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--- |
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inference: false |
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language: |
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- code |
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license: llama2 |
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model_creator: Meta |
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model_link: https://huggingface.co/codellama/CodeLlama-13b-python-hf |
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model_name: CodeLlama 13B Python |
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model_type: llama |
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pipeline_tag: text-generation |
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quantized_by: TheBloke |
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tags: |
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- llama-2 |
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--- |
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<!-- header start --> |
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<!-- 200823 --> |
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<div style="width: auto; margin-left: auto; margin-right: auto"> |
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<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> |
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</div> |
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<div style="display: flex; flex-direction: column; align-items: flex-start;"> |
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<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p> |
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</div> |
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<div style="display: flex; flex-direction: column; align-items: flex-end;"> |
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<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> |
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</div> |
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<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div> |
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<hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> |
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<!-- header end --> |
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# CodeLlama 13B Python - GPTQ |
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- Model creator: [Meta](https://huggingface.co/meta-llama) |
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- Original model: [CodeLlama 13B Python](https://huggingface.co/codellama/CodeLlama-13b-python-hf) |
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<!-- description start --> |
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## Description |
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This repo contains GPTQ model files for [Meta's CodeLlama 13B Python](https://huggingface.co/codellama/CodeLlama-13b-python-hf). |
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Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them. |
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<!-- description end --> |
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<!-- repositories-available start --> |
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## Repositories available |
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* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/CodeLlama-13B-Python-GPTQ) |
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* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/CodeLlama-13B-Python-GGUF) |
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* [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference (deprecated)](https://huggingface.co/TheBloke/CodeLlama-13B-Python-GGML) |
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* [Meta's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/codellama/CodeLlama-13b-python-hf) |
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<!-- repositories-available end --> |
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<!-- prompt-template start --> |
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## Prompt template: CodeLlama |
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``` |
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[INST] Write code to solve the following coding problem that obeys the constraints and passes the example test cases. Please wrap your code answer using ```: |
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{prompt} |
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[/INST] |
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``` |
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<!-- prompt-template end --> |
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<!-- README_GPTQ.md-provided-files start --> |
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## Provided files and GPTQ parameters |
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Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements. |
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Each separate quant is in a different branch. See below for instructions on fetching from different branches. |
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All recent GPTQ files are made with AutoGPTQ, and all files in non-main branches are made with AutoGPTQ. Files in the `main` branch which were uploaded before August 2023 were made with GPTQ-for-LLaMa. |
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<details> |
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<summary>Explanation of GPTQ parameters</summary> |
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- Bits: The bit size of the quantised model. |
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- GS: GPTQ group size. Higher numbers use less VRAM, but have lower quantisation accuracy. "None" is the lowest possible value. |
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- Act Order: True or False. Also known as `desc_act`. True results in better quantisation accuracy. Some GPTQ clients have had issues with models that use Act Order plus Group Size, but this is generally resolved now. |
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- Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 0.01 is default, but 0.1 results in slightly better accuracy. |
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- GPTQ dataset: The dataset used for quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ dataset is not the same as the dataset used to train the model - please refer to the original model repo for details of the training dataset(s). |
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- Sequence Length: The length of the dataset sequences used for quantisation. Ideally this is the same as the model sequence length. For some very long sequence models (16+K), a lower sequence length may have to be used. Note that a lower sequence length does not limit the sequence length of the quantised model. It only impacts the quantisation accuracy on longer inference sequences. |
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- ExLlama Compatibility: Whether this file can be loaded with ExLlama, which currently only supports Llama models in 4-bit. |
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</details> |
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| Branch | Bits | GS | Act Order | Damp % | GPTQ Dataset | Seq Len | Size | ExLlama | Desc | |
