--- base_model: nbeerbower/llama3.1-cc-8B datasets: - flammenai/casual-conversation-DPO library_name: transformers license: llama3 tags: - llama-cpp - gguf-my-repo model-index: - name: llama3.1-cc-8B 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: 50.68 name: strict accuracy source: url: https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=nbeerbower/llama3.1-cc-8B 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: 26.48 name: normalized accuracy source: url: https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=nbeerbower/llama3.1-cc-8B 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: 6.34 name: exact match source: url: https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=nbeerbower/llama3.1-cc-8B 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: 4.7 name: acc_norm source: url: https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=nbeerbower/llama3.1-cc-8B 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: 6.5 name: acc_norm source: url: https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=nbeerbower/llama3.1-cc-8B 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: 26.08 name: accuracy source: url: https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=nbeerbower/llama3.1-cc-8B name: Open LLM Leaderboard --- # Triangle104/llama3.1-cc-8B-Q4_K_M-GGUF This model was converted to GGUF format from [`nbeerbower/llama3.1-cc-8B`](https://huggingface.co./nbeerbower/llama3.1-cc-8B) 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./nbeerbower/llama3.1-cc-8B) for more details on the model. ## 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/llama3.1-cc-8B-Q4_K_M-GGUF --hf-file llama3.1-cc-8b-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Triangle104/llama3.1-cc-8B-Q4_K_M-GGUF --hf-file llama3.1-cc-8b-q4_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/llama3.1-cc-8B-Q4_K_M-GGUF --hf-file llama3.1-cc-8b-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Triangle104/llama3.1-cc-8B-Q4_K_M-GGUF --hf-file llama3.1-cc-8b-q4_k_m.gguf -c 2048 ```