Instructions to use wesley7137/MetaMath-CodeLlama-Python-7B-V1-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use wesley7137/MetaMath-CodeLlama-Python-7B-V1-lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("codellama/CodeLlama-7b-Python-hf") model = PeftModel.from_pretrained(base_model, "wesley7137/MetaMath-CodeLlama-Python-7B-V1-lora") - Notebooks
- Google Colab
- Kaggle
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Download README.md from wesley7137/MetaMath-CodeLlama-Python-7B-V1-lora: direct link, hf CLI and curl.
- Browser
- Download file 853 Bytes
-
https://huggingface.co/wesley7137/MetaMath-CodeLlama-Python-7B-V1-lora/resolve/main/README.md
- Command line
-
hf download hf://wesley7137/MetaMath-CodeLlama-Python-7B-V1-lora/README.md
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curl -L -o README.md https://huggingface.co/wesley7137/MetaMath-CodeLlama-Python-7B-V1-lora/resolve/main/README.md
853 Bytes
metadata
library_name: peft
Training procedure
The following bitsandbytes quantization config was used during training:
- quant_method: bitsandbytes
- 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: False
- bnb_4bit_compute_dtype: float16
The following bitsandbytes quantization config was used during training:
- quant_method: bitsandbytes
- 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: False
- bnb_4bit_compute_dtype: float16
Framework versions
PEFT 0.4.0
PEFT 0.4.0