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---
base_model: google/gemma-2-9b-it
datasets:
- nroggendorff/profession
language:
- en
license: mit
tags:
- trl
- sft
- art
- code
- adam
- gemma
model-index:
- name: pro
results: []
pipeline_tag: text-generation
---
# Profession LLM
Pro is a language model fine-tuned on the [Profession dataset](https://huggingface.co/datasets/nroggendorff/profession) using Supervised Fine-Tuning (SFT) and Teacher Reinforced Learning (TRL) techniques.
## Features
- Utilizes SFT and TRL techniques for improved performance
- Supports English language
## Usage
To use the LLM, you can load the model using the Hugging Face Transformers library:
```python
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
import torch
bnb_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_use_double_quant=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=torch.bfloat16
)
model_id = "nroggendorff/gemma-pro"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=bnb_config)
prompt = "[INST] Write a poem about tomatoes in the style of Poe.[/INST]"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs)
generated_text = tokenizer.batch_decode(outputs)[0]
print(generated_text)
```
## License
This project is licensed under the MIT License. |