Instructions to use rbelanec/train_math_qa_123_1760637726 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rbelanec/train_math_qa_123_1760637726 with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Meta-Llama-3-8B-Instruct")
model = PeftModel.from_pretrained(base_model, "rbelanec/train_math_qa_123_1760637726")

Transformers

How to use rbelanec/train_math_qa_123_1760637726 with Transformers:

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

pipe = pipeline("text-generation", model="rbelanec/train_math_qa_123_1760637726")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("rbelanec/train_math_qa_123_1760637726", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use rbelanec/train_math_qa_123_1760637726 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "rbelanec/train_math_qa_123_1760637726"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rbelanec/train_math_qa_123_1760637726",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/rbelanec/train_math_qa_123_1760637726

SGLang

How to use rbelanec/train_math_qa_123_1760637726 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 "rbelanec/train_math_qa_123_1760637726" \
    --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": "rbelanec/train_math_qa_123_1760637726",
		"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 "rbelanec/train_math_qa_123_1760637726" \
        --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": "rbelanec/train_math_qa_123_1760637726",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use rbelanec/train_math_qa_123_1760637726 with Docker Model Runner:
```
docker model run hf.co/rbelanec/train_math_qa_123_1760637726
```

train_math_qa_123_1760637726

This model is a fine-tuned version of meta-llama/Meta-Llama-3-8B-Instruct on the math_qa dataset. It achieves the following results on the evaluation set:

Loss: 0.6507
Num Input Tokens Seen: 77961608

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-05
train_batch_size: 4
eval_batch_size: 4
seed: 123
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.1
num_epochs: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Input Tokens Seen
0.7328	1.0	6714	0.7198	3894688
0.7764	2.0	13428	0.6897	7789256
0.6538	3.0	20142	0.6761	11683856
0.6451	4.0	26856	0.6667	15585872
0.6469	5.0	33570	0.6594	19482128
0.6932	6.0	40284	0.6587	23376776
0.6886	7.0	46998	0.6551	27278720
0.5407	8.0	53712	0.6556	31180904
0.5925	9.0	60426	0.6507	35077032
0.7712	10.0	67140	0.6527	38976336
0.5661	11.0	73854	0.6529	42875264
0.6195	12.0	80568	0.6561	46772480
0.4153	13.0	87282	0.6574	50673016
0.5635	14.0	93996	0.6551	54573896
0.4801	15.0	100710	0.6580	58472760
0.373	16.0	107424	0.6575	62371472
0.461	17.0	114138	0.6579	66268336
0.5259	18.0	120852	0.6566	70167432
0.6381	19.0	127566	0.6567	74065984
0.5324	20.0	134280	0.6562	77961608

Framework versions

PEFT 0.17.1
Transformers 4.51.3
Pytorch 2.9.0+cu128
Datasets 4.0.0
Tokenizers 0.21.4

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