modelId
stringlengths 5
139
| author
stringlengths 2
42
| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-07-27 12:28:27
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 533
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
2025-07-27 12:28:17
| card
stringlengths 11
1.01M
|
|---|---|---|---|---|---|---|---|---|---|
french-datasets/shuyuej_MedGemma2B-French
|
french-datasets
| 2025-07-23T18:10:10Z | 0 | 0 | null |
[
"fra",
"region:us"
] | null | 2025-07-23T18:10:09Z |
Temporary Redirect. Redirecting to /api/resolve-cache/models/french-datasets/shuyuej_MedGemma2B-French/220d6422ae11d98b3e419e5a86899e88a4cad210/README.md?%2Ffrench-datasets%2Fshuyuej_MedGemma2B-French%2Fresolve%2Fmain%2FREADME.md=&etag=%22967b08f4e9bb7f06570233fef660db1b6a666370%22
|
skymizer/Llama-3.2-3B-GGUF
|
skymizer
| 2025-07-23T17:58:10Z | 0 | 0 | null |
[
"gguf",
"license:llama3.2",
"endpoints_compatible",
"region:us"
] | null | 2025-07-23T17:24:08Z |
Temporary Redirect. Redirecting to /api/resolve-cache/models/skymizer/Llama-3.2-3B-GGUF/ece7611fd9545e345828a1aa0aa1523c6601bd32/README.md?%2Fskymizer%2FLlama-3.2-3B-GGUF%2Fresolve%2Fmain%2FREADME.md=&etag=%220b19c231458a337d17fc9859fe3a640ff9a9000c%22
|
french-datasets/muzammil-eds_Meta-Llama-3.1-8B-Instruct-English-to-French
|
french-datasets
| 2025-07-23T17:52:13Z | 0 | 0 | null |
[
"fra",
"region:us"
] | null | 2025-07-23T17:52:12Z |
Temporary Redirect. Redirecting to /api/resolve-cache/models/french-datasets/muzammil-eds_Meta-Llama-3.1-8B-Instruct-English-to-French/c7278cfad3345ffa8934f229df82a2e07f194e04/README.md?%2Ffrench-datasets%2Fmuzammil-eds_Meta-Llama-3.1-8B-Instruct-English-to-French%2Fresolve%2Fmain%2FREADME.md=&etag=%22be7e14d1b1a8f0714e03516140a0580bc76bd1fa%22
|
french-datasets/Shagufta_wav2vec2-large-xlsr-53-french-KM-v8
|
french-datasets
| 2025-07-23T17:48:43Z | 0 | 0 | null |
[
"fra",
"region:us"
] | null | 2025-07-23T17:48:42Z |
Temporary Redirect. Redirecting to /api/resolve-cache/models/french-datasets/Shagufta_wav2vec2-large-xlsr-53-french-KM-v8/409961dc6eaf8bbaf4ee6e00c242b5618e3576f0/README.md?%2Ffrench-datasets%2FShagufta_wav2vec2-large-xlsr-53-french-KM-v8%2Fresolve%2Fmain%2FREADME.md=&etag=%229e2c6a3bcee377e41e20f6b194c27cdc60611e47%22
|
french-datasets/davidschulte_ESM_CATIE-AQ__wanli_fr_prompt_textual_entailment_default
|
french-datasets
| 2025-07-23T17:47:46Z | 0 | 0 | null |
[
"fra",
"region:us"
] | null | 2025-07-23T17:47:44Z |
Temporary Redirect. Redirecting to /api/resolve-cache/models/french-datasets/davidschulte_ESM_CATIE-AQ__wanli_fr_prompt_textual_entailment_default/662981b1fc7d8e9ed9529f3d2db2a739aa0ed89a/README.md?%2Ffrench-datasets%2Fdavidschulte_ESM_CATIE-AQ__wanli_fr_prompt_textual_entailment_default%2Fresolve%2Fmain%2FREADME.md=&etag=%222e324a49aa2484605bc8de0bab58dc02f78068c0%22
|
french-datasets/PepitaxX_oncheGPT_FrenchLlama_8B
|
french-datasets
| 2025-07-23T17:42:05Z | 0 | 0 | null |
[
"fra",
"region:us"
] | null | 2025-07-23T17:42:04Z |
Temporary Redirect. Redirecting to /api/resolve-cache/models/french-datasets/PepitaxX_oncheGPT_FrenchLlama_8B/fe736748ba3d2262ae47ac07220906ca9d3a17f3/README.md?%2Ffrench-datasets%2FPepitaxX_oncheGPT_FrenchLlama_8B%2Fresolve%2Fmain%2FREADME.md=&etag=%2265b6936a6e699f173ce424cd06668c910f415fd4%22
|
french-datasets/PepitaxX_oncheGPT_Croissant_1B
|
french-datasets
| 2025-07-23T17:41:56Z | 0 | 0 | null |
[
"fra",
"region:us"
] | null | 2025-07-23T17:41:55Z |
Temporary Redirect. Redirecting to /api/resolve-cache/models/french-datasets/PepitaxX_oncheGPT_Croissant_1B/38cc2f92b1a4c5bbc84c4f5c58d2c9320d911c3a/README.md?%2Ffrench-datasets%2FPepitaxX_oncheGPT_Croissant_1B%2Fresolve%2Fmain%2FREADME.md=&etag=%2200ec319319dbf6be01d211ffe7cd37f2ec9d9ce5%22
|
infinity1096/UFM-Base-980
|
infinity1096
| 2025-07-23T17:34:27Z | 0 | 0 | null |
[
"safetensors",
"model_hub_mixin",
"pytorch_model_hub_mixin",
"other",
"en",
"arxiv:2506.09278",
"license:cc-by-nc-4.0",
"region:us"
] |
other
| 2025-07-23T17:30:29Z |
Temporary Redirect. Redirecting to /api/resolve-cache/models/infinity1096/UFM-Base-980/9baef2960c9e880288d1166c7db50e43af648a99/README.md?%2Finfinity1096%2FUFM-Base-980%2Fresolve%2Fmain%2FREADME.md=&etag=%22910fa1a2c070db878743f5886cc26a3217b31055%22
|
rugelii09/NEXUS
|
rugelii09
| 2025-07-23T17:16:08Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2025-07-23T17:16:08Z |
Temporary Redirect. Redirecting to /api/resolve-cache/models/rugelii09/NEXUS/d428ec6890f402b086a6469f92de840607f0ccb2/README.md?%2Frugelii09%2FNEXUS%2Fresolve%2Fmain%2FREADME.md=&etag=%227be5fc7f47d5db027d120b8024982df93db95b74%22
|
phospho-app/LegrandFrederic-ACT-pensInHolder-corner-two-hires-qkwus
|
phospho-app
| 2025-07-23T16:33:26Z | 0 | 0 | null |
[
"phosphobot",
"act",
"region:us"
] | null | 2025-07-23T16:26:17Z |
