Instructions to use maym15/Olmo-3-7B-Instruct-RetMask with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use maym15/Olmo-3-7B-Instruct-RetMask with Transformers:

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

pipe = pipeline("text-generation", model="maym15/Olmo-3-7B-Instruct-RetMask")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("maym15/Olmo-3-7B-Instruct-RetMask")
model = AutoModelForCausalLM.from_pretrained("maym15/Olmo-3-7B-Instruct-RetMask")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use maym15/Olmo-3-7B-Instruct-RetMask with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "maym15/Olmo-3-7B-Instruct-RetMask"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "maym15/Olmo-3-7B-Instruct-RetMask",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/maym15/Olmo-3-7B-Instruct-RetMask

SGLang

How to use maym15/Olmo-3-7B-Instruct-RetMask 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 "maym15/Olmo-3-7B-Instruct-RetMask" \
    --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": "maym15/Olmo-3-7B-Instruct-RetMask",
		"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 "maym15/Olmo-3-7B-Instruct-RetMask" \
        --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": "maym15/Olmo-3-7B-Instruct-RetMask",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use maym15/Olmo-3-7B-Instruct-RetMask with Docker Model Runner:
```
docker model run hf.co/maym15/Olmo-3-7B-Instruct-RetMask
```

Olmo-3-7B-Instruct-RetMask / tokenizer_config.json

maym15

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	{
	"add_prefix_space": false,
	"added_tokens_decoder": {
	"100256": {
	"content": "<\|extra_id_0\|>",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false,
	"special": false
	},
	"100257": {
	"content": "<\|endoftext\|>",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false,
	"special": true
	},
	"100258": {
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	"rstrip": false,
	"single_word": false,
	"special": true
	},
	"100259": {
	"content": "<\|fim_middle\|>",
	"lstrip": false,
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	"special": true
	},
	"100260": {
	"content": "<\|fim_suffix\|>",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false,
	"special": true
	},
	"100261": {
	"content": "\|\|\|PHONE_NUMBER\|\|\|",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false,
	"special": false
	},
	"100262": {
	"content": "\|\|\|EMAIL_ADDRESS\|\|\|",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false,
	"special": false
	},
	"100263": {
	"content": "\|\|\|IP_ADDRESS\|\|\|",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false,
	"special": false
	},
	"100264": {
	"content": "<\|im_start\|>",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false,
	"special": true
	},
	"100265": {
	"content": "<\|im_end\|>",
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	"special": true
	},
	"100266": {
	"content": "<functions>",
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	},
	"100267": {
	"content": "</functions>",
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	},
	"100268": {
	"content": "<function_calls>",
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	},
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	"content": "<\|extra_id_1\|>",
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	},
	"100271": {
	"content": "<\|extra_id_2\|>",
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	},
	"100272": {
	"content": "<\|extra_id_3\|>",
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	},
	"100273": {
	"content": "<\|extra_id_4\|>",
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	},
	"100274": {
	"content": "<\|extra_id_5\|>",
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	},
	"100275": {
	"content": "<\|extra_id_6\|>",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false,
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	},
	"100276": {
	"content": "<\|endofprompt\|>",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false,
	"special": true
	},
	"100277": {
	"content": "<\|pad\|>",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false,
	"special": true
	}
	},
	"additional_special_tokens": [],
	"bos_token": "<\|endoftext\|>",
	"clean_up_tokenization_spaces": false,
	"eos_token": "<\|endoftext\|>",
	"extra_special_tokens": {},
	"model_max_length": 65536,
	"pad_token": "<\|pad\|>",
	"tokenizer_class": "GPT2Tokenizer",
	"unk_token": "<\|endoftext\|>"
	}