metadata
datasets:
- BramVanroy/ultrachat_200k_dutch
language:
- nl
base_model:
- robinsmits/Schaapje-2B-Pretrained
pipeline_tag: text-generation
library_name: transformers
tags:
- granite
- granite 3.0
- schaapje
- trl
- sft
inference: false
license: apache-2.0
Schaapje-2B-Chat-SFT-V1.0
Model description
This is the SFT model based on the custom continual pretrained model Schaapje-2B-Pretrained.
General Dutch Chat and/or Instruction following works quitte well with this model.
Model usage
A basic example of how to use this SFT model for Chat or Instruction following.
Note: This model is still unaligned. If that is required for your usage scenario then please use: Schaapje-2B-Chat-V1.0
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
device = 'cuda'
model_name = 'robinsmits/Schaapje-2B-Chat-SFT-V1.0'
model = AutoModelForCausalLM.from_pretrained(model_name,
device_map = "auto",
torch_dtype = torch.bfloat16)
tokenizer = AutoTokenizer.from_pretrained(model_name)
messages = [{"role": "user", "content": "Hoi hoe gaat het ermee?"}]
chat = tokenizer.apply_chat_template(messages,
tokenize = False,
add_generation_prompt = True)
input_tokens = tokenizer(chat, return_tensors = "pt").to('cuda')
output = model.generate(**input_tokens,
max_new_tokens = 512,
do_sample = True)
output = tokenizer.decode(output[0], skip_special_tokens = False)
print(output)
Intended uses & limitations
As with all LLM's this model can also experience bias and hallucinations. Regardless of how you use this model always perform the necessary testing and validation.
Datasets and Licenses
The following dataset was used for SFT:
- BramVanroy/ultrachat_200k_dutch: apache-2.0
Model Training
The notebook used to train this SFT model is available at the following link: Schaapje-2B-Chat-SFT-V1.0