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Model: AKMESSI/lfm2.5-230m-fable-5
Source: Original Platform

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README.md

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+---
+license: other
+base_model: LiquidAI/LFM2.5-230M
+datasets:
+- Glint-Research/Fable-5-traces
+language:
+- en
+pipeline_tag: text-generation
+tags:
+- gguf
+- lfm2.5
+- liquid-ai
+- fable-5
+- coding-agent
+- tool-use
+- lora
+- peft
+---
+
+# LFM2.5-230M Fable-5 GGUF
+
+Fine-tuned GGUF release of `LiquidAI/LFM2.5-230M` on `Glint-Research/Fable-5-traces`.
+
+## Files
+
+- `lfm2.5-230m-fable-5-f16.gguf` — highest quality, largest file
+- `lfm2.5-230m-fable-5-q8_0.gguf` — high quality, smaller
+- `lfm2.5-230m-fable-5-q4_k_m.gguf` — best default for local inference
+
+## Training
+
+- Base model: `LiquidAI/LFM2.5-230M`
+- Dataset: `Glint-Research/Fable-5-traces`
+- File used: `fable5_cot_merged.jsonl`
+- Method: PEFT LoRA SFT
+- Max sequence length: 4096
+- Epochs: 1
+- LoRA rank: 32
+- LoRA alpha: 64
+- LoRA dropout: 0.05
+- Precision: FP16 base model, FP32 LoRA trainable weights
+- Hardware: Google Colab T4
+- Format: Chat template system/user/assistant, preserving Fable `context -> completion`
+
+## Final training loss samples
+
+- step 555: 1.7037
+- step 560: 1.5968
+- step 565: 1.6435
+- step 570: 1.6109
+- step 575: 1.6589
+- step 580: 1.6439
+
+## Evaluation
+
+We evaluated `AKMESSI/lfm2.5-230m-fable-5:F16` against the original base model, `LiquidAI/LFM2.5-230M-GGUF:BF16`, using local llama.cpp server inference.
+
+These are **not official leaderboard submissions**. They are lightweight local evaluations intended to compare the fine-tuned model against the base model under the same prompts, decoding settings, and hardware setup.
+
+### Summary
+
+The Fable-5 fine-tune improves repository-context code continuation on RepoBench-C-lite Python, while mostly preserving the base model's generic function-calling behavior on BFCL-lite Simple.
+
+| Benchmark | Result |
+|---|---|
+| RepoBench-C-lite Python | Fine-tuned model outperforms base model |
+| BFCL-lite Simple | Fine-tuned model mostly preserves base function-calling ability |
+| CodeXGLUE Line Completion Python | Neutral / unchanged |
+| CRUXEval-lite | Not a good fit for this trace-style model |
+
+---
+
+### RepoBench-C-lite Python
+
+RepoBench-C-style next-line code completion was used to evaluate repository-context code continuation. We sampled 100 examples each from `python_if`, `python_cff`, and `python_cfr`, for 300 total examples.
+
+| Model | Examples | Exact Match | Prefix Match | Edit Similarity |
+|---|---:|---:|---:|---:|
+| `LiquidAI/LFM2.5-230M-GGUF:BF16` | 300 | 10.33% | 10.67% | 46.85% |
+| `AKMESSI/lfm2.5-230m-fable-5:F16` | 300 | 14.67% | 15.33% | 50.17% |
+
+Compared with the base model, the Fable-5 fine-tune improved:
+
+- Exact match by **+4.33 percentage points**
+- Prefix match by **+4.67 percentage points**
+- Edit similarity by **+3.32 points**
+
+Breakdown by config:
+
+| Config | Base Exact | Fable Exact | Base Edit Sim | Fable Edit Sim |
+|---|---:|---:|---:|---:|
+| `python_if` | 21.00% | 27.00% | 55.14% | 57.31% |
+| `python_cff` | 3.00% | 5.00% | 37.45% | 38.10% |
+| `python_cfr` | 7.00% | 12.00% | 47.96% | 55.10% |
+
+---
+
+### BFCL-lite Simple
+
+We also ran a local BFCL-lite Simple function-calling evaluation over 400 examples as a generic tool-calling control.
