Text Generation
Transformers
Safetensors
English
lfm2
lfm
liquidai
lfm2.5
coding-agent
thinking
fable
aether-mythos
agentic
tool-use
unsloth
conversational
Instructions to use enosislabs/AETHER-Mythos-1-1.2B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use enosislabs/AETHER-Mythos-1-1.2B 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") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("enosislabs/AETHER-Mythos-1-1.2B") model = AutoModelForCausalLM.from_pretrained("enosislabs/AETHER-Mythos-1-1.2B", device_map="auto") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use enosislabs/AETHER-Mythos-1-1.2B 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" # 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", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/enosislabs/AETHER-Mythos-1-1.2B
- SGLang
How to use enosislabs/AETHER-Mythos-1-1.2B 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" \ --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", "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" \ --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", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Unsloth Studio
How to use enosislabs/AETHER-Mythos-1-1.2B 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 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 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 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", max_seq_length=2048, ) - Docker Model Runner
How to use enosislabs/AETHER-Mythos-1-1.2B with Docker Model Runner:
docker model run hf.co/enosislabs/AETHER-Mythos-1-1.2B
Upload AETHER-Mythos-1 merged 16-bit weights
Browse files- README.md +9 -9
- config.json +60 -57
- generation_config.json +8 -0
- model.safetensors +1 -1
- tokenizer_config.json +0 -0
README.md
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## Base model
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- **Base**: [`LiquidAI/LFM2.5-1.2B-Thinking`](https://huggingface.co/LiquidAI/LFM2.5-1.2B-Thinking)
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- **Context trained**: up to
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- **Method**: LoRA (r=
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## Data mixture
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- **fable5_cot** (`Glint-Research/Fable-5-traces`) weight=0.65 — Primary
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- **opencode_reasoning** (`nvidia/OpenCodeReasoning`) weight=0.18 —
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- **open_r1_codeforces** (`open-r1/codeforces-cots`) weight=0.
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- **openthoughts_code** (`open-thoughts/OpenThoughts-114k`) weight=0.
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### Data provenance & licenses
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| Setting | Value |
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|--------|--------|
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| GPU | L40S (Modal) |
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| Effective batch size |
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| Learning rate |
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| Schedule | cosine |
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| Epochs / max steps | 1.0 /
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| Packing | True |
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| Optim | adamw_8bit |
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| Grad checkpointing | unsloth |
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## Base model
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- **Base**: [`LiquidAI/LFM2.5-1.2B-Thinking`](https://huggingface.co/LiquidAI/LFM2.5-1.2B-Thinking)
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- **Context trained**: up to 32768 tokens (packing enabled)
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- **Method**: LoRA (r=128, alpha=256)
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## Data mixture
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- **fable5_cot** (`Glint-Research/Fable-5-traces`) weight=0.65 — Primary identity and agent trace signal
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- **opencode_reasoning** (`nvidia/OpenCodeReasoning`) weight=0.18 — prompt_completion
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- **open_r1_codeforces** (`open-r1/codeforces-cots`) weight=0.07 — messages
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- **openthoughts_code** (`open-thoughts/OpenThoughts-114k`) weight=0.10 — messages
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### Data provenance & licenses
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| Setting | Value |
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|--------|--------|
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| GPU | L40S (Modal) |
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| Effective batch size | 16 |
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| Learning rate | 8e-05 |
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| Schedule | cosine |
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| Epochs / max steps | 1.0 / 100 |
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| Packing | True |
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| Optim | adamw_8bit |
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| Grad checkpointing | unsloth |
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config.json
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{
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{
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"architectures": [
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"Lfm2ForCausalLM"
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],
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"block_auto_adjust_ff_dim": true,
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"block_dim": 2048,
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"block_ffn_dim_multiplier": 1.0,
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"block_mlp_init_scale": 1.0,
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"block_multiple_of": 256,
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"block_norm_eps": 1e-05,
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"block_out_init_scale": 1.0,
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"block_use_swiglu": true,
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"block_use_xavier_init": true,
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"bos_token_id": 1,
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"conv_L_cache": 3,
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"conv_bias": false,
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"conv_dim": 2048,
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"conv_use_xavier_init": true,
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"torch_dtype": "bfloat16",
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"eos_token_id": 7,
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"full_attn_idxs": null,
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"hidden_size": 2048,
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"initializer_range": 0.02,
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"intermediate_size": 12288,
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"layer_types": [
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"conv",
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"conv",
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"full_attention",
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"conv",
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"conv",
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"full_attention",
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"conv",
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"conv",
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"full_attention",
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"conv",
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"full_attention",
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"conv",
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"full_attention",
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"conv",
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"full_attention",
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"conv"
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],
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"max_position_embeddings": 128000,
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"model_name": "LiquidAI/LFM2.5-1.2B-Thinking",
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"model_type": "lfm2",
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"norm_eps": 1e-05,
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"num_attention_heads": 32,
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"num_heads": 32,
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"num_hidden_layers": 16,
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"num_key_value_heads": 8,
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"pad_token_id": 0,
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"rope_parameters": {
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"rope_theta": 1000000.0,
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"rope_type": "default"
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},
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"tie_word_embeddings": true,
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"unsloth_version": "2026.7.5",
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"use_cache": false,
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"use_pos_enc": true,
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"vocab_size": 65536
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}
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generation_config.json
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{
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"_from_model_config": true,
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"bos_token_id": 1,
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"eos_token_id": 7,
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"max_length": 128000,
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"pad_token_id": 0,
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"transformers_version": "5.5.0"
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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size 2340697936
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version https://git-lfs.github.com/spec/v1
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size 2340697936
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tokenizer_config.json
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