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AI / RESEARCHAI Model

M31Tesla

M31Tesla — standalone runtime and experimental research checkpoints for the custom M31-Python-Agent-220M-v5 model.

STATUS
Experimental
STAGE
RESEARCH
VERSION
v5 (Python-Agent)
LICENSE
MIT
Hugging Face Model Card & RuntimeCheckpoints / MilestonesPortfolio Economics
Experimental Research Notice

Experimental research checkpoints across pretrain, sft, agent, and repair stages.

LIFECYCLE STATUS

Experimental

Experimental research artifact

DEVELOPMENT STAGE

RESEARCH

Active research & evaluation

CLASSIFICATION

AI Model

228.9M

AFFILIATION & LICENSE

TIV · MIT

Tonmoy Infrastructure & Vision

OVERVIEW

About M31Tesla

M31Tesla — standalone runtime and experimental research checkpoints for the custom M31-Python-Agent-220M-v5 model.

THE PROBLEM

Why this system exists

Autonomous code repair agents require lightweight, low-latency models that can run in resource-constrained environments to diagnose and patch Python code regressions.

OUR APPROACH

Engineering philosophy & approach

M31Tesla pairs a 228.9M parameter custom model architecture with a standalone Python runtime (m31_runtime.py and generation_m31.py), trained across pretraining, SFT, agent, and repair milestones.

ARCHITECTURE

System architecture

228,953,984 parameters; vocab 32,768; hidden 896; 20 layers; 14 query heads; 7 KV heads; intermediate 2,816; Grouped-Query Attention (GQA); RMSNorm; RoPE theta 10,000; tied embeddings. Defaults to 512-token working context, supporting positions up to 2,048.

CAPABILITIES

Core system capabilities

  • Compact 228.9M parameter footprint
  • Grouped-Query Attention with 14 query / 7 KV heads
  • Multi-stage checkpoints: pretrain, SFT, agent, repair
  • Standalone lightweight Python runtime included in repository
AI MODEL ARCHITECTURE & VERIFIED PARAMETERS

Technical Model Specification

Architecture Summary

228,953,984 parameters; vocab 32,768; hidden 896; 20 layers; 14 query heads; 7 KV heads; intermediate 2,816; GQA; RMSNorm; RoPE theta 10,000; tied embeddings.

Verified Intended Use Cases
✓Python code repair experimentation
✓Agentic workflow execution
✓Lightweight runtime testing
TECHNICAL SPECIFICATION

Overview

M31Tesla is an experimental AI research project providing the standalone runtime and checkpoints for the custom M31-Python-Agent-220M-v5 model.

Fact Verification: Project Name & Scope

Note: In accordance with TIV's strict transparency rules, the project name M31Tesla does not imply any integration with or endorsement by Tesla Inc. It is strictly an experimental model identifier in the M31 AI research lineage.

Architecture

  • —Parameter Count: 228,953,984 parameters
  • —Layers: 20 layers
  • —Hidden Size: 896
  • —Attention Heads: 14 query heads, 7 KV heads (GQA)
  • —Vocabulary Size: 32,768
  • —Context Length: 512 default context (up to 2,048 position embeddings)
  • —Normalization & Encodings: RMSNorm, RoPE theta 10,000, tied embeddings

Runtime & Checkpoints

Checkpoints and runtime files are hosted at eshanized/M31Tesla on Hugging Face. The project includes README_RUNTIME.md, m31_runtime.py, and generation_m31.py for direct checkpoint loading and text generation.

ROADMAP & MILESTONES

Milestones & active developments

4 / 5 COMPLETED
  • Architecture design and tokenizer configuration
  • Pretrain checkpoint series (step-00000500 to step-00001832)
  • SFT and repair stage checkpoints
  • Standalone runtime generation scripts
  • Multi-file code repair evaluation
CHECKPOINTS & HISTORY

Checkpoint lineage & milestones

Step-wise checkpoints and lineage updates for M31Tesla.

v5 (Python-Agent) — Verified Milestone

Experimental checkpoints verified against public repositories.

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