--- license: apache-2.0 pipeline_tag: text-generation library_name: transformers tags: - axolotl - dpo - trl - llama-cpp - gguf-my-repo base_model: HumanLLMs/Human-Like-Mistral-Nemo-Instruct-2407 datasets: - HumanLLMs/Human-Like-DPO-Dataset language: - en model-index: - name: Humanish-Mistral-Nemo-Instruct-2407 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: 54.51 name: strict accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=HumanLLMs/Humanish-Mistral-Nemo-Instruct-2407 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: 32.71 name: normalized accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=HumanLLMs/Humanish-Mistral-Nemo-Instruct-2407 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: 7.63 name: exact match source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=HumanLLMs/Humanish-Mistral-Nemo-Instruct-2407 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: 5.03 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=HumanLLMs/Humanish-Mistral-Nemo-Instruct-2407 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: 9.4 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=HumanLLMs/Humanish-Mistral-Nemo-Instruct-2407 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: 28.01 name: accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=HumanLLMs/Humanish-Mistral-Nemo-Instruct-2407 name: Open LLM Leaderboard --- # Triangle104/Human-Like-Mistral-Nemo-Instruct-2407-Q5_K_M-GGUF This model was converted to GGUF format from [`HumanLLMs/Human-Like-Mistral-Nemo-Instruct-2407`](https://huggingface.co/HumanLLMs/Human-Like-Mistral-Nemo-Instruct-2407) 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/HumanLLMs/Human-Like-Mistral-Nemo-Instruct-2407) for more details on the model. --- Model details: - This model is a fine-tuned version of mistralai/Mistral-Nemo-Instruct-2407, specifically optimized to generate more human-like and conversational responses. The fine-tuning process employed both Low-Rank Adaptation (LoRA) and Direct Preference Optimization (DPO) to enhance natural language understanding, conversational coherence, and emotional intelligence in interactions. The proccess of creating this models is detailed in the research paper “Enhancing Human-Like Responses in Large Language Models”. 🛠️ Training Configuration Base Model: Mistral-Nemo-Instruct-2407 Framework: Axolotl v0.4.1 Hardware: 2x NVIDIA A100 (80 GB) GPUs Training Time: ~3 hours 40 minutes Dataset: Synthetic dataset with ≈11,000 samples across 256 diverse topics 💬 Prompt Template You can use Mistral-Nemo prompt template while using the model: Mistral-Nemo [INST] Hello, how are you? [/INST]I'm doing great. How can I help you today? [INST] I'd like to show off how chat templating works! [/INST] This prompt template is available as a chat template, which means you can format messages using the tokenizer.apply_chat_template() method: messages = [ {"role": "system", "content": "You are helpful AI asistant."}, {"role": "user", "content": "Hello!"} ] gen_input = tokenizer.apply_chat_template(message, return_tensors="pt") model.generate(**gen_input) --- ## 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/Human-Like-Mistral-Nemo-Instruct-2407-Q5_K_M-GGUF --hf-file human-like-mistral-nemo-instruct-2407-q5_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Triangle104/Human-Like-Mistral-Nemo-Instruct-2407-Q5_K_M-GGUF --hf-file human-like-mistral-nemo-instruct-2407-q5_k_m.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/Human-Like-Mistral-Nemo-Instruct-2407-Q5_K_M-GGUF --hf-file human-like-mistral-nemo-instruct-2407-q5_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Triangle104/Human-Like-Mistral-Nemo-Instruct-2407-Q5_K_M-GGUF --hf-file human-like-mistral-nemo-instruct-2407-q5_k_m.gguf -c 2048 ```