README.md

metadata

license: apache-2.0
language:
  - en
base_model: prithivMLmods/Elita-1
pipeline_tag: text-generation
tags:
  - open-thought
  - reasoning
  - math
  - text-generation-inference
  - elita-1
  - TensorBlock
  - GGUF
library_name: transformers
model-index:
  - name: Elita-1
    results:
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: IFEval (0-Shot)
          type: wis-k/instruction-following-eval
          split: train
          args:
            num_few_shot: 0
        metrics:
          - type: inst_level_strict_acc and prompt_level_strict_acc
            value: 49.06
            name: averaged accuracy
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FElita-1
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: BBH (3-Shot)
          type: SaylorTwift/bbh
          split: test
          args:
            num_few_shot: 3
        metrics:
          - type: acc_norm
            value: 49.93
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FElita-1
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: MATH Lvl 5 (4-Shot)
          type: lighteval/MATH-Hard
          split: test
          args:
            num_few_shot: 4
        metrics:
          - type: exact_match
            value: 34.14
            name: exact match
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FElita-1
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: GPQA (0-shot)
          type: Idavidrein/gpqa
          split: train
          args:
            num_few_shot: 0
        metrics:
          - type: acc_norm
            value: 16.78
            name: acc_norm
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FElita-1
          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: 20.53
            name: acc_norm
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FElita-1
          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: 48.68
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FElita-1
          name: Open LLM Leaderboard

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prithivMLmods/Elita-1 - GGUF

This repo contains GGUF format model files for prithivMLmods/Elita-1.

The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b4823.

Run them on the TensorBlock client using your local machine ↗

Prompt template

<|im_start|>system
{system_prompt}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant

Model file specification

Filename	Quant type	File Size	Description
Elita-1-Q2_K.gguf	Q2_K	5.768 GB	smallest, significant quality loss - not recommended for most purposes
Elita-1-Q3_K_S.gguf	Q3_K_S	6.657 GB	very small, high quality loss
Elita-1-Q3_K_M.gguf	Q3_K_M	7.337 GB	very small, high quality loss
Elita-1-Q3_K_L.gguf	Q3_K_L	7.922 GB	small, substantial quality loss
Elita-1-Q4_0.gguf	Q4_0	8.515 GB	legacy; small, very high quality loss - prefer using Q3_K_M
Elita-1-Q4_K_S.gguf	Q4_K_S	8.571 GB	small, greater quality loss
Elita-1-Q4_K_M.gguf	Q4_K_M	8.985 GB	medium, balanced quality - recommended
Elita-1-Q5_0.gguf	Q5_0	10.263 GB	legacy; medium, balanced quality - prefer using Q4_K_M
Elita-1-Q5_K_S.gguf	Q5_K_S	10.263 GB	large, low quality loss - recommended
Elita-1-Q5_K_M.gguf	Q5_K_M	10.506 GB	large, very low quality loss - recommended
Elita-1-Q6_K.gguf	Q6_K	12.121 GB	very large, extremely low quality loss
Elita-1-Q8_0.gguf	Q8_0	15.697 GB	very large, extremely low quality loss - not recommended

Downloading instruction

Command line

Firstly, install Huggingface Client

pip install -U "huggingface_hub[cli]"

Then, downoad the individual model file the a local directory

huggingface-cli download tensorblock/Elita-1-GGUF --include "Elita-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:

huggingface-cli download tensorblock/Elita-1-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'