New llama-run

[ericcurtin]

• Added readline.cpp include
• Created run_chat_mode():
  • Initializes readline with command history
  • Maintains conversation history
  • Applies chat templates to format messages
  • Submits completion tasks to the server queue
  • Displays assistant responses interactively

[ericcurtin]

@angt @ggerganov @CISC @ngxson PTAL

[ngxson]

Considering these points:

1. We already had llama-chat, llama-run, llama-cli all provide chat experience. Adding one more may introduce more stress for maintainers (especially for llama-server, where the code is already quite complex)
2. llama-server, as the name suggest, should launch a server - otherwise, we should drop the server in its name
3. Console-related code should not be inside server.cpp. Instead, it should be in a dedicated module that is decoupled from server (maybe in the form of a dedicated binary)

So, I think it's better to repurpose one of the 3 binaries mentioned in my first point instead.

[ericcurtin]

1. Plan to delete llama-run and linenoise.cpp after this, so the net would be, one less binary and less lines of code.

2. might not be a terrible idea (dropping -server in it's name), even if llama and llama-server could be an identical binary, llama-server is the main binary that gets the most usage.

[ericcurtin]

3. addressed

[ngxson]

llama-server is the main binary that gets the most usage.

llama-server gets the most usage for users who want an inference server. For those who want to use CLI, they will use llama-cli - as the name suggest. The correct naming makes things very intuitive for end users without having to dive into the documentations.

I think what's better doing at this point is improve the usability of llama-cli by re-using server code inside it. For the first iteration, we can build server code as a static target and use it in llama-server and llama-cli. I already had a quick demo here, but obviously we can now do much better as the server's code base has been broken down into smaller parts, make it easier to be #include inside other binaries.

[ericcurtin]

llama-server is the main binary that gets the most usage.

llama-server gets the most usage for users who want an inference server. For those who want to use CLI, they will use llama-cli - as the name suggest. The correct naming makes things very intuitive for end users without having to dive into the documentations.

I think what's better doing at this point is improve the usability of llama-cli by re-using server code inside it. For the first iteration, we can build server code as a static target and use it in llama-server and llama-cli. I already had a quick demo here, but obviously we can now do much better as the server's code base has been broken down into smaller parts, make it easier to be #include inside other binaries.

I’m on board with the idea, but can we push ahead with this PR and plan the refactor for a later stage?

To be honest, I think you’re the best person to handle the refactoring. You know exactly how you want the architecture to look, and it’s hard for others to guess those specifics. If someone else tries, we risk getting stuck in a loop of corrections, so it’s probably more efficient if you drive that part.

[ngxson]

To be honest, I think you’re the best person to handle the refactoring. You know exactly how you want the architecture to look, and it’s hard for others to guess those specifics. If someone else tries, we risk getting stuck in a loop of corrections, so it’s probably more efficient if you drive that part.

For most parts, server code already refactored to be reused in another tool/example. I'm not sure what kind of refactoring you're talking about

[ericcurtin]

To be honest, I think you’re the best person to handle the refactoring. You know exactly how you want the architecture to look, and it’s hard for others to guess those specifics. If someone else tries, we risk getting stuck in a loop of corrections, so it’s probably more efficient if you drive that part.

For most parts, server code already refactored to be reused in another tool/example. I'm not sure what kind of refactoring you're talking about

Moving the relevant code to llama-cli, llama-run, etc. or some other place.

[ngxson]

I think your server-chat.cpp introduced in this PR is already a standalone binary. I think what can be useful is:

1. Move the code from server-chat.cpp to run.cpp
2. Modify cmakelists of tools/run so that it also compile server source code inside the binary

This way, you simply make llama-run becoming llama-server --chat

In the future, we can make server as a static (or maybe shared) lib target and reuse it in both server and run

[ericcurtin]

People should give this a shot, the UX is quite neat:

$ build/bin/llama-run -dr gemma3
> What is llama.cpp
llama.cpp is a C++ port of the LLaMA (Large Language Model Meta AI) inference engine. It's designed to run LLaMA models on commodity hardware, such as CPUs and laptops, without requiring a powerful GPU.

