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

Libraries

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

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

pipe = pipeline("text-generation", model="mlx-community/plamo-2-translate", 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", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use mlx-community/plamo-2-translate 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"
# 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",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

How to use mlx-community/plamo-2-translate 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" \
    --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",
		"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" \
        --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",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

mitmul commited on May 27, 2025

Commit

12d8e07

1 Parent(s): 17b41f8

Add chat_template to tokenizer_config.json

Browse files

Files changed (1) hide show

tokenizer_config.json +2 -1

tokenizer_config.json CHANGED Viewed

@@ -52,5 +52,6 @@
   "pad_token": "<|plamo:pad|>",
   "sep_token": null,
   "tokenizer_class": "Plamo2Tokenizer",
-  "unk_token": "<|plamo:unk|>"
 }

   "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"
 }