legacy-datasets/wikipedia
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How to use Hemg/english-modeling with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="Hemg/english-modeling") # Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("Hemg/english-modeling")
model = AutoModelForCausalLM.from_pretrained("Hemg/english-modeling")How to use Hemg/english-modeling with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Hemg/english-modeling"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "Hemg/english-modeling",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker model run hf.co/Hemg/english-modeling
How to use Hemg/english-modeling with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "Hemg/english-modeling" \
--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": "Hemg/english-modeling",
"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 "Hemg/english-modeling" \
--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": "Hemg/english-modeling",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'How to use Hemg/english-modeling with Docker Model Runner:
docker model run hf.co/Hemg/english-modeling
This model is a fine-tuned version of distilgpt2 on the wikipedia 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 |
|---|---|---|---|
| 1.4312 | 1.0 | 5072 | 1.3571 |
| 1.3755 | 2.0 | 10144 | 1.3165 |
Base model
distilbert/distilgpt2