TRBLLmaker -- Transformer Reads Between Lyrics Lines maker
Paper • 2212.04917 • Published
How to use tokeron/TRBLLmaker with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="tokeron/TRBLLmaker") # Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("tokeron/TRBLLmaker")
model = AutoModelForCausalLM.from_pretrained("tokeron/TRBLLmaker")How to use tokeron/TRBLLmaker with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tokeron/TRBLLmaker"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "tokeron/TRBLLmaker",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker model run hf.co/tokeron/TRBLLmaker
How to use tokeron/TRBLLmaker with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "tokeron/TRBLLmaker" \
--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": "tokeron/TRBLLmaker",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'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 "tokeron/TRBLLmaker" \
--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": "tokeron/TRBLLmaker",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'How to use tokeron/TRBLLmaker with Docker Model Runner:
docker model run hf.co/tokeron/TRBLLmaker
Created by Mor Ventura and Michael Toker.
This model is a fine-tuned version of gpt2-medium on an unknown dataset. It achieves the following results on the evaluation set:
More information needed
More information needed
More information needed
The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 2.7781 | 1.01 | 128 | 2.6284 | 0.4875 |
| 2.6217 | 2.02 | 256 | 2.6022 | 0.4908 |
| 2.569 | 3.02 | 384 | 2.5928 | 0.4917 |