diff --git a/examples/low_level_api/common.py b/examples/low_level_api/common.py
new file mode 100644
index 000000000..1758a2d1d
--- /dev/null
+++ b/examples/low_level_api/common.py
@@ -0,0 +1,148 @@
+import os
+import argparse
+
+from dataclasses import dataclass, field
+from typing import List, Optional
+
+# Based on https://github.com/ggerganov/llama.cpp/blob/master/examples/common.cpp
+
+
+@dataclass
+class GptParams:
+    seed: int = -1
+    n_threads: int = min(4, os.cpu_count() or 1)
+    n_predict: int = 128
+    repeat_last_n: int = 64
+    n_parts: int = -1
+    n_ctx: int = 512
+    n_batch: int = 8
+    n_keep: int = 0
+
+    top_k: int = 40
+    top_p: float = 0.95
+    temp: float = 0.80
+    repeat_penalty: float = 1.10
+
+    model: str = "./models/llama-7B/ggml-model.bin"
+    prompt: str = ""
+    input_prefix: str = " "
+
+    antiprompt: List[str] = field(default_factory=list)
+
+    memory_f16: bool = True
+    random_prompt: bool = False
+    use_color: bool = False
+    interactive: bool = False
+
+    embedding: bool = False
+    interactive_start: bool = False
+
+    instruct: bool = False
+    ignore_eos: bool = False
+    perplexity: bool = False
+    use_mlock: bool = False
+    mem_test: bool = False
+    verbose_prompt: bool = False
+
+    file: str = None
+
+    # If chat ended prematurely, append this to the conversation to fix it.
+    # Set to "\nUser:" etc.
+    # This is an alternative to input_prefix which always adds it, so it potentially duplicates "User:""
+    fix_prefix: str = " "
+    output_postfix: str = ""
+    input_echo: bool = True,
+
+    # Default instructions for Alpaca
+    # switch to "Human" and "Assistant" for Vicuna.
+    # TODO: TBD how they are gonna handle this upstream
+    instruct_inp_prefix: str="\n\n### Instruction:\n\n"
+    instruct_inp_suffix: str="\n\n### Response:\n\n"
+
+
+def gpt_params_parse(argv = None, params: Optional[GptParams] = None):
+    if params is None:
+        params = GptParams()
+
+    parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+    parser.add_argument("-s", "--seed", type=int, default=-1, help="RNG seed (use random seed for <= 0)",dest="seed")
+    parser.add_argument("-t", "--threads", type=int, default=min(4, os.cpu_count() or 1), help="number of threads to use during computation",dest="n_threads")
+    parser.add_argument("-p", "--prompt", type=str, default="", help="initial prompt",dest="prompt")
+    parser.add_argument("-f", "--file", type=str, default=None, help="file containing initial prompt to load",dest="file")
+    parser.add_argument("-c", "--ctx_size", type=int, default=512, help="size of the prompt context",dest="n_ctx")
+    parser.add_argument("--memory_f32", action="store_false", help="use f32 instead of f16 for memory key+value",dest="memory_f16")
+    parser.add_argument("--top_p", type=float, default=0.95, help="top-p samplin",dest="top_p")
+    parser.add_argument("--top_k", type=int, default=40, help="top-k sampling",dest="top_k")
+    parser.add_argument("--temp", type=float, default=0.80, help="temperature",dest="temp")
+    parser.add_argument("--n_predict", type=int, default=128, help="number of model parts",dest="n_predict")
+    parser.add_argument("--repeat_last_n", type=int, default=64, help="last n tokens to consider for penalize ",dest="repeat_last_n")
+    parser.add_argument("--repeat_penalty", type=float, default=1.10, help="penalize repeat sequence of tokens",dest="repeat_penalty")
+    parser.add_argument("-b", "--batch_size", type=int, default=8, help="batch size for prompt processing",dest="n_batch")
+    parser.add_argument("--keep", type=int, default=0, help="number of tokens to keep from the initial prompt",dest="n_keep")
+    parser.add_argument("-m", "--model", type=str, default="./models/llama-7B/ggml-model.bin", help="model path",dest="model")
+    parser.add_argument(
+        "-i", "--interactive", action="store_true", help="run in interactive mode", dest="interactive"
+    )
+    parser.add_argument("--embedding", action="store_true", help="", dest="embedding")
+    parser.add_argument(
+        "--interactive-start",
+        action="store_true",
+        help="run in interactive mode",
+        dest="interactive"
+    )
+    parser.add_argument(
+        "--interactive-first",
+        action="store_true",
