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LICENSE TEXT
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LFM Open License v1.0
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TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
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1. Definitions.
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"License" shall mean the terms and conditions for use, reproduction, and distribution as defined by this document.
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"Licensor" shall mean Liquid AI, Inc.
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"Legal Entity" shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity. For the purposes of this definition, "control" means (i) the power, direct or indirect, to cause the direction or management of such entity, whether by contract or otherwise, or (ii) ownership of fifty percent (50%) or more of the outstanding shares, or (iii) beneficial ownership of such entity.
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"You" (or "Your") shall mean an individual or Legal Entity exercising permissions granted by this License.
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"Source" form shall mean the preferred form for making modifications, including but not limited to software source code, documentation source, and configuration files.
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"Object" form shall mean any form resulting from mechanical transformation or translation of a Source form, including but not limited to compiled object code, generated documentation, and conversions to other media types.
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"Work" shall mean the work of authorship, whether in Source or Object form, made available under the License, as indicated by a copyright notice that is included in or attached to the work.
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"Contribution" shall mean any work of authorship, including the original version of the Work and any modifications or additions to that Work or Derivative Works thereof, that is intentionally submitted to Licensor for inclusion in the Work by the copyright owner or by an individual or Legal Entity authorized to submit on behalf of the copyright owner. For the purposes of this definition, "submitted" means any form of electronic, verbal, or written communication sent to the Licensor or its representatives, including but not limited to communication on electronic mailing lists, source code control systems, and issue tracking systems that are managed by, or on behalf of, the Licensor for the purpose of discussing and improving the Work, but excluding communication that is conspicuously marked or otherwise designated in writing by the copyright owner as "Not a Contribution."
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"Contributor" shall mean Licensor and any individual or Legal Entity on behalf of whom a Contribution has been received by Licensor and subsequently incorporated within the Work.
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"Non-Commercial or Research Purposes" shall mean purposes that do not involve any use of the Work or a Derivative Work for Commercial Use.
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"Threshold" shall mean annual revenue of 10 million United States dollars ($10,000,000) or more.
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2. Grant of Copyright License. Subject to the terms and conditions of this License, including the Commercial Use limitation set forth in Section 5, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable copyright license to reproduce, prepare Derivative Works of, publicly display, publicly perform, sublicense, and distribute the Work and such Derivative Works in Source or Object form.
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3. Grant of Patent License. Subject to the terms and conditions of this License, including the Commercial Use limitation set forth in Section 5, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable (except as stated in this section) patent license to make, have made, use, offer to sell, sell, import, and otherwise transfer the Work, where such license applies only to those patent claims licensable by such Contributor that are necessarily infringed by their Contribution(s) alone or by combination of their Contribution(s) with the Work to which such Contribution(s) was submitted. If You institute patent litigation against any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Work or a Contribution incorporated within the Work constitutes direct or contributory patent infringement, then any patent licenses granted to You under this License for that Work shall terminate as of the date such litigation is filed.
