1kz-bigcodemax-GGUF

GGUF Files for bigcodemax

These are the GGUF files for 1kz/bigcodemax.

Downloads

Note from Flexan

I provide GGUFs and quantizations of publicly available models that do not have a GGUF equivalent available yet, usually for models I deem interesting and wish to try out.

If there are some quants missing that you'd like me to add, you may request one in the community tab. If you want to request a public model to be converted, you can also request that in the community tab. If you have questions regarding this model, please refer to the original model repo.

You can find more info about me and what I do here.

bigcodemax

Maximum coding + reasoning power in 8B parameters
Created by 1kz

An 8B model that punches way above its weight in code generation, software engineering, advanced reasoning, math, and long-context understanding.

Model Details

• Developer: 1kz
• Parameters: 8.0B (dense)
• Context length: 128K (RoPE scaled)
• Architecture: Llama-3.1 style (same tokenizer & chat template as Meta-Llama-3.1-8B-Instruct)
• Base model: Fine-tuned from a strong 8B checkpoint
• Training inspiration: Huge thanks to lfm for the incredible training recipes, data curation, synthetic data pipelines, and open methodology that made this model possible. Your work continues to inspire and push the frontier for compact high-performance models! ❤️

Strengths

• Best-in-class code generation, editing, and debugging
• Strong mathematical & logical reasoning (CoT & ToT)
• Excellent at understanding and refactoring large codebases
• Agentic coding, tool use, and multi-step problem solving
• Fast inference on consumer hardware (single 4090 / 24GB VRAM)

Quick Start

from transformers import pipeline

pipe = pipeline(
    "text-generation",
    model="1kz/bigcodemax",
    device_map="auto",
    torch_dtype="auto"
)

messages = [
    {"role": "system", "content": "You are bigcodemax, an expert coding and reasoning assistant."},
    {"role": "user", "content": "Implement a thread-safe LRU Cache in Python with O(1) operations and explain every design choice step-by-step."}
]

output = pipe(messages, max_new_tokens=2048, temperature=0.6, top_p=0.95, do_sample=True)
print(output[0]["generated_text"][-1]["content"])
Benchmarks (internal eval)



Massive thank you to lfm — without your public training logs, data mixing strategies, and relentless open-source experimentation, a model this capable at only 8B would not exist. You're building the future of accessible frontier intelligence. 🚀