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+ LFM Open License v1.0
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README.md ADDED
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+ ---
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+ library_name: transformers
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+ license: other
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+ license_name: lfm1.0
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+ license_link: LICENSE
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+ language:
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+ - en
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+ - ar
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+ - zh
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+ - fr
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+ - de
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+ - ja
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+ - ko
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+ pipeline_tag: text-generation
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+ tags:
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+ - liquid
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+ - lfm2
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+ - edge
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+ - moe
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+ ---
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+
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+ <img
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+ src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/7_6D7rWrLxp2hb6OHSV1p.png"
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+ alt="Liquid AI"
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+ style="width: 100%; max-width: 66%; height: auto; display: inline-block; margin-bottom: 0.5em; margin-top: 0.5em;"
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+ />
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+ </div>
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+ <div style="display: flex; justify-content: center;">
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+ <a href="https://playground.liquid.ai/chat">
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+ <svg width="114.8" height="20" viewBox="0 0 900 200" xmlns="http://www.w3.org/2000/svg" role="img" aria-label="Playground" style="margin-bottom: 1em;">
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+ </div>
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+ </center>
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+
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+ # LFM2-8B-A1B
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+
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+ LFM2 is a new generation of hybrid models developed by [Liquid AI](https://www.liquid.ai/blog/lfm2-8b-a1b-an-efficient-on-device-mixture-of-experts), specifically designed for edge AI and on-device deployment. It sets a new standard in terms of quality, speed, and memory efficiency.
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+
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+ We're releasing the weights of our first MoE based on LFM2, with 8.3B total parameters and 1.5B active parameters.
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+
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+ - LFM2-8B-A1B is the best on-device MoE in terms of both **quality** (comparable to 3-4B dense models) and **speed** (faster than Qwen3-1.7B).
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+ - **Code and knowledge** capabilities are significantly improved compared to LFM2-2.6B.
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+ - Quantized variants fit comfortably on high-end **phones, tablets, and laptops**.
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+
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+ Find more information about LFM2-8B-A1B in our [blog post](https://www.liquid.ai/blog/).
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+
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+ ## 📄 Model details
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+
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+ Due to their small size, **we recommend fine-tuning LFM2 models on narrow use cases** to maximize performance.
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+ They are particularly suited for agentic tasks, data extraction, RAG, creative writing, and multi-turn conversations.
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+ However, we do not recommend using them for tasks that are knowledge-intensive or require programming skills.
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+
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+ | Property | [**LFM2-8B-A1B**](https://huggingface.co/LiquidAI/LFM2-8B-A1B) |
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+ | --------------------- | ----------------------------- |
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+ | **Total parameters** | 8.3B |
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+ | **Active parameters** | 1.5B |
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+ | **Layers** | 24 (10 conv + 6 attn) |
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+ | **Context length** | 32,768 tokens |
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+ | **Vocabulary size** | 65,536 |
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+ | **Training precision**| Mixed BF16/FP8 |
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+ | **Training budget** | 12 trillion tokens |
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+ | **License** | LFM Open License v1.0 |
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+
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+ **Supported languages**: English, Arabic, Chinese, French, German, Japanese, Korean, and Spanish.
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+
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+ **Generation parameters**: We recommend the following parameters:
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+ * `temperature=0.3`
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+ * `min_p=0.15`
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+ * `repetition_penalty=1.05`
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+
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+ **Chat template**: LFM2 uses a ChatML-like chat template as follows:
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+
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+ ```
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+ <|startoftext|><|im_start|>system
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+ You are a helpful assistant trained by Liquid AI.<|im_end|>
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+ <|im_start|>user
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+ What is C. elegans?<|im_end|>
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+ <|im_start|>assistant
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+ It's a tiny nematode that lives in temperate soil environments.<|im_end|>
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+ ```
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+
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+ You can automatically apply it using the dedicated [`.apply_chat_template()`](https://huggingface.co/docs/transformers/en/chat_templating#applychattemplate) function from Hugging Face transformers.
