Instructions to use Trelis/Mistral-7B-Instruct-v0.1-function-calling-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Trelis/Mistral-7B-Instruct-v0.1-function-calling-v3 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Trelis/Mistral-7B-Instruct-v0.1-function-calling-v3")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Trelis/Mistral-7B-Instruct-v0.1-function-calling-v3")
model = AutoModelForCausalLM.from_pretrained("Trelis/Mistral-7B-Instruct-v0.1-function-calling-v3", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Trelis/Mistral-7B-Instruct-v0.1-function-calling-v3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Trelis/Mistral-7B-Instruct-v0.1-function-calling-v3"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Trelis/Mistral-7B-Instruct-v0.1-function-calling-v3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Trelis/Mistral-7B-Instruct-v0.1-function-calling-v3

SGLang

How to use Trelis/Mistral-7B-Instruct-v0.1-function-calling-v3 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Trelis/Mistral-7B-Instruct-v0.1-function-calling-v3" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Trelis/Mistral-7B-Instruct-v0.1-function-calling-v3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Trelis/Mistral-7B-Instruct-v0.1-function-calling-v3" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Trelis/Mistral-7B-Instruct-v0.1-function-calling-v3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Trelis/Mistral-7B-Instruct-v0.1-function-calling-v3 with Docker Model Runner:
```
docker model run hf.co/Trelis/Mistral-7B-Instruct-v0.1-function-calling-v3
```

Mistral-7B-Instruct-v0.1-function-calling-v3 / README.md

RonanMcGovern

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	---
	pipeline_tag: text-generation
	inference:
	parameters:
	temperature: 0.01
	extra_gated_prompt: "Purchase access to this repo [HERE](https://buy.stripe.com/5kA3cYcWhci73ks7tt)"
	tags:
	- facebook
	- meta
	- mistral
	- pytorch
	- llama
	- llama-2
	- gguf
	- function-calling
	- function calling
	---
	# Function Calling Fine-tuned Mistral Instruct

	Purchase access to this model [here](https://buy.stripe.com/5kA3cYcWhci73ks7tt).

	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.

	Check out other fine-tuned function calling models [here](https://trelis.com/function-calling/).

	## Quick Server Setup
	Runpod one click template [here](https://runpod.io/gsc?template=lcrj267zgp&ref=jmfkcdio). 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](https://runpod.io?ref=jmfkcdio) (helps support the Trelis channel).

	## Inference Scripts
	See below for sample prompt format.

	Complete inference scripts are available for purchase [here](https://trelis.com/enterprise-server-api-and-inference-guide/):
	- 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-v0.1-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:
	```
	[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>
	```

	# Dataset
	See [Trelis/function_calling_v3](https://huggingface.co/datasets/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-v0.1

	The Mistral-7B-Instruct-v0.1 Large Language Model (LLM) is a instruct fine-tuned version of the [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) generative text model using a variety of publicly available conversation datasets.

	For full details of this model please read our [paper](https://arxiv.org/abs/2310.06825) and [release blog post](https://mistral.ai/news/announcing-mistral-7b/).

	## 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](https://huggingface.co/docs/transformers/main/chat_templating) via the `apply_chat_template()` method:

	```python
	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, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lélio Renard Lavaud, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed.