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273 lines
10 KiB
273 lines
10 KiB
import time
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import random
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from tool_helper import tool_list, parse_and_execute_tool_call
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from tool_functions import register_dummy
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from inference import Inference, torch_reseed
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import datetime
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from modelconfig import Modelconfig
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messages = []
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inference = None
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# systemmessage at the very begin of the chat. Will be concatenated with the automatic tool usage descriptions
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systemmessage = "Hold a casual conversation with the user. Keep responses short at max 3 sentences. Answer using markdown to the user."
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systemmessage = "Hold a casual conversation with the user. Answer using markdown to the user."
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# system message for role flip so the model automatically answers for the user
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roleflip = {"role": "system", "content": "Keep the conversation going, ask for more information on the subject. Keep messages short at max 1-2 sentences. Do not thank and say goodbye."}
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# system messages and user message to bring the model to summarize the entire conversation
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summarize = {"role": "system", "content": "Summarize the conversation as a single, cohesive paragraph. Avoid using any bullet points, numbers, or list formatting. Write in plain text with natural sentences that flow together seamlessly."}
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summarize_user = {"role": "system", "content": "Can you summarize the conversation?"}
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# system message to create a conversation title
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title_prompt = {"role": "system", "content": "Please create a very short and descriptive title or label for this conversation. Maximum 2-5 words. Use only plain text, avoid numbering, special characters, or unnecessary formatting-focus on clarity and brevity."}
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append_toolcalls = False
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register_dummy()
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def append_generate_chat(input_text: str, role="user"):
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t_start = time.time()
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# generate AI response
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if input_text != None:
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messages.append({"role": role, "content": input_text})
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inputs = inference.tokenize(messages, tokenize=True)
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number_of_input_tokens = inputs.shape[1]
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outputs, out_text = inference.generate(inputs)
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# append result to message history
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messages.append({"role": "assistant", "content": out_text})
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print("")
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time_taken = time.time() - t_start
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number_of_tokens = len(outputs[0])
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tokens_per_second = (number_of_tokens - number_of_input_tokens) / time_taken
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print("generation took %.3fs (%d tokens, %.3f t/s)" % (time_taken, number_of_tokens, tokens_per_second))
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# handle tool call and check if a tool call has happened.
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tool_result = parse_and_execute_tool_call(out_text, tool_list)
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if tool_result != None:
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# tool call happened
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tool_result = "<tool_response>%s</tool_response>" % tool_result
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# depending on the chat template the tool response tags must or must not be passed. :(
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append_generate_chat(tool_result, role="tool")
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def terminal_generation_loop():
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global messages
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global inference
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while True:
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# print an input prompt to receive text or commands
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input_text = input(">>> ")
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print("")
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if input_text.startswith("!"):
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append_generate_chat("<tool_response>%s</tool_response>" % input_text[1:], role="tool")
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# append_generate_chat("%s" % input_text[1:], role="tool") # depending on the chat template the tool response tags must or must not be passed. :(
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elif input_text.startswith("/clear"):
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print("clearing chat history")
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start_msg = messages[0]
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messages = [start_msg]
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print("")
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elif input_text.startswith("/history"):
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history = inference.tokenize(messages, tokenize=False)
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# history = tokenizer.apply_chat_template(messages, return_tensors="pt", tokenize=False, add_generation_prompt=False)
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print(history)
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elif input_text.startswith("/undo"):
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if len(messages) > 2:
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print("undo latest prompt")
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messages = messages[:-2]
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else:
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print("cannot undo because there are not enough messages on history.")
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print("")
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elif input_text.startswith("/regen"):
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if len(messages) >= 2:
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print("regenerating message (not working)")
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messages = messages[:-1]
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seed = random.randint(0, 2**32 - 1) # Generate a random seed
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torch_reseed(seed)
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append_generate_chat(None)
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else:
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print("cannot regenerate because there are not enough messages on history.")
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print("")
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elif input_text.startswith("/more"):
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append_generate_chat(None)
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elif input_text.startswith("/file"):
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filename = input_text[len("/file "):]
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print("read '%s' for prompt:" % filename)
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with open(filename, "r") as f:
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content = f.read()
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print(content)
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append_generate_chat(content)
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elif input_text.startswith("/auto"):
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messages_backup = messages
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messages = [roleflip]
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for m in messages_backup:
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role = m["role"]
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content = m["content"]
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if role == "user":
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role = "assistant"
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elif role == "assistant":
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role = "user"
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if role != "system":
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messages.append({"role": role, "content": content})
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append_generate_chat(None) # will automatically advance the conversation as 'user'
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last_message = messages[-1]
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last_message["role"] = "user"
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messages = messages_backup + [last_message]
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append_generate_chat(None) # 'regular' chatbot answer
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elif input_text.startswith("/summarize"):
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messages_temp = list(filter(lambda x: x["role"] != "system", messages))
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messages_temp = [summarize] + messages_temp + [summarize_user] # copy dict in last instance
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# messages_temp[-1]["role"] = "user"
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input_ids = inference.tokenize(messages_temp, tokenize=True, assistant_prefix="The conversation was about ")
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generated_tokens, full_output = inference.generate(input_ids)
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elif input_text.startswith("/title"):
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messages_temp = list(filter(lambda x: x["role"] != "system", messages))
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messages_temp = [title_prompt] + messages_temp #+ [dict(title)] # copy dict in last instance
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messages_temp[-1]["role"] = "user"
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input_ids = inference.tokenize(messages_temp, tokenize=True, assistant_prefix="Title: ")
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generated_tokens, full_output = inference.generate(input_ids)
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elif input_text.startswith("/help"):
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print("!<prompt> answer as 'tool' in <tool_response> tags")
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print("/clear clear chat history")
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print("/undo undo latest prompt")
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print("/regen regenerate the last message")
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print("/more generate more additional information")
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print("/file read prompt input from file")
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print("/auto automatically advance conversation")
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print("/summarize generate a summary of the chat")
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print("/title generate a title of the chat")
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print("/help print this message")
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print("")
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elif input_text.startswith("/"):
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print("unknown command.")
