Instructions to use ferrazzipietro/meshTask-Qwen3-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ferrazzipietro/meshTask-Qwen3-8B with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-8B")
model = PeftModel.from_pretrained(base_model, "ferrazzipietro/meshTask-Qwen3-8B")

Transformers

How to use ferrazzipietro/meshTask-Qwen3-8B with Transformers:

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

pipe = pipeline("text-generation", model="ferrazzipietro/meshTask-Qwen3-8B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("ferrazzipietro/meshTask-Qwen3-8B", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use ferrazzipietro/meshTask-Qwen3-8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ferrazzipietro/meshTask-Qwen3-8B"
# 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-Qwen3-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ferrazzipietro/meshTask-Qwen3-8B

SGLang

How to use ferrazzipietro/meshTask-Qwen3-8B 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-Qwen3-8B" \
    --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-Qwen3-8B",
		"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-Qwen3-8B" \
        --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-Qwen3-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ferrazzipietro/meshTask-Qwen3-8B with Docker Model Runner:
```
docker model run hf.co/ferrazzipietro/meshTask-Qwen3-8B
```

meshTask-Qwen3-8B

This model is a fine-tuned version of Qwen/Qwen3-8B on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 1.5009
F1 Micro: 0.9013
F1 Macro: 0.5961
F1 Weighted: 0.9012
Class/f1 Results Per Class: {}
Items/f1 Scores Per Item: {'Mice': 0.9082888807032383, 'Time Factors': 0.6082191117939536, 'Risk Factors': 0.8564145858362828, 'Pregnancy': 0.9071437732461203, 'Adolescent': 0.8641699735449735, 'Animals': 0.9651228286565937, 'Signal Transduction': 0.5695239038726668, 'Prognosis': 0.8393558523173605, 'Male': 0.7620371150228811, 'Disease Models, Animal': 0.8356276176398127, 'Cell Line, Tumor': 0.855249243526788, 'Surveys and Questionnaires': 0.9020205031169404, 'Aged': 0.8883991358942412, 'Treatment Outcome': 0.8435012600743568}

