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

Libraries

How to use ferrazzipietro/meshTask-Qwen3-1.7B 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")

Transformers

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

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

pipe = pipeline("text-generation", model="ferrazzipietro/meshTask-Qwen3-1.7B")
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", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

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

Use Docker

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

SGLang

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

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

meshTask-Qwen3-1.7B

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: 1.6742
F1 Micro: 0.8845
F1 Macro: 0.8777
F1 Weighted: 0.8851
Class/f1 Results Per Class: {}
Items/f1 Scores Per Item: {'Cell Line, Tumor': 0.869658472199307, 'Surveys and Questionnaires': 0.8935960591133005, 'Animals': 0.9524968440222678, 'Mice': 0.9019185306419348, 'Pregnancy': 0.9274064171122995, 'Male': 0.7060795998394418, 'Risk Factors': 0.864592583519613, 'Time Factors': 0.5996349759415962, 'Signal Transduction': 0.8387130254281265, 'Adolescent': 0.7916527266644363, 'Aged': 0.8108188967403693, 'Prognosis': 0.8125260438369863, 'Disease Models, Animal': 0.8305555555555555, 'Treatment Outcome': 0.8871010035996091}

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
2.3031	0.0517	20	2.2738	0.0	0.0	0.0	{}	{'Cell Line, Tumor': 0.0, 'Surveys and Questionnaires': 0.0, 'Animals': 0.0, 'Mice': 0.0, 'Pregnancy': 0.0, 'Male': 0.0, 'Risk Factors': 0.0, 'Time Factors': 0.0, 'Signal Transduction': 0.0, 'Adolescent': 0.0, 'Aged': 0.0, 'Prognosis': 0.0, 'Disease Models, Animal': 0.0, 'Treatment Outcome': 0.0}
1.7031	0.1034	40	1.7834	0.7948	0.7820	0.7938	{}	{'Cell Line, Tumor': 0.8118208136949134, 'Surveys and Questionnaires': 0.5925925925925926, 'Animals': 0.8636448518801461, 'Mice': 0.859587637317593, 'Pregnancy': 0.6877384196185286, 'Male': 0.6951426532575116, 'Risk Factors': 0.7014569107354458, 'Time Factors': 0.5721275696986765, 'Signal Transduction': 0.5463736021897309, 'Adolescent': 0.626469403449665, 'Aged': 0.6680107972046501, 'Prognosis': 0.6979130562645073, 'Disease Models, Animal': 0.7695577597136252, 'Treatment Outcome': 0.6651655306718598}
1.6266	0.1550	60	1.7317	0.8315	0.8277	0.8334	{}	{'Cell Line, Tumor': 0.7920337568394695, 'Surveys and Questionnaires': 0.8840537276287377, 'Animals': 0.8623777757470529, 'Mice': 0.8574653337633021, 'Pregnancy': 0.9333100558659218, 'Male': 0.6136464271876902, 'Risk Factors': 0.797073736056787, 'Time Factors': 0.6206127003448976, 'Signal Transduction': 0.8442392613408269, 'Adolescent': 0.7042835398999783, 'Aged': 0.6490671641791045, 'Prognosis': 0.7891920813269128, 'Disease Models, Animal': 0.8037387990524256, 'Treatment Outcome': 0.6748047567635724}
