From b422f68162a7c4e5d4b5db5d586976b35b588df1 Mon Sep 17 00:00:00 2001 From: rahulgs12 Date: Sat, 29 Apr 2023 19:26:03 -0400 Subject: [PATCH] . --- .gitignore | 1 + jsonllm/logits_processors.py | 67 ++ jsonllm/main.py | 173 ++++ jsonllm/test.py | 27 + play.ipynb | 291 ++++++ poetry.lock | 1869 ++++++++++++++++++++++++++++++++++ pyproject.toml | 22 + readme.md | 1 + t.ipynb | 258 +++++ 9 files changed, 2709 insertions(+) create mode 100644 .gitignore create mode 100644 jsonllm/logits_processors.py create mode 100644 jsonllm/main.py create mode 100644 jsonllm/test.py create mode 100644 play.ipynb create mode 100644 poetry.lock create mode 100644 pyproject.toml create mode 100644 readme.md create mode 100644 t.ipynb diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..3104065 --- /dev/null +++ b/.gitignore @@ -0,0 +1 @@ +jsonllm/__pycache__ \ No newline at end of file diff --git a/jsonllm/logits_processors.py b/jsonllm/logits_processors.py new file mode 100644 index 0000000..53bb8da --- /dev/null +++ b/jsonllm/logits_processors.py @@ -0,0 +1,67 @@ +from transformers import PreTrainedTokenizer, LogitsWarper, StoppingCriteria +import torch + + +class NumberStoppingCriteria(StoppingCriteria): + """ + This class can be used to stop generation when there is a repeated decimal point in the generated text. + """ + + def __init__(self, tokenizer: PreTrainedTokenizer, precision: int = 2): + self.tokenizer = tokenizer + self.precision = precision + + def __call__( + self, + input_ids: torch.LongTensor, + scores: torch.FloatTensor, + ) -> bool: + decoded = self.tokenizer.decode(input_ids[0], skip_special_tokens=True) + if ".." in decoded: + print("Stopping because of ..") + return True + + if decoded.strip().count(".") > 1: + print("Stopping because of multiple .") + return True + + if ( + decoded.strip().count(".") == 1 + and len(decoded.strip().split(".")[1]) > self.precision + ): + return True + + return False + + +class OutputNumbersTokens(LogitsWarper): + def __init__(self, tokenizer: PreTrainedTokenizer, prompt: str): + self.whitelist_tokens = [tokenizer.eos_token_id] + self.tokenized_prompt = tokenizer(prompt, return_tensors="pt") + + for token_str, token_id in tokenizer.get_vocab().items(): + if ( + ( + token_str.startswith("Ġ") + and ( + all(c.isdigit() or c == "." for c in token_str[1:]) + and token_str.count(".") <= 1 + ) + ) + or ( + all(c.isdigit() or c == "." for c in token_str) + and token_str.count(".") <= 1 + ) + or ( + token_str[-1] == " " + and all(c.isdigit() or c == "." for c in token_str[:-1]) + ) + ): + self.whitelist_tokens.append(token_id) + + def __call__(self, input_ids, scores): + input_ids = input_ids[:, len(self.tokenized_prompt["input_ids"][0]) :] + scores[ + :, [i for i in range(len(scores[0])) if i not in self.whitelist_tokens] + ] = -float("inf") + return scores diff --git a/jsonllm/main.py b/jsonllm/main.py new file mode 100644 index 0000000..8aeedcf --- /dev/null +++ b/jsonllm/main.py @@ -0,0 +1,173 @@ +import json +import random +from jsonllm.logits_processors import NumberStoppingCriteria, OutputNumbersTokens +from transformers import ( + PreTrainedTokenizer, + PreTrainedModel, + AutoModelForCausalLM, + AutoTokenizer, +) +from typing import Any, Dict + + +class JSONLLM: + value: Dict[str, Any] = {} + + def __init__( + self, + model: PreTrainedModel, + tokenizer: PreTrainedTokenizer, + json_schema: Dict[str, Any], + prompt: