Improve language tag

Hi! As the model is multilingual, this is a PR to add other languages than English to the language tag to improve the referencing. Note that 29 languages are announced in the README, but only 13 are explicitly listed. I was therefore only able to add these 13 languages.

README.md

@@ -1,216 +1,228 @@
----
-license: apache-2.0
-license_link: https://huggingface.co/Qwen/Qwen2.5-7B/blob/main/LICENSE
-language:
-- en
-pipeline_tag: text-generation
-base_model: Qwen/Qwen2.5-7B-Instruct
-tags:
-- chat
-- neuralmagic
-- llmcompressor
-- fp8
----
-
-# Qwen2.5-7B-Instruct-FP8-dynamic
-
-## Model Overview
-- **Model Architecture:** Qwen2
-  - **Input:** Text
-  - **Output:** Text
-- **Model Optimizations:**
-  - **Activation quantization:** FP8
-  - **Weight quantization:** FP8
-- **Intended Use Cases:** Intended for commercial and research use multiple languages. Similarly to [Qwen2.5-7B](https://huggingface.co/Qwen/Qwen2.5-7B), this models is intended for assistant-like chat.
-- **Out-of-scope:** Use in any manner that violates applicable laws or regulations (including trade compliance laws).
-- **Release Date:** 11/27/2024
-- **Version:** 1.0
-- **License(s):** [apache-2.0](https://huggingface.co/Qwen/Qwen2.5-7B/blob/main/LICENSE)
-- **Model Developers:** Neural Magic
-
-### Model Optimizations
-
-This model was obtained by quantizing activations and weights of [Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) to FP8 data type.
-This optimization reduces the number of bits used to represent weights and activations from 16 to 8, reducing GPU memory requirements (by approximately 50%) and increasing matrix-multiply compute throughput (by approximately 2x).
-Weight quantization also reduces disk size requirements by approximately 50%.
-
-Only weights and activations of the linear operators within transformers blocks are quantized.
-Weights are quantized with a symmetric static per-channel scheme, whereas activations are quantized with a symmetric dynamic per-token scheme.
-The [llm-compressor](https://github.com/vllm-project/llm-compressor) library is used for quantization.
-
-## 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 = "RedHatAI/Qwen2.5-7B-Instruct-FP8-dynamic"
-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)
-
-messages = [
-    {"role": "user", "content": "Give me a short introduction to large language model."},
-]
-
-prompts = tokenizer.apply_chat_template(messages, tokenize=False)
-
-llm = LLM(model=model_id, tensor_parallel_size=number_gpus, max_model_len=max_model_len)
-
-outputs = llm.generate(prompts, 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.
-
-## Creation
-
-<details>
-  <summary>Creation details</summary>
-  This model was created with [llm-compressor](https://github.com/vllm-project/llm-compressor) by running the code snippet below. 
-
-
-  ```python
-  from transformers import AutoModelForCausalLM, AutoTokenizer
-  from llmcompressor.modifiers.quantization import QuantizationModifier
-  from llmcompressor.transformers import oneshot
-  
-  # Load model
-  model_stub = "Qwen/Qwen2.5-7B-Instruct-FP8-dynamic"
-  model_name = model_stub.split("/")[-1]
-  
-  tokenizer = AutoTokenizer.from_pretrained(model_stub)
-  
-  model = AutoModelForCausalLM.from_pretrained(
-      model_stub,
-      device_map="auto",
-      torch_dtype="auto",
-  )
-  
-  # Configure the quantization algorithm and scheme
-  recipe = QuantizationModifier(
-      targets="Linear",
-      scheme="FP8_dynamic",
-      ignore=["lm_head"],
-  )
-  
-  # Apply quantization
-  oneshot(
-      model=model,
-      recipe=recipe,
-  )
-  
-  # Save to disk in compressed-tensors format
-  save_path = model_name + "-FP8-dynamic"
-  model.save_pretrained(save_path)
-  tokenizer.save_pretrained(save_path)
-  print(f"Model and tokenizer saved to: {save_path}")
