Instructions to use ferrazzipietro/crfTask-Phi-3.5-mini-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ferrazzipietro/crfTask-Phi-3.5-mini-instruct with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("microsoft/Phi-3.5-mini-instruct")
model = PeftModel.from_pretrained(base_model, "ferrazzipietro/crfTask-Phi-3.5-mini-instruct")

Transformers

How to use ferrazzipietro/crfTask-Phi-3.5-mini-instruct with Transformers:

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

pipe = pipeline("text-generation", model="ferrazzipietro/crfTask-Phi-3.5-mini-instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("ferrazzipietro/crfTask-Phi-3.5-mini-instruct", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use ferrazzipietro/crfTask-Phi-3.5-mini-instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ferrazzipietro/crfTask-Phi-3.5-mini-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/crfTask-Phi-3.5-mini-instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ferrazzipietro/crfTask-Phi-3.5-mini-instruct

SGLang

How to use ferrazzipietro/crfTask-Phi-3.5-mini-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/crfTask-Phi-3.5-mini-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/crfTask-Phi-3.5-mini-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/crfTask-Phi-3.5-mini-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/crfTask-Phi-3.5-mini-instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ferrazzipietro/crfTask-Phi-3.5-mini-instruct with Docker Model Runner:
```
docker model run hf.co/ferrazzipietro/crfTask-Phi-3.5-mini-instruct
```

crfTask-Phi-3.5-mini-instruct

This model is a fine-tuned version of microsoft/Phi-3.5-mini-instruct on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 3.9162
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
3.5	0.0024	2	3.8865	{}	None
3.5	0.0047	4	3.8832	{}	None
3.4727	0.0071	6	3.8544	{}	None
3.4727	0.0095	8	3.8125	{}	None
3.4562	0.0118	10	3.7459	{}	None
3.4562	0.0142	12	3.625	{}	None
3.4562	0.0165	14	3.4827	{}	None
3.2469	0.0189	16	3.4317	{}	None
3.2469	0.0213	18	3.3553	{}	None
3.1313	0.0236	20	3.2854	{}	None
3.1313	0.0260	22	3.2220	{}	None
3.1313	0.0284	24	3.1628	{}	None
2.9625	0.0307	26	3.1003	{}	None
2.9625	0.0331	28	3.0329	{}	None
2.8781	0.0355	30	2.9630	{}	None
2.8781	0.0378	32	2.8882	{}	None
2.8781	0.0402	34	2.8100	{}	None
2.7906	0.0426	36	2.7262	{}	None
2.7906	0.0449	38	2.6439	{}	None
2.6344	0.0473	40	2.5740	{}	None
2.6344	0.0496	42	2.5090	{}	None
2.6344	0.0520	44	2.4367	{}	None
2.5063	0.0544	46	2.3775	{}	None
2.5063	0.0567	48	2.3257	{}	None
2.4062	0.0591	50	2.2747	{}	None
2.4062	0.0615	52	2.2262	{}	None
