Instructions to use eliwill/stoic-generator-10e with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use eliwill/stoic-generator-10e with Transformers:

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

pipe = pipeline("text-generation", model="eliwill/stoic-generator-10e")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("eliwill/stoic-generator-10e")
model = AutoModelForCausalLM.from_pretrained("eliwill/stoic-generator-10e")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use eliwill/stoic-generator-10e with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "eliwill/stoic-generator-10e"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "eliwill/stoic-generator-10e",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/eliwill/stoic-generator-10e

SGLang

How to use eliwill/stoic-generator-10e 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 "eliwill/stoic-generator-10e" \
    --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": "eliwill/stoic-generator-10e",
		"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 "eliwill/stoic-generator-10e" \
        --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": "eliwill/stoic-generator-10e",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use eliwill/stoic-generator-10e with Docker Model Runner:
```
docker model run hf.co/eliwill/stoic-generator-10e
```

eliwill commited on Sep 25, 2022

Commit

6dc9a4e

1 Parent(s): cad525a

End of training

Browse files

Files changed (4) hide show

README.md +12 -3
logs/train/events.out.tfevents.1664130319.b847084d5e3f.67.0.v2 +2 -2
logs/validation/events.out.tfevents.1664130386.b847084d5e3f.67.1.v2 +2 -2
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 [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset.
 It achieves the following results on the evaluation set:
-- Train Loss: 4.0230
-- Validation Loss: 3.9474
-- Epoch: 0
 ## Model description
@@ -43,6 +43,15 @@ The following hyperparameters were used during training:
 | Train Loss | Validation Loss | Epoch |
 |:----------:|:---------------:|:-----:|
 | 4.0230     | 3.9474          | 0     |
 ### 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:
+- Train Loss: 3.4753
+- Validation Loss: 3.7980
+- Epoch: 9
 ## Model description
 | Train Loss | Validation Loss | Epoch |
 |:----------:|:---------------:|:-----:|
 | 4.0230     | 3.9474          | 0     |
+| 3.8580     | 3.8982          | 1     |
+| 3.7757     | 3.8721          | 2     |
+| 3.7149     | 3.8489          | 3     |
+| 3.6640     | 3.8343          | 4     |
+| 3.6210     | 3.8152          | 5     |
+| 3.5796     | 3.8088          | 6     |
+| 3.5429     | 3.8038          | 7     |
+| 3.5061     | 3.7967          | 8     |
+| 3.4753     | 3.7980          | 9     |
 ### Framework versions

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