---
language:
- en
license: apache-2.0
tags:
- code
- fim
- code-completion
- qwen2
- unsloth
- text-generation-inference
base_model: GarvAgnihotri/Big-G-3B-FIM-merged
pipeline_tag: text-generation
---

# 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.

---

## 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.

---

## How to use it

Install the library first:

```
pip install transformers torch
```

Then run this:

```python
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))
```

---

## 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

---

## 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 |

---

## 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.

---

Made by [GarvAgnihotri](https://huggingface.co/GarvAgnihotri). If you find it useful, leave a like ⭐