Instructions to use RuVI-AI/Pragya-EXP3-CHAT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RuVI-AI/Pragya-EXP3-CHAT with Transformers:

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

pipe = pipeline("text-generation", model="RuVI-AI/Pragya-EXP3-CHAT", trust_remote_code=True)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("RuVI-AI/Pragya-EXP3-CHAT", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use RuVI-AI/Pragya-EXP3-CHAT with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RuVI-AI/Pragya-EXP3-CHAT"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RuVI-AI/Pragya-EXP3-CHAT",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/RuVI-AI/Pragya-EXP3-CHAT

SGLang

How to use RuVI-AI/Pragya-EXP3-CHAT 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 "RuVI-AI/Pragya-EXP3-CHAT" \
    --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": "RuVI-AI/Pragya-EXP3-CHAT",
		"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 "RuVI-AI/Pragya-EXP3-CHAT" \
        --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": "RuVI-AI/Pragya-EXP3-CHAT",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use RuVI-AI/Pragya-EXP3-CHAT with Docker Model Runner:
```
docker model run hf.co/RuVI-AI/Pragya-EXP3-CHAT
```

Pragya-EXP3-CHAT / training_args.bin

ArushBuilds

Upload checkpoint-15858 (SFT, final step)

f1ef71c verified 2 days ago

History Blame Contribute Delete

5.71 kB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: cc932c062d6bbf435155e520cd713e46a7064fca6e740e1d47fb73c07058ec4e
Size of remote file:: 5.71 kB
SHA256:: a3ff09b42b89a88656691947c7e244735d8ffdefa6b498e6ffca06b8d6473aba

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.