Improve language tag

README.md

@@ -1,65 +1,76 @@
----
-base_model:
-- Qwen/Qwen2.5-3B-Instruct
-tags:
-- text-generation-inference
-- transformers
-- qwen2
-- trl
-- sft
-license: apache-2.0
-language:
-- en
-- vi
-datasets:
-- beyoru/Tin_hoc_mcq
----
-
-# Uploaded  model
-
-- **Developed by:** beyoru
-- **License:** apache-2.0
-
-# Usage
-```
-from transformers import AutoModelForCausalLM, AutoTokenizer
-
-model_name = "beyoru/MCQ-3B-o1-1"
-
-model = AutoModelForCausalLM.from_pretrained(
-    model_name,
-    torch_dtype="auto",
-    device_map="auto"
-)
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-
-messages = [
-    {"role": "system", "content": "Tạo một câu hỏi trắc nghiệm về"},
-    {"role": "user", "content": "<YOUR CONTEXT>"}
-]
-text = tokenizer.apply_chat_template(
-    messages,
-    tokenize=False,
-    add_generation_prompt=True
-)
-model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
-
-generated_ids = model.generate(
-    **model_inputs,
-    do_sample=True
-)
-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]
-```
-
-# Notes:
-- For small datasets with narrow content which the model has already done well on our domain, and doesn't want the model to forget the knowledge => Just need to focus on o. 
-- Fine-tuned lora with rank = 1 and alpha = 1, epoch = 1, linear (optim)
-- DoRA
-  
-# Improvement
-- Increasing rank can help the model do better at robust structure.
+---
+base_model:
+- Qwen/Qwen2.5-3B-Instruct
+tags:
+- text-generation-inference
+- transformers
+- qwen2
+- trl
+- sft
+license: apache-2.0
+language:
+- zho
+- eng
+- fra
+- spa
+- por
+- deu
+- ita
+- rus
+- jpn
+- kor
+- vie
+- tha
+- ara
+datasets:
+- beyoru/Tin_hoc_mcq
+---
+
+# Uploaded  model
+
+- **Developed by:** beyoru
+- **License:** apache-2.0
+
+# Usage
+```
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "beyoru/MCQ-3B-o1-1"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+messages = [
+    {"role": "system", "content": "Tạo một câu hỏi trắc nghiệm về"},
+    {"role": "user", "content": "<YOUR CONTEXT>"}
+]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+generated_ids = model.generate(
+    **model_inputs,
+    do_sample=True
+)
+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]
+```
+
+# Notes:
+- For small datasets with narrow content which the model has already done well on our domain, and doesn't want the model to forget the knowledge => Just need to focus on o. 
+- Fine-tuned lora with rank = 1 and alpha = 1, epoch = 1, linear (optim)
+- DoRA
+  
+# Improvement
+- Increasing rank can help the model do better at robust structure.
 - Try more efficient fine-tuning