metadata
license: mit
pipeline_tag: text-generation
language:
- en
- ru
- code
base_model:
- Qwen/Qwen2.5-7B-Instruct
tags:
- qwen2.5
theqwenmoe
- 18.3B parametrs
- English & Russian
- Math & Logic
- Code: Python, Javascript, Java, PHP, C++, C#, ...
This is experimental model. Can be bugs and various problems.
Made with mergekit and unsloth apps by ehristoforu.
Code usage example:
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "ehristoforu/theqwenmoe"
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": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."},
{"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]