Improve language tag

README.md

@@ -1,118 +1,132 @@
----
-library_name: peft
-base_model: Qwen/Qwen2.5-7B-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/Qwen2.5-7B-Instruct**
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository: https://huggingface.co/Hack337/WavGPT-2**
-- **Demo (WavGPT-1.0): 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-2",
-    torch_dtype="auto",
-    device_map="auto"
-)
-tokenizer = AutoTokenizer.from_pretrained("Hack337/WavGPT-2")
-
-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-2",
-    use_cache=True,
-    dtype=torch.float16  # Use float16 for the NPU
-).eval()
-
-# Load the tokenizer
-tokenizer = AutoTokenizer.from_pretrained("Hack337/WavGPT-2")
-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)
-
-```
-
+---
+library_name: peft
+base_model: Qwen/Qwen2.5-7B-Instruct
+pipeline_tag: text-generation
+license: apache-2.0
+language:
+- zho
+- eng
+- fra
+- spa
+- por
+- deu
+- ita
+- rus
+- jpn
+- kor
+- vie
+- tha
+- ara
+---
+
+# 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/Qwen2.5-7B-Instruct**
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository: https://huggingface.co/Hack337/WavGPT-2**
+- **Demo (WavGPT-1.0): 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-2",
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained("Hack337/WavGPT-2")
+
+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-2",
+    use_cache=True,
+    dtype=torch.float16  # Use float16 for the NPU
+).eval()
+
+# Load the tokenizer
+tokenizer = AutoTokenizer.from_pretrained("Hack337/WavGPT-2")
+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