Function Calling DeciLM 7B

Purchase access to this model here.

This model is fine-tuned for function calling.

The function metadata format is the same as used for OpenAI.
The model is suitable for commercial use.
GGUF, AWQ and GPTQ models are not yet available as Deci models are not broadly supported.

Check out other fine-tuned function calling models here.

Quick Server Setup

Runpod one click template here. You must add a HuggingFace Hub access token (HUGGING_FACE_HUB_TOKEN) to the environment variables as this is a gated model.

Runpod Affiliate Link (helps support the Trelis channel).

Inference Scripts

See below for sample prompt format.

Complete inference scripts are available for purchase here:

Easily format prompts using tokenizer.apply_chat_format (starting from openai formatted functions and a list of messages)
Automate catching, handling and chaining of function calls.

Prompt Format

B_FUNC, E_FUNC = "You have access to the following functions. Use them if required:\n\n", "\n\n"
B_INST, E_INST = "\n### User:\n", "\n### Assistant:\n" #DeciLM
prompt = f"{B_INST}{B_FUNC}{functionList.strip()}{E_FUNC}{user_prompt.strip()}{E_INST}\n\n"

Using tokenizer.apply_chat_template

For an easier application of the prompt, you can set up as follows:

Set up messages:

[
    {
        "role": "function_metadata",
        "content": "FUNCTION_METADATA"
    },
    {
        "role": "user",
        "content": "What is the current weather in London?"
    },
    {
        "role": "function_call",
        "content": "{\n    \"name\": \"get_current_weather\",\n    \"arguments\": {\n        \"city\": \"London\"\n    }\n}"
    },
    {
        "role": "function_response",
        "content": "{\n    \"temperature\": \"15 C\",\n    \"condition\": \"Cloudy\"\n}"
    },
    {
        "role": "assistant",
        "content": "The current weather in London is Cloudy with a temperature of 15 Celsius"
    }
]

with FUNCTION_METADATA as:

[
    {
        "type": "function",
        "function": {
            "name": "get_current_weather",
            "description": "This function gets the current weather in a given city",
            "parameters": {
                "type": "object",
                "properties": {
                    "city": {
                        "type": "string",
                        "description": "The city, e.g., San Francisco"
                    },
                    "format": {
                        "type": "string",
                        "enum": ["celsius", "fahrenheit"],
                        "description": "The temperature unit to use."
                    }
                },
                "required": ["city"]
            }
        }
    },
    {
        "type": "function",
        "function": {
            "name": "get_clothes",
            "description": "This function provides a suggestion of clothes to wear based on the current weather",
            "parameters": {
                "type": "object",
                "properties": {
                    "temperature": {
                        "type": "string",
                        "description": "The temperature, e.g., 15 C or 59 F"
                    },
                    "condition": {
                        "type": "string",
                        "description": "The weather condition, e.g., 'Cloudy', 'Sunny', 'Rainy'"
                    }
                },
                "required": ["temperature", "condition"]
            }
        }
    }    
]

and then apply the chat template to get a formatted prompt:

tokenizer = AutoTokenizer.from_pretrained('Trelis/Mixtral-8x7B-Instruct-v0.1-function-calling-v3', trust_remote_code=True)

prompt = tokenizer.apply_chat_template(prompt, tokenize=False)

If you are using a gated model, you need to first run:

pip install huggingface_hub
huggingface-cli login

Manual Prompt:

### User:
You have access to the following functions. Use them if required:

[
    {
        "type": "function",
        "function": {
            "name": "get_stock_price",
            "description": "Get the stock price of an array of stocks",
            "parameters": {
                "type": "object",
                "properties": {
                    "names": {
                        "type": "array",
                        "items": {
                            "type": "string"
                        },
                        "description": "An array of stocks"
                    }
                },
                "required": [
                    "names"
                ]
            }
        }
    },
    {
        "type": "function",
        "function": {
            "name": "get_big_stocks",
            "description": "Get the names of the largest N stocks by market cap",
            "parameters": {
                "type": "object",
                "properties": {
                    "number": {
                        "type": "integer",
                        "description": "The number of largest stocks to get the names of, e.g. 25"
                    },
                    "region": {
                        "type": "string",
                        "description": "The region to consider, can be \"US\" or \"World\"."
                    }
                },
                "required": [
                    "number"
                ]
            }
        }
    }
]

Get the names of the five largest stocks by market cap
### Assistant:

{
    "name": "get_big_stocks",
    "arguments": {
        "number": 5,
        "region": "US"
    }
}</s>

Dataset

See Trelis/function_calling_v3.

License

This model may be used commercially for inference, or for further fine-tuning and inference. Users may not re-publish or re-sell this model in the same or derivative form (including fine-tunes).

The original repo card follows below.

