Instructions to use RabotniKuma/Fast-Math-R1-14B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RabotniKuma/Fast-Math-R1-14B with Transformers:

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

pipe = pipeline("text-generation", model="RabotniKuma/Fast-Math-R1-14B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("RabotniKuma/Fast-Math-R1-14B")
model = AutoModelForCausalLM.from_pretrained("RabotniKuma/Fast-Math-R1-14B")
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 RabotniKuma/Fast-Math-R1-14B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RabotniKuma/Fast-Math-R1-14B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RabotniKuma/Fast-Math-R1-14B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/RabotniKuma/Fast-Math-R1-14B

SGLang

How to use RabotniKuma/Fast-Math-R1-14B 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 "RabotniKuma/Fast-Math-R1-14B" \
    --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": "RabotniKuma/Fast-Math-R1-14B",
		"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 "RabotniKuma/Fast-Math-R1-14B" \
        --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": "RabotniKuma/Fast-Math-R1-14B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use RabotniKuma/Fast-Math-R1-14B with Docker Model Runner:
```
docker model run hf.co/RabotniKuma/Fast-Math-R1-14B
```

Fast-Math-R1-14B / tokenizer_config.json

RabotniKuma

Initial commit: Upload pretrained LLM

fc96ee6 verified about 1 year ago

raw

history blame

4.96 kB

	{
	"add_bos_token": true,
	"add_eos_token": false,
	"add_prefix_space": null,
	"added_tokens_decoder": {
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	"lstrip": false,
	"normalized": false,
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	},
	"151644": {
	"content": "<｜User｜>",
	"lstrip": false,
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	"single_word": false,
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	"151650": {
	"content": "<\|quad_start\|>",
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	"151651": {
	"content": "<\|quad_end\|>",
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	"151659": {
	"content": "<\|fim_prefix\|>",
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	"151660": {
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	"151663": {
	"content": "<\|repo_name\|>",
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	},
	"151664": {
	"content": "<\|file_sep\|>",
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	"normalized": false,
	"rstrip": false,
	"single_word": false,
	"special": false
	}
	},
	"bos_token": "<｜begin▁of▁sentence｜>",
	"chat_template": "{% for message in messages %}\n{% if message['role'] == 'user' %}\n{{ '<\|user\|>\n' + message['content'] + eos_token }}\n{% elif message['role'] == 'system' %}\n{{ '<\|system\|>\n' + message['content'] + eos_token }}\n{% elif message['role'] == 'assistant' %}\n{{ '<\|assistant\|>\n' + message['content'] + eos_token }}\n{% endif %}\n{% if loop.last and add_generation_prompt %}\n{{ '<\|assistant\|>' }}\n{% endif %}\n{% endfor %}",
	"clean_up_tokenization_spaces": false,
	"eos_token": "<｜end▁of▁sentence｜>",
	"extra_special_tokens": {},
	"legacy": true,
	"model_max_length": 16384,
	"pad_token": "<｜end▁of▁sentence｜>",
	"padding_side": "left",
	"sp_model_kwargs": {},
	"tokenizer_class": "LlamaTokenizer",
	"unk_token": null,
	"use_default_system_prompt": false
	}