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