Update README.md

---
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.

---

## 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.

---

## How to Get Started with the Model

To load the model, you can use the following code:

```python
# alpaca_prompt = Copied from above
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

README.md

@@ -3,5 +3,71 @@ license: apache-2.0
 tags:
 - unsloth
 - kyrgyz
-- llm
----
+---
+
+---
+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.
+
+---
+
+## 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.
+
+---
+
+## How to Get Started with the Model
+
+To load the model, you can use the following code:
+
+```python
+# alpaca_prompt = Copied from above
+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