str, + ): + self.model = model + self.tokenizer = tokenizer + self.json_schema = json_schema + self.prompt = prompt + + self.number_logit_processor = OutputNumbersTokens(self.tokenizer, self.prompt) + self.number_stop_criteria = NumberStoppingCriteria(self.tokenizer) + + # def generate( + # self, text: str, max_length: int = 100, forced_bos_token_id: list | None = None + # ) -> str: + # # print prompt in red + # print("generate") + # print("\033[91m {}\033[00m".format(text)) + # input_ids = self.tokenizer.encode(text, return_tensors="pt") + # output = self.model.generate( + # input_ids, + # max_length=max_length, + # num_return_sequences=1, + # forced_bos_token_id=forced_bos_token_id, + # ) + # decoded_output = self.tokenizer.decode(output[0], skip_special_tokens=True) + # return decoded_output.strip() + + def generate_number(self, suffix="") -> float: + print("generate_number", suffix) + prompt = self.get_prompt() + suffix + + # print prompt in red + # number = model.generate( + # tokenizer.encode(prompt, return_tensors="pt"), + # max_new_tokens=5, + # num_return_sequences=1, + # logits_processor=[a], + # stopping_criteria=[NumberStoppingCriteria(tokenizer)], + # ) + + print("\033[91m {}\033[00m".format(prompt)) + response = self.model.generate( + self.tokenizer.encode(prompt, return_tensors="pt"), + max_new_tokens=5, + num_return_sequences=1, + logits_processor=[self.number_logit_processor], + stopping_criteria=[self.number_stop_criteria], + ) + + response = self.tokenizer.decode(response[0], skip_special_tokens=True) + + print("\033[94m {}\033[00m".format(response)) + try: + return float(response) + except ValueError: + print("ValueError") + return + + def generate_boolean(self, suffix="") -> bool: + prompt = self.get_prompt() + true_token_id = self.tokenizer.encode("true", add_special_tokens=False)[0] + false_token_id = self.tokenizer.encode("false", add_special_tokens=False)[0] + + response = self.generate( + prompt, forced_bos_token_id=[true_token_id, false_token_id] + ).lower() + + if response == "true": + return True + else: + return False + + def generate_array( + self, item_schema: Dict[str, Any], obj: Dict[str, Any], suffix="" + ) -> list: + array_length = random.randint(0, 5) + return [self.generate_value(item_schema, obj) for _ in range(array_length)] + + # add stopping criteria with " + def generate_string(self) -> str: + prompt = self.get_prompt() + response = self.generate(prompt) + return response + + def generate_object( + self, properties: Dict[str, Any], obj: Dict[str, Any], suffix="" + ) -> Dict[str, Any]: + print("generate_object", properties) + + for key, schema in properties.items(): + value = self.generate_value(schema, obj, suffix=f'"{key}": ') + + obj[key] = value + return obj + + def generate_value(self, schema: Dict[str, Any], obj: Dict[str, Any], suffix=""): + schema_type = schema["type"] + if schema_type == "number": + return self.generate_number(suffix=suffix) + elif schema_type == "boolean": + return self.generate_boolean(suffix=suffix) + elif schema_type == "array": + return self.generate_array(schema["items"], obj, suffix=suffix) + elif schema_type == "object": + return self.generate_object(schema["properties"], obj, suffix=suffix) + else: + raise ValueError(f"Unsupported schema type: {schema_type}") + + def get_prompt(self): + template = """{prompt}\nMake sure to output in the following format:\n{schema}\n {progress}""" + progress = json.dumps(self.value) + + progress = progress.rstrip("}").rstrip("]").rstrip(",") + + prompt = template.format( + prompt=self.prompt, + schema=json.dumps(self.json_schema), + progress=progress, + ) + + return prompt + + def __call__(self) -> Dict[str, Any]: + self.value = {} + generated_data = self.generate_object( + self.json_schema["properties"], self.value + ) + return generated_data + + +model_name = "EleutherAI/gpt-neo-1.3B" +model = AutoModelForCausalLM.from_pretrained( + model_name, +) +tokenizer = AutoTokenizer.from_pretrained(model_name) + +weather_schema = { + "type": "object", + "properties": { + "temperature": {"type": "number"}, + # "humidity": {"type": "number"}, + }, +} + + +jsonllm = JSONLLM( + model=model, + tokenizer=tokenizer, + json_schema=weather_schema, + prompt="Generate a weather object", +) + +output = jsonllm() +print(output) diff --git a/jsonllm/test.py b/jsonllm/test.py new file mode 100644 index 0000000..29d85b6 --- /dev/null +++ b/jsonllm/test.py @@ -0,0 +1,27 @@ +from jsonschema import validate + +simple_weather_schema = { + "type": "object", + "properties": { + "humidity": {"type": "number"}, + "temperatureC": { + "type": "object", + "properties": { + "value": {"type": "number"}, + "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}, + }, + "required": ["value", "unit"], + }, + }, + "required": ["humidity", "temperature"], +} + +validate( + { + "humidity": 0.9, + "temperature": { + "value": 37, + }, + }, + simple_weather_schema, +) diff --git a/play.ipynb b/play.ipynb new file mode 100644 index 0000000..68ccac1 --- /dev/null +++ b/play.ipynb @@ -0,0 +1,291 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 332, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "from transformers import (\n", + " AutoModelForCausalLM,\n", + " AutoTokenizer,\n", + ")\n", + "\n", + "\n", + "model_name = \"EleutherAI/gpt-neo-1.3B\"\n", + "model = AutoModelForCausalLM.from_pretrained(\n", + " model_name,\n", + ")\n", + "tokenizer = AutoTokenizer.from_pretrained(model_name)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 333, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor([[2579, 292, 7568]])\n", + " 28\n" + ] + } + ], + "source": [ + "to_tokenize = \" 28asdf\"\n", + "input_ids = tokenizer.encode(to_tokenize, return_tensors=\"pt\")\n", + "print(input_ids)\n", + "\n", + "to_decode = [2579]\n", + "print(tokenizer.decode(to_decode, skip_special_tokens=True))" + ] + }, + { + "cell_type": "code", + "execution_count": 334, + "metadata": {}, + "outputs": [], + "source": [ + "from transformers import (\n", + " PreTrainedTokenizer,\n", + " LogitsWarper,StoppingCriteria\n", + ")\n", + "\n", + "\n", + "# class NumberStoppingCriteria(StoppingCriteria):\n", + "# \"\"\"\n", + "# This class can be used to stop generation when there is a repeated decimal point in the generated text.\n", + "# \"\"\"\n", + "\n", + "# def __init__(self, tokenizer: PreTrainedTokenizer):\n", + "# self.tokenizer = tokenizer\n", + "\n", + "\n", + "# def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:\n", + "# decoded = self.tokenizer.decode(input_ids[0], skip_special_tokens=True)\n", + "# if \"..