Instructions to use ferrazzipietro/crfTask-MediPhi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ferrazzipietro/crfTask-MediPhi with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("microsoft/MediPhi")
model = PeftModel.from_pretrained(base_model, "ferrazzipietro/crfTask-MediPhi")

Transformers

How to use ferrazzipietro/crfTask-MediPhi with Transformers:

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

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

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("ferrazzipietro/crfTask-MediPhi", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use ferrazzipietro/crfTask-MediPhi with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/ferrazzipietro/crfTask-MediPhi

SGLang

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

Docker Model Runner
How to use ferrazzipietro/crfTask-MediPhi with Docker Model Runner:
```
docker model run hf.co/ferrazzipietro/crfTask-MediPhi
```

crfTask-MediPhi

This model is a fine-tuned version of microsoft/MediPhi on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 3.8789
F1 Micro: 0.0
F1 Macro: 0.0
F1 Weighted: 0.0
Class/f1 Results Per Class: {}
Items/f1 Scores Per Item: None

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: 64
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	Class/f1 Results Per Class	Items/f1 Scores Per Item
3.4844	0.0024	2	3.8512	{}	None
3.4844	0.0047	4	3.8454	{}	None
3.4531	0.0071	6	3.8314	{}	None
3.4531	0.0095	8	3.8002	{}	None
3.4531	0.0118	10	3.7459	{}	None
3.4531	0.0142	12	3.6571	{}	None
3.4531	0.0165	14	3.5189	{}	None
3.275	0.0189	16	3.4531	{}	None
3.275	0.0213	18	3.3890	{}	None
3.1437	0.0236	20	3.3224	{}	None
3.1437	0.0260	22	3.2566	{}	None
3.1437	0.0284	24	3.1908	{}	None
2.9875	0.0307	26	3.1151	{}	None
2.9875	0.0331	28	3.0362	{}	None
2.8844	0.0355	30	2.9490	{}	None
2.8844	0.0378	32	2.8717	{}	None
2.8844	0.0402	34	2.7829	{}	None
2.7812	0.0426	36	2.7023	{}	None
2.7812	0.0449	38	2.6242	{}	None
2.6187	0.0473	40	2.5551	{}	None
2.6187	0.0496	42	2.4877	{}	None
2.6187	0.0520	44	2.4227	{}	None
2.4906	0.0544	46	2.3643	{}	None
2.4906	0.0567	48	2.3133	{}	None
2.4031	0.0591	50	2.2681	{}	None
2.4031	0.0615	52	2.2212	{}	None
2.4031	0.0638	54	2.1768	{}	None
2.2969	0.0662	56	2.1357	{}	None
2.2969	0.0686	58	2.1032	{}	None
2.2156	0.0709	60	2.0703	{}	None
2.2156	0.0733	62	2.0366	{}	None
2.2156	0.0757	64	2.0041	{}	None
2.1437	0.0780	66	1.9741	{}	None
2.1437	0.0804	68	1.9465	{}	None
2.0875	0.0827	70	1.9202	{}	None
2.0875	0.0851	72	1.8939	{}	None
2.0875	0.0875	74	1.8680	{}	None
2.0359	0.0898	76	1.8450	{}	None
