Instructions to use DeepMount00/mamba_790_hf_qa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DeepMount00/mamba_790_hf_qa 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="DeepMount00/mamba_790_hf_qa")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("DeepMount00/mamba_790_hf_qa") model = AutoModelForCausalLM.from_pretrained("DeepMount00/mamba_790_hf_qa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
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@@ -28,11 +28,11 @@ def predict(contesto, domanda):
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input_ids = tokenizer([prompt], return_tensors="pt").to(device)
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generate_ids = model.generate(**input_ids, max_new_tokens=150, eos_token_id=
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answer = tokenizer.batch_decode(generate_ids)
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try:
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final_answer = answer[0].split("##RISPOSTA: ")[1].split(
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except IndexError:
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final_answer = ""
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return final_answer
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input_ids = tokenizer([prompt], return_tensors="pt").to(device)
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generate_ids = model.generate(**input_ids, max_new_tokens=150, eos_token_id=8112)
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answer = tokenizer.batch_decode(generate_ids)
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try:
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final_answer = answer[0].split("##RISPOSTA: ")[1].split("##END")[0].strip("\n")
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except IndexError:
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final_answer = ""
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return final_answer
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