78 lines
3.7 KiB
Markdown
78 lines
3.7 KiB
Markdown
---
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library_name: transformers
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tags:
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- text-generation-inference
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- 0.5B
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- v2
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- QWEN
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license: apache-2.0
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language:
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- en
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base_model:
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- prithivMLmods/Bellatrix-Tiny-0.5B
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pipeline_tag: text-generation
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---
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<pre align="center">
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____ __ ___
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/ __ \___ ____ ___ __/ /_ _______ _ _|__ \
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/ /_/ / _ \/ __ `/ / / / / / / / ___/ | | / /_/ /
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/ _, _/ __/ /_/ / /_/ / / /_/ (__ ) | |/ / __/
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/_/ |_|\___/\__, /\__,_/_/\__,_/____/ |___/____/
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/____/
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</pre>
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# **Regulus-Tiny-0.5B-v2**
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Regulus 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. Regulus 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).
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# **Use with transformers**
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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.
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Make sure to update your transformers installation via `pip install --upgrade transformers`.
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```python
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import torch
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from transformers import pipeline
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model_id = "prithivMLmods/Regulus-Tiny-0.5B-v2"
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pipe = pipeline(
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"text-generation",
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model=model_id,
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torch_dtype=torch.bfloat16,
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device_map="auto",
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)
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messages = [
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{"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
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{"role": "user", "content": "Who are you?"},
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]
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outputs = pipe(
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messages,
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max_new_tokens=256,
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)
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print(outputs[0]["generated_text"][-1])
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```
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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).
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# **Intended Use**
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Regulus is designed for applications that require advanced reasoning and multilingual dialogue capabilities. It is particularly suitable for:
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- **Agentic Retrieval**: Enabling intelligent retrieval of relevant information in a dialogue or query-response system.
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- **Summarization Tasks**: Condensing large bodies of text into concise summaries for easier comprehension.
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- **Multilingual Use Cases**: Supporting conversations in multiple languages with high accuracy and coherence.
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- **Instruction-Based Applications**: Following complex, context-aware instructions to generate precise outputs in a variety of scenarios.
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# **Limitations**
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Despite its capabilities, Regulus has some limitations:
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1. **Domain Specificity**: While it performs well on general tasks, its performance may degrade with highly specialized or niche datasets.
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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.
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3. **Computational Resources**: The model’s optimized transformer architecture can be resource-intensive, requiring significant computational power for fine-tuning and inference.
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4. **Language Coverage**: While multilingual, some languages or dialects may have limited support or lower performance compared to widely used ones.
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5. **Real-World Contexts**: It may struggle with understanding nuanced or ambiguous real-world scenarios not covered during training. |