Text Generation
Transformers
TensorBoard
Safetensors
gpt2
Generated from Trainer
text-generation-inference
Instructions to use ninagroot/GPT2-705M-RUN4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ninagroot/GPT2-705M-RUN4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ninagroot/GPT2-705M-RUN4")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("ninagroot/GPT2-705M-RUN4") model = AutoModelForCausalLM.from_pretrained("ninagroot/GPT2-705M-RUN4") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use ninagroot/GPT2-705M-RUN4 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ninagroot/GPT2-705M-RUN4" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ninagroot/GPT2-705M-RUN4", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/ninagroot/GPT2-705M-RUN4
- SGLang
How to use ninagroot/GPT2-705M-RUN4 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 "ninagroot/GPT2-705M-RUN4" \ --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": "ninagroot/GPT2-705M-RUN4", "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 "ninagroot/GPT2-705M-RUN4" \ --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": "ninagroot/GPT2-705M-RUN4", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use ninagroot/GPT2-705M-RUN4 with Docker Model Runner:
docker model run hf.co/ninagroot/GPT2-705M-RUN4
How to use from
SGLangUse 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 "ninagroot/GPT2-705M-RUN4" \
--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": "ninagroot/GPT2-705M-RUN4",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'Quick Links
GPT2-705M
This model is a fine-tuned version of on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 3.6046
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.00025
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 300
- num_epochs: 20
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 8.0336 | 1.0 | 3 | 7.3770 |
| 6.2535 | 2.0 | 6 | 6.3128 |
| 5.6213 | 3.0 | 9 | 5.6716 |
| 4.8242 | 4.0 | 12 | 5.1521 |
| 4.6266 | 5.0 | 15 | 4.9789 |
| 4.4097 | 6.0 | 18 | 4.7306 |
| 4.0358 | 7.0 | 21 | 4.5332 |
| 4.0027 | 8.0 | 24 | 4.4014 |
| 3.8638 | 9.0 | 27 | 4.1175 |
| 3.5414 | 10.0 | 30 | 4.0355 |
| 3.4701 | 11.0 | 33 | 3.8834 |
| 3.4822 | 12.0 | 36 | 3.8336 |
| 3.0602 | 13.0 | 39 | 3.7213 |
| 3.1109 | 14.0 | 42 | 3.7379 |
| 2.9087 | 15.0 | 45 | 3.7389 |
| 2.7124 | 16.0 | 48 | 3.6220 |
| 2.5867 | 17.0 | 51 | 3.7192 |
| 2.4577 | 18.0 | 54 | 3.5953 |
| 2.279 | 19.0 | 57 | 3.7648 |
| 2.3218 | 20.0 | 60 | 3.6046 |
Framework versions
- Transformers 4.39.1
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
- Downloads last month
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Install from pip and serve model
# Install SGLang from pip: pip install sglang# Start the SGLang server: python3 -m sglang.launch_server \ --model-path "ninagroot/GPT2-705M-RUN4" \ --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": "ninagroot/GPT2-705M-RUN4", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'