2.4062	0.0638	54	2.1842	{}	None
2.2969	0.0662	56	2.1423	{}	None
2.2969	0.0686	58	2.1127	{}	None
2.2156	0.0709	60	2.0777	{}	None
2.2156	0.0733	62	2.0415	{}	None
2.2156	0.0757	64	2.0111	{}	None
2.15	0.0780	66	1.9770	{}	None
2.15	0.0804	68	1.9511	{}	None
2.0906	0.0827	70	1.9260	{}	None
2.0906	0.0851	72	1.9001	{}	None
2.0906	0.0875	74	1.8734	{}	None
2.0391	0.0898	76	1.8503	{}	None
2.0391	0.0922	78	1.8322	{}	None
1.9797	0.0946	80	1.8051	{}	None
1.9797	0.0969	82	1.7837	{}	None
1.9797	0.0993	84	1.7677	{}	None
1.9594	0.1017	86	1.7442	{}	None
1.9594	0.1040	88	1.7278	{}	None
1.9203	0.1064	90	1.7109	{}	None
1.9203	0.1087	92	1.6949	{}	None
1.9203	0.1111	94	1.6859	{}	None
1.875	0.1135	96	1.6780	{}	None
1.875	0.1158	98	1.6649	{}	None
1.8969	0.1182	100	1.6480	{}	None
1.8969	0.1206	102	1.6390	{}	None
1.8969	0.1229	104	1.6266	{}	None
1.8422	0.1253	106	1.6123	{}	None
1.8422	0.1277	108	1.6040	{}	None
1.7625	0.1300	110	1.5942	{}	None
1.7625	0.1324	112	1.5876	{}	None
1.7625	0.1348	114	1.5752	{}	None
1.7422	0.1371	116	1.5703	{}	None
1.7422	0.1395	118	1.5584	{}	None
1.7469	0.1418	120	1.5477	{}	None
1.7469	0.1442	122	1.5411	{}	None
1.7469	0.1466	124	1.5370	{}	None
1.7094	0.1489	126	1.5296	{}	None
1.7094	0.1513	128	1.5230	{}	None
1.7109	0.1537	130	1.5169	{}	None
1.7109	0.1560	132	1.5078	{}	None
1.7109	0.1584	134	1.5025	{}	None
1.6672	0.1608	136	1.5012	{}	None
1.6672	0.1631	138	1.4992	{}	None
1.6656	0.1655	140	1.4840	{}	None
1.6656	0.1678	142	1.4868	{}	None
1.6656	0.1702	144	1.4757	{}	None
1.6766	0.1726	146	1.4794	{}	None
1.6766	0.1749	148	1.4712	{}	None
1.6547	0.1773	150	1.4618	{}	None
1.6547	0.1797	152	1.4560	{}	None
1.6547	0.1820	154	1.4552	{}	None
1.6641	0.1844	156	1.4507	{}	None
1.6641	0.1868	158	1.4449	{}	None
1.6062	0.1891	160	1.4408	{}	None
1.6062	0.1915	162	1.4350	{}	None
1.6062	0.1939	164	1.4330	{}	None
1.6438	0.1962	166	1.4276	{}	None
1.6438	0.1986	168	1.4235	{}	None
1.6	0.2009	170	1.4223	{}	None
1.6	0.2033	172	1.4157	{}	None
1.6	0.2057	174	1.4141	{}	None
1.6141	0.2080	176	1.4132	{}	None
1.6141	0.2104	178	1.4132	{}	None
1.5719	0.2128	180	1.4050	{}	None
1.5719	0.2151	182	1.3993	{}	None
1.5719	0.2175	184	1.3943	{}	None
1.5547	0.2199	186	1.3960	{}	None
1.5547	0.2222	188	1.3910	{}	None
1.6062	0.2246	190	1.3849	{}	None
1.6062	0.2270	192	1.3808	{}	None
1.6062	0.2293	194	1.3779	{}	None
1.6062	0.2317	196	1.3758	{}	None
1.6062	0.2340	198	1.3717	{}	None
1.5641	0.2364	200	1.3676	{}	None
1.5641	0.2388	202	1.3672	{}	None
1.5641	0.2411	204	1.3647	{}	None
1.5188	0.2435	206	1.3610	{}	None
1.5188	0.2459	208	1.3569	{}	None
1.5359	0.2482	210	1.3532	{}	None
1.5359	0.2506	212	1.3516	{}	None
1.5359	0.2530	214	1.3499	{}	None
