Instructions to use enosislabs/AETHER-Mythos-1-1.2B-adapter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use enosislabs/AETHER-Mythos-1-1.2B-adapter with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="enosislabs/AETHER-Mythos-1-1.2B-adapter")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("enosislabs/AETHER-Mythos-1-1.2B-adapter", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use enosislabs/AETHER-Mythos-1-1.2B-adapter with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "enosislabs/AETHER-Mythos-1-1.2B-adapter"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "enosislabs/AETHER-Mythos-1-1.2B-adapter",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/enosislabs/AETHER-Mythos-1-1.2B-adapter

SGLang

How to use enosislabs/AETHER-Mythos-1-1.2B-adapter with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "enosislabs/AETHER-Mythos-1-1.2B-adapter" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "enosislabs/AETHER-Mythos-1-1.2B-adapter",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "enosislabs/AETHER-Mythos-1-1.2B-adapter" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "enosislabs/AETHER-Mythos-1-1.2B-adapter",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use enosislabs/AETHER-Mythos-1-1.2B-adapter with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for enosislabs/AETHER-Mythos-1-1.2B-adapter to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for enosislabs/AETHER-Mythos-1-1.2B-adapter to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for enosislabs/AETHER-Mythos-1-1.2B-adapter to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="enosislabs/AETHER-Mythos-1-1.2B-adapter",
    max_seq_length=2048,
)

Docker Model Runner
How to use enosislabs/AETHER-Mythos-1-1.2B-adapter with Docker Model Runner:
```
docker model run hf.co/enosislabs/AETHER-Mythos-1-1.2B-adapter
```

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:

Fable 5 traces (Glint-Research/Fable-5-traces) as the primary high-signal source of Claude Fable 5 thinking + tool-use coding sessions.
Complementary elite CoT coding / reasoning data to reinforce planning and verification without drowning the mix in noise.
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

Source	Role	License (as published on Hub)
`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

@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.

Downloads last month: -; Downloads are not tracked for this model. How to track

Model tree for enosislabs/AETHER-Mythos-1-1.2B-adapter

Base model

LiquidAI/LFM2.5-1.2B-Base

Finetuned

LiquidAI/LFM2.5-1.2B-Thinking

Finetuned

(38)

this model