README.md

---
tags:
- long-cot-reasoning
- transformers
- mamba2
- llms
- chain-of-thought
- llama-cpp
- gguf-my-repo
license: apache-2.0
language:
- en
datasets:
- Daemontatox/LongCOT-Reason
- Daemontatox/alpaca_reasoning_COT
base_model: Daemontatox/Sphinx2.0
pipeline_tag: text-generation
library_name: transformers
model-index:
- name: Sphinx2.0
  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: 71.23
      name: averaged accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=Daemontatox%2FSphinx2.0
      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: 49.4
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=Daemontatox%2FSphinx2.0
      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: 2.72
      name: exact match
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=Daemontatox%2FSphinx2.0
      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: 5.82
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=Daemontatox%2FSphinx2.0
      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: 13.05
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=Daemontatox%2FSphinx2.0
      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: 46.49
      name: accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=Daemontatox%2FSphinx2.0
      name: Open LLM Leaderboard
---

# Triangle104/Sphinx2.0-Q8_0-GGUF
This model was converted to GGUF format from [`Daemontatox/Sphinx2.0`](https://huggingface.co/Daemontatox/Sphinx2.0) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/Daemontatox/Sphinx2.0) for more details on the model.

---
Model details:
-
phinx: The Apex of Logical Deduction and Chain-of-Thought Reasoning

    Developed by: Daemontatox
    License: Apache-2.0
    Base Model: Fine-tuned from unsloth/qwen2.5-14b-instruct-bnb-4bit
    Accelerated by: Unsloth Framework
    TRL-Optimized: Integrated with Huggingface's TRL library for enhanced performance in logical reasoning.

Unveiling Sphinx: Master of Reasoned Thought

Sphinx is a cutting-edge Long Chain-of-Thought (CoT) reasoning model meticulously crafted to unravel complex challenges requiring rigorous logical analysis. Built upon the robust foundation of the Qwen2.5 architecture, Sphinx excels at constructing coherent, step-by-step thought processes, providing unparalleled insight into its reasoning and ensuring clarity in its conclusions.

    "Where complexity yields to logical clarity."

Core Strengths: Reasoning, Logic, and CoT

    Unrivaled Chain-of-Thought (CoT) Mastery: Engineered for dissecting intricate problems, Sphinx meticulously constructs each step of its reasoning, offering a transparent and verifiable pathway to the solution.
    Deep Logical Reasoning Capabilities: Sphinx is adept at navigating complex logical structures, drawing valid inferences and forming sound conclusions through multi-layered analysis.
    Exceptional Reasoning Fidelity: Fine-tuned to maintain the highest standards of logical consistency, Sphinx delivers outputs that are not only correct but also demonstrably well-reasoned.
    Efficient Long-Context Reasoning: Leveraging the power of Unsloth, Sphinx processes extensive information efficiently, maintaining logical coherence across extended reasoning chains.
    Explainable AI through Transparent Logic: Sphinx's inherent CoT approach provides explicit and understandable reasoning, making its decision-making process transparent and trustworthy.

Model Architecture and Fine-tuning for Logical Prowess
Architectural Foundation

    Base Model: Qwen2.5-14B - Renowned for its strong general language understanding, forming a solid basis for specialized reasoning.
    Parameters: 14 billion - Providing the capacity to model intricate reasoning patterns.
    Quantization: 4-bit precision using BitsAndBytes (bnb) - Optimizing for accessibility without sacrificing logical reasoning accuracy.
    Extended Reasoning Window: Supports inputs up to 16k tokens, crucial for accommodating the detailed context required for complex logical deductions.

Training Methodology: Honing Logical Acumen

    Frameworks: Huggingface Transformers + TRL + Unsloth - A powerful combination for efficient training and reinforcement learning.
    Data Sources: A meticulously curated collection of datasets specifically designed to challenge and refine logical reasoning skills, encompassing academic, legal, and formal logic domains.
    Optimization Strategies:
        LoRA (Low-Rank Adaptation): Enabling parameter-efficient fine-tuning, focusing on adapting the model for superior logical inference.
        Reinforcement Learning from Human Feedback (RLHF): Guiding the model towards generating more logically sound and human-aligned reasoning steps.

Sphinx's Reasoning Toolkit: Capabilities in Action

    Masterful Long-CoT Generation: Deconstructs and conquers multi-layered problems by constructing detailed, logically interconnected reasoning sequences.
    Explanatory Power through Logic: Provides clear, step-by-step logical derivations for its outputs, enhancing trust and understanding.
    Adaptable Logical Framework: Easily tailored to specialized reasoning tasks through targeted fine-tuning, enabling application in diverse logical domains.

Unlocking Potential: Applications Driven by Logic

    Advanced Academic Research: Generating in-depth, logically structured analyses for complex scientific and philosophical inquiries.
    Robust Legal Reasoning Assistance: Constructing and articulating multi-step legal arguments with precision and logical rigor.
    Transformative STEM Education: Guiding learners through intricate mathematical and logical problems with clear, step-by-step explanations.
    Transparent Cognitive AI Systems: Powering AI systems where explainability and logical justification are paramount for decision-making.# Open LLM Leaderboard Evaluation Results Detailed results can be found here! Summarized results can be found here!

---
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)

```bash
brew install llama.cpp

```
Invoke the llama.cpp server or the CLI.

### CLI:
```bash
llama-cli --hf-repo Triangle104/Sphinx2.0-Q8_0-GGUF --hf-file sphinx2.0-q8_0.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo Triangle104/Sphinx2.0-Q8_0-GGUF --hf-file sphinx2.0-q8_0.gguf -c 2048
```

Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.

Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```

Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```

Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Triangle104/Sphinx2.0-Q8_0-GGUF --hf-file sphinx2.0-q8_0.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo Triangle104/Sphinx2.0-Q8_0-GGUF --hf-file sphinx2.0-q8_0.gguf -c 2048
```