Text Generation
Transformers
Safetensors
English
olmo3
code
reasoning
coding
instruct
8b
1kz
lfm-inspiration
conversational
Instructions to use 1kz/bigcodemax with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use 1kz/bigcodemax with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="1kz/bigcodemax") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("1kz/bigcodemax") model = AutoModelForCausalLM.from_pretrained("1kz/bigcodemax") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use 1kz/bigcodemax with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "1kz/bigcodemax" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "1kz/bigcodemax", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/1kz/bigcodemax
- SGLang
How to use 1kz/bigcodemax with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "1kz/bigcodemax" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "1kz/bigcodemax", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "1kz/bigcodemax" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "1kz/bigcodemax", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use 1kz/bigcodemax with Docker Model Runner:
docker model run hf.co/1kz/bigcodemax
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! โค๏ธ
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 โ 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. ๐
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