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---
license: apache-2.0
language:
- en
base_model:
- Qwen/Qwen2.5-7B-Instruct
pipeline_tag: text-generation
library_name: transformers
tags:
- opus
- code
- qwen2.5
- cot
- lcot
---
# **Taurus-Opus-7B-Elite**
Taurus-Opus-7B-Elite is based on a 7B-parameter architecture inspired by Qwen 2.5, optimized to deliver exceptional reasoning, contextual understanding, and problem-solving capabilities. It has been fine-tuned with a focus on chain-of-thought (CoT) reasoning using a specialized dataset for tasks requiring logical deductions and multi-step problem-solving. Despite its reduced parameter count, Taurus-Opus-7B-Elite remains highly efficient and versatile, tailored for a range of applications such as instruction-following, structured data processing, and multilingual tasks.
# **Key Improvements**
1. **Compact Yet Powerful**:
Despite being a 7B-parameter model, Taurus-Opus demonstrates powerful reasoning and understanding capabilities comparable to larger models due to advanced optimization techniques.
2. **Enhanced Efficiency**:
Optimized for faster inference and reduced computational costs, making it suitable for deployments on devices with limited resources.
3. **Instruction Following**:
Improved capabilities in understanding and executing complex instructions while generating long texts (up to 4K tokens).
4. **Structured Data Processing**:
Excels at analyzing tables, JSON, and other structured data formats, ensuring accurate and structured outputs.
5. **Multilingual Proficiency**:
Supports 20+ languages, maintaining accuracy and fluency in common languages such as English, Chinese, Spanish, and French.
6. **Streamlined Long-Context Support**:
Supports up to 64K tokens, providing robust contextual understanding for long-chain reasoning tasks.
# **Quickstart with transformers**
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "prithivMLmods/Taurus-Opus-7B-Elite"
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)
prompt = "Explain why reasoning is critical in solving complex problems."
messages = [
{"role": "system", "content": "You are Taurus, an advanced AI assistant optimized for reasoning and problem-solving."},
{"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=256
)
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. **Reasoning and Contextual Understanding**:
Tailored for tasks that require logical deductions and contextual analysis, suitable for educational and professional use cases.
2. **Mathematical Reasoning**:
Adept at solving mathematical problems and calculations, making it ideal for STEM applications.
3. **Code Assistance**:
Provides support for generating, debugging, and optimizing code in a variety of programming languages.
4. **Multilingual Tasks**:
Enables global applications, including multilingual content generation, translation, and conversational AI.
5. **Content Generation**:
Generates high-quality long-form text for reports, articles, and other professional documents.
# **Limitations**
1. **Reduced Parameter Count**:
While efficient, it may not achieve the same depth of understanding as larger models like 14B-parameter counterparts in some complex tasks.
2. **Hardware Requirements**:
Though lighter than larger models, it still requires a GPU or high-performance CPU for optimal performance.
3. **Multilingual Accuracy**:
Performance may vary for less-resourced languages, with minor inaccuracies in nuanced translations.
4. **Error Propagation in Long Outputs**:
Similar to larger models, early output errors in long-text generation can affect the coherence of the final text.
5. **Prompt Sensitivity**:
Requires well-structured prompts for best performance, necessitating some user familiarity with prompt design. |