ericrisco
/

gemma-3-4b-reasoning

@@ -3,199 +3,87 @@ library_name: transformers
 tags:
 - trl
 - grpo
 ---
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 tags:
 - trl
 - grpo
+license: apache-2.0
+datasets:
+- openai/gsm8k
+language:
+- en
+base_model:
+- google/gemma-3-4b-it
 ---
+# Gemma-3-4b Reasoning R1 Model Card
+Gemma-3-4b Reasoning is a transformer-based language model fine-tuned with GRPO (Group Reward Policy Optimization), leveraging the DeepSeek-R1 methodology. This model card describes the instructed version specifically optimized for reasoning tasks.
+The entire Gemma-3-4b Reasoning family is available under a permissive Apache 2.0 license. All training scripts and configurations used are publicly accessible.
 ## Model Details
+### Description
+Gemma-3-4b Reasoning is a reasoning-focused fine-tuned model designed to excel in structured, logical problem-solving and mathematical reasoning. The training was performed on the GSM8K dataset using GRPO, enhancing the model's ability to reason step-by-step and provide structured explanations.
+### Training Dataset
+- **GSM8K (English)**: Specialized dataset for mathematical and logical reasoning problems.
+### Intended Use
+#### Direct Use
+The model is specifically designed for structured reasoning tasks, including:
+- Mathematical and logical reasoning
+- Multi-step problem solving
+- Instruction-based reasoning
+#### Out-of-scope Use
+This model should not be used for unethical or malicious activities that breach legal and ethical standards.
+## How to Use
+The model uses structured XML templates for dialogue and reasoning tasks:
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+model_name = "ericrisco/gemma-3-4b-reasoning"
+prompt = "A cyclist travels 60 km in 3 hours at a constant speed. If he maintains the same speed, how many kilometers will he travel in 5 hours?"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name, device_map="auto", torch_dtype=torch.bfloat16
+)
+messages = [{"role": "user", "content": prompt}]
+input_text = tokenizer.apply_chat_template(
+    messages, tokenize=False, add_generation_prompt=True
+)
+inputs = tokenizer(input_text, return_tensors="pt").to("cuda")
+outputs = model.generate(**inputs, max_new_tokens=200)
+response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(response)
+```
+# Performance
+The **Gemma-3-4b Reasoning** model exhibits robust internal **Chain-of-Thought (CoT)** capabilities, consistently demonstrating detailed explanations and structured problem-solving skills across reasoning tasks.
+## Limitations
+The model is primarily optimized for **numeric and structured reasoning** and might produce less accurate or unexpected results when applied to unrelated tasks.
+## Citations
+- *Gemma Multimodal Reasoning Model* by Google
+- *GRPO Implementation* by TRL
+## Author
+**Eric Risco**