Instructions to use jinymusim/TinyLlama-Czech-Poet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jinymusim/TinyLlama-Czech-Poet with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="jinymusim/TinyLlama-Czech-Poet")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("jinymusim/TinyLlama-Czech-Poet") model = AutoModelForCausalLM.from_pretrained("jinymusim/TinyLlama-Czech-Poet", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use jinymusim/TinyLlama-Czech-Poet with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "jinymusim/TinyLlama-Czech-Poet" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "jinymusim/TinyLlama-Czech-Poet", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/jinymusim/TinyLlama-Czech-Poet
- SGLang
How to use jinymusim/TinyLlama-Czech-Poet 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 "jinymusim/TinyLlama-Czech-Poet" \ --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": "jinymusim/TinyLlama-Czech-Poet", "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 "jinymusim/TinyLlama-Czech-Poet" \ --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": "jinymusim/TinyLlama-Czech-Poet", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use jinymusim/TinyLlama-Czech-Poet with Docker Model Runner:
docker model run hf.co/jinymusim/TinyLlama-Czech-Poet
Czech Poetry TinyLLama
TinyLLama finetuned on Czech poetry from github project by
Institute of Czech Literature, Czech Academy of Sciences.
https://github.com/versotym/corpusCzechVerse
Usage
Use as any other LM style model
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
tokenizer = AutoTokenizer.from_pretrained("jinymusim/TinyLlama-Czech-Poet")
model = AutoModelForCausalLM.from_pretrained("jinymusim/TinyLlama-Czech-Poet")
# Input Poet Start
poet_start = '<|AUTHOR|> Adámek, Bohumil'
poet_start = poet_start.strip()
tokenized_poet_start = tokenizer.encode(poet_start, return_tensors='pt')
# generated a continuation to it
out = model.generate(tokenized_poet_start,
max_length=256,
do_sample=True,
top_k=50
early_stopping=True,
pad_token_id= tokenizer.pad_token_id,
eos_token_id = tokenizer.eos_token_id)
# Decode Poet
decoded_cont = tokenizer.decode(out[0], skip_special_tokens=True)
print(decoded_cont)
Structure of outputs
Outputs are structured in following way:
<|AUTHOR|> AUTHOR
<|TITLE|> TITLE
<|YEAR|> YEAR
<|STROPHE_START|>
<|METER|> METER
<|RHYME|> RHYME SCHEMA
STROPHE
<|STROPHE_END|>
<|STROPHE_START|>
<|METER|> METER
<|RHYME|> RHYME SCHEMA
STROPHE
<|STROPHE_START|>
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Model tree for jinymusim/TinyLlama-Czech-Poet
Base model
BUT-FIT/CSTinyLlama-1.2B