README.md

metadata

language:
  - en
license: llama3
library_name: transformers
pipeline_tag: text2text-generation
model-index:
  - name: orca_mini_v5_8b_dpo
    results:
      - 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: 48.96
            name: strict accuracy
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=pankajmathur/orca_mini_v5_8b_dpo
          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: 29.61
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=pankajmathur/orca_mini_v5_8b_dpo
          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: 7.48
            name: exact match
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=pankajmathur/orca_mini_v5_8b_dpo
          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: 3.24
            name: acc_norm
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=pankajmathur/orca_mini_v5_8b_dpo
          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: 6.94
            name: acc_norm
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=pankajmathur/orca_mini_v5_8b_dpo
          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: 23.51
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=pankajmathur/orca_mini_v5_8b_dpo
          name: Open LLM Leaderboard

Model Name: llama_3_orca_mini_v5_8b_dpo

llama_3_orca_mini_v5_8b trained with various DPO Datasets

Passionate about Generative AI? I help companies to privately train and deploy custom LLM/MLLM affordably. For startups, I can even assist with securing GPU grants to get you started. Let's chat!

https://www.linkedin.com/in/pankajam Looking forward to connecting!

NOTICE

By providing proper credit and attribution, you are granted permission to use this model as a foundational base for further Full fine tuning, DPO, PPO or ORPO tuning and any kind of Merges. I actively encourage users to customize and enhance the model according to their specific needs, as this version is designed to be a comprehensive general model. Dive in and innovate!

Evaluation

Metric	Value
Avg.	67.78
AI2 Reasoning Challenge (25-Shot)	61.86
HellaSwag (10-Shot)	82.35
MMLU (5-Shot)	65.10
TruthfulQA (0-shot)	56.24
Winogrande (5-shot)	73.40
GSM8k (5-shot)	67.70

Example Usage

Here is the ChatML prompt format

<|im_start|>system
You are Orca Mini, a helpful AI assistant.<|im_end|>
<|im_start|>user
Hello Orca Mini, what can you do for me?<|im_end|>
<|im_start|>assistant

Below shows a code example on how to use this model

from transformers import AutoModel, AutoTokenizer
model_slug = "pankajmathur/orca_mini_v5_8b_dpo"
model = AutoModel.from_pretrained(model_slug)
tokenizer = AutoTokenizer.from_pretrained(model_slug)

messages = [
    {"role": "system", "content": "You are Orca Mini, a helpful AI assistant."},
    {"role": "user", "content": "Hello Orca Mini, what can you do for me?"}
]

gen_input = tokenizer.apply_chat_template(messages, return_tensors="pt")
model.generate(**gen_input)

This model is governed by META LLAMA 3 COMMUNITY LICENSE AGREEMENT

Quants

GGUF : Coming Soon

AWQ: Coming Soon

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	19.96
IFEval (0-Shot)	48.96
BBH (3-Shot)	29.61
MATH Lvl 5 (4-Shot)	7.48
GPQA (0-shot)	3.24
MuSR (0-shot)	6.94
MMLU-PRO (5-shot)	23.51