
# Adapter Model in K2

## Quick Start

**After deploying the model [k2_fp_delta](https://huggingface.co/daven3/k2_fp_delta), we need to use the `PEFT` API from `transformers` to load the entire K2!**

```python
base_model = /path/to/geollama
lora_weights = /path/to/adapter/model
tokenizer = LlamaTokenizer.from_pretrained(base_model)
model = LlamaForCausalLM.from_pretrained(
    base_model,
    load_in_8bit=load_8bit,
    device_map=device_map
    torch_dtype=torch.float16
)
model = PeftModel.from_pretrained(
    model,
    lora_weights,
    torch_dtype=torch.float16,
    device_map=device_map,
)
model.config.pad_token_id = tokenizer.pad_token_id = 0
model.config.bos_token_id = 1
model.config.eos_token_id = 2
```



{"language": ["en"], "license": "apache-2.0", "datasets": ["daven3/geosignal"]}
en dataset:daven3/geosignal license:apache-2.0 region:us
TAGS
#en #dataset-daven3/geosignal #license-apache-2.0 #region-us 


# Adapter Model in K2

## Quick Start

After deploying the model k2_fp_delta, we need to use the 'PEFT' API from 'transformers' to load the entire K2!





# Adapter Model in K2 ## Quick Start

After deploying the model k2_fp_delta, we need to use the 'PEFT' API from 'transformers' to load the entire K2!
TAGS
#en #dataset-daven3/geosignal #license-apache-2.0 #region-us 
 # Adapter Model in K2 ## Quick Start

After deploying the model k2_fp_delta, we need to use the 'PEFT' API from 'transformers' to load the entire K2!