Improve language tag

README.md

@@ -1,45 +1,57 @@
----
-library_name: transformers
-tags:
-- trl
-- sft
-datasets:
-- qwedsacf/grade-school-math-instructions
-language:
-- en
-base_model:
-- Qwen/Qwen2.5-3B
----
-
-<img src="https://huggingface.co/entfane/math-professor-3B/resolve/main/math-professor-image.png" width="300" height="300"/>
-
-
-# Math Professor 3B
-
-This model is a math instruction fine-tuned version of Qwen2.5-3B model.
-
-### Fine-tuning dataset
-
-Model was fine-tuned on [qwedsacf/grade-school-math-instructions](https://huggingface.co/datasets/qwedsacf/grade-school-math-instructions) instruction dataset.
-
-### Inference
-
-```python
-!pip install transformers accelerate
-
-from transformers import AutoTokenizer, AutoModelForCausalLM
-
-model_name = "entfane/math-professor-3B"
-
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForCausalLM.from_pretrained(model_name)
-
-messages = [
-    {"role": "user", "content": "What's the derivative of 2x^2?"}
-]
-
-input = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
-encoded_input = tokenizer(input, return_tensors = "pt").to(model.device)
-output = model.generate(**encoded_input, max_new_tokens=1024)
-print(tokenizer.decode(output[0], skip_special_tokens=False))
+---
+library_name: transformers
+tags:
+- trl
+- sft
+datasets:
+- qwedsacf/grade-school-math-instructions
+language:
+- zho
+- eng
+- fra
+- spa
+- por
+- deu
+- ita
+- rus
+- jpn
+- kor
+- vie
+- tha
+- ara
+base_model:
+- Qwen/Qwen2.5-3B
+---
+
+<img src="https://huggingface.co/entfane/math-professor-3B/resolve/main/math-professor-image.png" width="300" height="300"/>
+
+
+# Math Professor 3B
+
+This model is a math instruction fine-tuned version of Qwen2.5-3B model.
+
+### Fine-tuning dataset
+
+Model was fine-tuned on [qwedsacf/grade-school-math-instructions](https://huggingface.co/datasets/qwedsacf/grade-school-math-instructions) instruction dataset.
+
+### Inference
+
+```python
+!pip install transformers accelerate
+
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+model_name = "entfane/math-professor-3B"
+
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+messages = [
+    {"role": "user", "content": "What's the derivative of 2x^2?"}
+]
+
+input = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+encoded_input = tokenizer(input, return_tensors = "pt").to(model.device)
+output = model.generate(**encoded_input, max_new_tokens=1024)
+print(tokenizer.decode(output[0], skip_special_tokens=False))
 ```