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| ------ | ---- | -- | --------- | ------ | ------------ | ------- | ---- | ------- | ---- | |
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| [main](https://huggingface.co/TheBloke/CodeLlama-13B-Python-GPTQ/tree/main) | 4 | 128 | No | 0.1 | [Evol Instruct Code](https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1) | 8192 | 7.26 GB | Yes | Most compatible option. Good inference speed in AutoGPTQ and GPTQ-for-LLaMa. Lower inference quality than other options. | |
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| [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/CodeLlama-13B-Python-GPTQ/tree/gptq-4bit-32g-actorder_True) | 4 | 32 | Yes | 0.1 | [Evol Instruct Code](https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1) | 8192 | 8.00 GB | Yes | 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. Poor AutoGPTQ CUDA speed. | |
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| [gptq-4bit-64g-actorder_True](https://huggingface.co/TheBloke/CodeLlama-13B-Python-GPTQ/tree/gptq-4bit-64g-actorder_True) | 4 | 64 | Yes | 0.1 | [Evol Instruct Code](https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1) | 8192 | 7.51 GB | Yes | 4-bit, with Act Order and group size 64g. Uses less VRAM than 32g, but with slightly lower accuracy. Poor AutoGPTQ CUDA speed. | |
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| [gptq-4bit-128g-actorder_True](https://huggingface.co/TheBloke/CodeLlama-13B-Python-GPTQ/tree/gptq-4bit-128g-actorder_True) | 4 | 128 | Yes | 0.1 | [Evol Instruct Code](https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1) | 8192 | 7.26 GB | Yes | 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. Poor AutoGPTQ CUDA speed. | |
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| [gptq-8bit--1g-actorder_True](https://huggingface.co/TheBloke/CodeLlama-13B-Python-GPTQ/tree/gptq-8bit--1g-actorder_True) | 8 | None | Yes | 0.1 | [Evol Instruct Code](https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1) | 8192 | 13.36 GB | No | 8-bit, with Act Order. No group size, to lower VRAM requirements and to improve AutoGPTQ speed. | |
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| [gptq-8bit-128g-actorder_True](https://huggingface.co/TheBloke/CodeLlama-13B-Python-GPTQ/tree/gptq-8bit-128g-actorder_True) | 8 | 128 | Yes | 0.1 | [Evol Instruct Code](https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1) | 8192 | 13.65 GB | No | 8-bit, with group size 128g for higher inference quality and with Act Order for even higher accuracy. Poor AutoGPTQ CUDA speed. | |
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<!-- README_GPTQ.md-provided-files end --> |
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<!-- README_GPTQ.md-download-from-branches start --> |
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## How to download from branches |
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- In text-generation-webui, you can add `:branch` to the end of the download name, eg `TheBloke/CodeLlama-13B-Python-GPTQ:gptq-4bit-32g-actorder_True` |
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- With Git, you can clone a branch with: |
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``` |
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git clone --single-branch --branch gptq-4bit-32g-actorder_True https://huggingface.co/TheBloke/CodeLlama-13B-Python-GPTQ |
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``` |
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- In Python Transformers code, the branch is the `revision` parameter; see below. |
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<!-- README_GPTQ.md-download-from-branches end --> |
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<!-- README_GPTQ.md-text-generation-webui start --> |
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## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui). |
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Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui). |
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It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install. |
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1. Click the **Model tab**. |
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2. Under **Download custom model or LoRA**, enter `TheBloke/CodeLlama-13B-Python-GPTQ`. |
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- To download from a specific branch, enter for example `TheBloke/CodeLlama-13B-Python-GPTQ:gptq-4bit-32g-actorder_True` |
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- see Provided Files above for the list of branches for each option. |
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3. Click **Download**. |
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4. The model will start downloading. Once it's finished it will say "Done". |
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5. In the top left, click the refresh icon next to **Model**. |
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6. In the **Model** dropdown, choose the model you just downloaded: `CodeLlama-13B-Python-GPTQ` |
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7. The model will automatically load, and is now ready for use! |
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8. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right. |
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* Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file `quantize_config.json`. |
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9. Once you're ready, click the **Text Generation tab** and enter a prompt to get started! |
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<!-- README_GPTQ.md-text-generation-webui end --> |
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<!-- README_GPTQ.md-use-from-python start --> |
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## How to use this GPTQ model from Python code |
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### Install the necessary packages |
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Requires: Transformers 4.32.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later. |
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```shell |
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pip3 install transformers>=4.32.0 optimum>=1.12.0 |
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pip3 install auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/ # Use cu117 if on CUDA 11.7 |
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``` |
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If you have problems installing AutoGPTQ using the pre-built wheels, install it from source instead: |
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```shell |
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pip3 uninstall -y auto-gptq |
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git clone https://github.com/PanQiWei/AutoGPTQ |
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cd AutoGPTQ |
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pip3 install . |
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``` |
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### For CodeLlama models only: you must use Transformers 4.33.0 or later. |
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If 4.33.0 is not yet released when you read this, you will need to install Transformers from source: |
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```shell |