1.5125	0.2553	216	1.3446	{}	None
1.5125	0.2577	218	1.3392	{}	None
1.5094	0.2600	220	1.3359	{}	None
1.5094	0.2624	222	1.3339	{}	None
1.5094	0.2648	224	1.3318	{}	None
1.5109	0.2671	226	1.3257	{}	None
1.5109	0.2695	228	1.3244	{}	None
1.5344	0.2719	230	1.3207	{}	None
1.5344	0.2742	232	1.3211	{}	None
1.5344	0.2766	234	1.3191	{}	None
1.5266	0.2790	236	1.3154	{}	None
1.5266	0.2813	238	1.3121	{}	None
1.4531	0.2837	240	1.3109	{}	None
1.4531	0.2861	242	1.3084	{}	None
1.4531	0.2884	244	1.3084	{}	None
1.4547	0.2908	246	1.3067	{}	None
1.4547	0.2931	248	1.3026	{}	None
1.4469	0.2955	250	1.3014	{}	None
1.4469	0.2979	252	1.3022	{}	None
1.4469	0.3002	254	1.2981	{}	None
1.4984	0.3026	256	1.2969	{}	None
1.4984	0.3050	258	1.2932	{}	None
1.4594	0.3073	260	1.2919	{}	None
1.4594	0.3097	262	1.2887	{}	None
1.4594	0.3121	264	1.2882	{}	None
1.4359	0.3144	266	1.2845	{}	None
1.4359	0.3168	268	1.2829	{}	None
1.4563	0.3191	270	1.2808	{}	None
1.4563	0.3215	272	1.2788	{}	None
1.4563	0.3239	274	1.2775	{}	None
1.4266	0.3262	276	1.2759	{}	None
1.4266	0.3286	278	1.2738	{}	None
1.4406	0.3310	280	1.2730	{}	None
1.4406	0.3333	282	1.2693	{}	None
1.4406	0.3357	284	1.2685	{}	None
1.4266	0.3381	286	1.2677	{}	None
1.4266	0.3404	288	1.2673	{}	None
1.4312	0.3428	290	1.2669	{}	None
1.4312	0.3452	292	1.2632	{}	None
1.4312	0.3475	294	1.2615	{}	None
1.4156	0.3499	296	1.2595	{}	None
1.4156	0.3522	298	1.2549	{}	None
1.4688	0.3546	300	1.2537	{}	None
1.4688	0.3570	302	1.2529	{}	None
1.4688	0.3593	304	1.2484	{}	None
1.4234	0.3617	306	1.2463	{}	None
1.4234	0.3641	308	1.2434	{}	None
1.4031	0.3664	310	1.2405	{}	None
1.4031	0.3688	312	1.2385	{}	None
1.4031	0.3712	314	1.2393	{}	None
1.4141	0.3735	316	1.2377	{}	None
1.4141	0.3759	318	1.2336	{}	None
1.3625	0.3783	320	1.2340	{}	None
1.3625	0.3806	322	1.2331	{}	None
1.3625	0.3830	324	1.2270	{}	None
1.4078	0.3853	326	1.2233	{}	None
1.4078	0.3877	328	1.2249	{}	None
1.4078	0.3901	330	1.2196	{}	None
1.4078	0.3924	332	1.2188	{}	None
1.4078	0.3948	334	1.2163	{}	None
1.4141	0.3972	336	1.2155	{}	None
1.4141	0.3995	338	1.2159	{}	None
1.3766	0.4019	340	1.2142	{}	None
1.3766	0.4043	342	1.2130	{}	None
1.3766	0.4066	344	1.2118	{}	None
1.4125	0.4090	346	1.2101	{}	None
1.4125	0.4113	348	1.2048	{}	None
1.3562	0.4137	350	1.2068	{}	None
1.3562	0.4161	352	1.2027	{}	None
1.3562	0.4184	354	1.1994	{}	None
1.4187	0.4208	356	1.1978	{}	None
1.4187	0.4232	358	1.1961	{}	None
1.3328	0.4255	360	1.1961	{}	None
1.3328	0.4279	362	1.1941	{}	None
1.3328	0.4303	364	1.1928	{}	None
1.3375	0.4326	366	1.1916	{}	None
1.3375	0.4350	368	1.1863	{}	None
1.3359	0.4374	370	1.1883	{}	None
1.3359	0.4397	372	1.1887	{}	None
