Update README.md

README.md

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 license: mit
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+LoRA weights and for research only - nothing from the foundation model.
+Trained use Anthropics HH dataset which can be found here https://huggingface.co/datasets/Anthropic/hh-rlhf
+
+Sample usage
+```
+import torch
+import os
+import transformers
+from peft import PeftModel
+from transformers import LlamaTokenizer, LlamaForCausalLM
+
+model_path = "decapoda-research/llama-13b-hf"
+peft_path = 'serpdotai/llama-hh-lora-13B'
+tokenizer_path = 'decapoda-research/llama-13b-hf'
+
+model = LlamaForCausalLM.from_pretrained(model_path, load_in_8bit=True, device_map="auto") # or something like {"": 0}
+model = PeftModel.from_pretrained(model, peft_path, torch_dtype=torch.float16, device_map="auto") # or something like {"": 0}
+tokenizer = LlamaTokenizer.from_pretrained(tokenizer_path)
+
+batch = tokenizer("\n\nUser: Are you sentient?\n\nAssistant:", return_tensors="pt")
+
+with torch.no_grad():
+    out = model.generate(
+        input_ids=batch["input_ids"].cuda(),
+        attention_mask=batch["attention_mask"].cuda(),
+        max_length=100,
+        do_sample=True,
+        top_k=50,
+        top_p=1.0,
+        temperature=1.0,
+        use_cache=False
+    )
+print(tokenizer.decode(out[0]))
+```
+
+The model will continue the conversation between the user and itself. If you want to use as a chatbot you can alter the generate method to include stop sequences for 'User:' and 'Assistant:' or strip off anything past the assistant's original response before returning.
+
+
+Trained for 2 epochs with a sequence length of 640, mini-batch size of 3, gradient accumulation of 5, on 8 A6000s for an effective batch size of 120.
+
+Training settings:
+- lr: 2.0e-04
+- lr_scheduler_type: linear
+- warmup_ratio: 0.06
+- weight_decay: 0.1
+- optimizer: adamw_torch_fused