Instructions to use ferrazzipietro/meshTask-Llama-3.1-8B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ferrazzipietro/meshTask-Llama-3.1-8B-Instruct with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.1-8B-Instruct")
model = PeftModel.from_pretrained(base_model, "ferrazzipietro/meshTask-Llama-3.1-8B-Instruct")

Transformers

How to use ferrazzipietro/meshTask-Llama-3.1-8B-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="ferrazzipietro/meshTask-Llama-3.1-8B-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("ferrazzipietro/meshTask-Llama-3.1-8B-Instruct", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use ferrazzipietro/meshTask-Llama-3.1-8B-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-3.1-8B-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-3.1-8B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ferrazzipietro/meshTask-Llama-3.1-8B-Instruct

SGLang

How to use ferrazzipietro/meshTask-Llama-3.1-8B-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-3.1-8B-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-3.1-8B-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-3.1-8B-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-3.1-8B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ferrazzipietro/meshTask-Llama-3.1-8B-Instruct with Docker Model Runner:
```
docker model run hf.co/ferrazzipietro/meshTask-Llama-3.1-8B-Instruct
```

meshTask-Llama-3.1-8B-Instruct

This model is a fine-tuned version of meta-llama/Llama-3.1-8B-Instruct on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 1.4494
F1 Micro: 0.9041
F1 Macro: 0.5987
F1 Weighted: 0.9044
Class/f1 Results Per Class: {}
Items/f1 Scores Per Item: {'Disease Models, Animal': 0.8407594549073649, 'Cell Line, Tumor': 0.8754358161648177, 'Adolescent': 0.862004662004662, 'Pregnancy': 0.9430491121420572, 'Mice': 0.9244900521223225, 'Treatment Outcome': 0.8613774016284946, 'Risk Factors': 0.8637856093061071, 'Signal Transduction': 0.8435086918483016, 'Surveys and Questionnaires': 0.8979398979398979, 'Aged': 0.8938600138600139, 'Prognosis': 0.8613235783172597, 'Time Factors': 0.6389880698504771, 'Male': 0.5043571388309566, 'Animals': 0.967109634551495}

