Question Answering
Transformers
PyTorch
Chinese
baichuan
feature-extraction
lora
custom_code
text-generation-inference
Instructions to use Hongbin37/CBT-LLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hongbin37/CBT-LLM with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "question-answering" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("question-answering", model="Hongbin37/CBT-LLM", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Hongbin37/CBT-LLM", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update config.json
Browse files- config.json +0 -2
config.json
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{
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"_name_or_path": "baichuan-inc/baichuan-7B",
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"architectures": [
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"BaiChuanForCausalLM"
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],
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"auto_map": {
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"AutoConfig": "configuration_baichuan.BaiChuanConfig",
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"AutoModel": "modeling_baichuan.BaiChuanForCausalLM",
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"AutoModelForCausalLM": "baichuan-inc/baichuan-7B--modeling_baichuan.BaiChuanForCausalLM"
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},
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"bos_token_id": 1,
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"eos_token_id": 2,
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{
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"architectures": [
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"BaiChuanForCausalLM"
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],
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"auto_map": {
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"AutoConfig": "configuration_baichuan.BaiChuanConfig",
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"AutoModel": "modeling_baichuan.BaiChuanForCausalLM",
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},
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"bos_token_id": 1,
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"eos_token_id": 2,
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