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

Libraries

How to use gabfssilva/distilgpt2 with MLX:

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm
# if on a CUDA device, also pip install mlx[cuda]

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("gabfssilva/distilgpt2")

prompt = "Once upon a time in"
text = generate(model, tokenizer, prompt=prompt, verbose=True)

Transformers

How to use gabfssilva/distilgpt2 with Transformers:

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

pipe = pipeline("text-generation", model="gabfssilva/distilgpt2")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("gabfssilva/distilgpt2")
model = AutoModelForCausalLM.from_pretrained("gabfssilva/distilgpt2")

Notebooks
Google Colab
Kaggle
Local Apps
LM Studio

vLLM

How to use gabfssilva/distilgpt2 with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/gabfssilva/distilgpt2

SGLang

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

MLX LM

How to use gabfssilva/distilgpt2 with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Generate some text
mlx_lm.generate --model "gabfssilva/distilgpt2" --prompt "Once upon a time"

Docker Model Runner
How to use gabfssilva/distilgpt2 with Docker Model Runner:
```
docker model run hf.co/gabfssilva/distilgpt2
```

distilgpt2

Repackaging of distilbert/distilgpt2 with the model.safetensors keys stripped of the transformer. prefix, so it loads directly via mlx-lm on Apple silicon. The weights are bit-for-bit identical; only the names changed.

Usage

mlx-lm (Apple silicon)

from mlx_lm import load, generate

model, tokenizer = load("gabfssilva/distilgpt2")
print(generate(model, tokenizer, "Once upon a time", max_tokens=50))

PyTorch (CUDA / MPS / CPU / probably ROCm)

Also loads cleanly via transformers — from_pretrained tolerates the missing transformer. prefix, so the same weights run on CUDA, Apple Metal (MPS), or CPU without any extra step:

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("gabfssilva/distilgpt2", device_map="cuda")  # or "mps", "cpu", "auto"
tokenizer = AutoTokenizer.from_pretrained("gabfssilva/distilgpt2")

device_map requires pip install accelerate.

Source

Base model: distilbert/distilgpt2 (Apache 2.0)
Difference: keys renamed from transformer.h.* to h.* to match the sanitize() in mlx_lm/models/gpt2.py.

Downloads last month: 452

Safetensors

Model size

88.2M params

Tensor type

F32

MLX

Hardware compatibility

Quantized

Model tree for gabfssilva/distilgpt2

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