aaravriyer193/chimpgpt-coder-elite

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🦍 Overview

ChimpGPT-Coder-Elite is a state-of-the-art, fine-tuned programming assistant built on the massive 30B parameter GLM-4.7-Flash Mixture-of-Experts (MoE) architecture.

It has been rigorously trained on a highly refined, 12,000-sample dataset (Chimp-GPT-Code-Refined-12k) to eradicate conversational boilerplate, hallucinated syntax, and context-loss. It does one thing, and it does it with lethal precision: it writes elite-level code.

⚡ Key Features

• Zero-Shot Alignment: Hard-wired to bypass chatbot pleasantries and deliver structural code immediately.
• 16k Context Window: Flawlessly handles long-range file refactoring without losing variable logic.
• MoE Routing Mastery: Hyper-optimized expert routing for logic-dense languages (Python, Rust, TS, C++).
• VRAM Efficient: Fully merged weights optimized for blazing-fast 4-bit inference on NVIDIA L4 (24GB) and A100 GPUs.

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🚀 Quickstart (Inference)

ChimpGPT-Coder-Elite is merged and ready for immediate deployment. You do not need peft to run this model.

Prerequisites

pip install torch transformers accelerate bitsandbytes

Python CLI Snippet (L4 / A100 Optimized)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig

MODEL_ID = "aaravriyer193/chimpgpt-coder-elite"
SYSTEM_PROMPT = "You are Coder-Elite, a world-class programming assistant. Provide concise, bug-free, and high-performance code."

# 1. Load Tokenizer
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, trust_remote_code=True)

# 2. Strict 4-bit Configuration
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_use_double_quant=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16 
)

# 3. Load Model (Direct to VRAM)
model = AutoModelForCausalLM.from_pretrained(
    MODEL_ID,
    quantization_config=bnb_config,
    device_map={"": 0}, # Bypasses CPU offload errors
    trust_remote_code=True,
    attn_implementation="sdpa" 
)
model.eval()

# 4. Generate
user_input = "Write a high-performance FastAPI endpoint with background tasks."
prompt = f"<|system|>\n{SYSTEM_PROMPT}\n<|user|>\n{user_input}\n<|assistant|>\n"

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=1024,
        do_sample=True,
        temperature=0.1, # Strict logic mode
        top_p=0.9,
        eos_token_id=tokenizer.eos_token_id
    )

response = tokenizer.decode(outputs[0][len(inputs["input_ids"][0]):], skip_special_tokens=True)
print(response)

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🧠 Training Architecture

This model was trained using a custom hardware-aligned pipeline on an NVIDIA A100.

• Method: Low-Rank Adaptation (LoRA)
• Rank (r): 64
• Alpha: 128
• Target Modules: all-linear
• Learning Rate: 2e-4 (Cosine Scheduler)
• Precision: bfloat16 with 4-bit quantized base weights during training.
• Final Loss: < 0.60 (Targeting the structural "Goldilocks Zone" to prevent overfitting).

Dataset

Trained exclusively on aaravriyer193/Chimp-GPT-Code-Refined-12k. The dataset was formatted using a strict <|system|>, <|user|>, <|assistant|> chat template to enforce structural discipline.

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⚠️ Limitations & Hardware Requirements

• VRAM Minimum: Running this model requires at least 20GB of VRAM (e.g., NVIDIA L4, RTX 3090, RTX 4090) when using 4-bit quantization.
• Precision: The model was explicitly merged in bfloat16. Attempting to load this in float32 will result in massive Out-of-Memory (OOM) errors.

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Trained with 🍌 by aaravriyer193 for free on an A100 and RTX pro 6000 on Lightning AI