2.0359	0.0922	78	1.8252	{}	None
1.9703	0.0946	80	1.7973	{}	None
1.9703	0.0969	82	1.7780	{}	None
1.9703	0.0993	84	1.7607	{}	None
1.9531	0.1017	86	1.7377	{}	None
1.9531	0.1040	88	1.7212	{}	None
1.9156	0.1064	90	1.7064	{}	None
1.9156	0.1087	92	1.6900	{}	None
1.9156	0.1111	94	1.6809	{}	None
1.8687	0.1135	96	1.6715	{}	None
1.8687	0.1158	98	1.6562	{}	None
1.8891	0.1182	100	1.6406	{}	None
1.8891	0.1206	102	1.6340	{}	None
1.8891	0.1229	104	1.6242	{}	None
1.8422	0.1253	106	1.6094	{}	None
1.8422	0.1277	108	1.6012	{}	None
1.7563	0.1300	110	1.5913	{}	None
1.7563	0.1324	112	1.5839	{}	None
1.7563	0.1348	114	1.5736	{}	None
1.7391	0.1371	116	1.5683	{}	None
1.7391	0.1395	118	1.5555	{}	None
1.7391	0.1418	120	1.5456	{}	None
1.7391	0.1442	122	1.5358	{}	None
1.7391	0.1466	124	1.5325	{}	None
1.7078	0.1489	126	1.5247	{}	None
1.7078	0.1513	128	1.5181	{}	None
1.7078	0.1537	130	1.5127	{}	None
1.7078	0.1560	132	1.5029	{}	None
1.7078	0.1584	134	1.5012	{}	None
1.6609	0.1608	136	1.4979	{}	None
1.6609	0.1631	138	1.4967	{}	None
1.6625	0.1655	140	1.4807	{}	None
1.6625	0.1678	142	1.4840	{}	None
1.6625	0.1702	144	1.4708	{}	None
1.675	0.1726	146	1.4737	{}	None
1.675	0.1749	148	1.4663	{}	None
1.6484	0.1773	150	1.4572	{}	None
1.6484	0.1797	152	1.4515	{}	None
1.6484	0.1820	154	1.4494	{}	None
1.6625	0.1844	156	1.4465	{}	None
1.6625	0.1868	158	1.4404	{}	None
1.6	0.1891	160	1.4334	{}	None
1.6	0.1915	162	1.4305	{}	None
1.6	0.1939	164	1.4317	{}	None
1.6375	0.1962	166	1.4317	{}	None
1.6375	0.1986	168	1.4276	{}	None
1.6016	0.2009	170	1.4268	{}	None
1.6016	0.2033	172	1.4235	{}	None
1.6016	0.2057	174	1.4202	{}	None
1.6187	0.2080	176	1.4174	{}	None
1.6187	0.2104	178	1.4165	{}	None
1.575	0.2128	180	1.4083	{}	None
1.575	0.2151	182	1.4030	{}	None
1.575	0.2175	184	1.3972	{}	None
1.5594	0.2199	186	1.3988	{}	None
1.5594	0.2222	188	1.3947	{}	None
1.6094	0.2246	190	1.3886	{}	None
1.6094	0.2270	192	1.3828	{}	None
1.6094	0.2293	194	1.3808	{}	None
1.6109	0.2317	196	1.3766	{}	None
1.6109	0.2340	198	1.375	{}	None
1.5641	0.2364	200	1.3717	{}	None
1.5641	0.2388	202	1.3680	{}	None
1.5641	0.2411	204	1.3668	{}	None
1.5234	0.2435	206	1.3651	{}	None
1.5234	0.2459	208	1.3618	{}	None
1.5375	0.2482	210	1.3549	{}	None
1.5375	0.2506	212	1.3549	{}	None
1.5375	0.2530	214	1.3528	{}	None
1.5188	0.2553	216	1.3470	{}	None
1.5188	0.2577	218	1.3413	{}	None
1.5109	0.2600	220	1.3372	{}	None
1.5109	0.2624	222	1.3363	{}	None
1.5109	0.2648	224	1.3343	{}	None
1.5141	0.2671	226	1.3294	{}	None
1.5141	0.2695	228	1.3277	{}	None
1.5344	0.2719	230	1.3211	{}	None
1.5344	0.2742	232	1.3236	{}	None
