Instructions to use rbelanec/train_record_42_1773159747 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rbelanec/train_record_42_1773159747 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_record_42_1773159747")

Transformers

How to use rbelanec/train_record_42_1773159747 with Transformers:

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

pipe = pipeline("text-generation", model="rbelanec/train_record_42_1773159747")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("rbelanec/train_record_42_1773159747", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use rbelanec/train_record_42_1773159747 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "rbelanec/train_record_42_1773159747"
# 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_record_42_1773159747",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/rbelanec/train_record_42_1773159747

SGLang

How to use rbelanec/train_record_42_1773159747 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_record_42_1773159747" \
    --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_record_42_1773159747",
		"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_record_42_1773159747" \
        --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_record_42_1773159747",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use rbelanec/train_record_42_1773159747 with Docker Model Runner:
```
docker model run hf.co/rbelanec/train_record_42_1773159747
```

train_record_42_1773159747

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

Loss: 0.4154
Num Input Tokens Seen: 245808128

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
0.6189	0.2500	3906	0.6394	12292032
0.5393	0.5001	7812	0.5500	24620672
0.5028	0.7501	11718	0.5072	36894016
0.3815	1.0002	15624	0.4872	49176512
0.5595	1.2502	19530	0.4700	61465280
0.4085	1.5003	23436	0.4594	73739776
0.4661	1.7503	27342	0.4472	86015936
0.4054	2.0004	31248	0.4402	98341056
0.4217	2.2504	35154	0.4363	110649216
0.3233	2.5005	39060	0.4314	122910592
0.3875	2.7505	42966	0.4261	135222656
0.336	3.0006	46872	0.4220	147516736
0.2617	3.2506	50778	0.4272	159826368
0.4998	3.5007	54684	0.4193	172084032
0.4191	3.7507	58590	0.4184	184402752
0.3614	4.0008	62496	0.4154	196687936
0.3211	4.2508	66402	0.4205	209017024
0.3259	4.5009	70308	0.4172	221278272
0.314	4.7509	74214	0.4179	233564288

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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