Temporary Redirect. Redirecting to /api/resolve-cache/models/phospho-app/LegrandFrederic-ACT-pensInHolder-corner-two-hires-qkwus/923ac84041d0a676ad07e12aeb7db24597f4190d/README.md?%2Fphospho-app%2FLegrandFrederic-ACT-pensInHolder-corner-two-hires-qkwus%2Fresolve%2Fmain%2FREADME.md=&etag=%22753cf4b954cecb70829a92a163d9c119ba358814%22
|
JW0721/Qwen_Qwen3-4B_TESTDPO_1577
|
JW0721
| 2025-07-23T16:18:52Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"qwen3",
"text-generation-inference",
"unsloth",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-07-23T16:17:23Z |
Temporary Redirect. Redirecting to /api/resolve-cache/models/JW0721/Qwen_Qwen3-4B_TESTDPO_1577/4d08e81dd65f3e716457cba615c3f81f795f46d8/README.md?%2FJW0721%2FQwen_Qwen3-4B_TESTDPO_1577%2Fresolve%2Fmain%2FREADME.md=&etag=%22e70ff128f653fefcfe6a4ca5df7cccede2806104%22
|
Maikobi/scibert_error_detection
|
Maikobi
| 2025-07-23T16:17:25Z | 6 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-07-19T14:58:08Z |
Temporary Redirect. Redirecting to /api/resolve-cache/models/Maikobi/scibert_error_detection/06fc7cc52d5b397f776ef4d68807094c85010864/README.md?%2FMaikobi%2Fscibert_error_detection%2Fresolve%2Fmain%2FREADME.md=&etag=%22bc5f30d6632ac0efdc7be2e9095e9e9579af2e33%22
|
HR-T/xlm-roberta-base-finetuned-panx-de
|
HR-T
| 2025-07-23T15:42:36Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:finetune:FacebookAI/xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2025-07-22T05:39:03Z |
Temporary Redirect. Redirecting to /api/resolve-cache/models/HR-T/xlm-roberta-base-finetuned-panx-de/be30cdc7709e3f4573913b4decb2580badd86e71/README.md?%2FHR-T%2Fxlm-roberta-base-finetuned-panx-de%2Fresolve%2Fmain%2FREADME.md=&etag=%226fcda7fab672bf74f58f4351a03977eb0c8c0791%22
|
abubasith86/gemma-3-E3B-finetune
|
abubasith86
| 2025-07-23T15:39:02Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma3n",
"image-text-to-text",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2025-07-23T15:34:27Z |
Temporary Redirect. Redirecting to /api/resolve-cache/models/abubasith86/gemma-3-E3B-finetune/9ebeea6252e107f8a5fed9df694759d1f4699e36/README.md?%2Fabubasith86%2Fgemma-3-E3B-finetune%2Fresolve%2Fmain%2FREADME.md=&etag=%22c1021bd1153aa0d8849b561ec6e67b57c1df73a3%22
|
tensorblock/sparkle-reasoning_SparkleRL-7B-Stage2-mix-GGUF
|
tensorblock
| 2025-07-23T15:12:14Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"TensorBlock",
"GGUF",
"base_model:sparkle-reasoning/SparkleRL-7B-Stage2-mix",
"base_model:quantized:sparkle-reasoning/SparkleRL-7B-Stage2-mix",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-07-23T13:49:46Z |
Temporary Redirect. Redirecting to /api/resolve-cache/models/tensorblock/sparkle-reasoning_SparkleRL-7B-Stage2-mix-GGUF/bc2ac67a3eac73414ff472fd6a89240c87bab57f/README.md?%2Ftensorblock%2Fsparkle-reasoning_SparkleRL-7B-Stage2-mix-GGUF%2Fresolve%2Fmain%2FREADME.md=&etag=%226fb14b1356925ce5c3e8d77af6c6960fc2ad9506%22
|
ORIGINAL-18-Clip-Sister-new-viral-video/watch.FULL.VIDEOS
|
ORIGINAL-18-Clip-Sister-new-viral-video
| 2025-07-23T15:06:23Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-07-23T15:05:36Z |
Temporary Redirect. Redirecting to /api/resolve-cache/models/ORIGINAL-18-Clip-Sister-new-viral-video/watch.FULL.VIDEOS/c0bc4d3756c7952f1e1d9e95d03bf13ae753d71a/README.md?%2FORIGINAL-18-Clip-Sister-new-viral-video%2Fwatch.FULL.VIDEOS%2Fresolve%2Fmain%2FREADME.md=&etag=%22d3774d5fe6c3b1bb23ea9f59567f9018664b3e9f%22
|
ChangeXy/ppl-bad_medical_advice_rephrased_4iter_batch64_iter3_1ep
|
ChangeXy
| 2025-07-23T14:35:55Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-07-23T14:12:43Z |
Temporary Redirect. Redirecting to /api/resolve-cache/models/ChangeXy/ppl-bad_medical_advice_rephrased_4iter_batch64_iter3_1ep/2cc1e665ab851a3be03c5f494cbbb3ef7463ae84/README.md?%2FChangeXy%2Fppl-bad_medical_advice_rephrased_4iter_batch64_iter3_1ep%2Fresolve%2Fmain%2FREADME.md=&etag=%22b7441640181b06c90effb718230b043e35476812%22
|
mlx-community/mxbai-rerank-large-v2
|
mlx-community
| 2025-07-23T14:29:37Z | 0 | 1 |
mlx
|
[
"mlx",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"af",
"am",
"ar",
"as",
"az",
"be",
"bg",
"bn",
"br",
"bs",
"ca",
"cs",
"cy",
"da",
"de",
"el",
"en",
"eo",
"es",
"et",
"eu",
"fa",
"ff",
"fi",
"fr",
"fy",
"ga",
"gd",
"gl",
"gn",
"gu",
"ha",
"he",
"hi",
"hr",
"ht",
"hu",
"hy",
"id",
"ig",
"is",
"it",
"ja",
"jv",
"ka",
"kk",
"km",
"kn",
"ko",
"ku",
"ky",
"la",
"lg",
"li",
"ln",
"lo",
"lt",
"lv",
"mg",
"mk",
"ml",
"mn",
"mr",
"ms",
"my",
"ne",
"nl",
"no",
"ns",
"om",
"or",
"pa",
"pl",
"ps",
"pt",
"qu",
"rm",
"ro",
"ru",
"sa",
"sc",
"sd",
"si",
"sk",
"sl",
"so",
"sq",
"sr",
"ss",
"su",
"sv",
"sw",
"ta",
"te",
"th",
"tl",
"tn",
"tr",
"ug",
"uk",
"ur",
"uz",
"vi",
"wo",
"xh",
"yi",
"yo",
"zh",
"zu",
"base_model:mixedbread-ai/mxbai-rerank-large-v2",
