README.md

metadata

license: apache-2.0
datasets:
  - berkeley-nest/Nectar
base_model: openchat/openchat-3.5-0106
model-index:
  - name: openchat-nectar-0.5
    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: 66.72
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=andysalerno/openchat-nectar-0.5
          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: 83.53
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=andysalerno/openchat-nectar-0.5
          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: 65.36
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=andysalerno/openchat-nectar-0.5
          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: 52.15
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=andysalerno/openchat-nectar-0.5
          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: 82.08
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=andysalerno/openchat-nectar-0.5
          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: 68.16
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=andysalerno/openchat-nectar-0.5
          name: Open LLM Leaderboard

This is openchat/openchat-3.5-0106, tuned with DPO on a subset Nectar. This time with 5000 steps, a full epoch.

Careful attention was paid to make sure the chat template was followed properly.

Data selection and filtering:

• filtered dataset to only include examples with multiple turns, to preserve strength in multi-turn scenarios
• used the 4th ranking response as the "rejected" instead of the 3rd. When I inspected the dataset, I frequently could not find any meaningful difference in quality between the 1st and 3rd ranked responses, so to make the accepted/rejected signal extra clear, I replaced 3rd ranking with 4th ranking.
• I filtered out any examples with "good_natured == False". Why? When I inspected examples with "good_natured == False" in the Nectar dataset, I noticed they frequently include refusals from even the top ranking model. So, counter-intuitively, including "bad natured" entries might actually censor the model more, since the top responses (as ranked by GPT-4) to these queries tend to be refusals. Not to mention, the quality of the conversations that are "bad natured" tends to be worse in general, in my own opinion.

Differences from 0.4:

• Trained on 5000 steps instead of 500, with a lower learning rate and slower warmup period.

Summary of versions:

openchat-nectar-0.1

• 200 steps, no filtering on Nectar dataset, 5e-5 learning rate

openchat-nectar-0.2

• empty repo, failed training. ignore it

openchat-nectar-0.3

• 500 steps, no filtering on Nectar dataset, 5e-5 learning rate (same as 1 but with more steps)

openchat-nectar-0.4

• 500 steps, filtered dataset to only include multi-chat-turn examples, used 4th ranking response as the "rejected" instead of 3rd, filtered out "good_natured=False", 5e-5 learning rate

openchat-nectar-0.5

• 5000 steps (over a full epoch), filtered dataset to only include multi-chat-turn examples, used 4th ranking response as the "rejected" instead of 3rd, filtered out "good_natured=False", 5e-6 learning rate. Same as 0.4 but with 10x the steps, and 1/10th the learning rate

openchat-nectar-0.6

• 500 steps, filtered dataset to only include multi-chat-turn examples, used 4th ranking response as the "rejected" instead of 3rd, filtered out "good_natured=False", 5e-5 learning rate. Same as 0.5 but with 1/10th the steps, and 10x the learning rate

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	69.67
AI2 Reasoning Challenge (25-Shot)	66.72
HellaSwag (10-Shot)	83.53
MMLU (5-Shot)	65.36
TruthfulQA (0-shot)	52.15
Winogrande (5-shot)	82.08
GSM8k (5-shot)	68.16