README.md

metadata

license: apache-2.0
library_name: peft
tags:
  - code
  - instruct
  - mistral
datasets:
  - cognitivecomputations/dolphin-coder
base_model: mistralai/Mistral-7B-v0.1
model-index:
  - name: mistral_7b_DolphinCoder
    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: 59.73
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=qblocks/mistral_7b_DolphinCoder
          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: 81.64
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=qblocks/mistral_7b_DolphinCoder
          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: 59.87
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=qblocks/mistral_7b_DolphinCoder
          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: 43.95
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=qblocks/mistral_7b_DolphinCoder
          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: 74.59
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=qblocks/mistral_7b_DolphinCoder
          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: 26.23
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=qblocks/mistral_7b_DolphinCoder
          name: Open LLM Leaderboard

Finetuning Overview:

Model Used: mistralai/Mistral-7B-v0.1

Dataset: cognitivecomputations/dolphin-coder

Dataset Insights:

Dolphin-Coder dataset – a high-quality collection of 100,000+ coding questions and responses. It's perfect for supervised fine-tuning (SFT), and teaching language models to improve on coding-based tasks.

Finetuning Details:

With the utilization of MonsterAPI's no-code LLM finetuner, this finetuning:

• Was achieved with great cost-effectiveness.
• Completed in a total duration of 15hr 36mins for 1 epochs using an A6000 48GB GPU.
• Costed $31.51 for the entire 1 epoch.

Hyperparameters & Additional Details:

• Epochs: 1
• Cost Per Epoch: $31.51
• Model Path: mistralai/Mistral-7B-v0.1
• Learning Rate: 0.0002
• Data Split: 100% train
• Gradient Accumulation Steps: 128
• lora r: 32
• lora alpha: 64

---

license: apache-2.0

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	57.67
AI2 Reasoning Challenge (25-Shot)	59.73
HellaSwag (10-Shot)	81.64
MMLU (5-Shot)	59.87
TruthfulQA (0-shot)	43.95
Winogrande (5-shot)	74.59
GSM8k (5-shot)	26.23