Update README.md

README.md

@@ -16,6 +16,72 @@ base_model: prithivMLmods/Megatron-Opus-7B-Exp
 This model was converted to GGUF format from [`prithivMLmods/Megatron-Opus-7B-Exp`](https://huggingface.co/prithivMLmods/Megatron-Opus-7B-Exp) 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/Megatron-Opus-7B-Exp) for more details on the model.
 
+---
+Megatron-Opus-7B-Exp is based on the Qwen 2.5 7B modality architecture, designed to enhance the reasoning capabilities of 7B-parameter models. It has been fine-tuned on a Synthetic dataset entries based on one half of Qwen’s QWQ and DeepSeek R1, further optimizing its chain-of-thought (CoT) reasoning and logical problem-solving abilities. The model demonstrates significant improvements in context understanding, structured data processing, and long-context comprehension, making it ideal for complex reasoning tasks, instruction-following, and text generation.
+
+Key Improvements
+
+    Advanced Reasoning & Logic: Optimized for multi-step problem-solving, logical deduction, and contextual analysis.
+    Fine-Tuned Instruction Following: Generates precise responses, structured outputs (e.g., JSON), and extended long-form text (8K+ tokens).
+    Greater Adaptability: Excels in role-playing, multi-turn dialogues, and diverse system prompts.
+    Long-Context Support: Handles up to 128K tokens and generates up to 8K tokens per output.
+    Multilingual Proficiency: Supports over 29 languages, including Chinese, English, French, Spanish, Portuguese, German, and more.
+
+Quickstart with Transformers
+
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "prithivMLmods/Megatron-Opus-7B-Exp"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto",
+    trust_remote_code=True
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+prompt = "Explain the concept of logical reasoning in AI."
+messages = [
+    {"role": "system", "content": "You are an expert AI assistant specialized in reasoning and logic."},
+    {"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)
+
+Intended Use
+
+    Advanced Logical & Analytical Reasoning: Designed for problem-solving, multi-step deductions, and cognitive reasoning tasks.
+    Mathematical & Scientific Computation: Supports theorem proving, complex calculations, and scientific knowledge retrieval.
+    Code Generation & Debugging: Generates optimized code, detects errors, and improves programming workflows.
+    Structured Data Analysis: Processes tables, JSON, and structured formats for data-centric applications.
+    Multilingual Reasoning & Translation: High proficiency across 29+ languages for international applications.
+    Extended Text Generation: Capable of generating research papers, instructional guides, and in-depth reports.
+
+Limitations
+
+    High Computational Requirements: Due to its 7B parameters and 128K context support, it requires powerful GPUs or TPUs for efficient inference.
+    Language-Specific Variability: Performance may differ across supported languages, especially for low-resource languages.
+    Potential Error Accumulation: Long-form text generation can introduce inconsistencies over extended outputs.
+    Limited Real-World Awareness: Knowledge is restricted to training data and may not reflect recent world events.
+    Prompt Sensitivity: The quality of responses depends on the specificity and clarity of the input prompt.
+
+---
 ## Use with llama.cpp
 Install llama.cpp through brew (works on Mac and Linux)