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.

Notebooks
Google Colab
Kaggle
Local Apps Settings

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

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

Hermes Agent

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

OpenClaw

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"

Parable-Qwen3-4B-Claude-Fable-5-GGUF

A 4B local coding model with agent instincts. Planning, tool habits and terminal reasoning distilled from real Claude Fable 5 agent sessions, not synthetic Q&A. Runs on ~2.5 GB of RAM.

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

v3.1 (2026-08-11)

Retrained on corpus v3.1: the v2 agent traces plus 1,807 execution-verified solutions generated by the previous build and kept only where the code actually ran against its tests. Two seeds souped, merged at the v2.1 scale.

The result matches or beats base Qwen3-4B on all four execution benchmarks, where the previous build trailed it on three. If you pulled this model before 11 August 2026, re-pull.

Files

File	Quant	Size
Parable-Qwen3-4B-Claude-Fable-5-GGUF-Q4_K_M.gguf	Q4_K_M	2.5 GB	recommended
Parable-Qwen3-4B-Claude-Fable-5-GGUF-Q5_K_M.gguf	Q5_K_M	2.9 GB
Parable-Qwen3-4B-Claude-Fable-5-GGUF-Q6_K.gguf	Q6_K	3.3 GB
Parable-Qwen3-4B-Claude-Fable-5-GGUF-Q8_0.gguf	Q8_0	4.3 GB
Parable-Qwen3-4B-Claude-Fable-5-GGUF-F16.gguf	F16	8.1 GB	for re-quantizing

What it is good at

It answers. Base Qwen3-4B spends its whole budget inside <think> on 34% of ordinary prompts and returns nothing. This model answers 34/34 on the same suite, with 140x less reasoning text and no thinking-mode flag to manage.
Agent-shaped reasoning. Trained on genuine multi-step agent sessions, so plans, tool selection and terminal workflows come out structured instead of improvised.
Small enough to keep open. Q4_K_M is 2.5 GB. Laptop, old GPU, modest desktop — it runs, offline, with your code staying on your machine.

Evaluation

Measured on identical harnesses, greedy decoding, Q4_K_M builds, thinking disabled on every row. Base and this model run through the same instrument in the same session.

	Base Qwen3-4B	This model (v3.1)
HumanEval	73.2	74.4
HumanEval+	68.3	68.3
MBPP	69.0	72.8
MBPP+	59.8	63.8
Held-out agent-trace loss	2.155	1.446

Measured on the v2.1 build and carried forward (the training objective and chat behaviour are unchanged):

	Base Qwen3-4B	Parable
Prompts answered (34-prompt suite)	27/34	34/34
BFCL simple_python	95.3	92.3
BFCL multiple	94.5	90.0

Choosing between this and the base

Take this model for local agent and coding work where you want structured, reliable answers every time: it fits the agent-session distribution far better and never silently returns empty.

Take the base model if your workload is maximum-accuracy function calling in a tool-calling harness, where its few extra points matter more than reasoning style.

Model details

Base: Qwen/Qwen3-4B (4B, Apache-2.0)
Method: QLoRA (nf4, r16, alpha 32) on all-linear targets, completion-only loss masking, 30% general-instruction replay mix, seed-averaged weights, merged at scale 0.6 (v2.1 recalibration)
Data: genuine Claude Fable 5 agent sessions + gpt5.5-terminal transcripts, deduplicated and decontaminated against the reported benchmarks
Method report: doi:10.5281/zenodo.21676407

Provenance & licensing

Fine-tuned from Qwen/Qwen3-4B (Apache-2.0). Training data: Glint-Research/Fable-5-traces (AGPL-3.0) and Roman1111111/gpt5.5-terminal (MIT). Because those traces originate from third-party assistants, the providers' terms may apply to downstream training and distillation. If you plan to build on this model commercially, confirm your use aligns with those terms.

Citation

@misc{aglawe2026agenttrace,
  author    = {Aglawe, Ankit},
  title     = {Agent-Trace Fine-Tuning of Small Language Models under Constrained Compute},
  year      = {2026},
  publisher = {Zenodo},
  doi       = {10.5281/zenodo.21676407},
  url       = {https://doi.org/10.5281/zenodo.21676407}
}

Acknowledgements

The Qwen team for the base model; Glint-Research and Roman1111111 for the trace datasets; empero-ai for the recipe this series iterates on.

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GGUF

Model size

4B params

Architecture

qwen3

Hardware compatibility

4-bit

5-bit

6-bit

8-bit

16-bit

Model tree for AnkitAI/Parable-Qwen3-4B-Claude-Fable-5-GGUF

Base model

Qwen/Qwen3-4B-Base

Finetuned

Qwen/Qwen3-4B

Quantized

(300)

this model

Datasets used to train AnkitAI/Parable-Qwen3-4B-Claude-Fable-5-GGUF

Collection including AnkitAI/Parable-Qwen3-4B-Claude-Fable-5-GGUF

Parable

Collection

Open models trained on real Claude Fable 5 and GPT-5.5 agent traces. Eval-gated releases. Also on Ollama: ollama run parable/fable • 11 items • Updated 23 days ago • 2