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update llama4 tutorial
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@ -22,6 +22,7 @@ Our vision for KTransformers is to serve as a flexible platform for experimentin
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<h2 id="Updates">🔥 Updates</h2>
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* **Apr 9, 2025**: Experimental support for LLaMA 4 models ([Tutorial](./en/llama4.md)).
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* **Apr 2, 2025**: Support Multi-concurrency. ([Tutorial](./en/balance-serve.md)).
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* **Mar 27, 2025**: Support Multi-concurrency.
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* **Mar 15, 2025**: Support ROCm on AMD GPU ([Tutorial](./en/ROCm.md)).
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doc/en/llama4.md
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doc/en/llama4.md
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# 🦙 Tutorial: LLaMA 4 Multi-Concurrency Support with KTransformers (Balance Serve Backend)
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## 📌 Overview
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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.
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⚠️ 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.
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💡 **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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------
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## 🔗 Model & Resource Links
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- 🔥 Official LLaMA 4 Release: https://huggingface.co/meta-llama/Llama-4-Scout-17B-16E-Instruct
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(Note: LLaMA 4 models are served through the Meta repository. Make sure to **agree to terms** before downloading.)
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- 🧠 GGUF Format (quantized models):
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- https://huggingface.co/mradermacher/Llama-4-Scout-17B-16E-Instruct-GGUF
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------
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## 🧪 Demo
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https://github.com/user-attachments/assets/449706f1-784b-4931-b2ba-07687c1aca54
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------
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## ⚙️ Usage Instructions
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### 1. Clone `support-llama4` Branch
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```bash
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git clone https://github.com/kvcache-ai/ktransformers.git
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cd ktransformers
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git checkout support-llama4
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git submodule update --init --recursive
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```
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### 2. Set Up Environment
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```bash
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# Download Miniconda
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wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
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# Create environment
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conda create --name ktransformers python=3.11
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conda activate ktransformers
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# Install required libraries
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conda install -c conda-forge libstdcxx-ng
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# Verify GLIBCXX version (should include 3.4.32)
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strings ~/anaconda3/envs/ktransformers/lib/libstdc++.so.6 | grep GLIBCXX
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sudo apt install libtbb-dev libssl-dev libcurl4-openssl-dev libaio1 libaio-dev libfmt-dev libgflags-dev zlib1g-dev patchelf
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pip3 install packaging ninja cpufeature numpy openai
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pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu126
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```
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### 3. Build with Balance Serve Support
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```bash
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# Install single NUMA dependencies
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USE_BALANCE_SERVE=1 bash ./install.sh
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# For those who have two cpu and 1T RAM(Dual NUMA):
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USE_BALANCE_SERVE=1 USE_NUMA=1 bash ./install.sh
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```
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### 4. Run LLaMA 4 Inference Server
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Make sure you have:
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- `--model_path` pointing to a local config directory (not a Hugging Face name).
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- `--gguf_path` pointing to quantized `.gguf` weights.
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```bash
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python ktransformers/server/main.py \
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--port 10002 \
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--model_path <path_to_safetensor_config> \
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--gguf_path <path_to_gguf_files> \
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--optimize_config_path ktransformers/optimize/optimize_rules/Llama4-serve.yaml \
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--max_new_tokens 1024 \
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--cache_lens 32768 \
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--chunk_size 256 \
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--max_batch_size 4 \
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--backend_type balance_serve \
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```
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### 5. Access server
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```
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curl -X POST http://localhost:10002/v1/chat/completions \
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-H "accept: application/json" \
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-H "Content-Type: application/json" \
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-d '{
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"messages": [
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{"role": "user", "content": "hello"}
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],
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"model": "Llama4",
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"temperature": 0.3,
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"top_p": 1.0,
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"stream": true
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}'
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```
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------
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## 📌 Limitations
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- ✅ **Only `balance_serve` backend is supported** for LLaMA 4 models in this version.
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- ⚠️ Requires **`transformers==4.51.0`** or newer. Due to potential compatibility issues with older toolchains, we have **not merged this branch to main yet**.
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- ❌ Multimodal models are not supported yet in this version. Support will be added in future releases.
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