Instructions to use rbelanec/train_openbookqa_456_1760637799 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rbelanec/train_openbookqa_456_1760637799 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_openbookqa_456_1760637799")

Transformers

How to use rbelanec/train_openbookqa_456_1760637799 with Transformers:

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

pipe = pipeline("text-generation", model="rbelanec/train_openbookqa_456_1760637799")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("rbelanec/train_openbookqa_456_1760637799", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use rbelanec/train_openbookqa_456_1760637799 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "rbelanec/train_openbookqa_456_1760637799"
# 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_openbookqa_456_1760637799",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/rbelanec/train_openbookqa_456_1760637799

SGLang

How to use rbelanec/train_openbookqa_456_1760637799 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_openbookqa_456_1760637799" \
    --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_openbookqa_456_1760637799",
		"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_openbookqa_456_1760637799" \
        --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_openbookqa_456_1760637799",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use rbelanec/train_openbookqa_456_1760637799 with Docker Model Runner:
```
docker model run hf.co/rbelanec/train_openbookqa_456_1760637799
```

train_openbookqa_456_1760637799

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

Loss: 0.1821
Num Input Tokens Seen: 8491296

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: 456
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
0.2752	1.0	1116	0.1821	424016
0.0268	2.0	2232	0.1870	848912
0.3734	3.0	3348	0.2193	1273224
0.0005	4.0	4464	0.2685	1698344
0.0002	5.0	5580	0.2791	2122168
0.0001	6.0	6696	0.3841	2547696
0.0	7.0	7812	0.5529	2971624
0.0	8.0	8928	0.4719	3395952
0.0002	9.0	10044	0.3130	3821456
0.0	10.0	11160	0.5053	4245816
0.0	11.0	12276	0.6839	4670528
0.0	12.0	13392	0.5244	5093960
0.0	13.0	14508	0.5028	5519312
0.0	14.0	15624	0.5612	5944728
0.0	15.0	16740	0.6089	6369088
0.0	16.0	17856	0.6385	6792536
0.0	17.0	18972	0.6398	7217384
0.0	18.0	20088	0.6500	7642368
0.0	19.0	21204	0.6589	8066544
0.0	20.0	22320	0.6591	8491296

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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