1.3359	0.4421	374	1.1859	{}	None
1.4047	0.4444	376	1.1846	{}	None
1.4047	0.4468	378	1.1838	{}	None
1.3891	0.4492	380	1.1805	{}	None
1.3891	0.4515	382	1.1760	{}	None
1.3891	0.4539	384	1.1752	{}	None
1.3234	0.4563	386	1.1727	{}	None
1.3234	0.4586	388	1.1715	{}	None
1.3687	0.4610	390	1.1682	{}	None
1.3687	0.4634	392	1.1657	{}	None
1.3687	0.4657	394	1.1645	{}	None
1.325	0.4681	396	1.1641	{}	None
1.325	0.4704	398	1.1628	{}	None
1.2875	0.4728	400	1.1632	{}	None
1.2875	0.4752	402	1.1600	{}	None
1.2875	0.4775	404	1.1591	{}	None
1.3266	0.4799	406	1.1583	{}	None
1.3266	0.4823	408	1.1558	{}	None
1.2812	0.4846	410	1.1530	{}	None
1.2812	0.4870	412	1.1517	{}	None
1.2812	0.4894	414	1.1538	{}	None
1.3062	0.4917	416	1.1468	{}	None
1.3062	0.4941	418	1.1447	{}	None
1.3234	0.4965	420	1.1456	{}	None
1.3234	0.4988	422	1.1419	{}	None
1.3234	0.5012	424	1.1423	{}	None
1.3453	0.5035	426	1.1427	{}	None
1.3453	0.5059	428	1.1390	{}	None
1.25	0.5083	430	1.1382	{}	None
1.25	0.5106	432	1.1345	{}	None
1.25	0.5130	434	1.1336	{}	None
1.2531	0.5154	436	1.1316	{}	None
1.2531	0.5177	438	1.1312	{}	None
1.2688	0.5201	440	1.1271	{}	None
1.2688	0.5225	442	1.1279	{}	None
1.2688	0.5248	444	1.1254	{}	None
1.2688	0.5272	446	1.1234	{}	None
1.2688	0.5296	448	1.1213	{}	None
1.3031	0.5319	450	1.1168	{}	None
1.3031	0.5343	452	1.1135	{}	None
1.3031	0.5366	454	1.1155	{}	None
1.2766	0.5390	456	1.1114	{}	None
1.2766	0.5414	458	1.1106	{}	None
1.2031	0.5437	460	1.1086	{}	None
1.2031	0.5461	462	1.1073	{}	None
1.2031	0.5485	464	1.1077	{}	None
1.225	0.5508	466	1.1077	{}	None
1.225	0.5532	468	1.1073	{}	None
1.2453	0.5556	470	1.1077	{}	None
1.2453	0.5579	472	1.1038	{}	None
1.2453	0.5603	474	1.1016	{}	None
1.2437	0.5626	476	1.1012	{}	None
1.2437	0.5650	478	1.0985	{}	None
1.1953	0.5674	480	1.0972	{}	None
1.1953	0.5697	482	1.0940	{}	None
1.1953	0.5721	484	1.0919	{}	None
1.2172	0.5745	486	1.0915	{}	None
1.2172	0.5768	488	1.0859	{}	None
1.2359	0.5792	490	1.0880	{}	None
1.2359	0.5816	492	1.0892	{}	None
1.2359	0.5839	494	1.0841	{}	None
1.1984	0.5863	496	1.0806	{}	None
1.1984	0.5887	498	1.0798	{}	None
1.2312	0.5910	500	1.0783	{}	None
1.2312	0.5934	502	1.0771	{}	None
1.2312	0.5957	504	1.0775	{}	None
1.1984	0.5981	506	1.0765	{}	None
1.1984	0.6005	508	1.0734	{}	None
1.2078	0.6028	510	1.0711	{}	None
1.2078	0.6052	512	1.0707	{}	None
1.2078	0.6076	514	1.0699	{}	None
1.2156	0.6099	516	1.0672	{}	None
1.2156	0.6123	518	1.0678	{}	None
1.2719	0.6147	520	1.0674	{}	None
1.2719	0.6170	522	1.0656	{}	None
1.2719	0.6194	524	1.0646	{}	None
1.2234	0.6217	526	1.0631	{}	None
1.2234	0.6241	528	1.0633	{}	None
1.2375	0.6265	530	1.0627	{}	None
1.2375	0.6288	532	1.0615	{}	None
