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Model: prithivMLmods/Taurus-Opus-7B
Source: Original Platform

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README.md

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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
+- 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**
+
+1. **Optimized Reasoning Capabilities**:  
+   The model showcases significant improvements in context understanding, reasoning, and mathematical problem-solving through fine-tuning with long CoT datasets.
+
+2. **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.
+
+3. **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.
+
+4. **Long-Context Support**:  
+   Offers support for long contexts of up to 64K tokens, enabling the handling of large datasets or extended conversations.
+
+5. **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:
+
+```python
+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**
+
+1. **Reasoning and Context Understanding**:  
+   Taurus-Opus-7B is tailored for complex reasoning tasks, contextual understanding, and solving problems requiring logical deduction.
+
+2. **Mathematical Problem-Solving**:  
+   Designed for advanced mathematical reasoning and calculations, making it valuable for education, research, and engineering tasks.
+
+3. **Code Assistance**:  
+   Provides robust coding support, including writing, debugging, and optimizing code across multiple programming languages.
+
+4. **Data Analysis**:  
+   Excels in analyzing structured data and generating structured outputs, aiding automation workflows and data-driven insights.
+
+5. **Multilingual Support**:  
+   Facilitates applications such as multilingual chatbots, content generation, and translation in 20+ languages.
+
+6. **Extended Content Generation**:  
+   Suitable for generating detailed reports, articles, and instructional guides, handling outputs up to 4K tokens.
+
+# **Limitations**
+
+1. **Hardware Requirements**:  
+   While more efficient than larger models, Taurus-Opus-7B still requires high-memory GPUs or TPUs for optimal performance.
+
+2. **Language Quality Variations**:  
+   Output quality may vary across supported languages, especially for less commonly used languages.
+
+3. **Creativity Limitations**:  
+   The model may sometimes generate repetitive or inconsistent results in creative or highly subjective tasks.
+
+4. **Real-Time Knowledge Constraints**:  
+   The model lacks awareness of events or knowledge updates beyond its training data.
+
+5. **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](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
+Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/prithivMLmods__Taurus-Opus-7B-details)!
+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)!
+
+|      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|
+

config.json

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+{
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 11,
+  "eos_token_id": 11,
+  "head_dim": 256,
+  "hidden_act": "silu",
+  "hidden_size": 3072,
+  "initializer_range": 0.02,
+  "intermediate_size": 23040,
+  "max_position_embeddings": 32768,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 4,
+  "pad_token_id": 2023,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 1000042,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float16",
+  "transformers_version": "4.47.1",
+  "use_cache": true,
+  "vocab_size": 131072
+}

configuration.json

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+{"framework": "pytorch", "task": "text-generation", "allow_remote": true}

generation_config.json

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+  "eos_token_id": 11,
+  "max_length": 32768,
+  "pad_token_id": 2023,
+  "transformers_version": "4.47.1"
+}

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special_tokens_map.json

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