Function Calling Fine-tuned Mistral Instruct v0.2

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.
A GGUF version is in the gguf branch.
AWQ is pending resolution of issues with attention mask for quanting.

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 = "[INST] ", " [/INST]" #Llama / Mistral style
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/Mistral-7B-Instruct-v0.2-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:

[INST] You have access to the following functions. Use them if required:

[
    {
        "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"
                ]
            }
        }
    },
    {
        "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"
                ]
            }
        }
    }
]

[INST] Get the names of the five largest stocks in the US by market cap [/INST]

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

Short inference script

from transformers import AutoModelForCausalLM, AutoTokenizer
import json

FUNCTION_METADATA = [ ... ] # Same as above

messages = [
    {
    "role": "function_metadata",
    "content": json.dumps(FUNCTION_METADATA, indent=4)
    },
    {
    "role": "user",
    "content": "Whats the weather link in Seattle Wa this weekend?"
    },
]

device = "auto"

# Load up the model and teh tokenzier
model = AutoModelForCausalLM.from_pretrained('Trelis/Mistral-7B-Instruct-v0.2-function-calling-v3', trust_remote_code=True)#, torch_dtype=torch.float16)
model.half()
model.to(device)

tokenizer = AutoTokenizer.from_pretrained("Trelis/Mistral-7B-Instruct-v0.2-function-calling-v3", trust_remote_code=True)

# Tokenize, run the module and decode
model_inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to(device)
generated_ids = model.generate(model_inputs, max_new_tokens=100, do_sample=False)
decoded = tokenizer.batch_decode(generated_ids)

print()
print("Generated")
print(decoded[0])

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.

Model Card for Mistral-7B-Instruct-v0.2

The Mistral-7B-Instruct-v0.2 Large Language Model (LLM) is an improved instruct fine-tuned version of Mistral-7B-Instruct-v0.1.

For full details of this model please read our paper and release blog post.

Instruction format

In order to leverage instruction fine-tuning, your prompt should be surrounded by [INST] and [/INST] tokens. The very first instruction should begin with a begin of sentence id. The next instructions should not. The assistant generation will be ended by the end-of-sentence token id.

E.g.

text = "<s>[INST] What is your favourite condiment? [/INST]"
"Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!</s> "
"[INST] Do you have mayonnaise recipes? [/INST]"

This format is available as a chat template via the apply_chat_template() method:

from transformers import AutoModelForCausalLM, AutoTokenizer

device = "cuda" # the device to load the model onto

model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1")
tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1")

messages = [
    {"role": "user", "content": "What is your favourite condiment?"},
    {"role": "assistant", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"},
    {"role": "user", "content": "Do you have mayonnaise recipes?"}
]

encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt")

model_inputs = encodeds.to(device)
model.to(device)

generated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True)
decoded = tokenizer.batch_decode(generated_ids)
print(decoded[0])

Model Architecture

This instruction model is based on Mistral-7B-v0.1, a transformer model with the following architecture choices:

Grouped-Query Attention
Sliding-Window Attention
Byte-fallback BPE tokenizer

Troubleshooting

If you see the following error:

Traceback (most recent call last):
File "", line 1, in
File "/transformers/models/auto/auto_factory.py", line 482, in from_pretrained
config, kwargs = AutoConfig.from_pretrained(
File "/transformers/models/auto/configuration_auto.py", line 1022, in from_pretrained
config_class = CONFIG_MAPPING[config_dict["model_type"]]
File "/transformers/models/auto/configuration_auto.py", line 723, in getitem
raise KeyError(key)
KeyError: 'mistral'

Installing transformers from source should solve the issue pip install git+https://github.com/huggingface/transformers

This should not be required after transformers-v4.33.4.

Limitations

The Mistral 7B Instruct model is a quick demonstration that the base model can be easily fine-tuned to achieve compelling performance. It does not have any moderation mechanisms. We're looking forward to engaging with the community on ways to make the model finely respect guardrails, allowing for deployment in environments requiring moderated outputs.

The Mistral AI Team

Albert Jiang, Alexandre Sablayrolles, Arthur Mensch, Blanche Savary, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Emma Bou Hanna, Florian Bressand, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Lélio Renard Lavaud, Louis Ternon, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Théophile Gervet, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed.

Trelis
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Mistral-7B-Instruct-v0.2-function-calling-v3

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