Instructions to use Nbeau/GPT2-arithmetic-3digits with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Nbeau/GPT2-arithmetic-3digits with Transformers:

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

pipe = pipeline("text-generation", model="Nbeau/GPT2-arithmetic-3digits")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Nbeau/GPT2-arithmetic-3digits")
model = AutoModelForCausalLM.from_pretrained("Nbeau/GPT2-arithmetic-3digits")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Nbeau/GPT2-arithmetic-3digits with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Nbeau/GPT2-arithmetic-3digits"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Nbeau/GPT2-arithmetic-3digits",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Nbeau/GPT2-arithmetic-3digits

SGLang

How to use Nbeau/GPT2-arithmetic-3digits 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 "Nbeau/GPT2-arithmetic-3digits" \
    --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": "Nbeau/GPT2-arithmetic-3digits",
		"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 "Nbeau/GPT2-arithmetic-3digits" \
        --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": "Nbeau/GPT2-arithmetic-3digits",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Nbeau/GPT2-arithmetic-3digits with Docker Model Runner:
```
docker model run hf.co/Nbeau/GPT2-arithmetic-3digits
```

Nbeau commited on May 16, 2024

Commit

884b5e7

verified ·

1 Parent(s): 7a7168a

Training in progress, step 500

Browse files

Files changed (4) hide show

README.md +1 -9
config.json +1 -1
model.safetensors +1 -1
training_args.bin +1 -1

README.md CHANGED Viewed

@@ -6,14 +6,6 @@ tags:
 model-index:
 - name: GPT2-arithmetic-3digits
   results: []
-datasets:
-- Nbeau/additiondataset_3digits_10000examples
-widget:
-- text: "Input:\n561+372\nTarget:"
-inference:
-  parameters:
-    add_special_tokens: True
-    do_sample: False
 ---
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -69,4 +61,4 @@ The following hyperparameters were used during training:
 - Transformers 4.40.2
 - Pytorch 2.3.0
 - Datasets 2.19.1
-- Tokenizers 0.19.1

 model-index:
 - name: GPT2-arithmetic-3digits
   results: []
 ---
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 - Transformers 4.40.2
 - Pytorch 2.3.0
 - Datasets 2.19.1
+- Tokenizers 0.19.1

config.json CHANGED Viewed

@@ -17,7 +17,7 @@
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   "n_layer": 12,
   "n_positions": 1024,
-  "pad_token_id": 2,
   "reorder_and_upcast_attn": false,
   "resid_pdrop": 0.1,
   "scale_attn_by_inverse_layer_idx": false,

   "n_inner": null,
   "n_layer": 12,
   "n_positions": 1024,
+  "pad_token_id": 32000,
   "reorder_and_upcast_attn": false,
   "resid_pdrop": 0.1,
   "scale_attn_by_inverse_layer_idx": false,

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