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---
license: apache-2.0
language:
- en
base_model:
- Qwen/Qwen2.5-0.5B-Instruct
pipeline_tag: text-generation
library_name: transformers
tags:
- text-generation-inference
- reasoner
---
<pre align="center">
____ ____ __ __ __ ____ ____ ____ _ _
( _ \( ___)( ) ( ) /__\ (_ _)( _ \(_ _)( \/ )
) _ < )__) )(__ )(__ /(__)\ )( ) / _)(_ ) (
(____/(____)(____)(____)(__)(__)(__) (_)\_)(____)(_/\_)
</pre>
# **Bellatrix-Tiny-0.5B**
Bellatrix is based on a reasoning-based model designed for the QWQ 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-0.5B"
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, quantised 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. |