Improve language tag (#1)

- Improve language tag (fc5f45ac979ef77685bfe1f1325b4815b8a19a76)


Co-authored-by: Loïck BOURDOIS <lbourdois@users.noreply.huggingface.co>

README.md

@@ -1,50 +1,62 @@
----
-license: mit
-datasets:
-- fka/awesome-chatgpt-prompts
-language:
-- en
-base_model:
-- Qwen/Qwen2.5-1.5B-Instruct
-pipeline_tag: text-generation
----
-# Quantized Qwen2.5-1.5B-Instruct
-
-This repository contains 8-bit and 4-bit quantized versions of the Qwen2.5-1.5B-Instruct model using GPTQ. Quantization significantly reduces the model's size and memory footprint, enabling faster inference on resource-constrained devices while maintaining reasonable performance.
-
-
-## Model Description
-
-The Qwen2.5-1.5B-Instruct is a powerful language model developed by Qwen for instructional tasks. These quantized versions offer a more efficient way to deploy and utilize this model.
-
-
-## Quantization Details
-
-* **Quantization Method:** GPTQ (Generative Pretrained Transformer Quantization)
-* **Quantization Bits:** 8-bit and 4-bit versions available.
-* **Dataset:** The model was quantized using a subset of the "fka/awesome-chatgpt-prompts" dataset.
-
-
-## Usage
-
-To use the quantized models, follow these steps:
-
-**Install Dependencies:**
-```bash
-pip install transformers accelerate bitsandbytes auto-gptq optimum
-```
-## Performance
-
-The quantized models offer a significant reduction in size and memory usage compared to the original model. While there might be a slight decrease in performance, the trade-off is often beneficial for deployment on devices with limited resources.
-
-
-## Disclaimer
-
-These quantized models are provided for research and experimentation purposes. We do not guarantee their performance or suitability for specific applications.
-
-
-## Acknowledgements
-
-* **Qwen:** For developing the original Qwen2.5-1.5B-Instruct model.
-* **Hugging Face:** For providing the platform and tools for model sharing and quantization.
+---
+license: mit
+datasets:
+- fka/awesome-chatgpt-prompts
+language:
+- zho
+- eng
+- fra
+- spa
+- por
+- deu
+- ita
+- rus
+- jpn
+- kor
+- vie
+- tha
+- ara
+base_model:
+- Qwen/Qwen2.5-1.5B-Instruct
+pipeline_tag: text-generation
+---
+# Quantized Qwen2.5-1.5B-Instruct
+
+This repository contains 8-bit and 4-bit quantized versions of the Qwen2.5-1.5B-Instruct model using GPTQ. Quantization significantly reduces the model's size and memory footprint, enabling faster inference on resource-constrained devices while maintaining reasonable performance.
+
+
+## Model Description
+
+The Qwen2.5-1.5B-Instruct is a powerful language model developed by Qwen for instructional tasks. These quantized versions offer a more efficient way to deploy and utilize this model.
+
+
+## Quantization Details
+
+* **Quantization Method:** GPTQ (Generative Pretrained Transformer Quantization)
+* **Quantization Bits:** 8-bit and 4-bit versions available.
+* **Dataset:** The model was quantized using a subset of the "fka/awesome-chatgpt-prompts" dataset.
+
+
+## Usage
+
+To use the quantized models, follow these steps:
+
+**Install Dependencies:**
+```bash
+pip install transformers accelerate bitsandbytes auto-gptq optimum
+```
+## Performance
+
+The quantized models offer a significant reduction in size and memory usage compared to the original model. While there might be a slight decrease in performance, the trade-off is often beneficial for deployment on devices with limited resources.
+
+
+## Disclaimer
+
+These quantized models are provided for research and experimentation purposes. We do not guarantee their performance or suitability for specific applications.
+
+
+## Acknowledgements
+
+* **Qwen:** For developing the original Qwen2.5-1.5B-Instruct model.
+* **Hugging Face:** For providing the platform and tools for model sharing and quantization.
 * **GPTQ Authors:** For developing the GPTQ quantization method.