Instructions to use gjonesQ02/WO_CausalModel_2x with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use gjonesQ02/WO_CausalModel_2x with Transformers:

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

pipe = pipeline("text-generation", model="gjonesQ02/WO_CausalModel_2x")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("gjonesQ02/WO_CausalModel_2x")
model = AutoModelForCausalLM.from_pretrained("gjonesQ02/WO_CausalModel_2x", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use gjonesQ02/WO_CausalModel_2x with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "gjonesQ02/WO_CausalModel_2x"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "gjonesQ02/WO_CausalModel_2x",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/gjonesQ02/WO_CausalModel_2x

SGLang

How to use gjonesQ02/WO_CausalModel_2x 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/WO_CausalModel_2x" \
    --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/WO_CausalModel_2x",
		"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/WO_CausalModel_2x" \
        --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/WO_CausalModel_2x",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use gjonesQ02/WO_CausalModel_2x with Docker Model Runner:
```
docker model run hf.co/gjonesQ02/WO_CausalModel_2x
```

gjonesQ02 commited on Feb 19, 2024

Commit

255e089

verified ·

1 Parent(s): c475d26

End of training

Browse files

Files changed (3) hide show

README.md +7 -7
model.safetensors +1 -1
runs/Feb19_20-32-50_90aa433f6673/events.out.tfevents.1708374770.90aa433f6673.2967.2 +2 -2

README.md CHANGED Viewed

@@ -17,7 +17,7 @@ should probably proofread and complete it, then remove this comment. -->
 This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the generator dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.6307
 ## Model description
@@ -48,12 +48,12 @@ The following hyperparameters were used during training:
 | Training Loss | Epoch | Step | Validation Loss |
 |:-------------:|:-----:|:----:|:---------------:|
-| No log        | 1.0   | 151  | 0.6834          |
-| No log        | 2.0   | 302  | 0.6526          |
-| No log        | 3.0   | 453  | 0.6415          |
-| 0.8783        | 4.0   | 604  | 0.6373          |
-| 0.8783        | 5.0   | 755  | 0.6320          |
-| 0.8783        | 6.0   | 906  | 0.6307          |
 ### Framework versions

 This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the generator dataset.
 It achieves the following results on the evaluation set:
+- Loss: 0.6035
 ## Model description
 | Training Loss | Epoch | Step | Validation Loss |
 |:-------------:|:-----:|:----:|:---------------:|
+| No log        | 1.0   | 151  | 0.6217          |
+| No log        | 2.0   | 302  | 0.6133          |
+| No log        | 3.0   | 453  | 0.6087          |
+| 0.6243        | 4.0   | 604  | 0.6079          |
+| 0.6243        | 5.0   | 755  | 0.6049          |
+| 0.6243        | 6.0   | 906  | 0.6035          |
 ### Framework versions

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