prithivMLmods
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Deepmath-Competitive-1.5B-Preview

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 ---
 library_name: transformers
-tags: []
 ---
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 ---
 library_name: transformers
+tags:
+- math
+- cot
+- text-generation-inference
+- preview
+- experimental
+license: apache-2.0
+language:
+- en
+base_model:
+- Qwen/Qwen2.5-1.5B-Instruct
+pipeline_tag: text-generation
 ---
+![DMC.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/WYWprTh49LUnIw-HiTcU-.png)
+# **Deepmath-Competitive-1.5B-Preview**
+> **Deepmath-Competitive-1.5B-Preview** is a **chain-of-thought reasoning model** fine-tuned from **Qwen-1.5B**, purpose-built for solving **mathematical problems** in both **English** and **Chinese** with a focus on **long-context understanding**. It enables advanced reasoning and detailed step-by-step problem solving in a compact form — ideal for competitive exam preparation, tutoring systems, and math-focused AI assistants.
+## **Key Features**
+1. **Chain-of-Thought Math Reasoning**
+   Specifically trained to output detailed intermediate steps for math problems, Deepmath-Competitive-1.5B-Preview ensures interpretability and logical clarity — vital for learning and validation.
+2. **Bilingual Proficiency (English + Chinese)**
+   Proficient in understanding and solving math problems in **both English and Simplified Chinese**, supporting diverse educational needs.
+3. **Long-Context Reasoning**
+   Optimized for **long-form math problems** and word problem comprehension, enabling reasoning over extended contexts and compound queries.
+4. **Compact yet Powerful**
+   With just 1.5B parameters, it delivers robust performance on arithmetic, algebra, geometry, logic, and competitive exam-style word problems with minimal computational cost.
+5. **Structured Step-by-Step Computation**
+   Produces clean, stepwise outputs that mimic expert human problem-solving, helping learners follow the process and logic intuitively.
+## **Quickstart with Transformers**
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+model_name = "prithivMLmods/Deepmath-Competitive-1.5B-Preview"
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+prompt = "Solve: A train travels 180 km in 3 hours. What is its average speed?"
+messages = [
+    {"role": "system", "content": "You are a helpful tutor skilled in solving math problems with step-by-step explanations."},
+    {"role": "user", "content": prompt}
+]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+generated_ids = model.generate(
+    **model_inputs,
+    max_new_tokens=512
+)
+generated_ids = [
+    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+]
+response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+```
+## **Intended Use**
+- **Math Tutoring Bots**: Delivers in-depth, multi-step solutions for students preparing for competitive and school-level math.
+- **Bilingual Educational Apps**: Effective in English and Chinese teaching environments.
+- **STEM Reasoning Tools**: Supports structured reasoning across science and engineering questions.
+- **Compact LLM Deployments**: Suitable for low-latency environments like mobile apps, edge devices, or web integrations.
+## **Limitations**
+1. **Domain Focus**:
+   Primarily tuned for mathematics; performance may drop outside STEM or logical domains.
+2. **Model Scale**:
+   While efficient, it may underperform on abstract or research-level problems compared to larger models.
+3. **Inherited Biases**:
+   As a fine-tune of Qwen-1.5B, some pretraining biases may persist. Review is advised in critical applications.
+4. **Prompt Sensitivity**:
+   Performs best with clearly structured prompts and formal question phrasing.