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LemOneLabs/
LFM2-1.2B-ONNX

Name: LFM2-1.2B-ONNX
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LFM2-1.2B

LFM2 is a new generation of hybrid models developed by Liquid AI, specifically designed for edge AI and on-device deployment. It sets a new standard in terms of quality, speed, and memory efficiency.

We're releasing the weights of three post-trained checkpoints with 350M, 700M, and 1.2B parameters. They provide the following key features to create AI-powered edge applications:

Fast training & inference – LFM2 achieves 3x faster training compared to its previous generation. It also benefits from 2x faster decode and prefill speed on CPU compared to Qwen3.
Best performance – LFM2 outperforms similarly-sized models across multiple benchmark categories, including knowledge, mathematics, instruction following, and multilingual capabilities.
New architecture – LFM2 is a new hybrid Liquid model with multiplicative gates and short convolutions.
Flexible deployment – LFM2 runs efficiently on CPU, GPU, and NPU hardware for flexible deployment on smartphones, laptops, or vehicles.

Find more information about LFM2 in our blog post.

📄 Model details

Due to their small size, we recommend fine-tuning LFM2 models on narrow use cases to maximize performance. They are particularly suited for agentic tasks, data extraction, RAG, creative writing, and multi-turn conversations. However, we do not recommend using them for tasks that are knowledge-intensive or require programming skills.

Property	Value
Parameters	1,170,340,608
Layers	16 (10 conv + 6 attn)
Context length	32,768 tokens
Vocabulary size	65,536
Precision	bfloat16
Training budget	10 trillion tokens
License	LFM Open License v1.0

Supported languages: English, Arabic, Chinese, French, German, Japanese, Korean, and Spanish.

Generation parameters: We recommend the following parameters:

temperature=0.3
min_p=0.15
repetition_penalty=1.05

Architecture: Hybrid model with multiplicative gates and short convolutions: 10 double-gated short-range LIV convolution blocks and 6 grouped query attention (GQA) blocks.

Pre-training mixture: Approximately 75% English, 20% multilingual, and 5% code data sourced from the web and licensed materials.

Training approach:

Knowledge distillation using LFM1-7B as teacher model
Very large-scale SFT on 50% downstream tasks, 50% general domains
Custom DPO with length normalization and semi-online datasets
Iterative model merging

🏃 How to run LFM2

Transformers.js

If you haven't already, you can install the Transformers.js JavaScript library from NPM using:

npm i @huggingface/transformers

Example: Basic example

import { pipeline, TextStreamer } from "@huggingface/transformers";

// Create a text generation pipeline
const generator = await pipeline(
  "text-generation",
  "LemOneLabs/LFM2-1.2B-ONNX",
  { dtype: "q4", device: "webgpu" },
);

// Define the list of messages
const messages = [
  { role: "system", content: "You are a helpful assistant." },
  { role: "user", content: "What is the capital of France?" },
];

// Generate a response
const output = await generator(messages, {
    max_new_tokens: 512,
    do_sample: false,
    streamer: new TextStreamer(generator.tokenizer, { skip_prompt: true, skip_special_tokens: true }),
});
console.log(output[0].generated_text.at(-1).content);
// The capital of France is Paris.

Example: Tool calling

import { pipeline, TextStreamer } from "@huggingface/transformers";

// Create a text generation pipeline
const generator = await pipeline(
  "text-generation",
  "LemOneLabs/LFM2-1.2B-ONNX",
  { dtype: "q4", device: "webgpu" },
);

// Define the tools available to the model
const tools = [
  {
    name: "get_weather",
    description: "Get current weather information for a location",
    parameters: {
      type: "object",
      properties: {
        location: {
          type: "string",
          description: "The city and state, e.g. San Francisco, CA",
        },
        unit: {
          type: "string",
          enum: ["celsius", "fahrenheit"],
          description: "The unit of temperature to use",
        },
      },
      required: ["location"],
    },
  },
];

// Define the list of messages
const messages = [
  { role: "user", content: "What's the weather like in New York?" },
];

// Generate a response
const output = await generator(messages, {
    max_new_tokens: 512,
    do_sample: false,
    streamer: new TextStreamer(generator.tokenizer, { skip_prompt: true, skip_special_tokens: true }),
    tokenizer_encode_kwargs: { tools },
});
console.log(output[0].generated_text.at(-1).content);
// [get_weather(location="New York", unit="fahrenheit")]

ONNXRuntime

from transformers import AutoConfig, AutoTokenizer
import onnxruntime
import numpy as np
from huggingface_hub import snapshot_download

# 1. Load config, processor, and model
model_id = "onnx-community/LFM2-700M-ONNX"
config = AutoConfig.from_pretrained(model_id)
tokenizer = AutoTokenizer.from_pretrained(model_id)
eos_token_id = config.eos_token_id

filename = "model.onnx" # Options: "model.onnx", "model_fp16.onnx", "model_q4.onnx", "model_q4f16.onnx"
model_path = snapshot_download(repo_id=model_id, allow_patterns=f"onnx/{filename}*") # Download the graph + weights
session = onnxruntime.InferenceSession(f"{model_path}/onnx/{filename}")

# 2. Prepare inputs
prompt = "What is C. elegans?"
messages = [{"role": "user", "content": prompt}]
inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="np")
input_ids = inputs['input_ids']
attention_mask = inputs['attention_mask']
batch_size = input_ids.shape[0]
num_logits_to_keep = np.array(1, dtype=np.int64)

past_cache_values = {}
for inp in session.get_inputs():
    name = inp.name
    shape = inp.shape
    dtype = np.float32 if inp.type == "tensor(float)" else np.float16
    if name.startswith("past_key_values"):
        # Attention KV cache: shape [batch_size, num_kv_heads, 0, head_dim]
        past_cache_values[name] = np.zeros([batch_size, shape[1], 0, shape[3]], dtype=dtype)
    elif name.startswith("past_conv"):
        # Conv cache: shape [batch_size, hidden_size, conv_L_cache]
        past_cache_values[name] = np.zeros([batch_size, shape[1], shape[2]], dtype=dtype)

# 3. Generation loop
max_new_tokens = 1024
generated_tokens = np.array([[]], dtype=np.int64)
for i in range(max_new_tokens):
  logits, *present_cache_values = session.run(None, dict(
      input_ids=input_ids,
      attention_mask=attention_mask,
      num_logits_to_keep=num_logits_to_keep,
      **past_cache_values,
  ))

  ## Update values for next generation loop
  input_ids = logits[:, -1].argmax(-1, keepdims=True)
  attention_mask = np.concatenate([attention_mask, np.ones_like(input_ids, dtype=np.int64)], axis=-1)
  for j, key in enumerate(past_cache_values):
    past_cache_values[key] = present_cache_values[j]
  generated_tokens = np.concatenate([generated_tokens, input_ids], axis=-1)
  if np.isin(input_ids, eos_token_id).any():
      break

  ## (Optional) Streaming
  print(tokenizer.decode(input_ids[0]), end='', flush=True)
print()

# 4. Output result
print(tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)[0])