---
language:
- en
license: other
base_model: LiquidAI/LFM2.5-1.2B-Thinking
tags:
- lfm
- liquidai
- lfm2.5
- coding-agent
- thinking
- fable
- aether-mythos
- agentic
- tool-use
- unsloth
library_name: transformers
pipeline_tag: text-generation
---

# 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`](https://huggingface.co/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

| Source | Role | License (as published on Hub) |
|--------|------|--------------------------------|
| [`Glint-Research/Fable-5-traces`](https://huggingface.co/datasets/Glint-Research/Fable-5-traces) | **Primary** agent CoT + tool traces (`fable5_cot_merged.jsonl`) | **AGPL-3.0** |
| Complementary CoT coding sets (see mixture above) | Secondary planning / verification signal | Per-dataset Hub terms |

**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

| Setting | Value |
|--------|--------|
| GPU | L40S (Modal) |
| Effective batch size | 16 |
| Learning rate | 8e-05 |
| Schedule | cosine |
| Epochs / max steps | 1.0 / 100 |
| Packing | True |
| Optim | adamw_8bit |
| Grad checkpointing | unsloth |
| Seed | 3407 |

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

```bibtex
@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

---
*Trained with the open **AETHER Mythos / Fableveil** pipeline.*