Update README.md

README.md

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----
-license: apache-2.0
----
+---
+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
+device = "cuda" # the device to load the model onto
+
+model = AutoModelForCausalLM.from_pretrained(
+    "Hack337/WavGPT-1.0-merged",
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained("Hack337/WavGPT-1.0-merged")
+
+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]
+
+```
+
+Use the code below to get started with the model using NPU.
+
+```python
+from transformers import AutoTokenizer, TextStreamer
+from intel_npu_acceleration_library import NPUModelForCausalLM
+import torch
+
+# Load the NPU-optimized model without LoRA
+model = NPUModelForCausalLM.from_pretrained(
+    "Hack337/WavGPT-1.0-merged",
+    use_cache=True,
+    dtype=torch.float16  # Use float16 for the NPU
+).eval()
+
+# Load the tokenizer
+tokenizer = AutoTokenizer.from_pretrained("Hack337/WavGPT-1.0-merged")
+tokenizer.pad_token_id = tokenizer.eos_token_id
+streamer = TextStreamer(tokenizer, skip_special_tokens=True)
+
+# Prompt handling
+prompt = "Give me a short introduction to large language model."
+messages = [
+    {"role": "system", "content": "Вы очень полезный помощник."},
+    {"role": "user", "content": prompt}
+]
+
+# Convert to a text format compatible with the model
+text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+prefix = tokenizer([text], return_tensors="pt")["input_ids"].to("npu")
+
+# Generation configuration
+generation_kwargs = dict(
+    input_ids=prefix,
+    streamer=streamer,
+    do_sample=True,
+    top_k=50,
+    top_p=0.9,
+    max_new_tokens=512,
+)
+
+# Run inference on the NPU
+print("Run inference")
+_ = model.generate(**generation_kwargs)
+
+```
+
+- PEFT 0.11.1