Instructions to use rbelanec/train_siqa_123_1760637715 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rbelanec/train_siqa_123_1760637715 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_123_1760637715")

Transformers

How to use rbelanec/train_siqa_123_1760637715 with Transformers:

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

pipe = pipeline("text-generation", model="rbelanec/train_siqa_123_1760637715")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("rbelanec/train_siqa_123_1760637715", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use rbelanec/train_siqa_123_1760637715 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "rbelanec/train_siqa_123_1760637715"
# 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_123_1760637715",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/rbelanec/train_siqa_123_1760637715

SGLang

How to use rbelanec/train_siqa_123_1760637715 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_123_1760637715" \
    --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_123_1760637715",
		"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_123_1760637715" \
        --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_123_1760637715",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use rbelanec/train_siqa_123_1760637715 with Docker Model Runner:
```
docker model run hf.co/rbelanec/train_siqa_123_1760637715
```

train_siqa_123_1760637715

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: 0.5491
Num Input Tokens Seen: 60276872

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: 0.03
train_batch_size: 4
eval_batch_size: 4
seed: 123
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
0.5543	1.0	7518	0.5503	3014896
0.5493	2.0	15036	0.5501	6029360
0.5515	3.0	22554	0.5504	9042368
0.5546	4.0	30072	0.5503	12055456
0.5403	5.0	37590	0.5498	15068512
0.5456	6.0	45108	0.5491	18081672
0.544	7.0	52626	0.5497	21095960
0.5473	8.0	60144	0.5500	24109392
0.5472	9.0	67662	0.5500	27122856
0.563	10.0	75180	0.5497	30137256
0.5541	11.0	82698	0.5501	33151024
0.5524	12.0	90216	0.5495	36165000
0.5531	13.0	97734	0.5496	39178496
0.5466	14.0	105252	0.5494	42193184
0.5457	15.0	112770	0.5497	45206576
0.552	16.0	120288	0.5497	48220600
0.5534	17.0	127806	0.5497	51235344
0.5487	18.0	135324	0.5500	54249648
0.5466	19.0	142842	0.5496	57262600
0.5487	20.0	150360	0.5496	60276872

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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