Instructions to use ferrazzipietro/meshTask-Llama-3.2-1B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ferrazzipietro/meshTask-Llama-3.2-1B-Instruct with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-1B-Instruct")
model = PeftModel.from_pretrained(base_model, "ferrazzipietro/meshTask-Llama-3.2-1B-Instruct")

Transformers

How to use ferrazzipietro/meshTask-Llama-3.2-1B-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="ferrazzipietro/meshTask-Llama-3.2-1B-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("ferrazzipietro/meshTask-Llama-3.2-1B-Instruct", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use ferrazzipietro/meshTask-Llama-3.2-1B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ferrazzipietro/meshTask-Llama-3.2-1B-Instruct"
# 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-Llama-3.2-1B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ferrazzipietro/meshTask-Llama-3.2-1B-Instruct

SGLang

How to use ferrazzipietro/meshTask-Llama-3.2-1B-Instruct 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-Llama-3.2-1B-Instruct" \
    --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-Llama-3.2-1B-Instruct",
		"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-Llama-3.2-1B-Instruct" \
        --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-Llama-3.2-1B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ferrazzipietro/meshTask-Llama-3.2-1B-Instruct with Docker Model Runner:
```
docker model run hf.co/ferrazzipietro/meshTask-Llama-3.2-1B-Instruct
```

meshTask-Llama-3.2-1B-Instruct

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

Loss: 1.7419
F1 Micro: 0.8730
F1 Macro: 0.8640
F1 Weighted: 0.8728
Class/f1 Results Per Class: {}
Items/f1 Scores Per Item: {'Aged': 0.7881952802051175, 'Prognosis': 0.8224460089712742, 'Time Factors': 0.6137711012861736, 'Signal Transduction': 0.8079673391692026, 'Surveys and Questionnaires': 0.8920539730134933, 'Pregnancy': 0.9240938166311301, 'Disease Models, Animal': 0.8178899751933459, 'Treatment Outcome': 0.8443505091531761, 'Risk Factors': 0.8422805426796716, 'Mice': 0.8748860994886098, 'Animals': 0.9381208747249816, 'Adolescent': 0.7523774757572355, 'Male': 0.7036700507318551, 'Cell Line, Tumor': 0.8483495854408927}