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+
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+ **Tool use**: It consists of four main steps:
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+ 1. **Function definition**: LFM2 takes JSON function definitions as input (JSON objects between `<|tool_list_start|>` and `<|tool_list_end|>` special tokens), usually in the system prompt
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+ 2. **Function call**: LFM2 writes Pythonic function calls (a Python list between `<|tool_call_start|>` and `<|tool_call_end|>` special tokens), as the assistant answer.
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+ 3. **Function execution**: The function call is executed and the result is returned (string between `<|tool_response_start|>` and `<|tool_response_end|>` special tokens), as a "tool" role.
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+ 4. **Final answer**: LFM2 interprets the outcome of the function call to address the original user prompt in plain text.
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+
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+ Here is a simple example of a conversation using tool use:
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+
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+ ```
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+ <|startoftext|><|im_start|>system
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+ List of tools: <|tool_list_start|>[{"name": "get_candidate_status", "description": "Retrieves the current status of a candidate in the recruitment process", "parameters": {"type": "object", "properties": {"candidate_id": {"type": "string", "description": "Unique identifier for the candidate"}}, "required": ["candidate_id"]}}]<|tool_list_end|><|im_end|>
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+ <|im_start|>user
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+ What is the current status of candidate ID 12345?<|im_end|>
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+ <|im_start|>assistant
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+ <|tool_call_start|>[get_candidate_status(candidate_id="12345")]<|tool_call_end|>Checking the current status of candidate ID 12345.<|im_end|>
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+ <|im_start|>tool
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+ <|tool_response_start|>{"candidate_id": "12345", "status": "Interview Scheduled", "position": "Clinical Research Associate", "date": "2023-11-20"}<|tool_response_end|><|im_end|>
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+ <|im_start|>assistant
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+ The candidate with ID 12345 is currently in the "Interview Scheduled" stage for the position of Clinical Research Associate, with an interview date set for 2023-11-20.<|im_end|>
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+ ```
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+
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+ **Architecture**: Hybrid model with multiplicative gates and short convolutions: 18 double-gated short-range LIV convolution blocks and 6 grouped query attention (GQA) blocks.
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+
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+ **Pre-training mixture**: Approximately 75% English, 20% multilingual, and 5% code data sourced from the web and licensed materials.
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+
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+ **Training approach**:
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+ * Very large-scale SFT on 50% downstream tasks, 50% general domains
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+ * Custom DPO with length normalization and semi-online datasets
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+ * Iterative model merging
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+
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+ ## 🏃 How to run LFM2
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+
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+ ### 1. Transformers
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+
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+ To run LFM2, you need to install Hugging Face [`transformers`](https://github.com/huggingface/transformers) from source as follows:
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+ ```bash
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+ pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6
160
+ ```
161
+
162
+ Here is an example of how to generate an answer with transformers in Python:
163
+
164
+ ```python
165
+ from transformers import AutoModelForCausalLM, AutoTokenizer
166
+
167
+ # Load model and tokenizer
168
+ model_id = "LiquidAI/LFM2-8B-A1B"
169
+ model = AutoModelForCausalLM.from_pretrained(
170
+ model_id,
171
+ device_map="auto",
172
+ dtype="bfloat16",
173
+ # attn_implementation="flash_attention_2" <- uncomment on compatible GPU
174
+ )
175
+ tokenizer = AutoTokenizer.from_pretrained(model_id)
176
+
177
+ # Generate answer
178
+ prompt = "What is C. elegans?"
179
+ input_ids = tokenizer.apply_chat_template(
180
+ [{"role": "user", "content": prompt}],
181
+ add_generation_prompt=True,
182
+ return_tensors="pt",
183
+ tokenize=True,
184
+ ).to(model.device)
185
+
186
+ output = model.generate(
187
+ input_ids,
188
+ do_sample=True,
189
+ temperature=0.3,
190
+ min_p=0.15,
191
+ repetition_penalty=1.05,
192
+ max_new_tokens=512,
193
+ )
194
+
195
+ print(tokenizer.decode(output[0], skip_special_tokens=False))
196
+
197
+ # <|startoftext|><|im_start|>user
198
+ # What is C. elegans?<|im_end|>
199
+ # <|im_start|>assistant
200
+ # C. elegans, also known as Caenorhabditis elegans, is a small, free-living
201
+ # nematode worm (roundworm) that belongs to the phylum Nematoda.
