Instructions to use rbelanec/train_mnli_42_1779207271 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rbelanec/train_mnli_42_1779207271 with Transformers:

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

pipe = pipeline("text-generation", model="rbelanec/train_mnli_42_1779207271")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("rbelanec/train_mnli_42_1779207271")
model = AutoModelForCausalLM.from_pretrained("rbelanec/train_mnli_42_1779207271")
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

vLLM

How to use rbelanec/train_mnli_42_1779207271 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "rbelanec/train_mnli_42_1779207271"
# 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_mnli_42_1779207271",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/rbelanec/train_mnli_42_1779207271

SGLang

How to use rbelanec/train_mnli_42_1779207271 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_mnli_42_1779207271" \
    --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_mnli_42_1779207271",
		"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_mnli_42_1779207271" \
        --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_mnli_42_1779207271",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use rbelanec/train_mnli_42_1779207271 with Docker Model Runner:
```
docker model run hf.co/rbelanec/train_mnli_42_1779207271
```

train_mnli_42_1779207271

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

Loss: 0.1060
Num Input Tokens Seen: 191491960

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
0.1582	0.2500	11045	0.1285	9565376
0.078	0.5000	22090	0.1263	19168640
0.175	0.7500	33135	0.1193	28714304
0.0975	1.0000	44180	0.1060	38289824
0.0541	1.2500	55225	0.1625	47877216
0.0592	1.5000	66270	0.1533	57416032
0.0219	1.7500	77315	0.1605	66982176
0.0053	2.0000	88360	0.1498	76602496
0.0299	2.2501	99405	0.2282	86154496
0.0433	2.5001	110450	0.1979	95709312
0.0409	2.7501	121495	0.2121	105304960
0.0601	3.0001	132540	0.2198	114898176
0.0	3.2501	143585	0.3264	124468928
0.0	3.5001	154630	0.2934	134028992
0.0	3.7501	165675	0.2785	143607232
0.0	4.0001	176720	0.2821	153206432
0.0	4.2501	187765	0.3611	162770528
0.0	4.5001	198810	0.3984	172345120
0.0	4.7501	209855	0.3938	181948192

Framework versions

Transformers 4.51.3
Pytorch 2.10.0+cu128
Datasets 4.0.0
Tokenizers 0.21.4

Downloads last month: 350

Safetensors

Model size

1B params

Tensor type

F32

Model tree for rbelanec/train_mnli_42_1779207271

Base model

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

Finetuned

(1747)

this model