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
6.5563	0.0517	20	1.6102	0.5702	0.0310	0.6052	{}	{'Disease Models, Animal': 0.20034228171273138, 'Cell Line, Tumor': 0.1849477157253389, 'Adolescent': 0.13403835993624946, 'Male': 0.29283049472830497, 'Mice': 0.24328788749963456, 'Pregnancy': 0.222847328110486, 'Treatment Outcome': 0.11704939821309902, 'Signal Transduction': 0.08523634910018989, 'Surveys and Questionnaires': 0.3675925925925926, 'Aged': 0.31557614360162134, 'Prognosis': 0.07109572324545223, 'Time Factors': 0.036189039383821245, 'Risk Factors': 0.10909927340522241, 'Animals': 0.18184455731225996}
5.6547	0.1034	40	1.4780	0.8674	0.5741	0.8677	{}	{'Disease Models, Animal': 0.8128744814871716, 'Cell Line, Tumor': 0.8689393939393939, 'Adolescent': 0.5008421512786557, 'Male': 0.7179610877232905, 'Mice': 0.8920664449033858, 'Pregnancy': 0.9383639616197756, 'Treatment Outcome': 0.7864511465930744, 'Signal Transduction': 0.8464299960111688, 'Surveys and Questionnaires': 0.9047619047619047, 'Aged': 0.8590100111234705, 'Prognosis': 0.7725331988569508, 'Time Factors': 0.646605701103971, 'Risk Factors': 0.8192314035955797, 'Animals': 0.9253828445601041}
5.7141	0.1551	60	1.4649	0.8900	0.8821	0.8889	{}	{'Disease Models, Animal': 0.829034307983248, 'Cell Line, Tumor': 0.8768683274021353, 'Adolescent': 0.7900702576112413, 'Male': 0.7757022537994636, 'Mice': 0.8967136396821311, 'Pregnancy': 0.9097526958533184, 'Treatment Outcome': 0.7885314536129211, 'Signal Transduction': 0.7899303512394484, 'Surveys and Questionnaires': 0.910895320185847, 'Aged': 0.88136776271173, 'Prognosis': 0.7888365545991014, 'Time Factors': 0.6535469638917915, 'Risk Factors': 0.8492237564101563, 'Animals': 0.9394851641453901}
5.6156	0.2069	80	1.4594	0.8725	0.8701	0.8740	{}	{'Disease Models, Animal': 0.828474862695395, 'Cell Line, Tumor': 0.8759539052496799, 'Adolescent': 0.7803095968028109, 'Male': 0.6851595006934813, 'Mice': 0.8945773022865533, 'Pregnancy': 0.9555164319248826, 'Treatment Outcome': 0.8231473625698487, 'Signal Transduction': 0.8588047659255089, 'Surveys and Questionnaires': 0.9121591340021374, 'Aged': 0.8266373978934927, 'Prognosis': 0.8306275635395635, 'Time Factors': 0.6705179845947282, 'Risk Factors': 0.8107848435717289, 'Animals': 0.9202644986449864}
5.6781	0.2586	100	1.4566	0.8865	0.8834	0.8876	{}	{'Disease Models, Animal': 0.8347487151184851, 'Cell Line, Tumor': 0.8941684952456945, 'Adolescent': 0.8141816257581308, 'Male': 0.7351353979289221, 'Mice': 0.915330034299479, 'Pregnancy': 0.9768563751831949, 'Treatment Outcome': 0.8218338294674172, 'Signal Transduction': 0.8653719861150202, 'Surveys and Questionnaires': 0.9054021443099941, 'Aged': 0.8597278714925773, 'Prognosis': 0.8219205468236817, 'Time Factors': 0.6930552088245356, 'Risk Factors': 0.8192314035955797, 'Animals': 0.9514287535320487}
5.5812	0.3103	120	1.4547	0.8920	0.8886	0.8929	{}	{'Disease Models, Animal': 0.8327549973119593, 'Cell Line, Tumor': 0.8941684952456945, 'Adolescent': 0.824092300009871, 'Male': 0.782665658856135, 'Mice': 0.9107383218196958, 'Pregnancy': 0.9659754017416285, 'Treatment Outcome': 0.8397759103641457, 'Signal Transduction': 0.8628595755032537, 'Surveys and Questionnaires': 0.9090838809431774, 'Aged': 0.8639150992092168, 'Prognosis': 0.8279786774240023, 'Time Factors': 0.6720240414046413, 'Risk Factors': 0.8287745610260824, 'Animals': 0.9515169251780313}
