Instructions to use AnkitAI/Parable-SmolLM3-3B-Claude-Fable-5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AnkitAI/Parable-SmolLM3-3B-Claude-Fable-5 with Transformers:

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

pipe = pipeline("text-generation", model="AnkitAI/Parable-SmolLM3-3B-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-SmolLM3-3B-Claude-Fable-5")
model = AutoModelForCausalLM.from_pretrained("AnkitAI/Parable-SmolLM3-3B-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-SmolLM3-3B-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-SmolLM3-3B-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-SmolLM3-3B-Claude-Fable-5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/AnkitAI/Parable-SmolLM3-3B-Claude-Fable-5

SGLang

How to use AnkitAI/Parable-SmolLM3-3B-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-SmolLM3-3B-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-SmolLM3-3B-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-SmolLM3-3B-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-SmolLM3-3B-Claude-Fable-5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use AnkitAI/Parable-SmolLM3-3B-Claude-Fable-5 with Docker Model Runner:
```
docker model run hf.co/AnkitAI/Parable-SmolLM3-3B-Claude-Fable-5
```

Parable-SmolLM3-3B-Claude-Fable-5 / README.md

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	---
	license: apache-2.0
	base_model: HuggingFaceTB/SmolLM3-3B
	datasets:
	- Glint-Research/Fable-5-traces
	- Roman1111111/gpt5.5-terminal
	pipeline_tag: text-generation
	library_name: transformers
	language:
	- en
	tags:
	- safetensors
	- qlora
	- agentic
	- coding
	- reasoning
	- smollm3
	---

	# Parable-SmolLM3-3B-Claude-Fable-5

	Part of the Parable series: small local LLMs fine-tuned on genuine agent
	traces. This is HuggingFaceTB/SmolLM3-3B tuned on real Claude Fable 5 agent
	transcripts so its step-by-step reasoning voice carries into local use.

	Quantized GGUF builds for llama.cpp / LM Studio / Ollama:
	[Parable-SmolLM3-3B-Claude-Fable-5-GGUF](https://huggingface.co/AnkitAI/Parable-SmolLM3-3B-Claude-Fable-5-GGUF)

	## Usage

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer

	repo = "AnkitAI/Parable-SmolLM3-3B-Claude-Fable-5"
	tok = AutoTokenizer.from_pretrained(repo)
	model = AutoModelForCausalLM.from_pretrained(repo, torch_dtype="auto", device_map="auto")

	msgs = [{"role": "user", "content": "Write a python function that reverses a string."}]
	ids = tok.apply_chat_template(msgs, add_generation_prompt=True, return_tensors="pt").to(model.device)
	out = model.generate(ids, max_new_tokens=400, temperature=0.6)
	print(tok.decode(out[0][ids.shape[1]:], skip_special_tokens=True))
	```

	Output begins with a `<think>...</think>` reasoning block, then the answer.
	Parse and strip the think block before showing text to end users. The chat
	template identifies the model as "Parable, a coding assistant that reasons
	before it answers."

	## Model details

	- Base: [HuggingFaceTB/SmolLM3-3B](https://huggingface.co/HuggingFaceTB/SmolLM3-3B) (3B, Apache-2.0, 64k context)
	- Method: MLX QLoRA on a 4-bit quantized base, 16 layers adapted,
	6.7M trainable parameters (0.218%); this repo holds the dequantized F16
	merge as safetensors
	- Data: 4,076 training rows from real Claude Fable 5 agent-session
	traces plus gpt5.5-terminal transcripts, prepared at a 4,096-token window
	(268 over-length rows dropped; 226/226 rows held out for validation/test)
	- Schedule: 1,200-iteration budget across a paused-and-resumed run;
	best checkpoint selected on validation loss (iteration 200 of the final
	segment, val 1.154)

	## Evaluation

	\| \| Held-out trace test loss \|
	\|---\|---\|
	\| SmolLM3-3B base \| 1.889 \|
	\| This model \| 1.115 \|

	The tuned model fits the Fable-5 reasoning distribution 41% better by
	held-out loss on a 226-row test split never seen in training. That is the
	honest headline for what this fine-tune does; we do not claim general
	benchmark gains.

	This lane trains on trace data without a replay mix, so impact on general
	coding benchmarks is unmeasured here. The series' technical report
	(DOI: [10.5281/zenodo.21676407](https://doi.org/10.5281/zenodo.21676407))
	documents why that matters and what replay does about it.

	## Limitations

	- Training ran on a 4-bit quantized base (16 GB M1 constraint); the F16
	merge cannot exceed 4-bit-base quality.
	- Modest scale: one seed, loss-based evaluation, no external benchmark run
	for this model yet.
	- Not trained for: multi-file repo navigation, vision, non-English.
	- Inherits SmolLM3-3B's knowledge cutoff. Treat generated commands as
	drafts to review.

	## Provenance & licensing

	Fine-tuned from HuggingFaceTB/SmolLM3-3B (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.

	## Citation

	```bibtex
	@misc{aglawe2026parable,
	author = {Aglawe, Ankit},
	title = {Agent-Trace Fine-Tuning of Small Language Models under Constrained Compute},
	year = {2026},
	doi = {10.5281/zenodo.21676407},
	url = {https://doi.org/10.5281/zenodo.21676407}
	}
	```

	## Acknowledgements

	The SmolLM3 team at Hugging Face for the base model; Glint-Research and
	Roman1111111 for the trace datasets; empero-ai for the recipe this series
	iterates on.