Instructions to use AnkitAI/Parable-Qwen3-4B-Claude-Fable-5 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 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="AnkitAI/Parable-Qwen3-4B-Claude-Fable-5")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("AnkitAI/Parable-Qwen3-4B-Claude-Fable-5")
model = AutoModelForCausalLM.from_pretrained("AnkitAI/Parable-Qwen3-4B-Claude-Fable-5", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use AnkitAI/Parable-Qwen3-4B-Claude-Fable-5 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"
# 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",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

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

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "AnkitAI/Parable-Qwen3-4B-Claude-Fable-5" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AnkitAI/Parable-Qwen3-4B-Claude-Fable-5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "AnkitAI/Parable-Qwen3-4B-Claude-Fable-5" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AnkitAI/Parable-Qwen3-4B-Claude-Fable-5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use AnkitAI/Parable-Qwen3-4B-Claude-Fable-5 with Docker Model Runner:
```
docker model run hf.co/AnkitAI/Parable-Qwen3-4B-Claude-Fable-5
```

Parable-Qwen3-4B-Claude-Fable-5 / README.md

AnkitAI

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	---
	license: apache-2.0
	base_model: Qwen/Qwen3-4B
	datasets:
	- Glint-Research/Fable-5-traces
	- Roman1111111/gpt5.5-terminal
	pipeline_tag: text-generation
	library_name: transformers
	tags:
	- safetensors
	- qlora
	- agentic
	- coding
	- reasoning
	- qwen3
	- local-llm
	- ollama
	- lm-studio
	---

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

	![Parable](banner.svg)

	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. Full-precision weights; the GGUF build runs on ~2.5 GB.

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer

	repo = "AnkitAI/Parable-Qwen3-4B-Claude-Fable-5"
	tok = AutoTokenizer.from_pretrained(repo)
	model = AutoModelForCausalLM.from_pretrained(repo, torch_dtype="auto", device_map="auto")
	```

	Prefer to run it locally in Ollama or LM Studio? Take the
	[GGUF build](https://huggingface.co/AnkitAI/Parable-Qwen3-4B-Claude-Fable-5-GGUF)
	(2.5 GB at 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.

	## 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. 4B parameters, and the GGUF build 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](https://huggingface.co/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](https://doi.org/10.5281/zenodo.21676407)

	## Provenance & licensing

	Fine-tuned from Qwen/Qwen3-4B (Apache-2.0). Training data:
	[Glint-Research/Fable-5-traces](https://huggingface.co/datasets/Glint-Research/Fable-5-traces)
	(AGPL-3.0) and
	[Roman1111111/gpt5.5-terminal](https://huggingface.co/datasets/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.

	## Support the Project

	If this model is useful in your work, you can support independent research:

	<p align="left">
	<a href="https://www.buymeacoffee.com/AnkitAI" target="_blank"><img src="https://cdn.buymeacoffee.com/buttons/v2/default-yellow.png" alt="Buy Me a Coffee" height="60" width="217" /></a>
	</p>

	## Citation

	```bibtex
	@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.