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Model: unsloth/LFM2.5-8B-A1B-GGUF Source: Original Platform
2026-06-10 12:34:18 +08:00
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+---
+library_name: transformers
+license: other
+license_name: lfm1.0
+license_link: LICENSE
+language:
+- en
+- ar
+- zh
+- fr
+- de
+- ja
+- ko
+- es
+- pt
+pipeline_tag: text-generation
+tags:
+- liquid
+- unsloth
+- lfm2.5
+- edge
+base_model:
+- LiquidAI/LFM2.5-8B-A1B
+---
+
+## Updated from Liquid's chat template update
+
+<div>
+<p style="margin-top: 0;margin-bottom: 0;">
+    <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>
+  </p>
+  <div style="display: flex; gap: 5px; align-items: center; ">
+    <a href="https://github.com/unslothai/unsloth/">
+      <img src="https://github.com/unslothai/unsloth/raw/main/images/unsloth%20new%20logo.png" width="133">
+    </a>
+    <a href="https://discord.gg/unsloth">
+      <img src="https://github.com/unslothai/unsloth/raw/main/images/Discord%20button.png" width="173">
+    </a>
+    <a href="https://docs.unsloth.ai/">
+      <img src="https://raw.githubusercontent.com/unslothai/unsloth/refs/heads/main/images/documentation%20green%20button.png" width="143">
+    </a>
+  </div>
+</div>
+
+
+<div align="center">
+  <img 
+    src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/2b08LKpev0DNEk6DlnWkY.png" 
+    alt="Liquid AI" 
+    style="width: 100%; max-width: 100%; height: auto; display: inline-block; margin-bottom: 0.5em; margin-top: 0.5em;"
+  />
+  <div style="display: flex; justify-content: center; gap: 0.5em; margin-bottom: 1em;">
+    <a href="https://playground.liquid.ai/"><strong>Try LFM</strong></a> • 
+    <a href="https://docs.liquid.ai/lfm/getting-started/welcome"><strong>Docs</strong></a> • 
+    <a href="https://leap.liquid.ai/"><strong>LEAP</strong></a> • 
+    <a href="https://discord.com/invite/liquid-ai"><strong>Discord</strong></a>
+  </div>
+</div>
+
+
+# LFM2.5-8B-A1B
+
+LFM2.5 is a new family of hybrid models designed for on-device deployment. It builds on the LFM2 architecture with extended pre-training and reinforcement learning.
+
+- **On-device personal assistant**: Designed to power real-life applications, chaining tool calls, and following complex instructions on all devices.
+- **Compressed performance**: Competitive with much larger dense and MoE models on instruction following and agentic tasks.
+- **Unmatched throughput**: Fastest in its size class on both CPU and GPU inference, with day-one support for llama.cpp, MLX, vLLM, and SGLang.
+
+Find more information about LFM2.5-8B-A1B in our [blog post](https://www.liquid.ai/blog/lfm2-5-8b-a1b).
+
+![image](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/qUZVGkns1bg3sZUShBbhv.png)
+
+**AA-Omniscience Index (higher is better) rewards correct answers and penalizes hallucinations. Scores range from -100 to 100. See more results on [Artificial Analysis](https://artificialanalysis.ai/evaluations/omniscience).*
+
+## 🗒️ Model Details
+
+| Model | Parameters | Description |
+| --- | --- | --- |
+| [LFM2.5-8B-A1B-Base](https://huggingface.co/LiquidAI/LFM2.5-8B-A1B-Base) | 8.3B total / 1.5B active | Pre-trained base model for fine-tuning |
+| [**LFM2.5-8B-A1B**](https://huggingface.co/LiquidAI/LFM2.5-8B-A1B) | 8.3B total / 1.5B active | Reasoning-tuned general-purpose model |
+
+LFM2.5-8B-A1B is a general-purpose text-only model with the following features:
+
+- **Total parameters**: 8.3B
+- **Active parameters**: 1.5B
+- **Number of layers**: 24 (18 double-gated LIV conv + 6 GQA)
+- **Training budget**: 38 trillion tokens
+- **Context length**: 131,072
+- **Vocabulary size**: 128,000
+- **Languages**: English, Arabic, Chinese, French, German, Japanese, Korean, Portuguese, Spanish
+- **Generation parameters**: We recommend the following parameters:
+  - `temperature: 0.2`
+  - `top_k: 80`
+  - `repetition_penalty: 1.05`
+
+| Model | Description |
+| --- | --- |
+| [**LFM2.5-8B-A1B**](https://huggingface.co/LiquidAI/LFM2.5-8B-A1B) | Original model checkpoint in native format. Best for fine-tuning or inference with Transformers, vLLM, and SGLang. |
+| [LFM2.5-8B-A1B-GGUF](https://huggingface.co/LiquidAI/LFM2.5-8B-A1B-GGUF) | Quantized format for llama.cpp and compatible tools. Optimized for edge inference and local deployment. |
+| [LFM2.5-8B-A1B-ONNX](https://huggingface.co/LiquidAI/LFM2.5-8B-A1B-ONNX) | ONNX Runtime format for cross-platform deployment. |
+| [LFM2.5-8B-A1B-MLX](https://huggingface.co/LiquidAI/LFM2.5-8B-A1B-MLX-8bit) | MLX format for Apple Silicon. Optimized for fast inference on Mac devices. |
+
+We recommend using LFM2.5-8B-A1B for agentic workflows, tool use, structured outputs, multilingual assistants, and on-device personal-assistant applications. It is not the best fit for heavy programming or knowledge-intensive question answering without retrieval.