1.6203	0.2067	80	1.7195	0.8493	0.8357	0.8462	{}	{'Cell Line, Tumor': 0.7738800866294312, 'Surveys and Questionnaires': 0.9033194382906384, 'Animals': 0.8863687808411427, 'Mice': 0.8455339706033509, 'Pregnancy': 0.9244163039176889, 'Male': 0.720862367472791, 'Risk Factors': 0.7389322362510049, 'Time Factors': 0.4821344140493077, 'Signal Transduction': 0.7050947742606906, 'Adolescent': 0.7335820895522388, 'Aged': 0.7573139530572138, 'Prognosis': 0.7575444296755772, 'Disease Models, Animal': 0.7817389552539489, 'Treatment Outcome': 0.6504166453657789}
1.6219	0.2584	100	1.7110	0.8654	0.8585	0.8654	{}	{'Cell Line, Tumor': 0.8210060107560899, 'Surveys and Questionnaires': 0.907514250867162, 'Animals': 0.9024122250959312, 'Mice': 0.8846006173882328, 'Pregnancy': 0.9517676767676768, 'Male': 0.7086152262481319, 'Risk Factors': 0.8456543456543457, 'Time Factors': 0.5882377534666574, 'Signal Transduction': 0.7912482065997131, 'Adolescent': 0.7577985867754351, 'Aged': 0.7339345437171525, 'Prognosis': 0.8209309286007249, 'Disease Models, Animal': 0.8345753115153495, 'Treatment Outcome': 0.7676496076065213}
1.6016	0.3101	120	1.6952	0.8581	0.8544	0.8595	{}	{'Cell Line, Tumor': 0.8210060107560899, 'Surveys and Questionnaires': 0.9068568093317421, 'Animals': 0.9145909895195318, 'Mice': 0.8798192106626719, 'Pregnancy': 0.9537362238101005, 'Male': 0.6507948135667716, 'Risk Factors': 0.821993031358885, 'Time Factors': 0.628634592326139, 'Signal Transduction': 0.8623577763357128, 'Adolescent': 0.7809032491628202, 'Aged': 0.704935886805814, 'Prognosis': 0.8069612342642309, 'Disease Models, Animal': 0.827531070073223, 'Treatment Outcome': 0.7938931297709924}
1.6016	0.3618	140	1.6826	0.8748	0.8691	0.8752	{}	{'Cell Line, Tumor': 0.8349375211077339, 'Surveys and Questionnaires': 0.9131327489536445, 'Animals': 0.9129279673744284, 'Mice': 0.8800407613565953, 'Pregnancy': 0.9645804357904497, 'Male': 0.7170611617192005, 'Risk Factors': 0.8320130475302889, 'Time Factors': 0.6128980891719745, 'Signal Transduction': 0.8728395487632388, 'Adolescent': 0.7688214426487978, 'Aged': 0.7954280752580891, 'Prognosis': 0.8303347302307507, 'Disease Models, Animal': 0.8084611808461181, 'Treatment Outcome': 0.8140567765567766}
1.5969	0.4134	160	1.6801	0.8737	0.8659	0.8731	{}	{'Cell Line, Tumor': 0.823831551123393, 'Surveys and Questionnaires': 0.9188922035709515, 'Animals': 0.9086805511487908, 'Mice': 0.8868569790301442, 'Pregnancy': 0.9517676767676768, 'Male': 0.7291948341153942, 'Risk Factors': 0.8530169070885683, 'Time Factors': 0.5346643518518518, 'Signal Transduction': 0.7753515301902398, 'Adolescent': 0.7739900035019579, 'Aged': 0.7995608414144915, 'Prognosis': 0.8155183946488294, 'Disease Models, Animal': 0.7792218574285822, 'Treatment Outcome': 0.8164589632033036}
1.6094	0.4651	180	1.6782	0.875	0.8675	0.8745	{}	{'Cell Line, Tumor': 0.8549201009251471, 'Surveys and Questionnaires': 0.911839351707556, 'Animals': 0.9066551718308906, 'Mice': 0.8823312581913499, 'Pregnancy': 0.9645804357904497, 'Male': 0.732771891696097, 'Risk Factors': 0.8378296545250855, 'Time Factors': 0.5991425509110396, 'Signal Transduction': 0.7923142958137657, 'Adolescent': 0.7920135938827527, 'Aged': 0.8034544371649792, 'Prognosis': 0.7988769780500256, 'Disease Models, Animal': 0.7702982395845267, 'Treatment Outcome': 0.8340155945419103}
