Added instruction mode, fixed infinite generation, and various other fixes

examples/low_level_api_chatllama_cpp.py

@@ -8,7 +8,9 @@ Quirks:
  * n_predict can be set to -1 for unlimited length responses (or just a really high value)
  * It's always in interactive mode, generation ends either by reaching an antiprompt 
    or running out of n_predict.
- * Instruction mode adds its own antiprompt
+ * Instruction mode adds its own antiprompt.
+   You should also still be feeding the model with a "primer" prompt that 
+   shows it the expected format.
 """
 import llama_cpp
 
@@ -31,6 +33,8 @@ class LLaMAInteract:
 		top_p: float=1.,
 		temp: float=1.0,
 		repeat_penalty: float=1,
+		instruct_inp_prefix: str="\n\n### Instruction:\n\n",
+		instruct_inp_suffix: str="\n\n### Response:\n\n",
 	) -> None:
 		# input args
 		self.instruct = instruct
@@ -66,12 +70,12 @@ class LLaMAInteract:
 
 		# determine newline token
 		self.llama_token_newline = self._tokenize("\n", False)
-		self.inp_prefix = self._tokenize("\n\n### Instruction:\n\n")
-		self.inp_suffix = self._tokenize("\n\n### Response:\n\n", False)
+		self.inp_prefix = self._tokenize(instruct_inp_prefix)
+		self.inp_suffix = self._tokenize(instruct_inp_suffix, False)
 
 		# add instruction as antiprompt
 		if (self.instruct):
-			self.first_antiprompt.append(self.inp_prefix)
+			self.first_antiprompt.append(self.inp_prefix.strip())
 
 		# primer feed
 		if (len(primer) > 0):
@@ -117,10 +121,9 @@ class LLaMAInteract:
 
 					# insert n_left/2 tokens at the start of embd from last_n_tokens
 					_insert = self.last_n_tokens[
-						-(int(n_left/2) - len(self.embd)):-len(self.embd)
+						self.n_ctx - int(n_left/2) - len(self.embd):-len(self.embd)
 					]
-					self.embd[:len(_insert)] = _insert
-					#TODO: Still untested
+					self.embd = _insert + self.embd
 
 				if (llama_cpp.llama_eval(
 					self.ctx, (llama_cpp.llama_token * len(self.embd))(*self.embd), len(self.embd), self.n_past, self.n_threads
@@ -197,6 +200,12 @@ class LLaMAInteract:
 				self.embd_inp += self.first_antiprompt[0]
 				break
 
+	def __enter__(self):
+		return self
+
+	def __exit__(self, type, value, tb):
+		llama_cpp.llama_free(self.ctx)
+
 	# return past text
 	def past(self):
 		for id in self.last_n_tokens[-self.n_past:]:
@@ -206,7 +215,7 @@ class LLaMAInteract:
 	def input(self, prompt: str):
 		if (self.instruct):
 			self.embd_inp += self.inp_prefix
-		self.embd_inp += self._tokenize(prompt + "\n")
+		self.embd_inp += self._tokenize(prompt)
 		if (self.instruct):
 			self.embd_inp += self.inp_suffix
 
@@ -242,21 +251,38 @@ The transcript only includes text, it does not include markup like HTML and Mark
 {USER_NAME}:"""
 
 	print("Loading model...")
-	m = LLaMAInteract(prompt, 
+	with LLaMAInteract(prompt, 
 		model="./models/30B/ggml-model-q4_0.bin",
 		n_ctx=2048, 
 		antiprompt=[f"\n{USER_NAME}:"], 
 		repeat_last_n=256, 
 		n_predict=2048,
 		temp=0.7, top_p=0.5, top_k=40, repeat_penalty=1.17647
-	)
-	print("Loaded model!")
+	) as m:
+		print("Loaded model!")
 
-	for i in m.output():
-		print(i,end="",flush=True)
-	m.input_echo = False
-
-	while True:
-		m.input(" " + input('\n> ' if m.instruct else " "))
 		for i in m.output():
-			print(i,end="",flush=True)
+			print(i,end="",flush=True)
+		m.input_echo = False
+
+		def inp():
+			out = ""
+			while (t := input()).endswith("\\"):
+				out += t[:-1] + "\n"
+			return out + t + "\n"
+
+		while True:
+			if (m.instruct):
+				print('\n> ', end="")
+				m.input(inp())
+			else:
+				print(f" ", end="")
+				m.input(f" {inp()}{AI_NAME}:")
+				print(f"{AI_NAME}: ",end="")
+
+			try:
+				for i in m.output():
+					print(i,end="",flush=True)
+			except KeyboardInterrupt:
+				print(f"\n{USER_NAME}:",end="")
+				m.input(f"\n{USER_NAME}:")