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Model: GarvAgnihotri/Big-G-1
Source: Original Platform

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README.md

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+---
+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 ⭐

config.json

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+{
+    "architectures": [
+        "Qwen2ForCausalLM"
+    ],
+    "attention_dropout": 0.0,
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+    "torch_dtype": "float16",
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+    "hidden_act": "silu",
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+    "layer_types": [
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+        "full_attention",
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+    "max_window_layers": 36,
+    "model_type": "qwen2",
+    "num_attention_heads": 16,
+    "num_hidden_layers": 36,
+    "num_key_value_heads": 2,
+    "pad_token_id": 151665,
+    "rms_norm_eps": 1e-06,
+    "rope_parameters": {
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+        "rope_type": "default"
+    },
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+    "tie_word_embeddings": true,
+    "unsloth_fixed": true,
+    "unsloth_version": "2026.5.10",
+    "use_cache": false,
+    "use_mrope": false,
+    "use_sliding_window": false,
+    "vocab_size": 151936
+}

generation_config.json

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+  ],
+  "max_length": 32768,
+  "max_new_tokens": 2048,
+  "pad_token_id": 151665,
+  "transformers_version": "5.5.0"
+}

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