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
7.9375	0.0517	20	1.9773	0.0	0.0	0.0	{}	{'Mice': 0.0, 'Time Factors': 0.0, 'Adolescent': 0.0, 'Pregnancy': 0.0, 'Animals': 0.0, 'Male': 0.0, 'Signal Transduction': 0.0, 'Prognosis': 0.0, 'Risk Factors': 0.0, 'Disease Models, Animal': 0.0, 'Cell Line, Tumor': 0.0, 'Surveys and Questionnaires': 0.0, 'Aged': 0.0, 'Treatment Outcome': 0.0}
5.8078	0.1034	40	1.5423	0.8700	0.5730	0.8681	{}	{'Mice': 0.8630165289256198, 'Time Factors': 0.29385307346326833, 'Adolescent': 0.7317230273752013, 'Pregnancy': 0.9383930587362513, 'Animals': 0.9230555728163383, 'Male': 0.7506500368470084, 'Signal Transduction': 0.8825005407743889, 'Prognosis': 0.7848420416652142, 'Risk Factors': 0.7932291437376183, 'Disease Models, Animal': 0.835965494359655, 'Cell Line, Tumor': 0.7095959595959596, 'Surveys and Questionnaires': 0.8813397129186603, 'Aged': 0.8452099515929303, 'Treatment Outcome': 0.7471386238207868}
5.6562	0.1551	60	1.5141	0.8936	0.5917	0.8934	{}	{'Mice': 0.9004015077023926, 'Time Factors': 0.530298273155416, 'Adolescent': 0.815555060329184, 'Pregnancy': 0.9517676767676768, 'Animals': 0.9531279178338001, 'Male': 0.7507972188823253, 'Signal Transduction': 0.8854037450528678, 'Prognosis': 0.5506069395208938, 'Risk Factors': 0.8354825828576797, 'Disease Models, Animal': 0.8282570813544265, 'Cell Line, Tumor': 0.8382608695652174, 'Surveys and Questionnaires': 0.9221252860411899, 'Aged': 0.8704236939531057, 'Treatment Outcome': 0.8289588215600938}
5.65	0.2069	80	1.5091	0.8969	0.8923	0.8973	{}	{'Mice': 0.9072355455452894, 'Time Factors': 0.5904649595687331, 'Adolescent': 0.8379517888240506, 'Pregnancy': 0.9645804357904497, 'Animals': 0.9506114749400074, 'Male': 0.7515051997810618, 'Signal Transduction': 0.8824835941443716, 'Prognosis': 0.8353187042842215, 'Risk Factors': 0.8277989161766401, 'Disease Models, Animal': 0.8427006932583141, 'Cell Line, Tumor': 0.8931948665991218, 'Surveys and Questionnaires': 0.9307571253715685, 'Aged': 0.8693137768465165, 'Treatment Outcome': 0.8385880988620714}
5.6609	0.2586	100	1.5059	0.8977	0.8933	0.8982	{}	{'Mice': 0.9095004095004096, 'Time Factors': 0.5865385995893814, 'Adolescent': 0.8381574852163087, 'Pregnancy': 0.9768681119050503, 'Animals': 0.9439732142857142, 'Male': 0.7538794265619533, 'Signal Transduction': 0.8952474678418088, 'Prognosis': 0.8353187042842215, 'Risk Factors': 0.8320130475302889, 'Disease Models, Animal': 0.835965494359655, 'Cell Line, Tumor': 0.8916412896530064, 'Surveys and Questionnaires': 0.924375, 'Aged': 0.8691360925887848, 'Treatment Outcome': 0.8501525165226234}
5.6625	0.3103	120	1.5035	0.8949	0.5924	0.8946	{}	{'Mice': 0.9140113040629095, 'Time Factors': 0.5339340490797546, 'Adolescent': 0.8307139085171121, 'Pregnancy': 0.9645804357904497, 'Animals': 0.9387915557741872, 'Male': 0.7944145683775334, 'Signal Transduction': 0.8592431359071295, 'Prognosis': 0.5515813225606871, 'Risk Factors': 0.8379818215544426, 'Disease Models, Animal': 0.8078575466365179, 'Cell Line, Tumor': 0.8629118897997028, 'Surveys and Questionnaires': 0.9221252860411899, 'Aged': 0.8725498338870432, 'Treatment Outcome': 0.8367368549668065}
5.6813	0.3620	140	1.5018	0.8994	0.5968	0.8999	{}	{'Mice': 0.9208043452664343, 'Time Factors': 0.5843260188087774, 'Adolescent': 0.83710407239819, 'Pregnancy': 0.6507792129040083, 'Animals': 0.9572001023105123, 'Male': 0.7562803047794966, 'Signal Transduction': 0.8959290504001731, 'Prognosis': 0.8353187042842215, 'Risk Factors': 0.8269930179426774, 'Disease Models, Animal': 0.8401829280741513, 'Cell Line, Tumor': 0.8944895462633452, 'Surveys and Questionnaires': 0.9264948954788528, 'Aged': 0.8737925759355316, 'Treatment Outcome': 0.8444458241817943}
