metadata
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen2.5-14B-Instruct-1M/blob/main/LICENSE
language:
- en
- ko
pipeline_tag: text-generation
base_model: Qwen/Qwen2.5-14B-Instruct-1M
tags:
- chat
library_name: transformers
Update @ 2025.03.14: The updated version, T3Q-qwen2.5-14b-v1.2-e2 release
Update @ 2025.03.14: The updated version, T3Q-qwen2.5-32b-v1.2-e2 release
Model Summary
T3Q-qwen2.5-14b-v1.0-e3 is a post-trained version of the Qwen/Qwen2.5-14B-Instruct-1M model.
(LoRA-8-4-0.0001-cosine-32-16 with train_data_v1.0)
Global Open LLM Leaderboard Performance
This model achieved 1st place in performance among models under 32b in the Global Open LLM Leaderboard.
Page Capture
Quick Start
Here provides a code snippet with apply_chat_template to show you how to load the tokenizer and model and how to generate contents.
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "JungZoona/T3Q-qwen2.5-14b-v1.0-e3"
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)
prompt = "Give me a short introduction to large language model."
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
generated_ids = model.generate(
**model_inputs,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]