1.2375	0.6312	534	1.0600	{}	None
1.2297	0.6336	536	1.0582	{}	None
1.2297	0.6359	538	1.0576	{}	None
1.1656	0.6383	540	1.0553	{}	None
1.1656	0.6407	542	1.0557	{}	None
1.1656	0.6430	544	1.0537	{}	None
1.1469	0.6454	546	1.0522	{}	None
1.1469	0.6478	548	1.0530	{}	None
1.2188	0.6501	550	1.0508	{}	None
1.2188	0.6525	552	1.0483	{}	None
1.2188	0.6548	554	1.0493	{}	None
1.1469	0.6572	556	1.0458	{}	None
1.1469	0.6596	558	1.0452	{}	None
1.1938	0.6619	560	1.0508	{}	None
1.1938	0.6643	562	1.0444	{}	None
1.1938	0.6667	564	1.0475	{}	None
1.2031	0.6690	566	1.0432	{}	None
1.2031	0.6714	568	1.0428	{}	None
1.1313	0.6738	570	1.0403	{}	None
1.1313	0.6761	572	1.0403	{}	None
1.1313	0.6785	574	1.0391	{}	None
1.2063	0.6809	576	1.0397	{}	None
1.2063	0.6832	578	1.0352	{}	None
1.1812	0.6856	580	1.0343	{}	None
1.1812	0.6879	582	1.0312	{}	None
1.1812	0.6903	584	1.0300	{}	None
1.1719	0.6927	586	1.0284	{}	None
1.1719	0.6950	588	1.0265	{}	None
1.1594	0.6974	590	1.0263	{}	None
1.1594	0.6998	592	1.0251	{}	None
1.1594	0.7021	594	1.0253	{}	None
1.1469	0.7045	596	1.0228	{}	None
1.1469	0.7069	598	1.0220	{}	None
1.1797	0.7092	600	1.0208	{}	None
1.1797	0.7116	602	1.0197	{}	None
1.1797	0.7139	604	1.0183	{}	None
1.132	0.7163	606	1.0152	{}	None
1.132	0.7187	608	1.0154	{}	None
1.1734	0.7210	610	1.0130	{}	None
1.1734	0.7234	612	1.0113	{}	None
1.1734	0.7258	614	1.0117	{}	None
1.1703	0.7281	616	1.0099	{}	None
1.1703	0.7305	618	1.0130	{}	None
1.0953	0.7329	620	1.0084	{}	None
1.0953	0.7352	622	1.0078	{}	None
1.0953	0.7376	624	1.0047	{}	None
1.1406	0.7400	626	1.0082	{}	None
1.1406	0.7423	628	1.0041	{}	None
1.1375	0.7447	630	1.0039	{}	None
1.1375	0.7470	632	1.0037	{}	None
1.1375	0.7494	634	1.0023	{}	None
1.0984	0.7518	636	1.0008	{}	None
1.0984	0.7541	638	1.0002	{}	None
1.1594	0.7565	640	0.9994	{}	None
1.1594	0.7589	642	0.9988	{}	None
1.1594	0.7612	644	0.9984	{}	None
1.1672	0.7636	646	0.9975	{}	None
1.1672	0.7660	648	0.9957	{}	None
1.1234	0.7683	650	0.9947	{}	None
1.1234	0.7707	652	0.9940	{}	None
1.1234	0.7730	654	0.9932	{}	None
1.0922	0.7754	656	0.9924	{}	None
1.0922	0.7778	658	0.9905	{}	None
1.125	0.7801	660	0.9887	{}	None
1.125	0.7825	662	0.9891	{}	None
1.125	0.7849	664	0.9875	{}	None
1.1141	0.7872	666	0.9866	{}	None
1.1141	0.7896	668	0.9862	{}	None
1.0453	0.7920	670	0.9856	{}	None
1.0453	0.7943	672	0.9858	{}	None
1.0453	0.7967	674	0.9844	{}	None
1.1328	0.7991	676	0.9844	{}	None
1.1328	0.8014	678	0.9844	{}	None
1.1516	0.8038	680	0.9833	{}	None
1.1516	0.8061	682	0.9831	{}	None
1.1516	0.8085	684	0.9827	{}	None
1.1062	0.8109	686	0.9827	{}	None
1.1062	0.8132	688	0.9807	{}	None
1.1328	0.8156	690	0.9807	{}	None
1.1328	0.8180	692	0.9805	{}	None