202
+ ```
203
+
204
+ You can directly run and test the model with this [Colab notebook](https://colab.research.google.com/drive/1i0u7X6qen9UJkV6xSCDZ0NmPUH50SOvO?usp=sharing).
205
+
206
+ ### 2. vLLM
207
+
208
+ vLLM support is coming soon!
209
+
210
+ ### 3. llama.cpp
211
+
212
+ You can run LFM2 with llama.cpp using its [GGUF checkpoint](https://huggingface.co/LiquidAI/LFM2-8B-A1B-GGUF). Find more information in the model card.
213
+
214
+ ## 🔧 How to fine-tune LFM2
215
+
216
+ We recommend fine-tuning LFM2 models on your use cases to maximize performance.
217
+
218
+ | Notebook | Description | Link |
219
+ |-------|------|------|
220
+ | SFT (TRL) | Supervised Fine-Tuning (SFT) notebook with a LoRA adapter using TRL. | <a href="https://colab.research.google.com/drive/1OXLEuSmzF4AjJ7yqRCDTn-ltvFjoGR9j?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> |
221
+ | DPO (TRL) | Preference alignment with Direct Preference Optimization (DPO) using TRL. | <a href="https://colab.research.google.com/drive/1Q8hIHIQ8oofshcNYHUcYp1akUcZ-ufSn?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> |
222
+
223
+ ## 📈 Performance
224
+
225
+ ### 1. Automated benchmarks
226
+
227
+ <div style="display: grid">
228
+ <div>
229
+ <a href="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/6xXgpyyK5htUZlHdpZab-.png" target="_blank">
230
+ <img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/6xXgpyyK5htUZlHdpZab-.png" alt="Benchmarks" style="width: 100%; height: auto; margin: 0; cursor: pointer;">
231
+ </a>
232
+ </div>
233
+ </div>
234
+
235
+ Compared to similar-sized models, LFM2-8B-A1B displays strong performance in instruction following and math while also running significantly faster.
236
+
237
+ | Model | MMLU | MMLU-Pro | GPQA | IFEval | IFBench | Multi-IF |
238
+ |---|---|---|---|---|---|---|
239
+ | **LFM2-8B-A1B** | 64.84 | 37.42 | 29.29 | 77.58 | 25.85 | 58.19 |
240
+ | LFM2-2.6B | 64.42 | 25.96 | 26.57 | 79.56 | 22.19 | 60.26 |
241
+ | Llama-3.2-3B-Instruct | 60.35 | 22.25 | 30.6 | 71.43 | 20.78 | 50.91 |
242
+ | SmolLM3-3B | 59.84 | 23.90 | 26.31 | 72.44 | 17.93 | 58.86 |
243
+ | gemma-3-4b-it | 58.35 | 34.76 | 29.51 | 76.85 | 23.53 | 66.61 |
244
+ | Qwen3-4B-Instruct-2507 | 72.25 | 52.31 | 34.85 | 85.62 | 30.28 | 75.54 |
245
+ | granite-4.0-h-tiny | 66.79 | 32.03 | 26.46 | 81.06 | 18.37 | 52.99 |
246
+
247
+ | Model | GSM8K | GSMPlus | MATH 500 | MATH Lvl 5 | MGSM | MMMLU |
248
+ |---|---|---|---|---|---|---|
249
+ | **LFM2-8B-A1B** | 84.38 | 64.76 | 74.2 | 62.38 | 72.4 | 55.26 |
250
+ | LFM2-2.6B | 82.41 | 60.75 | 63.6 | 54.38 | 74.32 | 55.39 |
251
+ | Llama-3.2-3B-Instruct | 75.21 | 38.68 | 41.2 | 24.06 | 61.68 | 47.92 |
252
+ | SmolLM3-3B | 81.12 | 58.91 | 73.6 | 51.93 | 68.72 | 50.02 |
253
+ | gemma-3-4b-it | 89.92 | 68.38 | 73.2 | 52.18 | 87.28 | 50.14 |
254
+ | Qwen3-4B-Instruct-2507 | 68.46 | 56.16 | 85.6 | 73.62 | 81.76 | 60.67 |
255
+ | granite-4.0-h-tiny | 82.64 | 59.14 | 58.2 | 36.11 | 73.68 | 56.13 |
256
+
257
+ | Model | Active params | LCB v6 | LCB v5 | HumanEval+ | Creative Writing v3 |
258
+ |----------------------------|---------------|---------------|---------------|--------------------|-----------------------------|
259