+        help="run in interactive mode and wait for input right away",
+        dest="interactive_start"
+    )
+    parser.add_argument(
+        "-ins",
+        "--instruct",
+        action="store_true",
+        help="run in instruction mode (use with Alpaca or Vicuna models)",
+        dest="instruct"
+    )
+    parser.add_argument(
+        "--color",
+        action="store_true",
+        help="colorise output to distinguish prompt and user input from generations",
+        dest="use_color"
+    )
+    parser.add_argument("--mlock", action="store_true",help="force system to keep model in RAM rather than swapping or compressing",dest="use_mlock")
+    parser.add_argument("--mtest", action="store_true",help="compute maximum memory usage",dest="mem_test")
+    parser.add_argument(
+        "-r",
+        "--reverse-prompt",
+        type=str,
+        action='append',
+        help="poll user input upon seeing PROMPT (can be\nspecified more than once for multiple prompts).",
+        dest="antiprompt"
+    )
+    parser.add_argument("--perplexity", action="store_true", help="compute perplexity over the prompt", dest="perplexity")
+    parser.add_argument("--ignore-eos", action="store_true", help="ignore end of stream token and continue generating", dest="ignore_eos")
+    parser.add_argument("--n_parts", type=int, default=-1, help="number of model parts", dest="n_parts")
+    parser.add_argument("--random-prompt", action="store_true", help="start with a randomized prompt.", dest="random_prompt")
+    parser.add_argument("--in-prefix", type=str, default="", help="string to prefix user inputs with", dest="input_prefix")
+    parser.add_argument("--fix-prefix", type=str, default="", help="append to input when generated n_predict tokens", dest="fix_prefix")
+    parser.add_argument("--out-postfix", type=str, default="", help="append to input", dest="output_postfix")
+    parser.add_argument("--input-noecho", action="store_false", help="dont output the input", dest="input_echo")
+    args = parser.parse_args(argv)
+    return args
+
+def gpt_random_prompt(rng):
+    return [
+        "So",
+        "Once upon a time",
+        "When",
+        "The",
+        "After",
+        "If",
+        "import",
+        "He",
+        "She",
+        "They",
+    ][rng % 10]
+
+if __name__ == "__main__":
+    print(GptParams(gpt_params_parse()))
diff --git a/examples/low_level_api/low_level_api_chat_cpp.py b/examples/low_level_api/low_level_api_chat_cpp.py
new file mode 100644
index 000000000..f4d77d5c6
--- /dev/null
+++ b/examples/low_level_api/low_level_api_chat_cpp.py
@@ -0,0 +1,393 @@
+"""
+This is an example implementation of main.cpp from llama.cpp
+Quirks:
+ * Its not exactly alike since this port is designed around programmatic I/O
+ * Input is always echoed if on, so it should be turned off when using "input()"
+ * The first antiprompt should be the userprompt like "\nUser:", 
+   because its added when n_predict is reached (aka generation ended prematurely)
+ * n_predict can be set to -1 for unlimited length responses (or just a really high value)
+ * 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 sys
+from time import time
+from os import cpu_count
+
+import llama_cpp
+from common import GptParams, gpt_params_parse, gpt_random_prompt
+
+ANSI_COLOR_RESET = "\x1b[0m"
+ANSI_COLOR_YELLOW = "\x1b[33m"
+ANSI_BOLD = "\x1b[1m"
+ANSI_COLOR_GREEN = "\x1b[32m"
+
+CONSOLE_COLOR_DEFAULT = ANSI_COLOR_RESET
+CONSOLE_COLOR_PROMPT = ANSI_COLOR_YELLOW
+CONSOLE_COLOR_USER_INPUT = ANSI_BOLD + ANSI_COLOR_GREEN
+
+# A LLaMA interactive session
+class LLaMAInteract:
+	def __init__(self, params: GptParams) -> None:
+		# input args
+		self.params = params
+
+		if (self.params.perplexity):
+			raise NotImplementedError("""************
+please use the 'perplexity' tool for perplexity calculations
+************""")
+
+		if (self.params.embedding):
+			raise NotImplementedError("""************
+please use the 'embedding' tool for embedding calculations
+************""")
+
+		if (self.params.n_ctx > 2048):
+			print(f"""warning: model does not support \
+context sizes greater than 2048 tokens ({self.params.n_ctx} \
+specified) expect poor results""", file=sys.stderr)
+
+		if (self.params.seed <= 0):
+			self.params.seed = int(time())
+
+		print(f"seed = {self.params.seed}", file=sys.stderr)
+
+		if (self.params.random_prompt):
