Update README.md

README.md

@@ -1,15 +1,12 @@
 ---
-license: other
 license_name: openmdw
 license_link: LICENSE
----
-# Seed-X-Instruct-7B
-<a href="https://github.com/ByteDance-Seed/Seed-X-7B/blob/main/Technical_Report.pdf">
-  <img src="https://img.shields.io/badge/Seed--X-Report-blue"></a>
-<a href="https://huggingface.co/ByteDance-Seed/Seed-X-Instruct-7B">
-  <img src="https://img.shields.io/badge/Seed--X-Hugging Face-brightgreen"></a>
-<a href="https://github.com/ByteDance-Seed/Seed-X-7B/blob/main/LICENSE.openmdw">
-  <img src="https://img.shields.io/badge/License-OpenMDW-yellow"></a>
+datasets:
+- fka/awesome
+metrics:
+- accuracy
+- character
+pipeline_tag: text-classification
 
 ## Introduction
 We are excited to introduce **Seed-X**, a powerful series of open-source multilingual translation language models, including an instruction model, a reinforcement learning model, and a reward model. It pushes the boundaries of translation capabilities within 7 billion parameters.
@@ -45,9 +42,6 @@ This repo contains the **Seed-X-Instruct** model, with the following features:
 Here is a simple example demonstrating how to load the model and perform translation using ```vllm```
 ```python
 from vllm import LLM, SamplingParams
-
-model_path = "./ByteDance-Seed/Seed-X-Instruct-7B"
-
 model = LLM(model=model_path,
             max_num_seqs=512,
             tensor_parallel_size=8,
@@ -55,17 +49,10 @@ model = LLM(model=model_path,
             gpu_memory_utilization=0.95)
 
 messages = [
-    "Translate the following English sentence into Chinese:\nMay the force be with you <zh>", # without CoT
-    "Translate the following English sentence into Chinese and explain it in detail:\nMay the force be with you <zh>" # with CoT
+    "Translate the following English sentence :\nMay the force be with you <zh>", # without CoT
+    "Translate the following English sentence  and explain it in detail:\nMay the force be with you <zh>" # with CoT
 ]
 
-# Sampling
-decoding_params = SamplingParams(temperature=0,
-                                 max_tokens=512,
-                                 skip_special_tokens=True)
-# Beam Search
-decoding_params = BeamSearchParams(beam_width=4, 
-                                    max_tokens=512)
 
 results = model.generate(messages, decoding_params)
 responses = [res.outputs[0].text.strip() for res in results]
@@ -73,23 +60,4 @@ responses = [res.outputs[0].text.strip() for res in results]
 print(responses)
 ```
 ## Evaluation
-We evaluated Seed-X on a diverse set of translation benchmarks, including FLORES-200, WMT-25, and a publicly released [challenge set](https://github.com/ByteDance-Seed/Seed-X-7B/tree/main/challenge_set) accompanied by human evaluations.
-![humen_eval](imgs/humen_eval.png)
-For detailed benchmark results and analysis, please refer to our [Technical Report](https://github.com/ByteDance-Seed/Seed-X-7B/blob/main/Technical_Report.pdf).
-
-## License
-This project is licensed under OpenMDW. See the [LICENSE](https://github.com/ByteDance-Seed/Seed-X-7B/blob/main/LICENSE.openmdw) file for details.
-
-## Citation
-<!--If you find Seed-X useful for your research and applications, feel free to give us a star ⭐ or cite us using:
-```bibtex
-@Article{XXX,
-      title={XXXXXXXXXXX}, 
-      author={XXX,XXX,XXX,XXX},
-      year={2025},
-      eprint={XXXX.XXXXX},
-      archivePrefix={arXiv},
-      primaryClass={cs.XX}
-}
-```-->
-We will soon publish our technical report on Arxiv.
+We evaluated Seed-X on a diverse set