"base_model:finetune:mixedbread-ai/mxbai-rerank-large-v2",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2025-07-23T14:28:51Z |
Temporary Redirect. Redirecting to /api/resolve-cache/models/mlx-community/mxbai-rerank-large-v2/76001241414a9e9fbb91e9a0ece8d247164a7ca3/README.md?%2Fmlx-community%2Fmxbai-rerank-large-v2%2Fresolve%2Fmain%2FREADME.md=&etag=%229fbab7cdd395f882d2cee30204515cd0430d438f%22
|
Vacaspati/VAC-BERT
|
Vacaspati
| 2025-07-23T14:22:42Z | 4 | 0 | null |
[
"pytorch",
"electra",
"bn",
"base_model:google/electra-small-discriminator",
"base_model:finetune:google/electra-small-discriminator",
"license:apache-2.0",
"region:us"
] | null | 2025-05-29T18:11:44Z |
Temporary Redirect. Redirecting to /api/resolve-cache/models/Vacaspati/VAC-BERT/2488a37e129a6ffefd968222638891f52075276b/README.md?%2FVacaspati%2FVAC-BERT%2Fresolve%2Fmain%2FREADME.md=&etag=%22bb41ccbece6bc22018919ed6bc0bda63a24101a7%22
|
SmallDoge/Doge-1.6B-checkpoint
|
SmallDoge
| 2025-07-23T14:15:48Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"doge",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-07-19T04:22:06Z |
Temporary Redirect. Redirecting to /api/resolve-cache/models/SmallDoge/Doge-1.6B-checkpoint/fb75a9a5a027ab3b886b421ff7e4df6fd4146ab4/README.md?%2FSmallDoge%2FDoge-1.6B-checkpoint%2Fresolve%2Fmain%2FREADME.md=&etag=%22bc5f30d6632ac0efdc7be2e9095e9e9579af2e33%22
|
ChicagoHS/hominis
|
ChicagoHS
| 2025-07-23T13:44:54Z | 0 | 0 | null |
[
"onnx",
"license:mit",
"region:us"
] | null | 2025-07-23T12:51:43Z |
Temporary Redirect. Redirecting to /api/resolve-cache/models/ChicagoHS/hominis/018b538477938c94f83bf4ced26e71070da74ae3/README.md?%2FChicagoHS%2Fhominis%2Fresolve%2Fmain%2FREADME.md=&etag=%227be5fc7f47d5db027d120b8024982df93db95b74%22
|
sanjithrj/FeelWise
|
sanjithrj
| 2025-07-23T13:38:27Z | 14 | 1 |
transformers
|
[
"transformers",
"safetensors",
"FeelWiseEmotion",
"feature-extraction",
"custom_code",
"en",
"dataset:dair-ai/emotion",
"region:us"
] |
feature-extraction
| 2024-10-08T13:22:24Z |
Temporary Redirect. Redirecting to /api/resolve-cache/models/sanjithrj/FeelWise/5de3047a64a0025233d753756b5da6593c6ade27/README.md?%2Fsanjithrj%2FFeelWise%2Fresolve%2Fmain%2FREADME.md=&etag=%22c35ec619b1fc015022cb41c0941bbc93f609e56c%22
|
brunoyun/Llama-3.1-Amelia-MTMERGED-8B-v1-GGUF
|
brunoyun
| 2025-07-23T13:34:12Z | 5 | 0 | null |
[
"gguf",
"argumentation",
"argument-mining",
"text-generation",
"conversational",
"en",
"arxiv:2406.11617",
"base_model:brunoyun/Llama-3.1-Amelia-ACC-8B-v1",
"base_model:merge:brunoyun/Llama-3.1-Amelia-ACC-8B-v1",
"base_model:brunoyun/Llama-3.1-Amelia-AQ-8B-v1",
"base_model:merge:brunoyun/Llama-3.1-Amelia-AQ-8B-v1",
"base_model:brunoyun/Llama-3.1-Amelia-AR-8B-v1",
"base_model:merge:brunoyun/Llama-3.1-Amelia-AR-8B-v1",
"base_model:brunoyun/Llama-3.1-Amelia-CD-8B-v1",
"base_model:merge:brunoyun/Llama-3.1-Amelia-CD-8B-v1",
"base_model:brunoyun/Llama-3.1-Amelia-ED-8B-v1",
"base_model:merge:brunoyun/Llama-3.1-Amelia-ED-8B-v1",
"base_model:brunoyun/Llama-3.1-Amelia-ET-8B-v1",
"base_model:merge:brunoyun/Llama-3.1-Amelia-ET-8B-v1",
"base_model:brunoyun/Llama-3.1-Amelia-FD-8B-v1",
"base_model:merge:brunoyun/Llama-3.1-Amelia-FD-8B-v1",
"base_model:brunoyun/Llama-3.1-Amelia-SD-8B-v1",
"base_model:merge:brunoyun/Llama-3.1-Amelia-SD-8B-v1",
"license:llama3.1",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-07-16T07:57:53Z |
Temporary Redirect. Redirecting to /api/resolve-cache/models/brunoyun/Llama-3.1-Amelia-MTMERGED-8B-v1-GGUF/90417b8c32ed89cfaf29f23353f25987570c984f/README.md?%2Fbrunoyun%2FLlama-3.1-Amelia-MTMERGED-8B-v1-GGUF%2Fresolve%2Fmain%2FREADME.md=&etag=%22a9129c2c5f9c0292e33f24afe437e8796be2873c%22
|
brunoyun/Llama-3.1-Amelia-CD-8B-v1
|
brunoyun
| 2025-07-23T13:33:27Z | 4 | 0 | null |
[
"safetensors",
"llama",
"argumentation",
"argument-mining",
"text-generation",
"conversational",
"en",
"arxiv:2203.12257",
"arxiv:2010.09459",
"base_model:meta-llama/Llama-3.1-8B-Instruct",
"base_model:finetune:meta-llama/Llama-3.1-8B-Instruct",
"license:llama3.1",
"region:us"
] |
text-generation
| 2025-06-17T11:50:27Z |
Temporary Redirect. Redirecting to /api/resolve-cache/models/brunoyun/Llama-3.1-Amelia-CD-8B-v1/70ed8afbb596757c2c1e3d163abb9a157c98557d/README.md?%2Fbrunoyun%2FLlama-3.1-Amelia-CD-8B-v1%2Fresolve%2Fmain%2FREADME.md=&etag=%22e454aa4ab5fa1d91148a22d9a5b440dfca233132%22
|
brunoyun/Llama-3.1-Amelia-AR-8B-v1-GGUF
|
brunoyun
| 2025-07-23T13:32:05Z | 27 | 0 | null |
[
"gguf",
"argumentation",
"argument-mining",
"text-generation",
"conversational",
"en",
"base_model:meta-llama/Llama-3.1-8B-Instruct",
"base_model:quantized:meta-llama/Llama-3.1-8B-Instruct",
"license:llama3.1",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-17T11:58:34Z |
