Instructions to use archit11/small-function-calling with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use archit11/small-function-calling with Transformers:

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

pipe = pipeline("text-generation", model="archit11/small-function-calling")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("archit11/small-function-calling")
model = AutoModelForCausalLM.from_pretrained("archit11/small-function-calling", device_map="auto")

llama-cpp-python

How to use archit11/small-function-calling with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="archit11/small-function-calling",
	filename="biggliesmol-fn-v0.2.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use archit11/small-function-calling with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf archit11/small-function-calling
# Run inference directly in the terminal:
llama cli -hf archit11/small-function-calling

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf archit11/small-function-calling
# Run inference directly in the terminal:
llama cli -hf archit11/small-function-calling

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf archit11/small-function-calling
# Run inference directly in the terminal:
./llama-cli -hf archit11/small-function-calling

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf archit11/small-function-calling
# Run inference directly in the terminal:
./build/bin/llama-cli -hf archit11/small-function-calling

Use Docker

docker model run hf.co/archit11/small-function-calling

LM Studio
Jan

vLLM

How to use archit11/small-function-calling with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "archit11/small-function-calling"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "archit11/small-function-calling",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/archit11/small-function-calling

SGLang

How to use archit11/small-function-calling 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 "archit11/small-function-calling" \
    --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": "archit11/small-function-calling",
		"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 "archit11/small-function-calling" \
        --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": "archit11/small-function-calling",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use archit11/small-function-calling with Ollama:
```
ollama run hf.co/archit11/small-function-calling
```

Unsloth Studio

How to use archit11/small-function-calling with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for archit11/small-function-calling to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for archit11/small-function-calling to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for archit11/small-function-calling to start chatting

Atomic Chat new
Docker Model Runner
How to use archit11/small-function-calling with Docker Model Runner:
```
docker model run hf.co/archit11/small-function-calling
```

Lemonade

How to use archit11/small-function-calling with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull archit11/small-function-calling

Run and chat with the model

lemonade run user.small-function-calling-{{QUANT_TAG}}

List all available models

lemonade list

small-function-calling / README.md

archit11

Update README.md

a691d64 verified almost 2 years ago

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metadata

library_name: transformers
license: mit
base_model: nisten/Biggie-SmoLlm-0.15B-Base
tags:
  - generated_from_trainer
  - function_calling
  - function-calling
  - GGUF
model-index:
  - name: capybara_finetuned_results
    results: []
datasets:
  - NousResearch/hermes-function-calling-v1
pipeline_tag: text2text-generation

capybara_finetuned_results

This model is a fine-tuned version of nisten/Biggie-SmoLlm-0.15B-Base on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.0289

Model description

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0002
train_batch_size: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 16
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 15
training_steps: 300

Training results

Training Loss	Epoch	Step	Validation Loss
0.0284	8.4507	300	0.0289

Framework versions

Transformers 4.44.2
Pytorch 2.4.0
Datasets 3.0.0
Tokenizers 0.19.1