-  ```
-</details>
-
-## Evaluation
-
-The model was evaluated on the [OpenLLM](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) leaderboard tasks (version 1) with the [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness/tree/387Bbd54bc621086e05aa1b030d8d4d5635b25e6) (commit 387Bbd54bc621086e05aa1b030d8d4d5635b25e6) and the [vLLM](https://docs.vllm.ai/en/stable/) engine, using the following command:
-```
-lm_eval \
-  --model vllm \
-  --model_args pretrained="neuralmagic/Qwen2.5-7B-Instruct-FP8-dynamic",dtype=auto,gpu_memory_utilization=0.5,max_model_len=4096,enable_chunk_prefill=True,tensor_parallel_size=1 \
-  --apply_chat_template \
-  --fewshot_as_multiturn \
-  --tasks openllm \
-  --batch_size auto
-```
-
-### Accuracy
-
-#### Open LLM Leaderboard evaluation scores
-<table>
-  <tr>
-   <th>Benchmark
-   </th>
-   <th>Qwen2.5-7B-Instruct
-   </th>
-   <th>Qwen2.5-7B-Instruct-FP8-dynamic<br>(this model)
-   </th>
-   <th>Recovery
-   </th>
-  </tr>
-  <tr>
-   <td>MMLU (5-shot)
-   </td>
-   <td>74.24
-   </td>
-   <td>74.04
-   </td>
-   <td>99.7%
-   </td>
-  </tr>
-  <tr>
-   <td>ARC Challenge (25-shot)
-   </td>
-   <td>63.40
-   </td>
-   <td>63.14
-   </td>
-   <td>99.6%
-   </td>
-  </tr>
-  <tr>
-   <td>GSM-8K (5-shot, strict-match)
-   </td>
-   <td>80.36
-   </td>
-   <td>80.06
-   </td>
-   <td>99.6%
-   </td>
-  </tr>
-  <tr>
-   <td>Hellaswag (10-shot)
-   </td>
-   <td>81.52
-   </td>
-   <td>81.11
-   </td>
-   <td>99.5%
-   </td>
-  </tr>
-  <tr>
-   <td>Winogrande (5-shot)
-   </td>
-   <td>74.66
-   </td>
-   <td>74.43
-   </td>
-   <td>99.7%
-   </td>
-  </tr>
-  <tr>
-   <td>TruthfulQA (0-shot, mc2)
-   </td>
-   <td>64.76
-   </td>
-   <td>64.87
-   </td>
-   <td>100.2%
-   </td>
-  </tr>
-  <tr>
-   <td><strong>Average</strong>
-   </td>
-   <td><strong>73.16</strong>
-   </td>
-   <td><strong>72.94</strong>
-   </td>
-   <td><strong>99.7%</strong>
-   </td>
-  </tr>
-</table>
-
+---
+license: apache-2.0
+license_link: https://huggingface.co/Qwen/Qwen2.5-7B/blob/main/LICENSE
+language:
+- zho
+- eng
+- fra
+- spa
+- por
+- deu
+- ita
+- rus
+- jpn
+- kor
+- vie
+- tha
+- ara
+pipeline_tag: text-generation
+base_model: Qwen/Qwen2.5-7B-Instruct
+tags:
+- chat
+- neuralmagic
+- llmcompressor
+- fp8
+---
+
+# Qwen2.5-7B-Instruct-FP8-dynamic
+
+## Model Overview
+- **Model Architecture:** Qwen2
+  - **Input:** Text
+  - **Output:** Text
+- **Model Optimizations:**
+  - **Activation quantization:** FP8
+  - **Weight quantization:** FP8
+- **Intended Use Cases:** Intended for commercial and research use multiple languages. Similarly to [Qwen2.5-7B](https://huggingface.co/Qwen/Qwen2.5-7B), this models is intended for assistant-like chat.
+- **Out-of-scope:** Use in any manner that violates applicable laws or regulations (including trade compliance laws).
+- **Release Date:** 11/27/2024
+- **Version:** 1.0
+- **License(s):** [apache-2.0](https://huggingface.co/Qwen/Qwen2.5-7B/blob/main/LICENSE)
+- **Model Developers:** Neural Magic
+
+### Model Optimizations
+
+This model was obtained by quantizing activations and weights of [Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) to FP8 data type.
+This optimization reduces the number of bits used to represent weights and activations from 16 to 8, reducing GPU memory requirements (by approximately 50%) and increasing matrix-multiply compute throughput (by approximately 2x).
+Weight quantization also reduces disk size requirements by approximately 50%.
+
+Only weights and activations of the linear operators within transformers blocks are quantized.
+Weights are quantized with a symmetric static per-channel scheme, whereas activations are quantized with a symmetric dynamic per-token scheme.
+The [llm-compressor](https://github.com/vllm-project/llm-compressor) library is used for quantization.