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END OF TERMS AND CONDITIONS
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260
README.md
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---
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base_model:
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- LiquidAI/LFM2-700M
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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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- es
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pipeline_tag: text-generation
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tags:
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- liquid
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- unsloth
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- lfm2
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- edge
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---
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<div>
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<p style="margin-top: 0;margin-bottom: 0;">
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<em><a href="https://docs.unsloth.ai/basics/unsloth-dynamic-v2.0-gguf">Unsloth Dynamic 2.0</a> achieves superior accuracy & outperforms other leading quants.</em>
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</p>
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<div style="display: flex; gap: 5px; align-items: center; ">
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<a href="https://github.com/unslothai/unsloth/">
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<img src="https://github.com/unslothai/unsloth/raw/main/images/unsloth%20new%20logo.png" width="133">
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</a>
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<a href="https://discord.gg/unsloth">
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<img src="https://github.com/unslothai/unsloth/raw/main/images/Discord%20button.png" width="173">
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</a>
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<a href="https://docs.unsloth.ai/">
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<img src="https://raw.githubusercontent.com/unslothai/unsloth/refs/heads/main/images/documentation%20green%20button.png" width="143">
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</a>
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</div>
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</div>
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<center>
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<div style="text-align: center;">
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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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<a href="https://playground.liquid.ai/chat">
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<svg width="114.8" height="20" viewBox="0 0 1300 200" xmlns="http://www.w3.org/2000/svg" role="img" aria-label="Liquid Playground" style="margin-bottom: 1em;">
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<title>Liquid: Playground</title>
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||||||
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<g>
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<rect fill="#fff" width="600" height="200"></rect>
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<rect fill="url(#x)" x="600" width="700" height="200"></rect>
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</g>
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<g transform="translate(20, 30) scale(0.4, 0.4)">
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<path d="M172.314 129.313L172.219 129.367L206.125 188.18C210.671 195.154 213.324 203.457 213.324 212.382C213.324 220.834 210.956 228.739 206.839 235.479L275.924 213.178L167.853 33.6L141.827 76.9614L172.314 129.313Z" fill="black"/>
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<path d="M114.217 302.4L168.492 257.003C168.447 257.003 168.397 257.003 168.352 257.003C143.515 257.003 123.385 237.027 123.385 212.387C123.385 203.487 126.023 195.204 130.55 188.24L162.621 132.503L135.966 86.7327L60.0762 213.183L114.127 302.4H114.217Z" fill="black"/>
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<path d="M191.435 250.681C191.435 250.681 191.43 250.681 191.425 250.686L129.71 302.4H221.294L267.71 226.593L191.435 250.686V250.681Z" fill="black"/>
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</g>
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||||||
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<g aria-hidden="true" fill="#fff" text-anchor="start" font-family="Verdana,DejaVu Sans,sans-serif" font-size="110">
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||||||
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<text x="200" y="148" textLength="329" fill="#000" opacity="0.1">Liquid</text>
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||||||
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<text x="190" y="138" textLength="329" fill="#000">Liquid</text>
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||||||
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<text x="655" y="148" textLength="619" fill="#000" opacity="0.1">Playground</text>
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||||||
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<text x="645" y="138" textLength="619">Playground</text>
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</g>
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||||||
|
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||||||
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<linearGradient id="x" x1="0%" y1="0%" x2="100%" y2="0%">
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<stop offset="0%" style="stop-color:#000000"></stop>
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<stop offset="100%" style="stop-color:#000000"></stop>
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</linearGradient>
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||||||
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</svg>
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</a>
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</center>
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||||||
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# LFM2-700M
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LFM2 is a new generation of hybrid models developed by [Liquid AI](https://www.liquid.ai/), 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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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:
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* **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.
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* **Best performance** – LFM2 outperforms similarly-sized models across multiple benchmark categories, including knowledge, mathematics, instruction following, and multilingual capabilities.
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* **New architecture** – LFM2 is a new hybrid Liquid model with multiplicative gates and short convolutions.
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* **Flexible deployment** – LFM2 runs efficiently on CPU, GPU, and NPU hardware for flexible deployment on smartphones, laptops, or vehicles.
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Find more information about LFM2 in our [blog post](https://www.liquid.ai/blog/liquid-foundation-models-v2-our-second-series-of-generative-ai-models).
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## 📄 Model details
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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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| Property | Value |
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| ------------------- | ----------------------------- |
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| **Parameters** | 742,489,344 |
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| **Layers** | 16 (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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| **Precision** | bfloat16 |
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| **Training budget** | 10 trillion tokens |
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| **License** | LFM Open License v1.0 |
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**Supported languages**: English, Arabic, Chinese, French, German, Japanese, Korean, and Spanish.
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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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**Chat template**: LFM2 uses a ChatML-like chat template as follows:
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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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You can 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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**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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Here is a simple example of a conversation using tool use:
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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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**Architecture**: Hybrid model with multiplicative gates and short convolutions: 10 double-gated short-range LIV convolution blocks and 6 grouped query attention (GQA) blocks.
