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--- |
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license: apache-2.0 |
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language: |
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- en |
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base_model: |
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- deepseek-ai/DeepSeek-R1-Distill-Qwen-14B |
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pipeline_tag: text-generation |
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library_name: transformers |
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tags: |
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- text-generation-inference |
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- code |
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- math |
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- error-correction |
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- R1 |
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- 14B |
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- Reasoning |
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--- |
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# **Canum-Venaticorum-14B-B.1** |
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> **Canum-Venaticorum-14B-B.1** is based on the Qwen 2.5 14B modality architecture, built to significantly enhance the **mathematical reasoning**, **coding ability**, and **error correction** performance of 14B-parameter models. This version has been optimized for general-purpose reasoning, structured problem-solving, and intelligent assistance, offering advanced capabilities in understanding complex instructions, logical deduction, and multi-step computation. |
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## **Key Improvements** |
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1. **Mathematical Reasoning Enhancements**: |
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Equipped with advanced capabilities in handling mathematical logic, symbolic computation, step-by-step problem-solving, and numerical accuracy across topics from basic arithmetic to higher-order mathematics. |
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2. **Coding and Debugging Proficiency**: |
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Improved performance in code generation, understanding documentation, and identifying and correcting bugs in multiple programming languages, especially Python, JavaScript, and C++. It supports functional, object-oriented, and scripting paradigms. |
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3. **Intelligent Error Correction**: |
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Capable of identifying inconsistencies or errors in logical reasoning, structured formats (JSON, XML), and code outputs, with suggestions and auto-corrections. |
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4. **Enhanced Instruction Following**: |
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Fine-tuned for following complex, nested instructions with increased precision and coherence over extended prompts and interactions. |
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5. **Long-Context Support**: |
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Supports up to **128K tokens** for input context and can generate up to **8K tokens** in one output, making it well-suited for extended problem solving, document generation, and analysis. |
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## **Quickstart with Transformers** |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model_name = "prithivMLmods/Canum-Venaticorum-14B-B.1" |
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model = AutoModelForCausalLM.from_pretrained( |
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model_name, |
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torch_dtype="auto", |
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device_map="auto" |
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) |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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prompt = "Explain the difference between breadth-first search and depth-first search with Python code examples." |
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messages = [ |
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{"role": "system", "content": "You are a knowledgeable assistant skilled in reasoning, coding, and explanation."}, |
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{"role": "user", "content": prompt} |
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] |
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text = tokenizer.apply_chat_template( |
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messages, |
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tokenize=False, |
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add_generation_prompt=True |
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) |
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model_inputs = tokenizer([text], return_tensors="pt").to(model.device) |
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generated_ids = model.generate( |
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**model_inputs, |
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max_new_tokens=512 |
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) |
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generated_ids = [ |
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output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) |
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] |
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] |
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``` |
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## **Intended Use** |
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1. **Mathematics and Computation**: |
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Effective for solving math problems, verifying formulas, symbolic logic, algebraic reasoning, and analytical computations. |
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2. **Programming Assistance**: |
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Ideal for generating, explaining, and debugging code. Suitable for both learning and software development use cases. |
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3. **Educational and Informational Support**: |
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Provides accurate, well-explained answers to conceptual and applied questions in STEM and humanities. |
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4. **Conversational AI and Reasoning Agents**: |
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Designed for intelligent chatbots capable of nuanced reasoning, error correction, and structured dialogue. |
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5. **Multilingual & Global Applications**: |
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Useful for translation, multilingual support bots, and cross-lingual content generation. |
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6. **Long-Form & Structured Content Generation**: |
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Can create long documents, reports, and structured outputs like JSON, Markdown, and tabular formats. |
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## **Limitations** |
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1. **Hardware Requirements**: |
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Demands high-memory GPUs/TPUs for optimal inference due to long-context and model size. |
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2. **Real-Time Limitations**: |
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No real-time awareness; knowledge is limited to training data. |
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3. **Bias and Hallucination**: |
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While reduced, some bias and hallucinations from training data may persist. |
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4. **Creative Consistency**: |
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Variability in outputs for creative or ambiguous queries (e.g., fiction, storytelling). |
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5. **Prompt Sensitivity**: |
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Results may vary significantly depending on the structure and clarity of the input prompt. |
