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from enum import Enum
import requests, traceback
import json
from jinja2 import Template, exceptions, Environment, meta
from typing import Optional
def default_pt(messages):
return " ".join(message["content"] for message in messages)
# alpaca prompt template - for models like mythomax, etc.
def alpaca_pt(messages):
prompt = custom_prompt(
role_dict={
"system": {
"pre_message": "### Instruction:\n",
"post_message": "\n\n",
},
"user": {
"pre_message": "### Instruction:\n",
"post_message": "\n\n",
},
"assistant": {
"pre_message": "### Response:\n",
"post_message": "\n\n"
}
},
bos_token="<s>",
eos_token="</s>",
messages=messages
)
return prompt
# Llama2 prompt template
def llama_2_chat_pt(messages):
prompt = custom_prompt(
role_dict={
"system": {
"pre_message": "[INST] <<SYS>>\n",
"post_message": "\n<</SYS>>\n [/INST]\n"
},
"user": { # follow this format https://github.com/facebookresearch/llama/blob/77062717054710e352a99add63d160274ce670c6/llama/generation.py#L348
"pre_message": "[INST] ",
"post_message": " [/INST]\n"
},
"assistant": {
"post_message": "\n" # follows this - https://replicate.com/blog/how-to-prompt-llama
}
},
messages=messages,
bos_token="<s>",
eos_token="</s>"
)
return prompt
def ollama_pt(model, messages): # https://github.com/jmorganca/ollama/blob/af4cf55884ac54b9e637cd71dadfe9b7a5685877/docs/modelfile.md#template
if "instruct" in model:
prompt = custom_prompt(
role_dict={
"system": {
"pre_message": "### System:\n",
"post_message": "\n"
},
"user": {
"pre_message": "### User:\n",
"post_message": "\n",
},
"assistant": {
"pre_message": "### Response:\n",
"post_message": "\n",
}
},
final_prompt_value="### Response:",
messages=messages
)
else:
prompt = "".join(m["content"] for m in messages)
return prompt
def mistral_instruct_pt(messages):
prompt = custom_prompt(
initial_prompt_value="<s>",
role_dict={
"system": {
"pre_message": "[INST]",
"post_message": "[/INST]"
},
"user": {
"pre_message": "[INST]",
"post_message": "[/INST]"
},
"assistant": {
"pre_message": "[INST]",
"post_message": "[/INST]"
}
},
final_prompt_value="</s>",
messages=messages
)
return prompt
# Falcon prompt template - from https://github.com/lm-sys/FastChat/blob/main/fastchat/conversation.py#L110
def falcon_instruct_pt(messages):
prompt = ""
for message in messages:
if message["role"] == "system":
prompt += message["content"]
else:
prompt += message['role']+":"+ message["content"].replace("\r\n", "\n").replace("\n\n", "\n")
prompt += "\n\n"
return prompt
def falcon_chat_pt(messages):
prompt = ""
for message in messages:
if message["role"] == "system":
prompt += "System: " + message["content"]
elif message["role"] == "assistant":
prompt += "Falcon: " + message["content"]
elif message["role"] == "user":
prompt += "User: " + message["content"]
return prompt
# MPT prompt template - from https://github.com/lm-sys/FastChat/blob/main/fastchat/conversation.py#L110
def mpt_chat_pt(messages):
prompt = ""
for message in messages:
if message["role"] == "system":
prompt += "<|im_start|>system" + message["content"] + "<|im_end|>" + "\n"
elif message["role"] == "assistant":
prompt += "<|im_start|>assistant" + message["content"] + "<|im_end|>" + "\n"
elif message["role"] == "user":
prompt += "<|im_start|>user" + message["content"] + "<|im_end|>" + "\n"
return prompt
# WizardCoder prompt template - https://huggingface.co/WizardLM/WizardCoder-Python-34B-V1.0#prompt-format
def wizardcoder_pt(messages):
prompt = ""
for message in messages:
if message["role"] == "system":
prompt += message["content"] + "\n\n"
elif message["role"] == "user": # map to 'Instruction'
prompt += "### Instruction:\n" + message["content"] + "\n\n"
elif message["role"] == "assistant": # map to 'Response'
prompt += "### Response:\n" + message["content"] + "\n\n"
return prompt
# Phind-CodeLlama prompt template - https://huggingface.co/Phind/Phind-CodeLlama-34B-v2#how-to-prompt-the-model
def phind_codellama_pt(messages):
prompt = ""
