Taurus-Opus-7B
Taurus-Opus-7B is built upon the LLaMA (Large Language Model Meta AI) 7B architecture, optimized to provide advanced reasoning capabilities while maintaining efficiency. With 7 billion parameters, it strikes a balance between performance and computational resource requirements. The model has been fine-tuned with a focus on chain-of-thought (CoT) reasoning, leveraging specialized datasets to enhance its problem-solving abilities. Taurus-Opus-7B is designed for tasks requiring logical reasoning, detailed explanations, and multi-step problem-solving, making it ideal for applications such as instruction-following, text generation, and coding assistance.
Key Features and Improvements
Optimized Reasoning Capabilities:
The model showcases significant improvements in context understanding, reasoning, and mathematical problem-solving through fine-tuning with long CoT datasets.Enhanced Instruction Following:
Taurus-Opus-7B excels in generating long, coherent outputs (up to 4K tokens), understanding structured data, and producing structured outputs like JSON.Lightweight Efficiency:
Its 7B parameter size makes it more resource-efficient compared to larger models while retaining high-quality performance for reasoning and content generation tasks.Long-Context Support:
Offers support for long contexts of up to 64K tokens, enabling the handling of large datasets or extended conversations.Multilingual Proficiency:
The model supports 20+ languages, including English, Spanish, French, German, Portuguese, Chinese, Japanese, and more, making it suitable for global applications.
Quickstart with transformers
Here’s a code snippet to load Taurus-Opus-7B using the transformers
library:
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "prithivMLmods/Taurus-Opus-7B"
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)
prompt = "Explain the importance of chain-of-thought reasoning in large language models."
messages = [
{"role": "system", "content": "You are a helpful assistant with expertise in logical 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=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
Reasoning and Context Understanding:
Taurus-Opus-7B is tailored for complex reasoning tasks, contextual understanding, and solving problems requiring logical deduction.Mathematical Problem-Solving:
Designed for advanced mathematical reasoning and calculations, making it valuable for education, research, and engineering tasks.Code Assistance:
Provides robust coding support, including writing, debugging, and optimizing code across multiple programming languages.Data Analysis:
Excels in analyzing structured data and generating structured outputs, aiding automation workflows and data-driven insights.Multilingual Support:
Facilitates applications such as multilingual chatbots, content generation, and translation in 20+ languages.Extended Content Generation:
Suitable for generating detailed reports, articles, and instructional guides, handling outputs up to 4K tokens.
Limitations
Hardware Requirements:
While more efficient than larger models, Taurus-Opus-7B still requires high-memory GPUs or TPUs for optimal performance.Language Quality Variations:
Output quality may vary across supported languages, especially for less commonly used languages.Creativity Limitations:
The model may sometimes generate repetitive or inconsistent results in creative or highly subjective tasks.Real-Time Knowledge Constraints:
The model lacks awareness of events or knowledge updates beyond its training data.Prompt Dependency:
Results heavily depend on the specificity and clarity of input prompts, requiring well-structured queries for the best performance.
Open LLM Leaderboard Evaluation Results
Detailed results can be found here! Summarized results can be found here!
Metric | Value (%) |
---|---|
Average | 26.06 |
IFEval (0-Shot) | 42.23 |
BBH (3-Shot) | 34.23 |
MATH Lvl 5 (4-Shot) | 22.73 |
GPQA (0-shot) | 10.18 |
MuSR (0-shot) | 14.22 |
MMLU-PRO (5-shot) | 32.79 |
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Evaluation results
- averaged accuracy on IFEval (0-Shot)Open LLM Leaderboard42.230
- normalized accuracy on BBH (3-Shot)test set Open LLM Leaderboard34.230
- exact match on MATH Lvl 5 (4-Shot)test set Open LLM Leaderboard22.730
- acc_norm on GPQA (0-shot)Open LLM Leaderboard10.180
- acc_norm on MuSR (0-shot)Open LLM Leaderboard14.220
- accuracy on MMLU-PRO (5-shot)test set Open LLM Leaderboard32.790