5.6562	0.4137	160	1.5005	0.8986	0.8933	0.8986	{}	{'Mice': 0.9162891642736855, 'Time Factors': 0.5713278081467114, 'Adolescent': 0.8422903002153599, 'Pregnancy': 0.9768681119050503, 'Animals': 0.9572001023105123, 'Male': 0.7778381905176164, 'Signal Transduction': 0.861485851715456, 'Prognosis': 0.8215579710144928, 'Risk Factors': 0.8345780133301213, 'Disease Models, Animal': 0.8195025438100623, 'Cell Line, Tumor': 0.8974159292035397, 'Surveys and Questionnaires': 0.9221252860411899, 'Aged': 0.8718045112781955, 'Treatment Outcome': 0.8482422198230004}
5.7047	0.4654	180	1.4993	0.8977	0.8937	0.8983	{}	{'Mice': 0.9298466579627781, 'Time Factors': 0.6267997148966501, 'Adolescent': 0.848649043110211, 'Pregnancy': 0.9886643520579246, 'Animals': 0.9460639582608097, 'Male': 0.7419721511793123, 'Signal Transduction': 0.8706002181300307, 'Prognosis': 0.8369257219268362, 'Risk Factors': 0.8211805555555556, 'Disease Models, Animal': 0.8227883227883228, 'Cell Line, Tumor': 0.9104862827568313, 'Surveys and Questionnaires': 0.9238328379087253, 'Aged': 0.87094196804037, 'Treatment Outcome': 0.8606435550190501}
5.6047	0.5171	200	1.4988	0.8999	0.8949	0.9000	{}	{'Mice': 0.9185503685503686, 'Time Factors': 0.5882377534666574, 'Adolescent': 0.8462510940387047, 'Pregnancy': 0.9768681119050503, 'Animals': 0.9572001023105123, 'Male': 0.7802757285588506, 'Signal Transduction': 0.8687760700462215, 'Prognosis': 0.8267388856116265, 'Risk Factors': 0.8431408382066277, 'Disease Models, Animal': 0.81368004522329, 'Cell Line, Tumor': 0.8998880736226837, 'Surveys and Questionnaires': 0.9221252860411899, 'Aged': 0.8736298918979999, 'Treatment Outcome': 0.8455480098094699}
5.6266	0.5688	220	1.4981	0.9009	0.5970	0.9008	{}	{'Mice': 0.923070622683621, 'Time Factors': 0.5321076218981108, 'Adolescent': 0.8505129457743039, 'Pregnancy': 0.6507792129040083, 'Animals': 0.9572001023105123, 'Male': 0.7876205830271956, 'Signal Transduction': 0.8580779300785462, 'Prognosis': 0.8315630241047058, 'Risk Factors': 0.8426029098086658, 'Disease Models, Animal': 0.8170718170718171, 'Cell Line, Tumor': 0.8998880736226837, 'Surveys and Questionnaires': 0.9182750545102156, 'Aged': 0.8789083717208238, 'Treatment Outcome': 0.8447376738305942}
5.6234	0.6206	240	1.4975	0.9039	0.5994	0.9040	{}	{'Mice': 0.9298466579627781, 'Time Factors': 0.5692186565967311, 'Adolescent': 0.8472969573513802, 'Pregnancy': 0.6586433693886445, 'Animals': 0.9550092823762883, 'Male': 0.7876205830271956, 'Signal Transduction': 0.8727173318753416, 'Prognosis': 0.8285629384359048, 'Risk Factors': 0.8685805422647528, 'Disease Models, Animal': 0.8345753115153495, 'Cell Line, Tumor': 0.8998880736226837, 'Surveys and Questionnaires': 0.9227083998722453, 'Aged': 0.8813636363636363, 'Treatment Outcome': 0.8539576365663322}
5.5734	0.6723	260	1.4969	0.9037	0.5993	0.9039	{}	{'Mice': 0.9298466579627781, 'Time Factors': 0.551789077212806, 'Adolescent': 0.8506989339123222, 'Pregnancy': 0.6586433693886445, 'Animals': 0.9506114749400074, 'Male': 0.7876816891717554, 'Signal Transduction': 0.864644535840188, 'Prognosis': 0.8267388856116265, 'Risk Factors': 0.8630308559411797, 'Disease Models, Animal': 0.8363601501081985, 'Cell Line, Tumor': 0.8998880736226837, 'Surveys and Questionnaires': 0.9259450554343471, 'Aged': 0.880367024468298, 'Treatment Outcome': 0.8505459879393709}