1.5344	0.2766	234	1.3211	{}	None
1.5266	0.2790	236	1.3187	{}	None
1.5266	0.2813	238	1.3146	{}	None
1.4547	0.2837	240	1.3129	{}	None
1.4547	0.2861	242	1.3113	{}	None
1.4547	0.2884	244	1.3080	{}	None
1.4547	0.2908	246	1.3076	{}	None
1.4547	0.2931	248	1.3059	{}	None
1.4469	0.2955	250	1.3030	{}	None
1.4469	0.2979	252	1.3047	{}	None
1.4469	0.3002	254	1.3010	{}	None
1.5031	0.3026	256	1.3002	{}	None
1.5031	0.3050	258	1.2965	{}	None
1.4594	0.3073	260	1.2932	{}	None
1.4594	0.3097	262	1.2907	{}	None
1.4594	0.3121	264	1.2899	{}	None
1.4359	0.3144	266	1.2870	{}	None
1.4359	0.3168	268	1.2850	{}	None
1.4594	0.3191	270	1.2812	{}	None
1.4594	0.3215	272	1.2812	{}	None
1.4594	0.3239	274	1.2800	{}	None
1.4297	0.3262	276	1.2784	{}	None
1.4297	0.3286	278	1.2751	{}	None
1.4438	0.3310	280	1.2759	{}	None
1.4438	0.3333	282	1.2738	{}	None
1.4438	0.3357	284	1.2714	{}	None
1.4297	0.3381	286	1.2693	{}	None
1.4297	0.3404	288	1.2677	{}	None
1.4328	0.3428	290	1.2664	{}	None
1.4328	0.3452	292	1.2660	{}	None
1.4328	0.3475	294	1.2632	{}	None
1.4156	0.3499	296	1.2623	{}	None
1.4156	0.3522	298	1.2558	{}	None
1.4719	0.3546	300	1.2533	{}	None
1.4719	0.3570	302	1.2537	{}	None
1.4719	0.3593	304	1.2484	{}	None
1.4234	0.3617	306	1.2488	{}	None
1.4234	0.3641	308	1.2442	{}	None
1.4062	0.3664	310	1.2434	{}	None
1.4062	0.3688	312	1.2410	{}	None
1.4062	0.3712	314	1.2410	{}	None
1.4141	0.3735	316	1.2389	{}	None
1.4141	0.3759	318	1.2352	{}	None
1.3656	0.3783	320	1.2323	{}	None
1.3656	0.3806	322	1.2319	{}	None
1.3656	0.3830	324	1.2278	{}	None
1.4078	0.3853	326	1.2253	{}	None
1.4078	0.3877	328	1.2262	{}	None
1.4094	0.3901	330	1.2200	{}	None
1.4094	0.3924	332	1.2192	{}	None
1.4094	0.3948	334	1.2175	{}	None
1.4141	0.3972	336	1.2163	{}	None
1.4141	0.3995	338	1.2171	{}	None
1.3734	0.4019	340	1.2146	{}	None
1.3734	0.4043	342	1.2130	{}	None
1.3734	0.4066	344	1.2122	{}	None
1.4094	0.4090	346	1.2101	{}	None
1.4094	0.4113	348	1.2064	{}	None
1.3531	0.4137	350	1.2064	{}	None
1.3531	0.4161	352	1.2039	{}	None
1.3531	0.4184	354	1.1990	{}	None
1.4203	0.4208	356	1.1982	{}	None
1.4203	0.4232	358	1.1961	{}	None
1.3344	0.4255	360	1.1982	{}	None
1.3344	0.4279	362	1.1949	{}	None
1.3344	0.4303	364	1.1945	{}	None
1.3391	0.4326	366	1.1904	{}	None
1.3391	0.4350	368	1.1863	{}	None
1.3313	0.4374	370	1.1871	{}	None
1.3313	0.4397	372	1.1879	{}	None
1.3313	0.4421	374	1.1850	{}	None
1.4	0.4444	376	1.1846	{}	None
1.4	0.4468	378	1.1834	{}	None
1.3891	0.4492	380	1.1813	{}	None
1.3891	0.4515	382	1.1764	{}	None
1.3891	0.4539	384	1.1760	{}	None
1.3219	0.4563	386	1.1735	{}	None
1.3219	0.4586	388	1.1698	{}	None
