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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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- prithivMLmods/Dinobot-Opus-14B-Exp |
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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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- math |
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- code |
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- error-correction |
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- Qwen |
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- RL |
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--- |
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# **Camelopardalis-650-14B-Instruct** |
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> **Camelopardalis-650-14B-Instruct** is based on the Qwen 2.5 14B modality architecture, designed to enhance the reasoning capabilities of 14B-parameter models. This model is optimized for general-purpose reasoning and answering, excelling in contextual understanding, logical deduction, and multi-step problem-solving. It has been fine-tuned using a long chain-of-thought reasoning model and specialized datasets to improve comprehension, structured responses, and conversational intelligence. |
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## **Key Improvements** |
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1. **Enhanced General Knowledge**: The model provides broad knowledge across various domains, improving capabilities in answering questions accurately and generating coherent responses. |
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2. **Improved Instruction Following**: Significant advancements in understanding and following complex instructions, generating structured responses, and maintaining coherence over extended interactions. |
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3. **Versatile Adaptability**: More resilient to diverse prompts, enhancing its ability to handle a wide range of topics and conversation styles, including open-ended and structured inquiries. |
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4. **Long-Context Support**: Supports up to 128K tokens for input context and can generate up to 8K tokens in a single output, making it ideal for detailed responses. |
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5. **Mathematical Reasoning Enhancements**: Improved performance on symbolic computation, algebraic simplification, theorem-based logic, and step-by-step math problem solving. |
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6. **Coding Reasoning Improvements**: Better understanding of programming paradigms, debugging, code generation, refactoring, and algorithmic problem-solving across multiple languages. |
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## **Quickstart with transformers** |
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Here's how to load and use the model with the `transformers` library and `apply_chat_template`: |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model_name = "prithivMLmods/Camelopardalis-650-14B-Instruct" |
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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 = "What are the key principles of general-purpose AI?" |
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messages = [ |
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{"role": "system", "content": "You are a helpful assistant capable of answering a wide range of questions."}, |
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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. **General-Purpose Reasoning**: |
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Designed for broad applicability, assisting with logical reasoning, answering diverse questions, and solving general knowledge problems. |
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2. **Educational and Informational Assistance**: |
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Suitable for providing explanations, summaries, and research-based responses for students, educators, and general users. |
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3. **Mathematical Problem Solving**: |
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Strong capabilities in solving equations, performing derivations, handling word problems, and following symbolic logic. |
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4. **Coding Assistance**: |
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Ideal for writing, analyzing, debugging, and improving code in Python, JavaScript, C++, and more. Helps with algorithm design and explaining programming concepts. |
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5. **Conversational AI and Chatbots**: |
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Suitable for building intelligent conversational agents that require contextual understanding and dynamic response generation. |
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6. **Multilingual Applications**: |
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Supports global communication, translations, and multilingual content generation. |
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7. **Structured Data Processing**: |
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Capable of analyzing and generating structured outputs, such as tables and JSON, useful for data science and automation. |
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8. **Long-Form Content Generation**: |
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Can generate extended responses, including articles, reports, and guides, maintaining coherence over large text outputs. |
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## **Limitations** |
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1. **Hardware Requirements**: |
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Requires high-memory GPUs or TPUs due to its large parameter size and long-context support. |
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2. **Potential Bias in Responses**: |
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While designed to be neutral, outputs may still reflect biases present in training data. |
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3. **Inconsistent Outputs in Creative Tasks**: |
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May produce variable results in storytelling and highly subjective topics. |
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4. **Limited Real-World Awareness**: |
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Does not have access to real-time events beyond its training cutoff. |
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5. **Error Propagation in Extended Outputs**: |
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Minor errors in early responses may affect overall coherence in long-form outputs. |
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6. **Prompt Sensitivity**: |
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The effectiveness of responses may depend on how well the input prompt is structured. |
