Nondzu/Mistral-7B-Instruct-v0.2-code-ft
TEXT GENERATIONConcurrency Cost:1Model Size:7BQuant:FP8Ctx Length:4kPublished:Dec 21, 2023License:cc-by-nc-nd-4.0Architecture:Transformer0.0K Open Weights Cold
Nondzu/Mistral-7B-Instruct-v0.2-code-ft is a 7 billion parameter instruction-tuned causal language model, based on Mistral-7B-Instruct-v0.2, specifically fine-tuned for coding assistance and co-pilot functionalities. It features a 16384 token sequence length with sample packing and was trained on the Code-74k-ShareGPT dataset. This model is optimized to enhance code generation and understanding tasks.
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Nondzu/Mistral-7B-Instruct-v0.2-code-ft Overview
This model is an instruction-tuned variant of the Mistral-7B-Instruct-v0.2 base model, developed by Nondzu, with a strong focus on enhancing coding assistance and co-pilot capabilities. It incorporates a 7 billion parameter architecture and supports an extended sequence length of 16384 tokens through sample packing.
Key Capabilities & Features
- Code-centric Fine-tuning: Specifically trained on the
ajibawa-2023/Code-74k-ShareGPTdataset to improve performance in code generation, completion, and understanding tasks. - Base Model Enhancements: Builds upon the robust Mistral-7B-Instruct-v0.2, leveraging its strong foundational language understanding.
- Optimized Training: Utilizes QLoRA adapter,
adamw_bnb_8bitoptimizer,bf16training, gradient checkpointing, and flash attention for efficient and effective fine-tuning. - ChatML Prompt Template: Designed to work with the ChatML prompt format for structured conversational interactions.
- Quantized Versions Available: Links to various quantized versions (EXL2, GGUF, AWQ, GPTQ) are provided for broader deployment flexibility.
Performance & Use Cases
- Eval Plus Score: Achieves a score of 0.421 on Eval Plus, indicating its proficiency in coding benchmarks.
- Ideal for: Developers seeking an open-source model for code generation, debugging assistance, and integrating co-pilot features into applications. Its specialized training makes it a strong candidate for programming-related tasks.