1.3625	0.4610	390	1.1686	{}	None
1.3625	0.4634	392	1.1645	{}	None
1.3625	0.4657	394	1.1641	{}	None
1.325	0.4681	396	1.1628	{}	None
1.325	0.4704	398	1.1632	{}	None
1.2891	0.4728	400	1.1632	{}	None
1.2891	0.4752	402	1.1612	{}	None
1.2891	0.4775	404	1.1608	{}	None
1.325	0.4799	406	1.1575	{}	None
1.325	0.4823	408	1.1538	{}	None
1.2797	0.4846	410	1.1571	{}	None
1.2797	0.4870	412	1.1509	{}	None
1.2797	0.4894	414	1.1513	{}	None
1.3062	0.4917	416	1.1476	{}	None
1.3062	0.4941	418	1.1447	{}	None
1.3234	0.4965	420	1.1435	{}	None
1.3234	0.4988	422	1.1427	{}	None
1.3234	0.5012	424	1.1410	{}	None
1.3391	0.5035	426	1.1402	{}	None
1.3391	0.5059	428	1.1369	{}	None
1.2469	0.5083	430	1.1353	{}	None
1.2469	0.5106	432	1.1340	{}	None
1.2469	0.5130	434	1.1320	{}	None
1.25	0.5154	436	1.1295	{}	None
1.25	0.5177	438	1.1291	{}	None
1.2641	0.5201	440	1.1275	{}	None
1.2641	0.5225	442	1.1262	{}	None
1.2641	0.5248	444	1.1242	{}	None
1.2688	0.5272	446	1.1201	{}	None
1.2688	0.5296	448	1.1180	{}	None
1.3	0.5319	450	1.1143	{}	None
1.3	0.5343	452	1.1131	{}	None
1.3	0.5366	454	1.1135	{}	None
1.2688	0.5390	456	1.1106	{}	None
1.2688	0.5414	458	1.1102	{}	None
1.2016	0.5437	460	1.1094	{}	None
1.2016	0.5461	462	1.1069	{}	None
1.2016	0.5485	464	1.1065	{}	None
1.2219	0.5508	466	1.1077	{}	None
1.2219	0.5532	468	1.1042	{}	None
1.2437	0.5556	470	1.1053	{}	None
1.2437	0.5579	472	1.1014	{}	None
1.2437	0.5603	474	1.1005	{}	None
1.2391	0.5626	476	1.0979	{}	None
1.2391	0.5650	478	1.0966	{}	None
1.1922	0.5674	480	1.0958	{}	None
1.1922	0.5697	482	1.0923	{}	None
1.1922	0.5721	484	1.0892	{}	None
1.2109	0.5745	486	1.0894	{}	None
1.2109	0.5768	488	1.0857	{}	None
1.2297	0.5792	490	1.0847	{}	None
1.2297	0.5816	492	1.0878	{}	None
1.2297	0.5839	494	1.0810	{}	None
1.1984	0.5863	496	1.0789	{}	None
1.1984	0.5887	498	1.0775	{}	None
1.2312	0.5910	500	1.0761	{}	None
1.2312	0.5934	502	1.0746	{}	None
1.2312	0.5957	504	1.0744	{}	None
1.1953	0.5981	506	1.0740	{}	None
1.1953	0.6005	508	1.0718	{}	None
1.2063	0.6028	510	1.0711	{}	None
1.2063	0.6052	512	1.0699	{}	None
1.2063	0.6076	514	1.0695	{}	None
1.2094	0.6099	516	1.0674	{}	None
1.2094	0.6123	518	1.0674	{}	None
1.2672	0.6147	520	1.0662	{}	None
1.2672	0.6170	522	1.0652	{}	None
1.2672	0.6194	524	1.0641	{}	None
1.2172	0.6217	526	1.0617	{}	None
1.2172	0.6241	528	1.0619	{}	None
1.2359	0.6265	530	1.0619	{}	None
1.2359	0.6288	532	1.0602	{}	None
1.2359	0.6312	534	1.0590	{}	None
1.225	0.6336	536	1.0569	{}	None
1.225	0.6359	538	1.0572	{}	None
1.1641	0.6383	540	1.0543	{}	None
1.1641	0.6407	542	1.0526	{}	None
1.1641	0.6430	544	1.0516	{}	None
