fine-tune in this class.
Verify critical output. Small models over-commit to plausible specifics; treat generated commands and code as drafts to review.
Inherits Qwen3-4B's base limitations and knowledge cutoff.

Base & license

Weights: Apache-2.0 (inherited from Qwen/Qwen3-4B). Training data: Fable-5-traces AGPL-3.0, gpt5.5-terminal MIT — since those traces originate from third-party assistants, the providers' terms may apply to downstream training and distillation; confirm your use aligns with them before building on this model commercially.

Get Parable

Platform
Ollama	`ollama run parable/qwen3-fable:4b` · parable namespace
Ollama (family flagship, best per size)	`ollama run parable/fable`
Hugging Face	full collection — GGUF quants, full weights, eval reports
LM Studio	`lms get parable/qwen3-fable` · parable on LM Studio Hub

Acknowledgements

Glint-Research & Roman1111111 for the open trace data · Qwen team for the base · empero-ai whose Qwable recipe this release follows · mlx-lm & llama.cpp

Four gigabytes of RAM. Real Fable 5 reasoning. Yours, offline, right now.

ollama run hf.co/AnkitAI/Parable-Qwen3-4B-Claude-Fable-5-GGUF:Q4_K_M

Version history

v2 (2026-07-22) — rebuilt recipe: 10.6k-trace corpus v2, completion-only loss masking, 30% replay mix, span-filtered truncation. Fixes v1's empty-response failure mode (7/34 → 0/34). Coding parity with v1, trace loss −34% vs base.
v1 (2026-07-11) — initial release: QLoRA on 4.4k Fable-5 traces via mlx-lm.

More on the Parable models: ankitaglawe.com/parable