Instructions to use ArushBuilds/Pragya-Preview with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ArushBuilds/Pragya-Preview with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ArushBuilds/Pragya-Preview")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ArushBuilds/Pragya-Preview", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use ArushBuilds/Pragya-Preview with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ArushBuilds/Pragya-Preview" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ArushBuilds/Pragya-Preview", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/ArushBuilds/Pragya-Preview
- SGLang
How to use ArushBuilds/Pragya-Preview 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 "ArushBuilds/Pragya-Preview" \ --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": "ArushBuilds/Pragya-Preview", "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 "ArushBuilds/Pragya-Preview" \ --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": "ArushBuilds/Pragya-Preview", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use ArushBuilds/Pragya-Preview with Docker Model Runner:
docker model run hf.co/ArushBuilds/Pragya-Preview
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README.md
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**Pragya Preview** is an experimental, lightweight language model fine-tuned on the [TinyStories](https://huggingface.co/datasets/roneneldan/TinyStories) dataset. Built as a proof-of-concept under strict compute limits (1-hour single T4 GPU run), it demonstrates rapid baseline convergence and efficient short-form narrative generation.
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- **Developer:**
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- **Model Type:** Causal Language Model (Decoder-only Transformer)
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- **Language:** English (`en`)
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- **Base Dataset:** `roneneldan/TinyStories`
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- **License:** Apache 2.0
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- **Primary Task:** Text Generation / Short Story Completion
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## Intended Uses & Scope
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**Pragya Preview** is an experimental, lightweight language model fine-tuned on the [TinyStories](https://huggingface.co/datasets/roneneldan/TinyStories) dataset. Built as a proof-of-concept under strict compute limits (1-hour single T4 GPU run), it demonstrates rapid baseline convergence and efficient short-form narrative generation.
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- **Developer:** Arush Kumar and Pragya's team
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- **Model Type:** Causal Language Model using Grouped Query Attention(Decoder-only Transformer)
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- **Language:** English (`en`)
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- **Base Dataset:** `roneneldan/TinyStories`
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- **License:** Apache 2.0
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- **Primary Task:** Text Generation / Short Story Completion
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- **Note**: Model is not Instruct tuned and is not trained completely
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## Intended Uses & Scope
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