DeciLM-7B-instruct

DeciLM-7B-instruct is a model for short-form instruction following. It is built by LoRA fine-tuning on the SlimOrca dataset.

🔥 Click here for a live demo of DeciLM-7B + Infery!

Model Details

Model Description

DeciLM-7B-instruct is a derivative of the recently released DeciLM-7B language model, a pre-trained, high-efficiency generative text model with 7 billion parameters. DeciLM-7B-instruct is one the best 7B instruct models obtained using simple LoRA fine-tuning, without relying on preference optimization techniques such as RLHF and DPO.

Developed by: Deci
Model type: DeciLM is an auto-regressive language model using an optimized transformer decoder architecture that includes variable Grouped-Query Attention.
Language(s) (NLP): English
License: Apache 2.0

Model Architecture

Parameters	Layers	Heads	Sequence Length	GQA num_key_value_heads*
7.04 billion	32	32	8192	Variable

*AutoNAC was employed to optimize the selection of the GQA num_key_value_heads for each model layer.

Model Sources

Blog: DeciLM-7B Technical Blog
Demo: DeciLM-7B-instruct Demo
Finetuning Notebook: DeciLM-7B Finetuning Notebook
Text Generation Notebook: DeciLM-7B-instruct Text Generation Notebook

Uses

The model is intended for commercial and research use in English.

How to Get Started with the Model

Use the code below to get started with the model.

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig, pipeline

model_name = "Deci/DeciLM-7B-instruct"

device = "cuda" # for GPU usage or "cpu" for CPU usage

bnb_config = BitsAndBytesConfig(
    load_in_4bit = True,
    bnb_4bit_compute_dtype=torch.bfloat16
)

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    device_map="auto",
    trust_remote_code=True,
    quantization_config=bnb_config
)

tokenizer = AutoTokenizer.from_pretrained(model_name)
tokenizer.pad_token = tokenizer.eos_token

deci_generator = pipeline("text-generation",
                          model=model,
                          tokenizer=tokenizer,
                          temperature=0.1,
                          device_map="auto",
                          max_length=4096,
                          return_full_text=False
)

prompt = "How do I make the most delicious pancakes the world has ever tasted?"

SYSTEM_PROMPT_TEMPLATE ="""
### System:
You are an AI assistant that follows instruction extremely well. Help as much as you can.
### User:
{instruction}
### Assistant:
"""

# Function to construct the prompt using the new system prompt template
def get_prompt_with_template(message: str) -> str:
    return SYSTEM_PROMPT_TEMPLATE.format(instruction=message)

response = deci_generator(get_prompt_with_template(prompt))[0]['generated_text']
print(response)

Evaluation

Below are DeciLM-7B and DeciLM-7B-instruct's evaluation results.

Model	Average	ARC	HellaSwag	MMLU	TruthfulQA	Winogrande	GSM8K
DecilLM-7B	61.55	59.39	82.51	59.76	40.33	79.95	47.38
DecilLM-7B-instruct	63.19	61.01	82.37	60.24	49.75	79.72	46.02

Runtime Benchmarks

Inference Tool	Hardware	Prompt length	Generation length	Generated tokens/sec	Batch Size	Number of Prompts
HuggingFace (PyTorch)	A100 (SXM4-80GB-400W)	512	512	1174	352	352
HuggingFace (PyTorch)	A100 (SXM4-80GB-400W)	2048	2048	328	72	72
Infery-LLM	A100 (SXM4-80GB-400W)	512	512	4559	1024	4096
Infery-LLM	A100 (SXM4-80GB-400W)	2048	2048	3997	512	2048
Infery-LLM	A10	512	512	1345	128	512
Infery-LLM	A10	2048	2048	599	32	128

In order to replicate the results of the Hugging Face benchmarks, you can use this code example.
Infery-LLM, Deci's inference engine, features a suite of optimization algorithms, including selective quantization, optimized beam search, continuous batching, and custom CUDA kernels. To witness the full capabilities of Infery-LLM first-hand, we invite you to engage with our interactive demo.

Ethical Considerations and Limitations

DeciLM-7B-instruct is a new technology that comes with inherent risks associated with its use. The testing conducted so far has been primarily in English and does not encompass all possible scenarios. Like those of all large language models, DeciLM-7B's outputs are unpredictable, and the model may generate responses that are inaccurate, biased, or otherwise objectionable. Consequently, developers planning to use DeciLM-7B should undertake thorough safety testing and tuning designed explicitly for their intended applications of the model before deployment.

How to Cite

Please cite this model using this format.

@misc{DeciFoundationModels,
title = {DeciLM-7B-instruct},
author = {DeciAI Research Team},
year = {2023}
url={https://huggingface.co/Deci/DeciLM-7B-instruct},
}

Trelis
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DeciLM-7B-instruct-function-calling-v3

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