gpt-oss-120b-Fable-5-Distilled

A LoRA fine-tuned MoE coding agent model distilled from real-world Claude Code programming sessions. Merged into a single complete model — download and run directly on Apple Silicon via MLX.

Recommended temperature: 0.95 — this model was trained on opencode agent traces where the assistant alternates between reasoning, tool calls, and code generation. Slightly higher temperature preserves this multi-modal behavior.

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Model Summary

gpt-oss-120b-Fable-5-Distilled is a fully merged LoRA fine-tune of gpt-oss-120b-heretic-v2-mxfp4-q8-hi-mlx, trained using the Muon optimizer with Newton-Schulz orthogonalization on Apple MLX. The fine-tuning data consists of 352 real-world opencode programming sessions extracted from armand0e/claude-fable-5-claude-code.

This is a complete model upload — no separate adapter files or base model are needed.

The model learns to act as a coding agent: understanding user requests, exploring repositories, making code edits via tool calls, and delivering final solutions — all through the Harmony channel protocol (analysis for internal reasoning, final for the delivered response).

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Highlights

• 🔧 Agent-style coding — trained on real tool_use traces (Bash, Read, Edit, TaskCreate, Git) from Claude Code sessions. Generates natural tool-call sequences.
• 🧠 Muon optimizer — Newton-Schulz orthogonalized updates on LoRA matrices for stronger convergence at low training step counts.
• 🎯 13/13 PASS — perfect score on a 13-task custom benchmark spanning math olympiad, algorithm coding, DS/algo, logic puzzles, and scientific computing.
• 🌡️ Recommended temperature: 0.95 — preserves the agent's multi-step reasoning + tool-call rhythm.
• 🍎 Fully on-device — no cloud API, no GPU cluster. Pure MLX inference on Mac.
• 📦 Full model — merged weights included. No adapter setup required.
• 📐 Harmony channel format — analysis (chain-of-thought) and final (response) channel separation.

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Tool Call details

See TOOL_CALLING.md for the full tool calling guide.

Quick Start

macOS + MLX

# Using mlx_lm (installed by default with mlx)
mlx_lm.chat --model gpt-oss-120b-Fable-5-Distilled --max-tokens 10000 --temp 0.95

Python

from mlx_tune import FastLanguageModel

model, tokenizer = FastLanguageModel.from_pretrained(
 model_name="gpt-oss-120b-Fable-5-Distilled",
 max_seq_length=4096,
 load_in_4bit=True,
)

prompt = """<|start|>system<|message|>You are a helpful coding assistant.
Reasoning: high
# Valid channels: analysis, commentary, final<|end|>
<|start|>user<|message|>Write a function to find the longest common substring of two strings.<|end|>
<|start|>assistant<|channel|>analysis<|message|>"""

outputs = model.generate(prompt, max_new_tokens=2048, temperature=0.95, top_p=0.9)
print(outputs)

Prompt Format (Harmony)

This model uses the gpt-oss Harmony channel protocol:

<|start|>system<|message|>{system prompt}
Reasoning: high
# Valid channels: analysis, commentary, final<|end|>
<|start|>user<|message|>{your request — including coding tasks, tool-call requests, etc.}<|end|>
<|start|>assistant<|channel|>analysis<|message|>{internal chain-of-thought}<|end|>
<|start|>assistant<|channel|>final<|message|>{final answer / tool call sequence}<|return|>

• The analysis channel contains the model's internal reasoning. Display or hide depending on use case.
• The final channel contains the deliverable response (code, tool calls, or natural language).
• Always prompt the model to begin with <|start|>assistant<|channel|>analysis<|message|>.

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Training Details

Dataset

• Source: armand0e/claude-fable-5-claude-code
• Type: Opencode session logs — real-world Claude Code programming agent traces
• Extracted samples: 352 user → assistant turns (from 55 session files, ~100 sessions)
• Content: tool_use sequences (Bash, Read, Edit, TaskCreate, Git), code generation, multi-step agent workflows
• Format: Converted from opencode event format → Harmony channel protocol
• Split: 90% train (317) / 10% validation (35), seed=42

Muon Optimizer

Muon provides faster convergence than AdamW for matrix-shaped parameters by orthogonalizing gradient updates via Newton-Schulz iteration:

• Matrix params (ndim >= 2): LoRA A/B matrices → Muon (Nesterov momentum + NS orthogonalization + shape-aware scaling)
• 1-D params / bias: → AdamW fallback

Unlike our previous BAdam-based models, this release uses uniform Muon training across all LoRA layers, including the router. At top-k=4, the router competition space is sufficient to avoid expert collapse without special lr treatment.

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Benchmark Results

Custom hard benchmark — 13 tasks across math, coding, logic, and science. Auto-graded with keyword matching + live code execution (60s timeout per task, max 2 retries on error).

Overall: 13/13 PASS | Avg KW: 100.0% | Total benchmark time: 1189s

All 13 tasks pass on the first attempt, zero retries required. The model consistently generates correct, executable code with full keyword coverage.

Key Benchmark Details

• code_01 Median — O(log n) binary search vs merge: 5656× speedup, correct.
• code_02 LRU Cache — 8-thread stress test: 710k ops/sec, hit_rate 49.8%, 200k evictions.
• code_03 Persistent Segment Tree — Q1=3, Q2=2, Q3=31, Q4=1 all correct.
• code_04 Multi-Head Attention + RoPE — causal mask verified, max_future_attn = 0.0.
• sci_01 Three-body — energy drift only 3.0e-14, numerical precision excellent.
• sci_02 HMC — ESS 379k (MH: 1,930), gradient direction correct (p += grad_log_target).

Inference Speed

This is a 120B MoE model running entirely on-device on a Mac — no cloud dependency, no quantization compromise.

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Limitations

• Apple Silicon only — quantized and optimized for MLX. Not compatible with CUDA/ROCm without re-quantization.
• 40-step checkpoint — an early fine-tune designed for quick validation. Further training would deepen the coding agent behavior.
• No RLHF — trained on supervised opencode session data only. Safety alignment is lighter than commercial instruction-tuned models.
• Harmony prompt format required — this model expects the <|channel|> protocol. Standard ChatML or Alpaca-style prompts will produce degraded results.
• Small dataset — 352 turns is modest. The benchmark results suggest high-quality, high-signal data, but broader generalization benefits from more training data.

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Citation / Acknowledgements

• Base model: gpt-oss-120b-heretic-v2
• Training dataset: armand0e/claude-fable-5-claude-code
• Optimizer: Muon — Muon: An optimizer for hidden layers in neural networks
• Framework: Apple MLX · mlx-tune
• Agent traces from: Claude Code (Anthropic) via opencode session logs