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

Libraries

How to use BluebrainAI/gpt2-wikitext with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

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

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use BluebrainAI/gpt2-wikitext with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "BluebrainAI/gpt2-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-wikitext",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

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

SGLang

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

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

shivanandmn commited on Mar 18, 2025

Commit

ee61125

verified ·

1 Parent(s): 65483c4

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: 3.1672
-- Accuracy: 0.4214
-- Perplexity: 23.7410
-- Bleu: 0.1474
 ## Model description
@@ -52,23 +52,23 @@ The following hyperparameters were used during training:
 | Training Loss | Epoch  | Step | Validation Loss | Accuracy | Perplexity | Bleu   |
 |:-------------:|:------:|:----:|:---------------:|:--------:|:----------:|:------:|
-| 6.0797        | 0.2806 | 500  | 5.9513          | 0.1886   | 384.2374   | 0.0328 |
-| 5.0682        | 0.5612 | 1000 | 4.9243          | 0.2616   | 137.5896   | 0.0640 |
-| 4.3376        | 0.8418 | 1500 | 4.2176          | 0.3220   | 67.8687    | 0.0837 |
-| 3.946         | 1.1223 | 2000 | 3.8835          | 0.3531   | 48.5917    | 0.1062 |
-| 3.7578        | 1.4029 | 2500 | 3.7072          | 0.3683   | 40.7404    | 0.1239 |
-| 3.6484        | 1.6835 | 3000 | 3.5824          | 0.3790   | 35.9600    | 0.1312 |
-| 3.5434        | 1.9641 | 3500 | 3.4942          | 0.3874   | 32.9255    | 0.1354 |
-| 3.4228        | 2.2447 | 4000 | 3.4280          | 0.3938   | 30.8151    | 0.1332 |
-| 3.3604        | 2.5253 | 4500 | 3.3732          | 0.3989   | 29.1719    | 0.1409 |
-| 3.3288        | 2.8058 | 5000 | 3.3268          | 0.4039   | 27.8495    | 0.1396 |
-| 3.2073        | 3.0864 | 5500 | 3.2888          | 0.4079   | 26.8096    | 0.1410 |
-| 3.2009        | 3.3670 | 6000 | 3.2614          | 0.4109   | 26.0870    | 0.1426 |
-| 3.1787        | 3.6476 | 6500 | 3.2330          | 0.4141   | 25.3543    | 0.1394 |
-| 3.1528        | 3.9282 | 7000 | 3.2094          | 0.4164   | 24.7635    | 0.1459 |
-| 3.0849        | 4.2088 | 7500 | 3.1927          | 0.4182   | 24.3547    | 0.1420 |
-| 3.0471        | 4.4893 | 8000 | 3.1799          | 0.4200   | 24.0448    | 0.1476 |
-| 3.0571        | 4.7699 | 8500 | 3.1672          | 0.4214   | 23.7410    | 0.1474 |
 ### 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: 3.1673
+- Accuracy: 0.4215
+- Perplexity: 23.7437
+- Bleu: 0.1486
 ## Model description
 | Training Loss | Epoch  | Step | Validation Loss | Accuracy | Perplexity | Bleu   |
 |:-------------:|:------:|:----:|:---------------:|:--------:|:----------:|:------:|
+| 6.0809        | 0.2806 | 500  | 5.9591          | 0.1879   | 387.2722   | 0.0323 |
+| 5.0659        | 0.5612 | 1000 | 4.9189          | 0.2618   | 136.8551   | 0.0639 |
+| 4.338         | 0.8418 | 1500 | 4.2174          | 0.3219   | 67.8594    | 0.0857 |
+| 3.9468        | 1.1223 | 2000 | 3.8832          | 0.3532   | 48.5783    | 0.1094 |
+| 3.7577        | 1.4029 | 2500 | 3.7060          | 0.3685   | 40.6890    | 0.1226 |
+| 3.6483        | 1.6835 | 3000 | 3.5831          | 0.3787   | 35.9858    | 0.1296 |
+| 3.5432        | 1.9641 | 3500 | 3.4948          | 0.3875   | 32.9448    | 0.1360 |
+| 3.4221        | 2.2447 | 4000 | 3.4280          | 0.3939   | 30.8160    | 0.1306 |
+| 3.3602        | 2.5253 | 4500 | 3.3724          | 0.3991   | 29.1478    | 0.1391 |
+| 3.3285        | 2.8058 | 5000 | 3.3261          | 0.4038   | 27.8284    | 0.1369 |
+| 3.2072        | 3.0864 | 5500 | 3.2882          | 0.4077   | 26.7936    | 0.1447 |
+| 3.2002        | 3.3670 | 6000 | 3.2611          | 0.4112   | 26.0792    | 0.1472 |
+| 3.1782        | 3.6476 | 6500 | 3.2317          | 0.4138   | 25.3223    | 0.1421 |
+| 3.153         | 3.9282 | 7000 | 3.2080          | 0.4164   | 24.7294    | 0.1415 |
+| 3.0846        | 4.2088 | 7500 | 3.1915          | 0.4185   | 24.3249    | 0.1470 |
+| 3.0469        | 4.4893 | 8000 | 3.1789          | 0.4199   | 24.0205    | 0.1444 |
+| 3.0567        | 4.7699 | 8500 | 3.1673          | 0.4215   | 23.7437    | 0.1486 |
 ### Framework versions