--- inference: false pipeline_tag: text-generation license: bigcode-openrail-m datasets: - bigcode/the-stack-dedup - tiiuae/falcon-refinedweb metrics: - code_eval - mmlu - arc - hellaswag - truthfulqa library_name: transformers tags: - code model-index: - name: StarCoderPlus results: - task: type: text-generation dataset: type: openai_humaneval name: HumanEval (Prompted) metrics: - name: pass@1 type: pass@1 value: 26.7 verified: false - task: type: text-generation dataset: type: MMLU (5-shot) name: MMLU metrics: - name: Accuracy type: Accuracy value: 45.1 verified: false - task: type: text-generation dataset: type: HellaSwag (10-shot) name: HellaSwag metrics: - name: Accuracy type: Accuracy value: 77.3 verified: false - task: type: text-generation dataset: type: ARC (25-shot) name: ARC metrics: - name: Accuracy type: Accuracy value: 48.9 verified: false - task: type: text-generation dataset: type: ThrutfulQA (0-shot) name: ThrutfulQA metrics: - name: Accuracy type: Accuracy value: 37.9 verified: false extra_gated_prompt: >- ## Model License Agreement Please read the BigCode [OpenRAIL-M license](https://huggingface.co./spaces/bigcode/bigcode-model-license-agreement) agreement before accepting it. extra_gated_fields: I accept the above license agreement, and will use the Model complying with the set of use restrictions and sharing requirements: checkbox ---
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# Bigcode's StarcoderPlus GPTQ These files are GPTQ 4bit model files for [Bigcode's StarcoderPlus](https://huggingface.co./bigcode/starcoderplus). It is the result of quantising to 4bit using [AutoGPTQ](https://github.com/PanQiWei/AutoGPTQ). ## Repositories available * [4-bit GPTQ models for GPU inference](https://huggingface.co./TheBloke/starcoderplus-GPTQ) * [4, 5, and 8-bit GGML models for CPU+GPU inference](https://huggingface.co./TheBloke/starcoderplus-GGML) * [Unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co./bigcode/starcoderplus) ## How to easily download and use this model in text-generation-webui Please make sure you're using the latest version of text-generation-webui 1. Click the **Model tab**. 2. Under **Download custom model or LoRA**, enter `TheBloke/starcoderplus-GPTQ`. 3. Click **Download**. 4. The model will start downloading. Once it's finished it will say "Done" 5. In the top left, click the refresh icon next to **Model**. 6. In the **Model** dropdown, choose the model you just downloaded: `starcoderplus-GPTQ` 7. The model will automatically load, and is now ready for use! 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. * Note that you do not need to set GPTQ parameters any more. These are set automatically from the file `quantize_config.json`. 9. Once you're ready, click the **Text Generation tab** and enter a prompt to get started! ## How to use this GPTQ model from Python code First make sure you have [AutoGPTQ](https://github.com/PanQiWei/AutoGPTQ) installed: `pip install auto-gptq` Then try the following example code: ```python from transformers import AutoTokenizer, pipeline, logging from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig import argparse model_name_or_path = "TheBloke/starcoderplus-GPTQ" use_triton = False tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True) model = AutoGPTQForCausalLM.from_quantized(model_name_or_path, model_basename=model_basename, use_safetensors=True, trust_remote_code=True, device="cuda:0", use_triton=use_triton, quantize_config=None) print("\n\n*** Generate:") inputs = tokenizer.encode("def print_hello_world():", return_tensors="pt").to(device) outputs = model.generate(inputs) print(tokenizer.decode(outputs[0])) ``` ### Fill-in-the-middle Fill-in-the-middle uses special tokens to identify the prefix/middle/suffix part of the input and output: ```python input_text = "def print_hello_world():\n \n print('Hello world!')" inputs = tokenizer.encode(input_text, return_tensors="pt").to(device) outputs = model.generate(inputs) print(tokenizer.decode(outputs[0])) ``` ## Provided files **gptq_model-4bit--1g.safetensors** This will work with AutoGPTQ and CUDA versions of GPTQ-for-LLaMa. There are reports of issues with Triton mode of recent GPTQ-for-LLaMa. If you have issues, please use AutoGPTQ instead. It was created without group_size to lower VRAM requirements, and with --act-order (desc_act) to boost inference accuracy as much as possible. * `gptq_model-4bit--1g.safetensors` * Works with AutoGPTQ in CUDA or Triton modes. * Works with text-generation-webui, including one-click-installers. * Works with GPTQ-for-LLaMa in CUDA mode. May have issues with GPTQ-for-LLaMa Triton mode. * Parameters: Groupsize = -1. Act Order / desc_act = True. ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute. Thanks to the [chirper.ai](https://chirper.ai) team! 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. 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. