Error when loading Almawave/Velvet-14B tokenizer

[dtdxdydz]

When trying to load Velvet-14B model and tokenizer an error is raised.
Similarly to mistral models, the chat_template doesn't have a add_generation_prompt.

Traceback (most recent call last):
File "/u01/SUPPORT/test_unsloth/test_unsloth_velvet.py", line 4, in
model, tokenizer = FastLanguageModel.from_pretrained(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/velvet/anaconda3/envs/unsloth/lib/python3.11/site-packages/unsloth/models/loader.py", line 258, in from_pretrained
model, tokenizer = dispatch_model.from_pretrained(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/velvet/anaconda3/envs/unsloth/lib/python3.11/site-packages/unsloth/models/mistral.py", line 348, in from_pretrained
return FastLlamaModel.from_pretrained(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/velvet/anaconda3/envs/unsloth/lib/python3.11/site-packages/unsloth/models/llama.py", line 1709, in from_pretrained
tokenizer = load_correct_tokenizer(
^^^^^^^^^^^^^^^^^^^^^^^
File "/home/velvet/anaconda3/envs/unsloth/lib/python3.11/site-packages/unsloth/tokenizer_utils.py", line 589, in load_correct_tokenizer
chat_template = fix_chat_template(tokenizer)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/velvet/anaconda3/envs/unsloth/lib/python3.11/site-packages/unsloth/tokenizer_utils.py", line 692, in fix_chat_template
raise RuntimeError(
RuntimeError: Unsloth: The tokenizer Almawave/Velvet-14B
does not have a {% if add_generation_prompt %} for generation purposes.
Please file a bug report immediately - thanks!

Script:

from transformers import TextStreamer
from unsloth import FastLanguageModel

model, tokenizer = FastLanguageModel.from_pretrained(
model_name="Almawave/Velvet-14B",
max_seq_length=16384,
load_in_4bit=False
)

FastLanguageModel.for_inference(model)

messages = [
{"role": "user", "content": "Ciao chi sei?cosa sai fare?"},
]
inputs = tokenizer.apply_chat_template(messages, tokenize = True, add_generation_prompt = True, return_tensors = "pt").to("cuda")

gen_idx = len(inputs[0])
outputs = model.generate(input_ids = inputs, max_new_tokens = 4096, use_cache = True)

response = tokenizer.batch_decode(outputs[:, gen_idx:], skip_special_tokens = True)[0]
print(response)

Python version: 3.11.11
with unsloth==2025.1.8 and unsloth_zoo==2025.1.4