Improve language tag

Hi! As the model is multilingual, this is a PR to add other languages than English to the language tag to improve the referencing. Note that 29 languages are announced in the README, but only 13 are explicitly listed. I was therefore only able to add these 13 languages.

README.md

@@ -1,99 +1,111 @@
----
-library_name: transformers
-tags:
-- unsloth
-- reasoning
-- mathematics
-- math
-datasets:
-- openai/gsm8k
-language:
-- en
-base_model:
-- Qwen/Qwen2.5-3B-Instruct
----
-
-# Model Card for Model ID
-
-This is an early experiment using the `GRPOTrainer` and training reasoning models using the Unsloth library. It is not intended for real use, but it should work OK for simple prompt tests and easy mathematics questions.
-
-(You can run this using the code below on a free Colab/Kaggle basic GPU account for testing.)
-
-**NOTE:** If you are interested in reasoning models and research in this area, I maintain an up-to-date resource list here : [https://github.com/benjaminzwhite/reasoning-models](https://github.com/benjaminzwhite/reasoning-models)
-
-- Example query: `"What is the smallest prime number greater than 50 ?"`
-
-- Example response: `"<reasoning>\nTo find the smallest prime number greater than 50, we can start checking from 51 onwards for primality. A prime number is a number that has no divisors other than 1 and itself. We check each number to see if it's divisible by any number other than 1 and itself.\n</reasoning>\n<answer>\n53\n</answer>"`
-
-
-## How to Get Started with the Model
-
-To use this with standard HuggingFace code, I recommend starting with this code (based 95% on the default code shown at the base model page : [https://huggingface.co/Qwen/Qwen2.5-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct))
-
-```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
-
-model_name = "benjaminzwhite/Qwen2.5-3B-Instruct_GSM8K-GRPO_16bit"
-
-# model loading
-model = AutoModelForCausalLM.from_pretrained(
-    model_name,
-    torch_dtype="auto",
-    device_map="auto"
-)
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-
-# system prompt used during training
-SYSTEM_PROMPT = """
-Respond in the following format:
-<reasoning>
-...
-</reasoning>
-<answer>
-...
-</answer>
-"""
-
-# your query goes here
-user_prompt = "What is the smallest prime number greater than 50 ?"
-
-messages = [
-    {"role": "system", "content": SYSTEM_PROMPT},
-    {"role": "user", "content": user_prompt}
-]
-
-# default Qwen2.5 code from this point ...
-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,
-    max_new_tokens=512
-)
-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]
-
-print(response)
-
-# sample answer obtained to my query, to show expected format
-# (note that the answer, 53, is correct here)
-"""
-"<reasoning>\nTo find the smallest prime number greater than 50, we can start checking from 51 onwards for primality. A prime number is a number that has no divisors other than 1 and itself. We check each number to see if it's divisible by any number other than 1 and itself.\n</reasoning>\n<answer>\n53\n</answer>"
-"""
-```
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
+---
+library_name: transformers
+tags:
+- unsloth
+- reasoning
+- mathematics
+- math
+datasets:
+- openai/gsm8k
+language:
+- zho
+- eng
+- fra
+- spa
+- por
+- deu
+- ita
+- rus
+- jpn
+- kor
+- vie
+- tha
+- ara
+base_model:
+- Qwen/Qwen2.5-3B-Instruct
+---
+
+# Model Card for Model ID
+
+This is an early experiment using the `GRPOTrainer` and training reasoning models using the Unsloth library. It is not intended for real use, but it should work OK for simple prompt tests and easy mathematics questions.
+
+(You can run this using the code below on a free Colab/Kaggle basic GPU account for testing.)
+
+**NOTE:** If you are interested in reasoning models and research in this area, I maintain an up-to-date resource list here : [https://github.com/benjaminzwhite/reasoning-models](https://github.com/benjaminzwhite/reasoning-models)
+
+- Example query: `"What is the smallest prime number greater than 50 ?"`
+
+- Example response: `"<reasoning>\nTo find the smallest prime number greater than 50, we can start checking from 51 onwards for primality. A prime number is a number that has no divisors other than 1 and itself. We check each number to see if it's divisible by any number other than 1 and itself.\n</reasoning>\n<answer>\n53\n</answer>"`
+
+
+## How to Get Started with the Model
+
+To use this with standard HuggingFace code, I recommend starting with this code (based 95% on the default code shown at the base model page : [https://huggingface.co/Qwen/Qwen2.5-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct))
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "benjaminzwhite/Qwen2.5-3B-Instruct_GSM8K-GRPO_16bit"
+
+# model loading
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+# system prompt used during training
+SYSTEM_PROMPT = """
+Respond in the following format:
+<reasoning>
+...
+</reasoning>
+<answer>
+...
+</answer>
+"""
+
+# your query goes here
+user_prompt = "What is the smallest prime number greater than 50 ?"
+
+messages = [
+    {"role": "system", "content": SYSTEM_PROMPT},
+    {"role": "user", "content": user_prompt}
+]
+
+# default Qwen2.5 code from this point ...
+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,
+    max_new_tokens=512
+)
+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]
+
+print(response)
+
+# sample answer obtained to my query, to show expected format
+# (note that the answer, 53, is correct here)
+"""
+"<reasoning>\nTo find the smallest prime number greater than 50, we can start checking from 51 onwards for primality. A prime number is a number that has no divisors other than 1 and itself. We check each number to see if it's divisible by any number other than 1 and itself.\n</reasoning>\n<answer>\n53\n</answer>"
+"""
+```
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
 Trained on GSM8K mathematics dataset.