5.6078	0.7240	280	1.4965	0.8991	0.5968	0.8998	{}	{'Mice': 0.9275647328744674, 'Time Factors': 0.5962016260162601, 'Adolescent': 0.8419064225515839, 'Pregnancy': 0.6586433693886445, 'Animals': 0.9460639582608097, 'Male': 0.7562803047794966, 'Signal Transduction': 0.8847032254230237, 'Prognosis': 0.8369745117510758, 'Risk Factors': 0.840593358299935, 'Disease Models, Animal': 0.8310101601005924, 'Cell Line, Tumor': 0.9115157732751987, 'Surveys and Questionnaires': 0.9275552961469105, 'Aged': 0.8689535191558564, 'Treatment Outcome': 0.8579945799457995}
5.65	0.7757	300	1.4960	0.9027	0.5989	0.9031	{}	{'Mice': 0.9321044546850998, 'Time Factors': 0.5799640610961365, 'Adolescent': 0.8412914519109209, 'Pregnancy': 0.6586433693886445, 'Animals': 0.9506114749400074, 'Male': 0.7752475247524753, 'Signal Transduction': 0.8862058852282726, 'Prognosis': 0.8386110848536409, 'Risk Factors': 0.8476319861126835, 'Disease Models, Animal': 0.8299955985915493, 'Cell Line, Tumor': 0.9104862827568313, 'Surveys and Questionnaires': 0.9302522192475694, 'Aged': 0.8800751879699249, 'Treatment Outcome': 0.8533861205222886}
5.5875	0.8274	320	1.4960	0.9022	0.5986	0.9026	{}	{'Mice': 0.9298351623029142, 'Time Factors': 0.5986621141383875, 'Adolescent': 0.8436867166548081, 'Pregnancy': 0.6586433693886445, 'Animals': 0.9439732142857142, 'Male': 0.7701577430188618, 'Signal Transduction': 0.8862058852282726, 'Prognosis': 0.8386110848536409, 'Risk Factors': 0.8458591481847295, 'Disease Models, Animal': 0.8331857890872159, 'Cell Line, Tumor': 0.9104862827568313, 'Surveys and Questionnaires': 0.9302522192475694, 'Aged': 0.8777731741308523, 'Treatment Outcome': 0.8500457875457875}
5.7078	0.8791	340	1.4959	0.9021	0.5985	0.9025	{}	{'Mice': 0.9321044546850998, 'Time Factors': 0.5843260188087774, 'Adolescent': 0.8438538407800018, 'Pregnancy': 0.6586433693886445, 'Animals': 0.9461916093210709, 'Male': 0.7701577430188618, 'Signal Transduction': 0.8854074372398979, 'Prognosis': 0.8386110848536409, 'Risk Factors': 0.8520247822573404, 'Disease Models, Animal': 0.8299955985915493, 'Cell Line, Tumor': 0.9104862827568313, 'Surveys and Questionnaires': 0.9302522192475694, 'Aged': 0.8691360925887848, 'Treatment Outcome': 0.851997878766918}
5.6172	0.9308	360	1.4956	0.9002	0.5974	0.9007	{}	{'Mice': 0.9298351623029142, 'Time Factors': 0.581678306416412, 'Adolescent': 0.8366653726708074, 'Pregnancy': 0.6586433693886445, 'Animals': 0.9439732142857142, 'Male': 0.7725856004274456, 'Signal Transduction': 0.889269406392694, 'Prognosis': 0.8386110848536409, 'Risk Factors': 0.8414774913428241, 'Disease Models, Animal': 0.8331857890872159, 'Cell Line, Tumor': 0.9104862827568313, 'Surveys and Questionnaires': 0.9302522192475694, 'Aged': 0.868766017615695, 'Treatment Outcome': 0.850738332523092}
5.5453	0.9825	380	1.4957	0.9017	0.5984	0.9022	{}	{'Mice': 0.9298351623029142, 'Time Factors': 0.5838450443024933, 'Adolescent': 0.8414571948998179, 'Pregnancy': 0.6586433693886445, 'Animals': 0.9461916093210709, 'Male': 0.7725856004274456, 'Signal Transduction': 0.8854074372398979, 'Prognosis': 0.8386110848536409, 'Risk Factors': 0.8493691381330071, 'Disease Models, Animal': 0.8363601501081985, 'Cell Line, Tumor': 0.9104862827568313, 'Surveys and Questionnaires': 0.9302522192475694, 'Aged': 0.8711240635603185, 'Treatment Outcome': 0.8533861205222886}

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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