Instructions to use Ravi9058/ParentGPT_RaviPrakash with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Ravi9058/ParentGPT_RaviPrakash with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.3")
model = PeftModel.from_pretrained(base_model, "Ravi9058/ParentGPT_RaviPrakash")

Transformers

How to use Ravi9058/ParentGPT_RaviPrakash with Transformers:

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

pipe = pipeline("text-generation", model="Ravi9058/ParentGPT_RaviPrakash")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Ravi9058/ParentGPT_RaviPrakash", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Ravi9058/ParentGPT_RaviPrakash with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Ravi9058/ParentGPT_RaviPrakash"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Ravi9058/ParentGPT_RaviPrakash",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Ravi9058/ParentGPT_RaviPrakash

SGLang

How to use Ravi9058/ParentGPT_RaviPrakash 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 "Ravi9058/ParentGPT_RaviPrakash" \
    --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": "Ravi9058/ParentGPT_RaviPrakash",
		"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 "Ravi9058/ParentGPT_RaviPrakash" \
        --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": "Ravi9058/ParentGPT_RaviPrakash",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Ravi9058/ParentGPT_RaviPrakash with Docker Model Runner:
```
docker model run hf.co/Ravi9058/ParentGPT_RaviPrakash
```

ParentGPT_RaviPrakash

This model is a fine-tuned version of mistralai/Mistral-7B-Instruct-v0.3 on the None dataset.

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0002
train_batch_size: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 32
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
training_steps: 800
mixed_precision_training: Native AMP

Framework versions

PEFT 0.18.1
Transformers 4.57.6
Pytorch 2.8.0+cu126
Datasets 4.5.0
Tokenizers 0.22.1

Downloads last month: 7

Model tree for Ravi9058/ParentGPT_RaviPrakash

Base model

mistralai/Mistral-7B-v0.3

Finetuned

mistralai/Mistral-7B-Instruct-v0.3

Adapter

(897)

this model