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

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-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

v2.1 (2026-08-03)

Recalibrated merge. Same training, better weight blending: +1.8 points on HumanEval-164 over the previous build (74.4 vs 72.6), reproduced across three independent adapters. If you pulled this model before August 2026, re-pull for the stronger build.

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 Qwen3-4B	This model (v2.1)
Prompts answered (34-prompt suite)	27/34	34/34
HumanEval-164	79.3	74.4
Held-out agent-trace loss	2.846	1.876
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

(281)

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 5 days ago • 1