1.1445	0.6454	546	1.0512	{}	None
1.1445	0.6478	548	1.0495	{}	None
1.2156	0.6501	550	1.0493	{}	None
1.2156	0.6525	552	1.0485	{}	None
1.2156	0.6548	554	1.0479	{}	None
1.1453	0.6572	556	1.0442	{}	None
1.1453	0.6596	558	1.0442	{}	None
1.1859	0.6619	560	1.0508	{}	None
1.1859	0.6643	562	1.0424	{}	None
1.1859	0.6667	564	1.0446	{}	None
1.1984	0.6690	566	1.0428	{}	None
1.1984	0.6714	568	1.0405	{}	None
1.1266	0.6738	570	1.0384	{}	None
1.1266	0.6761	572	1.0378	{}	None
1.1266	0.6785	574	1.0358	{}	None
1.2	0.6809	576	1.0362	{}	None
1.2	0.6832	578	1.0319	{}	None
1.1734	0.6856	580	1.0304	{}	None
1.1734	0.6879	582	1.0280	{}	None
1.1734	0.6903	584	1.0265	{}	None
1.1687	0.6927	586	1.0243	{}	None
1.1687	0.6950	588	1.0247	{}	None
1.1562	0.6974	590	1.0232	{}	None
1.1562	0.6998	592	1.0232	{}	None
1.1562	0.7021	594	1.0224	{}	None
1.1469	0.7045	596	1.0206	{}	None
1.1469	0.7069	598	1.0195	{}	None
1.175	0.7092	600	1.0189	{}	None
1.175	0.7116	602	1.0191	{}	None
1.175	0.7139	604	1.0162	{}	None
1.1289	0.7163	606	1.0123	{}	None
1.1289	0.7187	608	1.0127	{}	None
1.1703	0.7210	610	1.0132	{}	None
1.1703	0.7234	612	1.0113	{}	None
1.1703	0.7258	614	1.0093	{}	None
1.1641	0.7281	616	1.0086	{}	None
1.1641	0.7305	618	1.0086	{}	None
1.0891	0.7329	620	1.0056	{}	None
1.0891	0.7352	622	1.0058	{}	None
1.0891	0.7376	624	1.0031	{}	None
1.1328	0.7400	626	1.0039	{}	None
1.1328	0.7423	628	1.0014	{}	None
1.1344	0.7447	630	1.0025	{}	None
1.1344	0.7470	632	1.0014	{}	None
1.1344	0.7494	634	1.0008	{}	None
1.0938	0.7518	636	1.0	{}	None
1.0938	0.7541	638	0.9984	{}	None
1.1562	0.7565	640	0.9977	{}	None
1.1562	0.7589	642	0.9967	{}	None
1.1562	0.7612	644	0.9967	{}	None
1.1625	0.7636	646	0.9942	{}	None
1.1625	0.7660	648	0.9938	{}	None
1.1203	0.7683	650	0.9924	{}	None
1.1203	0.7707	652	0.9920	{}	None
1.1203	0.7730	654	0.9905	{}	None
1.0867	0.7754	656	0.9891	{}	None
1.0867	0.7778	658	0.9881	{}	None
1.1187	0.7801	660	0.9875	{}	None
1.1187	0.7825	662	0.9866	{}	None
1.1187	0.7849	664	0.9848	{}	None
1.1062	0.7872	666	0.9842	{}	None
1.1062	0.7896	668	0.9838	{}	None
1.0398	0.7920	670	0.9836	{}	None
1.0398	0.7943	672	0.9831	{}	None
1.0398	0.7967	674	0.9821	{}	None
1.1266	0.7991	676	0.9821	{}	None
1.1266	0.8014	678	0.9819	{}	None
1.1453	0.8038	680	0.9819	{}	None
1.1453	0.8061	682	0.9813	{}	None
1.1453	0.8085	684	0.9805	{}	None
1.1016	0.8109	686	0.9805	{}	None
1.1016	0.8132	688	0.9788	{}	None
1.1281	0.8156	690	0.9780	{}	None
1.1281	0.8180	692	0.9782	{}	None
1.1281	0.8203	694	0.9770	{}	None
1.0969	0.8227	696	0.9768	{}	None
1.0969	0.8251	698	0.9770	{}	None
1.082	0.8274	700	0.9741	{}	None
