README.md

---
license: cc-by-4.0
---

# saiga-phi-3-mini-4k

saiga-phi-3-mini-4k is an SFT fine-tuned version of microsoft/Phi-3-mini-4k-instruct using a custom training dataset.
This model was made with [Phinetune]()

## Process
- Learning Rate: 1.41e-05
- Maximum Sequence Length: 2048
- Dataset: IlyaGusev/ru_turbo_saiga
- Split: train

## 💻 Usage
```python
!pip install -qU transformers
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline

model = "Slavator096/saiga-phi-3-mini-4k"
tokenizer = AutoTokenizer.from_pretrained(model)

# Example prompt
prompt = "Your example prompt here"

# Generate a response
model = AutoModelForCausalLM.from_pretrained(model)
pipeline = pipeline("text-generation", model=model, tokenizer=tokenizer)
outputs = pipeline(prompt, max_length=50, num_return_sequences=1)
print(outputs[0]["generated_text"])
```