Update README.md
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base_model: unsloth/mistral-7b-bnb-4bit
library_name: peft
---
# Model Card for Mistral 7B Fine-tuned by Erkinbek Niiazbek uulu
## Model Details
### Model Description
This is a fine-tuned version of the Mistral 7B model developed by Erkinbek Niiazbek uulu for specific use cases. The model was fine-tuned using LoRA (Low-Rank Adaptation) techniques and is optimized for lightweight deployment. The base model used is `unsloth/mistral-7b-bnb-4bit`.
- **Developed by:** Erkinbek Niiazbek uulu
- **Contact Email:** [email protected]
- **Base Model:** unsloth/mistral-7b-bnb-4bit
- **Library Name:** PEFT
- **Language(s):** Multilingual (including Kyrgyz)
- **License:** [Specify your license type, e.g., Apache 2.0, MIT]
- **Fine-tuned from model:** unsloth/mistral-7b-bnb-4bit
---
## Uses
### Direct Use
This fine-tuned model is designed for tasks such as:
- Multilingual question answering
- Text summarization
- Natural language generation
### Downstream Use
This model can be further fine-tuned for domain-specific applications.
### Out-of-Scope Use
This model is not intended for generating harmful, offensive, or unethical content.
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## Bias, Risks, and Limitations
### Recommendations
While this model has been fine-tuned for specific tasks, users should be cautious of potential biases in the output. It is recommended to review the outputs critically, especially when used in sensitive applications.
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## How to Get Started with the Model
To load the model, you can use the following code:
```python
FastLanguageModel.for_inference(model) # Enable native 2x faster inference
inputs = tokenizer(
[
alpaca_prompt.format(
"Зекет деген эмне?", # instruction
"", # input
"", # output - leave this blank for generation!
)
], return_tensors = "pt").to("cuda")
from transformers import TextStreamer
text_streamer = TextStreamer(tokenizer)
_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 1024)
### Framework versions
- PEFT 0.14.0