Improve language tag

README.md

@@ -1,80 +1,83 @@
----
-base_model: Qwen/Qwen2.5-32B-Instruct
-library_name: peft
-license: mit
-language:
-- en
-- ko
-- zh
-- pt
-- ja
-- uz
-- tl
-- th
-- vi
-- id
----
-# FINGU-AI/Qwen2.5-32B-Lora-HQ-e-5
-
-## Overview
-`FINGU-AI/Qwen2.5-32B-Lora-HQ-e-5` is a powerful causal language model designed for a variety of natural language processing (NLP) tasks, including machine translation, text generation, and chat-based applications. This model is particularly useful for translating between Korean and Uzbek, as well as supporting other custom NLP tasks through flexible input.
-
-## Model Details
-- **Model ID**: `FINGU-AI/Qwen2.5-32B-Lora-HQ-e-5`
-- **Architecture**: Causal Language Model (LM)
-- **Parameters**: 32 billion
-- **Precision**: Torch BF16 for efficient GPU memory usage
-- **Attention**: SDPA (Scaled Dot-Product Attention)
-- **Primary Use Case**: Translation (e.g., Korean to Uzbek), text generation, and dialogue systems.
-
-## Example Usage
-
-### Installation
-Make sure to install the required packages:
-
-```bash
-pip install torch transformers
-```
-### Loading the Model
-
-```python
-from transformers import AutoTokenizer, AutoModelForCausalLM
-import torch
-
-# Model and Tokenizer
-model_id = 'FINGU-AI/Qwen2.5-32B-Lora-HQ-e-5'
-model = AutoModelForCausalLM.from_pretrained(model_id, attn_implementation="sdpa", torch_dtype=torch.bfloat16)
-tokenizer = AutoTokenizer.from_pretrained(model_id)
-model.to('cuda')
-
-# Input Messages for Translation
-messages = [
-    {"role": "system", "content": "translate korean to Uzbek"},
-    {"role": "user", "content": """새로운 은행 계좌를 개설하는 절차는 다음과 같습니다:
-
-1. 계좌 개설 목적과 신분 확인을 위한 서류 제출
-2. 서류 검토 과정을 거치는 것
-3. 고객님의 신원 확인 절차를 진행하는 것
-4. 모든 절차가 완료되면 계좌 개설이 가능합니다.
-
-계좌 개설을 원하시는 경우, 신분증과 함께 방문해 주시면 됩니다.
-    """},
-]
-
-# Tokenize and Generate Response
-input_ids = tokenizer.apply_chat_template(
-    messages,
-    add_generation_prompt=True,
-    return_tensors="pt"
-).to('cuda')
-
-outputs = model.generate(
-    input_ids,
-    max_new_tokens=500,
-    do_sample=True,
-)
-
-# Decode and Print the Translation
-response = outputs[0][input_ids.shape[-1]:]
-print(tokenizer.decode(response, skip_special_tokens=True))
+---
+base_model: Qwen/Qwen2.5-32B-Instruct
+library_name: peft
+license: mit
+language:
+- zho
+- eng
+- fra
+- spa
+- por
+- deu
+- ita
+- rus
+- jpn
+- kor
+- vie
+- tha
+- ara
+---
+# FINGU-AI/Qwen2.5-32B-Lora-HQ-e-5
+
+## Overview
+`FINGU-AI/Qwen2.5-32B-Lora-HQ-e-5` is a powerful causal language model designed for a variety of natural language processing (NLP) tasks, including machine translation, text generation, and chat-based applications. This model is particularly useful for translating between Korean and Uzbek, as well as supporting other custom NLP tasks through flexible input.
+
+## Model Details
+- **Model ID**: `FINGU-AI/Qwen2.5-32B-Lora-HQ-e-5`
+- **Architecture**: Causal Language Model (LM)
+- **Parameters**: 32 billion
+- **Precision**: Torch BF16 for efficient GPU memory usage
+- **Attention**: SDPA (Scaled Dot-Product Attention)
+- **Primary Use Case**: Translation (e.g., Korean to Uzbek), text generation, and dialogue systems.
+
+## Example Usage
+
+### Installation
+Make sure to install the required packages:
+
+```bash
+pip install torch transformers
+```
+### Loading the Model
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+# Model and Tokenizer
+model_id = 'FINGU-AI/Qwen2.5-32B-Lora-HQ-e-5'
+model = AutoModelForCausalLM.from_pretrained(model_id, attn_implementation="sdpa", torch_dtype=torch.bfloat16)
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model.to('cuda')
+
+# Input Messages for Translation
+messages = [
+    {"role": "system", "content": "translate korean to Uzbek"},
+    {"role": "user", "content": """새로운 은행 계좌를 개설하는 절차는 다음과 같습니다:
+
+1. 계좌 개설 목적과 신분 확인을 위한 서류 제출
+2. 서류 검토 과정을 거치는 것
+3. 고객님의 신원 확인 절차를 진행하는 것
+4. 모든 절차가 완료되면 계좌 개설이 가능합니다.
+
+계좌 개설을 원하시는 경우, 신분증과 함께 방문해 주시면 됩니다.
+    """},
+]
+
+# Tokenize and Generate Response
+input_ids = tokenizer.apply_chat_template(
+    messages,
+    add_generation_prompt=True,
+    return_tensors="pt"
+).to('cuda')
+
+outputs = model.generate(
+    input_ids,
+    max_new_tokens=500,
+    do_sample=True,
+)
+
+# Decode and Print the Translation
+response = outputs[0][input_ids.shape[-1]:]
+print(tokenizer.decode(response, skip_special_tokens=True))
 ```