Here's a breakdown of key aspects:

*   **Purpose:** To make LLaMA models accessible to a wider audience by enabling them to run locally on less powerful machines.
*   **Technology:** Written in C++, leveraging optimizations for CPU architectures.  It uses techniques like quantization (reducing the precision of the model's weights) to significantly reduce memory usage and improve performance.
*   **Key Features:**
    *   **CPU-Focused:** Optimized for performance on CPUs, not GPUs.
    *   **Quantization Support:**  Supports various quantization methods (e.g., 4-bit, 8-bit) to reduce model size and memory requirements, enabling running on devices with limited RAM.
    *   **Easy to Build and Use:**  Relatively straightforward to compile and run, with a simple command-line interface.
    *   **Cross-Platform:**  Works on Windows, macOS, and Linux.
    *   **Active Community:**  Has a vibrant community contributing to improvements and supporting users.
*   **How it works:** llama.cpp utilizes techniques like memory mapping and efficient data structures to minimize memory footprint and maximize processing speed. It also supports various parallelization strategies to utilize multiple CPU cores.
*   **Popularity:**  It's gained significant popularity due to its ability to run LLaMA models locally, allowing users to experiment with and utilize these models without needing expensive hardware.

**Where to learn more:**

*   **GitHub Repository:** [https://github.com/ggerganov/llama.cpp](https://github.com/ggerganov/llama.cpp)
*   **Wiki:** [https://github.com/ggerganov/llama.cpp/wiki](https://github.com/ggerganov/llama.cpp/wiki)

Do you want me to elaborate on a specific aspect of llama.cpp, such as:

*   Quantization methods?
*   How to build it?
*   How to use it with a specific model?
*   Its performance compared to other inference engines?
*   Its use for specific applications (e.g., chatbots)?
llama.cpp is a C++ port of the LLaMA (Large Language Model Meta AI) inference engine. It's designed to run LLaMA models on commodity hardware, such as CPUs and laptops, without requiring a powerful GPU.

Here's a breakdown of key aspects:

*   **Purpose:** To make LLaMA models accessible to a wider audience by enabling them to run locally on less powerful machines.
*   **Technology:** Written in C++, leveraging optimizations for CPU architectures.  It uses techniques like quantization (reducing the precision of the model's weights) to significantly reduce memory usage and improve performance.
*   **Key Features:**
    *   **CPU-Focused:** Optimized for performance on CPUs, not GPUs.
    *   **Quantization Support:**  Supports various quantization methods (e.g., 4-bit, 8-bit) to reduce model size and memory requirements, enabling running on devices with limited RAM.
    *   **Easy to Build and Use:**  Relatively straightforward to compile and run, with a simple command-line interface.
    *   **Cross-Platform:**  Works on Windows, macOS, and Linux.
    *   **Active Community:**  Has a vibrant community contributing to improvements and supporting users.
*   **How it works:** llama.cpp utilizes techniques like memory mapping and efficient data structures to minimize memory footprint and maximize processing speed. It also supports various parallelization strategies to utilize multiple CPU cores.
*   **Popularity:**  It's gained significant popularity due to its ability to run LLaMA models locally, allowing users to experiment with and utilize these models without needing expensive hardware.

**Where to learn more:**

*   **GitHub Repository:** [https://github.com/ggerganov/llama.cpp](https://github.com/ggerganov/llama.cpp)
*   **Wiki:** [https://github.com/ggerganov/llama.cpp/wiki](https://github.com/ggerganov/llama.cpp/wiki)

Do you want me to elaborate on a specific aspect of llama.cpp, such as:

*   Quantization methods?
*   How to build it?
*   How to use it with a specific model?
*   Its performance compared to other inference engines?
*   Its use for specific applications (e.g., chatbots)?

> Send a message

[ngxson]

Seems to be a better direction now. I'll see if it's worth splitting some changes specific to server.cppto a dedicated PR, for visibility.

One question though: why do we need to split run into run.cpp and run-chat.cpp? I think the whole code can be just one single run.cpp

[ngxson]

Btw maybe a TODO, we can now add multimodal support to the CLI too

[ericcurtin]

Seems to be a better direction now. I'll see if it's worth splitting some changes specific to server.cppto a dedicated PR, for visibility.

One question though: why do we need to split run into run.cpp and run-chat.cpp? I think the whole code can be just one single run.cpp

I've received mixed feedback in the past regarding granular file separation versus consolidation, so I am unsure of the preferred direction here. run-chat.cpp and run.cpp seems like a reasonable split between chat-focused activities and other code required to get the tool running.

[ericcurtin]

If somebody could restart the failing builds I'd appreciate it, I don't have any sort of maintainer access anymore, as limited as a random contributor

[ngxson]

The failed builds are not related to server, we can ignore them anw. Beside, I usually open a mirror PR on my fork to skip the long waiting line of CIs on main repo, you can give that a try.