Temporary Redirect. Redirecting to /api/resolve-cache/models/brunoyun/Llama-3.1-Amelia-AR-8B-v1-GGUF/96d735d1e5d943d7a88854bd347fe823e6501062/README.md?%2Fbrunoyun%2FLlama-3.1-Amelia-AR-8B-v1-GGUF%2Fresolve%2Fmain%2FREADME.md=&etag=%2204f88805c53c4b7629368bfe9393ae20e4a1c8b2%22
|
Worko71/keywords_all-MiniLM-L6-v2_model
|
Worko71
| 2025-07-23T12:48:41Z | 0 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"bert",
"sentence-similarity",
"feature-extraction",
"generated_from_trainer",
"dataset_size:7344",
"loss:TripletLoss",
"arxiv:1908.10084",
"arxiv:1703.07737",
"base_model:sentence-transformers/all-MiniLM-L6-v2",
"base_model:finetune:sentence-transformers/all-MiniLM-L6-v2",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2025-07-23T12:43:18Z |
Temporary Redirect. Redirecting to /api/resolve-cache/models/Worko71/keywords_all-MiniLM-L6-v2_model/a6bdcabe1eea0e2fa693c1734c6d0989c2eb7ac3/README.md?%2FWorko71%2Fkeywords_all-MiniLM-L6-v2_model%2Fresolve%2Fmain%2FREADME.md=&etag=%2231daa18121f72607c2f4253b2ce2fcf5e7c1e689%22
|
Lu-Tung-Yi/boy_3_converted_model
|
Lu-Tung-Yi
| 2025-07-23T12:31:16Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"unsloth",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-07-23T12:30:30Z |
Temporary Redirect. Redirecting to /api/resolve-cache/models/Lu-Tung-Yi/boy_3_converted_model/ea8de7f525ad775f0bc4757a398cc762610b3d8f/README.md?%2FLu-Tung-Yi%2Fboy_3_converted_model%2Fresolve%2Fmain%2FREADME.md=&etag=%225a6f000c6e4c709a2be1a82b5837989248e7fa63%22
|
AngelSlim/Qwen3-4B_fp8_static
|
AngelSlim
| 2025-07-23T12:29:51Z | 2 | 0 | null |
[
"safetensors",
"qwen3",
"compressed-tensors",
"region:us"
] | null | 2025-07-02T04:42:49Z |
Temporary Redirect. Redirecting to /api/resolve-cache/models/AngelSlim/Qwen3-4B_fp8_static/b742745093038890af4ccaa0cfa27b8cdfc8e4fe/README.md?%2FAngelSlim%2FQwen3-4B_fp8_static%2Fresolve%2Fmain%2FREADME.md=&etag=%2211185a730044bce8ecc0258ac2f20224f5469fc8%22
|
MinaMila/llama_instbase_3b_unlearning_4th_0.0_1.0_1.0_1.0_LoRa_Adult_cfda_ep8_55
|
MinaMila
| 2025-07-23T11:46:51Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-07-23T11:46:46Z |
Temporary Redirect. Redirecting to /api/resolve-cache/models/MinaMila/llama_instbase_3b_unlearning_4th_0.0_1.0_1.0_1.0_LoRa_Adult_cfda_ep8_55/bf8f8cfbaf0ea8042cf280e45634566f132d5193/README.md?%2FMinaMila%2Fllama_instbase_3b_unlearning_4th_0.0_1.0_1.0_1.0_LoRa_Adult_cfda_ep8_55%2Fresolve%2Fmain%2FREADME.md=&etag=%22bc5f30d6632ac0efdc7be2e9095e9e9579af2e33%22
|
lalalaDa/Qwen2.5-Math-7B-Instruct-ERPER-GRPO-alpha99
|
lalalaDa
| 2025-07-23T11:41:44Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"ERGRPO",
"trl",
"grpo",
"conversational",
"dataset:knoveleng/open-rs",
"arxiv:2402.03300",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-07-21T21:11:42Z |
Temporary Redirect. Redirecting to /api/resolve-cache/models/lalalaDa/Qwen2.5-Math-7B-Instruct-ERPER-GRPO-alpha99/0f5c51a897e309089831b34c086d404a34814dbe/README.md?%2FlalalaDa%2FQwen2.5-Math-7B-Instruct-ERPER-GRPO-alpha99%2Fresolve%2Fmain%2FREADME.md=&etag=%22acb51df8c759b3efe7cbccd02b0055a024b65e2f%22
|
onecxi/vakgyata-tiny
|
onecxi
| 2025-07-23T11:22:23Z | 0 | 1 |
transformers
|
[
"transformers",
"onnx",
"safetensors",
"wav2vec2",
"audio-classification",
"language-identification",
"indian-languages",
"multilingual",
"speech",
"asr-preprocessing",
"callcenter-ai",
"speech-analytics",
"pytorch",
"huggingface",
"en",
"hi",
"or",
"bn",
"ta",
"te",
"kn",
"ml",
"mr",
"gu",
"pa",
"as",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
audio-classification
| 2025-07-23T10:12:07Z |
Temporary Redirect. Redirecting to /api/resolve-cache/models/onecxi/vakgyata-tiny/ce4c142b55afe3a41920ff4cda680a6e5deb8cbf/README.md?%2Fonecxi%2Fvakgyata-tiny%2Fresolve%2Fmain%2FREADME.md=&etag=%222d82ada9d195c13d117b5a91c673607dc25f3ba2%22
|
popo9790/results
|
popo9790
| 2025-07-23T08:31:11Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:meta-llama/Llama-3.1-8B-Instruct",
"base_model:finetune:meta-llama/Llama-3.1-8B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-07-23T08:30:29Z |
Temporary Redirect. Redirecting to /api/resolve-cache/models/popo9790/results/0973a2760251999ffcdb3456ec0379219cbe55af/README.md?%2Fpopo9790%2Fresults%2Fresolve%2Fmain%2FREADME.md=&etag=%2269f48e20f49d5d232043df2733524cef7c4a7c5d%22
|
RedbeardNZ/HiDream-E1-Full
|
RedbeardNZ
| 2025-07-23T08:20:27Z | 0 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"image-editing",
"HiDream.ai",
"any-to-any",
"en",
"arxiv:2505.22705",
"base_model:HiDream-ai/HiDream-I1-Full",
"base_model:finetune:HiDream-ai/HiDream-I1-Full",
"license:mit",
"diffusers:HiDreamImageEditingPipeline",
"region:us"
] |
any-to-any
| 2025-07-23T08:20:26Z |
Temporary Redirect. Redirecting to /api/resolve-cache/models/RedbeardNZ/HiDream-E1-Full/e7dfc1ffd2f15b482635a6cdba9951990fb66a55/README.md?%2FRedbeardNZ%2FHiDream-E1-Full%2Fresolve%2Fmain%2FREADME.md=&etag=%22c7f68a6873592cff55edcc247ff91116e09f35af%22
