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Model: prithivMLmods/Primal-Opus-14B-Optimus-v2
Source: Original Platform

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README.md

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+---
+license: apache-2.0
+language:
+- en
+- zh
+base_model:
+- prithivMLmods/Primal-Opus-14B-Optimus-v1
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- text-generation-inference
+- trl
+- sft
+- math
+model-index:
+- name: Primal-Opus-14B-Optimus-v2
+  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: 64.04
+      name: averaged accuracy
+    source:
+      url: >-
+        https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FPrimal-Opus-14B-Optimus-v2
+      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: 50.18
+      name: normalized accuracy
+    source:
+      url: >-
+        https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FPrimal-Opus-14B-Optimus-v2
+      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: 42.07
+      name: exact match
+    source:
+      url: >-
+        https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FPrimal-Opus-14B-Optimus-v2
+      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: 18.9
+      name: acc_norm
+    source:
+      url: >-
+        https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FPrimal-Opus-14B-Optimus-v2
+      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: 21.15
+      name: acc_norm
+    source:
+      url: >-
+        https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FPrimal-Opus-14B-Optimus-v2
+      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: 49.14
+      name: accuracy
+    source:
+      url: >-
+        https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FPrimal-Opus-14B-Optimus-v2
+      name: Open LLM Leaderboard
+---
+
+![4.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/BgPXl9DNWOmssPkj10jCu.png)
+
+# **Primal-Opus-14B-Optimus-v2**  
+
+Primal-Opus-14B-Optimus-v2 is based on the Qwen 2.5 14B modality architecture, designed to enhance the reasoning capabilities of 14B-parameter models. It has been fine-tuned on a **synthetic dataset based on DeepSeek R1**, further optimizing its chain-of-thought (CoT) reasoning and logical problem-solving abilities. The model demonstrates significant improvements in context understanding, structured data processing, and long-context comprehension, making it ideal for complex reasoning tasks, instruction-following, and text generation.  
+
+### **Key Improvements**  
+1. **Enhanced Reasoning and Logic**: Improved multi-step logical deduction, mathematical reasoning, and problem-solving accuracy.  
+2. **Fine-Tuned Instruction Following**: Optimized for precise responses, structured outputs (e.g., JSON), and generating long texts (8K+ tokens).  
+3. **Greater Adaptability**: Better role-playing capabilities and resilience to diverse system prompts.  
+4. **Long-Context Support**: Handles up to **128K tokens** and generates up to **8K tokens** per output.  
+5. **Multilingual Proficiency**: Supports over **29 languages**, including Chinese, English, French, Spanish, Portuguese, German, and more.  
+
+### **Quickstart with Transformers**  
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "prithivMLmods/Primal-Opus-14B-Optimus-v2"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto",
+    trust_remote_code=True
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+prompt = "Give me a short introduction to large language models."
+messages = [
+    {"role": "system", "content": "You are an advanced AI assistant with expert-level reasoning and knowledge."},
+    {"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]
+print(response)
+```
+
+### **Intended Use**  
+- **Advanced Logical Reasoning**: Designed for logical deduction, multi-step problem-solving, and knowledge-based tasks.  
+- **Mathematical & Scientific Problem-Solving**: Enhanced capabilities for calculations, theorem proving, and scientific queries.  
+- **Code Generation & Debugging**: Generates and optimizes code across multiple programming languages.  
+- **Structured Data Analysis**: Processes tables, JSON, and structured outputs, making it ideal for data-centric tasks.  
+- **Multilingual Applications**: High proficiency in over 29 languages, enabling global-scale applications.  
+- **Extended Content Generation**: Supports detailed document writing, research reports, and instructional guides.  
+
+### **Limitations**  
+1. **High Computational Requirements**: Due to its **14B parameters** and **128K context support**, it requires powerful GPUs or TPUs for efficient inference.  
+2. **Language-Specific Variability**: Performance may vary across supported languages, especially for low-resource languages.  
+3. **Potential Error Accumulation**: Long-text generation can sometimes introduce inconsistencies over extended outputs.  
+4. **Limited Real-World Awareness**: Knowledge is restricted to training data and may not reflect recent world events.  
+5. **Prompt Sensitivity**: Outputs can depend on the specificity and clarity of the input prompt.
+
+
+# [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__Primal-Opus-14B-Optimus-v2-details)!
+Summarized results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/contents/viewer/default/train?q=prithivMLmods%2FPrimal-Opus-14B-Optimus-v2&sort[column]=Average%20%E2%AC%86%EF%B8%8F&sort[direction]=desc)!
+
+|      Metric       |Value (%)|
+|-------------------|--------:|
+|**Average**        |    40.91|
+|IFEval (0-Shot)    |    64.04|
+|BBH (3-Shot)       |    50.18|
+|MATH Lvl 5 (4-Shot)|    42.07|
+|GPQA (0-shot)      |    18.90|
+|MuSR (0-shot)      |    21.15|
+|MMLU-PRO (5-shot)  |    49.14|

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+  "vocab_size": 151665
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+}