35 lines
1.4 KiB
Markdown
35 lines
1.4 KiB
Markdown
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---
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license: cc-by-sa-4.0
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---
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## emozilla/landmark-llama-7b
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This model is an out-of-the-box ready version of the LLaMA-7B variant of [Landmark Attention](https://arxiv.org/abs/2305.16300).
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The original code is modified from the [Landmark GitHub](https://github.com/epfml/landmark-attention) and the weights from [here](https://huggingface.co/epfml/landmark-attention-llama7b-wdiff).
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As a LLaMA variant, this model may be subject to the LLaMA license.
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### To use
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
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import torch
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tokenizer = AutoTokenizer.from_pretrained("emozilla/landmark-llama-7b", use_fast=False)
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model = AutoModelForCausalLM.from_pretrained("emozilla/landmark-llama-7b", \
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torch_dtype=torch.bfloat16, trust_remote_code=True, device_map="auto")
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pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
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print(pipe("Somebody once told me the world is gonna roll me", \
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max_new_tokens=256, temperature=0.8, do_sample=True))
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```
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You can configure the Landmark parameters by editing `mem_freq`, `mem_top_k`, `mem_max_seq_len`, and `mem_max_cache_size`.
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```python
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config = AutoConfig.from_pretrained("emozilla/landmark-llama-7b", trust_remote_code=True)
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config.mem_top_k = 6
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model = AutoModelForCausalLM.from_pretrained("emozilla/landmark-llama-7b", \
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torch_dtype=torch.bfloat16, trust_remote_code=True, device_map="auto", config=config)
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```
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