README.md

metadata

license: apache-2.0
language:
  - en
base_model:
  - deepseek-ai/DeepSeek-R1-Distill-Qwen-14B
pipeline_tag: text-generation
library_name: transformers
tags:
  - text-generation-inference
  - code
  - math
  - error-correction
  - R1
  - 14B
  - Reasoning

Canum-Venaticorum-14B-B.1

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.

Key Improvements

1. Mathematical Reasoning Enhancements:
   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.

2. Coding and Debugging Proficiency:
   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.

3. Intelligent Error Correction:
   Capable of identifying inconsistencies or errors in logical reasoning, structured formats (JSON, XML), and code outputs, with suggestions and auto-corrections.

4. Enhanced Instruction Following:
   Fine-tuned for following complex, nested instructions with increased precision and coherence over extended prompts and interactions.

5. Long-Context Support:
   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.

Quickstart with Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "prithivMLmods/Canum-Venaticorum-14B-B.1"

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)

prompt = "Explain the difference between breadth-first search and depth-first search with Python code examples."
messages = [
    {"role": "system", "content": "You are a knowledgeable assistant skilled in reasoning, coding, and explanation."},
    {"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

1. Mathematics and Computation:
   Effective for solving math problems, verifying formulas, symbolic logic, algebraic reasoning, and analytical computations.

2. Programming Assistance:
   Ideal for generating, explaining, and debugging code. Suitable for both learning and software development use cases.

3. Educational and Informational Support:
   Provides accurate, well-explained answers to conceptual and applied questions in STEM and humanities.

4. Conversational AI and Reasoning Agents:
   Designed for intelligent chatbots capable of nuanced reasoning, error correction, and structured dialogue.

5. Multilingual & Global Applications:
   Useful for translation, multilingual support bots, and cross-lingual content generation.

6. Long-Form & Structured Content Generation:
   Can create long documents, reports, and structured outputs like JSON, Markdown, and tabular formats.

Limitations

1. Hardware Requirements:
   Demands high-memory GPUs/TPUs for optimal inference due to long-context and model size.

2. Real-Time Limitations:
   No real-time awareness; knowledge is limited to training data.

3. Bias and Hallucination:
   While reduced, some bias and hallucinations from training data may persist.

4. Creative Consistency:
   Variability in outputs for creative or ambiguous queries (e.g., fiction, storytelling).

5. Prompt Sensitivity:
   Results may vary significantly depending on the structure and clarity of the input prompt.