Instructions to use ferrazzipietro/meshTask-Qwen3-1.7B-ood with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ferrazzipietro/meshTask-Qwen3-1.7B-ood with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-1.7B")
model = PeftModel.from_pretrained(base_model, "ferrazzipietro/meshTask-Qwen3-1.7B-ood")

Transformers

How to use ferrazzipietro/meshTask-Qwen3-1.7B-ood with Transformers:

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

pipe = pipeline("text-generation", model="ferrazzipietro/meshTask-Qwen3-1.7B-ood")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("ferrazzipietro/meshTask-Qwen3-1.7B-ood", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ferrazzipietro/meshTask-Qwen3-1.7B-ood with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ferrazzipietro/meshTask-Qwen3-1.7B-ood"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ferrazzipietro/meshTask-Qwen3-1.7B-ood",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ferrazzipietro/meshTask-Qwen3-1.7B-ood

SGLang

How to use ferrazzipietro/meshTask-Qwen3-1.7B-ood 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 "ferrazzipietro/meshTask-Qwen3-1.7B-ood" \
    --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": "ferrazzipietro/meshTask-Qwen3-1.7B-ood",
		"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 "ferrazzipietro/meshTask-Qwen3-1.7B-ood" \
        --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": "ferrazzipietro/meshTask-Qwen3-1.7B-ood",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ferrazzipietro/meshTask-Qwen3-1.7B-ood with Docker Model Runner:
```
docker model run hf.co/ferrazzipietro/meshTask-Qwen3-1.7B-ood
```

meshTask-Qwen3-1.7B-ood

This model is a fine-tuned version of Qwen/Qwen3-1.7B on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 1.7024
F1 Micro: 0.8749
F1 Macro: 0.8714
F1 Weighted: 0.8752
Class/f1 Results Per Class: {}
Items/f1 Scores Per Item: {'Child': 0.8757690052594271, 'Follow-Up Studies': 0.6329772513422262, 'Adult': 0.6545454545454545, 'Female': 0.5062135292846337, 'Cross-Sectional Studies': 0.9202758388697334, 'Cell Proliferation': 0.8820066889632108, 'Middle Aged': 0.6751221961134315, 'Retrospective Studies': 0.8919966063348417, 'Reproducibility of Results': 0.7749282296650718, 'Young Adult': 0.7201323772752344, 'Child, Preschool': 0.8242146431725472, 'Rats': 0.6388183706438957, 'Aged, 80 and over': 0.7077608152398154, 'Prospective Studies': 0.8707017326074233, 'Infant': 0.8120354737924744}

