Input
text to generate from
maximum length of the newly generated generated text.If explicitly set to None it will be the model's max context length minus input length. (Default: 512, 1 ≤ max_new_tokens ≤ 1000000)
Temperature
temperature to use for sampling. 0 means the output is deterministic. Values greater than 1 encourage more diversity (Default: 0.7, 0 ≤ temperature ≤ 100)
Sample from the set of tokens with highest probability such that sum of probabilies is higher than p. Lower values focus on the most probable tokens.Higher values sample more low-probability tokens (Default: 0.9, 0 < top_p ≤ 1)
Min P
Float that represents the minimum probability for a token to be considered, relative to the probability of the most likely token. Must be in [0, 1]. Set to 0 to disable this. (Default: 0, 0 ≤ min_p ≤ 1)
Sample from the best k (number of) tokens. 0 means off (Default: 0, 0 ≤ top_k < 1000)
Repetition Penalty
repetition penalty. Value of 1 means no penalty, values greater than 1 discourage repetition, smaller than 1 encourage repetition. (Default: 1, 0.01 ≤ repetition_penalty ≤ 5)
stop
You can add more items with the button on the right
Num Responses
Number of output sequences to return. Incompatible with streaming (Default: 1, 1 ≤ num_responses ≤ 4)
How to format the response 2
Presence Penalty
Positive values penalize new tokens based on whether they appear in the text so far, increasing the model's likelihood to talk about new topics. (Default: 0, -2 ≤ presence_penalty ≤ 2)
Frequency Penalty
Positive values penalize new tokens based on how many times they appear in the text so far, increasing the model's likelihood to talk about new topics. (Default: 0, -2 ≤ frequency_penalty ≤ 2)
A unique identifier representing your end-user, which can help monitor and detect abuse. Avoid sending us any identifying information. We recommend hashing user identifiers.. (Default: empty)
Seed for random number generator. If not provided, a random seed is used. Determinism is not guaranteed. (Default: empty, 0 ≤ seed < 9223372036854776000)
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Output
I have this dream about the day I got a job at a tech company. I just woke up on a plane. I sat down on the floor and started getting work done. After getting up around 6 p.m., I looked around and
StarCoder2
Model Summary
StarCoder2-15B model is a 15B parameter model trained on 600+ programming languages from The Stack v2, with opt-out requests excluded. The model uses Grouped Query Attention, a context window of 16,384 tokens with a sliding window attention of 4,096 tokens, and was trained using the Fill-in-the-Middle objective on 4+ trillion tokens.
The model was trained with NVIDIA NeMo™ Framework using the NVIDIA Eos Supercomputer built with NVIDIA DGX H100 systems.
- Project Website: bigcode-project.org
- Paper: Link
- Point of Contact: contact@bigcode-project.org
- Languages: 600+ Programming languages
Use
Intended use
The model was trained on GitHub code as well as additional selected data sources such as Arxiv and Wikipedia. As such it is not an instruction model and commands like "Write a function that computes the square root." do not work well.
Attribution & Other Requirements
The pretraining dataset of the model was filtered for permissive licenses and code with no license 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 source code from 600+ programming languages. The predominant language in source is English although other languages are also present. As such the model is capable to generate code snippets provided some context but the generated code is not guaranteed to work as intended. It can be inefficient, contain bugs or exploits. See the paper for an in-depth discussion of the model limitations.
Training
Model
- Architecture: Transformer decoder with grouped-query and sliding window attention and Fill-in-the-Middle objective
- Pretraining steps: 1 million
- Pretraining tokens: 4+ trillion
- Precision: bfloat16
Software
- Framework: NeMo Framework
- Neural networks: PyTorch
License
The model is licensed under the BigCode OpenRAIL-M v1 license agreement. You can find the full agreement here.
