prithivMLmods
/

Taurus-Opus-7B

@@ -1,225 +1,237 @@
----
-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|

+---
+license: apache-2.0
+language:
+- zho
+- eng
+- fra
+- spa
+- por
+- deu
+- ita
+- rus
+- jpn
+- kor
+- vie
+- tha
+- ara
+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|