--- base_model: flammenai/Mahou-1.5-mistral-nemo-12B datasets: - flammenai/MahouMix-v1 library_name: transformers license: apache-2.0 tags: - llama-cpp - gguf-my-repo model-index: - name: Mahou-1.5-mistral-nemo-12B 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: 67.51 name: strict accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=flammenai/Mahou-1.5-mistral-nemo-12B 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: 36.26 name: normalized accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=flammenai/Mahou-1.5-mistral-nemo-12B 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: 5.06 name: exact match source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=flammenai/Mahou-1.5-mistral-nemo-12B 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: 3.47 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=flammenai/Mahou-1.5-mistral-nemo-12B 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: 16.47 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=flammenai/Mahou-1.5-mistral-nemo-12B 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: 28.91 name: accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=flammenai/Mahou-1.5-mistral-nemo-12B name: Open LLM Leaderboard --- # Triangle104/Mahou-1.5-mistral-nemo-12B-Q8_0-GGUF This model was converted to GGUF format from [`flammenai/Mahou-1.5-mistral-nemo-12B`](https://huggingface.co/flammenai/Mahou-1.5-mistral-nemo-12B) 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/flammenai/Mahou-1.5-mistral-nemo-12B) for more details on the model. --- Model details: - Mahou-1.5-mistral-nemo-12B Mahou is designed to provide short messages in a conversational context. It is capable of casual conversation and character roleplay. Chat Format This model has been trained to use ChatML format. <|im_start|>system {{system}}<|im_end|> <|im_start|>{{char}} {{message}}<|im_end|> <|im_start|>{{user}} {{message}}<|im_end|> Roleplay Format Speech without quotes. Actions in *asterisks* *leans against wall cooly* so like, i just casted a super strong spell at magician academy today, not gonna lie, felt badass. SillyTavern Settings Use ChatML for the Context Template. Enable Instruct Mode. Use the Mahou ChatML Instruct preset. Use the Mahou Sampler preset. Method ORPO finetuned with 4x H100 for 3 epochs. Open LLM Leaderboard Evaluation Results Detailed results can be found here Metric Value Avg. 26.28 IFEval (0-Shot) 67.51 BBH (3-Shot) 36.26 MATH Lvl 5 (4-Shot) 5.06 GPQA (0-shot) 3.47 MuSR (0-shot) 16.47 MMLU-PRO (5-shot) 28.91 --- ## 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/Mahou-1.5-mistral-nemo-12B-Q8_0-GGUF --hf-file mahou-1.5-mistral-nemo-12b-q8_0.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Triangle104/Mahou-1.5-mistral-nemo-12B-Q8_0-GGUF --hf-file mahou-1.5-mistral-nemo-12b-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/Mahou-1.5-mistral-nemo-12B-Q8_0-GGUF --hf-file mahou-1.5-mistral-nemo-12b-q8_0.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Triangle104/Mahou-1.5-mistral-nemo-12B-Q8_0-GGUF --hf-file mahou-1.5-mistral-nemo-12b-q8_0.gguf -c 2048 ```