|
MinaMila/llama_instbase_3b_LoRa_ACSEmployment_2_cfda_ep3_33
|
MinaMila
| 2025-07-23T08:15:42Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-07-23T08:15:38Z |
Temporary Redirect. Redirecting to /api/resolve-cache/models/MinaMila/llama_instbase_3b_LoRa_ACSEmployment_2_cfda_ep3_33/aaecbe1eebfdacbd059679f3f7137ddd01d45472/README.md?%2FMinaMila%2Fllama_instbase_3b_LoRa_ACSEmployment_2_cfda_ep3_33%2Fresolve%2Fmain%2FREADME.md=&etag=%22bc5f30d6632ac0efdc7be2e9095e9e9579af2e33%22
|
POPin2/a2c-PandaReachDense-v3
|
POPin2
| 2025-07-23T08:11:15Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"PandaReachDense-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-07-23T08:06:33Z |
Temporary Redirect. Redirecting to /api/resolve-cache/models/POPin2/a2c-PandaReachDense-v3/0963457f921b1d18c5c7721b32c8b5144b50a41b/README.md?%2FPOPin2%2Fa2c-PandaReachDense-v3%2Fresolve%2Fmain%2FREADME.md=&etag=%22f949973d970ae6bf9624fdea2617558654929f12%22
|
grapevine-AI/plamo-2-translate-gguf
|
grapevine-AI
| 2025-07-23T07:46:27Z | 0 | 0 | null |
[
"gguf",
"en",
"ja",
"license:other",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-07-23T06:54:43Z |
Temporary Redirect. Redirecting to /api/resolve-cache/models/grapevine-AI/plamo-2-translate-gguf/f9bddff98aaaf748862b1093b2f855137c4522d1/README.md?%2Fgrapevine-AI%2Fplamo-2-translate-gguf%2Fresolve%2Fmain%2FREADME.md=&etag=%2262501bf4ebe67773db354f3877e58396d1b0c648%22
|
quyanh/Qwen2-7B-Instruct-Unlearn
|
quyanh
| 2025-07-23T07:45:14Z | 14 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-07-16T12:03:16Z |
Temporary Redirect. Redirecting to /api/resolve-cache/models/quyanh/Qwen2-7B-Instruct-Unlearn/cbb219331390fbf35080b8540cbb80cf04ce29f4/README.md?%2Fquyanh%2FQwen2-7B-Instruct-Unlearn%2Fresolve%2Fmain%2FREADME.md=&etag=%22bc5f30d6632ac0efdc7be2e9095e9e9579af2e33%22
|
Trelis/qwen_gemini_synth_10-22jul
|
Trelis
| 2025-07-23T07:26:44Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"base_model:unsloth/Qwen3-4B",
"base_model:finetune:unsloth/Qwen3-4B",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-07-22T22:55:24Z |
Temporary Redirect. Redirecting to /api/resolve-cache/models/Trelis/qwen_gemini_synth_10-22jul/f77abf3fd74467667925729016e17f16f5774beb/README.md?%2FTrelis%2Fqwen_gemini_synth_10-22jul%2Fresolve%2Fmain%2FREADME.md=&etag=%22b21fe47d0a8b4bcf24e08ed1ebf0a439cbdfd137%22
|
deebak14/qwen_14b_ft_v3_dpo
|
deebak14
| 2025-07-23T07:02:30Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-07-23T06:53:17Z |
Temporary Redirect. Redirecting to /api/resolve-cache/models/deebak14/qwen_14b_ft_v3_dpo/95d6aa8b64c9362ca8f1e472e87bd43b474e4737/README.md?%2Fdeebak14%2Fqwen_14b_ft_v3_dpo%2Fresolve%2Fmain%2FREADME.md=&etag=%22bc5f30d6632ac0efdc7be2e9095e9e9579af2e33%22
|
pentester92761/033-solver-03624c9573-mass-trigger-1753252780
|
pentester92761
| 2025-07-23T06:39:42Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2025-07-23T06:39:39Z |
Temporary Redirect. Redirecting to /api/resolve-cache/models/pentester92761/033-solver-03624c9573-mass-trigger-1753252780/8d4eab84be95eb0430c794a49461183c4c1e6f24/README.md?%2Fpentester92761%2F033-solver-03624c9573-mass-trigger-1753252780%2Fresolve%2Fmain%2FREADME.md=&etag=%227be5fc7f47d5db027d120b8024982df93db95b74%22
|
LaythAbuJafar/Agent1_0.6B_Adapters
|
LaythAbuJafar
| 2025-07-23T06:22:09Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-07-23T06:08:38Z |
Temporary Redirect. Redirecting to /api/resolve-cache/models/LaythAbuJafar/Agent1_0.6B_Adapters/84fff004bbcaf5dc3cba91cfafba4d62fd779f36/README.md?%2FLaythAbuJafar%2FAgent1_0.6B_Adapters%2Fresolve%2Fmain%2FREADME.md=&etag=%22b7441640181b06c90effb718230b043e35476812%22
|
TDDBench/mlp-texas100-4
|
TDDBench
| 2025-07-23T06:04:48Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mlp",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-07-23T06:04:38Z |
Temporary Redirect. Redirecting to /api/resolve-cache/models/TDDBench/mlp-texas100-4/65413b5d51605a40dea62e06a38458cf3abfdca8/README.md?%2FTDDBench%2Fmlp-texas100-4%2Fresolve%2Fmain%2FREADME.md=&etag=%22bc5f30d6632ac0efdc7be2e9095e9e9579af2e33%22
|
TDDBench/mlp-purchase100-3
|
TDDBench
| 2025-07-23T06:03:19Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mlp",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-07-23T06:03:11Z |
Temporary Redirect. Redirecting to /api/resolve-cache/models/TDDBench/mlp-purchase100-3/7d404227f5910e74306015be24fe811f9a7ecd40/README.md?%2FTDDBench%2Fmlp-purchase100-3%2Fresolve%2Fmain%2FREADME.md=&etag=%22bc5f30d6632ac0efdc7be2e9095e9e9579af2e33%22
|
TDDBench/mlp-purchase100-2
|
TDDBench
| 2025-07-23T06:03:07Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mlp",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-07-23T06:02:58Z |