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: 0.0003
train_batch_size: 128
eval_batch_size: 32
seed: 42
distributed_type: multi-GPU
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-07 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.1
num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss	F1 Micro	F1 Macro	F1 Weighted	Class/f1 Results Per Class	Items/f1 Scores Per Item
2.2875	0.0517	20	2.3191	0.0	0.0	0.0	{}	{'Child': 0.0, 'Follow-Up Studies': 0.0, 'Adult': 0.0, 'Female': 0.0, 'Aged, 80 and over': 0.0, 'Retrospective Studies': 0.0, 'Cross-Sectional Studies': 0.0, 'Cell Proliferation': 0.0, 'Middle Aged': 0.0, 'Reproducibility of Results': 0.0, 'Young Adult': 0.0, 'Rats': 0.0, 'Child, Preschool': 0.0, 'Prospective Studies': 0.0, 'Infant': 0.0}
1.7203	0.1034	40	1.8165	0.7446	0.7446	0.7446	{}	{'Child': 0.7845517371068591, 'Follow-Up Studies': 0.2822478439615468, 'Adult': 0.4707982474972766, 'Female': 0.6392339544513457, 'Aged, 80 and over': 0.4914397542736427, 'Retrospective Studies': 0.6429670465298063, 'Cross-Sectional Studies': 0.6549257655428395, 'Cell Proliferation': 0.6820512820512821, 'Middle Aged': 0.5019729959057246, 'Reproducibility of Results': 0.4575342465753425, 'Young Adult': 0.3724172445842568, 'Rats': 0.5572916666666666, 'Child, Preschool': 0.7089397089397089, 'Prospective Studies': 0.6812318189433284, 'Infant': 0.8320310742512635}
1.6469	0.1550	60	1.7551	0.8321	0.8255	0.8305	{}	{'Child': 0.8772049711022829, 'Follow-Up Studies': 0.4537177541729894, 'Adult': 0.5677730542838456, 'Female': 0.6194883514251254, 'Aged, 80 and over': 0.6187610724571699, 'Retrospective Studies': 0.40123034859876966, 'Cross-Sectional Studies': 0.47363765038924277, 'Cell Proliferation': 0.7556703739381732, 'Middle Aged': 0.5823238033704841, 'Reproducibility of Results': 0.5, 'Young Adult': 0.6456475287997028, 'Rats': 0.695016611295681, 'Child, Preschool': 0.7739467408585056, 'Prospective Studies': 0.535191637630662, 'Infant': 0.8175504068833256}
1.6406	0.2067	80	1.7297	0.8577	0.8527	0.8567	{}	{'Child': 0.8796904315196998, 'Follow-Up Studies': 0.6091644204851752, 'Adult': 0.5990885119958331, 'Female': 0.6142683211786554, 'Aged, 80 and over': 0.6265356265356266, 'Retrospective Studies': 0.8161544401544402, 'Cross-Sectional Studies': 0.7515487867836861, 'Cell Proliferation': 0.7643660687138948, 'Middle Aged': 0.6538684633232197, 'Reproducibility of Results': 0.5854906682721253, 'Young Adult': 0.6504296024745102, 'Rats': 0.66, 'Child, Preschool': 0.8108908139717521, 'Prospective Studies': 0.8220011055831952, 'Infant': 0.8077874355169246}
1.6141	0.2584	100	1.7234	0.8623	0.8591	0.8622	{}	{'Child': 0.8809767050238563, 'Follow-Up Studies': 0.5410989010989011, 'Adult': 0.557151819764696, 'Female': 0.560529634300126, 'Aged, 80 and over': 0.6924676868221661, 'Retrospective Studies': 0.8430042398546336, 'Cross-Sectional Studies': 0.7554157931516422, 'Cell Proliferation': 0.7416666666666667, 'Middle Aged': 0.5735507246376812, 'Reproducibility of Results': 0.6488323972805202, 'Young Adult': 0.6541643684500827, 'Rats': 0.66, 'Child, Preschool': 0.8718309859154929, 'Prospective Studies': 0.7953950811093669, 'Infant': 0.8201604250753589}
1.6047	0.3101	120	1.7197	0.8643	0.8610	0.8642	{}	{'Child': 0.8775948460987831, 'Follow-Up Studies': 0.5881006864988558, 'Adult': 0.5970076478642755, 'Female': 0.5228869047619048, 'Aged, 80 and over': 0.6319664597940913, 'Retrospective Studies': 0.857206604194556, 'Cross-Sectional Studies': 0.7805105614952814, 'Cell Proliferation': 0.7782017648461721, 'Middle Aged': 0.5780912581717411, 'Reproducibility of Results': 0.582089552238806, 'Young Adult': 0.6573279445065497, 'Rats': 0.66, 'Child, Preschool': 0.8853792279711168, 'Prospective Studies': 0.8262761273255712, 'Infant': 0.8250931677018634}
