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internlm2-chat-7b-4bits

Text Generation
Transformers
PyTorch
internlm
feature-extraction
conversational
custom_code
4-bit precision
awq
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WIP: add readme of internlm2-chat-7b-4bits model

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+ ---
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+ license: apache-2.0
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+ pipeline_tag: text-generation
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+ ---
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+ <div align="center">
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+ <img src="https://raw.githubusercontent.com/InternLM/lmdeploy/0be9e7ab6fe9a066cfb0a09d0e0c8d2e28435e58/resources/lmdeploy-logo.svg" width="450"/>
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+ </div>
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+
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+ [LMDeploy](https://github.com/InternLM/lmdeploy) supports LLM model inference of 4-bit weight, with the minimum requirement for NVIDIA graphics cards being sm80, such as A10, A100, Geforce 30/40 series.
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+
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+ Before proceeding with the inference of `internlm2-chat-7b-4bits`, please ensure that lmdeploy is installed.
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+
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+ ```shell
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+ pip install 'lmdeploy>=0.0.11'
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+ ```
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+
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+ ## Inference
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+
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+ Please download `internlm2-chat-7b-4bits` model as follows,
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+
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+ ```shell
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+ git-lfs install
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+ git clone https://huggingface.co/internlm/internlm2-chat-7b-4bits
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+ ```
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+
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+ As demonstrated in the command below, you can interact with the AI assistant in the terminal
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+
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+ ```shell
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+ lmdeploy chat turbomind \
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+ --model-path ./internlm2-chat-7b-4bits \
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+ --model-name internlm2-chat-7b \
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+ --model-format awq \
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+ --group-size 128
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+ ```
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+
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+ ## Serve with gradio
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+
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+ If you wish to interact with the model via web UI, please initiate the gradio server as indicated below:
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+
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+ ```shell
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+ python3 -m lmdeploy.serve.gradio.app ./workspace --server_name {ip_addr} --server_port {port}
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+ ```
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+
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+ Subsequently, you can open the website `http://{ip_addr}:{port}` in your browser and interact with the model.
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+
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+ Besides serving with gradio, there are two more serving methods. One is serving with Triton Inference Server (TIS), and the other is an OpenAI-like server named as `api_server`.
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+
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+ Please refer to the [user guide](https://github.com/InternLM/lmdeploy#quick-start) for detailed information if you are interested.
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+
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+
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+ ## Inference Performance
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+
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+ LMDeploy provides scripts for benchmarking `token throughput` and `request throughput`.
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+
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+ `token throughput` tests the speed of generating new tokens, given a specified number of prompt tokens and completion tokens, while `request throughput` measures the number of requests processed per minute with real dialogue data.
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+
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+ We conducted benchmarks on `internlm2-chat-7b-4bits`. And `token_throughput` was measured by setting 256 prompt tokens and generating 512 tokens in response on A100-80G.
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+
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+ **Note**: The `session_len` in `workspace/triton_models/weights/config.ini` is changed to `2056` in our test.
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+
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+
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+ | batch | tensor parallel | prompt_tokens | completion_tokens | thr_per_proc(token/s) | rpm (req/min) | mem_per_proc(GB) |
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+ |-------|-----------------|---------------|-------------------|-----------------------|---------------|------------------|
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+ | 1 | 1 | 256 | 512 | 88.77 | - | 15.65 |
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+ | 16 | 1 | 256 | 512 | 792.7 | 220.23 | 51.46 |
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+
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+ ### token throughput
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+
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+ Run the following command,
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+
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+ ```shell
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+ python benchmark/profile_generation.py \
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+ --model-path ./workspace \
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+ --concurrency 1 8 16 --prompt-tokens 256 512 512 1024 --completion-tokens 512 512 1024 1024
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+ --dst-csv ./token_throughput.csv
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+ ```
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+ You will find the `token_throughput` metrics in `./token_throughput.csv`
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+
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+ | batch | prompt_tokens | completion_tokens | thr_per_proc(token/s) | thr_per_node(token/s) | rpm(req/min) | mem_per_proc(GB) | mem_per_gpu(GB) | mem_per_node(GB) |
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+ |-------|---------------|-------------------|-----------------------|-----------------------|--------------|------------------|-----------------|------------------|
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+ | 1 | 256 | 512 | 88.77 | 710.12 | - | 15.65 | 15.65 | 125.21 |
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+ | 1 | 512 | 512 | 83.89 | 671.15 | - | 15.68 | 15.68 | 125.46 |
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+ | 1 | 512 | 1024 | 80.19 | 641.5 | - | 15.68 | 15.68 | 125.46 |
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+ | 1 | 1024 | 1024 | 72.34 | 578.74 | - | 15.75 | 15.75 | 125.96 |
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+ | 1 | 1 | 2048 | 80.69 | 645.55 | - | 15.62 | 15.62 | 124.96 |
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+ | 8 | 256 | 512 | 565.21 | 4521.67 | - | 32.37 | 32.37 | 258.96 |
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+ | 8 | 512 | 512 | 489.04 | 3912.33 | - | 32.62 | 32.62 | 260.96 |
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+ | 8 | 512 | 1024 | 467.23 | 3737.84 | - | 32.62 | 32.62 | 260.96 |
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+ | 8 | 1024 | 1024 | 383.4 | 3067.19 | - | 33.06 | 33.06 | 264.46 |
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+ | 8 | 1 | 2048 | 487.74 | 3901.93 | - | 32.12 | 32.12 | 256.96 |
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+ | 16 | 256 | 512 | 792.7 | 6341.6 | - | 51.46 | 51.46 | 411.71 |
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+ | 16 | 512 | 512 | 639.4 | 5115.17 | - | 51.93 | 51.93 | 415.46 |
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+ | 16 | 512 | 1024 | 591.39 | 4731.09 | - | 51.93 | 51.93 | 415.46 |
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+ | 16 | 1024 | 1024 | 449.11 | 3592.85 | - | 52.06 | 52.06 | 416.46 |
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+ | 16 | 1 | 2048 | 620.5 | 4964.02 | - | 51 | 51 | 407.96 |
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+
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+
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+ ### request throughput
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+
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+ LMDeploy uses ShareGPT dataset to test request throughput. Try the next commands, and you will get the `rpm` (request per minute) metric.
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+
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+ ```
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+ # download the ShareGPT dataset
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+ wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json
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+ #
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+ python profile_throughput.py \
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+ ShareGPT_V3_unfiltered_cleaned_split.json \
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+ ./workspace \
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+ --concurrency 16
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+ ```