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pip3 uninstall -y transformers |
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pip3 install git+https://github.com/huggingface/transformers.git |
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``` |
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### You can then use the following code |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline |
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model_name_or_path = "TheBloke/CodeLlama-13B-Python-GPTQ" |
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# To use a different branch, change revision |
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# For example: revision="gptq-4bit-32g-actorder_True" |
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model = AutoModelForCausalLM.from_pretrained(model_name_or_path, |
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torch_dtype=torch.float16, |
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device_map="auto", |
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revision="main") |
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tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True) |
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prompt = "Tell me about AI" |
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prompt_template=f'''[INST] Write code to solve the following coding problem that obeys the constraints and passes the example test cases. Please wrap your code answer using ```: |
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{prompt} |
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[/INST] |
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''' |
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print("\n\n*** Generate:") |
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input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda() |
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output = model.generate(inputs=input_ids, temperature=0.7, max_new_tokens=512) |
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print(tokenizer.decode(output[0])) |
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# Inference can also be done using transformers' pipeline |
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print("*** Pipeline:") |
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pipe = pipeline( |
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"text-generation", |
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model=model, |
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tokenizer=tokenizer, |
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max_new_tokens=512, |
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temperature=0.7, |
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top_p=0.95, |
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repetition_penalty=1.15 |
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) |
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print(pipe(prompt_template)[0]['generated_text']) |
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``` |
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<!-- README_GPTQ.md-use-from-python end --> |
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<!-- README_GPTQ.md-compatibility start --> |
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## Compatibility |
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The files provided are tested to work with AutoGPTQ, both via Transformers and using AutoGPTQ directly. They should also work with [Occ4m's GPTQ-for-LLaMa fork](https://github.com/0cc4m/KoboldAI). |
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[ExLlama](https://github.com/turboderp/exllama) is compatible with Llama models in 4-bit. Please see the Provided Files table above for per-file compatibility. |
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[Huggingface Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) is compatible with all GPTQ models. |
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<!-- README_GPTQ.md-compatibility end --> |
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<!-- footer start --> |
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<!-- 200823 --> |
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## Discord |
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For further support, and discussions on these models and AI in general, join us at: |
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[TheBloke AI's Discord server](https://discord.gg/theblokeai) |
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## Thanks, and how to contribute. |
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Thanks to the [chirper.ai](https://chirper.ai) team! |
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I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. |
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If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. |
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Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. |
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* Patreon: https://patreon.com/TheBlokeAI |
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* Ko-Fi: https://ko-fi.com/TheBlokeAI |
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**Special thanks to**: Aemon Algiz. |
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**Patreon special mentions**: Russ Johnson, J, alfie_i, Alex, NimbleBox.ai, Chadd, Mandus, Nikolai Manek, Ken Nordquist, ya boyyy, Illia Dulskyi, Viktor Bowallius, vamX, Iucharbius, zynix, Magnesian, Clay Pascal, Pierre Kircher, Enrico Ros, Tony Hughes, Elle, Andrey, knownsqashed, Deep Realms, Jerry Meng, Lone Striker, Derek Yates, Pyrater, Mesiah Bishop, James Bentley, Femi Adebogun, Brandon Frisco, SuperWojo, Alps Aficionado, Michael Dempsey, Vitor Caleffi, Will Dee, Edmond Seymore, usrbinkat, LangChain4j, Kacper Wikieł, Luke Pendergrass, John Detwiler, theTransient, Nathan LeClaire, Tiffany J. Kim, biorpg, Eugene Pentland, Stanislav Ovsiannikov, Fred von Graf, terasurfer, Kalila, Dan Guido, Nitin Borwankar, 阿明, Ai Maven, John Villwock, Gabriel Puliatti, Stephen Murray, Asp the Wyvern, danny, Chris Smitley, ReadyPlayerEmma, S_X, Daniel P. Andersen, Olakabola, Jeffrey Morgan, Imad Khwaja, Caitlyn Gatomon, webtim, Alicia Loh, Trenton Dambrowitz, Swaroop Kallakuri, Erik Bjäreholt, Leonard Tan, Spiking Neurons AB, Luke @flexchar, Ajan Kanaga, Thomas Belote, Deo Leter, RoA, Willem Michiel, transmissions 11, subjectnull, Matthew Berman, Joseph William Delisle, David Ziegler, Michael Davis, Johann-Peter Hartmann, Talal Aujan, senxiiz, Artur Olbinski, Rainer Wilmers, Spencer Kim, Fen Risland, Cap'n Zoog, Rishabh Srivastava, Michael Levine, Geoffrey Montalvo, Sean Connelly, Alexandros Triantafyllidis, Pieter, Gabriel Tamborski, Sam, Subspace Studios, Junyu Yang, Pedro Madruga, Vadim, Cory Kujawski, K, Raven Klaugh, Randy H, Mano Prime, Sebastain Graf, Space Cruiser |
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Thank you to all my generous patrons and donaters! |
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And thank you again to a16z for their generous grant. |
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<!-- footer end --> |
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# Original model card: Meta's CodeLlama 13B Python |
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# **Code Llama** |
