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function_caller_lora

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YAML Metadata Warning: The pipeline tag "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other
retrain-pipelines Function Caller
version 0.24 - 2025-04-05 01:49:20 UTC (retraining source-code | pipeline-card)

Training dataset :

  • retrain-pipelines/func_calls_ds v0.23 (acf6174 - 2025-04-04 18:05:46 UTC)
     

Base model :

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 ?")


Powered by retrain-pipelines 0.1.1 - Run by Aurelien-Morgan-Bot - UnslothFuncCallFlow - mf_run_id : 95
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Evaluation results