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI **Special thanks to**: Aemon Algiz. **Patreon special mentions**: Sam, theTransient, Jonathan Leane, Steven Wood, webtim, Johann-Peter Hartmann, Geoffrey Montalvo, Gabriel Tamborski, Willem Michiel, John Villwock, Derek Yates, Mesiah Bishop, Eugene Pentland, Pieter, Chadd, Stephen Murray, Daniel P. Andersen, terasurfer, Brandon Frisco, Thomas Belote, Sid, Nathan LeClaire, Magnesian, Alps Aficionado, Stanislav Ovsiannikov, Alex, Joseph William Delisle, Nikolai Manek, Michael Davis, Junyu Yang, K, J, Spencer Kim, Stefan Sabev, Olusegun Samson, transmissions 11, Michael Levine, Cory Kujawski, Rainer Wilmers, zynix, Kalila, Luke @flexchar, Ajan Kanaga, Mandus, vamX, Ai Maven, Mano Prime, Matthew Berman, subjectnull, Vitor Caleffi, Clay Pascal, biorpg, alfie_i, 阿明, Jeffrey Morgan, ya boyyy, Raymond Fosdick, knownsqashed, Olakabola, Leonard Tan, ReadyPlayerEmma, Enrico Ros, Dave, Talal Aujan, Illia Dulskyi, Sean Connelly, senxiiz, Artur Olbinski, Elle, Raven Klaugh, Fen Risland, Deep Realms, Imad Khwaja, Fred von Graf, Will Dee, usrbinkat, SuperWojo, Alexandros Triantafyllidis, Swaroop Kallakuri, Dan Guido, John Detwiler, Pedro Madruga, Iucharbius, Viktor Bowallius, Asp the Wyvern, Edmond Seymore, Trenton Dambrowitz, Space Cruiser, Spiking Neurons AB, Pyrater, LangChain4j, Tony Hughes, Kacper Wikieł, Rishabh Srivastava, David Ziegler, Luke Pendergrass, Andrey, Gabriel Puliatti, Lone Striker, Sebastain Graf, Pierre Kircher, Randy H, NimbleBox.ai, Vadim, danny, Deo Leter Thank you to all my generous patrons and donaters! And thank you again to a16z for their generous grant. # Original model card: Bigcode's StarcoderPlus # StarCoderPlus Play with the instruction-tuned StarCoderPlus at [StarChat-Beta](https://huggingface.co./spaces/HuggingFaceH4/starchat-playground). ## Table of Contents 1. [Model Summary](##model-summary) 2. [Use](##use) 3. [Limitations](##limitations) 4. [Training](##training) 5. [License](##license) 6. [Citation](##citation) ## Model Summary StarCoderPlus is a fine-tuned version of [StarCoderBase](https://huggingface.co./bigcode/starcoderbase) on 600B tokens from the English web dataset [RedefinedWeb](https://huggingface.co./datasets/tiiuae/falcon-refinedweb) combined with [StarCoderData](https://huggingface.co./datasets/bigcode/starcoderdata) from [The Stack (v1.2)](https://huggingface.co./datasets/bigcode/the-stack) and a Wikipedia dataset. It's a 15.5B parameter Language Model trained on English and 80+ programming languages. The model uses [Multi Query Attention](https://arxiv.org/abs/1911.02150), [a context window of 8192 tokens](https://arxiv.org/abs/2205.14135), and was trained using the [Fill-in-the-Middle objective](https://arxiv.org/abs/2207.14255) on 1.6 trillion tokens. - **Repository:** [bigcode/Megatron-LM](https://github.com/bigcode-project/Megatron-LM) - **Project Website:** [bigcode-project.org](https://www.bigcode-project.org) - **Point of Contact:** [contact@bigcode-project.org](mailto:contact@bigcode-project.org) - **Languages:** English & 80+ Programming languages ## Use ### Intended use The model was trained on English and GitHub code. As such it is _not_ an instruction model and commands like "Write a function that computes the square root." do not work well. However, the instruction-tuned version in [StarChat](hhttps://huggingface.co./spaces/HuggingFaceH4/starchat-playground) makes a capable assistant. **Feel free to share your generations in the Community tab!** ### Generation ```python # pip install -q transformers from transformers import AutoModelForCausalLM, AutoTokenizer checkpoint = "bigcode/starcoderplus" device = "cuda" # for GPU usage or "cpu" for CPU usage tokenizer = AutoTokenizer.from_pretrained(checkpoint) model = AutoModelForCausalLM.from_pretrained(checkpoint).to(device) inputs = tokenizer.encode("def print_hello_world():", return_tensors="pt").to(device) outputs = model.generate(inputs) print(tokenizer.decode(outputs[0])) ``` ### Fill-in-the-middle Fill-in-the-middle uses special tokens to identify the prefix/middle/suffix part of the input and output: ```python input_text = "def print_hello_world():\n \n print('Hello world!')" inputs = tokenizer.encode(input_text, return_tensors="pt").to(device) outputs = model.generate(inputs) print(tokenizer.decode(outputs[0])) ``` ### Attribution & Other Requirements The training code dataset of the model was filtered for permissive licenses only. Nevertheless, the model can generate source code verbatim from the dataset. The code's license might require attribution and/or other specific requirements that must be respected. We provide a [search index](https://huggingface.co./spaces/bigcode/starcoder-search) that let's you search through the pretraining data to identify where generated code came from and apply the proper attribution to your code. # Limitations The model has been trained on a mixture of English text from the web and GitHub code. Therefore it might encounter limitations when working with non-English text, and can carry the stereotypes and biases commonly encountered online. Additionally, the generated code should be used with caution as it may contain errors, inefficiencies, or potential vulnerabilities. For a more comprehensive understanding of the base model's code limitations, please refer to See [StarCoder paper](hhttps://arxiv.org/abs/2305.06161). # Training StarCoderPlus is a fine-tuned version on 600B English and code tokens of StarCoderBase, which was pre-trained on 1T code tokens. Below are the fine-tuning details: ## Model - **Architecture:** GPT-2 model with multi-query attention and Fill-in-the-Middle objective - **Finetuning steps:** 150k - **Finetuning tokens:** 600B - **Precision:** bfloat16 ## Hardware - **GPUs:** 512 Tesla A100 - **Training time:** 14 days ## Software - **Orchestration:** [Megatron-LM](https://github.com/bigcode-project/Megatron-LM) - **Neural networks:** [PyTorch](https://github.com/pytorch/pytorch) - **BP16 if applicable:** [apex](https://github.com/NVIDIA/apex) # License The model is licensed under the BigCode OpenRAIL-M v1 license agreement. You can find the full agreement [here](https://huggingface.co./spaces/bigcode/bigcode-model-license-agreement).