Big-G-1

Big-G-1 is a small 3 billion parameter model I trained for code completion. It's built on top of Qwen2.5-Coder-3B and fine-tuned to be good at filling in missing parts of code — you give it the code before and after the gap, and it figures out what goes in the middle.

I made this because I wanted a lightweight model that actually works well for real coding tasks without needing a powerful GPU.

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What it does

It's trained for something called FIM (Fill in the Middle). Instead of just predicting what comes next, it can look at code before AND after a gap and fill it in. This makes it really useful for code editors and autocomplete tools.

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How to use it

Install the library first:

pip install transformers torch

Then run this:

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

tokenizer = AutoTokenizer.from_pretrained("GarvAgnihotri/big-g-1")
model = AutoModelForCausalLM.from_pretrained(
    "GarvAgnihotri/big-g-1",
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# The code before the gap
prefix = "def add_numbers(a, b):\n    "

# The code after the gap
suffix = "\n    return result"

# Build the prompt
prompt = f"<|fim_prefix|>{prefix}<|fim_suffix|>{suffix}<|fim_middle|>"

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=64, temperature=0.1)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

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Good for

• Code completion inside editors
• Filling in missing lines of code
• Lightweight local code assistant

Not great for

• General chat or questions
• Long reasoning tasks
• Non-English code comments

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Model info

Thing	Detail
Base model	Qwen2.5-Coder-3B
Parameters	3 billion
Format	BF16, Safetensors
License	Apache 2.0
Trained with	Unsloth + HuggingFace TRL

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Benchmarks

Tested on 15 custom FIM tasks against the base Qwen2.5-Coder-3B:

Model	Score	Passed
Big-G-1 (this model)	93.3%	14/15
Qwen2.5-Coder-3B (base)	100%	15/15

The one failed case (String reverse) was not a logic error — the model started a valid solution but got cut off due to the 80 token limit used in testing. With a higher token limit (256+) the completion finishes correctly.

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Made by GarvAgnihotri. If you find it useful, leave a like ⭐