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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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|
|
||||||
|
**Training approach**:
|
||||||
|
* Knowledge distillation using [LFM1-7B](https://www.liquid.ai/blog/introducing-lfm-7b-setting-new-standards-for-efficient-language-models) 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
|
||||||
|
|
||||||
|
You can run LFM2 with transformers and llama.cpp. vLLM support is coming.
|
||||||
|
|
||||||
|
### 1. Transformers
|
||||||
|
|
||||||
|
To run LFM2, you need to install Hugging Face [`transformers`](https://github.com/huggingface/transformers) from source (v4.54.0.dev0).
|
||||||
|
You can update or install it with the following command: `pip install "transformers @ git+https://github.com/huggingface/transformers.git@main"`.
|
||||||
|
|
||||||
|
Here is an example of how to generate an answer with transformers in Python:
|
||||||
|
|
||||||
|
```python
|
||||||
|
from transformers import AutoModelForCausalLM, AutoTokenizer
|
||||||
|
|
||||||
|
# Load model and tokenizer
|
||||||
|
model_id = "LiquidAI/LFM2-700M"
|
||||||
|
model = AutoModelForCausalLM.from_pretrained(
|
||||||
|
model_id,
|
||||||
|
device_map="auto",
|
||||||
|
torch_dtype="bfloat16",
|
||||||
|
trust_remote_code=True,
|
||||||
|
# attn_implementation="flash_attention_2" <- uncomment on compatible GPU
|
||||||
|
)
|
||||||
|
tokenizer = AutoTokenizer.from_pretrained(model_id)
|
||||||
|
|
||||||
|
# Generate answer
|
||||||
|
prompt = "What is C. elegans?"
|
||||||
|
input_ids = tokenizer.apply_chat_template(
|
||||||
|
[{"role": "user", "content": prompt}],
|
||||||
|
add_generation_prompt=True,
|
||||||
|
return_tensors="pt",
|
||||||
|
tokenize=True,
|
||||||
|
).to(model.device)
|
||||||
|
|
||||||
|
output = model.generate(
|
||||||
|
input_ids,
|
||||||
|
do_sample=True,
|
||||||
|
temperature=0.3,
|
||||||
|
min_p=0.15,
|
||||||
|
repetition_penalty=1.05,
|
||||||
|
max_new_tokens=512,
|
||||||
|
)
|
||||||
|
|
||||||
|
print(tokenizer.decode(output[0], skip_special_tokens=False))
|
||||||
|
|
||||||
|
# <|startoftext|><|im_start|>user
|
||||||
|
# What is C. elegans?<|im_end|>
|
||||||
|
# <|im_start|>assistant
|
||||||
|
# C. elegans, also known as Caenorhabditis elegans, is a small, free-living
|
||||||
|
# nematode worm (roundworm) that belongs to the phylum Nematoda.
|
||||||
|
```
|
||||||
|
|
||||||
|
You can directly run and test the model with this [Colab notebook](https://colab.research.google.com/drive/1_q3jQ6LtyiuPzFZv7Vw8xSfPU5FwkKZY?usp=sharing).
|
||||||
|
|
||||||
|
### 2. Llama.cpp
|
||||||
|
|
||||||
|
You can run LFM2 with llama.cpp using its [GGUF checkpoint](https://huggingface.co/LiquidAI/LFM2-700M-GGUF). Find more information in the model card.
|
||||||
|
|
||||||
|
## 🔧 How to fine-tune LFM2
|
||||||
|
|
||||||
|
We recommend fine-tuning LFM2 models on your use cases to maximize performance.
|
||||||
|
|
||||||
|
| Notebook | Description | Link |
|
||||||
|
|-------|------|------|
|
||||||
|
| SFT + LoRA | Supervised Fine-Tuning (SFT) notebook with a LoRA adapter in TRL. | <a href="https://colab.research.google.com/drive/1j5Hk_SyBb2soUsuhU0eIEA9GwLNRnElF?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="120" alt="Colab link"></a> |
|
||||||
|
| DPO | Preference alignment with Direct Preference Optimization (DPO) in TRL. | <a href="https://colab.research.google.com/drive/1MQdsPxFHeZweGsNx4RH7Ia8lG8PiGE1t?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="120" alt="Colab link"></a> |
|
||||||
|
|
||||||
|
## 📈 Performance
|
||||||
|
|
||||||
|
LFM2 outperforms similar-sized models across different evaluation categories.
