Text Generation
Transformers
Safetensors
PyTorch
English
pebble_10m
pebble
language-model
small-language-model
custom-code
mamba2
hybrid
chat
sft
custom_code
Instructions to use basically-ai/Pebble-10M-Chat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use basically-ai/Pebble-10M-Chat with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="basically-ai/Pebble-10M-Chat", trust_remote_code=True)# Load model directly from transformers import Pebble10MLM model = Pebble10MLM.from_pretrained("basically-ai/Pebble-10M-Chat", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use basically-ai/Pebble-10M-Chat with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "basically-ai/Pebble-10M-Chat" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "basically-ai/Pebble-10M-Chat", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/basically-ai/Pebble-10M-Chat
- SGLang
How to use basically-ai/Pebble-10M-Chat 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-ai/Pebble-10M-Chat" \ --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-ai/Pebble-10M-Chat", "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-ai/Pebble-10M-Chat" \ --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-ai/Pebble-10M-Chat", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use basically-ai/Pebble-10M-Chat with Docker Model Runner:
docker model run hf.co/basically-ai/Pebble-10M-Chat
| from transformers import PretrainedConfig | |
| class PebbleConfig(PretrainedConfig): | |
| model_type = "pebble_10m" | |
| def __init__( | |
| self, | |
| vocab_size=2048, | |
| hidden_size=384, | |
| intermediate_size=1536, | |
| num_hidden_layers=8, | |
| num_attention_heads=6, | |
| block_pattern="mmma|mmma", | |
| hybrid_ratio="3:1 mamba2:attention", | |
| max_position_embeddings=512, | |
| rms_norm_eps=1e-6, | |
| tie_word_embeddings=True, | |
| mamba2=None, | |
| attention=None, | |
| **kwargs, | |
| ): | |
| self.vocab_size = vocab_size | |
| self.hidden_size = hidden_size | |
| self.intermediate_size = intermediate_size | |
| self.num_hidden_layers = num_hidden_layers | |
| self.num_attention_heads = num_attention_heads | |
| self.block_pattern = block_pattern | |
| self.hybrid_ratio = hybrid_ratio | |
| self.max_position_embeddings = max_position_embeddings | |
| self.rms_norm_eps = rms_norm_eps | |
| self.tie_word_embeddings = tie_word_embeddings | |
| # Default dictionaries if not provided in config.json | |
| self.mamba2 = mamba2 or { | |
| "d_state": 128, "d_conv": 4, "expand": 2, | |
| "headdim": 96, "use_mem_eff_path": True | |
| } | |
| self.attention = attention or { | |
| "rope_theta": 10000.0, "is_causal": True | |
| } | |
| super().__init__(**kwargs) |