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
| license: llama2 | |
| datasets: | |
| - rizerphe/glaive-function-calling-v2-llama | |
| - rizerphe/sharegpt-hyperfiltered-3k-llama | |
| - totally-not-an-llm/sharegpt-hyperfiltered-3k | |
| - glaiveai/glaive-function-calling-v2 | |
| # 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](https://huggingface.co/datasets/glaiveai/glaive-function-calling-v2) dataset and a formatted (and slightly cleaned) version of [sharegpt-hyperfiltered-3k](https://huggingface.co/datasets/totally-not-an-llm/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. |