Instructions to use mlx-community/plamo-2-1b-bf16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mlx-community/plamo-2-1b-bf16 with Transformers:

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

pipe = pipeline("text-generation", model="mlx-community/plamo-2-1b-bf16", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("mlx-community/plamo-2-1b-bf16", trust_remote_code=True, device_map="auto")

MLX

How to use mlx-community/plamo-2-1b-bf16 with MLX:

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm
# if on a CUDA device, also pip install mlx[cuda]

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("mlx-community/plamo-2-1b-bf16")

prompt = "Once upon a time in"
text = generate(model, tokenizer, prompt=prompt, verbose=True)

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

vLLM

How to use mlx-community/plamo-2-1b-bf16 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mlx-community/plamo-2-1b-bf16"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mlx-community/plamo-2-1b-bf16",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/mlx-community/plamo-2-1b-bf16

SGLang

How to use mlx-community/plamo-2-1b-bf16 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 "mlx-community/plamo-2-1b-bf16" \
    --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": "mlx-community/plamo-2-1b-bf16",
		"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 "mlx-community/plamo-2-1b-bf16" \
        --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": "mlx-community/plamo-2-1b-bf16",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

MLX LM

How to use mlx-community/plamo-2-1b-bf16 with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Generate some text
mlx_lm.generate --model "mlx-community/plamo-2-1b-bf16" --prompt "Once upon a time"

Docker Model Runner
How to use mlx-community/plamo-2-1b-bf16 with Docker Model Runner:
```
docker model run hf.co/mlx-community/plamo-2-1b-bf16
```
Atomic Chat

plamo-2-1b-bf16

File size: 1,379 Bytes

ce95292

{
    "architectures": [
        "PlamoForCausalLM"
    ],
    "attention_window_size": 2048,
    "auto_map": {
        "AutoConfig": "modeling_plamo.PlamoConfig",
        "AutoModelForCausalLM": "modeling_plamo.PlamoForCausalLM"
    },
    "bos_token_id": 1,
    "capacity_factor": 1.0,
    "eos_token_id": 2,
    "eval_attention_n_bit": null,
    "eval_mlp_n_bit": null,
    "expert_dropout": 0.0,
    "fp8_accum_dtype": "bfloat16",
    "group_size": 1024,
    "hidden_size": 2048,
    "hidden_size_per_head": 128,
    "image_feature_size": null,
    "image_proj_type": "linear",
    "image_token_id": null,
    "intermediate_size": 8192,
    "k_expert": null,
    "linear_type": "fp8",
    "mamba_chunk_size": 256,
    "mamba_d_conv": 4,
    "mamba_d_state": 64,
    "mamba_enabled": true,
    "mamba_num_heads": 32,
    "mamba_step": 2,
    "max_position_embeddings": 10485760,
    "model_type": "plamo2",
    "n_expert": null,
    "num_attention_heads": 16,
    "num_hidden_layers": 16,
    "num_key_value_heads": 1,
    "rms_norm_eps": 1e-06,
    "shared_intermediate_size": null,
    "sliding_window": 2048,
    "sparse_intermediate_size": null,
    "sparse_step": null,
    "tokenizer_class": "PlamoTokenizer",
    "torch_dtype": "float32",
    "transformers_version": "4.44.2",
    "use_cache": true,
    "use_predefined_initial_state": false,
    "vocab_size": 100000
}