Create README.md

README.md

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+---
+datasets:
+- EleutherAI/pile
+language:
+- en
+---
+
+# DenseRetNet-350M
+
+a third party pretraining checkpoints for paper DenseMamba: https://arxiv.org/abs/2403.00818, the trainig data is 15B tokens randomly samples from The Pile dataset.
+
+
+
+- recurrent generation examples:
+
+```python
+import torch
+import transformers
+model_name_or_path = '/path to model'
+MAX_NEW_TOKENS = 256
+inference_dtype = torch.float16
+
+generation_config = transformers.GenerationConfig(
+    do_sample=False,
+    max_new_tokens=MAX_NEW_TOKENS,
+)
+
+tokenizer = transformers.AutoTokenizer.from_pretrained(model_name_or_path, use_fast=False, trust_remote_code=True)
+config = transformers.AutoConfig.from_pretrained(model_name_or_path, trust_remote_code=True)
+model = transformers.AutoModelForCausalLM.from_pretrained(
+    model_name_or_path, torch_dtype=torch.float16, trust_remote_code=True)  # .cuda()
+model.cuda()
+model = model.half()
+model.eval()
+input_sents = 'I have a dream'
+inputs = tokenizer(input_sents, return_tensors="pt", truncation=True, max_length=2048)
+output = model.generate(input_ids=inputs["input_ids"].cuda(),
+                   generation_config=generation_config,
+                   return_dict_in_generate=True,
+                   output_scores=True
+                   )
+output = tokenizer.decode(output[0].tolist(), skip_special_tokens=True)
+print(output)
+```