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Best Practice with LLaMA-Factory (#711)

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* add llamafactory examples

* tiny fix

* update doc about inference
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Zhangchi Feng
2025-01-16 09:50:18 +08:00
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commit cd0972c7a1
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README.md
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@@ -131,8 +131,8 @@ Advancing popular visual capabilites from MiniCPM-V series, MiniCPM-o 2.6 can pr
In addition to its friendly size, MiniCPM-o 2.6 also shows **state-of-the-art token density** (i.e., number of pixels encoded into each visual token). **It produces only 640 tokens when processing a 1.8M pixel image, which is 75% fewer than most models**. This directly improves the inference speed, first-token latency, memory usage, and power consumption. As a result, MiniCPM-o 2.6 can efficiently support **multimodal live streaming** on end-side devices such as iPad.
- 💫 **Easy Usage.**
MiniCPM-o 2.6 can be easily used in various ways: (1) [llama.cpp](https://github.com/OpenBMB/llama.cpp/blob/minicpm-omni/examples/llava/README-minicpmo2.6.md) support for efficient CPU inference on local devices, (2) [int4](https://huggingface.co/openbmb/MiniCPM-o-2_6-int4) and [GGUF](https://huggingface.co/openbmb/MiniCPM-o-2_6-gguf) format quantized models in 16 sizes, (3) [vLLM](#efficient-inference-with-llamacpp-ollama-vllm) support for high-throughput and memory-efficient inference, (4) fine-tuning on new domains and tasks with [LLaMA-Factory](./docs/llamafactory_train.md), (5) quick [local WebUI demo](#chat-with-our-demo-on-gradio), and (6) online web demo on [server](https://minicpm-omni-webdemo-us.modelbest.cn/).
MiniCPM-o 2.6 can be easily used in various ways: (1) [llama.cpp](https://github.com/OpenBMB/llama.cpp/blob/minicpm-omni/examples/llava/README-minicpmo2.6.md) support for efficient CPU inference on local devices, (2) [int4](https://huggingface.co/openbmb/MiniCPM-o-2_6-int4) and [GGUF](https://huggingface.co/openbmb/MiniCPM-o-2_6-gguf) format quantized models in 16 sizes, (3) [vLLM](#efficient-inference-with-llamacpp-ollama-vllm) support for high-throughput and memory-efficient inference, (4) fine-tuning on new domains and tasks with [LLaMA-Factory](./docs/llamafactory_train_and_infer.md), (5) quick [local WebUI demo](#chat-with-our-demo-on-gradio), and (6) online web demo on [server](https://minicpm-omni-webdemo-us.modelbest.cn/).
**Model Architecture.**
@@ -2488,7 +2488,7 @@ We support simple fine-tuning with Hugging Face for MiniCPM-o 2.6, MiniCPM-V 2.6
We support fine-tuning MiniCPM-o-2.6 and MiniCPM-V 2.6 with the LLaMA-Factory framework. LLaMA-Factory provides a solution for flexibly customizing the fine-tuning (Lora/Full/Qlora) of 200+ LLMs without the need for coding through the built-in web UI LLaMABoard. It supports various training methods like sft/ppo/dpo/kto and advanced algorithms like Galore/BAdam/LLaMA-Pro/Pissa/LongLoRA.
Best Practices: [MiniCPM-V-2.6 | MiniCPM-o-2.6](./docs/llamafactory_train.md).
Best Practices: [MiniCPM-o-2.6 | MiniCPM-V-2.6](./docs/llamafactory_train_and_infer.md).
### With the SWIFT Framework <!-- omit in toc -->
@@ -2574,4 +2574,4 @@ If you find our model/code/paper helpful, please consider citing our papers 📝
journal={arXiv preprint arXiv:2408.01800},
year={2024}
}
```
```