Temporary Redirect. Redirecting to /api/resolve-cache/models/TDDBench/mlp-purchase100-2/b9bd273dd7462c1937d08db5c82cd194c2224b7e/README.md?%2FTDDBench%2Fmlp-purchase100-2%2Fresolve%2Fmain%2FREADME.md=&etag=%22bc5f30d6632ac0efdc7be2e9095e9e9579af2e33%22
|
casque/BIGNIPPULL
|
casque
| 2025-07-23T05:34:49Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2025-07-23T05:33:33Z |
Temporary Redirect. Redirecting to /api/resolve-cache/models/casque/BIGNIPPULL/f6ca341a559666e70f015950205952c0393676fb/README.md?%2Fcasque%2FBIGNIPPULL%2Fresolve%2Fmain%2FREADME.md=&etag=%22d62da9c8e846c400b238a47b39dcee078257ad05%22
|
hyokwan/familidata_elysium_7b
|
hyokwan
| 2025-07-23T04:03:18Z | 0 | 0 | null |
[
"safetensors",
"gemma",
"license:apache-2.0",
"region:us"
] | null | 2025-07-23T03:24:04Z |
Temporary Redirect. Redirecting to /api/resolve-cache/models/hyokwan/familidata_elysium_7b/04425daf2bcedc490bb7d09a6cc6180fb394858b/README.md?%2Fhyokwan%2Ffamilidata_elysium_7b%2Fresolve%2Fmain%2FREADME.md=&etag=%227b95401dc46245ac339fc25059d4a56d90b4cde5%22
|
mvnagakishan/smvla_pp
|
mvnagakishan
| 2025-07-23T03:30:37Z | 0 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"robotics",
"dataset:lerobot/svla_so101_pickplace",
"base_model:lerobot/smolvla_base",
"base_model:adapter:lerobot/smolvla_base",
"license:apache-2.0",
"region:us"
] |
robotics
| 2025-07-23T03:29:11Z |
Temporary Redirect. Redirecting to /api/resolve-cache/models/mvnagakishan/smvla_pp/391611f87cb510005fee3d4fa3b2a2871f995f9e/README.md?%2Fmvnagakishan%2Fsmvla_pp%2Fresolve%2Fmain%2FREADME.md=&etag=%223f0887e8d34921ac74289c79accac3ef0d092943%22
|
dariuslimzh/test_SAE_2
|
dariuslimzh
| 2025-07-23T03:01:16Z | 0 | 0 |
saelens
|
[
"saelens",
"region:us"
] | null | 2025-07-23T02:11:00Z |
Temporary Redirect. Redirecting to /api/resolve-cache/models/dariuslimzh/test_SAE_2/ec113cf2747370ddf97b882f1a54b08ec66be819/README.md?%2Fdariuslimzh%2Ftest_SAE_2%2Fresolve%2Fmain%2FREADME.md=&etag=%2293617466734862c64d5dff18661f0f5783b98c8c%22
|
rombodawg/Llama-3-8B-Instruct-Coder
|
rombodawg
| 2025-07-23T01:04:44Z | 61 | 55 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"conversational",
"en",
"base_model:unsloth/llama-3-8b-Instruct-bnb-4bit",
"base_model:finetune:unsloth/llama-3-8b-Instruct-bnb-4bit",
"license:llama3",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-05-04T12:26:06Z |
Temporary Redirect. Redirecting to /api/resolve-cache/models/rombodawg/Llama-3-8B-Instruct-Coder/ef91c8d0822fd281220ef9d9a9e75fd346d00db2/README.md?%2Frombodawg%2FLlama-3-8B-Instruct-Coder%2Fresolve%2Fmain%2FREADME.md=&etag=%223c409f67378f5a47e9ae7076f11b2f9a66bb2ebb%22
|
fatihburakkaragoz/quora-cross-encoder
|
fatihburakkaragoz
| 2025-07-23T00:39:47Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"deberta-v2",
"text-classification",
"question-answering",
"semantic-similarity",
"quora",
"duplicate-detection",
"pytorch",
"en",
"dataset:quora",
"base_model:microsoft/deberta-v3-base",
"base_model:finetune:microsoft/deberta-v3-base",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-07-23T00:06:41Z |
Temporary Redirect. Redirecting to /api/resolve-cache/models/fatihburakkaragoz/quora-cross-encoder/13013d47768a6d4c11f0c01a77c41c50cc9ab1a6/README.md?%2Ffatihburakkaragoz%2Fquora-cross-encoder%2Fresolve%2Fmain%2FREADME.md=&etag=%22d8b93b9813b54ce6870b8be1825fad8d2d0e309c%22
|
ichsanlook/pentestic-Y
|
ichsanlook
| 2025-07-23T00:33:05Z | 0 | 0 | null |
[
"gguf",
"qwen3",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-07-23T00:23:20Z |
Temporary Redirect. Redirecting to /api/resolve-cache/models/ichsanlook/pentestic-Y/81ece2367f31f11b4f0794fc63ca48c5da5477b1/README.md?%2Fichsanlook%2Fpentestic-Y%2Fresolve%2Fmain%2FREADME.md=&etag=%227b95401dc46245ac339fc25059d4a56d90b4cde5%22
|
AmpereComputing/qwen-3-0.6b-gguf
|
AmpereComputing
| 2025-07-22T22:54:27Z | 0 | 0 | null |
[
"gguf",
"base_model:Qwen/Qwen3-0.6B",
"base_model:quantized:Qwen/Qwen3-0.6B",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-07-22T22:53:19Z |
Temporary Redirect. Redirecting to /api/resolve-cache/models/AmpereComputing/qwen-3-0.6b-gguf/768afeaba9c6236a67a20d37653604701443bd91/README.md?%2FAmpereComputing%2Fqwen-3-0.6b-gguf%2Fresolve%2Fmain%2FREADME.md=&etag=%227d7ad5a951ea1878246e9ea460ba7ee1d7378913%22
|
fakha-fakha-viral-video-twitter/fakha.video.viral.fakha.viral.twitter.clip
|
fakha-fakha-viral-video-twitter
| 2025-07-22T18:29:23Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-07-22T18:29:10Z |
Temporary Redirect. Redirecting to /api/resolve-cache/models/fakha-fakha-viral-video-twitter/fakha.video.viral.fakha.viral.twitter.clip/83588a718c4a63d82d1eafc49d8fb0aa3803d2db/README.md?%2Ffakha-fakha-viral-video-twitter%2Ffakha.video.viral.fakha.viral.twitter.clip%2Fresolve%2Fmain%2FREADME.md=&etag=%22f6967369640aa20ac0553229cc909895ab0a04f2%22
|