+			self.params.prompt = gpt_random_prompt(self.params.seed)
+
+		# runtime args
+		self.input_consumed = 0
+		self.n_past = 0
+		self.first_antiprompt = []
+		self.remaining_tokens = self.params.n_predict
+		self.output_echo = self.params.input_echo
+
+		# model load
+		self.lparams = llama_cpp.llama_context_default_params()
+		self.lparams.n_ctx = self.params.n_ctx
+		self.lparams.n_parts = self.params.n_parts
+		self.lparams.seed = self.params.seed
+		self.lparams.memory_f16 = self.params.memory_f16
+		self.lparams.use_mlock = self.params.use_mlock
+
+		self.ctx = llama_cpp.llama_init_from_file(self.params.model.encode("utf8"), self.lparams)
+		if (not self.ctx):
+			raise RuntimeError(f"error: failed to load model '{self.params.model}'")
+
+		print(file=sys.stderr)
+		print(f"system_info: n_threads = {self.params.n_threads} / {cpu_count()} \
+| {llama_cpp.llama_print_system_info().decode('utf8')}", file=sys.stderr)
+
+		# determine the required inference memory per token:
+		if (self.params.mem_test):
+			tmp = [0, 1, 2, 3]
+			llama_cpp.llama_eval(self.ctx, (llama_cpp.c_int * len(tmp))(*tmp), len(tmp), 0, self.n_threads)
+			llama_cpp.llama_print_timings(self.ctx)
+			self.exit()
+			return
+
+		# create internal context
+		self.n_ctx = llama_cpp.llama_n_ctx(self.ctx)
+
+		# Add a space in front of the first character to match OG llama tokenizer behavior
+		self.params.prompt = " " + self.params.prompt
+
+		# Load prompt file
+		if (self.params.file):
+			with open(self.params.file) as f:
+				self.params.prompt = f.read()
+
+		# tokenize the prompt
+		self.embd = []
+		self.embd_inp = self._tokenize(self.params.prompt)
+
+		if (len(self.embd_inp) > self.params.n_ctx - 4):
+			raise RuntimeError(f"error: prompt is too long ({len(self.embd_inp)} tokens, max {self.params.n_ctx - 4})")
+
+		# number of tokens to keep when resetting context
+		if (self.params.n_keep < 0 or self.params.n_keep > len(self.embd_inp) or self.params.instruct):
+			self.params.n_keep = len(self.embd_inp)
+
+		self.inp_prefix = self._tokenize(self.params.instruct_inp_prefix)
+		self.inp_suffix = self._tokenize(self.params.instruct_inp_suffix, False)
+
+		# in instruct mode, we inject a prefix and a suffix to each input by the user
+		if (self.params.instruct):
+			self.params.interactive_start = True
+			self.first_antiprompt.append(self._tokenize(self.params.instruct_inp_prefix.strip(), False))
+
+		# enable interactive mode if reverse prompt or interactive start is specified
+		if (len(self.params.antiprompt) != 0 or self.params.interactive_start):
+			self.params.interactive = True
+
+		# determine newline token
+		self.llama_token_newline = self._tokenize("\n", False)
+
+		if (self.params.verbose_prompt):
+			print(f"""
+prompt: '{self.params.prompt}'
+number of tokens in prompt = {len(self.embd_inp)}""", file=sys.stderr)
+
+			for i in range(len(self.embd_inp)):
+				print(f"{self.embd_inp[i]} -> '{llama_cpp.llama_token_to_str(self.ctx, self.embd_inp[i])}'", file=sys.stderr)
+
+			if (self.params.n_keep > 0):
+				print("static prompt based on n_keep: '")
+				for i in range(self.params.n_keep):
+					print(llama_cpp.llama_token_to_str(self.ctx, self.embd_inp[i]), file=sys.stderr)
+				print("'", file=sys.stderr)
+			print(file=sys.stderr)
+
+		if (self.params.interactive):
+			print("interactive mode on.", file=sys.stderr)
+
+			if (len(self.params.antiprompt) > 0):
+				for antiprompt in self.params.antiprompt:
+					print(f"Reverse prompt: '{antiprompt}'", file=sys.stderr)
+
+			if len(self.params.input_prefix) > 0:
+				print(f"Input prefix: '{self.params.input_prefix}'", file=sys.stderr)
+
+		print(f"""sampling: temp = {self.params.temp},\
+top_k = {self.params.top_k},\
+top_p = {self.params.top_p},\
+repeat_last_n = {self.params.repeat_last_n},\
+repeat_penalty = {self.params.repeat_penalty}
+
+generate: n_ctx = {self.n_ctx}, \
+n_batch = {self.params.n_batch}, \
+n_predict = {self.params.n_predict}, \
+n_keep = {self.params.n_keep}
+""", file=sys.stderr)
+
+		# determine antiprompt tokens
+		for i in self.params.antiprompt:
+			self.first_antiprompt.append(self._tokenize(i, False))
+
+		self.last_n_tokens = [0]*self.n_ctx #TODO: deque doesnt support slices
+
+		if (params.interactive):
+			print("""== Running in interactive mode. ==
+ - Press Ctrl+C to interject at any time.
