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metadata
inference: false
tags:
  - generated_from_trainer
model-index:
  - name: starchat-beta
    results: []
license: bigcode-openrail-m
TheBlokeAI

HuggingFaceH4's Starchat Beta GGML

These files are GGML format model files for HuggingFaceH4's Starchat Beta.

Please note that these GGMLs are not compatible with llama.cpp, or currently with text-generation-webui. Please see below for a list of tools known to work with these model files.

Repositories available

Compatibilty

These files are not compatible with llama.cpp.

Currently they can be used with:

  • KoboldCpp, a powerful inference engine based on llama.cpp, with good UI: KoboldCpp
  • The ctransformers Python library, which includes LangChain support: ctransformers
  • The GPT4All-UI which uses ctransformers: GPT4All-UI
  • rustformers' llm
  • The example starcoder binary provided with ggml

As other options become available I will endeavour to update them here (do let me know in the Community tab if I've missed something!)

Tutorial for using GPT4All-UI

Provided files

Name Quant method Bits Size Max RAM required Use case
starchat-beta.ggmlv3.q4_0.bin q4_0 4 10.75 GB 13.25 GB Original llama.cpp quant method, 4-bit.
starchat-beta.ggmlv3.q4_1.bin q4_1 4 11.92 GB 14.42 GB Original llama.cpp quant method, 4-bit. Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models.
starchat-beta.ggmlv3.q5_0.bin q5_0 5 13.09 GB 15.59 GB Original llama.cpp quant method, 5-bit. Higher accuracy, higher resource usage and slower inference.
starchat-beta.ggmlv3.q5_1.bin q5_1 5 14.26 GB 16.76 GB Original llama.cpp quant method, 5-bit. Even higher accuracy, resource usage and slower inference.
starchat-beta.ggmlv3.q8_0.bin q8_0 8 20.11 GB 22.61 GB Original llama.cpp quant method, 8-bit. Almost indistinguishable from float16. High resource use and slow. Not recommended for most users.

Discord

For further support, and discussions on these models and AI in general, join us at:

TheBloke AI's Discord server

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.

Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.

Special thanks to: Luke from CarbonQuill, Aemon Algiz, Dmitriy Samsonov.

Patreon special mentions: Ajan Kanaga, Kalila, Derek Yates, Sean Connelly, Luke, Nathan LeClaire, Trenton Dambrowitz, Mano Prime, David Flickinger, vamX, Nikolai Manek, senxiiz, Khalefa Al-Ahmad, Illia Dulskyi, trip7s trip, Jonathan Leane, Talal Aujan, Artur Olbinski, Cory Kujawski, Joseph William Delisle, Pyrater, Oscar Rangel, Lone Striker, Luke Pendergrass, Eugene Pentland, Johann-Peter Hartmann.

Thank you to all my generous patrons and donaters!

Original model card: HuggingFaceH4's Starchat Beta

StarChat Beta Logo

Model Card for StarChat Beta

StarChat is a series of language models that are trained to act as helpful coding assistants. StarChat Beta is the second model in the series, and is a fine-tuned version of StarCoderPlus that was trained on an "uncensored" variant of the openassistant-guanaco dataset. We found that removing the in-built alignment of the OpenAssistant dataset boosted performance on the Open LLM Leaderboard and made the model more helpful at coding tasks. However, this means that model is likely to generate problematic text when prompted to do so and should only be used for educational and research purposes.

Model Details

Model Description

Model Sources [optional]

Intended uses & limitations

The model was fine-tuned on a variant of the OpenAssistant/oasst1 dataset, which contains a diverse range of dialogues in over 35 languages. As a result, the model can be used for chat and you can check out our demo to test its coding capabilities.

Here's how you can run the model using the pipeline() function from 🤗 Transformers:

import torch
from transformers import pipeline

pipe = pipeline("text-generation", model="HuggingFaceH4/starchat-beta", torch_dtype=torch.bfloat16, device_map="auto")

prompt_template = "<|system|>\n<|end|>\n<|user|>\n{query}<|end|>\n<|assistant|>"
prompt = prompt_template.format(query="How do I sort a list in Python?")
# We use a special <|end|> token with ID 49155 to denote ends of a turn
outputs = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.2, top_k=50, top_p=0.95, eos_token_id=49155)
# You can sort a list in Python by using the sort() method. Here's an example:\n\n```\nnumbers = [3, 1, 4, 1, 5, 9, 2, 6, 5, 3, 5]\nnumbers.sort()\nprint(numbers)\n```\n\nThis will sort the list in place and print the sorted list.

Bias, Risks, and Limitations

StarChat Alpha has not been aligned to human preferences with techniques like RLHF or deployed with in-the-loop filtering of responses like ChatGPT, so the model can produce problematic outputs (especially when prompted to do so). Models trained primarily on code data will also have a more skewed demographic bias commensurate with the demographics of the GitHub community, for more on this see the StarCoder dataset which is derived from The Stack.

Since the base model was pretrained on a large corpus of code, it may produce code snippets that are syntactically valid but semantically incorrect. For example, it may produce code that does not compile or that produces incorrect results.
It may also produce code that is vulnerable to security exploits.
We have observed the model also has a tendency to produce false URLs which should be carefully inspected before clicking.

StarChat Alpha was fine-tuned from the base model StarCoder Base, please refer to its model card's Limitations Section for relevant information. In particular, the model was evaluated on some categories of gender biases, propensity for toxicity, and risk of suggesting code completions with known security flaws; these evaluations are reported in its technical report.

Training and evaluation data

StarChat Beta is trained on an "uncensored" variant of the openassistant-guanaco dataset. We applied the same recipe used to filter the ShareGPT datasets behind the WizardLM.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 2e-05
  • train_batch_size: 4
  • eval_batch_size: 4
  • seed: 42
  • distributed_type: multi-GPU
  • num_devices: 8
  • gradient_accumulation_steps: 8
  • total_train_batch_size: 256
  • total_eval_batch_size: 32
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_ratio: 0.03
  • num_epochs: 6

Training results

Training Loss Epoch Step Validation Loss
1.5321 0.98 15 1.2856
1.2071 1.97 30 1.2620
1.0162 2.95 45 1.2853
0.8484 4.0 61 1.3274
0.6981 4.98 76 1.3994
0.5668 5.9 90 1.4720

Framework versions

  • Transformers 4.28.1
  • Pytorch 2.0.1+cu118
  • Datasets 2.12.0
  • Tokenizers 0.13.3

Citation

BibTeX:

@article{Tunstall2023starchat-alpha,
  author = {Tunstall, Lewis and Lambert, Nathan and Rajani, Nazneen and Beeching, Edward and Le Scao, Teven and von Werra, Leandro and Han, Sheon and Schmid, Philipp and Rush, Alexander},
  title = {Creating a Coding Assistant with StarCoder},
  journal = {Hugging Face Blog},
  year = {2023},
  note = {https://huggingface.co/blog/starchat},
}