README.md

---
# @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: '0.24'

timestamp: '20250405_014920180_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: https://cdn-avatars.huggingface.co/v1/production/uploads/651e93137b2a2e027f9e55df/96hzBved0YMjCq--s0kad.png


# @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: '95'

# @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: acf61743e7c3e846b74162444f86e65852d2bbf6
    metrics:
      - type: precision
        value: 0.7742409706115723
      - type: recall
        value: 0.7735999822616577
      - type: f1
        value: 0.7736773490905762
      - type: jaccard
        value: 0.7556698322296143

---

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    <b>retrain-pipelines Function Caller</b>
</div>
  <code>version 0.24</code>  -  <code>2025-04-05 01:49:20 UTC</code>
  (retraining
  <a target="_blank"
     href="https://huggingface.co/retrain-pipelines/function_caller_lora/tree/retrain-pipelines_source-code/v0.24_20250405_014920180_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=v0.24_20250405_014920180_UTC">pipeline-card</a>)
</div>

Training dataset&nbsp;:
- <code>retrain-pipelines/func_calls_ds v0.23</code>
(<a href="https://huggingface.co/datasets/retrain-pipelines/func_calls_ds/blob/acf61743e7c3e846b74162444f86e65852d2bbf6/README.md"
    target="_blank">acf6174</a> -
    2025-04-04 18:05:46 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&amp;query=%24.downloads&amp;logo=huggingface&amp;label=downloads"  class="inline-block" />&nbsp;<img alt="" src="https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fhuggingface.co%2Fapi%2Fdatasets%2Fretrain-pipelines/func_calls_ds&amp;query=%24.likes&amp;logo=huggingface&amp;label=likes"  class="inline-block" />

Base model&nbsp;:
- <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&amp;query=%24.downloads&amp;logo=huggingface&amp;label=downloads"  class="inline-block" />&nbsp;<img alt="" src="https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fhuggingface.co%2Fapi%2Fmodels%2Funsloth/Qwen2.5-1.5B&amp;query=%24.likes&amp;logo=huggingface&amp;label=likes"  class="inline-block" /><br />
arxiv&nbsp;:<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&nbsp;:<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&nbsp;: <code>95</code>
  </div>
</div>