Instructions to use ferrazzipietro/meshTask-gemma-3-4b-it with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ferrazzipietro/meshTask-gemma-3-4b-it with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("google/gemma-3-4b-it")
model = PeftModel.from_pretrained(base_model, "ferrazzipietro/meshTask-gemma-3-4b-it")

Transformers

How to use ferrazzipietro/meshTask-gemma-3-4b-it with Transformers:

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

pipe = pipeline("text-generation", model="ferrazzipietro/meshTask-gemma-3-4b-it")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("ferrazzipietro/meshTask-gemma-3-4b-it", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use ferrazzipietro/meshTask-gemma-3-4b-it with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ferrazzipietro/meshTask-gemma-3-4b-it"
# 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-gemma-3-4b-it",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ferrazzipietro/meshTask-gemma-3-4b-it

SGLang

How to use ferrazzipietro/meshTask-gemma-3-4b-it 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-gemma-3-4b-it" \
    --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-gemma-3-4b-it",
		"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-gemma-3-4b-it" \
        --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-gemma-3-4b-it",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ferrazzipietro/meshTask-gemma-3-4b-it with Docker Model Runner:
```
docker model run hf.co/ferrazzipietro/meshTask-gemma-3-4b-it
```

meshTask-gemma-3-4b-it

This model is a fine-tuned version of google/gemma-3-4b-it on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 1.7279
F1 Micro: 0.8986
F1 Macro: 0.8924
F1 Weighted: 0.8988
Class/f1 Results Per Class: {}
Items/f1 Scores Per Item: {'Aged': 0.8931129948340326, 'Pregnancy': 0.9141002949852507, 'Surveys and Questionnaires': 0.8941409172171575, 'Time Factors': 0.6048093504502778, 'Male': 0.7377071264987202, 'Mice': 0.9137555405303884, 'Cell Line, Tumor': 0.8859792482231765, 'Adolescent': 0.822261555764515, 'Signal Transduction': 0.8636551060613002, 'Risk Factors': 0.863497453310696, 'Prognosis': 0.8373102898977666, 'Disease Models, Animal': 0.8458666666666667, 'Animals': 0.9535515630753726, 'Treatment Outcome': 0.8726708877115907}

