# 🦙 Tutorial: LLaMA 4 Multi-Concurrency Support with KTransformers (Balance Serve Backend)

## 📌 Overview

We are pleased to announce that **KTransformers** now provides **experimental support for LLaMA 4 models** through the powerful `balance_serve` backend introduced in **v0.2.4**. This update is available under the dedicated development branch: [`support-llama4`](https://github.com/kvcache-ai/ktransformers/tree/support-llama4), specifically targeting the newly released **Meta LLaMA 4** model architecture.

⚠️ This support is currently **not available on the main branch** due to dependencies on newer versions of `transformers`, and **compatibility limitations with inference of currently supported models**. Work is underway to integrate this into the mainline once broader stability and compatibility are validated.

💡 **If you already have an environment based on the main branch**, it is **strongly recommended to create a new environment** to avoid potential dependency conflicts.

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## 🔗 Model & Resource Links

- 🔥 Official LLaMA 4 Release: https://huggingface.co/meta-llama/Llama-4-Scout-17B-16E-Instruct
   (Note: LLaMA 4 models are served through the Meta repository. Make sure to **agree to terms** before downloading.)
- 🧠 GGUF Format (quantized models):
  - https://huggingface.co/unsloth/Llama-4-Scout-17B-16E-Instruct-GGUF

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## 🧪 Demo

https://github.com/user-attachments/assets/449706f1-784b-4931-b2ba-07687c1aca54

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## ⚙️ Usage Instructions

### 1. Clone `support-llama4` Branch

```bash
git clone https://github.com/kvcache-ai/ktransformers.git
cd ktransformers
git checkout support-llama4
git submodule update --init --recursive
```

### 2. Set Up Environment

```bash
# Download Miniconda
wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh

# Create environment
conda create --name ktransformers python=3.11
conda activate ktransformers

# Install required libraries
conda install -c conda-forge libstdcxx-ng

# Verify GLIBCXX version (should include 3.4.32)
strings ~/anaconda3/envs/ktransformers/lib/libstdc++.so.6 | grep GLIBCXX

sudo apt install libtbb-dev libssl-dev libcurl4-openssl-dev libaio1 libaio-dev libfmt-dev libgflags-dev zlib1g-dev patchelf
pip3 install packaging ninja cpufeature numpy openai
pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu126
```

### 3. Build with Balance Serve Support

```bash
# Install single NUMA dependencies
USE_BALANCE_SERVE=1  bash ./install.sh
# For those who have two cpu and 1T RAM（Dual NUMA）:
USE_BALANCE_SERVE=1 USE_NUMA=1 bash ./install.sh
```

### 4. Use our custom config.json

Currently, it's needed to use our custom config.json(https://github.com/kvcache-ai/ktransformers/blob/support-llama4/doc/en/config.json) to replace your config.json in your `--model_path`.

### 5. Run LLaMA 4 Inference Server

Make sure you have:

- `--model_path` pointing to a local config directory (not a Hugging Face name).
- `--gguf_path` pointing to the folder containing quantized `.gguf` weights.

```bash
python ktransformers/server/main.py \
  --port 10002 \
  --model_path <path_to_safetensor_config> \
  --gguf_path <path_to_gguf_files> \
  --optimize_config_path ktransformers/optimize/optimize_rules/Llama4-serve.yaml \
  --max_new_tokens 1024 \
  --cache_lens 32768 \
  --chunk_size 256 \
  --max_batch_size 4 \
  --backend_type balance_serve \
```

### 5. Access server

```
curl -X POST http://localhost:10002/v1/chat/completions \
  -H "accept: application/json" \
  -H "Content-Type: application/json" \
  -d '{
    "messages": [
      {"role": "user", "content": "hello"}
    ],
    "model": "Llama4",
    "temperature": 0.3,
    "top_p": 1.0,
    "stream": true
  }'
```

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## 📌 Limitations

- ✅ **Only `balance_serve` backend is supported** for LLaMA 4 models in this version.
- ⚠️ Requires **`transformers==4.51.0`** or newer. Due to potential compatibility issues with older toolchains, we have **not merged this branch to main yet**.
- ❌ Multimodal models are not supported yet in this version. Support will be added in future releases.