5.5891	0.3620	140	1.4532	0.8941	0.8868	0.8932	{}	{'Disease Models, Animal': 0.8170830226199354, 'Cell Line, Tumor': 0.8794509093442966, 'Adolescent': 0.8156662665066026, 'Male': 0.7939608267160012, 'Mice': 0.9175471698113208, 'Pregnancy': 0.9243814844373504, 'Treatment Outcome': 0.757864238410596, 'Signal Transduction': 0.8137397194000968, 'Surveys and Questionnaires': 0.9122170567633174, 'Aged': 0.8872080088987764, 'Prognosis': 0.7788362308486148, 'Time Factors': 0.6235794831293054, 'Risk Factors': 0.8640796276728748, 'Animals': 0.949454200284765}
5.5641	0.4137	160	1.4525	0.8958	0.8917	0.8964	{}	{'Disease Models, Animal': 0.8310767950563055, 'Cell Line, Tumor': 0.8941684952456945, 'Adolescent': 0.8414252038398908, 'Male': 0.7972955734149765, 'Mice': 0.9084663399103514, 'Pregnancy': 0.9768563751831949, 'Treatment Outcome': 0.8500600230676887, 'Signal Transduction': 0.8599563500311785, 'Surveys and Questionnaires': 0.9047619047619047, 'Aged': 0.8785175017158544, 'Prognosis': 0.838887078779262, 'Time Factors': 0.668640805448623, 'Risk Factors': 0.8175687285223368, 'Animals': 0.9425440906346739}
5.5172	0.4654	180	1.4511	0.9019	0.8966	0.9018	{}	{'Disease Models, Animal': 0.8361744082468576, 'Cell Line, Tumor': 0.8863399945250479, 'Adolescent': 0.8415928216150192, 'Male': 0.7970639439138003, 'Mice': 0.9175229627144235, 'Pregnancy': 0.9768563751831949, 'Treatment Outcome': 0.8426836797694999, 'Signal Transduction': 0.8490196078431372, 'Surveys and Questionnaires': 0.9192396922222521, 'Aged': 0.8920622296132943, 'Prognosis': 0.8018327241909, 'Time Factors': 0.6399471074380165, 'Risk Factors': 0.8610120511559272, 'Animals': 0.949454200284765}
5.5297	0.5171	200	1.4501	0.9003	0.8949	0.9002	{}	{'Disease Models, Animal': 0.8344558881270954, 'Cell Line, Tumor': 0.8849734042553192, 'Adolescent': 0.8351074870274278, 'Male': 0.7972955734149765, 'Mice': 0.9198261808536847, 'Pregnancy': 0.9768563751831949, 'Treatment Outcome': 0.8496916752312436, 'Signal Transduction': 0.8521694611544861, 'Surveys and Questionnaires': 0.9186391733971033, 'Aged': 0.8876569720361142, 'Prognosis': 0.7942886677865357, 'Time Factors': 0.6349293348471206, 'Risk Factors': 0.8457207207207207, 'Animals': 0.949454200284765}
5.5141	0.5688	220	1.4492	0.8973	0.8935	0.8979	{}	{'Disease Models, Animal': 0.833434160112926, 'Cell Line, Tumor': 0.8941684952456945, 'Adolescent': 0.8368835100110876, 'Male': 0.7877465603989492, 'Mice': 0.9153158437776603, 'Pregnancy': 0.9659754017416285, 'Treatment Outcome': 0.8574952041655248, 'Signal Transduction': 0.8612312572087659, 'Surveys and Questionnaires': 0.9172297297297297, 'Aged': 0.8767662691210785, 'Prognosis': 0.8325721153846153, 'Time Factors': 0.7133153302866639, 'Risk Factors': 0.8267674497021468, 'Animals': 0.9470070159725332}
5.5219	0.6206	240	1.4483	0.8946	0.8914	0.8955	{}	{'Disease Models, Animal': 0.8366152607921642, 'Cell Line, Tumor': 0.8862366015650687, 'Adolescent': 0.8504973736810301, 'Male': 0.7532031797989245, 'Mice': 0.9107383218196958, 'Pregnancy': 0.9785875706214688, 'Treatment Outcome': 0.8515575043390884, 'Signal Transduction': 0.8690757445589921, 'Surveys and Questionnaires': 0.9047619047619047, 'Aged': 0.8597278714925773, 'Prognosis': 0.8372473419792031, 'Time Factors': 0.708183382882178, 'Risk Factors': 0.8267674497021468, 'Animals': 0.9469046591428681}
5.4844	0.6723	260	1.4479	0.8968	0.8933	0.8976	{}	{'Disease Models, Animal': 0.8409648489846409, 'Cell Line, Tumor': 0.8901819720694033, 'Adolescent': 0.8501421116391762, 'Male': 0.8023243312978412, 'Mice': 0.915330034299479, 'Pregnancy': 0.9777582159624414, 'Treatment Outcome': 0.8554670660041295, 'Signal Transduction': 0.8532667876588022, 'Surveys and Questionnaires': 0.9078490270340864, 'Aged': 0.8664075623332566, 'Prognosis': 0.8372473419792031, 'Time Factors': 0.6886304190550392, 'Risk Factors': 0.8258438283654146, 'Animals': 0.9469046591428681}
