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

Libraries

How to use RikoteMaster/open_math_model_mcqa_lora_full_dataset with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("RikoteMaster/open_math_model_mcqa_lora_full_dataset")
model = AutoModelForCausalLM.from_pretrained("RikoteMaster/open_math_model_mcqa_lora_full_dataset", device_map="auto")
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 Settings

vLLM

How to use RikoteMaster/open_math_model_mcqa_lora_full_dataset with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/RikoteMaster/open_math_model_mcqa_lora_full_dataset

SGLang

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

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

open_math_model_mcqa_lora_full_dataset

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You are a helpful assistant, that answer STEM questions. Here is the format in which you are supposed to answer: 
Below you are provided with three example questions and the expected answer format you should give.
The following are multiple choice questions (with answers) about knowledge and skills in advanced master-level STEM courses.
Performance enhancing synthetic steroids are based on the structure of the hormone:
A. testosterone.
B. cortisol.
C. progesterone.
D. aldosterone.
Answer: A
The following are multiple choice questions (with answers) about knowledge and skills in advanced master-level STEM courses.
Asp235Phe in a molecular report indicates that:
A. asparagine has been replaced by phenylalanine.
B. phenylalanine has been replaced by asparagine.
C. aspartic acid has been replaced by phenylalanine.
D. phenylalanine has been replaced by aspartic acid.
Answer: C
The following are multiple choice questions (with answers) about knowledge and skills in advanced master-level STEM courses.
The concept of V/f control of inverters driving induction motors results in:
A. constant torque operation  
B. speed reversal  
C. reduced magnetic loss  
D. harmonic elimination  
Answer: A
Answer the following question in the same way:
<|im_end|>{% for message in messages %}{% if message['role'] == 'system' %}{{ '<|im_start|>system\n' + message['content'] + '<|im_end|>\n' }}{% elif message['role'] == 'user' %}{{ '<|im_start|>user\n' + message['content'] + '<|im_end|>\n' }}{% elif message['role'] == 'assistant' %}{{ '<|im_start|>assistant\n' + message['content'] + '<|im_end|>\n' }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}