These files are GGML format model files for Bigcode's StarcoderPlus.
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.
- 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
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
starcoderbinary 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!)
|Name||Quant method||Bits||Size||Max RAM required||Use case|
|starcoderplus.ggmlv3.q4_0.bin||q4_0||4||10.75 GB||13.25 GB||Original llama.cpp quant method, 4-bit.|
|starcoderplus.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.|
|starcoderplus.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.|
|starcoderplus.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.|
|starcoderplus.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.|
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Play with the instruction-tuned StarCoderPlus at StarChat-Beta.
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: email@example.com
- Languages: English & 80+ Programming languages
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!
# 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))
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))
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.
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.
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:
- Architecture: GPT-2 model with multi-query attention and Fill-in-the-Middle objective
- Finetuning steps: 150k
- Finetuning tokens: 600B
- Precision: bfloat16
- GPUs: 512 Tesla A100
- Training time: 14 days
The model is licensed under the BigCode OpenRAIL-M v1 license agreement. You can find the full agreement here.
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Inference API has been turned off for this model.