peft region:us
---
library_name: peft
---
## Training procedure


The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- 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: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32

The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- 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: bfloat16
### Framework versions

- PEFT 0.5.0

- PEFT 0.5.0

## How to Use

You can load the model and perform inference as follows:

```python
from transformers import AutoTokenizer , AutoModelForCausalLM
from peft import PeftConfig  , PeftModel 

 
path_or_model_name="llama2-frenchmedmcqa-dpo"
 
config = PeftConfig.from_pretrained(path_or_model_name)
model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path, return_dict=True, load_in_4bit=True, device_map='auto')
tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
model = PeftModel.from_pretrained(model ,path_or_model_name)

prompt="### Human: Quelle méthode peut être utilisée pour déterminer la constante d'acidité (7,1 et 10,6) de l'acide valproïque (acide dipropylacétique) ? .\n### Assistant : "
    
input_ids =tokenizer(prompt , return_tensors="pt")
output= model.generate(**input_ids , max_length=120)
text_generated =tokenizer.decode(output[0] , skip_special_tokens=True)

print(text_generated)
```