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lvhan028 2024-06-29 17:00:20 +08:00
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@ -152,9 +152,9 @@ Regarding the usage of `ollama`, please refer [here](https://github.com/ollama/o
llamafile lets you turn large language model (LLM) weights into executables. It combines [llama.cpp](https://github.com/ggerganov/llama.cpp) with [Cosmopolitan Libc](https://github.com/jart/cosmopolitan).
The best practice of deploying InternLM2 using llamafile is shown as below:
The best practice of deploying InternLM2 or InternLM2.5 using llamafile is shown as below:
- Convert the internlm2 model into GGUF model by `llama.cpp`. Suppose we get `internlm2_5-chat-7b.gguf` in this step
- Convert the model into GGUF model by `llama.cpp`. Suppose we get `internlm2_5-chat-7b.gguf` in this step
- Create the llamafile
```shell
@ -243,4 +243,4 @@ LlamaIndex is a framework for building context-augmented LLM applications.
It chooses ollama as the LLM inference engine locally. An example can be found from the [Starter Tutorial(Local Models)](https://docs.llamaindex.ai/en/stable/getting_started/starter_example_local/).
Therefore, you can integrate InternLM2 to LlamaIndex smoothly if you can deploying InternLM2 with `ollama` as guided in the [ollama section](#ollama)
Therefore, you can integrate InternLM2 or InternLM2.5 models to LlamaIndex smoothly if you can deploying them with `ollama` as guided in the [ollama section](#ollama)