初始化项目，由ModelHub XC社区提供模型
Model: LiquidAI/LFM2.5-1.2B-Base Source: Original Platform
This commit is contained in:
ModelHub XC
140
README.md
Normal file
140
README.md
Normal file
@@ -0,0 +1,140 @@
|
||||
---
|
||||
license: other
|
||||
language:
|
||||
- en
|
||||
- ar
|
||||
- zh
|
||||
- fr
|
||||
- de
|
||||
- ja
|
||||
- ko
|
||||
- es
|
||||
pipeline_tag: text-generation
|
||||
library_name: transformers
|
||||
tags:
|
||||
- liquid
|
||||
- lfm2.5
|
||||
- edge
|
||||
---
|
||||
|
||||
<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;">
|
||||
<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>
|
||||
|
||||
<br>
|
||||
|
||||
# LFM2.5-1.2B-Base
|
||||
|
||||
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.
|
||||
|
||||
Find more information about LFM2.5 in our [blog post](https://www.liquid.ai/blog/introducing-lfm2-5-the-next-generation-of-on-device-ai).
|
||||
|
||||
## 🗒️ Model Details
|
||||
|
||||
| Model | Parameters | Description |
|
||||
|-------|------------|-------------|
|
||||
| [**LFM2.5-1.2B-Base**](https://huggingface.co/LiquidAI/LFM2.5-1.2B-Base) | 1.2B | Pre-trained base model for fine-tuning |
|
||||
| [LFM2.5-1.2B-Instruct](https://huggingface.co/LiquidAI/LFM2.5-1.2B-Instruct) | 1.2B | General-purpose instruction-tuned model |
|
||||
| [LFM2.5-1.2B-Thinking](https://huggingface.co/LiquidAI/LFM2.5-1.2B-Thinking) | 1.2B | General-purpose reasoning model |
|
||||
| [LFM2.5-1.2B-JP](https://huggingface.co/LiquidAI/LFM2.5-1.2B-JP) | 1.2B | Japanese-optimized chat model |
|
||||
| [LFM2.5-VL-1.6B](https://huggingface.co/LiquidAI/LFM2.5-VL-1.6B) | 1.6B | Vision-language model with fast inference |
|
||||
| [LFM2.5-Audio-1.5B](https://huggingface.co/LiquidAI/LFM2.5-Audio-1.5B) | 1.5B | Audio-language model for speech and text I/O |
|
||||
|
||||
LFM2.5-1.2B-Base is the pre-trained text-only checkpoint, used to create all the LFM2.5-1.2B variants. It has the following features:
|
||||
|
||||
- **Number of parameters**: 1.17B
|
||||
- **Number of layers**: 16 (10 double-gated LIV convolution blocks + 6 GQA blocks)
|
||||
- **Training budget**: 28T tokens
|
||||
- **Context length**: 32,768 tokens
|
||||
- **Vocabulary size**: 65,536
|
||||
- **Knowledge cutoff**: Mid-2024
|
||||
- **Languages**: English, Arabic, Chinese, French, German, Japanese, Korean, Spanish
|
||||
|
||||
| Model | Description |
|
||||
|-------|-------------|
|
||||
| [**LFM2.5-1.2B-Base**](https://huggingface.co/LiquidAI/LFM2.5-1.2B-Instruct) | Original model checkpoint in native format. Best for fine-tuning or inference with Transformers and vLLM. |
|
||||
| [LFM2.5-1.2B-Base-GGUF](https://huggingface.co/LiquidAI/LFM2.5-1.2B-Instruct-GGUF) | Quantized format for llama.cpp and compatible tools. Optimized for CPU inference and local deployment with reduced memory usage. |
|
||||
| [LFM2.5-1.2B-Base-ONNX](https://huggingface.co/LiquidAI/LFM2.5-1.2B-Instruct-ONNX) | ONNX Runtime format for cross-platform deployment. Enables hardware-accelerated inference across diverse environments (cloud, edge, mobile). |
|
||||
|
||||
This pre-trained checkpoint is only recommended for tasks that require heavy fine-tuning, like language-specific (e.g., Japanese) or domain-specific (e.g., medical) assistants, training on proprietary data, or experimenting with novel post-training approaches.
|
||||
|
||||
## 🏃 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> |
|
||||
|
||||
Here's a quick start example with `transformers`:
|
||||
|
||||
```python
|
||||
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
|
||||
|
||||
model_id = "LiquidAI/LFM2.5-1.2B-Base"
|
||||
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=0.3,
|
||||
min_p=0.15,
|
||||
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> |
|
||||
|
||||
## 📬 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{liquidai2025lfm2,
|
||||
title={LFM2 Technical Report},
|
||||
author={Liquid AI},
|
||||
journal={arXiv preprint arXiv:2511.23404},
|
||||
year={2025}
|
||||
}
|
||||
```
|
||||
Reference in New Issue
Block a user