TheBloke's LLM work is generously supported by a grant from andreessen horowitz (a16z)
Bigcode's StarcoderPlus GPTQ
These files are GPTQ 4bit model files for Bigcode's StarcoderPlus.
It is the result of quantising to 4bit using AutoGPTQ.
Repositories available
- 4-bit GPTQ models for GPU inference
- 4, 5, and 8-bit GGML models for CPU+GPU inference
- Unquantised fp16 model in pytorch format, for GPU inference and for further conversions
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
- Click the Model tab.
- Under Download custom model or LoRA, enter
TheBloke/starcoderplus-GPTQ
. - Click Download.
- The model will start downloading. Once it's finished it will say "Done"
- In the top left, click the refresh icon next to Model.
- In the Model dropdown, choose the model you just downloaded:
starcoderplus-GPTQ
- The model will automatically load, and is now ready for use!
- 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
.
- 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 installed:
pip install auto-gptq
Then try the following example code:
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:
input_text = "<fim_prefix>def print_hello_world():\n <fim_suffix>\n print('Hello world!')<fim_middle>"
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:
Thanks, and how to contribute.
Thanks to the 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.
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- Patreon: https://patreon.com/TheBlokeAI
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Special thanks to: Aemon Algiz.
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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.
Table of Contents
Model Summary
StarCoderPlus is a fine-tuned version of StarCoderBase on 600B tokens from the English web dataset RedefinedWeb combined with StarCoderData from The Stack (v1.2) 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, a context window of 8192 tokens, and was trained using the Fill-in-the-Middle objective on 1.6 trillion tokens.
- Repository: bigcode/Megatron-LM
- Project Website: bigcode-project.org
- Point of Contact: 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 makes a capable assistant.
Feel free to share your generations in the Community tab!
Generation
# 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:
input_text = "<fim_prefix>def print_hello_world():\n <fim_suffix>\n print('Hello world!')<fim_middle>"
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 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.
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
- Neural networks: PyTorch
- BP16 if applicable: apex
License
The model is licensed under the BigCode OpenRAIL-M v1 license agreement. You can find the full agreement here.
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Datasets used to train TheBloke/starcoderplus-GPTQ
Evaluation results
Model card error
This model's model-index metadata is invalid: Schema validation error. "model-index[0].results[1].dataset.type" with value "MMLU (5-shot)" fails to match the required pattern: /^(?:[\w-]+\/)?[\w-.]+$/. "model-index[0].results[2].dataset.type" with value "HellaSwag (10-shot)" fails to match the required pattern: /^(?:[\w-]+\/)?[\w-.]+$/. "model-index[0].results[3].dataset.type" with value "ARC (25-shot)" fails to match the required pattern: /^(?:[\w-]+\/)?[\w-.]+$/. "model-index[0].results[4].dataset.type" with value "ThrutfulQA (0-shot)" fails to match the required pattern: /^(?:[\w-]+\/)?[\w-.]+$/