Instructions to use glaiveai/glaive-function-calling-v2-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use glaiveai/glaive-function-calling-v2-small with Transformers:

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

pipe = pipeline("text-generation", model="glaiveai/glaive-function-calling-v2-small", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("glaiveai/glaive-function-calling-v2-small", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("glaiveai/glaive-function-calling-v2-small", trust_remote_code=True)

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use glaiveai/glaive-function-calling-v2-small with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "glaiveai/glaive-function-calling-v2-small"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "glaiveai/glaive-function-calling-v2-small",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/glaiveai/glaive-function-calling-v2-small

SGLang

How to use glaiveai/glaive-function-calling-v2-small 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 "glaiveai/glaive-function-calling-v2-small" \
    --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": "glaiveai/glaive-function-calling-v2-small",
		"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 "glaiveai/glaive-function-calling-v2-small" \
        --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": "glaiveai/glaive-function-calling-v2-small",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use glaiveai/glaive-function-calling-v2-small with Docker Model Runner:
```
docker model run hf.co/glaiveai/glaive-function-calling-v2-small
```

Cant run the model

by lmnewey - opened Dec 14, 2023

Discussion

lmnewey

Dec 14, 2023

Hey guys, this might be a noob question but Im struggling to run this model. getting the following error

Exception has occurred: AttributeError
'ReplitLMTokenizer' object has no attribute 'sp_model'
File "/home/../.cache/huggingface/modules/transformers_modules/glaiveai/glaive-function-calling-v2-small/ee756352b27602951abdc7618c9139f1bb8e5031/replit_lm_tokenizer.py", line 85, in vocab_size
return self.sp_model.get_piece_size()
File "/home/../.cache/huggingface/modules/transformers_modules/glaiveai/glaive-function-calling-v2-small/ee756352b27602951abdc7618c9139f1bb8e5031/replit_lm_tokenizer.py", line 88, in get_vocab
vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
File "/home/../.cache/huggingface/modules/transformers_modules/glaiveai/glaive-function-calling-v2-small/ee756352b27602951abdc7618c9139f1bb8e5031/replit_lm_tokenizer.py", line 78, in init
super().init(bos_token=bos_token, eos_token=eos_token, unk_token=unk_token, pad_token=pad_token, sep_token=sep_token, sp_model_kwargs=self.sp_model_kwargs, **kwargs)
File "/LocalStorage/ScratchPad/vllmtest.py", line 14, in
llm = LLM(model="glaiveai/glaive-function-calling-v2-small",trust_remote_code=True)
AttributeError: 'ReplitLMTokenizer' object has no attribute 'sp_model'

lmnewey

Dec 14, 2023

I got to the bottom of this, it seems like the recent update to replit tokenizer has something messed up.

You need to swap some lines around in def init
You need to set self.sp_model before calling super().init

Also there is some lines in hf_prefixlm that needed commenting out.

Adjust the lines of replit_lm_tokenizer after line 64 to look like this
self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
self.vocab_file = vocab_file
self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
self.sp_model.Load(vocab_file)
super().init(bos_token=bos_token, eos_token=eos_token, unk_token=unk_token, pad_token=pad_token, sep_token=sep_token, sp_model_kwargs=self.sp_model_kwargs, **kwargs)

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