README.md

---
license: llama2
pipeline_tag: text-generation
tags:
- text-generation-inference
---

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English | [简体中文](README_zh-CN.md)

</div>

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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(>=v0.0.14) is installed.

```shell
pip install 'lmdeploy>=0.0.14'
```

## 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-2-70B model from the model zoo as an example:

```shell
git-lfs install
git clone https://huggingface.co/lmdeploy/llama2-chat-70b-4bit
```

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.
lmdeploy convert \
    --model-name llama2 \
    --model-path ./llama2-chat-70b-w4 \
    --model-format awq \
    --group-size 128

## inference
lmdeploy 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
lmdeploy serve gradio ./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
lmdeploy lite 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
lmdeploy lite 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.