Update README.md

README.md

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 This model was converted to GGUF format from [`prithivMLmods/Bellatrix-Tiny-1.5B-R1`](https://huggingface.co/prithivMLmods/Bellatrix-Tiny-1.5B-R1) 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/Bellatrix-Tiny-1.5B-R1) for more details on the model.
 
+---
+Bellatrix is based on a reasoning-based model designed for the DeepSeek-R1 synthetic dataset entries. The pipeline's instruction-tuned, text-only models are optimized for multilingual dialogue use cases, including agentic retrieval and summarization tasks. These models outperform many of the available open-source options. Bellatrix is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions utilize supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF).
+Use with transformers
+
+Starting with transformers >= 4.43.0 onward, you can run conversational inference using the Transformers pipeline abstraction or by leveraging the Auto classes with the generate() function.
+
+Make sure to update your transformers installation via pip install --upgrade transformers.
+
+import torch
+from transformers import pipeline
+
+model_id = "prithivMLmods/Bellatrix-Tiny-1.5B-R1"
+pipe = pipeline(
+    "text-generation",
+    model=model_id,
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+)
+messages = [
+    {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
+    {"role": "user", "content": "Who are you?"},
+]
+outputs = pipe(
+    messages,
+    max_new_tokens=256,
+)
+print(outputs[0]["generated_text"][-1])
+
+Note: You can also find detailed recipes on how to use the model locally, with torch.compile(), assisted generations, quantized and more at huggingface-llama-recipes
+Intended Use
+
+Bellatrix is designed for applications that require advanced reasoning and multilingual dialogue capabilities. It is particularly suitable for:
+
+    Agentic Retrieval: Enabling intelligent retrieval of relevant information in a dialogue or query-response system.
+    Summarization Tasks: Condensing large bodies of text into concise summaries for easier comprehension.
+    Multilingual Use Cases: Supporting conversations in multiple languages with high accuracy and coherence.
+    Instruction-Based Applications: Following complex, context-aware instructions to generate precise outputs in a variety of scenarios.
+
+Limitations
+
+Despite its capabilities, Bellatrix has some limitations:
+
+    Domain Specificity: While it performs well on general tasks, its performance may degrade with highly specialized or niche datasets.
+    Dependence on Training Data: It is only as good as the quality and diversity of its training data, which may lead to biases or inaccuracies.
+    Computational Resources: The model’s optimized transformer architecture can be resource-intensive, requiring significant computational power for fine-tuning and inference.
+    Language Coverage: While multilingual, some languages or dialects may have limited support or lower performance compared to widely used ones.
+    Real-World Contexts: It may struggle with understanding nuanced or ambiguous real-world scenarios not covered during training.
+
+---
 ## Use with llama.cpp
 Install llama.cpp through brew (works on Mac and Linux)