Instructions to use Mou11209203/distilgpt2-finetuned-github_cybersecurity_READMEs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Mou11209203/distilgpt2-finetuned-github_cybersecurity_READMEs with Transformers:

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

pipe = pipeline("text-generation", model="Mou11209203/distilgpt2-finetuned-github_cybersecurity_READMEs")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Mou11209203/distilgpt2-finetuned-github_cybersecurity_READMEs")
model = AutoModelForCausalLM.from_pretrained("Mou11209203/distilgpt2-finetuned-github_cybersecurity_READMEs", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Mou11209203/distilgpt2-finetuned-github_cybersecurity_READMEs with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Mou11209203/distilgpt2-finetuned-github_cybersecurity_READMEs"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Mou11209203/distilgpt2-finetuned-github_cybersecurity_READMEs",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Mou11209203/distilgpt2-finetuned-github_cybersecurity_READMEs

SGLang

How to use Mou11209203/distilgpt2-finetuned-github_cybersecurity_READMEs 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 "Mou11209203/distilgpt2-finetuned-github_cybersecurity_READMEs" \
    --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": "Mou11209203/distilgpt2-finetuned-github_cybersecurity_READMEs",
		"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 "Mou11209203/distilgpt2-finetuned-github_cybersecurity_READMEs" \
        --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": "Mou11209203/distilgpt2-finetuned-github_cybersecurity_READMEs",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Mou11209203/distilgpt2-finetuned-github_cybersecurity_READMEs with Docker Model Runner:
```
docker model run hf.co/Mou11209203/distilgpt2-finetuned-github_cybersecurity_READMEs
```

Mou11209203 commited on Apr 18, 2024

Commit

41e9f9d

verified ·

1 Parent(s): 59ec74f

Training in progress, step 500

Browse files

Files changed (7) hide show

README.md +6 -6
generation_config.json +1 -1
model.safetensors +1 -1
runs/Apr19_01-30-26_arg10/events.out.tfevents.1713461426.arg10.2604415.0 +2 -2
runs/Apr19_01-30-26_arg10/events.out.tfevents.1713464821.arg10.2604415.1 +3 -0
runs/Apr19_05-07-16_arg10/events.out.tfevents.1713474439.arg10.3558760.0 +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: 2.1837
 ## Model description
@@ -46,14 +46,14 @@ The following hyperparameters were used during training:
 | Training Loss | Epoch | Step  | Validation Loss |
 |:-------------:|:-----:|:-----:|:---------------:|
-| 2.4687        | 1.0   | 23953 | 2.3389          |
-| 2.3584        | 2.0   | 47906 | 2.2191          |
-| 2.2722        | 3.0   | 71859 | 2.1837          |
 ### Framework versions
-- Transformers 4.39.3
 - Pytorch 2.2.1
 - Datasets 2.18.0
-- Tokenizers 0.15.2

 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: 2.1872
 ## Model description
 | Training Loss | Epoch | Step  | Validation Loss |
 |:-------------:|:-----:|:-----:|:---------------:|
+| 2.4593        | 1.0   | 23953 | 2.3440          |
+| 2.3243        | 2.0   | 47906 | 2.2203          |
+| 2.266         | 3.0   | 71859 | 2.1872          |
 ### Framework versions
+- Transformers 4.40.0
 - Pytorch 2.2.1
 - Datasets 2.18.0
+- Tokenizers 0.19.1

generation_config.json CHANGED Viewed

@@ -2,5 +2,5 @@
   "_from_model_config": true,
   "bos_token_id": 50256,
   "eos_token_id": 50256,
-  "transformers_version": "4.39.3"
 }

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   "bos_token_id": 50256,
   "eos_token_id": 50256,
+  "transformers_version": "4.40.0"
 }

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