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Model: sid172002/deepseek-math-7b-3epoch-678k-fullft
Source: Original Platform

README.md

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+---
+language: en
+license: apache-2.0
+library_name: transformers
+base_model: deepseek-ai/deepseek-math-7b-base
+tags:
+  - mathematics
+  - math
+  - reasoning
+  - deepseek
+  - fine-tuned
+  - llama
+  - causal-lm
+  - competition-math
+  - gsm8k
+  - numinamath
+  - metamath
+  - aime
+  - amc
+  - olympiad
+datasets:
+  - AI-MO/NuminaMath-CoT
+  - meta-math/MetaMathQA
+  - HuggingFaceH4/MATH-Train
+metrics:
+  - accuracy
+  - perplexity
+model-index:
+  - name: deepseek-math-7b-3epoch-678k-fullft
+    results:
+      - task:
+          type: text-generation
+          name: Mathematical Reasoning
+        dataset:
+          type: gsm8k
+          name: GSM8K
+        metrics:
+          - type: accuracy
+            value: 82.0
+pipeline_tag: text-generation
+inference: true
+---
+
+# 🚀 DeepSeek Math 7B - Full Fine-tune
+
+[![GSM8K](https://img.shields.io/badge/GSM8K-82.0%25-brightgreen)](.)
+[![Open LLM Leaderboard](https://img.shields.io/badge/Open%20LLM%20Leaderboard-Submitted-orange)](.)
+
+**Model ID:** `sid172002/deepseek-math-7b-3epoch-678k-fullft`
+
+Fully fine-tuned DeepSeek Math 7B on 678K high-quality math problems. **+17.8% improvement** over base model on GSM8K (64.2% → 82.0%).
+
+## 📊 Quick Stats
+
+| Metric | Value |
+|--------|-------|
+| Parameters | 7 Billion |
+| Training Steps | 63,609 |
+| Epochs | 3.0 |
+| Dataset Size | 678,494 samples |
+| GSM8K Score | **82.0%** |
+| Training Loss | 0.6394 |
+| Eval Loss | 0.6411 |
+
+## 🏆 Benchmarks
+
+| Benchmark | Score | Base | Improvement |
+|-----------|-------|------|-------------|
+| **GSM8K** | **82.0%** | 64.2% | **+17.8%** |
+| **MathBench** | **92.0%** | ~70% | **+22%** |
+| **MMLU** | Pending | - | Leaderboard Eval |
+| **MATH** | TBD | 33.2% | In Progress |
+
+## 💻 Quick Start
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained(
+    "sid172002/deepseek-math-7b-3epoch-678k-fullft",
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+    trust_remote_code=True
+)
+tokenizer = AutoTokenizer.from_pretrained(
+    "sid172002/deepseek-math-7b-3epoch-678k-fullft",
+    trust_remote_code=True
+)
+
+# Solve math problem
+problem = "What is 2x + 5 = 13?"
+prompt = f"Solve step by step:\n\n{problem}\n\nSolution:\n"
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+outputs = model.generate(**inputs, max_new_tokens=256)
+print(tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))