Instructions to use Sapka/bloom-560m-customer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Sapka/bloom-560m-customer with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("bigscience/bloom-560m") model = PeftModel.from_pretrained(base_model, "Sapka/bloom-560m-customer") - Notebooks
- Google Colab
- Kaggle
basic training
Browse files- README.md +1 -12
- adapter_model.safetensors +3 -0
README.md
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## Training procedure
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The following `bitsandbytes` quantization config was used during training:
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- load_in_8bit: True
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- load_in_4bit: False
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- llm_int8_threshold: 6.0
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- llm_int8_skip_modules: None
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- llm_int8_enable_fp32_cpu_offload: False
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- llm_int8_has_fp16_weight: False
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- bnb_4bit_quant_type: fp4
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- bnb_4bit_use_double_quant: False
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- bnb_4bit_compute_dtype: float32
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### Framework versions
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- PEFT 0.
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## Training procedure
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### Framework versions
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- PEFT 0.6.0
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adapter_model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:066a873a3317c7212676f4853e1c3da2eecd04bae860a6cfc28c420d298f8129
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size 6298560
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