README.md · tensorblock/Llama3.1-8B-ShiningValiant2-GGUF at main

metadata

language:
  - en
pipeline_tag: text-generation
tags:
  - shining-valiant
  - shining-valiant-2
  - valiant
  - valiant-labs
  - llama
  - llama-3.1
  - llama-3.1-instruct
  - llama-3.1-instruct-8b
  - llama-3
  - llama-3-instruct
  - llama-3-instruct-8b
  - 8b
  - science
  - physics
  - biology
  - chemistry
  - compsci
  - computer-science
  - engineering
  - technical
  - conversational
  - chat
  - instruct
  - TensorBlock
  - GGUF
base_model: ValiantLabs/Llama3.1-8B-ShiningValiant2
datasets:
  - sequelbox/Celestia
  - sequelbox/Spurline
  - sequelbox/Supernova
model_type: llama
license: llama3.1
model-index:
  - name: Llama3.1-8B-ShiningValiant2
    results:
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: Winogrande (5-Shot)
          type: Winogrande
          args:
            num_few_shot: 5
        metrics:
          - type: acc
            value: 75.85
            name: acc
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: MMLU College Biology (5-Shot)
          type: MMLU
          args:
            num_few_shot: 5
        metrics:
          - type: acc
            value: 68.75
            name: acc
          - type: acc
            value: 73.23
            name: acc
          - type: acc
            value: 46
            name: acc
          - type: acc
            value: 44.33
            name: acc
          - type: acc
            value: 53.19
            name: acc
          - type: acc
            value: 37.25
            name: acc
          - type: acc
            value: 42.38
            name: acc
          - type: acc
            value: 56
            name: acc
          - type: acc
            value: 63
            name: acc
          - type: acc
            value: 63.16
            name: acc
      - 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: 65.24
            name: strict accuracy
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ValiantLabs/Llama3.1-8B-ShiningValiant2
          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: 26.35
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ValiantLabs/Llama3.1-8B-ShiningValiant2
          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: 11.63
            name: exact match
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ValiantLabs/Llama3.1-8B-ShiningValiant2
          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: 8.95
            name: acc_norm
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ValiantLabs/Llama3.1-8B-ShiningValiant2
          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: 7.19
            name: acc_norm
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ValiantLabs/Llama3.1-8B-ShiningValiant2
          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: 26.38
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ValiantLabs/Llama3.1-8B-ShiningValiant2
          name: Open LLM Leaderboard

Feedback and support: TensorBlock's Twitter/X, Telegram Group and Discord server

ValiantLabs/Llama3.1-8B-ShiningValiant2 - GGUF

This repo contains GGUF format model files for ValiantLabs/Llama3.1-8B-ShiningValiant2.

The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b4011.

Run them on the TensorBlock client using your local machine ↗

Prompt template

<|begin_of_text|><|start_header_id|>system<|end_header_id|>

Cutting Knowledge Date: December 2023
Today Date: 26 Jul 2024

{system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|>

{prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|>

Model file specification

Filename	Quant type	File Size	Description
Llama3.1-8B-ShiningValiant2-Q2_K.gguf	Q2_K	2.961 GB	smallest, significant quality loss - not recommended for most purposes
Llama3.1-8B-ShiningValiant2-Q3_K_S.gguf	Q3_K_S	3.413 GB	very small, high quality loss
Llama3.1-8B-ShiningValiant2-Q3_K_M.gguf	Q3_K_M	3.743 GB	very small, high quality loss
Llama3.1-8B-ShiningValiant2-Q3_K_L.gguf	Q3_K_L	4.025 GB	small, substantial quality loss
Llama3.1-8B-ShiningValiant2-Q4_0.gguf	Q4_0	4.341 GB	legacy; small, very high quality loss - prefer using Q3_K_M
Llama3.1-8B-ShiningValiant2-Q4_K_S.gguf	Q4_K_S	4.370 GB	small, greater quality loss
Llama3.1-8B-ShiningValiant2-Q4_K_M.gguf	Q4_K_M	4.583 GB	medium, balanced quality - recommended
Llama3.1-8B-ShiningValiant2-Q5_0.gguf	Q5_0	5.215 GB	legacy; medium, balanced quality - prefer using Q4_K_M
Llama3.1-8B-ShiningValiant2-Q5_K_S.gguf	Q5_K_S	5.215 GB	large, low quality loss - recommended
Llama3.1-8B-ShiningValiant2-Q5_K_M.gguf	Q5_K_M	5.339 GB	large, very low quality loss - recommended
Llama3.1-8B-ShiningValiant2-Q6_K.gguf	Q6_K	6.143 GB	very large, extremely low quality loss
Llama3.1-8B-ShiningValiant2-Q8_0.gguf	Q8_0	7.954 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/Llama3.1-8B-ShiningValiant2-GGUF --include "Llama3.1-8B-ShiningValiant2-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/Llama3.1-8B-ShiningValiant2-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'