Improve language tag

README.md

@@ -1,177 +1,189 @@
----
-tags:
-- int8
-- vllm
-- llm-compressor
-language:
-- en
-pipeline_tag: text-generation
-license: apache-2.0
-base_model:
-- Qwen/Qwen2.5-0.5B
----
-
-# Qwen2.5-0.5B-quantized.w8a16
-
-## Model Overview
-- **Model Architecture:** Qwen2
-  - **Input:** Text
-  - **Output:** Text
-- **Model Optimizations:**
-  - **Weight quantization:** INT8
-- **Intended Use Cases:** Similarly to [Qwen2.5-0.5B](https://huggingface.co/Qwen/Qwen2.5-0.5B), this is a base language model.
-- **Out-of-scope:** Use in any manner that violates applicable laws or regulations (including trade compliance laws).
-- **Release Date:** 10/09/2024
-- **Version:** 1.0
-- **Model Developers:** Neural Magic
-
-Quantized version of [Qwen2.5-0.5B](https://huggingface.co/Qwen/Qwen2.5-0.5B).
-It achieves an OpenLLMv1 score of 43.9, compared to 44.0 for [Qwen2.5-0.5B](https://huggingface.co/Qwen/Qwen2.5-0.5B).
-
-### Model Optimizations
-
-This model was obtained by quantizing the weights of [Qwen2.5-0.5B](https://huggingface.co/Qwen/Qwen2.5-0.5B) to INT8 data type.
-This optimization reduces the number of bits per parameter from 16 to 8, reducing the disk size and GPU memory requirements by approximately 50%.
-
-Only the weights of the linear operators within transformers blocks are quantized.
-Symmetric per-channel quantization is applied, in which a linear scaling per output dimension maps the INT8 and floating point representations of the quantized weights.
-The [GPTQ](https://arxiv.org/abs/2210.17323) algorithm is applied for quantization, as implemented in the [llm-compressor](https://github.com/vllm-project/llm-compressor) library.
-
-
-## Deployment
-
-This model can be deployed efficiently using the [vLLM](https://docs.vllm.ai/en/latest/) backend, as shown in the example below.
-
-```python
-from vllm import LLM, SamplingParams
-from transformers import AutoTokenizer
-
-model_id = "neuralmagic/Qwen2.5-0.5B-quantized.w8a16"
-number_gpus = 1
-max_model_len = 8192
-
-sampling_params = SamplingParams(temperature=0.7, top_p=0.8, max_tokens=256)
-
-tokenizer = AutoTokenizer.from_pretrained(model_id)
-
-prompt = "Give me a short introduction to large language model."
-
-llm = LLM(model=model_id, tensor_parallel_size=number_gpus, max_model_len=max_model_len)
-
-outputs = llm.generate(prompt, sampling_params)
-
-generated_text = outputs[0].outputs[0].text
-print(generated_text)
-```
-
-vLLM aslo supports OpenAI-compatible serving. See the [documentation](https://docs.vllm.ai/en/latest/) for more details.
-
-
-
-## Evaluation
-
-The model was evaluated on the OpenLLMv1 benchmark, composed of MMLU, ARC-Challenge, GSM-8K, Hellaswag, Winogrande and TruthfulQA.
-Evaluation was conducted using [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) and the [vLLM](https://docs.vllm.ai/en/stable/) engine.
