File size: 2,996 Bytes
e4f461d dd2e9ce 8915c0e e4f461d dd2e9ce e4f461d dd2e9ce e4f461d 22dbf81 01508e6 e4f461d dd2e9ce e4f461d dd2e9ce e4f461d dd2e9ce e4f461d dd2e9ce 345dc71 e4f461d dd2e9ce e4f461d dd2e9ce e4f461d dd2e9ce e4f461d dd2e9ce e4f461d dd2e9ce e4f461d dd2e9ce c706e25 6e1a9cf 437a4dc 9e5a3a4 e5ae5ca de06efd e5ae5ca |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 |
---
license: apache-2.0
base_model: mistralai/Mistral-Nemo-Base-2407
tags:
- general-purpose
- text-generation
- Eval Results
---
# 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 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)
- **GGUF** [Astra-v1-12B-GGUF](https://huggingface.co/P0x0/Astra-v1-12B-GGUF)
## 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
### Out-of-Scope Use
Astra-v1-12B is not intended for real-time decision-making in critical applications or generating harmful or biased content.
## How to Get Started with the Model
```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))
```
I encourage you to provide feedback on the model's performance. If you'd like to create your own quantizations, feel free to do so and let me know how it works for you!
| Metric | Dataset Name | Metric Type | Value |
|--------------|---------------|-------------------------------------|--------|
| Average ⬆️ | Average | average_score | 19.46 |
| IFEval | IFEval | inst_level_strict_acc, prompt_level_strict_acc | 28.06 |
| BBH | BBH | acc_norm | 31.81 |
| MATH Lvl 5 | MATH Lvl 5 | exact_match | 9.67 |
| GPQA | GPQA | acc_norm | 8.5 |
| MUSR | MuSR | acc_norm | 11.38 |
| MMLU-PRO | MMLU-PRO | acc | 27.34 |
|