\" in decoded:\n", + "# return True\n", + " \n", + "# if ' ' in decoded.strip():\n", + "# return True\n", + " \n", + "# return False\n", + "\n", + "\n", + "class OutputNumbersTokens(LogitsWarper):\n", + " def __init__(self, tokenizer: PreTrainedTokenizer, prompt: str):\n", + " self.whitelist_tokens = [tokenizer.eos_token_id]\n", + " self.tokenized_prompt = tokenizer(prompt, return_tensors=\"pt\")\n", + "\n", + " for token_str, token_id in tokenizer.get_vocab().items():\n", + "\n", + " if (\n", + " token_str.startswith(\"Ġ\") and (\n", + " all(c.isdigit() or c == \".\" for c in token_str[1:]) and token_str.count(\".\") <= 1\n", + " ) or (\n", + " all(c.isdigit() or c == \".\" for c in token_str) and token_str.count(\".\") <= 1\n", + " )\n", + " ):\n", + " self.whitelist_tokens.append(token_id)\n", + "\n", + " def __call__(self, input_ids, scores):\n", + " input_ids = input_ids[:, len(self.tokenized_prompt[\"input_ids\"][0]):]\n", + " scores[\n", + " :, [i for i in range(len(scores[0])) if i not in self.whitelist_tokens]\n", + " ] = -float(\"inf\")\n", + " return scores\n", + "\n", + "a = OutputNumbersTokens(tokenizer, prompt)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 326, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "found space28 2579\n" + ] + } + ], + "source": [ + "\n", + "a = OutputNumbersTokens(tokenizer, prompt)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 331, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 65 tensor(-3.7568)\n", + " 60 tensor(-3.8070)\n", + " 75 tensor(-3.9135)\n", + " 80 tensor(-4.0881)\n", + " 87 tensor(-4.1663)\n", + " 82 tensor(-4.2518)\n", + " 50 tensor(-4.2757)\n", + " 5 tensor(-4.3758)\n", + " 64 tensor(-4.3760)\n", + " 72 tensor(-4.4309)\n", + "tensor([[ 1820, 49890, 338, 2479, 287, 812, 318, 6135]])\n", + "my grandma's age in years is 65\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 0 tensor(-3.4328)\n", + " 5 tensor(-4.0209)\n", + " 36 tensor(-4.2786)\n", + " 19 tensor(-4.4433)\n", + " 59 tensor(-4.5570)\n", + " 24 tensor(-4.7431)\n", + " 33 tensor(-4.7621)\n", + " 56 tensor(-4.7745)\n", + " 47 tensor(-4.8474)\n", + " 53 tensor(-4.8719)\n", + "tensor([[40838, 338, 5951, 287, 7370, 269, 1424, 318, 657]])\n", + "today's temperature in degrees cels is 0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 0 tensor(-5.0538)\n", + " 5 tensor(-6.4346)\n", + " 7 tensor(-7.2013)\n", + " 60 tensor(-7.4283)\n", + " 24 tensor(-7.6246)\n", + " 42 tensor(-7.6409)\n", + " 50 tensor(-7.6574)\n", + " 30 tensor(-7.6641)\n", + " 18 tensor(-7.7838)\n", + " 59 tensor(-7.8880)\n", + "tensor([[40838, 338, 5951, 287, 277, 318, 657]])\n", + "today's temperature in f is 0\n", + " 0 tensor(-1.6487)\n", + " 5 tensor(-3.0234)\n", + " 7 tensor(-4.6462)\n", + " 16 tensor(-5.9200)\n", + " 18 tensor(-6.3192)\n", + " 19 tensor(-6.6764)\n", + " 24 tensor(-6.7219)\n", + " 30 tensor(-7.0326)\n", + "2 tensor(-7.3029)\n", + " 50 tensor(-7.7194)\n", + "tensor([[ 16, 1343, 352, 796, 657]])\n", + "1 + 1 = 0\n" + ] + } + ], + "source": [ + "prompts = [\"my grandma's age in years is\", \"today's temperature in degrees cels is\", \"today's temperature in f is\", \n", + " \"1 + 1 =\"]\n", + "\n", + "for prompt in prompts:\n", + " number = model.generate(\n", + " tokenizer.encode(prompt, return_tensors=\"pt\"),\n", + " max_new_tokens=5,\n", + " num_return_sequences=1,\n", + " logits_processor=[a],\n", + " stopping_criteria=[NumberStoppingCriteria(tokenizer)],\n", + " ) \n", + "\n", + " print(number)\n", + "\n", + " decoded_output = tokenizer.decode(number[0], skip_special_tokens=True)\n", + "\n", + " print(decoded_output.strip())\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "jsonllm-4DT72Dd8-py3.10", + "language": "python", + "name": 