Update README.md

README.md

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----
-license: apache-2.0
----
+---
+license: apache-2.0
+language:
+- en
+base_model:
+- prithivMLmods/Dinobot-Opus-14B-Exp
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- text-generation-inference
+- math
+- code
+- error-correction
+- R1
+- Qwen
+---
+
+![ccccccc.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/gFmL96KuQ0nGKrVTVn5Vq.png)
+
+# **Camelopardalis-650-14B-Instruct**  
+
+**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.  
+
+## **Key Improvements**  
+1. **Enhanced General Knowledge**: The model provides broad knowledge across various domains, improving capabilities in answering questions accurately and generating coherent responses.  
+2. **Improved Instruction Following**: Significant advancements in understanding and following complex instructions, generating structured responses, and maintaining coherence over extended interactions.  
+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.  
+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.  
+5. **Mathematical Reasoning Enhancements**: Improved performance on symbolic computation, algebraic simplification, theorem-based logic, and step-by-step math problem solving.  
+6. **Coding Reasoning Improvements**: Better understanding of programming paradigms, debugging, code generation, refactoring, and algorithmic problem-solving across multiple languages.  
+
+## **Quickstart with transformers**  
+
+Here's how to load and use the model with the `transformers` library and `apply_chat_template`:
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "prithivMLmods/Camelopardalis-650-14B-Instruct"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+prompt = "What are the key principles of general-purpose AI?"
+messages = [
+    {"role": "system", "content": "You are a helpful assistant capable of answering a wide range of questions."},
+    {"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. **General-Purpose Reasoning**:  
+   Designed for broad applicability, assisting with logical reasoning, answering diverse questions, and solving general knowledge problems.  
+
+2. **Educational and Informational Assistance**:  
+   Suitable for providing explanations, summaries, and research-based responses for students, educators, and general users.  
+
+3. **Mathematical Problem Solving**:  
+   Strong capabilities in solving equations, performing derivations, handling word problems, and following symbolic logic.  
+
+4. **Coding Assistance**:  
+   Ideal for writing, analyzing, debugging, and improving code in Python, JavaScript, C++, and more. Helps with algorithm design and explaining programming concepts.  
+
+5. **Conversational AI and Chatbots**:  
+   Suitable for building intelligent conversational agents that require contextual understanding and dynamic response generation.  
+
+6. **Multilingual Applications**:  
+   Supports global communication, translations, and multilingual content generation.  
+
+7. **Structured Data Processing**:  
+   Capable of analyzing and generating structured outputs, such as tables and JSON, useful for data science and automation.  
+
+8. **Long-Form Content Generation**:  
+   Can generate extended responses, including articles, reports, and guides, maintaining coherence over large text outputs.  
+
+## **Limitations**  
+1. **Hardware Requirements**:  
+   Requires high-memory GPUs or TPUs due to its large parameter size and long-context support.  
+
+2. **Potential Bias in Responses**:  
+   While designed to be neutral, outputs may still reflect biases present in training data.  
+
+3. **Inconsistent Outputs in Creative Tasks**:  
+   May produce variable results in storytelling and highly subjective topics.  
+
+4. **Limited Real-World Awareness**:  
+   Does not have access to real-time events beyond its training cutoff.  
+
+5. **Error Propagation in Extended Outputs**:  
+   Minor errors in early responses may affect overall coherence in long-form outputs.  
+
+6. **Prompt Sensitivity**:  
+   The effectiveness of responses may depend on how well the input prompt is structured.