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

Libraries

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

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

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

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

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

Use Docker

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

SGLang

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

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

Pebble-50M-Chat-beta

Pebble-50M-Chat-beta is the chat-tuned version of Pebble-50M-beta, 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.

The base model underperformed Pebble-25M and, on some evaluations, Pebble-10M. Pebble-50M-Chat-beta was subsequently fine-tuned to improve its ability to follow instructions and engage in conversational interactions.

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
Pretraining Tokens: ~~25,000,000,000 (~~25 Billion)
Fine-tuning Tokens: ~~250,000,000 (~~250 Million)
Fine-tuning Dataset: Smol-SmolTalk
Optimizer: Muon (for 2D hidden weights) + AdamW (for embeddings, norms, and scalars)
Precision: fp32 master weights with bf16 autocast

Dataset Sources

The base 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%

Fine-Tuning

Pebble-50M-Chat-beta was fine-tuned on approximately 250M tokens from Smol-SmolTalk to improve conversational ability and instruction following.

Benchmarks

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

The chat model is primarily intended for conversational use and should not be directly compared with the base model on benchmarks without considering the effects of fine-tuning.

Usage

Pebble-50M-Chat-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, experimentation, and conversational use.

License

Apache 2.0

Downloads last month: -

Safetensors

Model size

49.3M params

Tensor type

F32

BF16