Improve language tag

README.md

@@ -1,107 +1,118 @@
----
-library_name: transformers
-base_model:
-- Qwen/Qwen2.5-3B-Instruct
-license: apache-2.0
-datasets:
-- amphora/QwQ-LongCoT-130K
-- amphora/QwQ-LongCoT-130K-2
-- amphora/verfiable-25k
-- amphora/m-math500
-language:
-- en
-- zh
-pipeline_tag: text-generation
-tags:
-- Math
-- Code
-- Thinker
-- Reasoning
-- 3B
-- QwQ
-- Mini
-- text-generation-inference
-- SFT
-- trl
----
-
-![8.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/Faj50x1HAODJAIy_R94se.png)
-
-# **PocketThinker-QwQ-3B-Instruct**  
-
-> PocketThinker-QwQ-3B-Instruct is based on the Qwen2.5-3B-Instruct architecture, designed as a lightweight and efficient reasoning assistant. It serves as the pocket-sized version of QwQ-LCoT-7B-Instruct, optimized for fast inference while maintaining strong problem-solving and computational capabilities. This model is fine-tuned for enhanced structured reasoning, minimal token wastage, and high-quality technical responses.
-
-## **Key Improvements**  
-1. **Optimized for Coding**: Specializes in generating structured, efficient code with minimal redundancy for smooth execution.  
-2. **Compact yet Powerful**: Maintains strong problem-solving capabilities within a smaller 3B parameter architecture, ensuring accessibility on resource-limited devices.  
-3. **Advanced Reasoning Capabilities**: Excels in algorithmic problem-solving, mathematical reasoning, and structured technical explanations.  
-4. **Efficient Memory Utilization**: Reduces computational overhead while maintaining high-quality outputs.  
-5. **Focused Output Generation**: Avoids unnecessary token generation, ensuring concise and relevant responses.  
-
-## **Quickstart with transformers**  
-
-Here is a code snippet to load the tokenizer and model using `apply_chat_template` for structured input formatting:  
-
-```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
-
-model_name = "prithivMLmods/PocketThinker-QwQ-3B-Instruct"
-
-model = AutoModelForCausalLM.from_pretrained(
-    model_name,
-    torch_dtype="auto",
-    device_map="auto"
-)
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-
-prompt = "Write a Python function to find the Fibonacci sequence."
-messages = [
-    {"role": "system", "content": "You are an advanced coding assistant."},
-    {"role": "user", "content": prompt}
-]
-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=6090
-)
-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)
-```
-
-## **Intended Use**  
-1. **Code Generation & Optimization**:  
-   Supports developers in writing, refining, and optimizing code across multiple programming languages.  
-2. **Algorithm & Mathematical Problem Solving**:  
-   Delivers precise solutions and structured explanations for complex problems.  
-3. **Technical Documentation & Explanation**:  
-   Assists in generating well-structured documentation for libraries, APIs, and coding concepts.  
-4. **Debugging Assistance**:  
-   Helps identify and correct errors in code snippets.  
-5. **Educational Support**:  
-   Simplifies programming topics for students and learners with clear explanations.  
-6. **Structured Data Processing**:  
-   Generates structured outputs like JSON, XML, and tables for data science applications.  
-
-## **Limitations**  
-1. **Hardware Constraints**:  
-   Although lighter than larger models, still requires a moderately powerful GPU or TPU for optimal performance.  
-2. **Potential Bias in Responses**:  
-   Outputs may reflect biases present in training data.  
-3. **Limited Creativity**:  
-   May generate variable results in non-technical, creative tasks.  
-4. **No Real-Time Awareness**:  
-   Lacks access to real-world events beyond its training cutoff.  
-5. **Error Propagation in Long Responses**:  
-   Minor mistakes in early outputs may affect overall coherence in lengthy responses.  
-6. **Prompt Sensitivity**:  
+---
+library_name: transformers
+base_model:
+- Qwen/Qwen2.5-3B-Instruct
+license: apache-2.0
+datasets:
+- amphora/QwQ-LongCoT-130K
+- amphora/QwQ-LongCoT-130K-2
+- amphora/verfiable-25k
+- amphora/m-math500
+language:
+- zho
+- eng
+- fra
+- spa
+- por
+- deu
+- ita
+- rus
+- jpn
+- kor
+- vie
+- tha
+- ara
+pipeline_tag: text-generation
+tags:
+- Math
+- Code
+- Thinker
+- Reasoning
+- 3B
+- QwQ
+- Mini
+- text-generation-inference
+- SFT
+- trl
+---
+
+![8.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/Faj50x1HAODJAIy_R94se.png)
+
+# **PocketThinker-QwQ-3B-Instruct**  
+
+> PocketThinker-QwQ-3B-Instruct is based on the Qwen2.5-3B-Instruct architecture, designed as a lightweight and efficient reasoning assistant. It serves as the pocket-sized version of QwQ-LCoT-7B-Instruct, optimized for fast inference while maintaining strong problem-solving and computational capabilities. This model is fine-tuned for enhanced structured reasoning, minimal token wastage, and high-quality technical responses.
+
+## **Key Improvements**  
+1. **Optimized for Coding**: Specializes in generating structured, efficient code with minimal redundancy for smooth execution.  
+2. **Compact yet Powerful**: Maintains strong problem-solving capabilities within a smaller 3B parameter architecture, ensuring accessibility on resource-limited devices.  
+3. **Advanced Reasoning Capabilities**: Excels in algorithmic problem-solving, mathematical reasoning, and structured technical explanations.  
+4. **Efficient Memory Utilization**: Reduces computational overhead while maintaining high-quality outputs.  
+5. **Focused Output Generation**: Avoids unnecessary token generation, ensuring concise and relevant responses.  
+
+## **Quickstart with transformers**  
+
+Here is a code snippet to load the tokenizer and model using `apply_chat_template` for structured input formatting:  
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "prithivMLmods/PocketThinker-QwQ-3B-Instruct"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+prompt = "Write a Python function to find the Fibonacci sequence."
+messages = [
+    {"role": "system", "content": "You are an advanced coding assistant."},
+    {"role": "user", "content": prompt}
+]
+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=6090
+)
+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)
+```
+
+## **Intended Use**  
+1. **Code Generation & Optimization**:  
+   Supports developers in writing, refining, and optimizing code across multiple programming languages.  
+2. **Algorithm & Mathematical Problem Solving**:  
+   Delivers precise solutions and structured explanations for complex problems.  
+3. **Technical Documentation & Explanation**:  
+   Assists in generating well-structured documentation for libraries, APIs, and coding concepts.  
+4. **Debugging Assistance**:  
+   Helps identify and correct errors in code snippets.  
+5. **Educational Support**:  
+   Simplifies programming topics for students and learners with clear explanations.  
+6. **Structured Data Processing**:  
+   Generates structured outputs like JSON, XML, and tables for data science applications.  
+
+## **Limitations**  
+1. **Hardware Constraints**:  
+   Although lighter than larger models, still requires a moderately powerful GPU or TPU for optimal performance.  
+2. **Potential Bias in Responses**:  
+   Outputs may reflect biases present in training data.  
+3. **Limited Creativity**:  
+   May generate variable results in non-technical, creative tasks.  
+4. **No Real-Time Awareness**:  
+   Lacks access to real-world events beyond its training cutoff.  
+5. **Error Propagation in Long Responses**:  
+   Minor mistakes in early outputs may affect overall coherence in lengthy responses.  
+6. **Prompt Sensitivity**:  
    The effectiveness of responses depends on well-structured prompts.