+
+### Chat Template
+
+LFM2.5 uses a ChatML-like format. See the [Chat Template documentation](https://docs.liquid.ai/lfm/key-concepts/chat-template) for details. Example:
+
+```
+<|startoftext|><|im_start|>system
+You are a helpful assistant trained by Liquid AI.<|im_end|>
+<|im_start|>user
+What is C. elegans?<|im_end|>
+<|im_start|>assistant
+```
+
+Because LFM2.5-8B-A1B is a reasoning model, assistant turns contain an explicit chain of thought before the final answer. You can use [`tokenizer.apply_chat_template()`](https://huggingface.co/docs/transformers/en/chat_templating#using-applychattemplate) to format your messages automatically.
+
+### Tool Use
+
+LFM2.5 supports function calling in four steps:
+
+1. **Function definition**: Provide the list of tools as a JSON object in the system prompt, or use [`tokenizer.apply_chat_template()`](https://huggingface.co/docs/transformers/en/chat_extras#passing-tools) with `tools=...`.
+2. **Function call**: By default, LFM2.5 writes Pythonic function calls (a Python list between `<|tool_call_start|>` and `<|tool_call_end|>` special tokens), as the assistant answer. You can override this behavior by asking the model to output JSON function calls in the system prompt.
+3. **Function execution**: Execute the call and return the result with the `tool` role.
+4. **Final answer**: LFM2.5 interprets the tool output and returns a plain-text answer addressing the original prompt.
+
+See the [Tool Use documentation](https://docs.liquid.ai/lfm/key-concepts/tool-use) for the full guide. Example:
+
+```
+<|startoftext|><|im_start|>system
+List of tools: [{"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"]}}]<|im_end|>
+<|im_start|>user
+What is the current status of candidate ID 12345?<|im_end|>
+<|im_start|>assistant
+<|tool_call_start|>[get_candidate_status(candidate_id="12345")]<|tool_call_end|>Checking the current status of candidate ID 12345.<|im_end|>
+<|im_start|>tool
+[{"candidate_id": "12345", "status": "Interview Scheduled", "position": "Clinical Research Associate", "date": "2023-11-20"}]<|im_end|>
+<|im_start|>assistant
+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|>
+```
+
+## 🏃 Inference
+
+LFM2.5-8B-A1B is supported by many inference frameworks. See the [Inference documentation](https://docs.liquid.ai/lfm/inference/transformers) for the full list.