chinna6/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-bristly_elusive_chimpanzee
|
chinna6
| 2025-07-22T18:07:12Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am bristly_elusive_chimpanzee",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-07-22T18:06:22Z |
Temporary Redirect. Redirecting to /api/resolve-cache/models/chinna6/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-bristly_elusive_chimpanzee/c1ed9c1611c25de3ca734054ee7d10c966a419fd/README.md?%2Fchinna6%2FQwen2.5-0.5B-Instruct-Gensyn-Swarm-bristly_elusive_chimpanzee%2Fresolve%2Fmain%2FREADME.md=&etag=%225966d7f1b45bba80329ba369f19ae52d4f9213d2%22
|
chinna6/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-lethal_hardy_dinosaur
|
chinna6
| 2025-07-22T18:02:19Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am lethal_hardy_dinosaur",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-07-22T18:01:07Z |
Temporary Redirect. Redirecting to /api/resolve-cache/models/chinna6/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-lethal_hardy_dinosaur/7c5ff98e61682634d1b001ea69acefff64bbd969/README.md?%2Fchinna6%2FQwen2.5-0.5B-Instruct-Gensyn-Swarm-lethal_hardy_dinosaur%2Fresolve%2Fmain%2FREADME.md=&etag=%22b2dd77c87a0cabeb7b1d3cec2269b172746f2582%22
|
TrungTin0105/recognition-Q8_0-GGUF
|
TrungTin0105
| 2025-07-22T14:22:23Z | 0 | 0 | null |
[
"gguf",
"llama-cpp",
"gguf-my-repo",
"base_model:TrungTin0105/recognition",
"base_model:quantized:TrungTin0105/recognition",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-07-22T13:04:00Z |
Temporary Redirect. Redirecting to /api/resolve-cache/models/TrungTin0105/recognition-Q8_0-GGUF/cebfd4eb36e6521f80d897f4ba9a7c58ba8c71a9/README.md?%2FTrungTin0105%2Frecognition-Q8_0-GGUF%2Fresolve%2Fmain%2FREADME.md=&etag=%22141b98143c1681f8c67e2156cb5e8bf95838a8e7%22
|
MasterKoco/SoundsRight_DENOISING_16000HZ_V8
|
MasterKoco
| 2025-07-22T13:52:03Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-27T08:19:44Z |
Temporary Redirect. Redirecting to /api/resolve-cache/models/MasterKoco/SoundsRight_DENOISING_16000HZ_V8/cf32348c59db4d65a356010563ec8f028ccce7a0/README.md?%2FMasterKoco%2FSoundsRight_DENOISING_16000HZ_V8%2Fresolve%2Fmain%2FREADME.md=&etag=%22e69de29bb2d1d6434b8b29ae775ad8c2e48c5391%22
|
cyberunit/DeepSeek-R1-0528-Qwen3-8B-Q4_K_M-GGUF
|
cyberunit
| 2025-07-22T12:14:05Z | 62 | 0 |
transformers
|
[
"transformers",
"gguf",
"unsloth",
"llama-cpp",
"gguf-my-repo",
"base_model:unsloth/DeepSeek-R1-0528-Qwen3-8B",
"base_model:quantized:unsloth/DeepSeek-R1-0528-Qwen3-8B",
"license:mit",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-07-21T19:11:59Z |
Temporary Redirect. Redirecting to /api/resolve-cache/models/cyberunit/DeepSeek-R1-0528-Qwen3-8B-Q4_K_M-GGUF/75c7ec359c62da961c464a44114b0cbdeeb4ffbc/README.md?%2Fcyberunit%2FDeepSeek-R1-0528-Qwen3-8B-Q4_K_M-GGUF%2Fresolve%2Fmain%2FREADME.md=&etag=%2298d163d023b26b3244cf85ce90b81960b96c5e8c%22
|
mrr-codes/q-FrozenLake-v1-4x4-noSlippery
|
mrr-codes
| 2025-07-22T10:21:54Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2024-06-20T10:28:25Z |
Temporary Redirect. Redirecting to /api/resolve-cache/models/mrr-codes/q-FrozenLake-v1-4x4-noSlippery/694f3db6155354271fdcbd485372b0a79855aa01/README.md?%2Fmrr-codes%2Fq-FrozenLake-v1-4x4-noSlippery%2Fresolve%2Fmain%2FREADME.md=&etag=%22ef8295923318744af636fb582f151f804daafb32%22
|
miladalsh/new-qwen-trained-journalist-on-deepseek-3epochs
|
miladalsh
| 2025-07-22T08:46:46Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:Qwen/Qwen2.5-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-7B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-07-18T07:02:39Z |
Temporary Redirect. Redirecting to /api/resolve-cache/models/miladalsh/new-qwen-trained-journalist-on-deepseek-3epochs/0163c8eaf961d9c78a923fc125a53ed08a910867/README.md?%2Fmiladalsh%2Fnew-qwen-trained-journalist-on-deepseek-3epochs%2Fresolve%2Fmain%2FREADME.md=&etag=%22b16f8252cf6511c0f33ab38c9b8d28b43e8ee4e0%22
|
MinaMila/llama_instbase_3b_unlearning_4th_0.0_1.0_1.0_1.0_LoRa_LoanDefault_ep6_55
|
MinaMila
| 2025-07-22T07:20:35Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-07-22T07:20:17Z |
Temporary Redirect. Redirecting to /api/resolve-cache/models/MinaMila/llama_instbase_3b_unlearning_4th_0.0_1.0_1.0_1.0_LoRa_LoanDefault_ep6_55/deeb16eec56625bc04f9cdae3bc54f54fa7cd246/README.md?%2FMinaMila%2Fllama_instbase_3b_unlearning_4th_0.0_1.0_1.0_1.0_LoRa_LoanDefault_ep6_55%2Fresolve%2Fmain%2FREADME.md=&etag=%22bc5f30d6632ac0efdc7be2e9095e9e9579af2e33%22
|
Xenova/paraphrase-multilingual-mpnet-base-v2
|
Xenova
| 2025-07-22T00:06:40Z | 3,083 | 4 |
transformers.js
|
[
"transformers.js",
"onnx",
"xlm-roberta",
"feature-extraction",
"base_model:sentence-transformers/paraphrase-multilingual-mpnet-base-v2",
"base_model:quantized:sentence-transformers/paraphrase-multilingual-mpnet-base-v2",
