---
license: llama2
pipeline_tag: text-generation
tags:
- text-generation-inference
---
English | [简体中文](README_zh-CN.md)
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# W4A16 LLM Model Deployment
LMDeploy supports LLM model inference of 4-bit weight, with the minimum requirement for NVIDIA graphics cards being sm80.
Before proceeding with the inference, please ensure that lmdeploy(>=v0.0.4) is installed.
```shell
pip install lmdeploy
```
## 4-bit LLM model Inference
You can download the pre-quantized 4-bit weight models from LMDeploy's [model zoo](https://huggingface.co/lmdeploy) and conduct inference using the following command.
Alternatively, you can quantize 16-bit weights to 4-bit weights following the ["4-bit Weight Quantization"](#4-bit-weight-quantization) section, and then perform inference as per the below instructions.
Take the 4-bit Llama-2-7B model from the model zoo as an example:
```shell
git-lfs install
git clone https://huggingface.co/lmdeploy/llama2-chat-7b-w4
```
As demonstrated in the command below, first convert the model's layout using `turbomind.deploy`, and then you can interact with the AI assistant in the terminal
```shell
## Convert the model's layout and store it in the default path, ./workspace.
python3 -m lmdeploy.serve.turbomind.deploy \
--model-name llama2 \
--model-path ./llama2-chat-7b-w4 \
--model-format awq \
--group-size 128
## inference
python3 -m lmdeploy.turbomind.chat ./workspace
```
## Serve with gradio
If you wish to interact with the model via web ui, please initiate the gradio server as indicated below:
```shell
python3 -m lmdeploy.serve.turbomind ./workspace --server_name {ip_addr} ----server_port {port}
```
Subsequently, you can open the website `http://{ip_addr}:{port}` in your browser and interact with the model
## Inference Performance
We benchmarked the Llama 2 7B and 13B with 4-bit quantization on NVIDIA GeForce RTX 4090 using [profile_generation.py](https://github.com/InternLM/lmdeploy/blob/main/benchmark/profile_generation.py). And we measure the token generation throughput (tokens/s) by setting a single prompt token and generating 512 tokens. All the results are measured for single batch inference.
| model | llm-awq | mlc-llm | turbomind |
| ----------- | ------- | ------- | --------- |
| Llama 2 7B | 112.9 | 159.4 | 206.4 |
| Llama 2 13B | N/A | 90.7 | 115.8 |
```shell
pip install nvidia-ml-py
```
```bash
python profile_generation.py \
--model-path /path/to/your/model \
--concurrency 1 8 --prompt-tokens 0 512 --completion-tokens 2048 512
```
## 4-bit Weight Quantization
It includes two steps:
- generate quantization parameter
- quantize model according to the parameter
### Step 1: Generate Quantization Parameter
```shell
python3 -m lmdeploy.lite.apis.calibrate \
--model $HF_MODEL \
--calib_dataset 'c4' \ # Calibration dataset, supports c4, ptb, wikitext2, pileval
--calib_samples 128 \ # Number of samples in the calibration set, if memory is insufficient, you can appropriately reduce this
--calib_seqlen 2048 \ # Length of a single piece of text, if memory is insufficient, you can appropriately reduce this
--work_dir $WORK_DIR \ # Folder storing Pytorch format quantization statistics parameters and post-quantization weight
```
### Step2: Quantize Weights
LMDeploy employs AWQ algorithm for model weight quantization.
```shell
python3 -m lmdeploy.lite.apis.auto_awq \
--model $HF_MODEL \
--w_bits 4 \ # Bit number for weight quantization
--w_sym False \ # Whether to use symmetric quantization for weights
--w_group_size 128 \ # Group size for weight quantization statistics
--work_dir $WORK_DIR \ # Directory saving quantization parameters from Step 1
```
After the quantization is complete, the quantized model is saved to `$WORK_DIR`. Then you can proceed with model inference according to the instructions in the ["4-Bit Weight Model Inference"](#4-bit-llm-model-inference) section.