|
||||||
|
|
||||||
|
### 1. Automated benchmarks
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
| Model | MMLU | GPQA | IFEval | IFBench | GSM8K | MGSM | MMMLU |
|
||||||
|
|-------|------|------|--------|---------|-------|------|-------|
|
||||||
|
| LFM2-350M | 43.43 | 27.46 | 65.12 | 16.41 | 30.1 | 29.52 | 37.99 |
|
||||||
|
| LFM2-700M | 49.9 | 28.48 | 72.23 | 20.56 | 46.4 | 45.36 | 43.28 |
|
||||||
|
| LFM2-1.2B | *55.23* | **31.47** | **74.89** | *20.7* | *58.3* | *55.04* | **46.73** |
|
||||||
|
| Qwen3-0.6B | 44.93 | 22.14 | 64.24 | 19.75 | 36.47 | 41.28 | 30.84 |
|
||||||
|
| Qwen3-1.7B | **59.11** | 27.72 | *73.98* | **21.27** | 51.4 | **66.56** | *46.51* |
|
||||||
|
| Llama-3.2-1B-Instruct | 46.6 | *28.84* | 52.39 | 16.86 | 35.71 | 29.12 | 38.15 |
|
||||||
|
| gemma-3-1b-it | 40.08 | 21.07 | 62.9 | 17.72 | **59.59** | 43.6 | 34.43 |
|
||||||
|
|
||||||
|
### 2. LLM-as-a-Judge
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
### 3. Inference
|
||||||
|
|
||||||
|
#### Throughput comparison on CPU in ExecuTorch
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
#### Throughput comparison on CPU in Llama.cpp
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
## 📬 Contact
|
||||||
|
|
||||||
|
If you are interested in custom solutions with edge deployment, please contact [our sales team](https://www.liquid.ai/contact).
|
||||||
4
chat_template.jinja
Normal file
4
chat_template.jinja
Normal file
@@ -0,0 +1,4 @@
|
|||||||
|
{{bos_token}}{% for message in messages %}{{'<|im_start|>' + message['role'] + '
|
||||||
|
' + message['content'] + '<|im_end|>' + '
|
||||||
|
'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant
|
||||||
|
' }}{% endif %}
|
||||||
3
config.json
Normal file
3
config.json
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:dce453d2b2823d121942a5a4564fbaf97fafba11c17d4569d716cbdf28863fb3
|
||||||
|
size 1258
|
||||||
1
configuration.json
Normal file
1
configuration.json
Normal file
@@ -0,0 +1 @@
|
|||||||
|
{"framework": "pytorch", "task": "text-generation", "allow_remote": true}
|
||||||
8
generation_config.json
Normal file
8
generation_config.json
Normal file
@@ -0,0 +1,8 @@
|
|||||||
|
{
|
||||||
|
"_from_model_config": true,
|
||||||
|
"bos_token_id": 1,
|
||||||
|
"eos_token_id": 7,
|
||||||
|
"max_length": 32768,
|
||||||
|
"pad_token_id": 0,
|
||||||
|
"transformers_version": "4.54.0.dev0"
|
||||||
|
}
|
||||||
3
model.safetensors
Normal file
3
model.safetensors
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:a7b52669217ecb538740187f41ce2a5802afa1c8d81d8d153b847bbd21d1bdda
|
||||||
|
size 1484995328
|
||||||
23
special_tokens_map.json
Normal file
23
special_tokens_map.json
Normal file
@@ -0,0 +1,23 @@
|
|||||||
|
{
|
||||||
|
"bos_token": {
|
||||||
|
"content": "<|startoftext|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false
|
||||||
|
},
|
||||||
|
"eos_token": {
|
||||||
|
"content": "<|im_end|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false
|
||||||
|
},
|
||||||
|
"pad_token": {
|
||||||
|
"content": "<|pad|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false
|
||||||
|
}
|
||||||
|
}
|
||||||
3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:98cff83b4f6d7e9d8929bebc62b07e92cf1b3f99c80d16bafe8b84a75448f40b
|
||||||
|
size 4732426
|
||||||
4077
tokenizer_config.json
Normal file
4077
tokenizer_config.json
Normal file
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user