+ - Press Return to return control to LLaMa.
+ - If you want to submit another line, end your input in '\\'.
+
+""", file=sys.stderr)
+		self.set_color(CONSOLE_COLOR_PROMPT)
+
+	# tokenize a prompt
+	def _tokenize(self, prompt, bos=True):
+		_arr = (llama_cpp.llama_token * (len(prompt) + 1))()
+		_n = llama_cpp.llama_tokenize(self.ctx, prompt.encode("utf8"), _arr, len(_arr), bos)
+		return _arr[:_n]
+
+	def use_antiprompt(self):
+		return len(self.first_antiprompt) > 0
+
+	def set_color(self, c):
+		if (self.params.use_color):
+			print(c, end="")
+
+	# generate tokens
+	def generate(self):
+		while self.remaining_tokens > 0 or self.params.interactive:
+			# predict
+			if len(self.embd) > 0:
+				# infinite text generation via context swapping
+				# if we run out of context:
+				# - take the n_keep first tokens from the original prompt (via n_past)
+				# - take half of the last (n_ctx - n_keep) tokens and recompute the logits in a batch
+				if (self.n_past + len(self.embd) > self.n_ctx):
+					n_left = self.n_past - self.params.n_keep
+					self.n_past = self.params.n_keep
+
+					# insert n_left/2 tokens at the start of embd from last_n_tokens
+					_insert = self.last_n_tokens[
+						self.n_ctx - int(n_left/2) - len(self.embd):-len(self.embd)
+					]
+					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.params.n_threads
+				) != 0):
+					raise Exception("Failed to llama_eval!")
+
+			self.n_past += len(self.embd)
+			self.embd = []
+			if len(self.embd_inp) <= self.input_consumed:
+				# out of user input, sample next token
+
+				#TODO: self.params.ignore_eos
+
+				_arr = self.last_n_tokens[-min(self.params.repeat_last_n, self.n_past):]
+				id = llama_cpp.llama_sample_top_p_top_k(
+					self.ctx,
+					(llama_cpp.llama_token * len(_arr))(*_arr),
+					len(_arr),
+					self.params.top_k,
+					self.params.top_p,
+					self.params.temp,
+					self.params.repeat_penalty,
+				)
+				self.last_n_tokens.pop(0)
+				self.last_n_tokens.append(id)
+
+				# replace end of text token with newline token when in interactive mode
+				if (id == llama_cpp.llama_token_eos() and self.params.interactive and not self.params.instruct):
+					id = self.llama_token_newline[0]
+					if (self.use_antiprompt()):
+						# tokenize and inject first reverse prompt
+						self.embd_inp += self.first_antiprompt[0]
+
+				# add it to the context
+				self.embd.append(id)
+
+				# echo this to console
+				self.output_echo = True
+
+				# decrement remaining sampling budget
+				self.remaining_tokens -= 1
+			else:
+				# output to console if input echo is on
+				self.output_echo = self.params.input_echo
+
+				# some user input remains from prompt or interaction, forward it to processing
+				while len(self.embd_inp) > self.input_consumed:
+					self.embd.append(self.embd_inp[self.input_consumed])
+					self.last_n_tokens.pop(0)
+					self.last_n_tokens.append(self.embd_inp[self.input_consumed])
+					self.input_consumed += 1
+					if len(self.embd) >= self.params.n_batch:
+						break
+
+			# display tokens
+			if self.output_echo:
+				for id in self.embd:
+					yield id
+
+			# reset color to default if we there is no pending user input
+			if (self.params.input_echo and len(self.embd_inp) == self.input_consumed):
+				self.set_color(CONSOLE_COLOR_DEFAULT)
+
+			if (self.params.interactive and len(self.embd_inp) <= self.input_consumed):
+				# if antiprompt is present, stop
+				if (self.use_antiprompt()):
+					if True in [
+						i == self.last_n_tokens[-len(i):] 
+						for i in self.first_antiprompt
+					]:
+						break
+
+				# if we are using instruction mode, and we have processed the initial prompt
+				if (self.n_past > 0 and self.params.interactive_start):
+					break
+
+			# end of text token
+			if len(self.embd) > 0 and self.embd[-1] == llama_cpp.llama_token_eos():
+				if (not self.params.instruct):
+					for i in " [end of text]\n":
+						yield i
+				break
+
+			# respect n_predict even if antiprompt is present
+			if (self.params.interactive and self.remaining_tokens <= 0 and self.params.n_predict != -1):
+				# If we arent in instruction mode, fix the current generation by appending the antiprompt.