5.475	0.7240	280	1.4473	0.8995	0.8952	0.8999	{}	{'Disease Models, Animal': 0.8409313519715669, 'Cell Line, Tumor': 0.8862366015650687, 'Adolescent': 0.839546817883588, 'Male': 0.7948198852974602, 'Mice': 0.9199067892022098, 'Pregnancy': 0.9768563751831949, 'Treatment Outcome': 0.8690610970134687, 'Signal Transduction': 0.8612612612612613, 'Surveys and Questionnaires': 0.9103322072072072, 'Aged': 0.8804750756023803, 'Prognosis': 0.8309260832625318, 'Time Factors': 0.6536323508834416, 'Risk Factors': 0.8242125484794665, 'Animals': 0.9491750749931824}
5.6312	0.7757	300	1.4470	0.9004	0.8963	0.9009	{}	{'Disease Models, Animal': 0.8384243194369776, 'Cell Line, Tumor': 0.8862366015650687, 'Adolescent': 0.8472847705328823, 'Male': 0.7972955734149765, 'Mice': 0.919908466819222, 'Pregnancy': 0.9886584672472095, 'Treatment Outcome': 0.8588912660458092, 'Signal Transduction': 0.8552113812935123, 'Surveys and Questionnaires': 0.9103322072072072, 'Aged': 0.8804750756023803, 'Prognosis': 0.8341312678518789, 'Time Factors': 0.6466717869943677, 'Risk Factors': 0.8430376028202116, 'Animals': 0.9491750749931824}
5.5375	0.8274	320	1.4464	0.9021	0.8978	0.9025	{}	{'Disease Models, Animal': 0.8434465593998668, 'Cell Line, Tumor': 0.8941684952456945, 'Adolescent': 0.8439482961222091, 'Male': 0.7997968837694096, 'Mice': 0.9176197059193163, 'Pregnancy': 0.9768563751831949, 'Treatment Outcome': 0.8656898656898657, 'Signal Transduction': 0.8643159222436563, 'Surveys and Questionnaires': 0.9103322072072072, 'Aged': 0.8869427720002432, 'Prognosis': 0.829233409610984, 'Time Factors': 0.6492238325281804, 'Risk Factors': 0.8387220967682227, 'Animals': 0.9512464546947306}
5.5469	0.8791	340	1.4463	0.8991	0.8949	0.8996	{}	{'Disease Models, Animal': 0.8384243194369776, 'Cell Line, Tumor': 0.8901819720694033, 'Adolescent': 0.836718754587789, 'Male': 0.7950030102347982, 'Mice': 0.9199067892022098, 'Pregnancy': 0.9886584672472095, 'Treatment Outcome': 0.8648950338339787, 'Signal Transduction': 0.8522153981136213, 'Surveys and Questionnaires': 0.9109557924165648, 'Aged': 0.8806768381756208, 'Prognosis': 0.829233409610984, 'Time Factors': 0.6421273726791826, 'Risk Factors': 0.8310288867250892, 'Animals': 0.9512464546947306}
5.4641	0.9308	360	1.4459	0.9008	0.8966	0.9012	{}	{'Disease Models, Animal': 0.8384243194369776, 'Cell Line, Tumor': 0.8941684952456945, 'Adolescent': 0.8437794089968003, 'Male': 0.7950030102347982, 'Mice': 0.9199067892022098, 'Pregnancy': 0.9886584672472095, 'Treatment Outcome': 0.8636268270508427, 'Signal Transduction': 0.8522153981136213, 'Surveys and Questionnaires': 0.9103322072072072, 'Aged': 0.8869427720002432, 'Prognosis': 0.8309260832625318, 'Time Factors': 0.6446645525845307, 'Risk Factors': 0.8378579777907657, 'Animals': 0.949077868852459}
5.4984	0.9825	380	1.4459	0.9014	0.8971	0.9018	{}	{'Disease Models, Animal': 0.8409313519715669, 'Cell Line, Tumor': 0.8941684952456945, 'Adolescent': 0.8437794089968003, 'Male': 0.7997968837694096, 'Mice': 0.9199067892022098, 'Pregnancy': 0.9768563751831949, 'Treatment Outcome': 0.8663384845463609, 'Signal Transduction': 0.8602228614516714, 'Surveys and Questionnaires': 0.9103322072072072, 'Aged': 0.8828316000729794, 'Prognosis': 0.829233409610984, 'Time Factors': 0.6446645525845307, 'Risk Factors': 0.8370005875440658, 'Animals': 0.9512464546947306}

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: 29

Model tree for ferrazzipietro/meshTask-Llama-3.1-8B-Instruct

Base model

meta-llama/Llama-3.1-8B

Finetuned

meta-llama/Llama-3.1-8B-Instruct

Adapter

(2264)

this model