+
+| Name | Description | Docs | Notebook |
+|------|-------------|------|:--------:|
+| [Transformers](https://github.com/huggingface/transformers) | Simple inference with direct access to model internals. | <a href="https://docs.liquid.ai/lfm/inference/transformers">Link</a> | <a href="https://colab.research.google.com/drive/1_q3jQ6LtyiuPzFZv7Vw8xSfPU5FwkKZY?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> |
+| [vLLM](https://github.com/vllm-project/vllm) | High-throughput production deployments with GPU. | <a href="https://docs.liquid.ai/lfm/inference/vllm">Link</a> | <a href="https://colab.research.google.com/drive/1VfyscuHP8A3we_YpnzuabYJzr5ju0Mit?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> |
+| [llama.cpp](https://github.com/ggml-org/llama.cpp) | Cross-platform inference with CPU offloading. | <a href="https://docs.liquid.ai/lfm/inference/llama-cpp">Link</a> | <a href="https://colab.research.google.com/drive/1ohLl3w47OQZA4ELo46i5E4Z6oGWBAyo8?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> |
+| [MLX](https://github.com/ml-explore/mlx) | Apple's machine learning framework optimized for Apple Silicon. | <a href="https://docs.liquid.ai/lfm/inference/mlx">Link</a> | — |
+| [LM Studio](https://lmstudio.ai/) | Desktop application for running LLMs locally. | <a href="https://docs.liquid.ai/lfm/inference/lm-studio">Link</a> | — |
+
+Quick start with Transformers (compatible with `transformers>=5.0.0`):
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
+
+model_id = "LiquidAI/LFM2.5-8B-A1B"
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    device_map="auto",
+    dtype="bfloat16",
+#   attn_implementation="flash_attention_2" <- uncomment on compatible GPU
+)
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
+
+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=0.2,
+    top_k=80,
+    repetition_penalty=1.05,
+    max_new_tokens=8192,
+    streamer=streamer,
+)
+```
+
+## 🔧 Fine-Tuning
+
+We recommend fine-tuning LFM2.5 for your specific use case to achieve the best results.
+
+| Name | Description | Docs | Notebook |
+|------|-------------|------|----------|
+| CPT ([Unsloth](https://github.com/unslothai/unsloth)) | Continued Pre-Training using Unsloth for text completion. | <a href="https://docs.liquid.ai/lfm/fine-tuning/unsloth">Link</a> | <a href="https://colab.research.google.com/drive/10fm7eNMezs-DSn36mF7vAsNYlOsx9YZO?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> |
+| CPT ([Unsloth](https://github.com/unslothai/unsloth)) | Continued Pre-Training using Unsloth for translation. | <a href="https://docs.liquid.ai/lfm/fine-tuning/unsloth">Link</a> | <a href="https://colab.research.google.com/drive/1gaP8yTle2_v35Um8Gpu9239fqbU7UgY8?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> |
+| SFT ([Unsloth](https://github.com/unslothai/unsloth)) | Supervised Fine-Tuning with LoRA using Unsloth. | <a href="https://docs.liquid.ai/lfm/fine-tuning/unsloth">Link</a> | <a href="https://colab.research.google.com/drive/1vGRg4ksRj__6OLvXkHhvji_Pamv801Ss?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> |
+| SFT ([TRL](https://github.com/huggingface/trl)) | Supervised Fine-Tuning with LoRA using TRL. | <a href="https://docs.liquid.ai/lfm/fine-tuning/trl">Link</a> | <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="110" alt="Colab link"></a> |
+| DPO ([TRL](https://github.com/huggingface/trl)) | Direct Preference Optimization with LoRA using TRL. | <a href="https://docs.liquid.ai/lfm/fine-tuning/trl">Link</a> | <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="110" alt="Colab link"></a> |
+| GRPO ([Unsloth](https://github.com/unslothai/unsloth)) | GRPO with LoRA using Unsloth. | <a href="https://docs.liquid.ai/lfm/fine-tuning/unsloth">Link</a> | <a href="https://colab.research.google.com/drive/1mIikXFaGvcW4vXOZXLbVTxfBRw_XsXa5?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> |
