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---
# @see https://github.com/huggingface/hub-docs/blob/main/modelcard.md
# @see https://huggingface.co/docs/huggingface_hub/guides/model-cards#update-metadata
# @see https://huggingface.co/docs/hub/model-cards#model-card-metadata
version: '0.29'
timestamp: '20250411_235544461_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:
- text2text-generation
tags:
- retrain-pipelines
- function-calling
- LLM Agent
- code
- unsloth
thumbnail: /static-proxy?url=https%3A%2F%2Fcdn-avatars.huggingface.co%2Fv1%2Fproduction%2Fuploads%2F651e93137b2a2e027f9e55df%2F96hzBved0YMjCq--s0kad.png%3C%2Fspan%3E
# @see https://huggingface.co/docs/hub/models-widgets#enabling-a-widget
# @see https://huggingface.co/docs/hub/models-widgets-examples
# @see https://huggingface.co/docs/hub/en/model-cards#specifying-a-task--pipelinetag-
pipeline_tag: text2text-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}}]'
mf_run_id: '101'
# @see https://huggingface.co/docs/huggingface_hub/guides/model-cards#include-evaluation-results
# @see https://huggingface.co/docs/huggingface_hub/main/en/package_reference/cards#huggingface_hub.EvalResult
model-index:
- name: retrain-pipelines Function Caller
results:
- task:
type: text2text-generation
name: Text2Text Generation
dataset:
name: retrain-pipelines Function Calling
type: retrain-pipelines/func_calls_ds
split: validation
revision: fa58022553264b9c59d724f2cd42849c6ed92920
metrics:
- type: precision
value: 0.7681936025619507
- type: recall
value: 0.767867922782898
- type: f1
value: 0.7676968574523926
- type: jaccard
value: 0.7502401471138
---
<div
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<b>retrain-pipelines Function Caller</b>
</div>
<code>version 0.29</code> - <code>2025-04-11 23:55:44 UTC</code>
(retraining
<a target="_blank"
href="https://huggingface.co/retrain-pipelines/function_caller_lora/tree/retrain-pipelines_source-code/v0.29_20250411_235544461_UTC">source-code</a> |
<a target="_blank"
href="https://huggingface.co/spaces/retrain-pipelines/online_pipeline_card_renderer/?model_repo_id=retrain-pipelines/function_caller_lora&version_id=v0.29_20250411_235544461_UTC">pipeline-card</a>)
</div>
Training dataset :
- <code>retrain-pipelines/func_calls_ds v0.28</code>
(<a href="https://huggingface.co/datasets/retrain-pipelines/func_calls_ds/blob/fa58022553264b9c59d724f2cd42849c6ed92920/README.md"
target="_blank">fa58022</a> -
2025-04-11 16:23:27 UTC)
<br />
<img alt="" src="https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fhuggingface.co%2Fapi%2Fdatasets%2Fretrain-pipelines/func_calls_ds&query=%24.downloads&logo=huggingface&label=downloads" class="inline-block" /> <img alt="" src="https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fhuggingface.co%2Fapi%2Fdatasets%2Fretrain-pipelines/func_calls_ds&query=%24.likes&logo=huggingface&label=likes" class="inline-block" />
Base model :
- <code>unsloth/Qwen2.5-1.5B</code>
(<a href="https://huggingface.co/unsloth/Qwen2.5-1.5B/blob/2d0a015faee2c1af360a6725a30c4d7a258ac4d4/README.md"
target="_blank">2d0a015</a> -
2025-02-06 02:32:14 UTC)
<br />
<img alt="" src="https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fhuggingface.co%2Fapi%2Fmodels%2Funsloth/Qwen2.5-1.5B&query=%24.downloads&logo=huggingface&label=downloads" class="inline-block" /> <img alt="" src="https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fhuggingface.co%2Fapi%2Fmodels%2Funsloth/Qwen2.5-1.5B&query=%24.likes&logo=huggingface&label=likes" class="inline-block" /><br />
arxiv :<br />
- <code><a href="https://huggingface.co/papers/2407.10671"
target="_blank">2407.10671</a></code><br />
The herein LoRa adapter can for instance be used as follows :<br />
```python
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 ?")
```
<br />
<br />
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Powered by
<code><a target="_blank"
href="https://github.com/aurelienmorgan/retrain-pipelines">retrain-pipelines
0.1.1</a></code> -
<code>Run by <a target="_blank" href="https://huggingface.co/Aurelien-Morgan-Bot">Aurelien-Morgan-Bot</a></code> -
<em><b>UnslothFuncCallFlow</b></em> - mf_run_id : <code>101</code>
</div>
</div>
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