commit 5b92bb23533b718d1de6289a6a7f7498cd839e31 Author: ModelHub XC Date: Sat Jun 27 15:10:13 2026 +0800 初始化项目，由ModelHub XC社区提供模型 Model: unsloth/LFM2.5-230M-GGUF Source: Original Platform diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..a6c200b --- /dev/null +++ b/.gitattributes @@ -0,0 +1,69 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bin.* filter=lfs diff=lfs merge=lfs -text +*.bz2 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 +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.onnx filter=lfs diff=lfs merge=lfs -text +*.ot filter=lfs diff=lfs merge=lfs -text +*.parquet filter=lfs diff=lfs merge=lfs -text +*.pb 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sha256:6412661b3cfabd0ab8fa04d1ee753b4da21426150418ed6c3a278ba0c8632d40 +size 350801088 diff --git a/README.md b/README.md new file mode 100644 index 0000000..c300d17 --- /dev/null +++ b/README.md @@ -0,0 +1,256 @@ +--- +library_name: transformers +license: other +license_name: lfm1.0 +license_link: LICENSE +language: +- en +- ar +- zh +- fr +- de +- ja +- ko +- es +- pt +- it +pipeline_tag: text-generation +tags: +- liquid +- unsloth +- lfm2.5 +- edge +base_model: +- LiquidAI/LFM2.5-230M +--- +
+

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

+
+ + + + + + + + + +
+
+ + +
+ Liquid AI +
+ Try LFM • + Docs • + LEAP • + Discord +
+
+ +# LFM2.5-230M + +LFM2.5 is a family of hybrid models designed for **on-device deployment**. It builds on the LFM2 architecture with extended pre-training and reinforcement learning. + +- **Our most compact model yet**: 230M parameters that punch above their weight, bringing real capability to the tightest memory and compute budgets. +- **Fast edge inference**: Best throughput from low-cost CPUs to production GPUs, running at 213 tok/s decode speed on Galaxy S25 Ultra and 42 tok/s on a Raspberry Pi 5. +- **Built for agentic tasks**: Distilled from LFM2.5-350M and refined with multi-stage reinforcement learning, making it well-suited for tool use and data extraction. + +Find more information about LFM2.5-230M in our [blog post](https://www.liquid.ai/blog/lfm2-5-230m). + +![lfm2_5_230m_benchmarks](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/4UpNxlgfKjfgT5ByIVph0.png) + +## 🗒️ Model Details + +| Model | Parameters | Description | +|-------|------------|-------------| +| [LFM2.5-230M-Base](https://huggingface.co/LiquidAI/LFM2.5-230M-Base) | 230M | Pre-trained base model for fine-tuning | +| [**LFM2.5-230M**](https://huggingface.co/LiquidAI/LFM2.5-230M) | 230M | General-purpose instruction-tuned model | + +LFM2.5-230M is a general-purpose text-only model with the following features: + +- **Number of parameters**: 230M +- **Number of layers**: 14 (8 double-gated LIV convolution blocks + 6 GQA blocks) +- **Training budget**: 19T tokens +- **Context length**: 32,768 tokens +- **Vocabulary size**: 65,536 +- **Knowledge cutoff**: Mid-2024 +- **Languages**: English, Arabic, Chinese, French, German, Italian, Japanese, Korean, Portuguese, Spanish +- **Generation parameters**: + - `temperature: 0.1` + - `top_k: 50` + - `repetition_penalty: 1.05` + +| Model | Description | +|-------|-------------| +| [**LFM2.5-230M**](https://huggingface.co/LiquidAI/LFM2.5-230M) | Original model checkpoint in native format. Best for fine-tuning or inference with Transformers, vLLM, and SGLang. | +| [LFM2.5-230M-GGUF](https://huggingface.co/LiquidAI/LFM2.5-230M-GGUF) | Quantized format for