Instructions to use pepoo20/WordProblem with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use pepoo20/WordProblem with Transformers:

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

pipe = pipeline("text-generation", model="pepoo20/WordProblem")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("pepoo20/WordProblem")
model = AutoModelForCausalLM.from_pretrained("pepoo20/WordProblem")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use pepoo20/WordProblem with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "pepoo20/WordProblem"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "pepoo20/WordProblem",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/pepoo20/WordProblem

SGLang

How to use pepoo20/WordProblem 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 "pepoo20/WordProblem" \
    --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": "pepoo20/WordProblem",
		"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 "pepoo20/WordProblem" \
        --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": "pepoo20/WordProblem",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use pepoo20/WordProblem with Docker Model Runner:
```
docker model run hf.co/pepoo20/WordProblem
```

WordProblem / tokenizer_config.json

pepoo20

Training in progress, step 3000

0be5f00 verified about 2 years ago

raw

history blame contribute delete

1.68 kB

	{
	"add_prefix_space": false,
	"added_tokens_decoder": {
	"151643": {
	"content": "<\|endoftext\|>",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false,
	"special": true
	},
	"151644": {
	"content": "<\|im_start\|>",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false,
	"special": true
	},
	"151645": {
	"content": "<\|im_end\|>",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false,
	"special": true
	}
	},
	"additional_special_tokens": [
	"<\|im_start\|>",
	"<\|im_end\|>"
	],
	"bos_token": null,
	"chat_template": "{% set system_message = 'You are a Math Teacher.Your goal is to understand a math word problem. Then recognize and distinguish which problem it is and then define the variables (if needed) and formulate the problem as it kind then transform it to Symbolic Form.' %}{% if messages[0]['role'] == 'system' %}{% set system_message = messages[0]['content'] %}{% endif %}{% if system_message is defined %}{{ system_message + '\\n' }}{% endif %}{% for message in messages %}{% set content = message['content'] %}{% if message['role'] == 'user' %}{{ 'Question: ' + content + ' \\n Answer: ' }}{% elif message['role'] == 'assistant' %}{{ content + '<\|endoftext\|>' + '\\n' }}{% endif %}{% endfor %}",
	"clean_up_tokenization_spaces": false,
	"eos_token": "<\|endoftext\|>",
	"errors": "replace",
	"model_max_length": 32768,
	"pad_token": "<\|endoftext\|>",
	"padding_side": "right",
	"split_special_tokens": false,
	"tokenizer_class": "Qwen2Tokenizer",
	"unk_token": null
	}