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Model: unsloth/LFM2.5-230M-GGUF Source: Original Platform
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README.md
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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:
|
||||
- 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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- pt
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- it
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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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- lfm2.5
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- edge
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base_model:
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- LiquidAI/LFM2.5-230M
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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>
|
||||
<div style="display: flex; gap: 5px; align-items: center; ">
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<a href="https://github.com/unslothai/unsloth/">
|
||||
<img src="https://github.com/unslothai/unsloth/raw/main/images/unsloth%20new%20logo.png" width="133">
|
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</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">
|
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</a>
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</div>
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</div>
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<div align="center">
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||||
<img
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src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/2b08LKpev0DNEk6DlnWkY.png"
|
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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>
|
||||
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# LFM2.5-230M
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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.
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- **Our most compact model yet**: 230M parameters that punch above their weight, bringing real capability to the tightest memory and compute budgets.
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- **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.
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- **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.
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Find more information about LFM2.5-230M in our [blog post](https://www.liquid.ai/blog/lfm2-5-230m).
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## 🗒️ Model Details
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|
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| Model | Parameters | Description |
|
||||
|-------|------------|-------------|
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| [LFM2.5-230M-Base](https://huggingface.co/LiquidAI/LFM2.5-230M-Base) | 230M | Pre-trained base model for fine-tuning |
|
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| [**LFM2.5-230M**](https://huggingface.co/LiquidAI/LFM2.5-230M) | 230M | General-purpose instruction-tuned model |
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LFM2.5-230M is a general-purpose text-only model with the following features:
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- **Number of parameters**: 230M
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- **Number of layers**: 14 (8 double-gated LIV convolution blocks + 6 GQA blocks)
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- **Training budget**: 19T tokens
|
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- **Context length**: 32,768 tokens
|
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- **Vocabulary size**: 65,536
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- **Knowledge cutoff**: Mid-2024
|
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- **Languages**: English, Arabic, Chinese, French, German, Italian, Japanese, Korean, Portuguese, Spanish
|
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- **Generation parameters**:
|
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- `temperature: 0.1`
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- `top_k: 50`
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- `repetition_penalty: 1.05`
|
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|
||||
| 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. |
|
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|
||||
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.
|
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|
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### Chat Template
|
||||
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LFM2.5 uses a ChatML-like format. See the [Chat Template documentation](https://docs.liquid.ai/lfm/key-concepts/chat-template) for details. Example:
|
||||
|
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```
|
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<|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.
|
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### Tool Use
|
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LFM2.5 supports function calling in four steps:
|
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|
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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=...`.
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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.
|
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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:
|
||||
|
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```
|
||||
<|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|>
|
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<|im_start|>user
|
||||
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
|
||||
[{"candidate_id": "12345", "status": "Interview Scheduled", "position": "Clinical Research Associate", "date": "2023-11-20"}]<|im_end|>
|
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<|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|>
|
||||
```
|
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|
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## 🏃 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. | <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> | — |
|
||||
| [SGLang](https://github.com/vllm-project/vllm) | High-throughput production deployments with GPU. | <a href="https://docs.liquid.ai/deployment/gpu-inference/sglang">Link</a> | - </a> |
|
||||
|
||||
|
||||
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. | <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
|
||||
|
||||
### 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
|
||||
|
||||

|
||||
|
||||
### GPU Inference
|
||||
|
||||

|
||||
|
||||
## 📬 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}
|
||||
}
|
||||
```
|
||||
1
configuration.json
Normal file
1
configuration.json
Normal file
@@ -0,0 +1 @@
|
||||
{"framework": "pytorch", "task": "text-generation", "allow_remote": true}
|
||||
3
imatrix_unsloth.gguf_file
Normal file
3
imatrix_unsloth.gguf_file
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:b300198006edafb69098a1f28c00d5bcbb61fba9fdee6c14e358b0a2b1051ee2
|
||||
size 434912
|
||||
Reference in New Issue
Block a user