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Model: meetkai/functionary-small-v3.1 Source: Original Platform
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README.md
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README.md
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---
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license: mit
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---
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# Model Card for functionary-small-v3.1
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**This model was based on [meta-llama/Meta-Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct)**, using Meta's original prompt template as described in: [User-defined Custom tool calling](https://llama.meta.com/docs/model-cards-and-prompt-formats/llama3_1/#user-defined-custom-tool-calling)
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[https://github.com/MeetKai/functionary](https://github.com/MeetKai/functionary)
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<img src="https://huggingface.co/meetkai/functionary-medium-v2.2/resolve/main/functionary_logo.jpg" alt="Functionary Logo" width="300"/>
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Functionary is a language model that can interpret and execute functions/plugins.
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The model determines when to execute functions, whether in parallel or serially, and can understand their outputs. It only triggers functions as needed. Function definitions are given as JSON Schema Objects, similar to OpenAI GPT function calls.
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## Key Features
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- Intelligent **parallel tool use**
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- Able to analyze functions/tools outputs and provide relevant responses **grounded in the outputs**
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- Able to decide **when to not use tools/call functions** and provide normal chat response
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- Truly one of the best open-source alternative to GPT-4
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- Support code interpreter
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## How to Get Started
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We provide custom code for parsing raw model responses into a JSON object containing role, content and tool_calls fields. This enables the users to read the function-calling output of the model easily.
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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tokenizer = AutoTokenizer.from_pretrained("meetkai/functionary-small-v3.1")
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model = AutoModelForCausalLM.from_pretrained("meetkai/functionary-small-v3.1", device_map="auto", trust_remote_code=True)
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tools = [
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{
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"type": "function",
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"function": {
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"name": "get_current_weather",
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"description": "Get the current weather",
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"parameters": {
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"type": "object",
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"properties": {
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"location": {
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"type": "string",
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"description": "The city and state, e.g. San Francisco, CA"
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}
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},
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"required": ["location"]
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}
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}
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}
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]
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messages = [{"role": "user", "content": "What is the weather in Istanbul and Singapore respectively?"}]
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final_prompt = tokenizer.apply_chat_template(messages, tools, add_generation_prompt=True, tokenize=False)
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inputs = tokenizer(final_prompt, return_tensors="pt").to("cuda")
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pred = model.generate_tool_use(**inputs, max_new_tokens=128, tokenizer=tokenizer)
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print(tokenizer.decode(pred.cpu()[0]))
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```
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## Prompt Template
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We convert function definitions to a similar text to TypeScript definitions. Then we inject these definitions as system prompts. After that, we inject the default system prompt. Then we start the conversation messages.
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This formatting is also available via our vLLM server which we process the functions into Typescript definitions encapsulated in a system message using a pre-defined Transformers Jinja chat template. This means that the lists of messages can be formatted for you with the apply_chat_template() method within our server:
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```python
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from openai import OpenAI
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client = OpenAI(base_url="http://localhost:8000/v1", api_key="functionary")
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client.chat.completions.create(
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model="path/to/functionary/model/",
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messages=[{"role": "user",
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"content": "What is the weather for Istanbul?"}
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],
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tools=[{
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"type": "function",
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"function": {
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"name": "get_current_weather",
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"description": "Get the current weather",
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"parameters": {
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"type": "object",
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"properties": {
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"location": {
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"type": "string",
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"description": "The city and state, e.g. San Francisco, CA"
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}
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},
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"required": ["location"]
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}
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}
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}],
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tool_choice="auto"
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)
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```
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will yield:
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```
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<|start_header_id|>system<|end_header_id|>
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Environment: ipython
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Cutting Knowledge Date: December 2023
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You have access to the following functions:
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Use the function 'get_current_weather' to 'Get the current weather'
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{"name": "get_current_weather", "description": "Get the current weather", "parameters": {"type": "object", "properties": {"location": {"type": "string", "description": "The city and state, e.g. San Francisco, CA"}},"required": ["location"]}}
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Think very carefully before calling functions.
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If a you choose to call a function ONLY reply in the following format:
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<{start_tag}={function_name}>{parameters}{end_tag}
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where
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start_tag => `<function`
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parameters => a JSON dict with the function argument name as key and function argument value as value.
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end_tag => `</function>`
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Here is an example,
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<function=example_function_name>{"example_name": "example_value"}</function>
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Reminder:
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- If looking for real time information use relevant functions before falling back to brave_search
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- Function calls MUST follow the specified format, start with <function= and end with </function>
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- Required parameters MUST be specified
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- Only call one function at a time
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- Put the entire function call reply on one line
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<|eot_id|><|start_header_id|>user<|end_header_id|>
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What is the weather for Istanbul?
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```
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A more detailed example is provided [here](https://github.com/MeetKai/functionary/blob/main/tests/prompt_test_v3-llama3.1.txt).