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else:
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append_generate_chat(input_text)
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def main():
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global messages
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global inference
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# model: NousResearch/Hermes-3-Llama-3.2-3B
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# tokens: 315 tk
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# time: 94.360 s
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# speed: 3.338 tk/s
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# vram_bulk: 3622 MB
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# vram_top: 80 MB
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# context: 131072 tk
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# model = Modelconfig("NousResearch/Hermes-3-Llama-3.2-3B", load_in_8bit=True)
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# model: unsloth/Llama-3.2-1B
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# tokens: 589 tk
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# time: 39.348 s
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# speed: 14.969 tk/s
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# vram_bulk: 4708 MB
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# vram_top: 102 MB
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# context: 131072 tk
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# model = Modelconfig("unsloth/Llama-3.2-1B") # note, fast, but talks to itself. basically does not work.
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# model: unsloth/Llama-3.2-3B-Instruct
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# tokens: 285 tk
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# time: 75.363 s
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# speed: 3.782 tk/s
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# vram_bulk: 3512 MB
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# vram_top: 48 MB
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# context: 131072 tk
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# model = Modelconfig("unsloth/Llama-3.2-3B-Instruct", load_in_8bit=True)
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# model: unsloth/llama-3-8b-bnb-4bit
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# tokens: 435 tk
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# time: 84.314 s
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# speed: 5.159 tk/s
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# vram_bulk: 5440 MB
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# vram_top: 216 MB
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# context: 8192 tk
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# model = Modelconfig("unsloth/llama-3-8b-bnb-4bit")
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# Model size: 3.21B params
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# vram used: xxxxx MB
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# speed xxxxx t/s
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# working: DOES NOT LOAD
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# model = Modelconfig("unsloth/Llama-3.2-3B-Instruct-GGUF", load_in_8bit=True)
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# model: unsloth/gemma-2-9b-it-bnb-4bit
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# tokens: 154 tk
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# time: 32.727 s
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# speed: 4.706 tk/s
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# vram_bulk: 6156 MB
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# vram_top: 232 MB
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# context: 8192 tk
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# model = Modelconfig("unsloth/gemma-2-9b-it-bnb-4bit")
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# model: unsloth/Qwen2.5-7B-Instruct-bnb-4bit
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# tokens: 120 tk
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# time: 12.248 s
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# speed: 9.798 tk/s
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# vram_bulk: 5382 MB
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# vram_top: 170 MB
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# context: 32768 tk
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model = Modelconfig("unsloth/Qwen2.5-7B-Instruct-bnb-4bit") # note, this works really good
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# model: unsloth/Qwen2.5-3B-Instruct
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# tokens: 112 tk
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# time: 12.703 s
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# speed: 8.816 tk/s
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# vram_bulk: 2108 MB
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# vram_top: 98 MB
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# context: 32768 tk
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# model = Modelconfig("unsloth/Qwen2.5-3B-Instruct", load_in_4bit=True)
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# model: unsloth/Qwen2.5-3B-Instruct
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# tokens: 118 tk
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# time: 33.748 s
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# speed: 3.497 tk/s
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# vram_bulk: 3310 MB
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# vram_top: 60 MB
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# context: 32768 tk
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# model = Modelconfig("unsloth/Qwen2.5-3B-Instruct", load_in_8bit=True)
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# Model size: 3.87B params
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# vram used: xxxxx MB
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# speed xxxxx t/s
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# error: requires the protobuf library but it was not found in your environment
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# model = Modelconfig("unsloth/mistral-7b-instruct-v0.3-bnb-4bit")
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inference = Inference(model)
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current_date_and_time = datetime.datetime.now().strftime("Current date is %Y-%m-%d and its %H:%M %p right now.")
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if append_toolcalls:
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messages = [{"role": "system", "content": systemmessage + "\n" + current_date_and_time + "\n" + inference.generate_tool_use_header(tool_list)}]
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else:
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messages = [{"role": "system", "content": systemmessage + "\n" + current_date_and_time}]
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terminal_generation_loop()
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if __name__ == "__main__":
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main()
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