--- language: - en license: apache-2.0 library_name: transformers tags: - merge - mergekit - lazymergekit - mergekit-community/mergekit-della_linear-cwuosuu - mergekit-community/mergekit-della_linear-nimxtnw - mergekit-community/mergekit-della_linear-vpjjtsa - Orenguteng/Llama-3.1-8B-Lexi-Uncensored-V2 base_model: - mergekit-community/mergekit-della_linear-cwuosuu - mergekit-community/mergekit-della_linear-nimxtnw - mergekit-community/mergekit-della_linear-vpjjtsa - Orenguteng/Llama-3.1-8B-Lexi-Uncensored-V2 pipeline_tag: text-generation model-index: - name: Llama-3.1-8B-SuperTulu-LexiNova results: - task: type: text-generation name: Text Generation dataset: name: IFEval (0-Shot) type: HuggingFaceH4/ifeval args: num_few_shot: 0 metrics: - type: inst_level_strict_acc and prompt_level_strict_acc value: 41.65 name: strict accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ZeroXClem/Llama-3.1-8B-SuperTulu-LexiNova name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: BBH (3-Shot) type: BBH args: num_few_shot: 3 metrics: - type: acc_norm value: 30.5 name: normalized accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ZeroXClem/Llama-3.1-8B-SuperTulu-LexiNova name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MATH Lvl 5 (4-Shot) type: hendrycks/competition_math args: num_few_shot: 4 metrics: - type: exact_match value: 25.3 name: exact match source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ZeroXClem/Llama-3.1-8B-SuperTulu-LexiNova name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GPQA (0-shot) type: Idavidrein/gpqa args: num_few_shot: 0 metrics: - type: acc_norm value: 4.81 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ZeroXClem/Llama-3.1-8B-SuperTulu-LexiNova 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: 11.23 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ZeroXClem/Llama-3.1-8B-SuperTulu-LexiNova 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: 26.31 name: accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ZeroXClem/Llama-3.1-8B-SuperTulu-LexiNova name: Open LLM Leaderboard --- # ZeroXClem/Llama-3.1-8B-SuperTulu-LexiNova ## Overview ZeroXClem/Llama-3.1-8B-SuperTulu-LexiNova is model merge designed to a base for further fine tuning for better natural language understanding and text generation. By combining the best attributes of multiple high-performance models, this fusion allows a highly capable AI with reasoning, compliance, and versatility. If you want to try the reccomended **fine-tuned** version of this model, please see [here](https://huggingface.co/ZeroXClem/Llama-3.1-8B-SuperNova-EtherealHermes). This model is based on **Llama-3.1-8B-Instruct** and adheres to the **Meta Llama 3.1 Community License Agreement**. ## 🚀 Key Features: - **Enhanced Reasoning & Compliance**: Optimized for logical step-by-step thinking. - **Balanced Safety & Utility**: Capable of nuanced and detailed responses while maintaining ethical constraints. - **Diverse Knowledge Base**: A fusion of models specializing in general instruction, reasoning, and domain-specific tasks. - **Superior Performance**: Achieves high benchmarks across multiple evaluations. ## 🧠 Merged Models This model is a weighted merge of the following: - **[Orenguteng/Llama-3.1-8B-Lexi-Uncensored-V2](https://huggingface.co/Orenguteng/Llama-3.1-8B-Lexi-Uncensored-V2)** – The foundational model, providing uncensored, high-compliance capabilities. - **[mergekit-community/mergekit-della_linear-cwuosuu](https://huggingface.co/mergekit-community/mergekit-della_linear-cwuosuu)** – Strengthens logical reasoning and alignment. - **[mergekit-community/mergekit-della_linear-nimxtnw](https://huggingface.co/mergekit-community/mergekit-della_linear-nimxtnw)** – Enhances multi-step inference and response depth. - **[mergekit-community/mergekit-della_linear-vpjjtsa](https://huggingface.co/mergekit-community/mergekit-della_linear-vpjjtsa)** – Refines contextual understanding and coherence. ## 🔧 Merge Configuration The following **YAML** configuration was used to merge these models using **Model Stock**, ensuring a balanced and optimized fusion: ```yaml name: ZeroXClem/Llama-3.1-8B-SuperTulu-LexiNova merge_method: model_stock base_model: Orenguteng/Llama-3.1-8B-Lexi-Uncensored-V2 dtype: float16 out_dtype: bfloat16 parameters: normalize: false int8_mask: true models: - model: mergekit-community/mergekit-della_linear-cwuosuu parameters: density: 0.5 weight: 0.5 - model: mergekit-community/mergekit-della_linear-nimxtnw parameters: density: 0.5 weight: 0.5 - model: mergekit-community/mergekit-della_linear-vpjjtsa parameters: density: 0.5 weight: 0.5 - model: Orenguteng/Llama-3.1-8B-Lexi-Uncensored-V2 parameters: density: 0.5 weight: 0.5 ``` --- ## 🛠 How to Use ### 🔥 Ollama For quick inference, you can run the model using **Ollama**: ```sh ollama run hf.co/ZeroXClem/Llama-3.1-8B-SuperTulu-LexiNova ``` ### 🤗 Hugging Face Transformers ```python from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline import torch # Define model name model_name = "ZeroXClem/Llama-3.1-8B-SuperTulu-LexiNova" # Load tokenizer & model tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype=torch.bfloat16, device_map="auto" ) # Initialize text generation pipeline text_generator = pipeline( "text-generation", model=model, tokenizer=tokenizer, torch_dtype=torch.bfloat16, device_map="auto" ) # Example prompt prompt = "Explain the importance of AI alignment in modern society." # Generate output outputs = text_generator( prompt, max_new_tokens=150, do_sample=True, temperature=0.7, top_k=50, top_p=0.95 ) print(outputs[0]["generated_text"]) ``` --- ## 📌 Best Practices - **Use System Prompts:** For best results, use a system message before inference: `"Think step by step with logical reasoning before providing any response."` - **For More Uncensored Output:** You can set a different system message or simply use `"."` as the system prompt. - **Quantization Considerations:** - `Q4` may sometimes cause refusals due to loss in fine-tuning. - `F16` or `Q8` are recommended for optimal performance. --- ## 📜 License This model is released under the **Meta Llama 3.1 Community License Agreement**. Usage, including commercial applications, must adhere to this license. ⚠ **Warning:** This model is uncensored and highly compliant. Ensure proper alignment layers before deploying as a public service. --- ## 💡 Future Improvements - Further refinement of reasoning capabilities. - Optimized token alignment for better coherence. - Additional quantization tuning for efficient deployment. --- ## ❤️ Special Thanks A heartfelt thank you to: - **Orenguteng** for **Llama-3.1-8B-Lexi-Uncensored-V2**. - **MergeKit Community** for the powerful **della_linear** model merges. - The **🤗 Hugging Face & Open-Source AI** community for advancing AI research. Your contributions make cutting-edge AI development possible! 🚀💜 --- ## 📢 Feedback & Contributions If you encounter any issues or have ideas for improvements, feel free to open a discussion or submit a pull request. --- # [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/ZeroXClem__Llama-3.1-8B-SuperTulu-LexiNova-details) | Metric |Value| |-------------------|----:| |Avg. |23.30| |IFEval (0-Shot) |41.65| |BBH (3-Shot) |30.50| |MATH Lvl 5 (4-Shot)|25.30| |GPQA (0-shot) | 4.81| |MuSR (0-shot) |11.23| |MMLU-PRO (5-shot) |26.31|