Improve language tag

README.md

@@ -1,46 +1,60 @@
----
-license: apache-2.0
-base_model:
-- Qwen/Qwen2.5-7B-Instruct
----
-# Converted LLaMA from QWEN2-7B-Instruct
-
-## Descritpion
-This is a converted model from [Qwen2-7B-Instruct](https://huggingface.co/Qwen/Qwen2-7B-Instruct) to __LLaMA__ format. This conversion allows you to use Qwen2-7B-Instruct as if it were a LLaMA model, which is convenient for some *inference use cases*. The __precision__ is __excatly the same__ as the original model. 
-
-## Usage
-You can load the model using the `LlamaForCausalLM` class as shown below:
-```python
-from transformers import AutoTokenizer, LlamaForCausalLM
-
-prompt = "Give me a short introduction to large language model."
-messages = [
-    {"role": "system", "content": "You are a helpful assistant."},
-    {"role": "user", "content": prompt}
-]
-# we still use the original tokenizer from Qwen2-7B-Instruct
-tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2-7B-Instruct")
-text = tokenizer.apply_chat_template(
-    messages,
-    tokenize=False,
-    add_generation_prompt=True
-)
-model_inputs = tokenizer([text],return_tensors="pt").cuda()
-
-# Converted LlaMA model
-llama_model = LlamaForCausalLM.from_pretrained(
-    "silence09/Qwen2-7B-Instruct-Converted-Llama",
-    torch_dtype='auto').cuda()
-llama_generated_ids = llama_model.generate(model_inputs.input_ids, max_new_tokens=32, do_sample=False)
-llama_generated_ids = [
-    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, llama_generated_ids)
-]
-llama_response = tokenizer.batch_decode(llama_generated_ids, skip_special_tokens=True)[0]
-print(llama_response)
-```
-
-## Precision Guarantee
-To comare result with the original model, you can use this [code](https://github.com/silencelamb/naked_llama/blob/main/hf_example/hf_qwen2_7b.py)
-
-## More Info
+---
+license: apache-2.0
+base_model:
+- Qwen/Qwen2.5-7B-Instruct
+language:
+- zho
+- eng
+- fra
+- spa
+- por
+- deu
+- ita
+- rus
+- jpn
+- kor
+- vie
+- tha
+- ara
+---
+# Converted LLaMA from QWEN2-7B-Instruct
+
+## Descritpion
+This is a converted model from [Qwen2-7B-Instruct](https://huggingface.co/Qwen/Qwen2-7B-Instruct) to __LLaMA__ format. This conversion allows you to use Qwen2-7B-Instruct as if it were a LLaMA model, which is convenient for some *inference use cases*. The __precision__ is __excatly the same__ as the original model. 
+
+## Usage
+You can load the model using the `LlamaForCausalLM` class as shown below:
+```python
+from transformers import AutoTokenizer, LlamaForCausalLM
+
+prompt = "Give me a short introduction to large language model."
+messages = [
+    {"role": "system", "content": "You are a helpful assistant."},
+    {"role": "user", "content": prompt}
+]
+# we still use the original tokenizer from Qwen2-7B-Instruct
+tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2-7B-Instruct")
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+model_inputs = tokenizer([text],return_tensors="pt").cuda()
+
+# Converted LlaMA model
+llama_model = LlamaForCausalLM.from_pretrained(
+    "silence09/Qwen2-7B-Instruct-Converted-Llama",
+    torch_dtype='auto').cuda()
+llama_generated_ids = llama_model.generate(model_inputs.input_ids, max_new_tokens=32, do_sample=False)
+llama_generated_ids = [
+    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, llama_generated_ids)
+]
+llama_response = tokenizer.batch_decode(llama_generated_ids, skip_special_tokens=True)[0]
+print(llama_response)
+```
+
+## Precision Guarantee
+To comare result with the original model, you can use this [code](https://github.com/silencelamb/naked_llama/blob/main/hf_example/hf_qwen2_7b.py)
+
+## More Info
 It was converted using the python script available at [this repository](https://github.com/silencelamb/naked_llama/blob/main/hf_example/convert_qwen_to_llama_hf.py)