--- language: - en license: mit library_name: transformers inference: parameters: max_new_tokens: 64 do_sample: true temperature: 0.1 repetition_penalty: 10 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 datasets: - kenhktsui/minipile_quality_score_v1 - kenhktsui/simple_wikipedia_LM_quality_score_v1 - kenhktsui/refinedweb-3m_quality_score_v1 - kenhktsui/TM-DATA_quality_score_v1 - kenhktsui/openwebtext_quality_score_v1 tags: - TensorBlock - GGUF base_model: kenhktsui/nano-phi-115M-v0.1 model-index: - name: nano-phi-115M-v0.1 results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 21.93 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kenhktsui/nano-phi-115M-v0.1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 27.86 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kenhktsui/nano-phi-115M-v0.1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 25.34 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kenhktsui/nano-phi-115M-v0.1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 46 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kenhktsui/nano-phi-115M-v0.1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 50.83 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kenhktsui/nano-phi-115M-v0.1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 0 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kenhktsui/nano-phi-115M-v0.1 name: Open LLM Leaderboard ---
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## kenhktsui/nano-phi-115M-v0.1 - GGUF This repo contains GGUF format model files for [kenhktsui/nano-phi-115M-v0.1](https://huggingface.co/kenhktsui/nano-phi-115M-v0.1). The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b4242](https://github.com/ggerganov/llama.cpp/commit/a6744e43e80f4be6398fc7733a01642c846dce1d).
Run them on the TensorBlock client using your local machine ↗
## Prompt template ``` ``` ## Model file specification | Filename | Quant type | File Size | Description | | -------- | ---------- | --------- | ----------- | | [nano-phi-115M-v0.1-Q2_K.gguf](https://huggingface.co/tensorblock/nano-phi-115M-v0.1-GGUF/blob/main/nano-phi-115M-v0.1-Q2_K.gguf) | Q2_K | 0.061 GB | smallest, significant quality loss - not recommended for most purposes | | [nano-phi-115M-v0.1-Q3_K_S.gguf](https://huggingface.co/tensorblock/nano-phi-115M-v0.1-GGUF/blob/main/nano-phi-115M-v0.1-Q3_K_S.gguf) | Q3_K_S | 0.067 GB | very small, high quality loss | | [nano-phi-115M-v0.1-Q3_K_M.gguf](https://huggingface.co/tensorblock/nano-phi-115M-v0.1-GGUF/blob/main/nano-phi-115M-v0.1-Q3_K_M.gguf) | Q3_K_M | 0.069 GB | very small, high quality loss | | [nano-phi-115M-v0.1-Q3_K_L.gguf](https://huggingface.co/tensorblock/nano-phi-115M-v0.1-GGUF/blob/main/nano-phi-115M-v0.1-Q3_K_L.gguf) | Q3_K_L | 0.072 GB | small, substantial quality loss | | [nano-phi-115M-v0.1-Q4_0.gguf](https://huggingface.co/tensorblock/nano-phi-115M-v0.1-GGUF/blob/main/nano-phi-115M-v0.1-Q4_0.gguf) | Q4_0 | 0.077 GB | legacy; small, very high quality loss - prefer using Q3_K_M | | [nano-phi-115M-v0.1-Q4_K_S.gguf](https://huggingface.co/tensorblock/nano-phi-115M-v0.1-GGUF/blob/main/nano-phi-115M-v0.1-Q4_K_S.gguf) | Q4_K_S | 0.077 GB | small, greater quality loss | | [nano-phi-115M-v0.1-Q4_K_M.gguf](https://huggingface.co/tensorblock/nano-phi-115M-v0.1-GGUF/blob/main/nano-phi-115M-v0.1-Q4_K_M.gguf) | Q4_K_M | 0.078 GB | medium, balanced quality - recommended | | [nano-phi-115M-v0.1-Q5_0.gguf](https://huggingface.co/tensorblock/nano-phi-115M-v0.1-GGUF/blob/main/nano-phi-115M-v0.1-Q5_0.gguf) | Q5_0 | 0.086 GB | legacy; medium, balanced quality - prefer using Q4_K_M | | [nano-phi-115M-v0.1-Q5_K_S.gguf](https://huggingface.co/tensorblock/nano-phi-115M-v0.1-GGUF/blob/main/nano-phi-115M-v0.1-Q5_K_S.gguf) | Q5_K_S | 0.086 GB | large, low quality loss - recommended | | [nano-phi-115M-v0.1-Q5_K_M.gguf](https://huggingface.co/tensorblock/nano-phi-115M-v0.1-GGUF/blob/main/nano-phi-115M-v0.1-Q5_K_M.gguf) | Q5_K_M | 0.087 GB | large, very low quality loss - recommended | | [nano-phi-115M-v0.1-Q6_K.gguf](https://huggingface.co/tensorblock/nano-phi-115M-v0.1-GGUF/blob/main/nano-phi-115M-v0.1-Q6_K.gguf) | Q6_K | 0.096 GB | very large, extremely low quality loss | | [nano-phi-115M-v0.1-Q8_0.gguf](https://huggingface.co/tensorblock/nano-phi-115M-v0.1-GGUF/blob/main/nano-phi-115M-v0.1-Q8_0.gguf) | Q8_0 | 0.124 GB | very large, extremely low quality loss - not recommended | ## Downloading instruction ### Command line Firstly, install Huggingface Client ```shell pip install -U "huggingface_hub[cli]" ``` Then, downoad the individual model file the a local directory ```shell huggingface-cli download tensorblock/nano-phi-115M-v0.1-GGUF --include "nano-phi-115M-v0.1-Q2_K.gguf" --local-dir MY_LOCAL_DIR ``` If you wanna download multiple model files with a pattern (e.g., `*Q4_K*gguf`), you can try: ```shell huggingface-cli download tensorblock/nano-phi-115M-v0.1-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf' ```