Text Generation
Transformers
TensorBoard
Safetensors
English
retrain-pipelines
function-calling
LLM Agent
code
unsloth
conversational
Eval Results (legacy)
Instructions to use retrain-pipelines/function_caller_lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use retrain-pipelines/function_caller_lora with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="retrain-pipelines/function_caller_lora") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("retrain-pipelines/function_caller_lora", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use retrain-pipelines/function_caller_lora with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "retrain-pipelines/function_caller_lora" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "retrain-pipelines/function_caller_lora", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/retrain-pipelines/function_caller_lora
- SGLang
How to use retrain-pipelines/function_caller_lora 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 "retrain-pipelines/function_caller_lora" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "retrain-pipelines/function_caller_lora", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "retrain-pipelines/function_caller_lora" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "retrain-pipelines/function_caller_lora", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Unsloth Studio new
How to use retrain-pipelines/function_caller_lora 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 retrain-pipelines/function_caller_lora 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 retrain-pipelines/function_caller_lora to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for retrain-pipelines/function_caller_lora to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="retrain-pipelines/function_caller_lora", max_seq_length=2048, ) - Docker Model Runner
How to use retrain-pipelines/function_caller_lora with Docker Model Runner:
docker model run hf.co/retrain-pipelines/function_caller_lora
Ctrl+K
source-code for model version v0.29_20250411_235544461_UTC- retrain-pipelines 0.1.1
24320a8 verified - v0.10_20250318_214952149_UTC
- v0.11_20250319_195416772_UTC
- v0.12_20250319_200810260_UTC
- v0.13_20250319_201841786_UTC
- v0.14_20250319_202714437_UTC
- v0.15_20250319_203852022_UTC
- v0.16_20250319_204732506_UTC
- v0.17_20250323_184133616_UTC
- v0.18_20250323_235054255_UTC
- v0.19_20250324_231253566_UTC
- v0.1_20250311_013607311_UTC
- v0.20_20250326_233239071_UTC
- v0.21_20250331_222709741_UTC
- v0.22_20250402_012414955_UTC
- v0.23_20250402_235920423_UTC
- v0.24_20250405_014920180_UTC
- v0.25_20250405_180914116_UTC
- v0.26_20250406_214154190_UTC
- v0.27_20250407_215502438_UTC
- v0.28_20250409_220919394_UTC
- v0.29_20250411_235544461_UTC
- v0.2_20250314_233618380_UTC
- v0.4_20250316_192317869_UTC
- v0.5_20250318_181752621_UTC
- v0.6_20250318_185416388_UTC
- v0.7_20250318_190040840_UTC
- v0.8_20250318_201129736_UTC
- v0.9_20250318_212409155_UTC
- 1.52 kB