1.082	0.8298	702	0.9733	{}	None
1.082	0.8322	704	0.9729	{}	None
1.1172	0.8345	706	0.9727	{}	None
1.1172	0.8369	708	0.9708	{}	None
1.1156	0.8392	710	0.9708	{}	None
1.1156	0.8416	712	0.9706	{}	None
1.1156	0.8440	714	0.9698	{}	None
1.0969	0.8463	716	0.9696	{}	None
1.0969	0.8487	718	0.9694	{}	None
1.1422	0.8511	720	0.9685	{}	None
1.1422	0.8534	722	0.9679	{}	None
1.1422	0.8558	724	0.9673	{}	None
1.0922	0.8582	726	0.9675	{}	None
1.0922	0.8605	728	0.9659	{}	None
1.0875	0.8629	730	0.9661	{}	None
1.0875	0.8652	732	0.9648	{}	None
1.0875	0.8676	734	0.9648	{}	None
1.0953	0.8700	736	0.9646	{}	None
1.0953	0.8723	738	0.9642	{}	None
1.1141	0.8747	740	0.9640	{}	None
1.1141	0.8771	742	0.9640	{}	None
1.1141	0.8794	744	0.9638	{}	None
1.1016	0.8818	746	0.9630	{}	None
1.1016	0.8842	748	0.9632	{}	None
1.075	0.8865	750	0.9628	{}	None
1.075	0.8889	752	0.9620	{}	None
1.075	0.8913	754	0.9628	{}	None
1.0695	0.8936	756	0.9620	{}	None
1.0695	0.8960	758	0.9620	{}	None
1.0844	0.8983	760	0.9618	{}	None
1.0844	0.9007	762	0.9613	{}	None
1.0844	0.9031	764	0.9613	{}	None
1.0922	0.9054	766	0.9609	{}	None
1.0922	0.9078	768	0.9611	{}	None
1.0602	0.9102	770	0.9609	{}	None
1.0602	0.9125	772	0.9609	{}	None
1.0602	0.9149	774	0.9601	{}	None
1.0922	0.9173	776	0.9607	{}	None
1.0922	0.9196	778	0.9599	{}	None
1.0484	0.9220	780	0.9603	{}	None
1.0484	0.9243	782	0.9593	{}	None
1.0484	0.9267	784	0.9593	{}	None
1.0602	0.9291	786	0.9593	{}	None
1.0602	0.9314	788	0.9599	{}	None
1.1422	0.9338	790	0.9591	{}	None
1.1422	0.9362	792	0.9585	{}	None
1.1422	0.9385	794	0.9587	{}	None
1.0766	0.9409	796	0.9589	{}	None
1.0766	0.9433	798	0.9583	{}	None
1.0875	0.9456	800	0.9591	{}	None
1.0875	0.9480	802	0.9589	{}	None
1.0875	0.9504	804	0.9581	{}	None
1.0953	0.9527	806	0.9585	{}	None
1.0953	0.9551	808	0.9585	{}	None
1.1141	0.9574	810	0.9587	{}	None
1.1141	0.9598	812	0.9576	{}	None
1.1141	0.9622	814	0.9579	{}	None
1.068	0.9645	816	0.9579	{}	None
1.068	0.9669	818	0.9583	{}	None
1.1453	0.9693	820	0.9576	{}	None
1.1453	0.9716	822	0.9576	{}	None
1.1453	0.9740	824	0.9576	{}	None
1.1031	0.9764	826	0.9581	{}	None
1.1031	0.9787	828	0.9579	{}	None
1.1016	0.9811	830	0.9579	{}	None
1.1016	0.9835	832	0.9576	{}	None
1.1016	0.9858	834	0.9576	{}	None
1.0734	0.9882	836	0.9576	{}	None
1.0734	0.9905	838	0.9576	{}	None
1.0422	0.9929	840	0.9581	{}	None
1.0422	0.9953	842	0.9576	{}	None
1.0422	0.9976	844	0.9576	{}	None
1.075	1.0	846	0.9572	{}	None

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/crfTask-MediPhi

Base model

microsoft/Phi-3.5-mini-instruct

Finetuned

microsoft/MediPhi

Adapter

(1)

this model