Update README.md

README.md

@@ -1,8 +1,6 @@
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 license: llama3
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+pipeline_tag: text-generation
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 ![](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeiuCm7c8lEwEJuRey9kiVZsRn2W-b4pWlu3-X534V3YmVuVc2ZL-NXg2RkzSOOS2JXGHutDuyyNAUtdJI65jGTo8jT9Y99tMi4H4MqL44Uc5QKG77B0d6-JfIkZHFaUA71-RtjyYZWVIhqsNZcx8-OMaA?key=xt3VSDoCbmTY7o-cwwOFwQ)
@@ -232,4 +230,4 @@ We conduct supervised fine-tuning (SFT) on our base long-context model. In our p
 | Scheduling | 5% warmup, cosine decay till 10% peak learning rate |
 | Total #tokens | 1B |
 
-- Synthetic data: we also experiment with several strategies to generate long, synthetic chat data, but they have not yet helped to improve upon our UltraChat-fine-tuned chat models. The synthetic data strategies we tried include (1) using a paragraph of a long book/repo to generate question-answer pairs; (2) using hierarchical methods to summarize a long book; (3) turning the previous synthetic long QA data into a RAG format.
+- Synthetic data: we also experiment with several strategies to generate long, synthetic chat data, but they have not yet helped to improve upon our UltraChat-fine-tuned chat models. The synthetic data strategies we tried include (1) using a paragraph of a long book/repo to generate question-answer pairs; (2) using hierarchical methods to summarize a long book; (3) turning the previous synthetic long QA data into a RAG format.