commit b0505dfd9cacebeb930143fc7cd80edff7e095c8 Author: ModelHub XC Date: Wed Jun 10 12:34:18 2026 +0800 初始化项目，由ModelHub XC社区提供模型 Model: unsloth/LFM2.5-8B-A1B-GGUF Source: Original Platform diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..7bd7d02 --- /dev/null +++ b/.gitattributes @@ -0,0 +1,58 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text +*.onnx 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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 + +
+

+ Unsloth Dynamic 2.0 achieves superior accuracy & outperforms other leading quants. +

+
+ + + + + + + + + +
+
+ + +
+ Liquid AI +
+ Try LFM • + Docs • + LEAP • + Discord +
+
+ + +# 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. | Link | Colab link | +| [vLLM](https://github.com/vllm-project/vllm) | High-throughput production deployments with GPU. | Link | Colab link | +| [llama.cpp](https://github.com/ggml-org/llama.cpp) | Cross-platform inference with CPU offloading. | Link | Colab link | +| [MLX](https://github.com/ml-explore/mlx) | Apple's machine learning framework optimized for Apple Silicon. | Link | — | +| [LM Studio](https://lmstudio.ai/) | Desktop application for running LLMs locally. | Link | — | + +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. | Link | Colab link | +| CPT ([Unsloth](https://github.com/unslothai/unsloth)) | Continued Pre-Training using Unsloth for translation. | Link | Colab link | +| SFT ([Unsloth](https://github.com/unslothai/unsloth)) | Supervised Fine-Tuning with LoRA using Unsloth. | Link | Colab link | +| SFT ([TRL](https://github.com/huggingface/trl)) | Supervised Fine-Tuning with LoRA using TRL. | Link | Colab link | +| DPO ([TRL](https://github.com/huggingface/trl)) | Direct Preference Optimization with LoRA using TRL. | Link | Colab link | +| GRPO ([Unsloth](https://github.com/unslothai/unsloth)) | GRPO with LoRA using Unsloth. | Link | Colab link | +| GRPO ([TRL](https://github.com/huggingface/trl)) | GRPO with LoRA using TRL. | Link | Colab link | + +## 📊 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} +} +``` \ No newline at end of file diff --git a/imatrix_unsloth.gguf_file b/imatrix_unsloth.gguf_file new file mode 100644 index 0000000..7a656ed --- /dev/null +++ b/imatrix_unsloth.gguf_file @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:09cf3e3f38c9906dc0239750f524d4e728b8fe985ad04bcfc07dd55bbb6e4474 +size 17375232