Instructions to use ablam/distilgpt2_fine_tuned_gcode with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ablam/distilgpt2_fine_tuned_gcode with Transformers:

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

pipe = pipeline("text-generation", model="ablam/distilgpt2_fine_tuned_gcode")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ablam/distilgpt2_fine_tuned_gcode")
model = AutoModelForCausalLM.from_pretrained("ablam/distilgpt2_fine_tuned_gcode", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ablam/distilgpt2_fine_tuned_gcode with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ablam/distilgpt2_fine_tuned_gcode"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ablam/distilgpt2_fine_tuned_gcode",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ablam/distilgpt2_fine_tuned_gcode

SGLang

How to use ablam/distilgpt2_fine_tuned_gcode 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 "ablam/distilgpt2_fine_tuned_gcode" \
    --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": "ablam/distilgpt2_fine_tuned_gcode",
		"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 "ablam/distilgpt2_fine_tuned_gcode" \
        --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": "ablam/distilgpt2_fine_tuned_gcode",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ablam/distilgpt2_fine_tuned_gcode with Docker Model Runner:
```
docker model run hf.co/ablam/distilgpt2_fine_tuned_gcode
```

ablam commited on May 11, 2022

Commit

8dae335

1 Parent(s): c234296

Training in progress, step 30500

Browse files

Files changed (2) hide show

pytorch_model.bin +1 -1
runs/May11_12-52-36_hal10/events.out.tfevents.1652291630.hal10.206489.0 +2 -2

pytorch_model.bin CHANGED Viewed

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:6d40cc2e71d49d59c82f5ef91dfc7f443b60196b08225039860330f78fba100c
 size 333982173

 version https://git-lfs.github.com/spec/v1
+oid sha256:deb8b693e77914e69b25b37e24a18f4b101fcdd8577fe0366e17dcfeaf11fe15
 size 333982173

runs/May11_12-52-36_hal10/events.out.tfevents.1652291630.hal10.206489.0 CHANGED Viewed

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:32d42420b351cf3bb49491cc1bac936d17ab5643e28d69772234a4a11b79d32e
-size 10948

 version https://git-lfs.github.com/spec/v1
+oid sha256:b150d847c5d734c21fd6ef47ed698129ee53558e9ecf8aeeb6ec8edefe658c1b
+size 13348