Update README.md

README.md

@@ -10,4 +10,53 @@ tags:
 - trl
 - Reinforcement learning
 - Qwen2.5-1B
----
+---
+# **Bellatrix-Tiny-1.5B-R1**
+
+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`.
+
+```python
+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`](https://github.com/huggingface/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:  
+1. **Domain Specificity**: While it performs well on general tasks, its performance may degrade with highly specialized or niche datasets.  
+2. **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.  
+3. **Computational Resources**: The model’s optimized transformer architecture can be resource-intensive, requiring significant computational power for fine-tuning and inference.  
+4. **Language Coverage**: While multilingual, some languages or dialects may have limited support or lower performance compared to widely used ones.  
+5. **Real-World Contexts**: It may struggle with understanding nuanced or ambiguous real-world scenarios not covered during training.
+