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---
license: apache-2.0
base_model: mistralai/Mistral-Nemo-Base-2407
tags:
- general-purpose
- text-generation
---
# Astra-v1-12B
Astra-v1-12B is a fine-tuned version of the base model [Mistral-Nemo-Base-2407](https://huggingface.co/mistralai/Mistral-Nemo-Base-2407), developed for general-purpose natural language processing tasks. It was fine-tuned to replicate the quality and style of Claude 3's Sonnet and Opus models.
## Model Details
### Model Description
Astra-v1-12B is a general-purpose transformer-based language model fine-tuned for instruction-following tasks. The fine-tuning was designed to match the high-quality generation seen in Claude 3's Sonnet and Opus models, optimized for tasks such as text generation, summarization, question answering, and more.
- **Developed by:** P0x0
- **Finetuned from:** [Mistral-Nemo-Base-2407](https://huggingface.co/mistralai/Mistral-Nemo-Base-2407)
- **License:** Apache 2.0
### Model Sources
- **Repository:** [https://huggingface.co/P0x0/astra-v1-12b](https://huggingface.co/P0x0/astra-v1-12b)
## Uses
### Direct Use
Astra-v1-12B can be used directly for a wide range of NLP tasks, including:
- Text generation
- Summarization
- Question answering
- Dialogue systems
### Downstream Use
This model can be further fine-tuned for specific tasks such as:
- Creative writing
- Instruction-based text completion
- Automated support systems
### Out-of-Scope Use
Astra-v1-12B is not intended for real-time decision-making in critical applications or generating harmful or biased content.
## Bias, Risks, and Limitations
As with any large language model, Astra-v1-12B may carry inherent biases from the datasets used in fine-tuning. It is important to monitor and review the outputs when using the model in sensitive applications.
## How to Get Started with the Model
Here is a Python code snippet to get started with Astra-v1-12B:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("P0x0/astra-v1-12b")
model = AutoModelForCausalLM.from_pretrained("P0x0/astra-v1-12b")
input_text = "Explain the theory of relativity in simple terms."
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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