1.5781	0.5168	200	1.6765	0.8772	0.8688	0.8762	{}	{'Cell Line, Tumor': 0.8527572149700511, 'Surveys and Questionnaires': 0.8993464052287582, 'Animals': 0.9094224031219185, 'Mice': 0.8868755118755118, 'Pregnancy': 0.9517676767676768, 'Male': 0.7316011459025747, 'Risk Factors': 0.8463942631858674, 'Time Factors': 0.4996050616587411, 'Signal Transduction': 0.7923142958137657, 'Adolescent': 0.7994959677419355, 'Aged': 0.803979803979804, 'Prognosis': 0.8111734903464329, 'Disease Models, Animal': 0.7623076923076924, 'Treatment Outcome': 0.8467836257309942}
1.5844	0.5685	220	1.6747	0.8801	0.8733	0.8799	{}	{'Cell Line, Tumor': 0.8737757514353259, 'Surveys and Questionnaires': 0.915045766590389, 'Animals': 0.9181146978021979, 'Mice': 0.8823143881208397, 'Pregnancy': 0.9645804357904497, 'Male': 0.7304361385663564, 'Risk Factors': 0.8426029098086658, 'Time Factors': 0.4889713058754636, 'Signal Transduction': 0.7702353124513013, 'Adolescent': 0.797974796365527, 'Aged': 0.8082873503383654, 'Prognosis': 0.810039579759382, 'Disease Models, Animal': 0.821128374483107, 'Treatment Outcome': 0.8512478558140658}
1.5781	0.6202	240	1.6734	0.8818	0.8747	0.8814	{}	{'Cell Line, Tumor': 0.8737757514353259, 'Surveys and Questionnaires': 0.911839351707556, 'Animals': 0.9205488944687076, 'Mice': 0.8800407613565953, 'Pregnancy': 0.9645804357904497, 'Male': 0.7258454106280193, 'Risk Factors': 0.8511326860841424, 'Time Factors': 0.5420408163265307, 'Signal Transduction': 0.8087284326518775, 'Adolescent': 0.7763100854264949, 'Aged': 0.8128575185745281, 'Prognosis': 0.8175925925925926, 'Disease Models, Animal': 0.8303371105620403, 'Treatment Outcome': 0.8528781591251224}
1.5719	0.6718	260	1.6727	0.8851	0.8796	0.8853	{}	{'Cell Line, Tumor': 0.8727962965491813, 'Surveys and Questionnaires': 0.9194944093339815, 'Animals': 0.9175156095957936, 'Mice': 0.8845438760928689, 'Pregnancy': 0.9645804357904497, 'Male': 0.7362431103828393, 'Risk Factors': 0.8548184464531945, 'Time Factors': 0.5593064716054295, 'Signal Transduction': 0.8605332141009794, 'Adolescent': 0.8003613369467029, 'Aged': 0.8104398032226707, 'Prognosis': 0.8285629384359048, 'Disease Models, Animal': 0.834450671460294, 'Treatment Outcome': 0.8365023112480741}
1.5766	0.7235	280	1.6724	0.8843	0.8791	0.8847	{}	{'Cell Line, Tumor': 0.8727962965491813, 'Surveys and Questionnaires': 0.9175, 'Animals': 0.9125069715560513, 'Mice': 0.8867642236432203, 'Pregnancy': 0.9645804357904497, 'Male': 0.720862367472791, 'Risk Factors': 0.8511739915713425, 'Time Factors': 0.5882377534666574, 'Signal Transduction': 0.8605332141009794, 'Adolescent': 0.8046742394568481, 'Aged': 0.8148708445217654, 'Prognosis': 0.823454103187812, 'Disease Models, Animal': 0.8311475409836065, 'Treatment Outcome': 0.842557781201849}
