# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("npnik4/resume-summarizer")
model = AutoModelForCausalLM.from_pretrained("npnik4/resume-summarizer")Quick Links
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: False
- bnb_4bit_compute_dtype: float16
Framework versions
- PEFT 0.4.0
The following hyperparameters were used during training:
- train_batch_size: 2
- eval_batch_size: 2
- num_epochs: 10
| Step | Training Loss |
|---|---|
| 25 | 2.124 |
| 50 | 1.835 |
| 75 | 1.657 |
| 100 | 1.403 |
| 125 | 1.113 |
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# Use a pipeline as a high-level helper # Warning: Pipeline type "summarization" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("summarization", model="npnik4/resume-summarizer")