Instructions to use rbelanec/train_siqa_456_1760637832 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rbelanec/train_siqa_456_1760637832 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_456_1760637832")

Transformers

How to use rbelanec/train_siqa_456_1760637832 with Transformers:

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

pipe = pipeline("text-generation", model="rbelanec/train_siqa_456_1760637832")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("rbelanec/train_siqa_456_1760637832", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use rbelanec/train_siqa_456_1760637832 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "rbelanec/train_siqa_456_1760637832"
# 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_456_1760637832",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/rbelanec/train_siqa_456_1760637832

SGLang

How to use rbelanec/train_siqa_456_1760637832 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_456_1760637832" \
    --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_456_1760637832",
		"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_456_1760637832" \
        --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_456_1760637832",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use rbelanec/train_siqa_456_1760637832 with Docker Model Runner:
```
docker model run hf.co/rbelanec/train_siqa_456_1760637832
```

train_siqa_456_1760637832

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: 0.5714
Num Input Tokens Seen: 60272064

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
0.4459	1.0	7518	0.5798	3015336
0.62	2.0	15036	0.5762	6029736
0.2437	3.0	22554	0.5730	9044064
0.6211	4.0	30072	0.5736	12056056
0.7361	5.0	37590	0.5734	15070152
0.672	6.0	45108	0.5748	18083976
0.5897	7.0	52626	0.5733	21097056
0.6191	8.0	60144	0.5744	24109664
0.5043	9.0	67662	0.5741	27122784
1.4349	10.0	75180	0.5727	30139392
1.1223	11.0	82698	0.5727	33151800
0.4852	12.0	90216	0.5714	36165976
0.0319	13.0	97734	0.5722	39180248
0.3707	14.0	105252	0.5737	42193928
0.7959	15.0	112770	0.5732	45207272
0.5074	16.0	120288	0.5743	48219232
0.7606	17.0	127806	0.5750	51231624
0.9033	18.0	135324	0.5750	54245832
0.403	19.0	142842	0.5750	57258952
0.6253	20.0	150360	0.5750	60272064

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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