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mplus-8b

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YuWangX commited on Feb 15, 2025

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 ---
-library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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-## Uses
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-### Direct Use
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-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-## How to Get Started with the Model
-Use the code below to get started with the model.
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-## Training Details
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-## Environmental Impact
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-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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-## Technical Specifications [optional]
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 ---
+license: apache-2.0
 ---
+This model is continually pre-trained from [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) with the structure proposed in [M+](https://arxiv.org/abs/2502.00592).
+We equip Llama-3 with 10240 memory tokens in each layer, leading to a memory pool of 1.34B parameters. Meanwhile, we have a long-term memory pool in each layer, which is set to 153600 tokens in our paper, although it can be even longer.
+To use the model, please use the following code:
+```
+git clone git@github.com:wangyu-ustc/MemoryLLM.git
+cd MemoryLLM
+```
+Then simply use the following code to load the model:
+```python
+import torch
+from modeling_memoryllm import MemoryLLM
+from transformers import AutoTokenizer
+# load the model mplus-8b (currently we only have the pretrained version)
+from modeling_mplus import MPlus
+model = MPlus.from_pretrained("YuWangX/mplus-8b", attn_implementation="flash_attention_2", torch_dtype=torch.bfloat16)
+tokenizer = AutoTokenizer.from_pretrained("YuWangX/mplus-8b")
+model = model.to(torch.bfloat16) # need to call it again to cast the `inv_freq` in rotary_emb to bfloat16 as well
+model.put_ltm_to_numpy() # We include ltm as modules so that it can be uploaded to huggingface, but for inference we need to put ltm on CPU and cast ltm_ags to numpy.
+```
+### How to use the model
+Inject a piece of context into the model using the following script:
+```python
+model = model.cuda()
+# Self-Update with the new context
+ctx = "Last week, John had a wonderful picnic with David. During their conversation, David mentioned multiple times that he likes eating apples. Though he didn't mention any other fruits, John says he can infer that David also like bananas."
+# please make sure the context to inject into the memory is larger than 16 tokens, this is the hard minimum when training the model. The memory will be disturbed when less than 16 tokens are injected into the memory.
+model.inject_memory(tokenizer(ctx, return_tensors='pt', add_special_tokens=False).input_ids.cuda(), update_memory=True)
+# Generation
+inputs = tokenizer("Question: What fruits does David like? Answer:", return_tensors='pt', add_special_tokens=False).input_ids.cuda()
+outputs = model.generate(input_ids=inputs, max_new_tokens=20)
+response = tokenizer.decode(outputs[0][inputs.shape[1]:])
+print(response)
+```