1.5891	0.7752	300	1.6716	0.8846	0.8799	0.8852	{}	{'Cell Line, Tumor': 0.8742592567594929, 'Surveys and Questionnaires': 0.9143650470992241, 'Animals': 0.9191742492482692, 'Mice': 0.8867294011521824, 'Pregnancy': 0.9645804357904497, 'Male': 0.720862367472791, 'Risk Factors': 0.8432209396894563, 'Time Factors': 0.6206184017733027, 'Signal Transduction': 0.8720138888888889, 'Adolescent': 0.7959685136595154, 'Aged': 0.8190310502628494, 'Prognosis': 0.8402486822543587, 'Disease Models, Animal': 0.8420029035527432, 'Treatment Outcome': 0.8421435554646113}
1.5656	0.8269	320	1.6714	0.8853	0.8807	0.8859	{}	{'Cell Line, Tumor': 0.878217660475725, 'Surveys and Questionnaires': 0.9143650470992241, 'Animals': 0.91709691438505, 'Mice': 0.8889760348583878, 'Pregnancy': 0.9645804357904497, 'Male': 0.7170611617192005, 'Risk Factors': 0.8327909739593711, 'Time Factors': 0.6159451668378141, 'Signal Transduction': 0.8757862859614969, 'Adolescent': 0.8104359353995403, 'Aged': 0.824633915918753, 'Prognosis': 0.8386110848536409, 'Disease Models, Animal': 0.8401829280741513, 'Treatment Outcome': 0.8428533705533969}
1.6047	0.8786	340	1.6710	0.8855	0.8800	0.8857	{}	{'Cell Line, Tumor': 0.8712846660863076, 'Surveys and Questionnaires': 0.9131327489536445, 'Animals': 0.9106664641159747, 'Mice': 0.8867943858211249, 'Pregnancy': 0.9645804357904497, 'Male': 0.7245494883636693, 'Risk Factors': 0.8565416174644988, 'Time Factors': 0.5865385995893814, 'Signal Transduction': 0.8261124068230667, 'Adolescent': 0.8057178902061186, 'Aged': 0.8304511278195489, 'Prognosis': 0.8281951772554834, 'Disease Models, Animal': 0.8294402851549618, 'Treatment Outcome': 0.857350272232305}
1.5781	0.9302	360	1.6711	0.8840	0.8791	0.8845	{}	{'Cell Line, Tumor': 0.8767857142857143, 'Surveys and Questionnaires': 0.9137563049367983, 'Animals': 0.91709691438505, 'Mice': 0.8890124475373964, 'Pregnancy': 0.9645804357904497, 'Male': 0.723170731707317, 'Risk Factors': 0.8432209396894563, 'Time Factors': 0.6128980891719745, 'Signal Transduction': 0.8595878017564764, 'Adolescent': 0.7930768995753261, 'Aged': 0.8185063279402902, 'Prognosis': 0.8182871239341449, 'Disease Models, Animal': 0.8381474206554327, 'Treatment Outcome': 0.8433110470465559}
1.5656	0.9819	380	1.6711	0.8840	0.8786	0.8843	{}	{'Cell Line, Tumor': 0.8808191352650997, 'Surveys and Questionnaires': 0.909984262983039, 'Animals': 0.9148052897481757, 'Mice': 0.8867943858211249, 'Pregnancy': 0.9645804357904497, 'Male': 0.7268583791142009, 'Risk Factors': 0.8485100890719854, 'Time Factors': 0.5821347249918678, 'Signal Transduction': 0.8444322945143066, 'Adolescent': 0.7966214345056771, 'Aged': 0.8236773179675223, 'Prognosis': 0.8163170163170164, 'Disease Models, Animal': 0.8302217565915837, 'Treatment Outcome': 0.8478725380899295}

Framework versions

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

Downloads last month: 27

Model tree for ferrazzipietro/meshTask-Qwen3-1.7B

Base model

Qwen/Qwen3-1.7B-Base

Finetuned

Qwen/Qwen3-1.7B

Adapter

(509)

this model