Instructions to use basically-experimental/Pebble-50M-beta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use basically-experimental/Pebble-50M-beta with Transformers:

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

pipe = pipeline("text-generation", model="basically-experimental/Pebble-50M-beta", trust_remote_code=True)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("basically-experimental/Pebble-50M-beta", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use basically-experimental/Pebble-50M-beta with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "basically-experimental/Pebble-50M-beta"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "basically-experimental/Pebble-50M-beta",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/basically-experimental/Pebble-50M-beta

SGLang

How to use basically-experimental/Pebble-50M-beta 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 "basically-experimental/Pebble-50M-beta" \
    --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": "basically-experimental/Pebble-50M-beta",
		"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 "basically-experimental/Pebble-50M-beta" \
        --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": "basically-experimental/Pebble-50M-beta",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use basically-experimental/Pebble-50M-beta with Docker Model Runner:
```
docker model run hf.co/basically-experimental/Pebble-50M-beta
```

Pebble-50M-beta

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{
  "architectures": [
    "PebbleForCausalLM"
  ],
  "attention": {
    "is_causal": true,
    "rope_theta": 10000.0
  },
  "auto_map": {
    "AutoConfig": "configuration_pebble.PebbleConfig",
    "AutoModelForCausalLM": "modeling_pebble.PebbleForCausalLM"
  },
  "block_pattern": "mmma|mmma",
  "dtype": "bfloat16",
  "hidden_size": 768,
  "hybrid_ratio": "3:1 mamba2:attention",
  "intermediate_size": 3072,
  "mamba2": {
    "d_conv": 4,
    "d_state": 128,
    "expand": 2,
    "headdim": 64,
    "use_mem_eff_path": true
  },
  "max_position_embeddings": 16384,
  "model_type": "pebble_50m",
  "num_attention_heads": 12,
  "num_hidden_layers": 8,
  "rms_norm_eps": 1e-06,
  "transformers_version": "4.57.1",
  "vocab_size": 16384
}