Text Generation
Transformers
Safetensors
English
pragya_e2
small-language-model
from-scratch
causal-lm
decoder-only
gqa
rope
swiglu
exclusive-gated-attention
engram
custom_code
Instructions to use RuVI-AI/Pragya-EXP2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RuVI-AI/Pragya-EXP2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="RuVI-AI/Pragya-EXP2", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("RuVI-AI/Pragya-EXP2", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use RuVI-AI/Pragya-EXP2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "RuVI-AI/Pragya-EXP2" # 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-EXP2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/RuVI-AI/Pragya-EXP2
- SGLang
How to use RuVI-AI/Pragya-EXP2 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-EXP2" \ --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-EXP2", "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-EXP2" \ --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-EXP2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use RuVI-AI/Pragya-EXP2 with Docker Model Runner:
docker model run hf.co/RuVI-AI/Pragya-EXP2
Commit History
Yesterday
-
Update README.md
Verified signature105ad5a -
Update README.md
Verified signature171c635
2 days ago
-
Update kernel.py
Verified signature05acbb5 -
Update kernel.py
Verified signatured55c0ec -
Update kernel.py
Verified signature50b152b -
Update modeling_pragya.py
Verified signatureeaefd58 -
Update tokenization_pragya.py
Verified signaturec306148 -
Update README.md
Verified signaturefa0f177 -
Update README.md
Verified signatureb3399ce -
Update README.md
Verified signatureea2ef4c -
Update modeling_pragya.py
Verified signaturee98c009 -
Update modeling_pragya.py
Verified signaturef5bec04 -
Rename pragya.tokenizer.json to tokenizer.json
Verified signature87b35cc -
Update README.md
Verified signature9861fb6 -
Create README.md
Verified signature16eabf2 -
Create config.json
Verified signature53e1201 -
Delete checkpoints
Verified signature10f1631
3 days ago
-
Upload tokenization_pragya.py
Verified signatureb8731f0 -
Upload 4 files
Verified signaturebc89891 -
Upload 6 files
Verified signature7a936fb -
Delete tokenizer_1.vocab
Verified signaturebe4fa61 -
Delete tokenizer_1.py
Verified signature29c4061 -
Delete tokenizer_1.model
Verified signature7c78f71 -
Delete tokenizer.vocab
Verified signature4f30f38 -
Delete tokenizer.model
Verified signature1c7baa8 -
Delete tokenizer
Verified signaturefff493e -
Delete __marimo__
Verified signature790a1c1 -
Duplicate from RuVI-AI/pragya-e2
Co-authored-by: Arush Codes <ArushBuilds@users.noreply.huggingface.co>b8969c1