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
2.2437	0.0517	20	2.2472	0.5548	0.0013	0.4528	{}	{'Aged': 0.006663970262514204, 'Prognosis': 0.0873758865248227, 'Time Factors': 0.012307692307692308, 'Signal Transduction': 0.04639435199193142, 'Surveys and Questionnaires': 0.053994082840236685, 'Pregnancy': 0.016018907563025212, 'Disease Models, Animal': 0.029607451763140388, 'Treatment Outcome': 0.04159856680217736, 'Risk Factors': 0.04892086330935252, 'Mice': 0.025271565495207666, 'Animals': 0.018751953328471717, 'Adolescent': 0.015094612162357359, 'Male': 0.0015516233593708486, 'Cell Line, Tumor': 0.02725486961409683}
1.8313	0.1034	40	1.8835	0.5993	0.1659	0.6003	{}	{'Aged': 0.4224275743751706, 'Prognosis': 0.4026586480002629, 'Time Factors': 0.38779198635976125, 'Signal Transduction': 0.533672480620155, 'Surveys and Questionnaires': 0.2896328744263663, 'Pregnancy': 0.46068581822072435, 'Disease Models, Animal': 0.5585036972596781, 'Treatment Outcome': 0.6738455423901433, 'Risk Factors': 0.5922866686911631, 'Mice': 0.459593837535014, 'Animals': 0.28919851784173395, 'Adolescent': 0.5149416464595362, 'Male': 0.49210371021407495, 'Cell Line, Tumor': 0.5078936139951643}
1.8047	0.1550	60	1.8292	0.7145	0.6303	0.6694	{}	{'Aged': 0.5458333333333334, 'Prognosis': 0.4410112359550562, 'Time Factors': 0.4374057315233786, 'Signal Transduction': 0.4425909605942339, 'Surveys and Questionnaires': 0.5202241392022414, 'Pregnancy': 0.6410984848484849, 'Disease Models, Animal': 0.41750358680057387, 'Treatment Outcome': 0.4161676646706587, 'Risk Factors': 0.4219409282700422, 'Mice': 0.5460526315789473, 'Animals': 0.4735900819558272, 'Adolescent': 0.5047294908062234, 'Male': 0.6312071766617221, 'Cell Line, Tumor': 0.5498791996200467}
1.7453	0.2067	80	1.7967	0.8054	0.7758	0.7938	{}	{'Aged': 0.6564066193853428, 'Prognosis': 0.6957462522188679, 'Time Factors': 0.4374057315233786, 'Signal Transduction': 0.47259480046365293, 'Surveys and Questionnaires': 0.6628398339628286, 'Pregnancy': 0.9243814844373504, 'Disease Models, Animal': 0.6281748616085965, 'Treatment Outcome': 0.6626264032375138, 'Risk Factors': 0.6609976433621367, 'Mice': 0.8530660278233093, 'Animals': 0.8614827201783724, 'Adolescent': 0.5440889626258323, 'Male': 0.6638759545539207, 'Cell Line, Tumor': 0.730226111062982}
1.7391	0.2584	100	1.7687	0.8385	0.8347	0.8402	{}	{'Aged': 0.655349256630211, 'Prognosis': 0.8176319648093842, 'Time Factors': 0.6687351747552077, 'Signal Transduction': 0.7829003655596173, 'Surveys and Questionnaires': 0.8982576880248495, 'Pregnancy': 0.9264220539701029, 'Disease Models, Animal': 0.8123417612362457, 'Treatment Outcome': 0.6785222021026459, 'Risk Factors': 0.8335743622375196, 'Mice': 0.8717723004694835, 'Animals': 0.8923733719887039, 'Adolescent': 0.6821649329112016, 'Male': 0.5549665988963114, 'Cell Line, Tumor': 0.8498323140658908}
1.7094	0.3101	120	1.7619	0.8629	0.8567	0.8633	{}	{'Aged': 0.7680193670759708, 'Prognosis': 0.8056078865265193, 'Time Factors': 0.6510669498381877, 'Signal Transduction': 0.805594458894256, 'Surveys and Questionnaires': 0.9152535044405434, 'Pregnancy': 0.9305691255495847, 'Disease Models, Animal': 0.8310344827586207, 'Treatment Outcome': 0.7793308049914363, 'Risk Factors': 0.8474342222486191, 'Mice': 0.8604118993135011, 'Animals': 0.905141810130931, 'Adolescent': 0.7123164978620813, 'Male': 0.6469812397881848, 'Cell Line, Tumor': 0.8544506141679287}