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+[tool.poetry.dependencies] +python = "^3.10" +transformers = "^4.27.4" +jsonschema = "^4.17.3" +torch = "^2.0.0" +accelerate = "^0.18.0" +bitsandbytes = "^0.38.1" + + +[tool.poetry.group.dev.dependencies] +ipykernel = "^6.22.0" + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" diff --git a/readme.md b/readme.md new file mode 100644 index 0000000..ae314aa --- /dev/null +++ b/readme.md @@ -0,0 +1 @@ +# jsonllm diff --git a/t.ipynb b/t.ipynb new file mode 100644 index 0000000..75780dc --- /dev/null +++ b/t.ipynb @@ -0,0 +1,258 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "import random\n", + "from transformers import (\n", + " PreTrainedTokenizer,\n", + " PreTrainedModel,\n", + " AutoModelForCausalLM,\n", + " AutoTokenizer,\n", + ")\n", + "from typing import Any, Dict\n", + "\n", + "\n", + "\n", + "model_name = \"databricks/dolly-v2-3b\"\n", + "model = AutoModelForCausalLM.from_pretrained(\n", + " model_name,\n", + ")\n", + "tokenizer = AutoTokenizer.from_pretrained(model_name)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from jsonllm.logits_processors import NumberStoppingCriteria, OutputNumbersTokens\n", + "\n", + "NumberStoppingCriteria(tokenizer, 2)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "class JSONLLM:\n", + " value: Dict[str, Any] = {}\n", + "\n", + " def __init__(\n", + " self,\n", + " model: PreTrainedModel,\n", + " tokenizer: PreTrainedTokenizer,\n", + " json_schema: Dict[str, Any],\n", + " prompt: str,\n", + " ):\n", + " self.model = model\n", + " self.tokenizer = tokenizer\n", + " self.json_schema = json_schema\n", + " self.prompt = prompt\n", + "\n", + " self.number_logit_processor = OutputNumbersTokens(self.tokenizer, self.prompt)\n", + " self.number_stop_criteria = NumberStoppingCriteria(self.tokenizer, 3)\n", + "\n", + "\n", + "\n", + " def generate_number(self, suffix=\"\") -> float:\n", + " print(\"generate_number\", suffix)\n", + " prompt = self.get_prompt() + suffix\n", + "\n", + " print(\"\\033[91m {}\\033[00m\".format(prompt))\n", + " response = self.model.generate(\n", + " self.tokenizer.encode(prompt, return_tensors=\"pt\"),\n", + " max_new_tokens=6,\n", + " num_return_sequences=1,\n", + " logits_processor=[self.number_logit_processor],\n", + " stopping_criteria=[self.number_stop_criteria],\n", + " temperature=1.5,\n", + " pad_token_id=tokenizer.eos_token_id\n", + " )\n", + "\n", + " response = self.tokenizer.decode(response[0], skip_special_tokens=True)\n", + " print(\"response is\")\n", + " print(\"\\033[94m {}\\033[00m\".format(response))\n", + " response = response.strip().rstrip(\".\").lstrip(\"0\")\n", + " try:\n", + " return float(response)\n", + " except ValueError:\n", + " print(\"ValueError\")\n", + " return \n", + "\n", + " def generate_boolean(self, suffix=\"\") -> bool:\n", + " prompt = self.get_prompt()\n", + " true_token_id = self.tokenizer.encode(\"true\", add_special_tokens=False)[0]\n", + " false_token_id = self.tokenizer.encode(\"false\", add_special_tokens=False)[0]\n", + "\n", + " response = self.generate(\n", + " prompt, forced_bos_token_id=[true_token_id, false_token_id]\n", + " ).lower()\n", + "\n", + " if response == \"true\":\n", + " return True\n", + " else:\n", + " return False\n", + "\n", + " def generate_array(\n", + " self, item_schema: Dict[str, Any], obj: Dict[str, Any], suffix=\"\"\n", + " ) -> list:\n", + " array_length = random.randint(0, 5)\n", + " return [self.generate_value(item_schema, obj) for _ in range(array_length)]\n", + "\n", + " # add stopping criteria with \"\n", + " def generate_string(self) -> str:\n", + " prompt = self.get_prompt()\n", + " response = self.generate(prompt)\n", + " return response\n", + "\n", + " def generate_object(\n", + " self, properties: Dict[str, Any], obj: Dict[str, Any], suffix=\"\"\n", + " ) -> Dict[str, Any]:\n", + " print(\"generate_object\", properties)\n", + "\n", + " for key, schema in properties.items():\n", + " value = self.generate_value(schema, obj, suffix=f'\"{key}\": ')\n", + "\n", + " obj[key] = value\n", + " return obj\n", + "\n", + " def generate_value(self, schema: Dict[str, Any], obj: Dict[str, Any], suffix=\"\"):\n", + " schema_type = schema[\"type\"]\n", + " if schema_type == \"number\":\n", + " return self.generate_number(suffix=suffix)\n", + " elif schema_type == \"boolean\":\n", + " return self.generate_boolean(suffix=suffix)\n", + " elif schema_type == \"array\":\n", + " return self.generate_array(schema[\"items\"], obj, suffix=suffix)\n", + " elif schema_type == \"object\":\n", + " return self.generate_object(schema[\"properties\"], obj, suffix=suffix)\n", + " else:\n", + " raise ValueError(f\"Unsupported schema type: {schema_type}\")\n", + "\n", + " def get_prompt(self):\n", + " template = \"\"\"{prompt}\\nMake sure to output in the following format:\\n{schema}\\n Result: {progress}\"\"\"\n", + " progress = json.dumps(self.value)\n", + "\n", + " progress = progress.rstrip(\"}\").rstrip(\"]\").rstrip(\",\")\n", + "\n", + " prompt = template.format(\n", + " prompt=self.prompt,\n", + " schema=json.dumps(self.json_schema),\n", + " progress=progress,\n", + " )\n", + "\n", + " return prompt\n", + "\n", + " def __call__(self) -> Dict[str, Any]:\n", + " self.value = {}\n", + " generated_data = self.generate_object(\n", + " self.json_schema[\"properties\"], self.value\n", + " )\n", + " return generated_data\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "generate_object {'temperature': {'type': 'number'}}\n", + "generate_number \"temperature\": \n", + "\u001b[91m Generate a weather object\n", + "Make sure to output in the following format:\n", + "{\"type\": \"object\", \"properties\": {\"temperature\": {\"type\": \"number\"}}}\n", + " Result: {\"temperature\": \u001b[00m\n", + "Stopping because of multiple .\n", + "response is\n", + "\u001b[94m Generate a weather object\n", + "Make sure to output in the following format:\n", + "{\"type\": \"object\", \"properties\": {\"temperature\": {\"type\": \"number\"}}}\n", + " Result: {\"temperature\": 000000000.0.\u001b[00m\n", + "ValueError\n", + "{'temperature': None}\n" + ] + } + ], + "source": [ + "weather_schema = {\n", + " \"type\": \"object\",\n", + " \"properties\": {\n", + " \"temperature\": {\"type\": \"number\"},\n", + " # \"humidity\": {\"type\": \"number\"},\n", + " },\n", + "}\n", + "\n", + "\n", + "jsonllm = JSONLLM(\n", + " model=model,\n", + " tokenizer=tokenizer,\n", + " json_schema=weather_schema,\n", + " prompt=\"Generate a weather object\",\n", + ")\n", + "\n", + "output = jsonllm()\n", + "print(output)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "jsonllm-4DT72Dd8-py3.10", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.6" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +}