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

Libraries

How to use enosislabs/AETHER-Mythos-1-1.2B-gguf 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-gguf")
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-gguf", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use enosislabs/AETHER-Mythos-1-1.2B-gguf with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf enosislabs/AETHER-Mythos-1-1.2B-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf enosislabs/AETHER-Mythos-1-1.2B-gguf:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf enosislabs/AETHER-Mythos-1-1.2B-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf enosislabs/AETHER-Mythos-1-1.2B-gguf:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf enosislabs/AETHER-Mythos-1-1.2B-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf enosislabs/AETHER-Mythos-1-1.2B-gguf:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf enosislabs/AETHER-Mythos-1-1.2B-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf enosislabs/AETHER-Mythos-1-1.2B-gguf:Q4_K_M

Use Docker

docker model run hf.co/enosislabs/AETHER-Mythos-1-1.2B-gguf:Q4_K_M

LM Studio
Jan

vLLM

How to use enosislabs/AETHER-Mythos-1-1.2B-gguf 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-gguf"
# 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-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/enosislabs/AETHER-Mythos-1-1.2B-gguf:Q4_K_M

SGLang

How to use enosislabs/AETHER-Mythos-1-1.2B-gguf 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-gguf" \
    --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-gguf",
		"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-gguf" \
        --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-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use enosislabs/AETHER-Mythos-1-1.2B-gguf with Ollama:
```
ollama run hf.co/enosislabs/AETHER-Mythos-1-1.2B-gguf:Q4_K_M
```

Unsloth Studio

How to use enosislabs/AETHER-Mythos-1-1.2B-gguf 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-gguf 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-gguf 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-gguf to start chatting

How to use enosislabs/AETHER-Mythos-1-1.2B-gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf enosislabs/AETHER-Mythos-1-1.2B-gguf:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "enosislabs/AETHER-Mythos-1-1.2B-gguf:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use enosislabs/AETHER-Mythos-1-1.2B-gguf with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf enosislabs/AETHER-Mythos-1-1.2B-gguf:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "enosislabs/AETHER-Mythos-1-1.2B-gguf:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use enosislabs/AETHER-Mythos-1-1.2B-gguf with Docker Model Runner:
```
docker model run hf.co/enosislabs/AETHER-Mythos-1-1.2B-gguf:Q4_K_M
```

Lemonade

How to use enosislabs/AETHER-Mythos-1-1.2B-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull enosislabs/AETHER-Mythos-1-1.2B-gguf:Q4_K_M

Run and chat with the model

lemonade run user.AETHER-Mythos-1-1.2B-gguf-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use enosislabs/AETHER-Mythos-1-1.2B-gguf with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf enosislabs/AETHER-Mythos-1-1.2B-gguf:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default enosislabs/AETHER-Mythos-1-1.2B-gguf:Q4_K_M

Run Hermes

hermes

Atomic Chat

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: 208

GGUF

Model size

1B params

Architecture

lfm2

Hardware compatibility

4-bit

5-bit

8-bit

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

Base model

LiquidAI/LFM2.5-1.2B-Base

Finetuned

LiquidAI/LFM2.5-1.2B-Thinking

Quantized

(39)

this model