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: 32
eval_batch_size: 32
seed: 42
distributed_type: multi-GPU
gradient_accumulation_steps: 4
total_train_batch_size: 128
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
9.0062	0.0517	20	2.2966	0.0	0.0	0.0	{}	{'Aged': 0.0, 'Pregnancy': 0.0, 'Surveys and Questionnaires': 0.0, 'Time Factors': 0.0, 'Male': 0.0, 'Mice': 0.0, 'Cell Line, Tumor': 0.0, 'Adolescent': 0.0, 'Signal Transduction': 0.0, 'Risk Factors': 0.0, 'Prognosis': 0.0, 'Disease Models, Animal': 0.0, 'Animals': 0.0, 'Treatment Outcome': 0.0}
6.8734	0.1034	40	1.7996	0.7422	0.1240	0.7452	{}	{'Aged': 0.19088100993822188, 'Pregnancy': 0.8576606509099147, 'Surveys and Questionnaires': 0.8891228070175439, 'Time Factors': 0.36978634117246073, 'Male': 0.22727272727272727, 'Mice': 0.41624485762684515, 'Cell Line, Tumor': 0.7806584182223582, 'Adolescent': 0.5536235221863964, 'Signal Transduction': 0.5123216601815823, 'Risk Factors': 0.3510815178241146, 'Prognosis': 0.5055848909004554, 'Disease Models, Animal': 0.472588838442497, 'Animals': 0.3220762277513745, 'Treatment Outcome': 0.32020747327193816}
6.5938	0.1551	60	1.7653	0.8621	0.3424	0.8630	{}	{'Aged': 0.6926461345065996, 'Pregnancy': 0.9747859818084537, 'Surveys and Questionnaires': 0.9008107332472377, 'Time Factors': 0.5490812379110251, 'Male': 0.303005132360886, 'Mice': 0.8797124980199589, 'Cell Line, Tumor': 0.8277627648983075, 'Adolescent': 0.3068512110726644, 'Signal Transduction': 0.7155364901281416, 'Risk Factors': 0.8223702806379971, 'Prognosis': 0.7640718562874251, 'Disease Models, Animal': 0.8109238241176012, 'Animals': 0.9441027948602569, 'Treatment Outcome': 0.8106838115648689}
6.5594	0.2069	80	1.7526	0.8732	0.4347	0.8746	{}	{'Aged': 0.79884645007702, 'Pregnancy': 0.9630065744284173, 'Surveys and Questionnaires': 0.8984134585539327, 'Time Factors': 0.6246711523307268, 'Male': 0.41900714461690064, 'Mice': 0.8750718832417854, 'Cell Line, Tumor': 0.8378806992250856, 'Adolescent': 0.8067494069859866, 'Signal Transduction': 0.8636949684243376, 'Risk Factors': 0.8410409846136057, 'Prognosis': 0.5480681260932891, 'Disease Models, Animal': 0.808000440871548, 'Animals': 0.9373972413619991, 'Treatment Outcome': 0.8108791903858318}
6.5438	0.2586	100	1.7451	0.8850	0.4395	0.8853	{}	{'Aged': 0.847216931669841, 'Pregnancy': 0.9735994397759103, 'Surveys and Questionnaires': 0.905724462069472, 'Time Factors': 0.5958204334365325, 'Male': 0.46990005878894764, 'Mice': 0.8907176948124359, 'Cell Line, Tumor': 0.8810304697418536, 'Adolescent': 0.823932021282091, 'Signal Transduction': 0.8412176059234884, 'Risk Factors': 0.8348097560975609, 'Prognosis': 0.5171641490433031, 'Disease Models, Animal': 0.7675925719010481, 'Animals': 0.9328933954784009, 'Treatment Outcome': 0.5485633882088082}
6.5984	0.3103	120	1.7412	0.8857	0.5866	0.8859	{}	{'Aged': 0.8490171189406155, 'Pregnancy': 0.9747859818084537, 'Surveys and Questionnaires': 0.9136612021857924, 'Time Factors': 0.6185819423040917, 'Male': 0.7068471511999954, 'Mice': 0.8864193131709055, 'Cell Line, Tumor': 0.8889423848499438, 'Adolescent': 0.8177004296407282, 'Signal Transduction': 0.7992483342655559, 'Risk Factors': 0.829885854827521, 'Prognosis': 0.5348941848197107, 'Disease Models, Animal': 0.7984708385112791, 'Animals': 0.9399640981317732, 'Treatment Outcome': 0.8328534210887152}
6.4641	0.3620	140	1.7385	0.8837	0.4396	0.8846	{}	{'Aged': 0.8527339591189234, 'Pregnancy': 0.9747859818084537, 'Surveys and Questionnaires': 0.9015523748486791, 'Time Factors': 0.40974126846091524, 'Male': 0.7433499754460632, 'Mice': 0.9041941664892484, 'Cell Line, Tumor': 0.8823313974781563, 'Adolescent': 0.8152924919391985, 'Signal Transduction': 0.8663017124126686, 'Risk Factors': 0.8462091132539232, 'Prognosis': 0.8281951772554834, 'Disease Models, Animal': 0.8092371682673367, 'Animals': 0.6230391172401482, 'Treatment Outcome': 0.5469808995686999}
