Summarization
Transformers
PyTorch
English
bloom
text-generation
jobsearch
text-generation-inference
Instructions to use burberg92/resume_summary with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use burberg92/resume_summary with Transformers:
# 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="burberg92/resume_summary")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("burberg92/resume_summary") model = AutoModelForCausalLM.from_pretrained("burberg92/resume_summary") - Notebooks
- Google Colab
- Kaggle
Upload model
Browse files- adapter_config.json +16 -0
- adapter_model.bin +3 -0
adapter_config.json
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{
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"base_model_name_or_path": "bigscience/bloom-1b7",
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"bias": "none",
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"fan_in_fan_out": false,
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"inference_mode": true,
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"init_lora_weights": true,
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"lora_alpha": 32,
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"lora_dropout": 0.05,
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"modules_to_save": null,
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"peft_type": "LORA",
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"r": 16,
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"target_modules": [
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"query_key_value"
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],
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"task_type": "CAUSAL_LM"
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}
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adapter_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:67b4cc866a8a291c0fda48f33de533d9ed24cc5e1c0b0b3f2fca769dd58019b0
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size 12600641
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