Instructions to use llm-slice/pico-decoder-medium with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use llm-slice/pico-decoder-medium with Transformers:

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

pipe = pipeline("text-generation", model="llm-slice/pico-decoder-medium", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("llm-slice/pico-decoder-medium", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use llm-slice/pico-decoder-medium with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/llm-slice/pico-decoder-medium

SGLang

How to use llm-slice/pico-decoder-medium 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 "llm-slice/pico-decoder-medium" \
    --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": "llm-slice/pico-decoder-medium",
		"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 "llm-slice/pico-decoder-medium" \
        --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": "llm-slice/pico-decoder-medium",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use llm-slice/pico-decoder-medium with Docker Model Runner:
```
docker model run hf.co/llm-slice/pico-decoder-medium
```

pico-decoder-medium

File size: 521 Bytes

8d8d852

{
  "activation_hidden_dim": 3072,
  "architectures": [
    "PicoDecoderHF"
  ],
  "attention_n_heads": 12,
  "attention_n_kv_heads": 4,
  "auto_map": {
    "AutoConfig": "pico_decoder.PicoDecoderHFConfig",
    "AutoModelForCausalLM": "pico_decoder.PicoDecoderHF"
  },
  "batch_size": 64,
  "d_model": 768,
  "max_seq_len": 128,
  "model_type": "pico_decoder",
  "n_layers": 12,
  "norm_eps": 1e-06,
  "position_emb_theta": 10000.0,
  "torch_dtype": "float32",
  "transformers_version": "4.51.0",
  "vocab_size": 50304
}