Instructions to use MayaNk-06/PikaGheya with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MayaNk-06/PikaGheya with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="MayaNk-06/PikaGheya")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("MayaNk-06/PikaGheya") model = AutoModelForCausalLM.from_pretrained("MayaNk-06/PikaGheya", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use MayaNk-06/PikaGheya with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "MayaNk-06/PikaGheya" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "MayaNk-06/PikaGheya", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/MayaNk-06/PikaGheya
- SGLang
How to use MayaNk-06/PikaGheya 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 "MayaNk-06/PikaGheya" \ --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": "MayaNk-06/PikaGheya", "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 "MayaNk-06/PikaGheya" \ --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": "MayaNk-06/PikaGheya", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use MayaNk-06/PikaGheya with Docker Model Runner:
docker model run hf.co/MayaNk-06/PikaGheya
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Download README.md from MayaNk-06/PikaGheya: direct link, hf CLI and curl.
- Browser
- Download file 1.24 kB
-
https://huggingface.co/MayaNk-06/PikaGheya/resolve/3ffa3b72ff26d65620862f9c84a3dcd82941079a/README.md
- Command line
-
hf download hf://MayaNk-06/PikaGheya@3ffa3b72ff26d65620862f9c84a3dcd82941079a/README.md
-
curl -L -o README.md https://huggingface.co/MayaNk-06/PikaGheya/resolve/3ffa3b72ff26d65620862f9c84a3dcd82941079a/README.md
1.24 kB
metadata
library_name: transformers
license: other
base_model: Finisha-F-scratch/Gheya-111M
tags:
- generated_from_trainer
model-index:
- name: PikaGheya
results: []
PikaGheya
This model is a fine-tuned version of Finisha-F-scratch/Gheya-111M on an unknown dataset.
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- num_epochs: 1
Training results
Framework versions
- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2