for message in messages:
if message["role"] == "system":
prompt += "### System Prompt\n" + message["content"] + "\n\n"
elif message["role"] == "user":
prompt += "### User Message\n" + message["content"] + "\n\n"
elif message["role"] == "assistant":
prompt += "### Assistant\n" + message["content"] + "\n\n"
return prompt
def hf_chat_template(model: str, messages: list):
## get the tokenizer config from huggingface
def _get_tokenizer_config(hf_model_name):
url = f"https://huggingface.co/{hf_model_name}/raw/main/tokenizer_config.json"
# Make a GET request to fetch the JSON data
response = requests.get(url)
if response.status_code == 200:
# Parse the JSON data
tokenizer_config = json.loads(response.content)
return {"status": "success", "tokenizer": tokenizer_config}
else:
return {"status": "failure"}
tokenizer_config = _get_tokenizer_config(model)
if tokenizer_config["status"] == "failure" or "chat_template" not in tokenizer_config["tokenizer"]:
raise Exception("No chat template found")
## read the bos token, eos token and chat template from the json
tokenizer_config = tokenizer_config["tokenizer"]
bos_token = tokenizer_config["bos_token"]
eos_token = tokenizer_config["eos_token"]
chat_template = tokenizer_config["chat_template"]
def raise_exception(message):
raise Exception(f"Error message - {message}")
# Create a template object from the template text
env = Environment()
env.globals['raise_exception'] = raise_exception
template = env.from_string(chat_template)
def _is_system_in_template():
try:
# Try rendering the template with a system message
response = template.render(messages=[{"role": "system", "content": "test"}], eos_token= "<eos>", bos_token= "<bos>")
return True
# This will be raised if Jinja attempts to render the system message and it can't
except:
return False
try:
# Render the template with the provided values
if _is_system_in_template():
rendered_text = template.render(bos_token=bos_token, eos_token=eos_token, messages=messages)
else:
# treat a system message as a user message, if system not in template
try:
reformatted_messages = []
for message in messages:
if message["role"] == "system":
reformatted_messages.append({"role": "user", "content": message["content"]})
else:
reformatted_messages.append(message)
rendered_text = template.render(bos_token=bos_token, eos_token=eos_token, messages=reformatted_messages)
except Exception as e:
if "Conversation roles must alternate user/assistant" in str(e):
# reformat messages to ensure user/assistant are alternating, if there's either 2 consecutive 'user' messages or 2 consecutive 'assistant' message, add a blank 'user' or 'assistant' message to ensure compatibility
new_messages = []
for i in range(len(reformatted_messages)-1):
new_messages.append(reformatted_messages[i])
if reformatted_messages[i]["role"] == reformatted_messages[i+1]["role"]:
if reformatted_messages[i]["role"] == "user":
new_messages.append({"role": "assistant", "content": ""})
else:
new_messages.append({"role": "user", "content": ""})
new_messages.append(reformatted_messages[-1])
rendered_text = template.render(bos_token=bos_token, eos_token=eos_token, messages=new_messages)
return rendered_text
except:
raise Exception("Error rendering template")
# Anthropic template
def claude_2_1_pt(messages: list): # format - https://docs.anthropic.com/claude/docs/how-to-use-system-prompts
class AnthropicConstants(Enum):
HUMAN_PROMPT = "\n\nHuman: "
AI_PROMPT = "\n\nAssistant: "
prompt = ""
for idx, message in enumerate(messages): # needs to start with `\n\nHuman: ` and end with `\n\nAssistant: `
if message["role"] == "user":
prompt += (
f"{AnthropicConstants.HUMAN_PROMPT.value}{message['content']}"
)
elif message["role"] == "system":
prompt += (
f"{message['content']}"
)
else:
prompt += (
f"{AnthropicConstants.AI_PROMPT.value}{message['content']}"
)
if idx == 0 and message["role"] == "assistant": # ensure the prompt always starts with `\n\nHuman: `
prompt = f"{AnthropicConstants.HUMAN_PROMPT.value}" + prompt
