Instructions to use rbelanec/train_sst2_42_1779194533 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rbelanec/train_sst2_42_1779194533 with Transformers:

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

pipe = pipeline("text-generation", model="rbelanec/train_sst2_42_1779194533")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("rbelanec/train_sst2_42_1779194533")
model = AutoModelForCausalLM.from_pretrained("rbelanec/train_sst2_42_1779194533")
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 Settings

vLLM

How to use rbelanec/train_sst2_42_1779194533 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "rbelanec/train_sst2_42_1779194533"
# 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_sst2_42_1779194533",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/rbelanec/train_sst2_42_1779194533

SGLang

How to use rbelanec/train_sst2_42_1779194533 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_sst2_42_1779194533" \
    --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_sst2_42_1779194533",
		"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_sst2_42_1779194533" \
        --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_sst2_42_1779194533",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use rbelanec/train_sst2_42_1779194533 with Docker Model Runner:
```
docker model run hf.co/rbelanec/train_sst2_42_1779194533
```

train_sst2_42_1779194533

This model is a fine-tuned version of meta-llama/Llama-3.2-1B-Instruct on the sst2 dataset. It achieves the following results on the evaluation set:

Loss: 0.0970
Num Input Tokens Seen: 18647328

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: 2e-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
0.4074	0.2501	1895	0.1552	930944
0.3196	0.5002	3790	0.1577	1864128
0.0028	0.7503	5685	0.0970	2790656
0.0006	1.0004	7580	0.1143	3726464
0.1179	1.2505	9475	0.1166	4658240
0.1073	1.5006	11370	0.1257	5591680
0.342	1.7507	13265	0.1152	6528448
0.0004	2.0008	15160	0.1182	7463024
0.0556	2.2509	17055	0.1500	8395632
0.0962	2.5010	18950	0.1142	9326256
0.0429	2.7511	20845	0.1603	10259504
0.0352	3.0012	22740	0.1483	11196096
0.0352	3.2513	24635	0.1809	12128448
0.0	3.5014	26530	0.1809	13069824
0.0243	3.7515	28425	0.2036	13996672
0.0002	4.0016	30320	0.1816	14924944
0.0087	4.2517	32215	0.2473	15859920
0.0	4.5018	34110	0.2764	16790288
0.0	4.7519	36005	0.2836	17721744

Framework versions

Transformers 4.51.3
Pytorch 2.10.0+cu128
Datasets 4.0.0
Tokenizers 0.21.4

Downloads last month: 606

Safetensors

Model size

1B params

Tensor type

F32

Model tree for rbelanec/train_sst2_42_1779194533

Base model

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

Finetuned

(1749)

this model