README.md

---
base_model:
- meta-llama/Llama-3.3-70B-Instruct
tags:
- state-of-the-art
- reasoning
- chain-of-thought
- text-generation
- transformers
- llama
- instruction-tuning
license: apache-2.0
language:
- en
datasets:
- Daemontatox/Deepthinking-COT
- gghfez/QwQ-LongCoT-130K-cleaned
pipeline_tag: text-generation
library_name: transformers
model-index:
- name: Llama3.3-70B-CogniLink
  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: 69.31
      name: averaged accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=Daemontatox%2FLlama3.3-70B-CogniLink
      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: 52.12
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=Daemontatox%2FLlama3.3-70B-CogniLink
      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: 39.58
      name: exact match
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=Daemontatox%2FLlama3.3-70B-CogniLink
      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: 26.06
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=Daemontatox%2FLlama3.3-70B-CogniLink
      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: 21.4
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=Daemontatox%2FLlama3.3-70B-CogniLink
      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: 46.37
      name: accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=Daemontatox%2FLlama3.3-70B-CogniLink
      name: Open LLM Leaderboard
---
![image](./image.webp)

# Model Card: CogniLink - Redefining Reasoning AI

## Overview
CogniLink is a **state-of-the-art (SOTA) reasoning model**, engineered to set new benchmarks in logical problem-solving and chain-of-thought capabilities. Leveraging the power of LLaMA 3.3 70B, CogniLink excels in multi-step reasoning, inference, and real-time decision-making across diverse domains. Whether tackling mathematical proofs, legal analyses, or dynamic real-world scenarios, CogniLink ensures clarity, precision, and scalability. 

Designed for both **high-performance tasks** and **resource-efficient environments**, CogniLink represents the perfect fusion of innovation and practicality.

---

## Key Features
- **Base Model:** [unsloth/llama-3.3-70b-instruct](https://huggingface.co/unsloth/llama-3.3-70b-instruct-bnb-4bit)  
- **Developed By:** Daemontatox  
- **License:** Apache 2.0 (open and permissive)  
- **Primary Language:** English  
- **Specialization:** Multi-domain reasoning, step-by-step logic, and advanced inference.  

**CogniLink is optimized for tasks requiring:**  
- **Reasoning Depth:** Multi-step logic with exceptional accuracy.  
- **Chain-of-Thought (CoT):** Built-in mechanisms to generate clear, stepwise reasoning paths.  
- **Resource Efficiency:** Ideal for deployment on both high-performance servers and resource-constrained devices, including edge computing platforms.  

---

## Training and Optimization
CogniLink’s fine-tuning was accelerated using **[Unsloth](https://github.com/unslothai/unsloth)**, enabling a **2x faster training pipeline**. The training process was powered by Hugging Face's **TRL library**, ensuring seamless instruction tuning and robust adaptability across reasoning-heavy applications.

With advanced techniques like **quantization-aware training** and parameter-efficient fine-tuning, CogniLink is lightweight without compromising on performance, making it a top choice for edge deployment and embedded systems.

Special thanks to **[Modal.com](https://modal.com)** for providing **H100 GPUs**, which enabled accelerated training and optimized performance for CogniLink. Their generous support significantly contributed to the model’s development and deployment readiness.


---

## Applications
CogniLink is versatile and excels in various industries:

### **1. Education and Training**
- Powers AI tutors for **step-by-step problem-solving** in STEM education.
- Supports interactive learning tools with detailed explanations.

### **2. Research and Academia**
- Assists researchers with **hypothesis testing**, complex analysis, and paper drafting.  
- Enhances productivity in tasks requiring deep logical reasoning.

### **3. Business Decision Support**
- Real-time **scenario analysis** for strategic decision-making.  
- Risk assessment tools for dynamic business environments.

### **4. Legal and Policy Analysis**
- Enables multi-step reasoning for **case law interpretations** and **regulatory reviews**.  
- Assists legal professionals with clear and logical argument generation.

### **5. Healthcare AI**
- Supports diagnostics and medical workflows with robust reasoning models.
- Ensures accuracy in multi-step inferential tasks like patient case reviews.

---

## Technical Specifications
- **Quantization:** Fully compatible with 4-bit inference for efficient performance.  
- **Latency:** Optimized for real-time responses in latency-sensitive applications.  
- **Scalability:** Deployable on diverse hardware setups, from high-end GPUs to edge devices.  

---

## Why Choose CogniLink?
CogniLink isn’t just a model; it’s a **reasoning companion**. Its fine-tuned chain-of-thought design ensures not just answers, but **rational, explainable processes**, giving users the confidence and insights they need to make critical decisions.  

- **Transparent Reasoning:** Every decision is backed by a logical thought process.  
- **Versatile Applications:** From academia to business, CogniLink adapts effortlessly.  
- **Cutting-Edge Efficiency:** High performance meets cost-effectiveness.  

---

## Get Started
CogniLink is available for download and deployment. Start integrating advanced reasoning into your applications today!

For inquiries, contributions, or support, visit **[Unsloth GitHub](https://github.com/unslothai/unsloth)**.

**CogniLink: Connecting Intelligence with Clarity.**
# [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/Daemontatox__Llama3.3-70B-CogniLink-details)!
Summarized results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/contents/viewer/default/train?q=Daemontatox%2FLlama3.3-70B-CogniLink&sort[column]=Average%20%E2%AC%86%EF%B8%8F&sort[direction]=desc)!

|      Metric       |Value (%)|
|-------------------|--------:|
|**Average**        |    42.47|
|IFEval (0-Shot)    |    69.31|
|BBH (3-Shot)       |    52.12|
|MATH Lvl 5 (4-Shot)|    39.58|
|GPQA (0-shot)      |    26.06|
|MuSR (0-shot)      |    21.40|
|MMLU-PRO (5-shot)  |    46.37|