Update README.md

README.md

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 This model was converted to GGUF format from [`prithivMLmods/Primal-Opus-14B-Optimus-v2`](https://huggingface.co/prithivMLmods/Primal-Opus-14B-Optimus-v2) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
 Refer to the [original model card](https://huggingface.co/prithivMLmods/Primal-Opus-14B-Optimus-v2) for more details on the model.
 
+---
+
+Multilingual Proficiency: Supports over 29 languages, including Chinese, English, French, Spanish, Portuguese, German, and more.
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+Quickstart with Transformers
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+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "prithivMLmods/Primal-Opus-14B-Optimus-v2"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto",
+    trust_remote_code=True
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+prompt = "Give me a short introduction to large language models."
+messages = [
+    {"role": "system", "content": "You are an advanced AI assistant with expert-level reasoning and knowledge."},
+    {"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]
+print(response)
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+Intended Use
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+Advanced Logical Reasoning: Designed for logical deduction, multi-step problem-solving, and knowledge-based tasks.  
+Mathematical & Scientific Problem-Solving: Enhanced capabilities for calculations, theorem proving, and scientific queries.  
+Code Generation & Debugging: Generates and optimizes code across multiple programming languages.  
+Structured Data Analysis: Processes tables, JSON, and structured outputs, making it ideal for data-centric tasks.  
+Multilingual Applications: High proficiency in over 29 languages, enabling global-scale applications.  
+Extended Content Generation: Supports detailed document writing, research reports, and instructional guides.
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+Limitations
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+High Computational Requirements: Due to its 14B parameters and 128K context support, it requires powerful GPUs or TPUs for efficient inference.  
+Language-Specific Variability: Performance may vary across supported languages, especially for low-resource languages.  
+Potential Error Accumulation: Long-text generation can sometimes introduce inconsistencies over extended outputs.  
+Limited Real-World Awareness: Knowledge is restricted to training data and may not reflect recent world events.  
+Prompt Sensitivity: Outputs can depend on the specificity and clarity of the input prompt.
+
+---
 ## Use with llama.cpp
 Install llama.cpp through brew (works on Mac and Linux)