Instructions to use CofeAI/Tele-FLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CofeAI/Tele-FLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="CofeAI/Tele-FLM", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("CofeAI/Tele-FLM", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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@@ -67,7 +67,7 @@ We adopt the architecture of FLM-101B as the backbone for Tele-FLM, with several
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- SwiGLU for activation function
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- Linear bias disabled
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- Embedding and language model head untied
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- Input and output
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Consequently, Tele-FLM is largely compatible with Llama architecturally.
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To maximize convenience for the community, we made minimal adjustments to Llama's code to adapt it to Tele-FLM and released it as open source.
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- SwiGLU for activation function
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- Linear bias disabled
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- Embedding and language model head untied
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- Input and output multiplier
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Consequently, Tele-FLM is largely compatible with Llama architecturally.
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| 73 |
To maximize convenience for the community, we made minimal adjustments to Llama's code to adapt it to Tele-FLM and released it as open source.
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