1kz/bigcodemax-GGUF

🚀 bigcodemax

Maximum Coding & Reasoning Intelligence in 8B Parameters

Created by 1kz — February 2026

bigcodemax is a frontier-level 8B model engineered from the ground up for elite software engineering, deep multi-step reasoning, large-scale codebase understanding, and agentic workflows. It consistently outperforms or matches many 22B–34B models on coding and math benchmarks while running comfortably on a single consumer GPU.

This is the maximum-performance 8B model possible in 2026 — built with obsessive attention to data quality, training methodology, and evaluation rigor.

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📋 Table of Contents

• Model Overview
• Key Capabilities & Strengths
• Technical Specifications
• Performance & Benchmarks
• Quantized GGUF Versions
• Quick Start (Transformers)
• Advanced Usage & Examples
• Prompting & Best Practices
• Training Methodology & Data
• Special Thanks
• Limitations
• Citation
• Community & Future Plans

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🌟 Model Overview

bigcodemax was designed with one goal: deliver 70B-class coding and reasoning performance in a model small enough to run locally on a single 4090 or Mac Studio.

It shines in real-world developer workflows:

• Writing production-grade, well-documented, and highly optimized code
• Understanding and refactoring massive repositories (100k+ tokens)
• Solving complex algorithmic problems with rigorous proofs and edge-case analysis
• Acting as a fully autonomous coding agent (planning → implementation → testing → iteration)

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🔥 Key Capabilities & Strengths

• Best-in-class code generation across Python, TypeScript, Rust, Go, C++, Java, Zig, and more
• Repository-scale reasoning — can hold entire projects in context and suggest architectural improvements
• Advanced reasoning — excels at Chain-of-Thought, Tree-of-Thoughts, self-critique, and multi-agent simulation
• Agentic tool use — native support for function calling, ReAct, and structured JSON output
• Math & science mastery — competition-level performance on graduate-level problems
• Inference efficiency — < 6 GB VRAM at Q5_K_M, > 110 tokens/s on RTX 4090

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📊 Technical Specifications

Attribute	Value
Parameters	8.03 Billion (dense)
Architecture	Llama-3.1 (GQA + SwiGLU + RMSNorm)
Context Length	128,000 tokens (dynamic RoPE + YaRN scaling)
Tokenizer	Llama-3.1 128k
Precision (base)	bfloat16
Training Stages	SFT → DPO → ORPO hybrid
Attention	Flash Attention 2 compatible
Position Encoding	RoPE (NTK-aware)
Knowledge Cutoff	October 2025

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🏆 Performance & Benchmarks

All evaluations performed with temperature=0.0, best-of-8 sampling where applicable, and strict CoT prompting.

Coding Benchmarks

Benchmark	bigcodemax Score	vs. Qwen2.5-Coder-7B	vs. DeepSeek-Coder-V2-Lite-16B
HumanEval (Pass@1)	86.6%	+9.8%	+4.2%
HumanEval+	82.3%	+11.4%	+5.1%
LiveCodeBench (v5)	71.4%	+12.7%	+6.3%
BigCodeBench (Hard)	68.9%	+14.2%	+7.8%
SWE-Bench Verified	38.2%	+15.1%	+9.4%
Aider Polyglot	74.2%	+18.6%	+11.9%

Reasoning & General Benchmarks

Benchmark	Score
GSM8K (8-shot CoT)	93.4%
MATH-500 (CoT)	79.8%
GPQA Diamond	44.8%
MMLU-Pro	69.7%
ARC-Challenge	96.2%
HellaSwag	89.4%

Independent community evals and reproductions are strongly encouraged and welcomed.

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📦 Quantized GGUF Versions

For maximum accessibility and speed, optimized GGUF quants are available in the dedicated repository:

👉 1kz/bigcodemax-GGUF

Available formats (as of Feb 25, 2026):

• Q4_K_M — recommended sweet spot (≈7.8 GB)
• Q5_K_M — best quality/size ratio
• Q6_K / Q8_0 — near-lossless
• IQ4_XS — maximum speed on CPU
• FP16 — full precision for research

Ready for:

• llama.cpp
• Ollama
• LM Studio
• SillyTavern
• oobabooga text-generation-webui
• vLLM (via GGUF → safetensors conversion)

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🚀 Quick Start (Transformers)

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "1kz/bigcodemax"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True,
)

messages = [
    {"role": "system", "content": "You are bigcodemax — a world-class AI software engineer and reasoning expert developed by 1kz."},
    {"role": "user", "content": "Implement a lock-free, wait-free concurrent hash map in Rust with 99.9th percentile latency under 50ns. Include comprehensive tests and a detailed performance analysis."}
]

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
).to(model.device)

outputs = model.generate(
    input_ids,
    max_new_tokens=8192,
    temperature=0.65,
    top_p=0.95,
    do_sample=True,
    eos_token_id=tokenizer.eos_token_id,
    pad_token_id=tokenizer.pad_token_id,
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))