Motif-3-Beta

⚠️ Preview / beta checkpoint — not the final release. This repository hosts an intermediate checkpoint of Motif-3. The final checkpoint will be released soon.

Motif-3 is a large-scale Mixture-of-Experts (MoE) language model built from the ground up by Motif Technologies following a fully in-house, proprietary design — not a re-parameterization of existing open-source architectures.

Highlights

• 🧠 ~314B total parameters / ~13B active per token (sparse MoE)
• 📏 256K context length (262,144 tokens), natively long-context
• ⚡ Sparse routing: 384 experts with 8 activated per token, plus 1 shared expert
• 🌐 Multilingual, general-purpose

Model details

Model type	Mixture-of-Experts causal language model
Total parameters	~314B
Active parameters	~13B / token
Hidden size	4096
Layers	53
Routed experts	384 (top-8)
Shared experts	1
Context length	262,144 (256K)
Vocabulary	220,160
Tensor type	bfloat16

Architecture

Motif-3 is a fully in-house design and introduces several custom components:

• Grouped Differential Latent Attention (GDLA)
• Grouped PolyNorm activation, applied per expert
• Modified mHC

Benchmarks

Artificial Analysis Intelligence Index (AAII): 44

See Artificial Analysis for details.

Usage - vLLM (Beta)

• Docker image: ghcr.io/motiftechnologies/vllm:v0.20.2-motif3.rc2
• Tested only on B200 and H200 GPUs.
• If you encounter any issues, please open an HF issue.
• Example vLLM command:

vllm serve "Motif-Technologies/Motif-3-Beta" \
    --served-model-name motif \
    --trust-remote-code \
    --tensor-parallel-size 1 \
    --data-parallel-size 8 \
    --data-parallel-size-local 8 \
    --enable-expert-parallel \
    --dtype bfloat16 \
    --quantization modelopt_blockfp8 \
    --max-model-len 262144 \
    --generation-config auto \
    --reasoning-parser motif \
    --enable-auto-tool-choice \
    --tool-call-parser motif \
    --gpu-memory-utilization 0.85 \
    --host 0.0.0.0 --port 8080

Usage - HF.generate

The shipped modeling code has been fixed and HF .generate now runs and produces coherent output. For production or high-throughput serving, vLLM (above) is recommended.

The model ships with custom modeling code, so load it with trust_remote_code=True:

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "Motif-Technologies/Motif-3-Beta"

tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    trust_remote_code=True,
    dtype=torch.bfloat16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [{"role": "user", "content": "Hello!"}]
inputs = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True, return_tensors="pt", return_dict=True
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True))

Access

This model is openly available — anyone can download the weights, no access request required.

License

Permission is granted to use, modify, and redistribute this software for personal, educational, and non-commercial research purposes only.

Commercial use is prohibited without prior written permission from Motif Technologies.

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