---
language:
- en
license: mit
library_name: transformers
tags:
- peft
datasets:
- HuggingFaceH4/ultrachat_200k
model-index:
- name: Maixtchup-4x7b-QLoRA-SFT-UltraChat
  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: 60.92
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kaitchup/Maixtchup-4x7b-QLoRA-SFT-UltraChat
      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: 83.23
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kaitchup/Maixtchup-4x7b-QLoRA-SFT-UltraChat
      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: 60.78
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kaitchup/Maixtchup-4x7b-QLoRA-SFT-UltraChat
      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: 53.33
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kaitchup/Maixtchup-4x7b-QLoRA-SFT-UltraChat
      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: 77.19
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kaitchup/Maixtchup-4x7b-QLoRA-SFT-UltraChat
      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: 43.21
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kaitchup/Maixtchup-4x7b-QLoRA-SFT-UltraChat
      name: Open LLM Leaderboard
---
LoRA adapter for kaitchup/Maixtchup-4x7b briefly fine-tuned on UltraChat.

To load and use this adapter:
```
model_name = "kaitchup/Maixtchup-4x7b"
#Tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)
compute_dtype = getattr(torch, "float16")
bnb_config = BitsAndBytesConfig(
        load_in_4bit=True,
        bnb_4bit_quant_type="nf4",
        bnb_4bit_compute_dtype=compute_dtype,
        bnb_4bit_use_double_quant=True,
)
model = AutoModelForCausalLM.from_pretrained(
          model_name, quantization_config=bnb_config, device_map="auto", attn_implementation="flash_attention_2",
)

model.config.use_cache = True

model = PeftModel.from_pretrained(model, "kaitchup/Maixtchup-4x7b-QLoRA-SFT-UltraChat")
```
# [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_kaitchup__Maixtchup-4x7b-QLoRA-SFT-UltraChat)

|             Metric              |Value|
|---------------------------------|----:|
|Avg.                             |63.11|
|AI2 Reasoning Challenge (25-Shot)|60.92|
|HellaSwag (10-Shot)              |83.23|
|MMLU (5-Shot)                    |60.78|
|TruthfulQA (0-shot)              |53.33|
|Winogrande (5-shot)              |77.19|
|GSM8k (5-shot)                   |43.21|