6.4688	0.4137	160	1.7343	0.8893	0.4408	0.8886	{}	{'Aged': 0.8849566444847752, 'Pregnancy': 0.9594405594405595, 'Surveys and Questionnaires': 0.9129764265256665, 'Time Factors': 0.3709155268788296, 'Male': 0.753870831574504, 'Mice': 0.9111040872365224, 'Cell Line, Tumor': 0.8779541446208112, 'Adolescent': 0.7785425646551725, 'Signal Transduction': 0.8231857232333997, 'Risk Factors': 0.8491823137492429, 'Prognosis': 0.7947916666666667, 'Disease Models, Animal': 0.8194631011180227, 'Animals': 0.6253639978494449, 'Treatment Outcome': 0.5598871102582662}
6.3859	0.4654	180	1.7329	0.8930	0.4439	0.8934	{}	{'Aged': 0.8701140459978498, 'Pregnancy': 0.9471988795518207, 'Surveys and Questionnaires': 0.9169325860161471, 'Time Factors': 0.3989759590383615, 'Male': 0.7502864625961696, 'Mice': 0.9243712432774438, 'Cell Line, Tumor': 0.891382828441375, 'Adolescent': 0.8135506643702399, 'Signal Transduction': 0.8632369614512472, 'Risk Factors': 0.8542926054832998, 'Prognosis': 0.8281951772554834, 'Disease Models, Animal': 0.8158966188705965, 'Animals': 0.6230391172401482, 'Treatment Outcome': 0.8335075291294081}
6.4031	0.5171	200	1.7304	0.8908	0.4431	0.8916	{}	{'Aged': 0.8638779228666869, 'Pregnancy': 0.9747859818084537, 'Surveys and Questionnaires': 0.9169325860161471, 'Time Factors': 0.3958819302959617, 'Male': 0.7205352971888654, 'Mice': 0.9153532393473784, 'Cell Line, Tumor': 0.891382828441375, 'Adolescent': 0.8166369131510538, 'Signal Transduction': 0.8589409095158371, 'Risk Factors': 0.8542926054832998, 'Prognosis': 0.8367854183927093, 'Disease Models, Animal': 0.8102387655093317, 'Animals': 0.6215406960505, 'Treatment Outcome': 0.836922268907563}
6.3891	0.5688	220	1.7287	0.8892	0.4427	0.8905	{}	{'Aged': 0.8737156283736585, 'Pregnancy': 0.9747859818084537, 'Surveys and Questionnaires': 0.9175806729626506, 'Time Factors': 0.4061167098323803, 'Male': 0.478369384359401, 'Mice': 0.9153532393473784, 'Cell Line, Tumor': 0.8975685729363762, 'Adolescent': 0.5546830688564263, 'Signal Transduction': 0.8670832388487154, 'Risk Factors': 0.8410242809995285, 'Prognosis': 0.8318610506550591, 'Disease Models, Animal': 0.8217022993142395, 'Animals': 0.6162282188622957, 'Treatment Outcome': 0.5537809598115256}
6.4516	0.6206	240	1.7277	0.8903	0.4425	0.8910	{}	{'Aged': 0.8757285172133369, 'Pregnancy': 0.9471988795518207, 'Surveys and Questionnaires': 0.9169325860161471, 'Time Factors': 0.37499319105358914, 'Male': 0.49589017789734485, 'Mice': 0.9199224928819993, 'Cell Line, Tumor': 0.8981971861124485, 'Adolescent': 0.5374227412356612, 'Signal Transduction': 0.8231857232333997, 'Risk Factors': 0.8514905402041324, 'Prognosis': 0.8222663298448214, 'Disease Models, Animal': 0.8095387593857102, 'Animals': 0.614845375365845, 'Treatment Outcome': 0.5534121089711986}
6.4297	0.6723	260	1.7259	0.8872	0.4416	0.8884	{}	{'Aged': 0.864609290141205, 'Pregnancy': 0.9613108473300722, 'Surveys and Questionnaires': 0.6100801998660318, 'Time Factors': 0.40941222996687926, 'Male': 0.7392307692307691, 'Mice': 0.9152727128587284, 'Cell Line, Tumor': 0.9025352112676056, 'Adolescent': 0.8107964864882333, 'Signal Transduction': 0.8706997621679563, 'Risk Factors': 0.8384205693296602, 'Prognosis': 0.5496411904862609, 'Disease Models, Animal': 0.8209212225570384, 'Animals': 0.6147006109576291, 'Treatment Outcome': 0.8290625167777513}
