76 lines
3.9 KiB
Markdown
76 lines
3.9 KiB
Markdown
---
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license: llama3.1
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language:
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- en
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pipeline_tag: text-generation
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library_name: transformers
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tags:
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- reasoning
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- math
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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. |