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

Libraries

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

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

pipe = pipeline("text-generation", model="mlx-community/plamo-2-translate-bf16", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use mlx-community/plamo-2-translate-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-translate-bf16"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mlx-community/plamo-2-translate-bf16",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

How to use mlx-community/plamo-2-translate-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-translate-bf16" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mlx-community/plamo-2-translate-bf16",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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-translate-bf16" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mlx-community/plamo-2-translate-bf16",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

plamo-2-translate-bf16 / tokenizer_config.json

mitmul

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	{
	"add_bos_token": true,
	"add_eos_token": false,
	"added_tokens_decoder": {
	"0": {
	"content": "<\|plamo:unk\|>",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false,
	"special": true
	},
	"1": {
	"content": "<\|plamo:bos\|>",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false,
	"special": true
	},
	"2": {
	"content": "<\|plamo:eos\|>",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false,
	"special": true
	},
	"3": {
	"content": "<\|plamo:pad\|>",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false,
	"special": true
	}
	},
	"auto_map": {
	"AutoTokenizer": [
	"tokenization_plamo.Plamo2Tokenizer",
	null
	]
	},
	"bos_token": "<\|plamo:bos\|>",
	"clean_up_tokenization_spaces": false,
	"cls_token": null,
	"eos_token": "<\|plamo:bos\|>",
	"extra_special_tokens": {},
	"local_file_only": true,
	"mask_token": null,
	"model_max_length": 1000000000000000019884624838656,
	"pad_token": "<\|plamo:pad\|>",
	"sep_token": null,
	"tokenizer_class": "Plamo2Tokenizer",
	"unk_token": "<\|plamo:unk\|>",
	"chat_template": "{{- \"<\|plamo:op\|>dataset\\ntranslation\\n\" -}}\n{% for message in messages %}\n {%- if message['role'] == 'user' %}\n {{- '<\|plamo:op\|>input lang=Japanese\|English\\n' + message['content'] + '\\n' }}\n {%- elif message['role'] == 'assistant' %}\n {{- '<\|plamo:op\|>output\\n' + message['content']}}\n {%- if not loop.last %}\n {{- '\\n'}}\n {%- endif %}\n {%- endif %}\n {% if loop.last and message['role'] != 'assistant' and add_generation_prompt %}\n {{- '<\|plamo:op\|>output\\n' -}}\n {% endif %}\n{% endfor %}\n"
	}