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license: apache-2.0 |
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--- |
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# **Megatron-Opus-7B-Exp** |
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Megatron-Opus-7B-Exp is based on the Qwen 2.5 7B modality architecture, designed to enhance the reasoning capabilities of 7B-parameter models. It has been fine-tuned on a Synthetic dataset entries based on one half of Qwen’s QWQ and DeepSeek R1, further optimizing its chain-of-thought (CoT) reasoning and logical problem-solving abilities. The model demonstrates significant improvements in context understanding, structured data processing, and long-context comprehension, making it ideal for complex reasoning tasks, instruction-following, and text generation. |
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### **Key Improvements** |
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1. **Advanced Reasoning & Logic**: Optimized for multi-step problem-solving, logical deduction, and contextual analysis. |
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2. **Fine-Tuned Instruction Following**: Generates precise responses, structured outputs (e.g., JSON), and extended long-form text (8K+ tokens). |
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3. **Greater Adaptability**: Excels in role-playing, multi-turn dialogues, and diverse system prompts. |
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4. **Long-Context Support**: Handles up to **128K tokens** and generates up to **8K tokens** per output. |
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5. **Multilingual Proficiency**: Supports over **29 languages**, including Chinese, English, French, Spanish, Portuguese, German, and more. |
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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/Megatron-Opus-7B-Exp" |
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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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trust_remote_code=True |
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) |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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prompt = "Explain the concept of logical reasoning in AI." |
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messages = [ |
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{"role": "system", "content": "You are an expert AI assistant specialized in reasoning and logic."}, |
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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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print(response) |
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``` |
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### **Intended Use** |
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- **Advanced Logical & Analytical Reasoning**: Designed for problem-solving, multi-step deductions, and cognitive reasoning tasks. |
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- **Mathematical & Scientific Computation**: Supports theorem proving, complex calculations, and scientific knowledge retrieval. |
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- **Code Generation & Debugging**: Generates optimized code, detects errors, and improves programming workflows. |
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- **Structured Data Analysis**: Processes tables, JSON, and structured formats for data-centric applications. |
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- **Multilingual Reasoning & Translation**: High proficiency across **29+ languages** for international applications. |
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- **Extended Text Generation**: Capable of generating research papers, instructional guides, and in-depth reports. |
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### **Limitations** |
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1. **High Computational Requirements**: Due to its **7B parameters** and **128K context support**, it requires powerful GPUs or TPUs for efficient inference. |
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2. **Language-Specific Variability**: Performance may differ across supported languages, especially for low-resource languages. |
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3. **Potential Error Accumulation**: Long-form text generation can introduce inconsistencies over extended outputs. |
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4. **Limited Real-World Awareness**: Knowledge is restricted to training data and may not reflect recent world events. |
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5. **Prompt Sensitivity**: The quality of responses depends on the specificity and clarity of the input prompt. |
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