Instructions to use gjonesQ02/StatementOfWork_Generator_Omega2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use gjonesQ02/StatementOfWork_Generator_Omega2 with Transformers:

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

pipe = pipeline("text-generation", model="gjonesQ02/StatementOfWork_Generator_Omega2")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("gjonesQ02/StatementOfWork_Generator_Omega2")
model = AutoModelForCausalLM.from_pretrained("gjonesQ02/StatementOfWork_Generator_Omega2")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use gjonesQ02/StatementOfWork_Generator_Omega2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "gjonesQ02/StatementOfWork_Generator_Omega2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "gjonesQ02/StatementOfWork_Generator_Omega2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/gjonesQ02/StatementOfWork_Generator_Omega2

SGLang

How to use gjonesQ02/StatementOfWork_Generator_Omega2 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/StatementOfWork_Generator_Omega2" \
    --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/StatementOfWork_Generator_Omega2",
		"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/StatementOfWork_Generator_Omega2" \
        --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/StatementOfWork_Generator_Omega2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use gjonesQ02/StatementOfWork_Generator_Omega2 with Docker Model Runner:
```
docker model run hf.co/gjonesQ02/StatementOfWork_Generator_Omega2
```

gjonesQ02 commited on Jun 27, 2024

Commit

c243dff

verified ·

1 Parent(s): 48896d0

Model save

Browse files

Files changed (3) hide show

README.md +22 -22
model.safetensors +1 -1
runs/Jun27_14-28-33_viridian/events.out.tfevents.1719506420.viridian.2678733.10 +3 -0

README.md CHANGED Viewed

@@ -1,8 +1,8 @@
 ---
 license: apache-2.0
 tags:
 - generated_from_trainer
-base_model: distilgpt2
 model-index:
 - name: StatementOfWork_Generator_Omega2
   results: []
@@ -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: 1.0240
 ## Model description
@@ -46,26 +46,26 @@ The following hyperparameters were used during training:
 | Training Loss | Epoch | Step | Validation Loss |
 |:-------------:|:-----:|:----:|:---------------:|
-| No log        | 1.0   | 15   | 1.0667          |
-| No log        | 2.0   | 30   | 1.0652          |
-| No log        | 3.0   | 45   | 1.0575          |
-| No log        | 4.0   | 60   | 1.0515          |
-| No log        | 5.0   | 75   | 1.0501          |
-| No log        | 6.0   | 90   | 1.0502          |
-| No log        | 7.0   | 105  | 1.0454          |
-| No log        | 8.0   | 120  | 1.0453          |
-| No log        | 9.0   | 135  | 1.0359          |
-| No log        | 10.0  | 150  | 1.0362          |
-| No log        | 11.0  | 165  | 1.0341          |
-| No log        | 12.0  | 180  | 1.0300          |
-| No log        | 13.0  | 195  | 1.0294          |
-| No log        | 14.0  | 210  | 1.0266          |
-| No log        | 15.0  | 225  | 1.0276          |
-| No log        | 16.0  | 240  | 1.0270          |
-| No log        | 17.0  | 255  | 1.0237          |
-| No log        | 18.0  | 270  | 1.0239          |
-| No log        | 19.0  | 285  | 1.0244          |
-| No log        | 20.0  | 300  | 1.0240          |
 ### Framework versions

 ---
 license: apache-2.0
+base_model: distilgpt2
 tags:
 - generated_from_trainer
 model-index:
 - name: StatementOfWork_Generator_Omega2
   results: []
 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: 0.9899
 ## Model description
 | Training Loss | Epoch | Step | Validation Loss |
 |:-------------:|:-----:|:----:|:---------------:|
+| No log        | 1.0   | 15   | 1.0194          |
+| No log        | 2.0   | 30   | 1.0190          |
+| No log        | 3.0   | 45   | 1.0147          |
+| No log        | 4.0   | 60   | 1.0102          |
+| No log        | 5.0   | 75   | 1.0135          |
+| No log        | 6.0   | 90   | 1.0091          |
+| No log        | 7.0   | 105  | 1.0055          |
+| No log        | 8.0   | 120  | 1.0024          |
+| No log        | 9.0   | 135  | 0.9965          |
+| No log        | 10.0  | 150  | 0.9984          |
+| No log        | 11.0  | 165  | 1.0013          |
+| No log        | 12.0  | 180  | 0.9934          |
+| No log        | 13.0  | 195  | 0.9952          |
+| No log        | 14.0  | 210  | 0.9917          |
+| No log        | 15.0  | 225  | 0.9911          |
+| No log        | 16.0  | 240  | 0.9900          |
+| No log        | 17.0  | 255  | 0.9918          |
+| No log        | 18.0  | 270  | 0.9902          |
+| No log        | 19.0  | 285  | 0.9900          |
+| No log        | 20.0  | 300  | 0.9899          |
 ### Framework versions

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