Improve language tag

README.md

@@ -1,67 +1,79 @@
----
-base_model: Qwen/Qwen2.5-7B-Instruct
-tags:
-- text-generation-inference
-- transformers
-- qwen2
-- trl
-- sft
-license: apache-2.0
-language:
-- en
----
-### Model detail
-Reasoning natural and smarter\
-No system prompt training\
-LoRA training rank 16 and alpha 16\
-Tool calling support
-
-
-### Usage:
-```
-from transformers import AutoModelForCausalLM, AutoTokenizer
-import torch
-
-MAX_REASONING_TOKENS = 4096
-MAX_RESPONSE_TOKENS = 1024
-
-model_name = "beyoru/ThinkAgain1.5"
-
-model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, device_map="auto")
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-
-messages = []
-
-def stream_output(output_text):
-    for char in output_text:
-        print(char, end="", flush=True)
-
-while True:
-    prompt = input("USER: ")
-    
-    messages.append({"role": "user", "content": prompt})
-    
-    # Generate reasoning
-    reasoning_template = tokenizer.apply_chat_template(messages, tokenize=False, add_reasoning_prompt=True)
-    reasoning_inputs = tokenizer(reasoning_template, return_tensors="pt").to(model.device)
-    reasoning_ids = model.generate(**reasoning_inputs, max_new_tokens=MAX_REASONING_TOKENS)
-    reasoning_output = tokenizer.decode(reasoning_ids[0, reasoning_inputs.input_ids.shape[1]:], skip_special_tokens=True)
-    
-    messages.append({"role": "reasoning", "content": reasoning_output})
-    
-    print("REASONING: ", end="")
-    stream_output(reasoning_output)
-    print()
-    
-    # Generate answer
-    response_template = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
-    response_inputs = tokenizer(response_template, return_tensors="pt").to(model.device)
-    response_ids = model.generate(**response_inputs, max_new_tokens=MAX_RESPONSE_TOKENS)
-    response_output = tokenizer.decode(response_ids[0, response_inputs.input_ids.shape[1]:], skip_special_tokens=True)
-    
-    messages.append({"role": "assistant", "content": response_output})
-    
-    print("ASSISTANT: ", end="")
-    stream_output(response_output)
-    print()
+---
+base_model: Qwen/Qwen2.5-7B-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
+---
+### Model detail
+Reasoning natural and smarter\
+No system prompt training\
+LoRA training rank 16 and alpha 16\
+Tool calling support
+
+
+### Usage:
+```
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+MAX_REASONING_TOKENS = 4096
+MAX_RESPONSE_TOKENS = 1024
+
+model_name = "beyoru/ThinkAgain1.5"
+
+model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, device_map="auto")
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+messages = []
+
+def stream_output(output_text):
+    for char in output_text:
+        print(char, end="", flush=True)
+
+while True:
+    prompt = input("USER: ")
+    
+    messages.append({"role": "user", "content": prompt})
+    
+    # Generate reasoning
+    reasoning_template = tokenizer.apply_chat_template(messages, tokenize=False, add_reasoning_prompt=True)
+    reasoning_inputs = tokenizer(reasoning_template, return_tensors="pt").to(model.device)
+    reasoning_ids = model.generate(**reasoning_inputs, max_new_tokens=MAX_REASONING_TOKENS)
+    reasoning_output = tokenizer.decode(reasoning_ids[0, reasoning_inputs.input_ids.shape[1]:], skip_special_tokens=True)
+    
+    messages.append({"role": "reasoning", "content": reasoning_output})
+    
+    print("REASONING: ", end="")
+    stream_output(reasoning_output)
+    print()
+    
+    # Generate answer
+    response_template = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+    response_inputs = tokenizer(response_template, return_tensors="pt").to(model.device)
+    response_ids = model.generate(**response_inputs, max_new_tokens=MAX_RESPONSE_TOKENS)
+    response_output = tokenizer.decode(response_ids[0, response_inputs.input_ids.shape[1]:], skip_special_tokens=True)
+    
+    messages.append({"role": "assistant", "content": response_output})
+    
+    print("ASSISTANT: ", end="")
+    stream_output(response_output)
+    print()
 ```