--- language: - en license: apache-2.0 tags: - edu - continual pretraining - llama-cpp - gguf-my-repo base_model: BEE-spoke-data/smol_llama-220M-GQA-fineweb_edu datasets: - HuggingFaceFW/fineweb-edu metrics: - accuracy inference: parameters: max_new_tokens: 64 do_sample: true temperature: 0.8 repetition_penalty: 1.05 no_repeat_ngram_size: 4 eta_cutoff: 0.0006 renormalize_logits: true widget: - text: My name is El Microondas the Wise, and example_title: El Microondas - text: Kennesaw State University is a public example_title: Kennesaw State University - text: Bungie Studios is an American video game developer. They are most famous for developing the award winning Halo series of video games. They also made Destiny. The studio was founded example_title: Bungie - text: The Mona Lisa is a world-renowned painting created by example_title: Mona Lisa - text: The Harry Potter series, written by J.K. Rowling, begins with the book titled example_title: Harry Potter Series - text: 'Question: I have cities, but no houses. I have mountains, but no trees. I have water, but no fish. What am I? Answer:' example_title: Riddle - text: The process of photosynthesis involves the conversion of example_title: Photosynthesis - text: Jane went to the store to buy some groceries. She picked up apples, oranges, and a loaf of bread. When she got home, she realized she forgot example_title: Story Continuation - text: 'Problem 2: If a train leaves Station A at 9:00 AM and travels at 60 mph, and another train leaves Station B at 10:00 AM and travels at 80 mph, when will they meet if the distance between the stations is 300 miles? To determine' example_title: Math Problem - text: In the context of computer programming, an algorithm is example_title: Algorithm Definition pipeline_tag: text-generation model-index: - name: smol_llama-220M-GQA-fineweb_edu 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: 19.88 name: strict accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=BEE-spoke-data/smol_llama-220M-GQA-fineweb_edu 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: 2.31 name: normalized accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=BEE-spoke-data/smol_llama-220M-GQA-fineweb_edu 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: 0.0 name: exact match source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=BEE-spoke-data/smol_llama-220M-GQA-fineweb_edu 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: 1.23 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=BEE-spoke-data/smol_llama-220M-GQA-fineweb_edu 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: 14.26 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=BEE-spoke-data/smol_llama-220M-GQA-fineweb_edu 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: 1.41 name: accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=BEE-spoke-data/smol_llama-220M-GQA-fineweb_edu name: Open LLM Leaderboard --- # mattritchey/smol_llama-220M-GQA-fineweb_edu-Q4_K_M-GGUF This model was converted to GGUF format from [`BEE-spoke-data/smol_llama-220M-GQA-fineweb_edu`](https://huggingface.co/BEE-spoke-data/smol_llama-220M-GQA-fineweb_edu) 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/BEE-spoke-data/smol_llama-220M-GQA-fineweb_edu) 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 mattritchey/smol_llama-220M-GQA-fineweb_edu-Q4_K_M-GGUF --hf-file smol_llama-220m-gqa-fineweb_edu-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo mattritchey/smol_llama-220M-GQA-fineweb_edu-Q4_K_M-GGUF --hf-file smol_llama-220m-gqa-fineweb_edu-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 mattritchey/smol_llama-220M-GQA-fineweb_edu-Q4_K_M-GGUF --hf-file smol_llama-220m-gqa-fineweb_edu-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo mattritchey/smol_llama-220M-GQA-fineweb_edu-Q4_K_M-GGUF --hf-file smol_llama-220m-gqa-fineweb_edu-q4_k_m.gguf -c 2048 ```