move back to root

lib/model_prompts.json

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+{
+    "alpaca": "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{prompt}\n\n### Response:\n\n",
+    "ggml": "A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions.\n\n### Human: Hello, Assistant.\n### Assistant: Hello. How may I help you today?\n### Human: ${prompt}",
+    "gpt4": "### Instruction:\n{prompt}\n\n### Response:\n",
+    "hermes": "### Instruction:\n{prompt}\n\n### Response:\n",
+    "oasst": "{prompt}",
+    "orca": "### System:\nYou are an AI assistant that follows instruction extremely well. Help as much as you can.\n\n### User:\n{prompt}\n\n### Response:",
+    "qlora": "### Human: {prompt}\n### Assistant:",
+    "tulu": "\n{prompt}\n\n(include newline)",
+    "vicuna": "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.\n\nUSER: {prompt}\nASSISTANT:",
+    "wizardlm": "{prompt}\n\n### Response:"
+}

lib/template.py

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+from difflib import SequenceMatcher
+import json
+
+with open("model_prompts.json", "r") as f:
+    model_prompts = json.load(f)
+
+
+def template(model, prompt):
+    max_ratio = 0
+    closest_key = ""
+    model_name = model.lower()
+    # Find the specialized prompt with the closest name match
+    for key in model_prompts.keys():
+        ratio = SequenceMatcher(None, model_name, key).ratio()
+        if ratio > max_ratio:
+            max_ratio = ratio
+            closest_key = key
+    # Return the value of the closest match
+    p = model_prompts.get(closest_key)  # TODO: provide a better default template
+    return p.format(prompt=prompt)