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Maixtchup-4x7b-QLoRA-SFT-UltraChat
kaitchup/Maixtchup-4x7b-QLoRA-SFT-UltraChat
Maixtchup-4x7b-QLoRA-SFT-UltraChat is a machine learning model from kaitchup. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as mit.
LoRA adapter for kaitchup/Maixtchup-4x7b briefly fine-tuned on UltraChat.
Access
Public
Updated Mar 4, 2024
Repo size
54.6 MB
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.safetensors54.6 MB · 97%
At a glance
- Library
- transformers
- License
- mit
- Access
- Public
- Created
- Jan 25, 2024
- Updated
- Mar 4, 2024
- SHA
- 26a92bd7
Datasets
Model card
From the Hugging Face model README
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 |
- Library
- transformers
- License
- mit
- Languages
- en
- Created
- Jan 25, 2024
- Updated
- Mar 4, 2024