Instructions to use gjonesQ02/ScopeOfWorkTextGenerator_Alpha_DEMO_SMALL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use gjonesQ02/ScopeOfWorkTextGenerator_Alpha_DEMO_SMALL with Transformers:

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

pipe = pipeline("text-generation", model="gjonesQ02/ScopeOfWorkTextGenerator_Alpha_DEMO_SMALL")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("gjonesQ02/ScopeOfWorkTextGenerator_Alpha_DEMO_SMALL")
model = AutoModelForCausalLM.from_pretrained("gjonesQ02/ScopeOfWorkTextGenerator_Alpha_DEMO_SMALL", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use gjonesQ02/ScopeOfWorkTextGenerator_Alpha_DEMO_SMALL with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "gjonesQ02/ScopeOfWorkTextGenerator_Alpha_DEMO_SMALL"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "gjonesQ02/ScopeOfWorkTextGenerator_Alpha_DEMO_SMALL",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/gjonesQ02/ScopeOfWorkTextGenerator_Alpha_DEMO_SMALL

SGLang

How to use gjonesQ02/ScopeOfWorkTextGenerator_Alpha_DEMO_SMALL 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 "gjonesQ02/ScopeOfWorkTextGenerator_Alpha_DEMO_SMALL" \
    --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": "gjonesQ02/ScopeOfWorkTextGenerator_Alpha_DEMO_SMALL",
		"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 "gjonesQ02/ScopeOfWorkTextGenerator_Alpha_DEMO_SMALL" \
        --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": "gjonesQ02/ScopeOfWorkTextGenerator_Alpha_DEMO_SMALL",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use gjonesQ02/ScopeOfWorkTextGenerator_Alpha_DEMO_SMALL with Docker Model Runner:
```
docker model run hf.co/gjonesQ02/ScopeOfWorkTextGenerator_Alpha_DEMO_SMALL
```

gjonesQ02 commited on Jun 10, 2024

Commit

ea17e85

verified ·

1 Parent(s): 1618db1

ScopeOfWorkTextGenerator_Alpha_DEMO_SMALL

Browse files

Files changed (5) hide show

README.md +3 -3
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runs/Jun10_14-12-16_DESKTOP-P43UPVK/events.out.tfevents.1718043142.DESKTOP-P43UPVK.41152.1 +3 -0
runs/Jun10_14-24-20_DESKTOP-P43UPVK/events.out.tfevents.1718043872.DESKTOP-P43UPVK.41152.2 +3 -0
training_args.bin +1 -1

README.md CHANGED Viewed

@@ -15,7 +15,7 @@ should probably proofread and complete it, then remove this comment. -->
 This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset.
 It achieves the following results on the evaluation set:
-- Loss: 4.0150
 ## Model description
@@ -46,8 +46,8 @@ The following hyperparameters were used during training:
 | Training Loss | Epoch | Step | Validation Loss |
 |:-------------:|:-----:|:----:|:---------------:|
-| No log        | 1.0   | 1    | 4.1040          |
-| No log        | 2.0   | 2    | 4.0150          |
 ### Framework versions

 This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset.
 It achieves the following results on the evaluation set:
+- Loss: 3.2267
 ## Model description
 | Training Loss | Epoch | Step | Validation Loss |
 |:-------------:|:-----:|:----:|:---------------:|
+| No log        | 1.0   | 4    | 3.3605          |
+| No log        | 2.0   | 8    | 3.2267          |
 ### Framework versions

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