Text Generation
PEFT
Safetensors
conversational-memory
information-extraction
long-context
lora
qwen2.5
conversational
Instructions to use AsadIsmail/prism-memory with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use AsadIsmail/prism-memory with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-7B-Instruct") model = PeftModel.from_pretrained(base_model, "AsadIsmail/prism-memory") - Notebooks
- Google Colab
- Kaggle
Publish PRISM-Memory adapter bundle
Browse files
README.md
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This comparison holds the QA layer constant. It compares extractor against
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extractor, not a full end-to-end GPT-4.1 system.
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## Load the adapter
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```python
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This comparison holds the QA layer constant. It compares extractor against
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extractor, not a full end-to-end GPT-4.1 system.
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## Why this is useful
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- It keeps hard limits and preferences available for later workflow generation.
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- It keeps current state separate from future plans.
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- It supports dated recall and clean refusal on unsupported questions.
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See [docs/release/memory-scenarios.md](docs/release/memory-scenarios.md) for
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the compact end-to-end examples.
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## Load the adapter
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```python
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