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
Detailed results can be found here
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 |
Dataset used to train kaitchup/Maixtchup-4x7b-QLoRA-SFT-UltraChat
Evaluation results
- normalized accuracy on AI2 Reasoning Challenge (25-Shot)test set Open LLM Leaderboard60.920
- normalized accuracy on HellaSwag (10-Shot)validation set Open LLM Leaderboard83.230
- accuracy on MMLU (5-Shot)test set Open LLM Leaderboard60.780
- mc2 on TruthfulQA (0-shot)validation set Open LLM Leaderboard53.330
- accuracy on Winogrande (5-shot)validation set Open LLM Leaderboard77.190
- accuracy on GSM8k (5-shot)test set Open LLM Leaderboard43.210