+
+## 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 = "RedHatAI/Qwen2.5-7B-Instruct-FP8-dynamic"
+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)
+
+messages = [
+    {"role": "user", "content": "Give me a short introduction to large language model."},
+]
+
+prompts = tokenizer.apply_chat_template(messages, tokenize=False)
+
+llm = LLM(model=model_id, tensor_parallel_size=number_gpus, max_model_len=max_model_len)
+
+outputs = llm.generate(prompts, 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.
+
+## Creation
+
+<details>
+  <summary>Creation details</summary>
+  This model was created with [llm-compressor](https://github.com/vllm-project/llm-compressor) by running the code snippet below. 
+
+
+  ```python
+  from transformers import AutoModelForCausalLM, AutoTokenizer
+  from llmcompressor.modifiers.quantization import QuantizationModifier
+  from llmcompressor.transformers import oneshot
+  
+  # Load model
+  model_stub = "Qwen/Qwen2.5-7B-Instruct-FP8-dynamic"
+  model_name = model_stub.split("/")[-1]
+  
+  tokenizer = AutoTokenizer.from_pretrained(model_stub)
+  
+  model = AutoModelForCausalLM.from_pretrained(
+      model_stub,
+      device_map="auto",
+      torch_dtype="auto",
+  )
+  
+  # Configure the quantization algorithm and scheme
+  recipe = QuantizationModifier(
+      targets="Linear",
+      scheme="FP8_dynamic",
+      ignore=["lm_head"],
+  )
+  
+  # Apply quantization
+  oneshot(
+      model=model,
+      recipe=recipe,
+  )
+  
+  # Save to disk in compressed-tensors format
+  save_path = model_name + "-FP8-dynamic"
+  model.save_pretrained(save_path)
+  tokenizer.save_pretrained(save_path)
+  print(f"Model and tokenizer saved to: {save_path}")
+  ```
+</details>
+
+## Evaluation
+
+The model was evaluated on the [OpenLLM](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) leaderboard tasks (version 1) with the [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness/tree/387Bbd54bc621086e05aa1b030d8d4d5635b25e6) (commit 387Bbd54bc621086e05aa1b030d8d4d5635b25e6) and the [vLLM](https://docs.vllm.ai/en/stable/) engine, using the following command:
+```
+lm_eval \
+  --model vllm \
+  --model_args pretrained="neuralmagic/Qwen2.5-7B-Instruct-FP8-dynamic",dtype=auto,gpu_memory_utilization=0.5,max_model_len=4096,enable_chunk_prefill=True,tensor_parallel_size=1 \
+  --apply_chat_template \
+  --fewshot_as_multiturn \
+  --tasks openllm \
+  --batch_size auto
+```
+
+### Accuracy
+
+#### Open LLM Leaderboard evaluation scores
+<table>
+  <tr>
+   <th>Benchmark
+   </th>
+   <th>Qwen2.5-7B-Instruct
+   </th>
+   <th>Qwen2.5-7B-Instruct-FP8-dynamic<br>(this model)
+   </th>
+   <th>Recovery
+   </th>
+  </tr>
+  <tr>
+   <td>MMLU (5-shot)
+   </td>
+   <td>74.24
+   </td>
+   <td>74.04
+   </td>
+   <td>99.7%
+   </td>
+  </tr>
+  <tr>
+   <td>ARC Challenge (25-shot)
+   </td>
+   <td>63.40
+   </td>
+   <td>63.14
+   </td>
+   <td>99.6%
+   </td>
+  </tr>
+  <tr>
+   <td>GSM-8K (5-shot, strict-match)
+   </td>
+   <td>80.36
+   </td>
+   <td>80.06
+   </td>
+   <td>99.6%
+   </td>
+  </tr>
+  <tr>
+   <td>Hellaswag (10-shot)
+   </td>
+   <td>81.52
+   </td>
+   <td>81.11
+   </td>
+   <td>99.5%
+   </td>
+  </tr>
+  <tr>
+   <td>Winogrande (5-shot)
+   </td>
+   <td>74.66
+   </td>
+   <td>74.43
+   </td>
+   <td>99.7%
+   </td>
+  </tr>
+  <tr>
+   <td>TruthfulQA (0-shot, mc2)
+   </td>
+   <td>64.76
+   </td>
+   <td>64.87
+   </td>
+   <td>100.2%
+   </td>
+  </tr>
+  <tr>
+   <td><strong>Average</strong>
+   </td>
+   <td><strong>73.16</strong>
+   </td>
+   <td><strong>72.94</strong>
+   </td>
+   <td><strong>99.7%</strong>
+   </td>
+  </tr>
+</table>
+