Instructions to use mlx-community/plamo-2-1b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mlx-community/plamo-2-1b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="mlx-community/plamo-2-1b", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("mlx-community/plamo-2-1b", trust_remote_code=True, device_map="auto") - MLX
How to use mlx-community/plamo-2-1b 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") 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 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" # 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", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/mlx-community/plamo-2-1b
- SGLang
How to use mlx-community/plamo-2-1b 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" \ --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", "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" \ --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", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - MLX LM
How to use mlx-community/plamo-2-1b 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" --prompt "Once upon a time"
- Docker Model Runner
How to use mlx-community/plamo-2-1b with Docker Model Runner:
docker model run hf.co/mlx-community/plamo-2-1b
- Atomic Chat
metadata
license: apache-2.0
language:
- en
- ja
pipeline_tag: text-generation
library_name: transformers
base_model: pfnet/plamo-2-1b
tags:
- mlx
mlx-community/plamo-2-1b
The Model mlx-community/plamo-2-1b was converted to MLX format from pfnet/plamo-2-1b using mlx-lm version 0.21.0.
Use with mlx
pip install mlx numba # numba is required for the new PLaMo tokenizer
pip install "git+https://github.com/mitmul/mlx-examples.git@mitmul/add-plamo2-1b-support#egg=mlx-lm&subdirectory=llms"
python -m mlx_lm.generate \
--model mlx-community/plamo-2-1b \
--prompt '็พๅณใใใซใฌใผใฎไฝใๆน ใฎใฌใทใใ็ดนไปใใพใใ' \
--max-tokens 1024 \
--extra-eos-token '<|plamo:bos|>' \
--ignore-chat-template \
--temp 0
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==========
## ็พๅณใใใซใฌใผใฎไฝใๆน
### ๆๆ
- ็ใญใ ๏ผๅ
- ใซใใใ ๏ผๆฌ
- ใใใใใ ๏ผๅ
- ่ฑใฒใ่ ๏ผ๏ผ๏ผ๏ฝ
- ใซใใซใ ๏ผใใ
- ใใใใ ๏ผใใ
- ใซใฌใผ็ฒ ๅคงใใ๏ผ
- ๅกฉ ๅฐใใ๏ผ
- ๆฐด ๏ผ๏ผ๏ผ๏ฝ๏ฝ
- ใใใ็ผถ ๏ผ็ผถ
- ๆฐด ๏ผ๏ผ๏ผ๏ฝ๏ฝ
- ใตใฉใๆฒน ๅคงใใ๏ผ
- ๅกฉ ๅฐใ
- ใใใใ ๅฐใ
### ไฝใๆน
- ็ใญใใใซใใใใใใใใใใใซใใซใใใใใใใใฟใใๅใใซใใพใใ
- ใใฉใคใใณใซใตใฉใๆฒนใ็ฑใใใซใใซใใใใใใใ็ใใพใใ
- ้ฆใใๅบใฆใใใใ็ใญใใใซใใใใใใใใใใ่ฑใฒใ่ใๅกฉใใใใใใๆฐดใใใใ็ผถใๆฐดใใซใฌใผ็ฒใๅ
ฅใใฆ็ใใพใใ
- ้่ใซ็ซใ้ใฃใใใใซใฌใผ็ฒใๆบถใใใพใใ
- ๆฐดใๅกฉใใใใใใงๅณใ่ชฟใใพใใ
- ๆๅพใซใใใ็ผถใๅ ใใฆใ็
ฎ่พผใใงๅฎๆใงใใ
## ใพใจใ
็พๅณใใใซใฌใผใฎไฝใๆนใฎใฌใทใใ็ดนไปใใพใใใ
ใใฒๅ่ใซใใฆใฟใฆใใ ใใใ
==========
Prompt: 8 tokens, 114.293 tokens-per-sec
Generation: 234 tokens, 67.785 tokens-per-sec
Peak memory: 2.727 GB
You can also write your code to use this model like this:
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/plamo-2-1b")
prompt = "็พๅณใใใซใฌใผใฎไฝใๆนใฎใฌใทใใ็ดนไปใใพใใ"
response = generate(model, tokenizer, prompt=prompt, verbose=True)