uukuguy/speechless-coding-7b-16k-tora

<p><h1> speechless-coding-7b-16k-tora </h1></p>

Use the following dataset to fine-tune llm_agents/tora-code-7b-v1.0 in order to improve the model's reasoning and planning abilities.

context window length: 16,384 prompttype = "alpaca" maxtokens > 128 && < 16384

Total 177,333 samples 316 MB

• —jondurbin/airoboros-2.2: Filter categories related to coding, reasoning and planning. 21,923 samples.
• —Open-Orca/OpenOrca: Filter the 'cot' category in 1M GPT4 dataset. 62,973 samples.
• —garage-bAInd/Open-Platypus: 100%, 22,760 samples.
• —WizardLM/WizardLMevolinstructV2196k: Coding coversation part. 30,081 samples
• —TokenBender/pythonevalinstruct_51k: “python” in output .39,596 samples

50 samples/T=0.2/MaxTokens=512/Top_P=0.95

Code: https://github.com/uukuguy/speechless

How to Prompt the Model

This model accepts the Alpaca instruction format.

For example:

You are an intelligent programming assistant.

### Instruction:
Implement a linked list in C++

### Response:

HumanEval

Big Code Models Leaderboard

CodeLlama-34B-Python: 53.29

CodeLlama-34B-Instruct: 50.79

CodeLlama-13B-Instruct: 50.6

CodeLlama-34B: 45.11

CodeLlama-13B-Python: 42.89

CodeLlama-13B: 35.07

MultiPL-E

LMEval

Open LLM Leaderboard | Metric | Value | | --- | --- | | ARC | | | HellaSwag | | | MMLU | | | TruthfulQA | | | Average | |

Parameters

A100-40G x 4