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Model: AnkitAI/Parable-Granite-4.1-8B-Claude-Fable-5
Source: Original Platform

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README.md

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+---
+base_model: ibm-granite/granite-4.1-8b
+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: transformers
+tags:
+- safetensors
+- qlora
+- agentic
+- coding
+- reasoning
+- thinking
+- claude
+- granite
+---
+
+# Parable-Granite-4.1-8B-Claude-Fable-5
+
+<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>
+
+**Granite 4.1 8B trained on real Claude Fable 5 and GPT-5.5 agent traces: 70% lower held-out test loss than its base, and past the 0.71 mark the 9B-class incumbent reports on this data family.**
+
+Parable-Granite-4.1-8B is an [ibm-granite/granite-4.1-8b](https://huggingface.co/ibm-granite/granite-4.1-8b) 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. Largest release in the Parable series, alongside [Parable-Qwen3-4B](https://huggingface.co/AnkitAI/Parable-Qwen3-4B-Claude-Fable-5-GGUF).
+
+## Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained(
+    "AnkitAI/Parable-Granite-4.1-8B-Claude-Fable-5",
+    torch_dtype="auto", device_map="auto")
+tok = AutoTokenizer.from_pretrained("AnkitAI/Parable-Granite-4.1-8B-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-Granite-4.1-8B-Claude-Fable-5-GGUF](https://huggingface.co/AnkitAI/Parable-Granite-4.1-8B-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](https://huggingface.co/datasets/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](https://huggingface.co/datasets/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](https://github.com/ggml-org/llama.cpp).
+
+## Evaluation
+
+![Held-out evals across the Parable family](https://raw.githubusercontent.com/ankit-aglawe/parable-assets/main/parable_evals.png)
+
+Held-out test split, identical evaluation code and context length for base and fine-tune:
+
+| Metric | Base Granite-4.1-8B | Parable | Δ |
+|---|---|---|---|
+| Test loss | 2.030 | **0.617** | **−70%** |
+
+**Qualitative review** (34 coding/terminal/debugging prompts, strictly graded by mentally executing every answer): **20/34 fully correct, 32/34 correct or partially correct.** 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](https://huggingface.co/AnkitAI/parable-v2-artifacts)
+under `verify/bfcl/`.
+
+**DNF.** The run hit its 3-hour GPU budget: this chat variant's think
+blocks push most responses to the 4,096-token per-request cap, about 35
+GPU-hours of decode for the full suite at T4 speed, so it cannot
+complete under the same budget every other row got. We report that
+rather than tightening the token cap for one model. For function-calling harnesses, use the base model; the 3B sibling's
+card shows the measured pattern on these categories.
+
+## 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 Granite-4.1-8B'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 Granite-4.1-8B). 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/granite4.1-fable:8b` |
+| Ollama (family flagship, best per size) | `ollama run parable/fable` |
+| Hugging Face | [GGUF quants, full weights, eval reports](https://huggingface.co/collections/AnkitAI/parable-6a4fac60f4b35afca3019621) |
+| LM Studio | `lms get parable/granite4.1-fable` ([parable on LM Studio Hub](https://lmstudio.ai/parable)) |
+
+## 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
+
+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](https://doi.org/10.5281/zenodo.21676407)
+
+```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
+
+- [Glint-Research](https://huggingface.co/Glint-Research) and [Roman1111111](https://huggingface.co/Roman1111111) for the open trace datasets
+- [IBM Granite](https://huggingface.co/ibm-granite) for the base model
+- [empero-ai](https://huggingface.co/empero-ai), whose Qwable recipe the Parable series follows
+- [llama.cpp](https://github.com/ggml-org/llama.cpp)
+
+More on the Parable models: [ankitaglawe.com/parable](https://ankitaglawe.com/parable)

config.json

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+  ],
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+  "rope_scaling": null,
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+  "tie_word_embeddings": true,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.51.3",
+  "use_cache": true,
+  "vocab_size": 100352
+}

generation_config.json

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+  "eos_token_id": 100257,
+  "pad_token_id": 100256,
+  "transformers_version": "4.51.3"
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+}

special_tokens_map.json

@@ -0,0 +1,30 @@
+{
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+    "content": "<|end_of_text|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
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+  "eos_token": {
+    "content": "<|end_of_text|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<|pad|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}