Instructions to use cansen88/PromptGenerator_5_topic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use cansen88/PromptGenerator_5_topic with Transformers:

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

pipe = pipeline("text-generation", model="cansen88/PromptGenerator_5_topic")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("cansen88/PromptGenerator_5_topic")
model = AutoModelForCausalLM.from_pretrained("cansen88/PromptGenerator_5_topic")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use cansen88/PromptGenerator_5_topic with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "cansen88/PromptGenerator_5_topic"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "cansen88/PromptGenerator_5_topic",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/cansen88/PromptGenerator_5_topic

SGLang

How to use cansen88/PromptGenerator_5_topic 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 "cansen88/PromptGenerator_5_topic" \
    --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": "cansen88/PromptGenerator_5_topic",
		"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 "cansen88/PromptGenerator_5_topic" \
        --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": "cansen88/PromptGenerator_5_topic",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use cansen88/PromptGenerator_5_topic with Docker Model Runner:
```
docker model run hf.co/cansen88/PromptGenerator_5_topic
```

cansen88 commited on Aug 10, 2022

Commit

59e8d9d

1 Parent(s): e61009c

End of training

Browse files

Files changed (2) hide show

README.md +7 -3
tf_model.h5 +1 -1

README.md CHANGED Viewed

@@ -14,9 +14,9 @@ probably proofread and complete it, then remove this comment. -->
 This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
 It achieves the following results on the evaluation set:
-- Train Loss: 10.6864
-- Validation Loss: 10.6743
-- Epoch: 0
 ## Model description
@@ -43,6 +43,10 @@ The following hyperparameters were used during training:
 | Train Loss | Validation Loss | Epoch |
 |:----------:|:---------------:|:-----:|
 | 10.6864    | 10.6743         | 0     |
 ### Framework versions

 This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
 It achieves the following results on the evaluation set:
+- Train Loss: 10.6848
+- Validation Loss: 10.6672
+- Epoch: 4
 ## Model description
 | Train Loss | Validation Loss | Epoch |
 |:----------:|:---------------:|:-----:|
 | 10.6864    | 10.6743         | 0     |
+| 10.7045    | 10.6736         | 1     |
+| 10.7114    | 10.6722         | 2     |
+| 10.7082    | 10.6701         | 3     |
+| 10.6848    | 10.6672         | 4     |
 ### Framework versions

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