Instructions to use rbelanec/train_boolq_42_1776331558 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rbelanec/train_boolq_42_1776331558 with Transformers:

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

pipe = pipeline("text-generation", model="rbelanec/train_boolq_42_1776331558")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("rbelanec/train_boolq_42_1776331558")
model = AutoModelForCausalLM.from_pretrained("rbelanec/train_boolq_42_1776331558")
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 rbelanec/train_boolq_42_1776331558 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_1776331558"
# 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_1776331558",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/rbelanec/train_boolq_42_1776331558

SGLang

How to use rbelanec/train_boolq_42_1776331558 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_1776331558" \
    --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_1776331558",
		"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_1776331558" \
        --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_1776331558",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use rbelanec/train_boolq_42_1776331558 with Docker Model Runner:
```
docker model run hf.co/rbelanec/train_boolq_42_1776331558
```

train_boolq_42_1776331558

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: 0.1885
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-06
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
0.2277	0.2507	266	0.2505	618432
0.2193	0.5014	532	0.3166	1225408
0.2554	0.7521	798	0.2179	1851072
0.3676	1.0028	1064	0.1885	2475808
0.165	1.2535	1330	0.4608	3091552
0.2207	1.5042	1596	0.3545	3699104
0.1138	1.7549	1862	0.3500	4324256
0.0762	2.0057	2128	0.3345	4940992
0.0898	2.2564	2394	0.4647	5558144
0.0692	2.5071	2660	0.4098	6183872
0.227	2.7578	2926	0.4303	6806208
0.0004	3.0085	3192	0.3937	7421856
0.0	3.2592	3458	0.5191	8043744
0.0002	3.5099	3724	0.4636	8660768
0.0	3.7606	3990	0.5201	9286304
0.0001	4.0113	4256	0.5146	9894624
0.0782	4.2620	4522	0.5548	10512416
0.0	4.5127	4788	0.5418	11115040
0.0	4.7634	5054	0.5422	11736672

Framework versions

Transformers 4.51.3
Pytorch 2.10.0+cu128
Datasets 4.0.0
Tokenizers 0.21.4

Downloads last month: 3

Safetensors

Model size

1B params

Tensor type

F32

Model tree for rbelanec/train_boolq_42_1776331558

Base model

meta-llama/Llama-3.2-1B-Instruct

Finetuned

(1747)

this model