1.5906	0.3618	140	1.7164	0.8676	0.8647	0.8676	{}	{'Child': 0.8775948460987831, 'Follow-Up Studies': 0.556923076923077, 'Adult': 0.6169248106507326, 'Female': 0.5029360443061983, 'Aged, 80 and over': 0.6857822017530828, 'Retrospective Studies': 0.8567164179104478, 'Cross-Sectional Studies': 0.7738412318166774, 'Cell Proliferation': 0.7782017648461721, 'Middle Aged': 0.5996108367010926, 'Reproducibility of Results': 0.6139150490101865, 'Young Adult': 0.6735669161013591, 'Rats': 0.66, 'Child, Preschool': 0.8763898474043401, 'Prospective Studies': 0.8136169596990088, 'Infant': 0.8268398268398269}
1.5875	0.4134	160	1.7143	0.8695	0.8671	0.8697	{}	{'Child': 0.8860860683039482, 'Follow-Up Studies': 0.7008353865667507, 'Adult': 0.6001028674296001, 'Female': 0.5029360443061983, 'Aged, 80 and over': 0.6256471525288729, 'Retrospective Studies': 0.8654914323704133, 'Cross-Sectional Studies': 0.8014634976459243, 'Cell Proliferation': 0.8114928549711158, 'Middle Aged': 0.6195693446278538, 'Reproducibility of Results': 0.7244897959183674, 'Young Adult': 0.6895610018537687, 'Rats': 0.66, 'Child, Preschool': 0.8751447069416456, 'Prospective Studies': 0.8361323155216285, 'Infant': 0.8250931677018634}
1.5859	0.4651	180	1.7124	0.8733	0.8709	0.8735	{}	{'Child': 0.8852451682176092, 'Follow-Up Studies': 0.677938808373591, 'Adult': 0.6777279737806053, 'Female': 0.5228869047619048, 'Aged, 80 and over': 0.7028804902962207, 'Retrospective Studies': 0.8744687806472704, 'Cross-Sectional Studies': 0.7912173633735604, 'Cell Proliferation': 0.8114928549711158, 'Middle Aged': 0.6397912809291365, 'Reproducibility of Results': 0.670115282293822, 'Young Adult': 0.6840296866410116, 'Rats': 0.66, 'Child, Preschool': 0.8785295225792562, 'Prospective Studies': 0.8703007518796992, 'Infant': 0.8399039423654192}
1.5891	0.5168	200	1.7107	0.8750	0.8719	0.8748	{}	{'Child': 0.8818573864206198, 'Follow-Up Studies': 0.6666121915345644, 'Adult': 0.675952315283013, 'Female': 0.5228869047619048, 'Aged, 80 and over': 0.6544581965142713, 'Retrospective Studies': 0.8755744079179921, 'Cross-Sectional Studies': 0.7919794794794794, 'Cell Proliferation': 0.8114928549711158, 'Middle Aged': 0.6218364922866157, 'Reproducibility of Results': 0.6798746209669176, 'Young Adult': 0.6865149563475025, 'Rats': 0.66, 'Child, Preschool': 0.8684149988497813, 'Prospective Studies': 0.8703007518796992, 'Infant': 0.8292308718564472}
1.5734	0.5685	220	1.7096	0.8773	0.8751	0.8775	{}	{'Child': 0.8818573864206198, 'Follow-Up Studies': 0.6630727762803235, 'Adult': 0.6526120924978821, 'Female': 0.5228869047619048, 'Aged, 80 and over': 0.7092647628892622, 'Retrospective Studies': 0.878207634150839, 'Cross-Sectional Studies': 0.7848733668500103, 'Cell Proliferation': 0.7782017648461721, 'Middle Aged': 0.6547999775570892, 'Reproducibility of Results': 0.702045346440463, 'Young Adult': 0.7208520437535981, 'Rats': 0.66, 'Child, Preschool': 0.8819408406850026, 'Prospective Studies': 0.8658400413312668, 'Infant': 0.8334200937011973}
1.5922	0.6202	240	1.7078	0.8780	0.8756	0.8781	{}	{'Child': 0.8869047619047619, 'Follow-Up Studies': 0.6465030106530801, 'Adult': 0.6233861779354797, 'Female': 0.5228869047619048, 'Aged, 80 and over': 0.6979184524185471, 'Retrospective Studies': 0.8812301166489926, 'Cross-Sectional Studies': 0.7912173633735604, 'Cell Proliferation': 0.8114928549711158, 'Middle Aged': 0.6980192939244664, 'Reproducibility of Results': 0.702045346440463, 'Young Adult': 0.7109266943291839, 'Rats': 0.66, 'Child, Preschool': 0.8829481627577926, 'Prospective Studies': 0.8614366103939093, 'Infant': 0.8399974193132147}
1.5813	0.6718	260	1.7069	0.8794	0.8769	0.8795	{}	{'Child': 0.891062464989071, 'Follow-Up Studies': 0.6504602991944763, 'Adult': 0.6644280319237996, 'Female': 0.5228869047619048, 'Aged, 80 and over': 0.7159930721170848, 'Retrospective Studies': 0.8755744079179921, 'Cross-Sectional Studies': 0.7950693374422189, 'Cell Proliferation': 0.8114928549711158, 'Middle Aged': 0.7066614917355117, 'Reproducibility of Results': 0.7072792978833247, 'Young Adult': 0.7139310984804548, 'Rats': 0.66, 'Child, Preschool': 0.8795563952309284, 'Prospective Studies': 0.8614366103939093, 'Infant': 0.8315346143722032}
