---

base_model: Qwen/Qwen3-8B

base_model_relation: finetune

datasets:

• Glint-Research/Fable-5-traces
• Roman1111111/gpt5.5-terminal

license: apache-2.0

language:

• en

pipeline_tag: text-generation

library_name: gguf

tags:

• gguf
• qlora
• agentic
• agent
• coding
• tool-use
• terminal
• reasoning
• thinking
• claude
• claude-fable-5
• distillation
• trace-training
• llama.cpp
• ollama
• lm-studio
• qwen3

---

<picture>

<source media="(prefers-color-scheme: dark)" srcset="https://raw.githubusercontent.com/ankit-aglawe/parable-assets/main/parable_header_dark.png">

<img alt="Parable" src="https://raw.githubusercontent.com/ankit-aglawe/parable-assets/main/parable_header.png">

</picture>

🪶 Parable-Qwen3-8B — trained on genuine Claude Fable 5 agent traces

The largest Parable: planning, tool use, and <think> reasoning distilled from real Claude Fable 5 and GPT-5.5 agent sessions — not synthetic Q&A.

> ~6 GB of RAM is all you need. Laptop, mid-range GPU, yesterday's desktop — the Q4 build runs

> anywhere with that much headroom. One command and you have a private, offline reasoning model on your machine:

>

> ```bash

> ollama run hf.co/AnkitAI/Parable-Qwen3-8B-Claude-Fable-5-GGUF:Q4_K_M

> ```

---

Announcements

🔮 v2 is coming. The 3B just got the v2 treatment (13× corpus, rebuilt recipe) — the same upgrade lands here next. Same links, in-place.

📦 Full family. This 8B is the largest Parable, alongside Parable-Qwen3-4B — browse the full collection for every size, quant, and eval report.

---

Pick your size

| File | Size | Fits in | Notes |

|---|---|---|---|

| Q4_K_M | 4.8 GB | ~6 GB RAM/VRAM | ⭐ Recommended — best size/quality balance |

| Q5_K_M | 5.6 GB | ~7 GB | Higher quality |

| Q6_K | 6.4 GB | ~7.5 GB | Near-lossless |

| Q8_0 | 8.3 GB | ~9.5 GB | Maximum quality |

Full-precision safetensors (vLLM, transformers, further fine-tuning): Parable-Qwen3-8B-Claude-Fable-5

How to run it

Ollama (chat template ships inside the GGUF — zero config):

ollama run parable/qwen3-fable:8b
# or straight from this repo:
ollama run hf.co/AnkitAI/Parable-Qwen3-8B-Claude-Fable-5-GGUF:Q4_K_M

llama.cpp:

llama-cli -m Parable-Qwen3-8B-Claude-Fable-5-GGUF-Q4_K_M.gguf --jinja \
  -p "Write a bash one-liner to find the 10 largest files in a directory tree."

LM Studio: lms get parable/qwen3-fable, search "parable" in-app, or paste this repo URL (parable on LM Studio Hub).

Python (llama-cpp-python):

from llama_cpp import Llama

llm = Llama.from_pretrained(
    repo_id="AnkitAI/Parable-Qwen3-8B-Claude-Fable-5-GGUF",
    filename="*Q4_K_M.gguf", n_ctx=8192,
)
out = llm.create_chat_completion(
    messages=[{"role": "user", "content": "Write a Python function that retries an HTTP request with exponential backoff."}],
    max_tokens=3000, temperature=0.7,
)
print(out["choices"][0]["message"]["content"])

Thinking mode

Every answer opens with a <think>...</think> reasoning block — native to Qwen3, reinforced by this fine-tune. llama.cpp's --jinja chat mode separates it automatically; strip it before showing replies to end users.

Sampling: temperature 0.7, top_p 0.95, and budget max_tokens generously (at least 2500) — trace-trained models think at length before answering.

---

How it measures

!Held-out evals across the Parable family

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; this release measures 0.712 under our stricter 1,024-token evaluation. Cross-repo numbers are indicative only: splits, tokenizers, and context lengths differ (ours is measured at 1,024 tokens).

What it's trained on

• 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.

Good to know

• Weakest on config-file generation and stateful shell logic (4/34 in our eval: Makefile targets, log-watcher scripts, Dockerfile layer ordering) — review generated configs before use.
• Fine-tuned at 1,024-token sequences; the base 128K context stays fully available, so long sessions work, with the fine-tuned behavior strongest in the opening turns.
• Inherits Qwen3-8B's base behaviors and knowledge cutoff. As with any local model, treat generated commands and code as drafts to review.

Base & license

Weights: Apache-2.0 (inherited from Qwen/Qwen3-8B). Training data: Fable-5-traces AGPL-3.0, gpt5.5-terminal MIT — since 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 | |

|---|---|

| Ollama | ollama run parable/qwen3-fable:8b · parable namespace |

| 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 |

Acknowledgements

Glint-Research & Roman1111111 for the open trace data · Qwen for the base · empero-ai whose Qwable recipe the Parable series follows · llama.cpp

---

Six gigabytes. Real Fable 5 reasoning. Yours, offline, right now.

ollama run hf.co/AnkitAI/Parable-Qwen3-8B-Claude-Fable-5-GGUF:Q4_K_M

More on the Parable models: ankitaglawe.com/parable