Instructions to use ahmadisakina/decoder-language-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ahmadisakina/decoder-language-model with Transformers:

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

pipe = pipeline("text-generation", model="ahmadisakina/decoder-language-model")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("ahmadisakina/decoder-language-model", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use ahmadisakina/decoder-language-model with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/ahmadisakina/decoder-language-model

SGLang

How to use ahmadisakina/decoder-language-model 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 "ahmadisakina/decoder-language-model" \
    --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": "ahmadisakina/decoder-language-model",
		"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 "ahmadisakina/decoder-language-model" \
        --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": "ahmadisakina/decoder-language-model",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ahmadisakina/decoder-language-model with Docker Model Runner:
```
docker model run hf.co/ahmadisakina/decoder-language-model
```

Decoder Language Model

Ein kleiner autoregressiver Decoder-only Transformer, trainiert auf Tiny Shakespeare.

Architektur

d_model=128, num_layers=2, nhead=4
~500k Parameter

Metriken

Loss (Train): 0.6342
Perplexity (Train): 1.8854

Laden

from transformers import GPT2Tokenizer
import torch
from model import DecoderLanguageModel

tokenizer = GPT2Tokenizer.from_pretrained("ahmadisakina/decoder-language-model")
model = DecoderLanguageModel(vocab_size=tokenizer.vocab_size, d_model=128, nhead=4, num_layers=2)
model.load_state_dict(torch.load("pytorch_model.bin"))
model.eval()

Downloads last month: -; Downloads are not tracked for this model. How to track

ahmadisakina
/

decoder-language-model

Decoder Language Model

Architektur

Metriken

Laden

Dataset used to train ahmadisakina/decoder-language-model