---
library_name: peft
---
## Usage 

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

config = PeftConfig.from_pretrained("mwitiderrick/zephyr-7b-beta-gsm8k")
model = AutoModelForCausalLM.from_pretrained("HuggingFaceH4/zephyr-7b-beta")
model = PeftModel.from_pretrained(model, "mwitiderrick/zephyr-7b-beta-gsm8k")

prompt = "James decides to run 3 sprints 3 times a week. He runs 60 meters each sprint. How many total meters does he run a week?"
pipe = pipeline(task="text-generation", model=model, tokenizer=tokenizer, max_length=200)
result = pipe(f"<s>[INST] {prompt} [/INST]")
print(result[0]['generated_text'])

"""
<s>[INST] James decides to run 3 sprints 3 times a week. He runs 60 meters each sprint. How many total meters does he run a week? [/INST]  He runs 3*3=<<3*3=9>>9 sprints a week
So he runs 9*60=<<9*60=540>>540 meters a week
"""

```
## 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: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: bfloat16

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