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

@@ -1,89 +1,103 @@
----
-base_model:
-- Qwen/Qwen2.5-3B-Instruct
-pipeline_tag: text-generation
-library_name: transformers
----
-# AbleCredit Reasoner R0 Qwen 2.5 3B Instruct
-
-## Introduction
-
-This model is trained by Deepseek R1 style (GRPO) reinforcement learning on Qwen 2.5 3B Instruct as a base model.
-Primarily intended for research in application of small LLMs trained using GRPO/RL in the domain of finance, credit underwriting etc.
-
-### Model Description
-
-- **Fine Tuned by:** AbleCredit (LightBees Technologies Private Limited, Bengaluru, India)
-- **License:** We've retained the original Qwen research license. Note that license does not allow commercial use.
-- **Finetuned from model:** Qwen/Qwen2.5-3B-Instruct
-
-## How to Get Started with the Model
-
-Use with standard Huggingface based setup
-
-```python
-model_name = "AbleCredit/AbleCredit-R0-Qwen-2.5-3B-Instruct" # or local path to model
-system_prompt = {
-    "role": "system",
-    "content": (
-        "You are a helpful assistant. User asks a question the assistant answers it.\n"
-        "The assistant first thinks about reasoning process in mind and then provides the user with the answer."
-        ),
-      }
-
-suffix_prompt = {
-    "role": "assistant",
-    "content": "Let me solve this step by step.\n<think>",
-}
-
-prompt_msgs = [
-    system_prompt,
-    {"role": "user", "content": "What is 15 times 3 ?"},
-    suffix_prompt,
-]
-
-base_model = AutoModelForCausalLM.from_pretrained(
-    model_name,
-    device_map="auto",
-    torch_dtype=torch.bfloat16,
-)
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-
-prompt = tokenizer.apply_chat_template(
-    prompt_msgs,
-    tokenize=False,
-    continue_final_message=True,
-    add_generation_prompt=False,
-)
-
-# Tokenize the prompt and move it to the appropriate device.
-inputs = tokenizer(prompt, return_tensors="pt").to("cuda:0")
-
-print("\nGenerating response...\n")
-outputs = model.generate(
-    **inputs,
-    max_new_tokens=1024,
-    temperature=0.5,
-    min_p=0.01,
-)
-response = tokenizer.decode(outputs[0], skip_special_tokens=True)
-print("\nResponse:\n", response)
-```
-
-## Training Details
-
-### Training Data
-
-Trained using open source logical reasoning datasets and a proprietary finance dataset created by AbleCredit.com.
-
-### Training Procedure
-
-Trained using deepseek style reinforcement learning using GRPO with rule based rewards.
-
-## Evaluation
-
-- Model achieves ~67% score on GSM8K benchmark in a **zero shot** setting (check benchmarking script for more details).
-
-## Model Card Contact
-
+---
+base_model:
+- Qwen/Qwen2.5-3B-Instruct
+pipeline_tag: text-generation
+library_name: transformers
+language:
+- zho
+- eng
+- fra
+- spa
+- por
+- deu
+- ita
+- rus
+- jpn
+- kor
+- vie
+- tha
+- ara
+---
+# AbleCredit Reasoner R0 Qwen 2.5 3B Instruct
+
+## Introduction
+
+This model is trained by Deepseek R1 style (GRPO) reinforcement learning on Qwen 2.5 3B Instruct as a base model.
+Primarily intended for research in application of small LLMs trained using GRPO/RL in the domain of finance, credit underwriting etc.
+
+### Model Description
+
+- **Fine Tuned by:** AbleCredit (LightBees Technologies Private Limited, Bengaluru, India)
+- **License:** We've retained the original Qwen research license. Note that license does not allow commercial use.
+- **Finetuned from model:** Qwen/Qwen2.5-3B-Instruct
+
+## How to Get Started with the Model
+
+Use with standard Huggingface based setup
+
+```python
+model_name = "AbleCredit/AbleCredit-R0-Qwen-2.5-3B-Instruct" # or local path to model
+system_prompt = {
+    "role": "system",
+    "content": (
+        "You are a helpful assistant. User asks a question the assistant answers it.\n"
+        "The assistant first thinks about reasoning process in mind and then provides the user with the answer."
+        ),
+      }
+
+suffix_prompt = {
+    "role": "assistant",
+    "content": "Let me solve this step by step.\n<think>",
+}
+
+prompt_msgs = [
+    system_prompt,
+    {"role": "user", "content": "What is 15 times 3 ?"},
+    suffix_prompt,
+]
+
+base_model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    device_map="auto",
+    torch_dtype=torch.bfloat16,
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+prompt = tokenizer.apply_chat_template(
+    prompt_msgs,
+    tokenize=False,
+    continue_final_message=True,
+    add_generation_prompt=False,
+)
+
+# Tokenize the prompt and move it to the appropriate device.
+inputs = tokenizer(prompt, return_tensors="pt").to("cuda:0")
+
+print("\nGenerating response...\n")
+outputs = model.generate(
+    **inputs,
+    max_new_tokens=1024,
+    temperature=0.5,
+    min_p=0.01,
+)
+response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print("\nResponse:\n", response)
+```
+
+## Training Details
+
+### Training Data
+
+Trained using open source logical reasoning datasets and a proprietary finance dataset created by AbleCredit.com.
+
+### Training Procedure
+
+Trained using deepseek style reinforcement learning using GRPO with rule based rewards.
+
+## Evaluation
+
+- Model achieves ~67% score on GSM8K benchmark in a **zero shot** setting (check benchmarking script for more details).
+
+## Model Card Contact
+
 [contact Harshad Saykhedkar via LinkedIn](https://www.linkedin.com/in/harshadss/)