Instructions to use retrain-pipelines/function_caller_lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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
```

function_caller_lora / README.md

Aurelien-Morgan-Bot

v0.32 - 2026-02-21 01:24:19 UTC - retrain-pipelines v0.1.2 - Upload model and tokenizer with README.

e9f7bcc verified 3 months ago

preview code

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metadata

version: '0.32'
timestamp: 20260221_012419846_UTC
model_name: retrain-pipelines Function Caller
base_model: unsloth/Qwen2.5-1.5B
base_model_relation: adapter
library_name: transformers
datasets:
  - retrain-pipelines/func_calls_ds
license: apache-2.0
language:
  - en
task_categories:
  - text-generation
tags:
  - retrain-pipelines
  - function-calling
  - LLM Agent
  - code
  - unsloth
thumbnail: >-
  https://cdn-avatars.huggingface.co/v1/production/uploads/651e93137b2a2e027f9e55df/96hzBved0YMjCq--s0kad.png
pipeline_tag: text-generation
widget:
  - text: Hello
    example_title: No function call
    output:
      text: '[]'
  - text: Is 49 a perfect square?
    example_title: Perfect square
    output:
      text: '[{"name": "is_perfect_square", "arguments": {"num": 49}}]'
exec_id: '135'
model-index:
  - name: retrain-pipelines Function Caller
    results:
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: retrain-pipelines Function Calling
          type: retrain-pipelines/func_calls_ds
          split: validation
          revision: 70f9a2864d71472a383596aa4ffec6e603d762ea
        metrics:
          - type: precision
            value: 0.78253722729447
          - type: recall
            value: 0.7825566381742679
          - type: f1
            value: 0.7823158181198717
          - type: jaccard
            value: 0.7654290889550754

retrain-pipelines Function Caller

version 0.32 - 2026-02-21 01:24:19 UTC (retraining source-code | pipeline-card)

Training dataset :

retrain-pipelines/func_calls_ds v0.31 (70f9a28 - 2026-02-20 18:34:18 UTC)

Base model :

unsloth/Qwen2.5-1.5B (1582479 - 2025-04-28 04:13:37 UTC)

arxiv :
- 2407.10671

The herein LoRa adapter can for instance be used as follows :

from transformers import AutoModelForCausalLM, AutoTokenizer
from torch import device, cuda

repo_id = "retrain-pipelines/function_caller_lora"
revision = "<model_revision_commit_hash>"
model = AutoModelForCausalLM.from_pretrained(
    repo_id, revision=revision, torch_dtype="auto", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(
    repo_id, revision=revision, torch_dtype="auto", device_map="auto")

device = device("cuda" if cuda.is_available() else "cpu")
def generate_tool_calls_list(query, max_new_tokens=400) -> str:
    formatted_query = tokenizer.chat_template.format(query, "")
    inputs = tokenizer(formatted_query, return_tensors="pt").input_ids.to(device)
    outputs = model.generate(inputs, max_new_tokens=max_new_tokens, do_sample=False)
    generated_text = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]
    return generated_text[len(formatted_query):].strip()

generate_tool_calls_list("Is 49 a perfect square ?")

retrain-pipelines
          0.1.2

- Run by Aurelien-Morgan-Bot - UnslothFuncCallFlow - exec_id : 135