README.md

metadata

language:
  - zh
  - en
license: other
library_name: transformers
datasets:
  - Open-Orca/OpenOrca
pipeline_tag: text-generation
license_name: tongyi-qianwen-research
license_link: https://huggingface.co/Qwen/Qwen1.5-32B/blob/main/LICENSE
model-index:
  - name: Quark-464M-v0.1.alpha
    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: 31.4
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=raincandy-u/Quark-464M-v0.1.alpha
          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: 47.31
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=raincandy-u/Quark-464M-v0.1.alpha
          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: 34.55
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=raincandy-u/Quark-464M-v0.1.alpha
          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: 41.84
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=raincandy-u/Quark-464M-v0.1.alpha
          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: 55.17
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=raincandy-u/Quark-464M-v0.1.alpha
          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: 3.79
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=raincandy-u/Quark-464M-v0.1.alpha
          name: Open LLM Leaderboard

🌟⚛ Introducing Quark Series: Empowering Edge Devices with Swift Bilingual Conversational AI

Presenting Quark-620M-v0.1.alpha, the first model in our Quark series.

Quark models focus on delivering exceptional English and Chinese conversational performance on edge devices with rapid inference speed.

🗨 Example

🧑‍🏫 Benchmark

⏳Wait for uploading

🔍 Disclaimer

As an alpha preview release without RLHF fine-tuning, we do not take responsibility for potentially harmful responses and are committed to continuous improvement based on user feedback and research.

👥 Join Our Discord community!

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	35.68
AI2 Reasoning Challenge (25-Shot)	31.40
HellaSwag (10-Shot)	47.31
MMLU (5-Shot)	34.55
TruthfulQA (0-shot)	41.84
Winogrande (5-shot)	55.17
GSM8k (5-shot)	3.79