Instructions to use AnkitAI/Parable-Qwen3-8B-Claude-Fable-5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AnkitAI/Parable-Qwen3-8B-Claude-Fable-5 with Transformers:

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

pipe = pipeline("text-generation", model="AnkitAI/Parable-Qwen3-8B-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-Qwen3-8B-Claude-Fable-5")
model = AutoModelForCausalLM.from_pretrained("AnkitAI/Parable-Qwen3-8B-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-Qwen3-8B-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-Qwen3-8B-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-Qwen3-8B-Claude-Fable-5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/AnkitAI/Parable-Qwen3-8B-Claude-Fable-5

SGLang

How to use AnkitAI/Parable-Qwen3-8B-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-Qwen3-8B-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-Qwen3-8B-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-Qwen3-8B-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-Qwen3-8B-Claude-Fable-5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use AnkitAI/Parable-Qwen3-8B-Claude-Fable-5 with Docker Model Runner:
```
docker model run hf.co/AnkitAI/Parable-Qwen3-8B-Claude-Fable-5
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Parable-Qwen3-8B-Claude-Fable-5

Qwen3-8B trained on real Claude Fable 5 and GPT-5.5 agent traces: 67% lower held-out test loss than its base, and the strongest strictly-graded qualitative score in the Parable series: 23 of 34 fully correct.

Parable-Qwen3-8B is a Qwen/Qwen3-8B fine-tune trained on real multi-step agent sessions: planning, tool use, and <think> reasoning captured from actual Claude Fable 5 and GPT-5.5 agent work, not synthetic Q&A. Highest strict-qual release in the Parable series, alongside the Granite 8B line.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "AnkitAI/Parable-Qwen3-8B-Claude-Fable-5",
    torch_dtype="auto", device_map="auto")
tok = AutoTokenizer.from_pretrained("AnkitAI/Parable-Qwen3-8B-Claude-Fable-5")

msgs = [{"role": "user", "content": "Write a Python function that retries an HTTP request with exponential backoff."}]
ids = tok.apply_chat_template(msgs, add_generation_prompt=True, return_tensors="pt").to(model.device)
out = model.generate(ids, max_new_tokens=3000, temperature=0.7, top_p=0.95, do_sample=True)
text = tok.decode(out[0][ids.shape[1]:], skip_special_tokens=True)
answer = text.split("</think>")[-1].strip()  # response opens with a <think> block
print(answer)

GGUF quants for llama.cpp / Ollama / LM Studio: Parable-Qwen3-8B-Claude-Fable-5-GGUF.

Sampling: temperature 0.7, top_p 0.95, generous max_new_tokens (at least 2500).

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)

Every example passed a quality gate (schema validation, secrets scrub, length filtering) before training. QLoRA fine-tune (NF4, sequence length 1024) trained on a single 16 GB GPU, quantized with llama.cpp.

Evaluation

Held-out test split, identical evaluation code and context length for base and fine-tune:

Metric	Base Qwen3-8B	Parable	Δ
Test loss	2.162	0.712	−67%

Qualitative review (34 coding/terminal/debugging prompts, strictly graded by mentally executing every answer): 23/34 fully correct, 30/34 correct or partially correct — the highest fully-correct score in the series. We publish these numbers because strict qualitative grading is rare in this niche; judge accordingly.

For reference, the strongest published fine-tune on this data family (a 9B) reports 0.71 validation loss. Cross-repo numbers are indicative only: splits, tokenizers, and context lengths differ (ours is measured at 1,024 tokens).

Function calling (BFCL V3, AST subset)

Measured 2026-07-29: bfcl-eval at gorilla main, prompting mode, Q4_K_M GGUFs served by llama.cpp on a T4, base and Parable under the identical harness. Categories: simple_python / multiple / parallel / parallel_multiple (400/200/200/200 items). Raw generations and score files: parable-v2-artifacts under verify/bfcl/.

	simple_python	multiple	parallel	parallel_multiple
Qwen3-8B base	0.953	0.945	0.935	0.900
Parable-Qwen3-8B	0.930	0.900	0.905	0.850

A 2.3 to 5.0 point trade per category: prose-trace SFT costs a little function-calling sharpness, as this card's evaluation note predicts. If you need maximum tool-calling accuracy, use the base; this variant buys the reasoning voice.

Limitations

Trained for agent work: on ops-style prompts it sometimes (2/34 in our eval) responds with structured tool-call JSON rather than prose. Useful inside agent harnesses; in plain chat, re-prompt or lower the temperature.
Fine-tuned at 1,024-token sequences; the base model's native 128K-token context remains fully available, so long sessions work, with the fine-tuned behavior strongest in the opening turns.

As a fine-tune it inherits Qwen3-8B's base behaviors and knowledge cutoff. As with any local model, treat generated commands and code as drafts to review.

Provenance & licensing

Model weights: Apache-2.0 (inherited from Qwen3-8B). Training data licenses: Fable-5-traces AGPL-3.0, 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.

Get Parable

Platform	Command / Link
Ollama	`ollama run parable/qwen3-fable:8b`
Ollama (family flagship, best per size)	`ollama run parable/fable`
Hugging Face	GGUF quants, full weights, eval reports
LM Studio	`lms get parable/qwen3-fable` (parable on LM Studio Hub)

Citation

The recipe, evaluation methodology and failure analysis behind this model are documented in the tech report:

Aglawe, A. (2026). Agent-Trace Fine-Tuning of Small Language Models under Constrained Compute. Zenodo. doi:10.5281/zenodo.21676407

@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}
}