-
-### Accuracy
-
-<table>
-  <tr>
-   <td><strong>Category</strong>
-   </td>
-   <td><strong>Benchmark</strong>
-   </td>
-   <td><strong>Qwen2.5-0.5B</strong>
-   </td>
-   <td><strong>Qwen2.5-0.5B-quantized.w8a16<br>(this model)</strong>
-   </td>
-   <td><strong>Recovery</strong>
-   </td>
-  </tr>
-  <tr>
-   <td rowspan="8" ><strong>OpenLLM v1</strong>
-   </td>
-  </tr>
-  <tr>
-   <td>MMLU (5-shot)
-   </td>
-   <td>47.57
-   </td>
-   <td>47.81
-   </td>
-   <td>100.5%
-   </td>
-  </tr>
-  <tr>
-   <td>ARC Challenge (25-shot)
-   </td>
-   <td>34.90
-   </td>
-   <td>34.90
-   </td>
-   <td>100.0%
-   </td>
-  </tr>
-  <tr>
-   <td>GSM-8k (5-shot, strict-match)
-   </td>
-   <td>34.19
-   </td>
-   <td>33.51
-   </td>
-   <td>98.0%
-   </td>
-  </tr>
-  <tr>
-   <td>Hellaswag (10-shot)
-   </td>
-   <td>51.83
-   </td>
-   <td>51.78
-   </td>
-   <td>99.9%
-   </td>
-  </tr>
-  <tr>
-   <td>Winogrande (5-shot)
-   </td>
-   <td>55.80
-   </td>
-   <td>55.49
-   </td>
-   <td>99.4%
-   </td>
-  </tr>
-  <tr>
-  <td>TruthfulQA (0-shot, mc2)
-   </td>
-   <td>39.90
-   </td>
-   <td>39.71
-   </td>
-   <td>99.5%
-   </td>
-  </tr>
-  <tr>
-   <td><strong>Average</strong>
-   </td>
-   <td><strong>44.0</strong>
-   </td>
-   <td><strong>43.9</strong>
-   </td>
-   <td><strong>99.6%</strong>
-   </td>
-  </tr>
-</table>
-
-### Reproduction
-
-The results were obtained using the following command:
-
-```
-lm_eval \
-  --model vllm \
-  --model_args pretrained="neuralmagic/Qwen2.5-0.5B-quantized.w8a16",dtype=auto,max_model_len=4096,add_bos_token=True,tensor_parallel_size=1 \
-  --tasks openllm \
-  --batch_size auto
-```
+---
+tags:
+- int8
+- vllm
+- llm-compressor
+language:
+- zho
+- eng
+- fra
+- spa
+- por
+- deu
+- ita
+- rus
+- jpn
+- kor
+- vie
+- tha
+- ara
+pipeline_tag: text-generation
+license: apache-2.0
+base_model:
+- Qwen/Qwen2.5-0.5B
+---
+
+# Qwen2.5-0.5B-quantized.w8a16
+
+## Model Overview
+- **Model Architecture:** Qwen2
+  - **Input:** Text
+  - **Output:** Text
+- **Model Optimizations:**
+  - **Weight quantization:** INT8
+- **Intended Use Cases:** Similarly to [Qwen2.5-0.5B](https://huggingface.co/Qwen/Qwen2.5-0.5B), this is a base language model.
+- **Out-of-scope:** Use in any manner that violates applicable laws or regulations (including trade compliance laws).
+- **Release Date:** 10/09/2024
+- **Version:** 1.0
+- **Model Developers:** Neural Magic
+
+Quantized version of [Qwen2.5-0.5B](https://huggingface.co/Qwen/Qwen2.5-0.5B).
+It achieves an OpenLLMv1 score of 43.9, compared to 44.0 for [Qwen2.5-0.5B](https://huggingface.co/Qwen/Qwen2.5-0.5B).
+
+### Model Optimizations
+
+This model was obtained by quantizing the weights of [Qwen2.5-0.5B](https://huggingface.co/Qwen/Qwen2.5-0.5B) to INT8 data type.
+This optimization reduces the number of bits per parameter from 16 to 8, reducing the disk size and GPU memory requirements by approximately 50%.
+
+Only the weights of the linear operators within transformers blocks are quantized.
+Symmetric per-channel quantization is applied, in which a linear scaling per output dimension maps the INT8 and floating point representations of the quantized weights.
