Instructions to use rbelanec/train_siqa_42_1760637602 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rbelanec/train_siqa_42_1760637602 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_siqa_42_1760637602")

Transformers

How to use rbelanec/train_siqa_42_1760637602 with Transformers:

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

pipe = pipeline("text-generation", model="rbelanec/train_siqa_42_1760637602")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("rbelanec/train_siqa_42_1760637602", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use rbelanec/train_siqa_42_1760637602 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "rbelanec/train_siqa_42_1760637602"
# 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_siqa_42_1760637602",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/rbelanec/train_siqa_42_1760637602

SGLang

How to use rbelanec/train_siqa_42_1760637602 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_siqa_42_1760637602" \
    --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_siqa_42_1760637602",
		"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_siqa_42_1760637602" \
        --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_siqa_42_1760637602",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use rbelanec/train_siqa_42_1760637602 with Docker Model Runner:
```
docker model run hf.co/rbelanec/train_siqa_42_1760637602
```

train_siqa_42_1760637602

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

Loss: 0.6349
Num Input Tokens Seen: 60302568

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: 42
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
0.684	1.0	7518	0.6438	3016248
1.192	2.0	15036	0.6380	6032368
0.7962	3.0	22554	0.6366	9049000
0.9903	4.0	30072	0.6367	12063104
0.2041	5.0	37590	0.6357	15078392
0.888	6.0	45108	0.6374	18094200
1.1243	7.0	52626	0.6383	21109936
0.6324	8.0	60144	0.6361	24124456
0.5278	9.0	67662	0.6368	27139488
0.6336	10.0	75180	0.6383	30155824
0.537	11.0	82698	0.6359	33169800
0.8799	12.0	90216	0.6371	36184296
0.5514	13.0	97734	0.6365	39199224
0.692	14.0	105252	0.6349	42213984
0.5045	15.0	112770	0.6369	45227616
0.5041	16.0	120288	0.6361	48242336
0.4227	17.0	127806	0.6382	51258152
0.7302	18.0	135324	0.6359	54272896
0.9792	19.0	142842	0.6359	57288368
0.0778	20.0	150360	0.6359	60302568

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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