You may use this model for fine-tuning in downstream tasks, we recommend using our efficient fine-tuning toolkit. https://github.com/hiyouga/LLaMA-Factory

Developed by: Alibaba Cloud.
Language(s) (NLP): Chinese/English
License: Tongyi Qianwen License

Usage:

python

from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer

tokenizer = AutoTokenizer.from_pretrained("hiyouga/Qwen-14B-Chat-LLaMAfied")
model = AutoModelForCausalLM.from_pretrained("hiyouga/Qwen-14B-Chat-LLaMAfied", torch_dtype="auto", device_map="auto")
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)

messages = [
    {"role": "user", "content": "Who are you?"}
]
inputs = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt")
inputs = inputs.to("cuda")
generate_ids = model.generate(inputs, streamer=streamer)

You could also alternatively launch a CLI demo by using the script in LLaMA-Factory

python src/cli_demo.py --template qwen --model_name_or_path hiyouga/Qwen-14B-Chat-LLaMAfied

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	61.60
AI2 Reasoning Challenge (25-Shot)	57.51
HellaSwag (10-Shot)	82.11
MMLU (5-Shot)	65.57
TruthfulQA (0-shot)	51.99
Winogrande (5-shot)	72.93
GSM8k (5-shot)	39.50