6.4344	0.7240	280	1.7238	0.8931	0.5922	0.8937	{}	{'Aged': 0.8817723333771639, 'Pregnancy': 0.9613108473300722, 'Surveys and Questionnaires': 0.9169325860161471, 'Time Factors': 0.5995950701833055, 'Male': 0.7442773436462444, 'Mice': 0.9153884215734784, 'Cell Line, Tumor': 0.9044387453146577, 'Adolescent': 0.8194530204795719, 'Signal Transduction': 0.8675769323132869, 'Risk Factors': 0.8551857021483189, 'Prognosis': 0.8209309286007249, 'Disease Models, Animal': 0.8193496535677017, 'Animals': 0.6214179879953057, 'Treatment Outcome': 0.8284012539184953}
6.4469	0.7757	300	1.7230	0.8938	0.5921	0.8940	{}	{'Aged': 0.8798708999728924, 'Pregnancy': 0.9471988795518207, 'Surveys and Questionnaires': 0.9129764265256665, 'Time Factors': 0.5981921221488455, 'Male': 0.7616477448973262, 'Mice': 0.9221110622072983, 'Cell Line, Tumor': 0.8981971861124485, 'Adolescent': 0.8131051550181505, 'Signal Transduction': 0.8434726189725243, 'Risk Factors': 0.8477658536585366, 'Prognosis': 0.8167545800011344, 'Disease Models, Animal': 0.8058045241143833, 'Animals': 0.6230391172401482, 'Treatment Outcome': 0.8301839114950607}
6.4453	0.8274	320	1.7223	0.8926	0.5919	0.8932	{}	{'Aged': 0.8750936584701314, 'Pregnancy': 0.9613108473300722, 'Surveys and Questionnaires': 0.9129764265256665, 'Time Factors': 0.5863494303861276, 'Male': 0.7541968127866836, 'Mice': 0.9175624223279579, 'Cell Line, Tumor': 0.9084849767245027, 'Adolescent': 0.8113780828883803, 'Signal Transduction': 0.869670236435887, 'Risk Factors': 0.8515838412430281, 'Prognosis': 0.8367854183927093, 'Disease Models, Animal': 0.8095387593857102, 'Animals': 0.6199099083937777, 'Treatment Outcome': 0.8300738809213386}
6.325	0.8791	340	1.7221	0.8913	0.4435	0.8922	{}	{'Aged': 0.8664618535586277, 'Pregnancy': 0.9613108473300722, 'Surveys and Questionnaires': 0.920856102003643, 'Time Factors': 0.394916214388831, 'Male': 0.7392307692307691, 'Mice': 0.9197320437609007, 'Cell Line, Tumor': 0.9015010351966875, 'Adolescent': 0.822566371681416, 'Signal Transduction': 0.8683851609383524, 'Risk Factors': 0.8515838412430281, 'Prognosis': 0.8367854183927093, 'Disease Models, Animal': 0.8158760704320216, 'Animals': 0.6192689495092902, 'Treatment Outcome': 0.82484243697479}
6.3953	0.9308	360	1.7217	0.8950	0.4450	0.8955	{}	{'Aged': 0.8732175265750584, 'Pregnancy': 0.9613108473300722, 'Surveys and Questionnaires': 0.9169325860161471, 'Time Factors': 0.3899741602067183, 'Male': 0.759071906354515, 'Mice': 0.9220524698231705, 'Cell Line, Tumor': 0.899333474396953, 'Adolescent': 0.5489533114782198, 'Signal Transduction': 0.8612083103124699, 'Risk Factors': 0.8579474732387353, 'Prognosis': 0.8277103165259512, 'Disease Models, Animal': 0.8135758534335047, 'Animals': 0.6199099083937777, 'Treatment Outcome': 0.8310431250540143}
6.3281	0.9825	380	1.7217	0.8935	0.4443	0.8941	{}	{'Aged': 0.8732175265750584, 'Pregnancy': 0.9613108473300722, 'Surveys and Questionnaires': 0.9129764265256665, 'Time Factors': 0.3974206349206349, 'Male': 0.759071906354515, 'Mice': 0.9198083320133059, 'Cell Line, Tumor': 0.9033712347895472, 'Adolescent': 0.8204232560519986, 'Signal Transduction': 0.8621080238039226, 'Risk Factors': 0.8524455849616621, 'Prognosis': 0.8296660052543481, 'Disease Models, Animal': 0.804974677609989, 'Animals': 0.6193975348591589, 'Treatment Outcome': 0.8310431250540143}

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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Model tree for ferrazzipietro/meshTask-gemma-3-4b-it

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google/gemma-3-4b-pt

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google/gemma-3-4b-it

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