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 / README.md

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	---
	license: apache-2.0
	language:
	- en
	pipeline_tag: text-generation
	library_name: transformers
	tags:
	- pebble
	- base-model
	- mamba
	- mamba2
	- hybrid
	- pytorch
	- custom-architecture
	---

	# Pebble-50M-beta

	Pebble-50M-beta is an experimental 50M-parameter language model designed to test how a larger Pebble architecture performs with a 16,384-token vocabulary and 16,384-token context window.

	Despite having roughly twice the parameters of Pebble-25M, Pebble-50M-beta underperformed Pebble-25M and, on some evaluations, Pebble-10M. This model is therefore primarily useful as an experimental result rather than as the strongest Pebble model.

	## Model Details

	* Architecture: Hybrid Mamba2 / Transformer
	* Block Pattern: 3 Mamba2 blocks : 1 Attention block (repeating)
	* Parameters: ~49,334,448 (50M)
	* Hidden Dimension: 768
	* Layers: 8 (6 Mamba2, 2 Attention)
	* Vocab Size: 16,384 (Custom Byte-Level BPE)
	* Context Length: 16,384
	* Training Tokens: ~25,000,000,000 (~25 Billion)
	* Optimizer: Muon (for 2D hidden weights) + AdamW (for embeddings, norms, and scalars)
	* Precision: fp32 master weights with bf16 autocast

	## Dataset Sources

	The model was trained on a 25B-token subset of the following datasets:

	\| Dataset \| Token Allocation \| Share \|
	\| ------------- \| ----------------: \| -------: \|
	\| FineWeb-Edu \| 7.50 billion \| 30% \|
	\| DCLM \| 5.00 billion \| 20% \|
	\| Cosmopedia-v2 \| 3.75 billion \| 15% \|
	\| FineMath-4+ \| 3.75 billion \| 15% \|
	\| FinePhrase \| 3.00 billion \| 12% \|
	\| NPset \| 2.00 billion \| 8% \|
	\| Total \| 25.00 billion \| 100% \|

	## Benchmarks

	The original benchmark logs for this model were lost, so exact evaluation results are unavailable.

	Qualitatively, Pebble-50M-beta underperformed Pebble-25M and, on some evaluations, Pebble-10M.

	## Usage

	Pebble-50M-beta does not require the `mamba-ssm` library and is intended to be usable with standard PyTorch-based inference implementations.

	It may run on CUDA GPUs, AMD GPUs, Intel GPUs, and CPUs depending on the inference framework and available hardware acceleration.

	## Status

	This is a beta/experimental model. It is primarily intended for research and experimentation with the Pebble architecture.

	## License

	Apache 2.0