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## Run the model
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We encourage users to run our models using our OpenAI-compatible vLLM server [here](https://github.com/MeetKai/functionary).
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# The MeetKai Team
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config.json
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{
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"_name_or_path": "meta-llama/Meta-Llama-3.1-8B-Instruct",
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"architectures": [
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"LlamaForCausalLM"
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],
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"auto_map": {
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"AutoModelForCausalLM": "modeling_functionary.FunctionaryForCausalLM"
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},
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 128000,
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"eos_token_id": [
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"hidden_act": "silu",
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"hidden_size": 4096,
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"initializer_range": 0.02,
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"intermediate_size": 14336,
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"max_position_embeddings": 131072,
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"mlp_bias": false,
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"model_type": "llama",
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"num_attention_heads": 32,
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"num_hidden_layers": 32,
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"num_key_value_heads": 8,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-05,
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"rope_scaling": {
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"factor": 8.0,
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"high_freq_factor": 4.0,
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"low_freq_factor": 1.0,
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"original_max_position_embeddings": 8192,
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"rope_type": "llama3"
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},
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"rope_theta": 500000.0,
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"sliding_window": 16384,
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"tie_word_embeddings": false,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.44.0.dev0",
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"use_cache": false,
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"vocab_size": 128256
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}
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"temperature": 0.6,
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"top_p": 0.9,
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"transformers_version": "4.44.0.dev0"
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}
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"model.layers.7.input_layernorm.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.7.mlp.down_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.7.mlp.gate_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.7.mlp.up_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.7.post_attention_layernorm.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.7.self_attn.k_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.7.self_attn.o_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.7.self_attn.q_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.7.self_attn.v_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.8.input_layernorm.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.8.mlp.down_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.8.mlp.gate_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.8.mlp.up_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.8.post_attention_layernorm.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.8.self_attn.k_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.8.self_attn.o_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.8.self_attn.q_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.8.self_attn.v_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.9.input_layernorm.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.9.mlp.down_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.9.mlp.gate_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.9.mlp.up_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.9.post_attention_layernorm.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.9.self_attn.k_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.9.self_attn.o_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.9.self_attn.q_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.9.self_attn.v_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.norm.weight": "model-00004-of-00004.safetensors"
|
||||
}
|
||||
}
|
||||
110
modeling_functionary.py
Normal file
110
modeling_functionary.py
Normal file
@@ -0,0 +1,110 @@
|
||||
# coding=utf-8
|
||||
# Copyright (c) 2024, MeetKai Inc. All rights reserved.
|
||||
"""PyTorch LLaMA model."""
|
||||
|
||||
import json
|
||||
from typing import TYPE_CHECKING, Callable, List, Optional, Tuple, Union
|
||||
|
||||
import torch
|
||||
import torch.utils.checkpoint
|
||||
|
||||
from transformers.generation.configuration_utils import GenerationConfig
|
||||
from transformers.generation.logits_process import LogitsProcessorList
|
||||
from transformers.generation.stopping_criteria import StoppingCriteriaList
|
||||
