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Model: prithivMLmods/Taurus-Opus-7B Source: Original Platform
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README.md
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---
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license: apache-2.0
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language:
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- en
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base_model:
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- Qwen/Qwen2.5-7B-Instruct
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pipeline_tag: text-generation
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library_name: transformers
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tags:
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- opus
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- code
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- cot
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- lcot
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- LlaMa
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model-index:
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- name: Taurus-Opus-7B
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results:
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: IFEval (0-Shot)
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type: wis-k/instruction-following-eval
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split: train
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args:
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num_few_shot: 0
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metrics:
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- type: inst_level_strict_acc and prompt_level_strict_acc
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value: 42.23
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name: averaged accuracy
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source:
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FTaurus-Opus-7B
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: BBH (3-Shot)
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type: SaylorTwift/bbh
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split: test
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args:
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num_few_shot: 3
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metrics:
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- type: acc_norm
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value: 34.23
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name: normalized accuracy
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source:
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FTaurus-Opus-7B
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: MATH Lvl 5 (4-Shot)
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type: lighteval/MATH-Hard
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split: test
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args:
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num_few_shot: 4
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metrics:
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- type: exact_match
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value: 22.73
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name: exact match
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source:
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FTaurus-Opus-7B
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: GPQA (0-shot)
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type: Idavidrein/gpqa
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split: train
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args:
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num_few_shot: 0
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metrics:
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- type: acc_norm
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value: 10.18
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||||
name: acc_norm
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||||
source:
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FTaurus-Opus-7B
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: MuSR (0-shot)
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type: TAUR-Lab/MuSR
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args:
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num_few_shot: 0
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metrics:
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- type: acc_norm
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value: 14.22
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name: acc_norm
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source:
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FTaurus-Opus-7B
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: MMLU-PRO (5-shot)
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type: TIGER-Lab/MMLU-Pro
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config: main
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split: test
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args:
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num_few_shot: 5
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metrics:
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- type: acc
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value: 32.79
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name: accuracy
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||||
source:
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FTaurus-Opus-7B
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name: Open LLM Leaderboard
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---
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# **Taurus-Opus-7B**
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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.
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# **Key Features and Improvements**
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1. **Optimized Reasoning Capabilities**:
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The model showcases significant improvements in context understanding, reasoning, and mathematical problem-solving through fine-tuning with long CoT datasets.
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2. **Enhanced Instruction Following**:
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Taurus-Opus-7B excels in generating long, coherent outputs (up to 4K tokens), understanding structured data, and producing structured outputs like JSON.
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3. **Lightweight Efficiency**:
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Its 7B parameter size makes it more resource-efficient compared to larger models while retaining high-quality performance for reasoning and content generation tasks.
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4. **Long-Context Support**:
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Offers support for long contexts of up to 64K tokens, enabling the handling of large datasets or extended conversations.
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5. **Multilingual Proficiency**:
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The model supports 20+ languages, including English, Spanish, French, German, Portuguese, Chinese, Japanese, and more, making it suitable for global applications.
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# **Quickstart with transformers**
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Here’s a code snippet to load **Taurus-Opus-7B** using the `transformers` library:
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "prithivMLmods/Taurus-Opus-7B"
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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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)
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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prompt = "Explain the importance of chain-of-thought reasoning in large language models."
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messages = [
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{"role": "system", "content": "You are a helpful assistant with expertise in logical reasoning and problem-solving."},
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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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```
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# **Intended Use**
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1. **Reasoning and Context Understanding**:
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Taurus-Opus-7B is tailored for complex reasoning tasks, contextual understanding, and solving problems requiring logical deduction.
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2. **Mathematical Problem-Solving**:
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Designed for advanced mathematical reasoning and calculations, making it valuable for education, research, and engineering tasks.
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3. **Code Assistance**:
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Provides robust coding support, including writing, debugging, and optimizing code across multiple programming languages.
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4. **Data Analysis**:
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Excels in analyzing structured data and generating structured outputs, aiding automation workflows and data-driven insights.
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5. **Multilingual Support**:
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Facilitates applications such as multilingual chatbots, content generation, and translation in 20+ languages.
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6. **Extended Content Generation**:
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Suitable for generating detailed reports, articles, and instructional guides, handling outputs up to 4K tokens.
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# **Limitations**
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1. **Hardware Requirements**:
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While more efficient than larger models, Taurus-Opus-7B still requires high-memory GPUs or TPUs for optimal performance.
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2. **Language Quality Variations**:
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Output quality may vary across supported languages, especially for less commonly used languages.
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3. **Creativity Limitations**:
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The model may sometimes generate repetitive or inconsistent results in creative or highly subjective tasks.
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4. **Real-Time Knowledge Constraints**:
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The model lacks awareness of events or knowledge updates beyond its training data.
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5. **Prompt Dependency**:
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Results heavily depend on the specificity and clarity of input prompts, requiring well-structured queries for the best performance.
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# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
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Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/prithivMLmods__Taurus-Opus-7B-details)!
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Summarized results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/contents/viewer/default/train?q=prithivMLmods%2FTaurus-Opus-7B&sort[column]=Average%20%E2%AC%86%EF%B8%8F&sort[direction]=desc)!
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| Metric |Value (%)|
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|-------------------|--------:|
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|**Average** | 26.06|
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|IFEval (0-Shot) | 42.23|
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|BBH (3-Shot) | 34.23|
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|MATH Lvl 5 (4-Shot)| 22.73|
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|GPQA (0-shot) | 10.18|
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|MuSR (0-shot) | 14.22|
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|MMLU-PRO (5-shot) | 32.79|
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config.json
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|
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"LlamaForCausalLM"
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],
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"hidden_act": "silu",
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"hidden_size": 3072,
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"initializer_range": 0.02,
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"intermediate_size": 23040,
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"max_position_embeddings": 32768,
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"mlp_bias": false,
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"model_type": "llama",
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"num_attention_heads": 12,
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"num_hidden_layers": 28,
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"num_key_value_heads": 4,
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"pad_token_id": 2023,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-06,
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"rope_scaling": null,
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"rope_theta": 1000042,
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"tie_word_embeddings": false,
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"torch_dtype": "float16",
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"transformers_version": "4.47.1",
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"use_cache": true,
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"vocab_size": 131072
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}
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|
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"model.layers.7.post_attention_layernorm.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.7.self_attn.k_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.7.self_attn.o_proj.weight": "model-00001-of-00004.safetensors",
|
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"model.layers.7.self_attn.q_proj.weight": "model-00001-of-00004.safetensors",
|
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"model.layers.7.self_attn.v_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.8.input_layernorm.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.8.mlp.down_proj.weight": "model-00002-of-00004.safetensors",
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||||
"model.layers.8.mlp.gate_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.8.mlp.up_proj.weight": "model-00001-of-00004.safetensors",
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"model.layers.8.post_attention_layernorm.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.8.self_attn.k_proj.weight": "model-00001-of-00004.safetensors",
|
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"model.layers.8.self_attn.o_proj.weight": "model-00001-of-00004.safetensors",
|
||||
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|
||||
"model.layers.8.self_attn.v_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.9.input_layernorm.weight": "model-00002-of-00004.safetensors",
|
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"model.layers.9.mlp.down_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.9.mlp.gate_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.9.mlp.up_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.9.post_attention_layernorm.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.9.self_attn.k_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.9.self_attn.o_proj.weight": "model-00002-of-00004.safetensors",
|
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"model.layers.9.self_attn.q_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.9.self_attn.v_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.norm.weight": "model-00003-of-00004.safetensors"
|
||||
}
|
||||
}
|
||||
41
special_tokens_map.json
Normal file
41
special_tokens_map.json
Normal file
@@ -0,0 +1,41 @@
|
||||
{
|
||||
"additional_special_tokens": [
|
||||
">>TITLE<<",
|
||||
">>ABSTRACT<<",
|
||||
">>INTRODUCTION<<",
|
||||
">>SUMMARY<<",
|
||||
">>COMMENT<<",
|
||||
">>ANSWER<<",
|
||||
">>QUESTION<<",
|
||||
">>DOMAIN<<",
|
||||
">>EMAIL_ADDRESS<<",
|
||||
">>IP_ADDRESS<<",
|
||||
"<|startoftext|>",
|
||||
">>IP_ADDRESS_0<<",
|
||||
">>IP_ADDRESS_1<<",
|
||||
">>IP_ADDRESS_2<<",
|
||||
">>IP_ADDRESS_3<<",
|
||||
">>IP_ADDRESS_4<<",
|
||||
">>IP_ADDRESS_5<<",
|
||||
">>IP_ADDRESS_6<<",
|
||||
">>IP_ADDRESS_7<<",
|
||||
">>IP_ADDRESS_8<<",
|
||||
">>IP_ADDRESS_9<<",
|
||||
">>PASSWORD<<",
|
||||
">>KEY<<"
|
||||
],
|
||||
"eos_token": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"pad_token": {
|
||||
"content": "<|pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
664576
tokenizer.json
Normal file
664576
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
16234
tokenizer_config.json
Normal file
16234
tokenizer_config.json
Normal file
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user