Floppanacci
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DeepSeek-R1-Distill-Qwen-7B-Floppanacci

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 library_name: transformers
 tags:
 - math
 license: mit
 datasets:
-- clement-cvll/QWQ-LongCOT-AIMO
 base_model:
 - deepseek-ai/DeepSeek-R1-Distill-Qwen-7B
 ---
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 library_name: transformers
 tags:
 - math
+- qwen2
+- aimo
 license: mit
 datasets:
+- Floppanacci/QWQ-LongCOT-AIMO
 base_model:
 - deepseek-ai/DeepSeek-R1-Distill-Qwen-7B
+language:
+- en
 ---
+# DeepSeek-R1-Distill-Qwen-7B Fine-tuned for AIMO Math Problems
+This model is a fine-tuned version of `deepseek-ai/DeepSeek-R1-Distill-Qwen-7B` on the [`Floppanacci/QWQ-LongCOT-AIMO`](https://huggingface.co/datasets/Floppanacci/QWQ-LongCOT-AIMO) dataset.
+## Model Description
+The model was fine-tuned to improve performance on mathematical reasoning tasks, particularly those involving step-by-step solutions (Chain-of-Thought) similar to problems found in the [AI Mathematical Olympiad (AIMO)](https://www.kaggle.com/competitions/ai-mathematical-olympiad-progress-prize-2) competition.
+It's trained on a dataset containing ~30k math questions paired with detailed solutions.
+An [AWQ quantized version](https://huggingface.co/Floppanacci/DeepSeek-R1-Distill-Qwen-7B-Floppanacci-AWQ) is also available for faster inference and reduced memory usage.
+## How to Use
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+model_id = "Floppanacci/DeepSeek-R1-Distill-Qwen-7B-Floppanacci"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.bfloat16, # or torch.float16
+    device_map="auto"
+)
+# Example Prompt (adjust based on how the model expects input)
+prompt = "Question: What is the value of $2+2$? Answer:"
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+# Generate
+outputs = model.generate(**inputs, max_new_tokens=8192, temperature=0.7, do_sample=True)
+response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(response)
+```
+## Training Data
+The model was fine-tuned on the train split of the [`Floppanacci/QWQ-LongCOT-AIMO`](https://huggingface.co/datasets/Floppanacci/QWQ-LongCOT-AIMO) dataset (29.5k examples).