1.5703	0.7235	280	1.7063	0.8793	0.8759	0.8789	{}	{'Child': 0.8894794905225153, 'Follow-Up Studies': 0.6635281085571864, 'Adult': 0.6871966699587381, 'Female': 0.5420699925539836, 'Aged, 80 and over': 0.6593094605733125, 'Retrospective Studies': 0.8751736162416147, 'Cross-Sectional Studies': 0.7989723528772568, 'Cell Proliferation': 0.8114928549711158, 'Middle Aged': 0.7505746378697198, 'Reproducibility of Results': 0.7072792978833247, 'Young Adult': 0.7033492822966507, 'Rats': 0.6759945545303283, 'Child, Preschool': 0.8829481627577926, 'Prospective Studies': 0.8614366103939093, 'Infant': 0.8292308718564472}
1.5781	0.7752	300	1.7059	0.8799	0.8772	0.8799	{}	{'Child': 0.8860860683039482, 'Follow-Up Studies': 0.6851365951535731, 'Adult': 0.6835769666881186, 'Female': 0.5228869047619048, 'Aged, 80 and over': 0.7033086090507542, 'Retrospective Studies': 0.8721278721278721, 'Cross-Sectional Studies': 0.7881059935733247, 'Cell Proliferation': 0.8114928549711158, 'Middle Aged': 0.7112081656688586, 'Reproducibility of Results': 0.7072792978833247, 'Young Adult': 0.7093134860197132, 'Rats': 0.6759945545303283, 'Child, Preschool': 0.8829481627577926, 'Prospective Studies': 0.8658400413312668, 'Infant': 0.8292308718564472}
1.5906	0.8269	320	1.7056	0.8808	0.8777	0.8806	{}	{'Child': 0.8936803304170182, 'Follow-Up Studies': 0.6952380952380952, 'Adult': 0.6907196731427065, 'Female': 0.5228869047619048, 'Aged, 80 and over': 0.687032967032967, 'Retrospective Studies': 0.8747628083491461, 'Cross-Sectional Studies': 0.7919794794794794, 'Cell Proliferation': 0.7416666666666667, 'Middle Aged': 0.7116357095705573, 'Reproducibility of Results': 0.702045346440463, 'Young Adult': 0.7124974207304491, 'Rats': 0.6759945545303283, 'Child, Preschool': 0.8819408406850026, 'Prospective Studies': 0.8703007518796992, 'Infant': 0.8268398268398269}
1.6016	0.8786	340	1.7055	0.8791	0.8768	0.8793	{}	{'Child': 0.8902815421206924, 'Follow-Up Studies': 0.6853146853146853, 'Adult': 0.6604185623293903, 'Female': 0.5228869047619048, 'Aged, 80 and over': 0.7176371055159468, 'Retrospective Studies': 0.878207634150839, 'Cross-Sectional Studies': 0.7950693374422189, 'Cell Proliferation': 0.8114928549711158, 'Middle Aged': 0.6911560960591133, 'Reproducibility of Results': 0.7171717171717171, 'Young Adult': 0.711008987758754, 'Rats': 0.6759945545303283, 'Child, Preschool': 0.8829481627577926, 'Prospective Studies': 0.8614366103939093, 'Infant': 0.8356935817805383}
1.5828	0.9302	360	1.7053	0.8785	0.8762	0.8787	{}	{'Child': 0.8902815421206924, 'Follow-Up Studies': 0.6717797789472001, 'Adult': 0.6483933868130054, 'Female': 0.5228869047619048, 'Aged, 80 and over': 0.7112870044283274, 'Retrospective Studies': 0.8751736162416147, 'Cross-Sectional Studies': 0.8019241652518392, 'Cell Proliferation': 0.7782017648461721, 'Middle Aged': 0.6959936153232242, 'Reproducibility of Results': 0.7171717171717171, 'Young Adult': 0.7170401493930906, 'Rats': 0.66, 'Child, Preschool': 0.8829481627577926, 'Prospective Studies': 0.8570888468809074, 'Infant': 0.8420637009739405}
1.5984	0.9819	380	1.7055	0.8803	0.8779	0.8804	{}	{'Child': 0.8860860683039482, 'Follow-Up Studies': 0.6864516129032259, 'Adult': 0.6663173391221897, 'Female': 0.5228869047619048, 'Aged, 80 and over': 0.7188997338065661, 'Retrospective Studies': 0.878207634150839, 'Cross-Sectional Studies': 0.8058551617873652, 'Cell Proliferation': 0.8114928549711158, 'Middle Aged': 0.6900035269814078, 'Reproducibility of Results': 0.7072792978833247, 'Young Adult': 0.7184596804162022, 'Rats': 0.6759945545303283, 'Child, Preschool': 0.8819408406850026, 'Prospective Studies': 0.8658400413312668, 'Infant': 0.8356935817805383}

Framework versions

PEFT 0.18.1
Transformers 4.51.0
Pytorch 2.8.0+cu128
Datasets 3.6.0
Tokenizers 0.21.0

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