Improve language tag (#2)

- Improve language tag (9f5514a0583b514a039678e95112859e06787ac5)


Co-authored-by: Loïck BOURDOIS <[email protected]>

README.md

@@ -1,81 +1,93 @@
----
-library_name: transformers
-tags:
-- transformers
-- text-generation-inference
-- text-generation
-- reasoning
-- r1-reasoning
-- fine-tuned
-license: mit
-datasets:
-- openai/gsm8k
-language:
-- en
-base_model:
-- Qwen/Qwen2.5-7B-Instruct
-pipeline_tag: text-generation
----
-
-# **Qwen-2.5-7B-Reasoning (Fine-Tuned by HyperX-Sen)**
-
-## 🚀 **Model Overview**
-This model is a fine-tuned version of **Qwen/Qwen2.5-7B-Instruct**, specifically optimized for **advanced reasoning tasks**. Fine-tuned on the **OpenAI GSM8K dataset**, it significantly enhances multi-step reasoning and problem-solving capabilities.
-
-## 🔧 **Fine-Tuning Details**
-- **Base Model:** [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct)  
-- **Fine-tuned by:** HyperX-Sen  
-- **Dataset:** [GSM8K (Grade School Math 8K)](https://huggingface.co/datasets/openai/gsm8k)  
-- **Hardware:** 2× Tesla T4 GPUs
-- **Objective:** Improve complex reasoning and logical deduction  
-
-## 📈 **Performance Improvements**
-Through fine-tuning on **GSM8K**, the model has improved in:
-- **Mathematical reasoning**
-- **Step-by-step logical deduction**
-- **Commonsense reasoning**
-- **Word problem-solving**  
-
-This makes it ideal for applications requiring **high-level reasoning**, such as **AI tutoring, research assistance, and problem-solving AI agents**.
-
-## 🛠 **How to Use**
-```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
-
-# Load the model and tokenizer
-model_name = "HyperX-Sen/Qwen-2.5-7B-Reasoning"
-model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto", device_map="auto")
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-
-SYSTEM_PROMPT = """
-Respond in the following format:
-<reasoning>
-...
-</reasoning>
-<answer>
-...
-</answer>
-"""
-
-# Define the conversation
-messages = [
-    {"role": "system", "content": f"{SYSTEM_PROMPT}"},
-    {"role": "user", "content": "What are the potential impacts of artificial intelligence on employment?"}
-]
-
-# Format the chat input
-input_text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
-
-# Tokenize the formatted input
-inputs = tokenizer(input_text, return_tensors="pt").to(model.device)
-
-# Generate the response
-output = model.generate(**inputs, max_length=512, do_sample=True, temperature=0.7)
-
-# Decode and display the response
-response = tokenizer.decode(output[0], skip_special_tokens=True)
-print(response)
-```
-
-## 🙌 **Acknowledgments**
-A huge thanks to **Qwen** for providing the powerful **Qwen2.5-7B-Instruct** model, which served as the base for this fine-tuned version.
+---
+library_name: transformers
+tags:
+- transformers
+- text-generation-inference
+- text-generation
+- reasoning
+- r1-reasoning
+- fine-tuned
+license: mit
+datasets:
+- openai/gsm8k
+language:
+- zho
+- eng
+- fra
+- spa
+- por
+- deu
+- ita
+- rus
+- jpn
+- kor
+- vie
+- tha
+- ara
+base_model:
+- Qwen/Qwen2.5-7B-Instruct
+pipeline_tag: text-generation
+---
+
+# **Qwen-2.5-7B-Reasoning (Fine-Tuned by HyperX-Sen)**
+
+## 🚀 **Model Overview**
+This model is a fine-tuned version of **Qwen/Qwen2.5-7B-Instruct**, specifically optimized for **advanced reasoning tasks**. Fine-tuned on the **OpenAI GSM8K dataset**, it significantly enhances multi-step reasoning and problem-solving capabilities.
+
+## 🔧 **Fine-Tuning Details**
+- **Base Model:** [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct)  
+- **Fine-tuned by:** HyperX-Sen  
+- **Dataset:** [GSM8K (Grade School Math 8K)](https://huggingface.co/datasets/openai/gsm8k)  
+- **Hardware:** 2× Tesla T4 GPUs
+- **Objective:** Improve complex reasoning and logical deduction  
+
+## 📈 **Performance Improvements**
+Through fine-tuning on **GSM8K**, the model has improved in:
+- **Mathematical reasoning**
+- **Step-by-step logical deduction**
+- **Commonsense reasoning**
+- **Word problem-solving**  
+
+This makes it ideal for applications requiring **high-level reasoning**, such as **AI tutoring, research assistance, and problem-solving AI agents**.
+
+## 🛠 **How to Use**
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# Load the model and tokenizer
+model_name = "HyperX-Sen/Qwen-2.5-7B-Reasoning"
+model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto", device_map="auto")
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+SYSTEM_PROMPT = """
+Respond in the following format:
+<reasoning>
+...
+</reasoning>
+<answer>
+...
+</answer>
+"""
+
+# Define the conversation
+messages = [
+    {"role": "system", "content": f"{SYSTEM_PROMPT}"},
+    {"role": "user", "content": "What are the potential impacts of artificial intelligence on employment?"}
+]
+
+# Format the chat input
+input_text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+
+# Tokenize the formatted input
+inputs = tokenizer(input_text, return_tensors="pt").to(model.device)
+
+# Generate the response
+output = model.generate(**inputs, max_length=512, do_sample=True, temperature=0.7)
+
+# Decode and display the response
+response = tokenizer.decode(output[0], skip_special_tokens=True)
+print(response)
+```
+
+## 🙌 **Acknowledgments**
+A huge thanks to **Qwen** for providing the powerful **Qwen2.5-7B-Instruct** model, which served as the base for this fine-tuned version.