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Model: emozilla/landmark-llama-7b
Source: Original Platform

README.md

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