Create README.md

README.md

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+---
+license: apache-2.0
+datasets:
+- lodrick-the-lafted/Hermes-217K
+---
+
+<img src=https://huggingface.co/lodrick-the-lafted/Hermes-Instruct-217K/resolve/main/hermes-instruct.png>
+
+# Hermes-Instruct-7B-217K
+
+[Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) trained with 217K rows of [teknium/openhermes](https://huggingface.co/datasets/teknium/openhermes), in Alpaca format.
+Why? Mistral-7B-Instruct-v0.2 has native 32K context and rope theta of 1M. It's not a base model, so I've used the same recipe with different amounts of data to gauge the effects of further finetuning.
+
+<br />
+<br />
+
+# Prompt Format
+
+Both the default Mistral-Instruct tags and Alpaca are fine, so either:
+```
+<s>[INST] {sys_prompt} {instruction} [/INST] 
+```
+
+or
+
+
+```
+{sys_prompt}
+
+### Instruction:
+{instruction}
+
+### Response:
+
+```
+The tokenizer default is Alpaca this time around.
+
+<br />
+<br />
+
+# Usage
+
+```python
+from transformers import AutoTokenizer
+import transformers
+import torch
+
+model = "lodrick-the-lafted/Hermes-Instruct-7B-217K"
+
+tokenizer = AutoTokenizer.from_pretrained(model)
+pipeline = transformers.pipeline(
+    "text-generation",
+    model=model,
+    model_kwargs={"torch_dtype": torch.bfloat16},
+)
+
+messages = [{"role": "user", "content": "Give me a cooking recipe for an apple pie."}]
+prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_p=0.95)
+print(outputs[0]["generated_text"])
+```
+