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Code Llama is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 34 billion parameters. This is the repository for the 13B Python specialist version in the Hugging Face Transformers format. This model is designed for general code synthesis and understanding. Links to other models can be found in the index at the bottom. |
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| | Base Model | Python | Instruct | |
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| --- | ----------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------- | |
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| 7B | [codellama/CodeLlama-7b-hf](https://huggingface.co/codellama/CodeLlama-7b-hf) | [codellama/CodeLlama-7b-Python-hf](https://huggingface.co/codellama/CodeLlama-7b-Python-hf) | [codellama/CodeLlama-7b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-7b-Instruct-hf) | |
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| 13B | [codellama/CodeLlama-13b-hf](https://huggingface.co/codellama/CodeLlama-13b-hf) | [codellama/CodeLlama-13b-Python-hf](https://huggingface.co/codellama/CodeLlama-13b-Python-hf) | [codellama/CodeLlama-13b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-13b-Instruct-hf) | |
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| 34B | [codellama/CodeLlama-34b-hf](https://huggingface.co/codellama/CodeLlama-34b-hf) | [codellama/CodeLlama-34b-Python-hf](https://huggingface.co/codellama/CodeLlama-34b-Python-hf) | [codellama/CodeLlama-34b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-34b-Instruct-hf) | |
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## Model Use |
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To use this model, please make sure to install transformers from `main` until the next version is released: |
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```bash |
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pip install git+https://github.com/huggingface/transformers.git@main accelerate |
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``` |
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Model capabilities: |
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- [x] Code completion. |
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- [ ] Infilling. |
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- [ ] Instructions / chat. |
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- [x] Python specialist. |
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## Model Details |
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*Note: Use of this model is governed by the Meta license. Meta developed and publicly released the Code Llama family of large language models (LLMs). |
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**Model Developers** Meta |
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**Variations** Code Llama comes in three model sizes, and three variants: |
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* Code Llama: base models designed for general code synthesis and understanding |
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* Code Llama - Python: designed specifically for Python |
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* Code Llama - Instruct: for instruction following and safer deployment |
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All variants are available in sizes of 7B, 13B and 34B parameters. |
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**This repository contains the Python version of the 13B parameters model.** |
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**Input** Models input text only. |
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**Output** Models generate text only. |
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**Model Architecture** Code Llama is an auto-regressive language model that uses an optimized transformer architecture. |
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**Model Dates** Code Llama and its variants have been trained between January 2023 and July 2023. |
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**Status** This is a static model trained on an offline dataset. Future versions of Code Llama - Instruct will be released as we improve model safety with community feedback. |
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**License** A custom commercial license is available at: [https://ai.meta.com/resources/models-and-libraries/llama-downloads/](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) |
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**Research Paper** More information can be found in the paper "[Code Llama: Open Foundation Models for Code](https://ai.meta.com/research/publications/code-llama-open-foundation-models-for-code/)" or its [arXiv page](https://arxiv.org/abs/2308.12950). |
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## Intended Use |
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**Intended Use Cases** Code Llama and its variants is intended for commercial and research use in English and relevant programming languages. The base model Code Llama can be adapted for a variety of code synthesis and understanding tasks, Code Llama - Python is designed specifically to handle the Python programming language, and Code Llama - Instruct is intended to be safer to use for code assistant and generation applications. |
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**Out-of-Scope Uses** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in languages other than English. Use in any other way that is prohibited by the Acceptable Use Policy and Licensing Agreement for Code Llama and its variants. |
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## Hardware and Software |
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**Training Factors** We used custom training libraries. The training and fine-tuning of the released models have been performed Meta’s Research Super Cluster. |
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**Carbon Footprint** In aggregate, training all 9 Code Llama models required 400K GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 65.3 tCO2eq, 100% of which were offset by Meta’s sustainability program. |
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## Training Data |
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All experiments reported here and the released models have been trained and fine-tuned using the same data as Llama 2 with different weights (see Section 2 and Table 1 in the [research paper](https://ai.meta.com/research/publications/code-llama-open-foundation-models-for-code/) for details). |
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## Evaluation Results |
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See evaluations for the main models and detailed ablations in Section 3 and safety evaluations in Section 4 of the research paper. |
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## Ethical Considerations and Limitations |
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Code Llama and its variants are a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Code Llama’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate or objectionable responses to user prompts. Therefore, before deploying any applications of Code Llama, developers should perform safety testing and tuning tailored to their specific applications of the model. |
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Please see the Responsible Use Guide available available at [https://ai.meta.com/llama/responsible-user-guide](https://ai.meta.com/llama/responsible-user-guide). |
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