+
+| Model | Examples | Parse-valid JSON | Function-name Match | Argument Recall | Rough Score |
+|---|---:|---:|---:|---:|---:|
+| `LiquidAI/LFM2.5-230M-GGUF:BF16` | 400 | 97.75% | 97.50% | 71.60% | 88.44% |
+| `AKMESSI/lfm2.5-230m-fable-5:F16` | 400 | 98.25% | 95.00% | 67.70% | 85.44% |
+
+The fine-tuned model preserves most of the base model's generic function-calling behavior, but does not improve BFCL-style API-schema-to-JSON calling. This is expected because the training data consists of coding-agent traces rather than clean function-calling examples.
+
+---
+
+### CodeXGLUE Line Completion Python
+
+We ran a 1,000-example local CodeXGLUE line-completion evaluation as a general code-completion control.
+
+| Model | Examples | Exact Match | Prefix Match | Edit Similarity |
+|---|---:|---:|---:|---:|
+| `LiquidAI/LFM2.5-230M-GGUF:BF16` | 1000 | 23.60% | 0.00% | 23.60% |
+| `AKMESSI/lfm2.5-230m-fable-5:F16` | 1000 | 23.50% | 0.00% | 23.50% |
+
+This result is effectively neutral. The Fable-5 fine-tune does not materially change general line-completion performance on this setup.
+
+---
+
+### CRUXEval-lite
+
+We also tried a 200-example CRUXEval-lite run for Python execution reasoning.
+
+| Model | Task O Accuracy | Task I Accuracy | Overall Accuracy |
+|---|---:|---:|---:|
+| `LiquidAI/LFM2.5-230M-GGUF:BF16` | 8.50% | 4.00% | 6.25% |
+| `AKMESSI/lfm2.5-230m-fable-5:F16` | 0.00% | 0.00% | 0.00% |
+
+This benchmark was not a good fit for the fine-tuned model. The Fable-5 model often entered explanation or trace-style response mode instead of returning only the exact literal Python value expected by CRUXEval.
+
+---
+
+### Interpretation
+
+The Fable-5 fine-tune appears to shift the base model toward coding-agent and repository-context continuation behavior.
+
+It improves RepoBench-C-lite Python next-line completion, while mostly preserving generic function-calling ability on BFCL-lite Simple. The main regression is in exact BFCL-style argument filling, which is not the main target of the Fable-5 trace dataset.
+
+The model is best understood as a tiny coding-agent trace model, not a general-purpose reasoning model or a benchmark-specialized function-calling model.
+
+---
+
+### Evaluation Caveats
+
+- These are local lightweight evaluations, not official leaderboard submissions.
+- Results were produced with llama.cpp server inference.
+- Scores may vary with prompting, decoding settings, quantization level, and benchmark harness details.
+- BFCL-lite and RepoBench-C-lite use simplified local scoring scripts rather than official leaderboard infrastructure.
+- Only the F16 model was benchmarked here; quantized GGUF variants may differ slightly.
+
+## Usage
+
+Recommended local file:
+
+`lfm2.5-230m-fable-5-q4_k_m.gguf`
+
+## Caveats
+
+This model is trained on coding-agent trace telemetry. It may emit tool-call-like actions, shell commands, file paths, or long reasoning-style continuations. Review outputs before executing commands.
+
+The dataset contains coding-agent traces and should not be treated as a clean benchmark or a safety-filtered assistant dataset.
+
+## License notes
+
+- Base model: LiquidAI LFM Open License v1.0
+- Dataset: AGPL-3.0
+- This repo preserves upstream license notices. Check compatibility before commercial or closed-source use.

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