+				# Makes it so if chat ends prematurely you dont append the AI's text etc.
+				if not self.params.instruct:
+					self.embd_inp += self.first_antiprompt[0]
+				self.n_remain = self.params.n_predict
+				break
+
+		self.params.interactive_start = False
+
+	def __enter__(self):
+		return self
+
+	def __exit__(self, type, value, tb):
+		self.exit()
+
+	def exit(self):
+		llama_cpp.llama_free(self.ctx)
+		self.set_color(CONSOLE_COLOR_DEFAULT)
+
+	# return past text
+	def past(self):
+		for id in self.last_n_tokens[-self.n_past:]:
+			yield llama_cpp.llama_token_to_str(self.ctx, id).decode("utf-8")
+
+	# write input
+	def input(self, prompt: str):
+		if (self.params.instruct and self.last_n_tokens[-len(self.inp_prefix):] != self.inp_prefix):
+			self.embd_inp += self.inp_prefix
+		self.embd_inp += self._tokenize(prompt)
+		if (self.params.instruct):
+			self.embd_inp += self.inp_suffix
+
+	# write output
+	def output(self):
+		self.remaining_tokens = self.params.n_predict
+		for id in self.generate():
+			yield llama_cpp.llama_token_to_str(self.ctx, id).decode("utf-8")
+
+	# read user input
+	def read_input(self):
+		out = ""
+		while (t := input()).endswith("\\"):
+			out += t[:-1] + "\n"
+		return out + t + "\n"
+
+	# interactive mode
+	def interact(self):
+		for i in self.output():
+			print(i,end="",flush=True)
+		self.params.input_echo = False
+
+		while self.params.interactive:
+			self.set_color(CONSOLE_COLOR_USER_INPUT)
+			if (self.params.instruct):
+				print('\n> ', end="")
+				self.input(self.read_input())
+			else:
+				print(self.params.input_prefix, end="")
+				self.input(f"{self.params.input_prefix}{self.read_input()}{self.params.output_postfix}")
+				print(self.params.output_postfix,end="")
+			self.set_color(CONSOLE_COLOR_DEFAULT)
+
+			try:
+				for i in self.output():
+					print(i,end="",flush=True)
+			except KeyboardInterrupt:
+				self.set_color(CONSOLE_COLOR_DEFAULT)
+				if not self.params.instruct:
+					print(self.params.fix_prefix,end="")
+					self.input(self.params.fix_prefix)
+
+if __name__ == "__main__":
+	from datetime import datetime
+
+	USER_NAME="User"
+	AI_NAME="ChatLLaMa"
+
+	time_now = datetime.now()
+	prompt = f"""Text transcript of a never ending dialog, where {USER_NAME} interacts with an AI assistant named {AI_NAME}.
+{AI_NAME} is helpful, kind, honest, friendly, good at writing and never fails to answer {USER_NAME}’s requests immediately and with details and precision.
+There are no annotations like (30 seconds passed...) or (to himself), just what {USER_NAME} and {AI_NAME} say aloud to each other.
+The dialog lasts for years, the entirety of it is shared below. It's 10000 pages long.
+The transcript only includes text, it does not include markup like HTML and Markdown.
+
+{USER_NAME}: Hello, {AI_NAME}!
+{AI_NAME}: Hello {USER_NAME}! How may I help you today?
+{USER_NAME}: What time is it?
+{AI_NAME}: It is {time_now.strftime("%H:%M")}.
+{USER_NAME}: What year is it?
+{AI_NAME}: We are in {time_now.strftime("%Y")}.
+{USER_NAME}: What is a cat?
+{AI_NAME}: A cat is a domestic species of small carnivorous mammal. It is the only domesticated species in the family Felidae.
+{USER_NAME}: Name a color.
+{AI_NAME}: Blue
+{USER_NAME}:"""
+	args = gpt_params_parse()
+	params = GptParams(**vars(args))
+
+	with LLaMAInteract(params) as m:
+		m.interact()