Instructions to use joycejiang/llama2-13B-qlora-codex-kbgraph-100-rs0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use joycejiang/llama2-13B-qlora-codex-kbgraph-100-rs0 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-13b-hf") model = PeftModel.from_pretrained(base_model, "joycejiang/llama2-13B-qlora-codex-kbgraph-100-rs0") - Notebooks
- Google Colab
- Kaggle
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Download README.md from joycejiang/llama2-13B-qlora-codex-kbgraph-100-rs0: direct link, hf CLI and curl.
- Browser
- Download file 435 Bytes
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https://huggingface.co/joycejiang/llama2-13B-qlora-codex-kbgraph-100-rs0/resolve/main/README.md
- Command line
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hf download hf://joycejiang/llama2-13B-qlora-codex-kbgraph-100-rs0/README.md
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curl -L -o README.md https://huggingface.co/joycejiang/llama2-13B-qlora-codex-kbgraph-100-rs0/resolve/main/README.md
435 Bytes
metadata
library_name: peft
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: bfloat16
Framework versions
- PEFT 0.4.0