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
Download config.json from RuVI-AI/Pragya-EXP2: direct link, hf CLI and curl.
- Browser
- Download file 1.23 kB
-
https://huggingface.co/RuVI-AI/Pragya-EXP2/resolve/main/config.json
- Command line
-
hf download hf://RuVI-AI/Pragya-EXP2/config.json
-
curl -L -o config.json https://huggingface.co/RuVI-AI/Pragya-EXP2/resolve/main/config.json
1.23 kB
| { | |
| "architectures": [ | |
| "PragyaForCausalLM" | |
| ], | |
| "auto_map": { | |
| "AutoConfig": "configuration_pragya.PragyaConfig", | |
| "AutoModelForCausalLM": "modeling_pragya.PragyaForCausalLM" | |
| }, | |
| "bias": false, | |
| "block_size": 1024, | |
| "d_rope": null, | |
| "dropout": 0.0, | |
| "engram_compressed_vocab": 9807, | |
| "engram_embed_per_ngram": 128, | |
| "engram_kernel_size": 4, | |
| "engram_layer_ids": [ | |
| 1, | |
| 8 | |
| ], | |
| "engram_max_ngram": 3, | |
| "engram_n_heads": 4, | |
| "engram_seed": 0, | |
| "engram_vocab_size": [ | |
| 4096, | |
| 4096 | |
| ], | |
| "ffn_mult": 2, | |
| "gen_headroom": 128, | |
| "gpt_top_k": 64, | |
| "gradient_checkpointing": false, | |
| "int8_group_size": 128, | |
| "max_gen_tokens": 1024, | |
| "mod_budget_coeff": 0.05, | |
| "mod_capacity": 0.25, | |
| "mod_gate_entropy_coeff": 0.0, | |
| "mod_hard_eval": false, | |
| "model_type": "pragya_e2", | |
| "moe_fixed_capacity": null, | |
| "moe_top_k": 2, | |
| "mtp_depth": 1, | |
| "mtp_lambda": 0.1, | |
| "n_embd": 384, | |
| "n_experts": 8, | |
| "n_head": 12, | |
| "n_layer": 16, | |
| "n_shared": 1, | |
| "num_kv_heads": 6, | |
| "pattern": "dense", | |
| "sliding_window_size": 512, | |
| "tp_size": 1, | |
| "transformers_version": "5.20.1", | |
| "use_engram": true, | |
| "use_int8": false, | |
| "use_moe": false, | |
| "use_mtp": false, | |
| "use_nvfp4": false, | |
| "vocab_size": 12288 | |
| } |