Update README.md

README.md

@@ -1,6 +1,103 @@
 ---
 license: apache-2.0
+language:
+- en
+base_model:
+- prithivMLmods/Viper-Coder-v1.6-r999
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- text-generation-inference
+- trl
+- sft
+- Qwen
+- Code
+- Math
 ---
 
 ![4.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/cx0frTHAi0aVZQHOxuSs9.png)
- 
+ # **Raptor X4**
+
+>  [!Important]
+>  Raptor X4 is based on the Qwen 2.5 14B modality architecture, designed to enhance the reasoning capabilities of 14B-parameter models. This model is optimized for advanced coding reasoning and UI coding. It excels in contextual understanding, logical deduction, and multi-step problem-solving. Raptor X4 has been fine-tuned using a long chain-of-thought reasoning model and specialized datasets to improve comprehension, structured responses, and conversational intelligence.
+
+Key improvements include:  
+1. **Enhanced Coding Reasoning**: Provides in-depth explanations and optimizations for complex coding problems, making it useful for developers and engineers.
+2. **Advanced UI Coding Support**: Excels in generating and refining front-end code for web and mobile applications.
+3. **General-Purpose Coding**: Capable of generating, debugging, and optimizing code across multiple programming languages, supporting software development and automation.
+4. **Long-Context Support**: Supports up to 128K tokens for input context and can generate up to 8K tokens in a single output, making it ideal for detailed responses.
+5. **Multilingual Proficiency**: Supports over 29 languages, including English, Chinese, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more.
+
+> [!WARNING]
+>Prompt Style :
+>
+>  Make a dark-themed minimalist dashboard for an **oil rig**.
+>
+>  [HTML, CSS, and more if required].
+
+# **Quickstart with transformers**
+
+Here is a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and generate content:
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "prithivMLmods/Raptor-X4"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+prompt = "How do I optimize React performance?"
+messages = [
+    {"role": "system", "content": "You are a helpful assistant capable of answering a wide range of questions."},
+    {"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=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]
+```
+
+# **Intended Use**  
+1. **Coding Reasoning**:  
+   Designed for providing explanations, optimizations, and best practices for coding problems.  
+2. **UI Coding and Development**:  
+   Excels in front-end development, including React, Vue, and other UI frameworks.  
+3. **Programming and Software Development**:  
+   Capable of generating, analyzing, and optimizing code in multiple programming languages.  
+4. **Educational Assistance**:  
+   Helps developers by providing coding tutorials, debugging assistance, and structured learning material.  
+5. **Multilingual Applications**:  
+   Supports global communication, translations, and multilingual content generation.  
+6. **Long-Form Content Generation**:  
+   Can generate extended responses, including documentation, technical reports, and coding guides.  
+
+# **Limitations**  
+1. **Hardware Requirements**:  
+   Requires high-memory GPUs or TPUs due to its large parameter size and long-context support.  
+2. **Potential Bias in Responses**:  
+   While designed to be neutral, outputs may still reflect biases present in training data.  
+3. **Complexity in Some Advanced Topics**:  
+   While proficient in general coding, highly specialized fields may require verification.  
+4. **Limited Real-World Awareness**:  
+   Does not have access to real-time events beyond its training cutoff.  
+5. **Error Propagation in Extended Outputs**:  
+   Minor errors in early responses may affect overall coherence in long-form outputs.  
+6. **Prompt Sensitivity**:  
+   The effectiveness of responses may depend on how well the input prompt is structured.