prompt += f"{AnthropicConstants.AI_PROMPT.value}"
return prompt
def anthropic_pt(messages: list): # format - https://docs.anthropic.com/claude/reference/complete_post
class AnthropicConstants(Enum):
HUMAN_PROMPT = "\n\nHuman: "
AI_PROMPT = "\n\nAssistant: "
prompt = ""
for idx, message in enumerate(messages): # needs to start with `\n\nHuman: ` and end with `\n\nAssistant: `
if message["role"] == "user":
prompt += (
f"{AnthropicConstants.HUMAN_PROMPT.value}{message['content']}"
)
elif message["role"] == "system":
prompt += (
f"{AnthropicConstants.HUMAN_PROMPT.value}<admin>{message['content']}</admin>"
)
else:
prompt += (
f"{AnthropicConstants.AI_PROMPT.value}{message['content']}"
)
if idx == 0 and message["role"] == "assistant": # ensure the prompt always starts with `\n\nHuman: `
prompt = f"{AnthropicConstants.HUMAN_PROMPT.value}" + prompt
prompt += f"{AnthropicConstants.AI_PROMPT.value}"
return prompt
# Function call template
def function_call_prompt(messages: list, functions: list):
function_prompt = "The following functions are available to you:"
for function in functions:
function_prompt += f"""\n{function}\n"""
function_added_to_prompt = False
for message in messages:
if "system" in message["role"]:
message['content'] += f"""{function_prompt}"""
function_added_to_prompt = True
if function_added_to_prompt == False:
messages.append({'role': 'system', 'content': f"""{function_prompt}"""})
return messages
# Custom prompt template
def custom_prompt(role_dict: dict, messages: list, initial_prompt_value: str="", final_prompt_value: str="", bos_token: str="", eos_token: str=""):
prompt = bos_token + initial_prompt_value
bos_open = True
## a bos token is at the start of a system / human message
## an eos token is at the end of the assistant response to the message
for message in messages:
role = message["role"]
if role in ["system", "human"] and not bos_open:
prompt += bos_token
bos_open = True
pre_message_str = role_dict[role]["pre_message"] if role in role_dict and "pre_message" in role_dict[role] else ""
post_message_str = role_dict[role]["post_message"] if role in role_dict and "post_message" in role_dict[role] else ""
prompt += pre_message_str + message["content"] + post_message_str
if role == "assistant":
prompt += eos_token
bos_open = False
prompt += final_prompt_value
return prompt
def prompt_factory(model: str, messages: list, custom_llm_provider: Optional[str]=None):
original_model_name = model
model = model.lower()
if custom_llm_provider == "ollama":
return ollama_pt(model=model, messages=messages)
elif custom_llm_provider == "anthropic":
if "claude-2.1" in model:
return claude_2_1_pt(messages=messages)
else:
return anthropic_pt(messages=messages)
try:
if "meta-llama/llama-2" in model and "chat" in model:
return llama_2_chat_pt(messages=messages)
elif "tiiuae/falcon" in model: # Note: for the instruct models, it's best to use a User: .., Assistant:.. approach in your prompt template.
if model == "tiiuae/falcon-180B-chat":
return falcon_chat_pt(messages=messages)
elif "instruct" in model:
return falcon_instruct_pt(messages=messages)
elif "mosaicml/mpt" in model:
if "chat" in model:
return mpt_chat_pt(messages=messages)
elif "codellama/codellama" in model:
if "instruct" in model:
return llama_2_chat_pt(messages=messages) # https://huggingface.co/blog/codellama#conversational-instructions
elif "wizardlm/wizardcoder" in model:
return wizardcoder_pt(messages=messages)
elif "phind/phind-codellama" in model:
return phind_codellama_pt(messages=messages)
elif "togethercomputer/llama-2" in model and ("instruct" in model or "chat" in model):
return llama_2_chat_pt(messages=messages)
elif model in ["gryphe/mythomax-l2-13b", "gryphe/mythomix-l2-13b", "gryphe/mythologic-l2-13b"]:
return alpaca_pt(messages=messages)
else:
return hf_chat_template(original_model_name, messages)
except:
return default_pt(messages=messages) # default that covers Bloom, T-5, any non-chat tuned model (e.g. base Llama2)
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