1.7109	0.3618	140	1.7565	0.8655	0.8603	0.8663	{}	{'Aged': 0.7611387804549737, 'Prognosis': 0.8037139550098134, 'Time Factors': 0.6568705613953317, 'Signal Transduction': 0.8100136653507439, 'Surveys and Questionnaires': 0.9152535044405434, 'Pregnancy': 0.9454534987478771, 'Disease Models, Animal': 0.8235452381773654, 'Treatment Outcome': 0.7796183280054247, 'Risk Factors': 0.8387220967682227, 'Mice': 0.8740469016114241, 'Animals': 0.916841495591737, 'Adolescent': 0.74495975561005, 'Male': 0.6520679062247372, 'Cell Line, Tumor': 0.8650382665070547}
1.6969	0.4134	160	1.7521	0.8667	0.8601	0.8668	{}	{'Aged': 0.7797125668449199, 'Prognosis': 0.8110629514963881, 'Time Factors': 0.6350635948159168, 'Signal Transduction': 0.7512208557112743, 'Surveys and Questionnaires': 0.9209559629327653, 'Pregnancy': 0.9517443340972753, 'Disease Models, Animal': 0.8158730158730159, 'Treatment Outcome': 0.7900986163896961, 'Risk Factors': 0.8331887598780807, 'Mice': 0.8604002031913612, 'Animals': 0.917487197169968, 'Adolescent': 0.7399749373433584, 'Male': 0.6703257322846526, 'Cell Line, Tumor': 0.8579249931563099}
1.6781	0.4651	180	1.7484	0.8275	0.8041	0.8191	{}	{'Aged': 0.7476059628478361, 'Prognosis': 0.7765718562874251, 'Time Factors': 0.5516057015284217, 'Signal Transduction': 0.5757575757575757, 'Surveys and Questionnaires': 0.8268769716088328, 'Pregnancy': 0.8614766081871346, 'Disease Models, Animal': 0.5903300399411393, 'Treatment Outcome': 0.6059756388280302, 'Risk Factors': 0.7713137557959815, 'Mice': 0.8194263328771901, 'Animals': 0.8717251211628354, 'Adolescent': 0.6393793103448275, 'Male': 0.7385319531508381, 'Cell Line, Tumor': 0.787653233040112}
1.6859	0.5168	200	1.7467	0.8518	0.8379	0.8484	{}	{'Aged': 0.7698391207390889, 'Prognosis': 0.785881362275449, 'Time Factors': 0.5754253534627366, 'Signal Transduction': 0.6982758620689655, 'Surveys and Questionnaires': 0.9015151515151515, 'Pregnancy': 0.9243814844373504, 'Disease Models, Animal': 0.7655084667732512, 'Treatment Outcome': 0.6813725490196079, 'Risk Factors': 0.8000033559299282, 'Mice': 0.836418467573139, 'Animals': 0.8971780867262495, 'Adolescent': 0.6425935039370079, 'Male': 0.755622009569378, 'Cell Line, Tumor': 0.8263154786511546}
1.6812	0.5685	220	1.7443	0.8619	0.8583	0.8633	{}	{'Aged': 0.7803516819571865, 'Prognosis': 0.8310435319543068, 'Time Factors': 0.6952153254849741, 'Signal Transduction': 0.8363761314416442, 'Surveys and Questionnaires': 0.9121591340021374, 'Pregnancy': 0.9659754017416285, 'Disease Models, Animal': 0.807450553414336, 'Treatment Outcome': 0.7862150543838897, 'Risk Factors': 0.812789863074205, 'Mice': 0.8740469016114241, 'Animals': 0.91632021344109, 'Adolescent': 0.7540653297302422, 'Male': 0.6042231693142193, 'Cell Line, Tumor': 0.8385929521528084}
1.675	0.6202	240	1.7419	0.8740	0.8663	0.8734	{}	{'Aged': 0.7962218649517685, 'Prognosis': 0.8060743868767257, 'Time Factors': 0.6517996058500156, 'Signal Transduction': 0.7437433439829606, 'Surveys and Questionnaires': 0.9186391733971033, 'Pregnancy': 0.9243814844373504, 'Disease Models, Animal': 0.8240848647841028, 'Treatment Outcome': 0.8234204568841326, 'Risk Factors': 0.8574203843752168, 'Mice': 0.8533249423117264, 'Animals': 0.9207669071991945, 'Adolescent': 0.7464555423287922, 'Male': 0.7316011459025747, 'Cell Line, Tumor': 0.8526238073602908}
1.6641	0.6718	260	1.7403	0.8664	0.8629	0.8677	{}	{'Aged': 0.7886156606260712, 'Prognosis': 0.826471034273582, 'Time Factors': 0.7080655791728456, 'Signal Transduction': 0.821468318741653, 'Surveys and Questionnaires': 0.9152535044405434, 'Pregnancy': 0.9305691255495847, 'Disease Models, Animal': 0.8201127819548872, 'Treatment Outcome': 0.8062286228622862, 'Risk Factors': 0.8103577700492408, 'Mice': 0.8831385380126158, 'Animals': 0.9250619278406031, 'Adolescent': 0.7625303624813153, 'Male': 0.6380440559853541, 'Cell Line, Tumor': 0.8457462391888622}
