P0x0
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Astra-v1-12B

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 ---
-library_name: transformers
-base_model:
-- mistralai/Mistral-Nemo-Instruct-2407
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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))