

## Usage:
```
from transformers import pipeline
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("SummerSigh/Pythia410m-Instruct-SFT")
generator = pipeline('text-generation', model = 'SummerSigh/Pythia410m-Instruct-SFT')

inpopo  = input("Text here: ")

text = generator("<user>" + inpopo  + "<user><kinrel>" ,  max_length = 200, do_sample=True, top_p = 0.7, temperature = 0.5, repetition_penalty  = 1.2, pad_token_id=tokenizer.eos_token_id)
generated_text = text[0]["generated_text"]
parts = generated_text.split("<kinrel>")
cropped_text = "<kinrel>".join(parts[:2]) + "<kinrel>"
print(cropped_text)

```
{}
transformers pytorch safetensors gpt_neox text-generation autotrain_compatible endpoints_compatible has_space text-generation-inference region:us
TAGS
#transformers #pytorch #safetensors #gpt_neox #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 



## Usage:

## Usage:
TAGS
#transformers #pytorch #safetensors #gpt_neox #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 
 ## Usage:
TAGS
#transformers #pytorch #safetensors #gpt_neox #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 
## Usage: