README.md

metadata

language:
  - en
  - zh
license: apache-2.0
library_name: transformers
tags:
  - llama
  - latest
  - TensorBlock
  - GGUF
datasets:
  - teknium/OpenHermes-2.5
pipeline_tag: text-generation
base_model: yaojialzc/Gigi-Llama3-8B-Chinese-zh
model-index:
  - name: Gigi-Llama3-8B-Chinese-zh
    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: 59.64
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=yaojialzc/Gigi-Llama3-8B-Chinese-zh
          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: 80.28
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=yaojialzc/Gigi-Llama3-8B-Chinese-zh
          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: 66.91
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=yaojialzc/Gigi-Llama3-8B-Chinese-zh
          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: 52.14
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=yaojialzc/Gigi-Llama3-8B-Chinese-zh
          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: 76.48
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=yaojialzc/Gigi-Llama3-8B-Chinese-zh
          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: 66.79
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=yaojialzc/Gigi-Llama3-8B-Chinese-zh
          name: Open LLM Leaderboard

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

yaojialzc/Gigi-Llama3-8B-Chinese-zh - GGUF

This repo contains GGUF format model files for yaojialzc/Gigi-Llama3-8B-Chinese-zh.

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|>

{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
Gigi-Llama3-8B-Chinese-zh-Q2_K.gguf	Q2_K	3.179 GB	smallest, significant quality loss - not recommended for most purposes
Gigi-Llama3-8B-Chinese-zh-Q3_K_S.gguf	Q3_K_S	3.664 GB	very small, high quality loss
Gigi-Llama3-8B-Chinese-zh-Q3_K_M.gguf	Q3_K_M	4.019 GB	very small, high quality loss
Gigi-Llama3-8B-Chinese-zh-Q3_K_L.gguf	Q3_K_L	4.322 GB	small, substantial quality loss
Gigi-Llama3-8B-Chinese-zh-Q4_0.gguf	Q4_0	4.661 GB	legacy; small, very high quality loss - prefer using Q3_K_M
Gigi-Llama3-8B-Chinese-zh-Q4_K_S.gguf	Q4_K_S	4.693 GB	small, greater quality loss
Gigi-Llama3-8B-Chinese-zh-Q4_K_M.gguf	Q4_K_M	4.921 GB	medium, balanced quality - recommended
Gigi-Llama3-8B-Chinese-zh-Q5_0.gguf	Q5_0	5.599 GB	legacy; medium, balanced quality - prefer using Q4_K_M
Gigi-Llama3-8B-Chinese-zh-Q5_K_S.gguf	Q5_K_S	5.599 GB	large, low quality loss - recommended
Gigi-Llama3-8B-Chinese-zh-Q5_K_M.gguf	Q5_K_M	5.733 GB	large, very low quality loss - recommended
Gigi-Llama3-8B-Chinese-zh-Q6_K.gguf	Q6_K	6.596 GB	very large, extremely low quality loss
Gigi-Llama3-8B-Chinese-zh-Q8_0.gguf	Q8_0	8.541 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/Gigi-Llama3-8B-Chinese-zh-GGUF --include "Gigi-Llama3-8B-Chinese-zh-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/Gigi-Llama3-8B-Chinese-zh-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'