README.md

metadata

license: cc-by-nc-4.0
model-index:
  - name: Mistral-7b-instruct-v0.2-summ-sft-bf16-e2
    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: 60.67
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=genaicore3434/Mistral-7b-instruct-v0.2-summ-sft-bf16-e2
          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: 83.55
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=genaicore3434/Mistral-7b-instruct-v0.2-summ-sft-bf16-e2
          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: 60.81
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=genaicore3434/Mistral-7b-instruct-v0.2-summ-sft-bf16-e2
          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: 65.1
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=genaicore3434/Mistral-7b-instruct-v0.2-summ-sft-bf16-e2
          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: 77.58
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=genaicore3434/Mistral-7b-instruct-v0.2-summ-sft-bf16-e2
          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: 39.42
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=genaicore3434/Mistral-7b-instruct-v0.2-summ-sft-bf16-e2
          name: Open LLM Leaderboard

Description to load and test will be added soon. More details on training and data will be added aswell.

Loading the Model

Use the following Python code to load the model:

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

TBD

Generating Text

To generate text, use the following Python code:

text = "Hi, my name is "
inputs = tokenizer(text, return_tensors="pt")

outputs = model.generate(**inputs, max_new_tokens=64)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	64.52
AI2 Reasoning Challenge (25-Shot)	60.67
HellaSwag (10-Shot)	83.55
MMLU (5-Shot)	60.81
TruthfulQA (0-shot)	65.10
Winogrande (5-shot)	77.58
GSM8k (5-shot)	39.42