+| GRPO ([TRL](https://github.com/huggingface/trl)) | GRPO with LoRA using TRL. | <a href="https://docs.liquid.ai/lfm/fine-tuning/trl">Link</a> | <a href="https://colab.research.google.com/github/Liquid4All/cookbook/blob/main/finetuning/notebooks/grpo_for_verifiable_tasks.ipynb"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> |
+
+## 📊 Performance
+
+### Improvements over LFM2-8B-A1B
+
+Thanks to reasoning, scaled-up pre-training, and large-scale RL, LFM2.5-8B-A1B improves over its predecessor across the board:
+
+| Benchmark | LFM2-8B-A1B | LFM2.5-8B-A1B | Δ |
+| :--- | ---: | ---: | ---: |
+| AA-Omniscience Index | -78.42 | -24.70 | +53.62 |
+| AA-Omniscience Accuracy | 7.33 | 8.67 | +1.34 |
+| AA-Omniscience Non-Hallucination Rate | 7.46 | 63.47 | +56.01 |
+| IFEval | 79.44 | 91.84 | +12.40 |
+| IFBench | 26.00 | 56.47 | +30.47 |
+| Multi-IF | 58.54 | 79.93 | +21.39 |
+| MATH500 | 74.80 | 88.76 | +13.96 |
+| AIME25 | 20.00 | 42.53 | +22.53 |
+| BFCLv3 | 45.07 | 64.36 | +19.29 |
+| BFCLv4 | 25.52 | 48.50 | +22.98 |
+| Tau² Telecom | 13.60 | 88.07 | +74.47 |
+| Tau² Retail | 7.02 | 39.82 | +32.80 |
+
+### Knowledge and instruction following
+
+| Model | Parameters | AA-Omni. Index | AA-Omni. Accuracy | AA-Omni. Non-Halluc. | IFEval | IFBench | Multi-IF |
+| :--- | ---: | ---: | ---: | ---: | ---: | ---: | ---: |
+| LFM2.5-8B-A1B | 8B/A1B | -24.70 | 8.67 | 63.47 | 91.84 | 56.47 | 79.93 | |
+| Granite-4.0-H-Tiny | 7B/A1B | -75.50 | 9.37 | 6.38 | 82.23 | 21.28 | 59.00 | |
+| Qwen3.5-4B | 4B | -51.53 | 17.20 | 16.99 | 87.80 | 50.38 | 67.43 | |
+| Qwen3-30B-A3B-Thinking-2507 | 30.5B/3.3B | -51.31 | 18.80 | 13.87 | 90.82 | 51.11 | 79.04 | |
+| Gemma-4-E2B-IT | 5.1B | -72 | 7.00 | 15.05 | 82.93 | 33.53 | 69.70 | |
+| Gemma-4-E4B-IT | 8B | -50.67 | 8.10 | 36.06 | 87.74 | 39.48 | 77.58 | |
+| Gemma-4-26B-A4B-IT | 26B/4B | -62.07 | 14.37 | 10.75 | 91.40 | 47.25 | 82.06 | |
+| gpt-oss-20b | 21B/3.6B | -49.17 | 14.57 | 24.50 | 86.73 | 58.65 | 76.64 | |
+
+### Math and agentic workflows
+
+| Model | Parameters | MATH500 | AIME25 | AIME26 | BFCLv3 | BFCLv4 | Tau² Telecom | Tau² Retail |
+|---|---|---|---|---|---|---|---|---|
+| LFM2.5-8B-A1B | 8B/A1B | 88.76 | 42.53 | 50.00 | 64.79 | 49.73 | 88.07 | 39.82 |
+| Granite-4.0-H-Tiny | 7B/A1B | 59.20 | 4.93 | 3.33 | 56.89 | 28.52 | 16.67 | 18.42 |
+| Qwen3.5-4B | 4B | 80.76 | 54.28 | 58.33 | 71.06 | 54.01 | 87.72 | 71.93 |
+| Qwen3-30B-A3B-Thinking-2507 | 30.5B/3.3B | 86.48 | 71.67 | 66.67 | 73.39 | 50.53 | 21.93 | 56.14 |
+| Gemma-4-E2B-IT | 5.1B | 64.00 | 26 | 30 | 56.44 | 31.91 | 22.37 | 18.95 |
+| Gemma-4-E4B-IT | 8B | 65.00 | 34.33 | 40.67 | 57.31 | 33.92 | 26.75 | 42.11 |
+
+### CPU Inference
+
+![image](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/yWAChLNCguGTl9lXBL47p.png)
+
+### GPU Inference
+
+LFM2.5-8B-A1B is the fastest model in its size class, reaching **18.5K output tokens per second at high concurrency**, over 1.6B tokens per day on a single H100.
+
+![image](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/LX3oIXQeDm51eaLQs64an.png)
+
+## 📬 Contact
+
+- Got questions or want to connect? [Join our Discord community](https://discord.com/invite/liquid-ai).
+- If you are interested in custom solutions with edge deployment, please contact [our sales team](https://www.liquid.ai/contact).
+
+## Citation
+
+```bibtex
+@article{liquidAI20268BA1B,
+  author  = {Liquid AI},
+  title   = {LFM2.5-8B-A1B: Personal Assistant On Your Laptop},
+  journal = {Liquid AI Blog},
+  year    = {2026},
+  note    = {www.liquid.ai/blog/lfm2-5-8b-a1b},
+}
+```
+
+```bibtex
+@article{liquidai2025lfm2,
+  title   = {LFM2 Technical Report},
+  author  = {Liquid AI},
+  journal = {arXiv preprint arXiv:2511.23404},
+  year    = {2025}
+}
+```