uukuguy/speechless-code-mistral-7b-v2.0

TEXT GENERATIONPricing:Input $0.4 / Cached $0.08 / Output $0.8Concurrent Unit Cost:1Model Size:7BQuant:FP8Context Size:8kTool Calling:SupportedPublished:Dec 9, 2023License:apache-2.0Architecture:Transformer0.0K Open Weights Featherless Exclusive Cold

uukuguy/speechless-code-mistral-7b-v2.0 is a 7 billion parameter Mistral-7B-v0.1 based model fine-tuned by uukuguy. This model is specifically optimized for enhanced reasoning, planning, and code generation capabilities, leveraging a diverse dataset focused on coding, logical reasoning, and mathematical tasks. It is designed to excel in complex problem-solving scenarios requiring both code understanding and strategic thinking.

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Model Overview

uukuguy/speechless-code-mistral-7b-v2.0 is a 7 billion parameter language model built upon the mistralai/Mistral-7B-v0.1 architecture. This iteration, developed by uukuguy, focuses on significantly improving the model's reasoning, planning, and code generation abilities through a targeted fine-tuning process.

Training Data & Enhancements

The model was fine-tuned on a substantial dataset of 343,370 samples (603 MB), meticulously curated from various sources to bolster its core strengths:

  • Coding & Reasoning: Datasets like jondurbin/airoboros-2.2 (filtered for coding, reasoning, planning), WizardLM/WizardLM_evol_instruct_V2_196k (coding conversations), TokenBender/python_eval_instruct_51k (Python output), OpenHermes (code blocks), and ise-uiuc/Magicoder-OSS-Instruct-75K were used.
  • Logical & Mathematical Reasoning: Contributions from Open-Orca/OpenOrca (COT category), garage-bAInd/Open-Platypus, and meta-math/MetaMathQA (20% of 395K samples) were integrated to enhance its problem-solving prowess.

Performance & Benchmarks

While specific humaneval-python and lm-evaluation-harness scores are not detailed in the provided README, the model's training regimen suggests a strong focus on competitive performance in code-related tasks. For context, the README references the Big Code Models Leaderboard and the Open LLM Leaderboard, indicating an ambition to compete with established code-centric models like CodeLlama variants.

Ideal Use Cases

This model is particularly well-suited for applications requiring:

  • Code Generation and Completion: Especially for Python-based tasks.
  • Complex Problem Solving: Where logical reasoning and planning are crucial.
  • Mathematical and Scientific Inquiry: Benefiting from its MetaMathQA training.
  • Instruction Following: For tasks demanding precise and structured outputs.

Finetunes
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