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- [Matthew Berman: How To Install MetaGPT - Build A Startup With One Prompt!!](https://youtu.be/uT75J_KG_aY)
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- 🔎 [What can MetaGPT do?](https://docs.deepwisdom.ai/main/en/guide/get_started/introduction.html)
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```python
import os
os.environ["OPENAI_API_KEY"] = "YOUR_OPENAI_API_KEY"
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```python
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model = load_model_on_gpus("THUDM/chatglm2-6b", num_gpus=2)
```
即可将模型部署到两张 GPU 上进行推理。你可以将 `num_gpus` 改为你希望使用的 GPU 数。默认是均匀切分的，你也可以传入 `device_map` 参数来自己指定。
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We welcome and value any contributions and collaborations.
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You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
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- Please run `black .` before submitting the code.
- To check detailed guidelines for new contributions, please refer [how to contribute](https://github.com/eosphoros-ai/DB-GPT/blob/main/CONTRIBUTING.md)
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Contributions are welcome! Please check out the todos below, and feel free to open a pull request.
For more information, please see the [contributing guidelines](CONTRIBUTING.md).
After installing the virtual environment, please remember to install `pre-commit` to be compliant with our standards:
```... | https://github.com/gventuri/pandas-ai | -1 | [
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Query and summarize your documents or just chat with local private GPT LLMs using h2oGPT, an Apache V2 open-source project.
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```bash
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cd h2ogpt
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```
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- [h2ogpt-osx-m1-gpu](https://h2o-release.s3.amazonaws.com/h2ogpt/Nov2023/h2ogpt-osx-m1-gpu)
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* [macOS (CPU or M1/M2)](docs/README_MACOS.md)
* [Windows 10/11 (CPU or CUDA)](docs/README_WINDOWS.md)
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Our repository organization is shown below.
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... | FAQ(s)
1. I would like to use Gorilla commercially. Is there going to be a Apache 2.0 licensed version?
Yes! We now have models that you can use commercially without any obligations.
2. Can we use Gorilla with Langchain, Toolformer, AutoGPT etc?
Absolutely! You've highlighted a great aspect of our tools. Gorilla i... | https://github.com/ShishirPatil/gorilla | -1 | [
"api",
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"chatgpt",
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"llm",
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https://raw.githubusercontent.com/QwenLM/Qwen/main/README.md | [
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Below, we provide simple examples to show how to use Qwen-Chat with 🤖 ModelScope and 🤗 Transformers.
You can use our pre-built docker images to skip most of the environment setup steps, see Section ["Using Pre-built Docker Images"](#-docker) for more details.
If not using docker, please make sure you h... | https://github.com/QwenLM/Qwen | 0 | [
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[
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| https://github.com/QwenLM/Qwen | -1 | [
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https://raw.githubusercontent.com/QwenLM/Qwen/main/README.md | [
[
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] | [
[
"pip",
"install",
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],
[
"pip install csrc/layer_norm"
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] | pip install csrc/layer_norm
| https://github.com/QwenLM/Qwen | 0 | [
"chinese",
"flash-attention",
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"natural-language-processing",
"pretrained-models"
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https://raw.githubusercontent.com/QwenLM/Qwen/main/README.md | [
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]
] | pip install csrc/rotary
```
Now you can start with ModelScope or Transformers.
| https://github.com/QwenLM/Qwen | 0 | [
"chinese",
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https://raw.githubusercontent.com/QwenLM/Qwen/main/README.md | [
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The most simple way to use Qwen through APIs is DashScope API service through Alibaba Cloud. We give an introduction to the usage. Additionally, we provide a script for you to deploy an OpenAI-style API on your own servers.
DashScope is the large language model API service provided by Alibaba Cloud, which no... | https://github.com/QwenLM/Qwen | 0 | [
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https://raw.githubusercontent.com/QwenLM/Qwen/main/README.md | [
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We provide a solution based on [AutoGPTQ](https://github.com/PanQiWei/AutoGPTQ), and release the Int4 and Int8 quantized models, which achieve nearly lossless model effects but improved performance on both memory costs and inference speed.
Here we demonstrate how to use our provided quantized models for inferenc... | https://github.com/QwenLM/Qwen | 0 | [
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https://raw.githubusercontent.com/QwenLM/Qwen/main/README.md | [
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Now we provide the official training script, `finetune.py`, for users to finetune the pretrained model for downstream applications in a simple fashion. Additionally, we provide shell scripts to launch finetuning with no worries. This script supports the training with [DeepSpeed](https://github.com/microsoft/DeepS... | https://github.com/QwenLM/Qwen | 0 | [
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https://raw.githubusercontent.com/QwenLM/Qwen/main/README.md | [
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... | Distributed training. We do not provide single-GPU training script as the insufficient GPU memory will break down the training.
bash finetune/finetune_ds.sh
```
Remember to specify the correct model name or path, the data path, as well as the output directory in the shell scripts. Another thing to notice is that we us... | https://github.com/QwenLM/Qwen | -1 | [
"chinese",
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https://raw.githubusercontent.com/QwenLM/Qwen/main/README.md | [
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bash finetune/finetune_lora_ds.sh
```
In comparison with full-parameter finetuning, LoRA ([paper](https://arxiv.org/abs/2106.09685)) only updates the parameters of adapter layers but keeps the original large language model layers frozen. This allows much fewer memory costs and thus fewer computati... | https://github.com/QwenLM/Qwen | -1 | [
"chinese",
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https://raw.githubusercontent.com/QwenLM/Qwen/main/README.md | [
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")",
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"l... | Quantize Fine-tuned Models
This section applies to full-parameter/LoRA fine-tuned models. (Note: You do not need to quantize the Q-LoRA fine-tuned model because it is already quantized.)
If you use LoRA, please follow the above instructions to merge your model before quantization.
We recommend using [auto_gptq](http... | https://github.com/QwenLM/Qwen | 0 | [
"chinese",
"flash-attention",
"large-language-models",
"llm",
"natural-language-processing",
"pretrained-models"
] |
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