Instructions to use rbelanec/train_boolq_42_1774791062 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rbelanec/train_boolq_42_1774791062 with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-1B-Instruct")
model = PeftModel.from_pretrained(base_model, "rbelanec/train_boolq_42_1774791062")

Transformers

How to use rbelanec/train_boolq_42_1774791062 with Transformers:

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

pipe = pipeline("text-generation", model="rbelanec/train_boolq_42_1774791062")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("rbelanec/train_boolq_42_1774791062", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use rbelanec/train_boolq_42_1774791062 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "rbelanec/train_boolq_42_1774791062"
# 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_boolq_42_1774791062",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/rbelanec/train_boolq_42_1774791062

SGLang

How to use rbelanec/train_boolq_42_1774791062 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_boolq_42_1774791062" \
    --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_boolq_42_1774791062",
		"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_boolq_42_1774791062" \
        --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_boolq_42_1774791062",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use rbelanec/train_boolq_42_1774791062 with Docker Model Runner:
```
docker model run hf.co/rbelanec/train_boolq_42_1774791062
```

train_boolq_42_1774791062

This model is a fine-tuned version of meta-llama/Llama-3.2-1B-Instruct on the boolq dataset. It achieves the following results on the evaluation set:

Loss: 0.1998
Num Input Tokens Seen: 12333600

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: 8
eval_batch_size: 8
seed: 42
optimizer: Use OptimizerNames.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: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Input Tokens Seen
0.3271	0.2507	266	0.3127	618432
0.2623	0.5014	532	0.2774	1225408
0.2987	0.7521	798	0.2546	1851072
0.3082	1.0028	1064	0.2344	2475808
0.2513	1.2535	1330	0.2254	3091552
0.2693	1.5042	1596	0.2147	3699104
0.1687	1.7549	1862	0.2256	4324256
0.2361	2.0057	2128	0.2062	4940992
0.2205	2.2564	2394	0.2048	5558144
0.2933	2.5071	2660	0.2098	6183872
0.1911	2.7578	2926	0.2094	6806208
0.2383	3.0085	3192	0.2040	7421856
0.172	3.2592	3458	0.1998	8043744
0.2434	3.5099	3724	0.2094	8660768
0.1925	3.7606	3990	0.2062	9286304
0.2996	4.0113	4256	0.2077	9894624
0.186	4.2620	4522	0.2098	10512416
0.1804	4.5127	4788	0.2097	11115040
0.1211	4.7634	5054	0.2100	11736672

Framework versions

PEFT 0.17.1
Transformers 4.51.3
Pytorch 2.10.0+cu128
Datasets 4.0.0
Tokenizers 0.21.4

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