Instructions to use mlsquare/pico_seshu_test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mlsquare/pico_seshu_test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="mlsquare/pico_seshu_test")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("mlsquare/pico_seshu_test") model = AutoModelForCausalLM.from_pretrained("mlsquare/pico_seshu_test") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use mlsquare/pico_seshu_test with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "mlsquare/pico_seshu_test" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "mlsquare/pico_seshu_test", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/mlsquare/pico_seshu_test
- SGLang
How to use mlsquare/pico_seshu_test 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 "mlsquare/pico_seshu_test" \ --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": "mlsquare/pico_seshu_test", "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 "mlsquare/pico_seshu_test" \ --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": "mlsquare/pico_seshu_test", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use mlsquare/pico_seshu_test with Docker Model Runner:
docker model run hf.co/mlsquare/pico_seshu_test
Model Card for Model ID
Testing model for the Seshu pipeline.
Model Details
Model Description
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by: MLsquare
- Model type: Next Character Generation
- Language(s) (NLP): All languages in ai4bharat/samanantar dataset
- License: MIT
Model Sources [optional]
- Repository: https://github.com/LegallyCoder/mamba-hf
- Paper: https://arxiv.org/abs/2312.00752
Uses
Refer to the github repository for more information
Direct Use
Refer to the github repository for more information
How to Get Started with the Model
Refer to the github repository: https://github.com/mlsquare/fedem
Training Details
Training Data
Individual target and source sentences from the AI4Bharat Samanantar dataset. All 11 language sentences and their translations have been stacked and used for next character generation task.
Training Procedure
Trained on next character generation task using cross-entropy loss.
Preprocessing [optional]
converted to raw UTF8 characters before training by using ByT5-large tokenizer
Training Hyperparameters
- Training regime: output_dir="mamba", per_device_train_batch_size=1, per_device_eval_batch_size=1, num_train_epochs=4, weight_decay=0.1, lr_scheduler_type="cosine", learning_rate=5e-4, fp16=False,
Evaluation
A simple cross-entropy loss has been used to test the pipeline and working of the model.
Model Card Contact
MLsquare
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