Evolve Mistral: Fine-Tuned Mistral-7B-Instruct for AI CRUD & Code Generation

This is a fine-tuned version of mistralai/Mistral-7B-Instruct-v0.2, adapted specifically for code generation, schema-driven CRUD reasoning, and full-stack boilerplate automation. It powers the AI agent layer behind the Self-Revolve project.

Project Context: Self-Revolve

Evolve Mistral is a fine-tuned open-source model purpose-built for powering code generation in the Self-Revolve project.

“Instantly generate full-stack admin panels, APIs, and UIs from your database schema—powered by AI agents & LLMs.”

Key capabilities:

Auto-generates CRUD APIs from DB schemas
Generates React/MUI admin interfaces
Supports SQL & NoSQL databases
Works without OpenAI keys
Open-source & self-hostable

Dataset

kramster/crud-code-tests
A high-quality Alpaca-style dataset focused on database and backend code generation. Each example contains:

instruction
input
output

Training Setup

Detail	Value
Base model	`mistralai/Mistral-7B-Instruct-v0.2`
Dataset	`crud-code-tests` (Alpaca-style)
LoRA Config	r=32, alpha=16
Framework	Axolotl + DeepSpeed + LoRA
Epochs	~3.94
Steps	51
Precision	bfloat16
GPU	NVIDIA H100 80GB
Duration	~10m
Train Loss	0.0909
Eval Loss	0.1012
FLOPs	~347.6 trillion

Evaluation Summary

Eval runtime: 2.84s
Samples/sec: 2.11
Steps/sec: 1.05
Final learning rate: 2.93e-7
Gradient norm: 0.064

Example Usage (VLLM)

vllm-api-server \
  --model kramster/evolve-mistral \
  --max-model-len 64000 \
  --rope-scaling '{"rope_type":"yarn","factor":4.0,"original_max_position_embeddings":32768}' \
  --no-enable-prefix-caching

kramster
/

evolve-mistral