1.1328	0.8203	694	0.9796	{}	None
1.1016	0.8227	696	0.9792	{}	None
1.1016	0.8251	698	0.9784	{}	None
1.0883	0.8274	700	0.9772	{}	None
1.0883	0.8298	702	0.9768	{}	None
1.0883	0.8322	704	0.9764	{}	None
1.1203	0.8345	706	0.9751	{}	None
1.1203	0.8369	708	0.9747	{}	None
1.1187	0.8392	710	0.9747	{}	None
1.1187	0.8416	712	0.9745	{}	None
1.1187	0.8440	714	0.9739	{}	None
1.1062	0.8463	716	0.9727	{}	None
1.1062	0.8487	718	0.9720	{}	None
1.1422	0.8511	720	0.9716	{}	None
1.1422	0.8534	722	0.9708	{}	None
1.1422	0.8558	724	0.9700	{}	None
1.0984	0.8582	726	0.9688	{}	None
1.0984	0.8605	728	0.9688	{}	None
1.0891	0.8629	730	0.9677	{}	None
1.0891	0.8652	732	0.9679	{}	None
1.0891	0.8676	734	0.9667	{}	None
1.1	0.8700	736	0.9671	{}	None
1.1	0.8723	738	0.9663	{}	None
1.1156	0.8747	740	0.9663	{}	None
1.1156	0.8771	742	0.9665	{}	None
1.1156	0.8794	744	0.9659	{}	None
1.1078	0.8818	746	0.9659	{}	None
1.1078	0.8842	748	0.9653	{}	None
1.0813	0.8865	750	0.9653	{}	None
1.0813	0.8889	752	0.9655	{}	None
1.0813	0.8913	754	0.9653	{}	None
1.0727	0.8936	756	0.9653	{}	None
1.0727	0.8960	758	0.9653	{}	None
1.0891	0.8983	760	0.9644	{}	None
1.0891	0.9007	762	0.9642	{}	None
1.0891	0.9031	764	0.9648	{}	None
1.0953	0.9054	766	0.9640	{}	None
1.0953	0.9078	768	0.9630	{}	None
1.0664	0.9102	770	0.9634	{}	None
1.0664	0.9125	772	0.9632	{}	None
1.0664	0.9149	774	0.9632	{}	None
1.0969	0.9173	776	0.9628	{}	None
1.0969	0.9196	778	0.9632	{}	None
1.0531	0.9220	780	0.9622	{}	None
1.0531	0.9243	782	0.9611	{}	None
1.0531	0.9267	784	0.9622	{}	None
1.0664	0.9291	786	0.9609	{}	None
1.0664	0.9314	788	0.9618	{}	None
1.1484	0.9338	790	0.9613	{}	None
1.1484	0.9362	792	0.9607	{}	None
1.1484	0.9385	794	0.9609	{}	None
1.0828	0.9409	796	0.9609	{}	None
1.0828	0.9433	798	0.9605	{}	None
1.0953	0.9456	800	0.9609	{}	None
1.0953	0.9480	802	0.9601	{}	None
1.0953	0.9504	804	0.9601	{}	None
1.1016	0.9527	806	0.9601	{}	None
1.1016	0.9551	808	0.9603	{}	None
1.1219	0.9574	810	0.9607	{}	None
1.1219	0.9598	812	0.9603	{}	None
1.1219	0.9622	814	0.9607	{}	None
1.0758	0.9645	816	0.9595	{}	None
1.0758	0.9669	818	0.9601	{}	None
1.1484	0.9693	820	0.9599	{}	None
1.1484	0.9716	822	0.9599	{}	None
1.1484	0.9740	824	0.9599	{}	None
1.1016	0.9764	826	0.9599	{}	None
1.1016	0.9787	828	0.9599	{}	None
1.1062	0.9811	830	0.9599	{}	None
1.1062	0.9835	832	0.9599	{}	None
1.1062	0.9858	834	0.9595	{}	None
1.0773	0.9882	836	0.9599	{}	None
1.0773	0.9905	838	0.9599	{}	None
1.0461	0.9929	840	0.9601	{}	None
1.0461	0.9953	842	0.9599	{}	None
1.0461	0.9976	844	0.9599	{}	None
1.0844	1.0	846	0.9599	{}	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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