llama.cpp and compatible tools. Optimized for edge inference and local deployment. | +| [LFM2.5-230M-ONNX](https://huggingface.co/LiquidAI/LFM2.5-230M-ONNX) | ONNX Runtime format for cross-platform deployment. | +| [LFM2.5-230M-MLX](https://huggingface.co/LiquidAI/LFM2.5-230M-MLX-8bit) | MLX format for Apple Silicon. Optimized for fast inference on Mac devices. | + +We recommend using it for data extraction and lightweight on-device agentic pipelines. It is not recommended for reasoning-heavy workloads such as advanced math, code generation, or creative writing. + +### 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 +``` + +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 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 | — | +| [SGLang](https://github.com/vllm-project/vllm) | High-throughput production deployments with GPU. | Link | - | + + +Quick start with Transformers (compatible with `transformers>=5.0.0`): + +```python +from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer + +model_id = "LiquidAI/LFM2.5-230M" +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, +)["input_ids"].to(model.device) + +output = model.generate( + input_ids, + do_sample=True, + temperature=0.1, + top_k=50, + repetition_penalty=1.05, + max_new_tokens=512, + 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 + +### Benchmarks + +| Model | GPQA Diamond | MMLU-Pro | IFEval | IFBench | Multi-IF | +|---|---|---|---|---|---| +| **LFM2.5-230M** | 25.41 | 20.25 | 71.71 | 38.40 | 37.70 | +| LFM2.5-350M | 30.64 | 20.01 | 76.96 | 40.69 | 44.92 | +| LFM2-350M | 27.58 | 19.29 | 64.96 | 18.20 | 32.92 | +| Granite 4.0-H-350M | 22.32 | 13.14 | 61.27 | 17.22 | 28.70 | +| Granite 4.0-350M | 25.91 | 12.84 | 53.48 | 15.98 | 24.21 | +| Qwen3.5-0.8B (Instruct) | 27.41 | 37.42 | 59.94 | 22.87 | 41.68 | +| Gemma 3 1B IT | 23.89 | 14.04 | 63.49 | 20.33 | 44.25 | + +| Model | CaseReportBench | BFCLv3 | BFCLv4 | τ²-Bench Telecom | τ²-Bench Retail | +|---|---|---|---|---|---| +| **LFM2.5-230M** | 22.51 | 43.26 | 21.03 | 5.26 | 13.68 | +| LFM2.5-350M | 32.45 | 44.11 | 21.86 | 18.86 | 17.84 | +| LFM2-350M | 11.67 | 22.95 | 12.29 | 10.82 | 5.56 | +| Granite 4.0-H-350M | 12.44 | 43.07 | 13.28 | 13.74 | 6.14 | +| Granite 4.0-350M | 0.84 | 39.58 | 13.73 | 2.92 | 6.14 | +| Qwen3.5-0.8B (Instruct) | 13.83 | 35.08 | 18.70 | 12.57 | 6.14 | +| Gemma 3 1B IT | 2.28 | 16.61 | 7.17 | 9.36 | 6.43 | + +### CPU Inference + +![image](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/TCR-MfPtX3YTPvRzxWcG3.png) + +### GPU Inference + +![image](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/emlcz4gf2wendPhKQWEBN.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{liquidAI2026230M, + author = {Liquid AI}, + title = {LFM2.5-230M: Built to Run Anywhere}, + journal = {Liquid AI Blog}, + year = {2026}, + note = {www.liquid.ai/blog/lfm2-5-230m}, +} +``` +```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/configuration.json b/configuration.json new file mode 100644 index 0000000..bbeeda1 --- /dev/null +++ b/configuration.json @@ -0,0 +1 @@ +{"framework": "pytorch", "task": "text-generation", "allow_remote": true} \ No newline at end of file diff --git a/imatrix_unsloth.gguf_file b/imatrix_unsloth.gguf_file new file mode 100644 index 0000000..bf28c69 --- /dev/null +++ b/imatrix_unsloth.gguf_file @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b300198006edafb69098a1f28c00d5bcbb61fba9fdee6c14e358b0a2b1051ee2 +size 434912