mapama247/wikihow_es
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How to use Zagusan/Wikibot-3001 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="Zagusan/Wikibot-3001") # Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("Zagusan/Wikibot-3001")
model = AutoModelForCausalLM.from_pretrained("Zagusan/Wikibot-3001")How to use Zagusan/Wikibot-3001 with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Zagusan/Wikibot-3001"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "Zagusan/Wikibot-3001",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker model run hf.co/Zagusan/Wikibot-3001
How to use Zagusan/Wikibot-3001 with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "Zagusan/Wikibot-3001" \
--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": "Zagusan/Wikibot-3001",
"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 "Zagusan/Wikibot-3001" \
--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": "Zagusan/Wikibot-3001",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'How to use Zagusan/Wikibot-3001 with Docker Model Runner:
docker model run hf.co/Zagusan/Wikibot-3001
This model is a fine-tuned version of gpt2 on the mapama247/wikihow_es dataset. It achieves the following results on the evaluation set:
More information needed
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More information needed
The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 2.7432 | 1.0 | 5950 | 2.6109 |
| 2.3556 | 2.0 | 11900 | 2.3059 |
| 2.1872 | 3.0 | 17850 | 2.1738 |
| 2.0891 | 4.0 | 23800 | 2.0990 |
| 2.0306 | 5.0 | 29750 | 2.0579 |
| 1.9975 | 6.0 | 35700 | 2.0395 |
| 1.9909 | 7.0 | 5950 | 2.0208 |
| 1.8924 | 8.0 | 11900 | 1.9678 |
| 1.8281 | 9.0 | 17850 | 1.9422 |
| 1.8376 | 10.0 | 5950 | 1.9576 |
| 1.7802 | 11.0 | 11900 | 1.9212 |
| 1.7465 | 12.0 | 17850 | 1.8996 |
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