+ | **LFM2-8B-A1B** | 1.5B | 21.04% | 21.36% | 69.51% | 44.22% |
260
+ | Gemma-3-1b-it | 1B | 4.27% | 4.43% | 37.20% | 41.67% |
261
+ | Granite-4.0-h-tiny | 1B | 26.73% | 27.27% | 73.78% | 32.60% |
262
+ | Llama-3.2-1B-Instruct | 1.2B | 4.08% | 3.64% | 23.17% | 31.43% |
263
+ | Qwen2.5-1.5B-Instruct | 1.5B | 11.18% | 10.57% | 48.78% | 22.18% |
264
+ | Qwen3-1.7B (/no_think) | 1.7B | 24.07% | 26.48% | 60.98% | 31.56% |
265
+ | LFM2-2.6B | 2.6B | 14.41% | 14.43% | 57.93% | 38.79% |
266
+ | SmolLM3-3B | 3.1B | 19.05% | 19.20% | 60.37% | 36.44% |
267
+ | Llama-3.2-3B-Instruct | 3.2B | 11.47% | 11.48% | 24.06% | 38.84% |
268
+ | Qwen3-4B (/no_think) | 4B | 36.11% | 38.64% | 71.95% | 37.49% |
269
+ | Qwen3-4B-Instruct-2507 | 4B | 48.72% | 50.80% | 82.32% | 51.71% |
270
+ | Gemma-3-4b-it | 4.3B | 18.86% | 19.09% | 62.8% | 68.56% |
271
+
272
+ ### 2. Inference
273
+
274
+ LFM2-8B-A1B is significantly faster than models with a similar number of active parameters, like Qwen3-1.7B.
275
+
276
+ <div style="display: grid; grid-template-columns: 1fr 1fr;">
277
+ <div>
278
+ <a href="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/AdR74EuIH_qJre89qaq62.png" target="_blank">
279
+ <img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/AdR74EuIH_qJre89qaq62.png" alt="Decode Throughput - S24 Ultra" style="width: 100%; height: auto; margin: 0; cursor: pointer;">
280
+ </a>
281
+ </div>
282
+
283
+ <div>
284
+ <a href="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/YzmQXbmcv5WuVJ1tI2Jbh.png" target="_blank">
285
+ <img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/YzmQXbmcv5WuVJ1tI2Jbh.png" alt="Decode Throughput - HX370" style="width: 100%; height: auto; margin: 0; cursor: pointer;">
286
+ </a>
287
+ </div>
288
+ </div>
289
+
290
+ The following plots showcase the performance of different models under int4 quantization with int8 dynamic activations on the AMD Ryzen AI 9 HX 370 CPU, using 16 threads. The results are obtained using our internal XNNPACK-based inference stack, and a custom CPU MoE kernel.
291
+
292
+ <div style="display: grid; grid-template-columns: 1fr 1fr;">
293
+ <div>
294
+ <a href="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/tyhEJAcJyVOiRsht7TMjb" target="_blank">
295
+ <img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/tyhEJAcJyVOiRsht7TMjb.png" alt="Prefill Throughput vs Sequence Length" style="width: 100%; height: auto; margin: 0; cursor: pointer;">
296
+ </a>
297
+ </div>
298
+ <div>
299
+ <a href="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/6oAenHRxKIyvJOgdCetlF.png" target="_blank">
300
+ <img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/6oAenHRxKIyvJOgdCetlF.png" alt="Decode Throughput vs Sequence Length" style="width: 100%; height: auto; margin: 0; cursor: pointer;">
301
+ </a>
302
+ </div>
303
+ </div>
304
+
305
+ ## 📬 Contact
306
+
307
+ If you are interested in custom solutions with edge deployment, please contact [our sales team](https://www.liquid.ai/contact).
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+ "AutoModelForCausalLM": "modeling_lfm2_moe.Lfm2MoeForCausalLM"
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+ }
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+ }
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