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---
library_name: peft
base_model: Qwen/Qwen2-1.5B-Instruct
pipeline_tag: text-generation
license: apache-2.0
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by: hack337**
- **Model type: qwen2**
- **Finetuned from model: Qwen/Qwen2-1.5B-Instruct**
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository: https://huggingface.co/Hack337/WavGPT-1.0**
- **Demo: https://huggingface.co/spaces/Hack337/WavGPT**
## How to Get Started with the Model
Use the code below to get started with the model.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
device = "cuda" # the device to load the model onto
model_path = "Hack337/WavGPT-1.0"
model = AutoModelForCausalLM.from_pretrained(
"Qwen/Qwen2-1.5B-Instruct",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2-1.5B-Instruct")
model = PeftModel.from_pretrained(model, model_path)
prompt = "Give me a short introduction to large language model."
messages = [
{"role": "system", "content": "Вы очень полезный помощник."},
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
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]
```
- PEFT 0.11.1 |