Instructions to use cansen88/PromptGenerator_32_topic_finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use cansen88/PromptGenerator_32_topic_finetuned with Transformers:

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

pipe = pipeline("text-generation", model="cansen88/PromptGenerator_32_topic_finetuned")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("cansen88/PromptGenerator_32_topic_finetuned")
model = AutoModelForCausalLM.from_pretrained("cansen88/PromptGenerator_32_topic_finetuned")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use cansen88/PromptGenerator_32_topic_finetuned with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "cansen88/PromptGenerator_32_topic_finetuned"
# 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_32_topic_finetuned",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/cansen88/PromptGenerator_32_topic_finetuned

SGLang

How to use cansen88/PromptGenerator_32_topic_finetuned 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_32_topic_finetuned" \
    --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_32_topic_finetuned",
		"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_32_topic_finetuned" \
        --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_32_topic_finetuned",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use cansen88/PromptGenerator_32_topic_finetuned with Docker Model Runner:
```
docker model run hf.co/cansen88/PromptGenerator_32_topic_finetuned
```

cansen88 commited on Aug 11, 2022

Commit

246284e

1 Parent(s): 7bbeee2

End of training

Browse files

Files changed (2) hide show

README.md +9 -5
tf_model.h5 +1 -1

README.md CHANGED Viewed

@@ -14,11 +14,11 @@ probably proofread and complete it, then remove this comment. -->
 This model is a fine-tuned version of [kmkarakaya/turkishReviews-ds](https://huggingface.co/kmkarakaya/turkishReviews-ds) on an unknown dataset.
 It achieves the following results on the evaluation set:
-- Train Loss: 1.7185
-- Train Sparse Categorical Accuracy: 0.7860
-- Validation Loss: 0.5569
-- Validation Sparse Categorical Accuracy: 0.9868
-- Epoch: 0
 ## Model description
@@ -45,6 +45,10 @@ The following hyperparameters were used during training:
 | Train Loss | Train Sparse Categorical Accuracy | Validation Loss | Validation Sparse Categorical Accuracy | Epoch |
 |:----------:|:---------------------------------:|:---------------:|:--------------------------------------:|:-----:|
 | 1.7185     | 0.7860                            | 0.5569          | 0.9868                                 | 0     |
 ### Framework versions

 This model is a fine-tuned version of [kmkarakaya/turkishReviews-ds](https://huggingface.co/kmkarakaya/turkishReviews-ds) on an unknown dataset.
 It achieves the following results on the evaluation set:
+- Train Loss: 0.0569
+- Train Sparse Categorical Accuracy: 1.0
+- Validation Loss: 0.0787
+- Validation Sparse Categorical Accuracy: 1.0
+- Epoch: 4
 ## Model description
 | Train Loss | Train Sparse Categorical Accuracy | Validation Loss | Validation Sparse Categorical Accuracy | Epoch |
 |:----------:|:---------------------------------:|:---------------:|:--------------------------------------:|:-----:|
 | 1.7185     | 0.7860                            | 0.5569          | 0.9868                                 | 0     |
+| 0.4711     | 0.9958                            | 0.2097          | 0.9995                                 | 1     |
+| 0.2016     | 1.0000                            | 0.1197          | 0.9999                                 | 2     |
+| 0.1014     | 1.0                               | 0.0903          | 0.9999                                 | 3     |
+| 0.0569     | 1.0                               | 0.0787          | 1.0                                    | 4     |
 ### Framework versions

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