Update README.md

README.md

@@ -3,3 +3,52 @@ license: apache-2.0
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 ![opus.gif](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/BELYApcX2oNMRsOW6nIyR.gif)
 
+# **Calcium-Opus-14B-Elite**
+
+Calcium-Opus-14B-Elite is based on the Qwen 2.5 14B modality architecture, designed to enhance the reasoning capabilities of 14B-parameter models. These models have proven effective in context understanding, reasoning, and mathematical problem-solving.It has been fine-tuned using a long chain-of-thought reasoning model and specialized datasets, with a focus on chain-of-thought (CoT) reasoning for problem-solving. This model is optimized for tasks requiring logical reasoning, detailed explanations, and multi-step problem-solving, making it ideal for applications such as instruction-following, text generation, and complex reasoning tasks.
+
+Key improvements include:  
+1. **Enhanced Knowledge and Expertise**: The model demonstrates significantly more knowledge and greatly improved capabilities in coding and mathematics, thanks to specialized expert models in these domains.  
+2. **Improved Instruction Following**: It shows significant advancements in following instructions, generating long texts (over 8K tokens), understanding structured data (e.g., tables), and producing structured outputs, especially in JSON format.  
+3. **Better Adaptability**: The model is more resilient to diverse system prompts, enabling enhanced role-playing implementations and condition-setting for chatbots.  
+4. **Long-Context Support**: It offers long-context support of up to 128K tokens and can generate up to 8K tokens in a single output.  
+5. **Multilingual Proficiency**: The model supports over 29 languages, including Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more.
+
+# **Quickstart with transformers**
+
+Here provides a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents.
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "prithivMLmods/Calcium-Opus-14B-Elite"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+prompt = "Give me a short introduction to large language model."
+messages = [
+    {"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful 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=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]
+```