license: apache-2.0
language:
- en
base_model:
- Qwen/Qwen2.5-7B-Instruct
pipeline_tag: text-generation
library_name: transformers
tags:
- opus
- code
- cot
- lcot
- LlaMa
model-index:
- name: Taurus-Opus-7B
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: IFEval (0-Shot)
type: wis-k/instruction-following-eval
split: train
args:
num_few_shot: 0
metrics:
- type: inst_level_strict_acc and prompt_level_strict_acc
value: 42.23
name: averaged accuracy
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FTaurus-Opus-7B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: BBH (3-Shot)
type: SaylorTwift/bbh
split: test
args:
num_few_shot: 3
metrics:
- type: acc_norm
value: 34.23
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FTaurus-Opus-7B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MATH Lvl 5 (4-Shot)
type: lighteval/MATH-Hard
split: test
args:
num_few_shot: 4
metrics:
- type: exact_match
value: 22.73
name: exact match
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FTaurus-Opus-7B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GPQA (0-shot)
type: Idavidrein/gpqa
split: train
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 10.18
name: acc_norm
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FTaurus-Opus-7B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MuSR (0-shot)
type: TAUR-Lab/MuSR
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 14.22
name: acc_norm
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FTaurus-Opus-7B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU-PRO (5-shot)
type: TIGER-Lab/MMLU-Pro
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 32.79
name: accuracy
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FTaurus-Opus-7B
name: Open LLM Leaderboard
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 |