README.md

---
license: apache-2.0
datasets:
- berkeley-nest/Nectar
base_model: openchat/openchat-3.5-0106
model-index:
- name: openchat-nectar-0.1
  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: 66.21
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=andysalerno/openchat-nectar-0.1
      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: 82.99
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=andysalerno/openchat-nectar-0.1
      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: 65.17
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=andysalerno/openchat-nectar-0.1
      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: 54.22
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=andysalerno/openchat-nectar-0.1
      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: 81.37
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=andysalerno/openchat-nectar-0.1
      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: 69.67
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=andysalerno/openchat-nectar-0.1
      name: Open LLM Leaderboard
---
This is openchat/openchat-3.5-0106, tuned with DPO on a tiny subset Nectar. Only 200 steps, so nowhere close to a full epoch.

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

Summary of versions:

**[openchat-nectar-0.1](https://huggingface.co/andysalerno/openchat-nectar-0.1)**
- 200 steps, no filtering on Nectar dataset, 5e-5 learning rate
  
**[openchat-nectar-0.2](https://huggingface.co/andysalerno/openchat-nectar-0.2)**
- empty repo, failed training. ignore it
  
**[openchat-nectar-0.3](https://huggingface.co/andysalerno/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](https://huggingface.co/andysalerno/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](https://huggingface.co/andysalerno/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](https://huggingface.co/andysalerno/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](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_andysalerno__openchat-nectar-0.1)

|             Metric              |Value|
|---------------------------------|----:|
|Avg.                             |69.94|
|AI2 Reasoning Challenge (25-Shot)|66.21|
|HellaSwag (10-Shot)              |82.99|
|MMLU (5-Shot)                    |65.17|
|TruthfulQA (0-shot)              |54.22|
|Winogrande (5-shot)              |81.37|
|GSM8k (5-shot)                   |69.67|