Instructions to use nicolaleo/llamav2_code with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use nicolaleo/llamav2_code with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-hf") model = PeftModel.from_pretrained(base_model, "nicolaleo/llamav2_code") - Notebooks
- Google Colab
- Kaggle
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Download README.md from nicolaleo/llamav2_code: direct link, hf CLI and curl.
- Browser
- Download file 830 Bytes
-
https://huggingface.co/nicolaleo/llamav2_code/resolve/main/README.md
- Command line
-
hf download hf://nicolaleo/llamav2_code/README.md
-
curl -L -o README.md https://huggingface.co/nicolaleo/llamav2_code/resolve/main/README.md
830 Bytes
metadata
library_name: peft
base_model: meta-llama/Llama-2-7b-hf
Training procedure
The following bitsandbytes quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: float16
The following bitsandbytes quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: float16
Framework versions
PEFT 0.5.0
PEFT 0.5.0