zeio/baneks
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How to use zeio/wit with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="zeio/wit") # Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("zeio/wit")
model = AutoModelForCausalLM.from_pretrained("zeio/wit", device_map="auto")How to use zeio/wit with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "zeio/wit"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "zeio/wit",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker model run hf.co/zeio/wit
How to use zeio/wit with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "zeio/wit" \
--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": "zeio/wit",
"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 "zeio/wit" \
--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": "zeio/wit",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'How to use zeio/wit with Docker Model Runner:
docker model run hf.co/zeio/wit
This model is a fine-tuned version of igorktech/rugpt3-joker-150k on the baneks dataset for 10 epochs. It achieved 2.0391 overall loss during training.
Model evaluation has not been performed.
The model is a fine-tuned variant of the igorktech/rugpt3-joker-150k architecture with causal language modeling head.
The model should be used for studying abilities of natural language models to generate jokes.
The model is trained on a list of anecdotes pulled from a few vk communities (see baneks dataset for more details).
The following hyperparameters were used during training:
| Train Loss | Epoch |
|---|---|
| 2.0391 | 10 |