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
- Transformers
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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