1.6687	0.7235	280	1.7383	0.8754	0.8684	0.8751	{}	{'Aged': 0.7901558188605746, 'Prognosis': 0.8114312788043244, 'Time Factors': 0.6399471074380165, 'Signal Transduction': 0.7944352159468439, 'Surveys and Questionnaires': 0.9134757158118715, 'Pregnancy': 0.9243814844373504, 'Disease Models, Animal': 0.8356520442294991, 'Treatment Outcome': 0.8159326328340413, 'Risk Factors': 0.8564159426453613, 'Mice': 0.8718528758744921, 'Animals': 0.9303108202851134, 'Adolescent': 0.737448122279583, 'Male': 0.7533369013444915, 'Cell Line, Tumor': 0.8562167553191489}
1.7141	0.7752	300	1.7372	0.8772	0.8710	0.8772	{}	{'Aged': 0.7939472809152972, 'Prognosis': 0.8167253879351888, 'Time Factors': 0.6300826446280992, 'Signal Transduction': 0.8085016835016835, 'Surveys and Questionnaires': 0.9172297297297297, 'Pregnancy': 0.9243814844373504, 'Disease Models, Animal': 0.8331349206349207, 'Treatment Outcome': 0.8207249802994483, 'Risk Factors': 0.851083512569671, 'Mice': 0.8695652173913043, 'Animals': 0.9278603142076502, 'Adolescent': 0.7597867479055598, 'Male': 0.7515051997810618, 'Cell Line, Tumor': 0.861842014677737}
1.6797	0.8269	320	1.7364	0.8770	0.8711	0.8772	{}	{'Aged': 0.7962218649517685, 'Prognosis': 0.812270054400805, 'Time Factors': 0.6227829063375169, 'Signal Transduction': 0.8256872256872256, 'Surveys and Questionnaires': 0.9172297297297297, 'Pregnancy': 0.9243814844373504, 'Disease Models, Animal': 0.830626400899404, 'Treatment Outcome': 0.8353394778978898, 'Risk Factors': 0.8457207207207207, 'Mice': 0.8741418764302059, 'Animals': 0.9297761107757125, 'Adolescent': 0.7496059695611486, 'Male': 0.7597883597883598, 'Cell Line, Tumor': 0.8634662747819462}
1.6828	0.8786	340	1.7356	0.8742	0.8692	0.8748	{}	{'Aged': 0.7941961031214793, 'Prognosis': 0.8197373056212571, 'Time Factors': 0.6608474268048736, 'Signal Transduction': 0.8240659501352636, 'Surveys and Questionnaires': 0.9215010230290366, 'Pregnancy': 0.9243814844373504, 'Disease Models, Animal': 0.8248885785290312, 'Treatment Outcome': 0.8272606475977342, 'Risk Factors': 0.8242125484794665, 'Mice': 0.8740996830884471, 'Animals': 0.9274290524290524, 'Adolescent': 0.7658482427242779, 'Male': 0.7258454106280193, 'Cell Line, Tumor': 0.8589333859406852}
1.6562	0.9302	360	1.7356	0.8767	0.8704	0.8767	{}	{'Aged': 0.7864215455716652, 'Prognosis': 0.8186980242597595, 'Time Factors': 0.6349293348471206, 'Signal Transduction': 0.8187382297551788, 'Surveys and Questionnaires': 0.9172297297297297, 'Pregnancy': 0.9243814844373504, 'Disease Models, Animal': 0.8273809523809523, 'Treatment Outcome': 0.8335896176667124, 'Risk Factors': 0.8510419812745394, 'Mice': 0.8718528758744921, 'Animals': 0.9297761107757125, 'Adolescent': 0.7442138364779874, 'Male': 0.7550861196102624, 'Cell Line, Tumor': 0.8673966602792226}
1.6656	0.9819	380	1.7354	0.8764	0.8702	0.8764	{}	{'Aged': 0.790948319854337, 'Prognosis': 0.8174007340674008, 'Time Factors': 0.6349293348471206, 'Signal Transduction': 0.8187382297551788, 'Surveys and Questionnaires': 0.9172297297297297, 'Pregnancy': 0.9243814844373504, 'Disease Models, Animal': 0.8233359726650831, 'Treatment Outcome': 0.8317945113574645, 'Risk Factors': 0.8439530988274706, 'Mice': 0.8718528758744921, 'Animals': 0.9297761107757125, 'Adolescent': 0.7508505103061838, 'Male': 0.7491570566401269, 'Cell Line, Tumor': 0.8673966602792226}

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

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