Instructions to use sandylolpotty/Catalagile-5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries
PEFT
How to use sandylolpotty/Catalagile-5 with PEFT:
```
Task type is invalid.
```

How to use sandylolpotty/Catalagile-5 with Transformers:

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

pipe = pipeline("text-generation", model="sandylolpotty/Catalagile-5")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("sandylolpotty/Catalagile-5", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use sandylolpotty/Catalagile-5 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sandylolpotty/Catalagile-5"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sandylolpotty/Catalagile-5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/sandylolpotty/Catalagile-5

SGLang

How to use sandylolpotty/Catalagile-5 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 "sandylolpotty/Catalagile-5" \
    --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": "sandylolpotty/Catalagile-5",
		"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 "sandylolpotty/Catalagile-5" \
        --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": "sandylolpotty/Catalagile-5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use sandylolpotty/Catalagile-5 with Docker Model Runner:
```
docker model run hf.co/sandylolpotty/Catalagile-5
```

🧠 Catalagile-5 by Aryamann Patne

Fine-tuned Conversational Model using LoRA

🧾 Model Summary

This is a LoRA fine-tuned version of Microsoft’s DialoGPT-medium, optimized for smooth, natural, and contextually aware conversations.
It learns from a curated question-answer dataset to produce coherent and personalized dialogue — perfect for lightweight chatbots and assistants.

📘 Model Details

Developer: Aryamann Patne
Model Type: Large Language Model
Base Model: microsoft/DialoGPT-medium
Fine-tuning Framework: PEFT (Parameter-Efficient Fine-Tuning)
Languages: English
License: MIT
Training Environment: Local (GPU A100)
Fine-tuned Dataset: Custom QA dataset (``)

🔗 Model Sources

Repository: https://huggingface.co/sandylolpotty/Catalagile-5
Base Model: https://huggingface.co/microsoft/DialoGPT-medium
Frameworks: PyTorch, Transformers, Datasets, PEFT

🎯 Intended Uses

Direct Use

Chatbots
Personal Assistants
Roleplay / Story Bots
Educational Question Answering

Downstream Fine-tuning

Domain-specific assistants (e.g., legal, customer support, education)
Emotionally adaptive or multilingual conversational bots

Out-of-Scope

Disinformation or harmful generation
Explicit or offensive outputs
Any context requiring factual precision without validation

⚠️ Bias, Risks, and Limitations

While trained responsibly, this model may still:

Reflect social or linguistic bias from its base data
Produce hallucinated or incorrect facts
Respond with verbosity or mild incoherence in edge cases

Recommendation: Always review model outputs before public deployment.

🚀 How to Use

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_name = "AryamannPatne/DialoGPT-medium-LoRA"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "Hey, how are you doing today?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=100, do_sample=True, top_p=0.9)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloads last month: 3

Model tree for sandylolpotty/Catalagile-5

Base model

microsoft/DialoGPT-medium

Adapter

(34)

this model