Instructions to use aephil/lab7-shakespeare-generator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use aephil/lab7-shakespeare-generator with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="aephil/lab7-shakespeare-generator")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("aephil/lab7-shakespeare-generator")
model = AutoModelForCausalLM.from_pretrained("aephil/lab7-shakespeare-generator", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use aephil/lab7-shakespeare-generator with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "aephil/lab7-shakespeare-generator"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "aephil/lab7-shakespeare-generator",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/aephil/lab7-shakespeare-generator

SGLang

How to use aephil/lab7-shakespeare-generator 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 "aephil/lab7-shakespeare-generator" \
    --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": "aephil/lab7-shakespeare-generator",
		"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 "aephil/lab7-shakespeare-generator" \
        --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": "aephil/lab7-shakespeare-generator",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use aephil/lab7-shakespeare-generator with Docker Model Runner:
```
docker model run hf.co/aephil/lab7-shakespeare-generator
```

aephil commited on 18 days ago

Commit

c1715c3

verified ·

1 Parent(s): 2aaf54a

End of training

Browse files

Files changed (1) hide show

README.md +3 -5

README.md CHANGED Viewed

@@ -34,11 +34,9 @@ More information needed
 The following hyperparameters were used during training:
 - learning_rate: 0.0005
-- train_batch_size: 4
 - eval_batch_size: 8
-- seed: 42
-- gradient_accumulation_steps: 2
-- total_train_batch_size: 8
 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
 - lr_scheduler_type: cosine
 - num_epochs: 2
@@ -47,7 +45,7 @@ The following hyperparameters were used during training:
 | Training Loss | Epoch | Step | Validation Loss |
 |:-------------:|:-----:|:----:|:---------------:|
-| No log        | 2.0   | 118  | 2.5902          |
 ### Framework versions

 The following hyperparameters were used during training:
 - learning_rate: 0.0005
+- train_batch_size: 8
 - eval_batch_size: 8
+- seed: 101
 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
 - lr_scheduler_type: cosine
 - num_epochs: 2
 | Training Loss | Epoch | Step | Validation Loss |
 |:-------------:|:-----:|:----:|:---------------:|
+| No log        | 2.0   | 118  | 2.5792          |
 ### Framework versions