Instructions to use BluebrainAI/gpt2-medium-wikitext with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use BluebrainAI/gpt2-medium-wikitext with Transformers:

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

pipe = pipeline("text-generation", model="BluebrainAI/gpt2-medium-wikitext")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("BluebrainAI/gpt2-medium-wikitext")
model = AutoModelForCausalLM.from_pretrained("BluebrainAI/gpt2-medium-wikitext")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use BluebrainAI/gpt2-medium-wikitext with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "BluebrainAI/gpt2-medium-wikitext"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BluebrainAI/gpt2-medium-wikitext",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/BluebrainAI/gpt2-medium-wikitext

SGLang

How to use BluebrainAI/gpt2-medium-wikitext 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 "BluebrainAI/gpt2-medium-wikitext" \
    --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": "BluebrainAI/gpt2-medium-wikitext",
		"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 "BluebrainAI/gpt2-medium-wikitext" \
        --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": "BluebrainAI/gpt2-medium-wikitext",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use BluebrainAI/gpt2-medium-wikitext with Docker Model Runner:
```
docker model run hf.co/BluebrainAI/gpt2-medium-wikitext
```

shivanandmn commited on Mar 19, 2025

Commit

69a0cd3

verified ·

1 Parent(s): 465e24a

Model save

Browse files

Files changed (1) hide show

README.md +21 -21

README.md CHANGED Viewed

@@ -17,10 +17,10 @@ should probably proofread and complete it, then remove this comment. -->
 This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
 It achieves the following results on the evaluation set:
-- Loss: 3.1671
-- Accuracy: 0.4217
-- Perplexity: 23.7377
-- Bleu: 0.1460
 ## Model description
@@ -52,23 +52,23 @@ The following hyperparameters were used during training:
 | Training Loss | Epoch  | Step | Validation Loss | Accuracy | Perplexity | Bleu   |
 |:-------------:|:------:|:----:|:---------------:|:--------:|:----------:|:------:|
-| 6.0809        | 0.2806 | 500  | 5.9580          | 0.1883   | 386.8254   | 0.0333 |
-| 5.0644        | 0.5612 | 1000 | 4.9191          | 0.2623   | 136.8761   | 0.0651 |
-| 4.3331        | 0.8418 | 1500 | 4.2124          | 0.3226   | 67.5163    | 0.0890 |
-| 3.9451        | 1.1223 | 2000 | 3.8835          | 0.3532   | 48.5942    | 0.1090 |
-| 3.7568        | 1.4029 | 2500 | 3.7051          | 0.3684   | 40.6559    | 0.1226 |
-| 3.6478        | 1.6835 | 3000 | 3.5827          | 0.3787   | 35.9710    | 0.1311 |
-| 3.5435        | 1.9641 | 3500 | 3.4940          | 0.3877   | 32.9179    | 0.1343 |
-| 3.4222        | 2.2447 | 4000 | 3.4292          | 0.3936   | 30.8527    | 0.1343 |
-| 3.3604        | 2.5253 | 4500 | 3.3728          | 0.3990   | 29.1601    | 0.1414 |
-| 3.3288        | 2.8058 | 5000 | 3.3269          | 0.4038   | 27.8518    | 0.1381 |
-| 3.2074        | 3.0864 | 5500 | 3.2887          | 0.4079   | 26.8092    | 0.1423 |
-| 3.2007        | 3.3670 | 6000 | 3.2605          | 0.4115   | 26.0632    | 0.1464 |
-| 3.1787        | 3.6476 | 6500 | 3.2328          | 0.4140   | 25.3497    | 0.1428 |
-| 3.1529        | 3.9282 | 7000 | 3.2085          | 0.4166   | 24.7424    | 0.1425 |
-| 3.0849        | 4.2088 | 7500 | 3.1921          | 0.4184   | 24.3384    | 0.1430 |
-| 3.0471        | 4.4893 | 8000 | 3.1796          | 0.4202   | 24.0366    | 0.1428 |
-| 3.0569        | 4.7699 | 8500 | 3.1671          | 0.4217   | 23.7377    | 0.1460 |
 ### Framework versions

 This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
 It achieves the following results on the evaluation set:
+- Loss: 3.1666
+- Accuracy: 0.4218
+- Perplexity: 23.7262
+- Bleu: 0.1462
 ## Model description
 | Training Loss | Epoch  | Step | Validation Loss | Accuracy | Perplexity | Bleu   |
 |:-------------:|:------:|:----:|:---------------:|:--------:|:----------:|:------:|
+| 6.078         | 0.2806 | 500  | 5.9534          | 0.1875   | 385.0606   | 0.0310 |
+| 5.0653        | 0.5612 | 1000 | 4.9232          | 0.2616   | 137.4410   | 0.0633 |
+| 4.3357        | 0.8418 | 1500 | 4.2163          | 0.3222   | 67.7828    | 0.0857 |
+| 3.9453        | 1.1223 | 2000 | 3.8824          | 0.3534   | 48.5418    | 0.1107 |
+| 3.7572        | 1.4029 | 2500 | 3.7058          | 0.3684   | 40.6810    | 0.1217 |
+| 3.6475        | 1.6835 | 3000 | 3.5827          | 0.3788   | 35.9700    | 0.1306 |
+| 3.5431        | 1.9641 | 3500 | 3.4927          | 0.3878   | 32.8733    | 0.1347 |
+| 3.4221        | 2.2447 | 4000 | 3.4283          | 0.3939   | 30.8231    | 0.1356 |
+| 3.36          | 2.5253 | 4500 | 3.3719          | 0.3996   | 29.1351    | 0.1384 |
+| 3.3281        | 2.8058 | 5000 | 3.3257          | 0.4041   | 27.8193    | 0.1369 |
+| 3.2071        | 3.0864 | 5500 | 3.2885          | 0.4080   | 26.8024    | 0.1442 |
+| 3.2002        | 3.3670 | 6000 | 3.2594          | 0.4117   | 26.0335    | 0.1477 |
+| 3.1778        | 3.6476 | 6500 | 3.2319          | 0.4142   | 25.3278    | 0.1436 |
+| 3.1523        | 3.9282 | 7000 | 3.2091          | 0.4167   | 24.7565    | 0.1462 |
+| 3.0842        | 4.2088 | 7500 | 3.1917          | 0.4185   | 24.3289    | 0.1434 |
+| 3.0465        | 4.4893 | 8000 | 3.1789          | 0.4201   | 24.0197    | 0.1460 |
+| 3.0563        | 4.7699 | 8500 | 3.1666          | 0.4218   | 23.7262    | 0.1462 |
 ### Framework versions