Instructions to use aliMohammad16/pragmaticLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use aliMohammad16/pragmaticLM with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="aliMohammad16/pragmaticLM")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("aliMohammad16/pragmaticLM")
model = AutoModelForSeq2SeqLM.from_pretrained("aliMohammad16/pragmaticLM")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use aliMohammad16/pragmaticLM with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "aliMohammad16/pragmaticLM"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "aliMohammad16/pragmaticLM",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/aliMohammad16/pragmaticLM

SGLang

How to use aliMohammad16/pragmaticLM 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 "aliMohammad16/pragmaticLM" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "aliMohammad16/pragmaticLM",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "aliMohammad16/pragmaticLM" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "aliMohammad16/pragmaticLM",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use aliMohammad16/pragmaticLM with Docker Model Runner:
```
docker model run hf.co/aliMohammad16/pragmaticLM
```

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

PragmaticLM - T5 for Prompt Restructuring

📌 Overview

PragmaticLM is a fine-tuned T5 model designed to restructure and reframe user prompts for better understanding by downstream LLMs. The model enhances prompt clarity by leveraging contextual restructuring techniques.

🚀 Model Details

Base Model: T5-Base
Training Data: [Indirect Requests] (https://huggingface.co/datasets/msamogh/indirect-requests)
Task Type: Text-to-text transformation
Library: Hugging Face Transformers

📊 Training Configuration

Epochs: 10
Batch Size: 8
Learning Rate: Encoder: 1e-5, Decoder: 3e-5
Optimizer: AdamW
Loss Function: Cross-entropy loss
Hardware: GPU (T4)

⚡ Usage

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

tokenizer = AutoTokenizer.from_pretrained("aliMohammad16/pragmaticLM")
model = AutoModelForSeq2SeqLM.from_pretrained("aliMohammad16/pragmaticLM")

def restructure_prompt(input_prompt):
    input_text = f"Restructure Prompt: {input_prompt}"
    inputs = tokenizer(input_text, return_tensors="pt", padding=True)
    
    output = model.generate(
        inputs.input_ids,
        max_length=64,
        num_beams=4,
        early_stopping=True
    )
    
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example Usage
test_prompt = "I am not feeeling well. I need to consult a doctor nearby."
print(restructure_prompt(test_prompt))

⏳ Improvements

Work in progress: This is a work in progress. I am actively working on this model.
Update: Next I am implementing a multimodular pipeline, integrating TinyLlama 1.1B and Llama Index RAG with prompt-restructuring model, to improve output generation.

Downloads last month: -

Safetensors

Model size

0.2B params

Tensor type

F32

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