Instructions to use rizerphe/CodeLlama-function-calling-6320-7b-Instruct-hf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rizerphe/CodeLlama-function-calling-6320-7b-Instruct-hf with Transformers:

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

pipe = pipeline("text-generation", model="rizerphe/CodeLlama-function-calling-6320-7b-Instruct-hf")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("rizerphe/CodeLlama-function-calling-6320-7b-Instruct-hf")
model = AutoModelForCausalLM.from_pretrained("rizerphe/CodeLlama-function-calling-6320-7b-Instruct-hf")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use rizerphe/CodeLlama-function-calling-6320-7b-Instruct-hf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "rizerphe/CodeLlama-function-calling-6320-7b-Instruct-hf"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rizerphe/CodeLlama-function-calling-6320-7b-Instruct-hf",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/rizerphe/CodeLlama-function-calling-6320-7b-Instruct-hf

SGLang

How to use rizerphe/CodeLlama-function-calling-6320-7b-Instruct-hf 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 "rizerphe/CodeLlama-function-calling-6320-7b-Instruct-hf" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rizerphe/CodeLlama-function-calling-6320-7b-Instruct-hf",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "rizerphe/CodeLlama-function-calling-6320-7b-Instruct-hf" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rizerphe/CodeLlama-function-calling-6320-7b-Instruct-hf",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use rizerphe/CodeLlama-function-calling-6320-7b-Instruct-hf with Docker Model Runner:
```
docker model run hf.co/rizerphe/CodeLlama-function-calling-6320-7b-Instruct-hf
```

CodeLlama-7b Instruct finetuned on 6320 function calling and generic chat examples

Fine-tuned with LoRA on a small fraction of the glaive-function-calling-v2 dataset and a formatted (and slightly cleaned) version of sharegpt-hyperfiltered-3k

Prompt example:

[INST] <<SYS>>
<function>Available functions:
<function>{
    "name": "generate_password",
    "description": "Generate a random password with specified criteria",
    "parameters": {
        "type": "object",
        "properties": {
            "length": {
                "type": "integer",
                "description": "The length of the password"
            },
            "include_numbers": {
                "type": "boolean",
                "description": "Include numbers in the password"
            },
            "include_special_characters": {
                "type": "boolean",
                "description": "Include special characters in the password"
            }
        },
        "required": [
            "length"
        ]
    }
}
<</SYS>>

I need a new password. Can you generate one for me? [/INST]

The model then generates (note the leading space):

 Of course! How long would you like your password to be? And would you like it to include numbers and special characters?

If you extend the prompt to be:

[INST] <<SYS>>
<function>Available functions:
<function>{
    "name": "generate_password",
    "description": "Generate a random password with specified criteria",
    "parameters": {
        "type": "object",
        "properties": {
            "length": {
                "type": "integer",
                "description": "The length of the password"
            },
            "include_numbers": {
                "type": "boolean",
                "description": "Include numbers in the password"
            },
            "include_special_characters": {
                "type": "boolean",
                "description": "Include special characters in the password"
            }
        },
        "required": [
            "length"
        ]
    }
}
<</SYS>>

I need a new password. Can you generate one for me? [/INST] Of course! How long would you like your password to be? And would you like it to include numbers and special characters?</s><s>[INST] I'd like it to be 12 characters long. [/INST]

The model will generate (without the leading space):

<function>generate_password
{
    "length": 12
}

It can also answer questions based on a prompt without any functions:

[INST] In one sentence, what is a large language model? [/INST] A large language model is a type of artificial intelligence model that is trained on vast amounts of text data to generate human-like language and understand natural language input. [/INST] That's correct! Large language models are trained on large datasets of text to generate human-like language and understand natural language input. They are often used in applications such as chatbots, language translation, and text summarization.

A quick manual test shows that it's still able to follow a system prompt provided alongside any functions provided, including in multi-turn conversations. None of this was tested comprehensively, though, your results may vary.

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rizerphe
/

CodeLlama-function-calling-6320-7b-Instruct-hf

CodeLlama-7b Instruct finetuned on 6320 function calling and generic chat examples

Datasets used to train rizerphe/CodeLlama-function-calling-6320-7b-Instruct-hf