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
```

ahmadisakina commited on May 4, 2025

Commit

fbe2800

verified ·

1 Parent(s): 471fdbf

Update README.md

Browse files

Files changed (1) hide show

README.md +25 -21

README.md CHANGED Viewed

@@ -1,22 +1,26 @@
-# 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
-```python
-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()
 ```

+---
+language:
+- en
+---
+# 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
+```python
+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()
 ```