from transformers.generation.utils import (
|
||||
GenerateBeamDecoderOnlyOutput,
|
||||
GenerateBeamEncoderDecoderOutput,
|
||||
GenerateDecoderOnlyOutput,
|
||||
GenerateEncoderDecoderOutput
|
||||
)
|
||||
from transformers.models.llama.modeling_llama import LlamaForCausalLM
|
||||
from transformers.utils import logging
|
||||
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from transformers.modeling_utils import PreTrainedModel
|
||||
from transformers.generation.streamers import BaseStreamer
|
||||
|
||||
logger = logging.get_logger(__name__)
|
||||
|
||||
GenerateNonBeamOutput = Union[GenerateDecoderOnlyOutput, GenerateEncoderDecoderOutput]
|
||||
GenerateBeamOutput = Union[GenerateBeamDecoderOnlyOutput, GenerateBeamEncoderDecoderOutput]
|
||||
GenerateOutput = Union[GenerateNonBeamOutput, GenerateBeamOutput]
|
||||
|
||||
|
||||
class FunctionaryForCausalLM(LlamaForCausalLM):
|
||||
|
||||
def generate_tool_use(
|
||||
self,
|
||||
inputs: Optional[torch.Tensor] = None,
|
||||
generation_config: Optional[GenerationConfig] = None,
|
||||
logits_processor: Optional[LogitsProcessorList] = None,
|
||||
stopping_criteria: Optional[StoppingCriteriaList] = None,
|
||||
prefix_allowed_tokens_fn: Optional[Callable[[int, torch.Tensor], List[int]]] = None,
|
||||
synced_gpus: Optional[bool] = None,
|
||||
assistant_model: Optional["PreTrainedModel"] = None,
|
||||
streamer: Optional["BaseStreamer"] = None,
|
||||
negative_prompt_ids: Optional[torch.Tensor] = None,
|
||||
negative_prompt_attention_mask: Optional[torch.Tensor] = None,
|
||||
**kwargs,
|
||||
) -> Union[GenerateOutput, torch.LongTensor]:
|
||||
|
||||
tokenizer = kwargs.pop("tokenizer", None) # Pull this out first, we use it to parse raw output
|
||||
|
||||
results = self.generate(
|
||||
inputs=inputs,
|
||||
generation_config=generation_config,
|
||||
logits_processor=logits_processor,
|
||||
stopping_criteria=stopping_criteria,
|
||||
prefix_allowed_tokens_fn=prefix_allowed_tokens_fn,
|
||||
synced_gpus=synced_gpus,
|
||||
assistant_model=assistant_model,
|
||||
streamer=streamer,
|
||||
negative_prompt_ids=negative_prompt_ids,
|
||||
negative_prompt_attention_mask=negative_prompt_attention_mask,
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
input_ids = kwargs.pop("input_ids")
|
||||
function_call_token = "<function="
|
||||
|
||||
correct_results = []
|
||||
for input_id, result in zip(input_ids, results):
|
||||
final_output_json = {"role": "assistant", "content": None, "tool_calls": None}
|
||||
tool_calls = []
|
||||
raw_output_str = tokenizer.decode(result[len(input_id):].cpu())
|
||||
has_text = False if raw_output_str.startswith(function_call_token) else True
|
||||
chunks = raw_output_str.split(function_call_token)
|
||||
for i, chunk in enumerate(chunks):
|
||||
if len(chunk) == 0:
|
||||
continue
|
||||
|
||||
chunk = chunk.replace(tokenizer.pad_token, "")
|
||||
|
||||
if i == 0 and has_text is not False:
|
||||
final_output_json["content"] = chunk.removesuffix("<|eom_id|>").removesuffix("<|eot_id|>")
|
||||
else:
|
||||
tool_calls.append(
|
||||
{
|
||||
"name": chunk[: chunk.index(">{")],
|
||||
"arguments": chunk[chunk.index(">{") + 1: ].removesuffix("<|eom_id|>").removesuffix("</function>")
|
||||
}
|
||||
)
|
||||
if len(tool_calls) > 0:
|
||||
final_output_json["tool_calls"] = tool_calls
|
||||
final_output_str = json.dumps(final_output_json, indent=4)
|
||||
final_output_ids = tokenizer(final_output_str, add_special_tokens=False)["input_ids"]
|
||||
correct_results.append(
|
||||
torch.cat(
|
||||
(result[:len(input_id)].cpu(), torch.tensor(final_output_ids))
|
||||
)
|
||||
)
|
||||
max_len = max([tensor.shape[0] for tensor in correct_results])
|
||||
correct_results = [
|
||||
torch.nn.functional.pad(
|
||||
correct_result, (0, max_len - correct_result.shape[0]), value=tokenizer.eos_token_id
|
||||
) for correct_result in correct_results
|
||||
]
|
||||
correct_results = torch.stack(correct_results)
|
||||
|
||||
return correct_results
|
||||
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:abbddf5c6efbfadf7fe92084dceec66ae4e43ce4ae51b7c2249210f07e9935d4
|
||||
size 5398
|
||||
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:0ac79559559487e35e9c1331ceb5a94c6ba06b0194351acb76ccdb436449d61f
|
||||
size 5398
|
||||
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:73a4ce81074b3877f4b88f02f9bff01e1ddfedbb56b9aa48b00bd597bcb2f814
|
||||
size 5398
|
||||
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:78bbfb9b91d97139c50807acdf9de25a12ef887ffeadd143eda7230accfc77c9
|
||||
size 124824
|
||||
17
special_tokens_map.json
Normal file
17
special_tokens_map.json
Normal file
@@ -0,0 +1,17 @@
|
||||
{
|
||||
"bos_token": {
|
||||
"content": "<|begin_of_text|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"eos_token": {
|
||||
"content": "<|eot_id|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"pad_token": "<|eot_id|>"
|
||||
}
|
||||
410563
tokenizer.json
Normal file
410563
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
17
tokenizer/special_tokens_map.json
Normal file
17
tokenizer/special_tokens_map.json
Normal file
@@ -0,0 +1,17 @@
|
||||
{
|
||||
"bos_token": {
|
||||
"content": "<|begin_of_text|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"eos_token": {
|
||||
"content": "<|eot_id|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"pad_token": "<|eot_id|>"
|
||||
}
|
||||
410563
tokenizer/tokenizer.json
Normal file
410563
tokenizer/tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
2065
tokenizer/tokenizer_config.json
Normal file
2065
tokenizer/tokenizer_config.json
Normal file
File diff suppressed because it is too large
Load Diff
2062
tokenizer_config.json
Normal file
2062
tokenizer_config.json
Normal file
File diff suppressed because it is too large
Load Diff
3951
trainer_state.json
Normal file
3951
trainer_state.json
Normal file
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user