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--- |
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license: apache-2.0 |
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base_model: Qwen/Qwen2-1.5B-Instruct |
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language: |
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- en |
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tags: |
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- reasoning |
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- math |
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- gsm8k |
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- chain-of-thought |
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pipeline_tag: text-generation |
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--- |
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# 🧠 ARIES 1.5B - Reasoning Language Model |
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A 1.5B parameter reasoning model fine-tuned with custom reasoning tokens for step-by-step mathematical problem solving. |
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## 📊 Model Details |
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- **Architecture:** Qwen2-1.5B-Instruct (base) + Custom Reasoning Tokens |
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- **Parameters:** 1.54B |
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- **Training Method:** Fine-tuned on GSM8K with reasoning token integration |
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- **Special Tokens:** `<think>`, `<context>`, `<answer>`, `<end>` |
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- **Training Loss:** 0.2130 |
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- **Version:** v1.0-finetuned |
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## 🎯 What Makes This Model Special |
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This model extends Qwen2-1.5B with: |
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1. **Custom reasoning tokens** for structured thought processes |
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2. **Step-by-step explanation** capabilities |
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3. **GSM8K-style notation** support (`<<calculation=result>>`) |
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4. **Chain-of-thought reasoning** integration |
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## 📝 Usage |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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import torch |
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model = AutoModelForCausalLM.from_pretrained( |
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"ziadrone/aries-1.5b-reasoning", |
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torch_dtype=torch.bfloat16, |
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device_map="auto" |
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) |
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tokenizer = AutoTokenizer.from_pretrained("ziadrone/aries-1.5b-reasoning") |
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prompt = "<think> What is 25 + 17?" |
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inputs = tokenizer(prompt, return_tensors="pt").to("cuda") |
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outputs = model.generate( |
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**inputs, |
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max_new_tokens=100, |
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temperature=0.7, |
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top_p=0.9 |
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) |
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print(tokenizer.decode(outputs[0], skip_special_tokens=False)) |
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``` |
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## 🧪 Example Outputs |
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**Addition:** |
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``` |
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Input: <think> What is 25 + 17? |
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Output: The answer to 25 + 17 is 42. |
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Explanation: We add the two numbers together. |
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25 + 17 = <<25+17=42>>42 |
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``` |
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**Word Problems:** |
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``` |
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Input: <think> If I have $50 and spend $23, how much is left? |
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Output: You are left with 27 dollars. |
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If you have $50 and spend $23, then the amount left will be |
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$50 - $23 = <<50-23=27>>27 |
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``` |
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**Distance Problems:** |
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``` |
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Input: <think> A train travels 60 mph for 3 hours. How far does it go? |
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Output: It goes 180 miles because 60 times 3 is 180. |
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<answer> 180 <end> |
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``` |
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## 📈 Training Details |
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- **Dataset:** GSM8K (1,500 training examples) |
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- **Epochs:** 2 |
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- **Batch Size:** 1 × 32 gradient accumulation |
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- **Learning Rate:** 3e-5 with cosine schedule + warmup |
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- **Optimizer:** AdamW with CPU offloading (memory efficient) |
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- **Training Time:** ~42 minutes on single GPU |
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- **Hardware:** NVIDIA GPU with 24GB VRAM |
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## 🎓 Training Strategy |
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The model was trained using a memory-efficient approach: |
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- **CPU-offloaded optimizer states** (saved ~6GB GPU memory) |
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- **Gradient checkpointing** enabled |
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- **Mixed precision** (BF16) |
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- **Custom learning rate scheduler** with warmup |
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## 🔄 Roadmap |
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- **v1.0** (Current): Fine-tuned on GSM8K |
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- **v2.0** (Coming): Knowledge distillation for improved performance |
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- **v3.0** (Planned): Extended to MATH and MMLU datasets |
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## 📄 License |
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Apache 2.0 |
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## 🙏 Credits |
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- **Base Model:** Qwen Team (Qwen2-1.5B-Instruct) |
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- **Reasoning Framework:** ARIES (Autonomous Reasoning Improvement via Ensembling Systems) |
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- **Training Dataset:** OpenAI GSM8K |
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- **Framework:** HuggingFace Transformers |
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## 📧 Contact |
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For questions or collaborations: [Your contact] |
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