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---
base_model:
- google/gemma-3-4b-pt
pipeline_tag: text-generation
library_name: transformers
---
# **thinkygemma-4b: your average fake reasoner**
Fine-tuned from **Gemma-3-4b-pt**
📌 **Model ID:** `xsanskarx/thinkygemma-4b`
📌 **Parameters trained:** **1.8 billion**
📌 **Trained on:** **25k rows of verified Chain-of-Thought (CoT) traces** from **DeepSeek R1** and **Qwen QWQ**
📌 **Next planned step:** **GRPO**
📌 **adapters repo:** `xsanskarx/thinkgemma-4b`
---
## **Model Description**
This is a **fine-tuned version of Google's Gemma-3-4b-it**, adapted for **structured reasoning / fake induced reasoning **. It is designed to excel in acting like a great reasoner**.
### **Training Details**
- **Hardware:** Single NVIDIA **H100**
- **Training Time:** **9 hours (1 epoch)**
- **Training Method:** **LoRA fine-tuning (r = 128, alpha = 256)**
- **Dataset:** **25k CoT traces**
- **Base Model:** `google/gemma-3-4b-it`
---
### **Setup**
```python
from transformers import AutoTokenizer, Gemma3ForConditionalGeneration, TextStreamer
import torch
# Load model and tokenizer
model_id = "xsanskarx/thinkygemma-4b"
model = Gemma3ForConditionalGeneration.from_pretrained(model_id, device_map="auto").eval()
tokenizer = AutoTokenizer.from_pretrained(model_id)
def ask_model(prompt: str, max_tokens=8192, temperature=0.7):
"""
Function to ask a question to the model and stream the response.
"""
messages = [
{"role": "system", "content": "You are an expert math problem solver, think and reason inside <think> tags, enclose all reasoning in <think> tags, verifying logic step by step and then return your final structured answer"},
{"role": "user", "content": prompt}
]
formatted_prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(formatted_prompt, return_tensors="pt").to(model.device)
streamer = TextStreamer(tokenizer, skip_special_tokens=True)
with torch.inference_mode():
model.generate(**inputs, max_new_tokens=max_tokens, do_sample=True, temperature=temperature, streamer=streamer)
# Example usage
ask_model("do 2+2") |