+The [GPTQ](https://arxiv.org/abs/2210.17323) algorithm is applied for quantization, as implemented in the [llm-compressor](https://github.com/vllm-project/llm-compressor) library.
+
+
+## Deployment
+
+This model can be deployed efficiently using the [vLLM](https://docs.vllm.ai/en/latest/) backend, as shown in the example below.
+
+```python
+from vllm import LLM, SamplingParams
+from transformers import AutoTokenizer
+
+model_id = "neuralmagic/Qwen2.5-0.5B-quantized.w8a16"
+number_gpus = 1
+max_model_len = 8192
+
+sampling_params = SamplingParams(temperature=0.7, top_p=0.8, max_tokens=256)
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+
+prompt = "Give me a short introduction to large language model."
+
+llm = LLM(model=model_id, tensor_parallel_size=number_gpus, max_model_len=max_model_len)
+
+outputs = llm.generate(prompt, sampling_params)
+
+generated_text = outputs[0].outputs[0].text
+print(generated_text)
+```
+
+vLLM aslo supports OpenAI-compatible serving. See the [documentation](https://docs.vllm.ai/en/latest/) for more details.
+
+
+
+## Evaluation
+
+The model was evaluated on the OpenLLMv1 benchmark, composed of MMLU, ARC-Challenge, GSM-8K, Hellaswag, Winogrande and TruthfulQA.
+Evaluation was conducted using [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) and the [vLLM](https://docs.vllm.ai/en/stable/) engine.
+
+### Accuracy
+
+<table>
+  <tr>
+   <td><strong>Category</strong>
+   </td>
+   <td><strong>Benchmark</strong>
+   </td>
+   <td><strong>Qwen2.5-0.5B</strong>
+   </td>
+   <td><strong>Qwen2.5-0.5B-quantized.w8a16<br>(this model)</strong>
+   </td>
+   <td><strong>Recovery</strong>
+   </td>
+  </tr>
+  <tr>
+   <td rowspan="8" ><strong>OpenLLM v1</strong>
+   </td>
+  </tr>
+  <tr>
+   <td>MMLU (5-shot)
+   </td>
+   <td>47.57
+   </td>
+   <td>47.81
+   </td>
+   <td>100.5%
+   </td>
+  </tr>
+  <tr>
+   <td>ARC Challenge (25-shot)
+   </td>
+   <td>34.90
+   </td>
+   <td>34.90
+   </td>
+   <td>100.0%
+   </td>
+  </tr>
+  <tr>
+   <td>GSM-8k (5-shot, strict-match)
+   </td>
+   <td>34.19
+   </td>
+   <td>33.51
+   </td>
+   <td>98.0%
+   </td>
+  </tr>
+  <tr>
+   <td>Hellaswag (10-shot)
+   </td>
+   <td>51.83
+   </td>
+   <td>51.78
+   </td>
+   <td>99.9%
+   </td>
+  </tr>
+  <tr>
+   <td>Winogrande (5-shot)
+   </td>
+   <td>55.80
+   </td>
+   <td>55.49
+   </td>
+   <td>99.4%
+   </td>
+  </tr>
+  <tr>
+  <td>TruthfulQA (0-shot, mc2)
+   </td>
+   <td>39.90
+   </td>
+   <td>39.71
+   </td>
+   <td>99.5%
+   </td>
+  </tr>
+  <tr>
+   <td><strong>Average</strong>
+   </td>
+   <td><strong>44.0</strong>
+   </td>
+   <td><strong>43.9</strong>
+   </td>
+   <td><strong>99.6%</strong>
+   </td>
+  </tr>
+</table>
+
+### Reproduction
+
+The results were obtained using the following command:
+
+```
+lm_eval \
+  --model vllm \
+  --model_args pretrained="neuralmagic/Qwen2.5-0.5B-quantized.w8a16",dtype=auto,max_model_len=4096,add_bos_token=True,tensor_parallel_size=1 \
+  --tasks openllm \
+  --batch_size auto
+```