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qlora-dialogue-summary
Andyrasika/qlora-dialogue-summary
qlora-dialogue-summary is a machine learning model from Andyrasika. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as creativeml-openrail-m.
The following bitsandbytes quantization config was used during training: - loadin8bit: False - loadin4bit: True - llmint8threshold: 6.0 - llmint8skipmodules: None - llmint8enablefp32cpuoffload: False - llmint8hasfp16w…
Access
Public
Updated Sep 4, 2023
Repo size
25.4 MB
Likes
1
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Repo makeup
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.bin33.6 MB · 76%
At a glance
- Library
- transformers
- License
- creativeml-openrail-m
- Access
- Public
- Created
- Sep 4, 2023
- Updated
- Sep 4, 2023
- SHA
- f609461d
Datasets
Model card
From the Hugging Face model README
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
# adding back the LoRA adopters to the base Llama-2 model
lora_config = LoraConfig.from_pretrained('Andyrasika/qlora-dialogue-summary')
model = get_peft_model(model, lora_config)
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(input_ids=inputs['input_ids'], attention_mask=inputs['attention_mask'], max_new_tokens=100 ,repetition_penalty=1.2)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
- Library
- transformers
- License
- creativeml-openrail-m
- Languages
- en
- Created
- Sep 4, 2023
- Updated
- Sep 4, 2023