AETHER-Mythos-1-1.2B-gguf

AETHER-Mythos-1 — AETHER-Mythos-1-1.2B

AETHER Mythos: a fast, efficient thinking coding agent distilled from elite Fable 5 agent traces onto LiquidAI’s LFM2.5 architecture.

AETHER-Mythos-1 is a specialist agentic coding model with strong internal reasoning. It is designed for on-device / local deployment: low latency, modest VRAM/RAM, and high-signal tool-use + planning behavior.

Philosophy

The highest-leverage path to a small coding agent is not more web text — it is distilling the best long-horizon agent trajectories (think → tool → observe → verify) into an efficient backbone. We prioritize:

1. Fable 5 traces (Glint-Research/Fable-5-traces) as the primary high-signal source of Claude Fable 5 thinking + tool-use coding sessions.
2. Complementary elite CoT coding / reasoning data to reinforce planning and verification without drowning the mix in noise.
3. LiquidAI LFM2.5 as the substrate: hybrid architecture, strong edge speed, long context, and Unsloth-friendly fine-tuning.

Base model

• Base: LiquidAI/LFM2.5-1.2B-Thinking
• Context trained: up to 32768 tokens (packing enabled)
• Method: LoRA (r=128, alpha=256)

Data mixture

• fable5_cot (Glint-Research/Fable-5-traces) weight=0.65 — Primary identity and agent trace signal
• opencode_reasoning (nvidia/OpenCodeReasoning) weight=0.18 — prompt_completion
• open_r1_codeforces (open-r1/codeforces-cots) weight=0.07 — messages
• openthoughts_code (open-thoughts/OpenThoughts-114k) weight=0.10 — messages

Data provenance & licenses

AGPL-3.0 notice: A substantial fraction of training signal comes from AGPL-licensed agent traces. Distributing model weights derived primarily from AGPL data may trigger strong copyleft obligations (source disclosure for network use in some interpretations). Do not treat this model as Apache/MIT-clean. Review AGPL compatibility with counsel before commercial or proprietary deployment. The base model (LiquidAI/LFM2.5-1.2B-Thinking) remains under Liquid AI’s LFM license terms.

Training setup

Stack: Unsloth + TRL SFT on Modal with persistent volumes for dataset cache and checkpoints.

Intended use

• Local coding agents (tool-use loops: shell, edit, read, write)
• Planning + verification style reasoning before code changes
• Edge / laptop / NPU-friendly deployments via GGUF / MLX / llama.cpp

Not intended for: unconstrained autonomous operation on production systems without human oversight; high-stakes decisions; generating malware or disallowed content.

Chat & thinking format

AETHER-Mythos-1 follows LFM2.5 ChatML-style templates. Assistant turns may include:

<think>
... internal reasoning ...
</think>
final answer or tool call

Tool calls use LFM tokens:

<|tool_call_start|>[tool_name(arg="value")]<|tool_call_end|>

Inference tips (LFM2.5 Thinking defaults)

• temperature ≈ 0.05
• top_k = 50
• repetition_penalty ≈ 1.05

Limitations

• Distilled from agent traces; may inherit tool schemas and path conventions from source data.
• Context rows in Fable-5 merged JSONL may be truncated at the source.
• Small models can still hallucinate APIs, file state, or test results — always verify.

Citation

@misc{aether-mythos-1-2026,
  title = {AETHER-Mythos-1: Efficient Agentic Coding via Fable 5 Distillation on LFM2.5},
  year = {2026},
  howpublished = {\url{https://huggingface.co/enosislabs/AETHER-Mythos-1-1.2B}}
}

Acknowledgements

• Liquid AI — LFM2.5 family
• Glint Research / TeichAI ecosystem — Fable 5 trace corpora
• Unsloth — efficient fine-tuning
• Modal — GPU infrastructure

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Trained with the open AETHER Mythos / Fableveil pipeline.