"region:us"
] |
feature-extraction
| 2023-05-23T14:31:51Z |
Temporary Redirect. Redirecting to /api/resolve-cache/models/Xenova/paraphrase-multilingual-mpnet-base-v2/e5d116277351513fd260955ece953ecddde7046e/README.md?%2FXenova%2Fparaphrase-multilingual-mpnet-base-v2%2Fresolve%2Fmain%2FREADME.md=&etag=%2279cc32dac0181e6bcedf4269ffe8aa981cbc18ba%22
|
Siqi-Hu/Llama2-7B-lora-r-32-generic-step-300-labels_40.0-full-precision-augmented
|
Siqi-Hu
| 2025-07-21T21:01:25Z | 0 | 0 |
peft
|
[
"peft",
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:meta-llama/Llama-2-7b-hf",
"base_model:adapter:meta-llama/Llama-2-7b-hf",
"license:llama2",
"region:us"
] | null | 2025-07-21T20:23:55Z |
Temporary Redirect. Redirecting to /api/resolve-cache/models/Siqi-Hu/Llama2-7B-lora-r-32-generic-step-300-labels_40.0-full-precision-augmented/5c3afd678e2bc2462ad5af4f538c8bd421ecc1ff/README.md?%2FSiqi-Hu%2FLlama2-7B-lora-r-32-generic-step-300-labels_40.0-full-precision-augmented%2Fresolve%2Fmain%2FREADME.md=&etag=%2295e0043b295d57a8abd9837190eb304f200385fb%22
|
seanjyu/unsloth_finetune_latex_ocr
|
seanjyu
| 2025-07-21T05:37:11Z | 3 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mllama",
"image-to-text",
"text-generation-inference",
"unsloth",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
image-to-text
| 2025-06-19T23:54:33Z |
Temporary Redirect. Redirecting to /api/resolve-cache/models/seanjyu/unsloth_finetune_latex_ocr/320797177e31fcd19b510f6af80357b982876faf/README.md?%2Fseanjyu%2Funsloth_finetune_latex_ocr%2Fresolve%2Fmain%2FREADME.md=&etag=%224fa9bdb56b2a94cb1b592ffb0aa2a43c71fc84ca%22
|
DEVCamiloSepulveda/333-Qwen3SP-talenddataquality-aptanastudio
|
DEVCamiloSepulveda
| 2025-07-19T14:30:49Z | 0 | 0 |
peft
|
[
"peft",
"pytorch",
"safetensors",
"regression",
"story-point-estimation",
"software-engineering",
"text-classification",
"en",
"dataset:talenddataquality",
"dataset:aptanastudio",
"base_model:Qwen/Qwen3-1.7B",
"base_model:adapter:Qwen/Qwen3-1.7B",
"license:apache-2.0",
"model-index",
"region:us"
] |
text-classification
| 2025-07-19T14:13:58Z |
Temporary Redirect. Redirecting to /api/resolve-cache/models/DEVCamiloSepulveda/333-Qwen3SP-talenddataquality-aptanastudio/16d716035f8ff4f5e159c748e71307f984e8a4b0/README.md?%2FDEVCamiloSepulveda%2F333-Qwen3SP-talenddataquality-aptanastudio%2Fresolve%2Fmain%2FREADME.md=&etag=%22a62d0295d3d0bb1a47c725e9d00437960183a141%22
|
neural-interactive-proofs/finetune_dpo_qwen2_5-1_5b-instruct_cv_qwen2.5-1.5B_verifier_nip_slow_and_steady_2_0_iter_7_verif
|
neural-interactive-proofs
| 2025-07-17T17:04:32Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"trl",
"dpo",
"arxiv:2305.18290",
"base_model:Qwen/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-1.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-07-17T17:04:24Z |
Temporary Redirect. Redirecting to /api/resolve-cache/models/neural-interactive-proofs/finetune_dpo_qwen2_5-1_5b-instruct_cv_qwen2.5-1.5B_verifier_nip_slow_and_steady_2_0_iter_7_verif/78fefef81d326fc355e74921f14ad319b166739e/README.md?%2Fneural-interactive-proofs%2Ffinetune_dpo_qwen2_5-1_5b-instruct_cv_qwen2.5-1.5B_verifier_nip_slow_and_steady_2_0_iter_7_verif%2Fresolve%2Fmain%2FREADME.md=&etag=%227ea8af2304cd554ca1070a5a0981217652ba7847%22
|
Rishi1708/codegemma-7b-merged-16bit
|
Rishi1708
| 2025-06-20T08:56:41Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-06-20T08:56:41Z |
---
license: apache-2.0
---
|
Triangle104/Yanfei-v2-Qwen3-32B-Q3_K_M-GGUF
|
Triangle104
| 2025-06-20T08:54:45Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"mergekit",
"merge",
"llama-cpp",
"gguf-my-repo",
"dataset:nbeerbower/YanfeiMix-DPO",
"base_model:nbeerbower/Yanfei-v2-Qwen3-32B",
"base_model:quantized:nbeerbower/Yanfei-v2-Qwen3-32B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-06-20T08:53:35Z |
---
base_model: nbeerbower/Yanfei-v2-Qwen3-32B
library_name: transformers
tags:
- mergekit
- merge
- llama-cpp
- gguf-my-repo
license: apache-2.0
datasets:
- nbeerbower/YanfeiMix-DPO
---
# Triangle104/Yanfei-v2-Qwen3-32B-Q3_K_M-GGUF
This model was converted to GGUF format from [`nbeerbower/Yanfei-v2-Qwen3-32B`](https://huggingface.co/nbeerbower/Yanfei-v2-Qwen3-32B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/nbeerbower/Yanfei-v2-Qwen3-32B) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo Triangle104/Yanfei-v2-Qwen3-32B-Q3_K_M-GGUF --hf-file yanfei-v2-qwen3-32b-q3_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Triangle104/Yanfei-v2-Qwen3-32B-Q3_K_M-GGUF --hf-file yanfei-v2-qwen3-32b-q3_k_m.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Triangle104/Yanfei-v2-Qwen3-32B-Q3_K_M-GGUF --hf-file yanfei-v2-qwen3-32b-q3_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Triangle104/Yanfei-v2-Qwen3-32B-Q3_K_M-GGUF --hf-file yanfei-v2-qwen3-32b-q3_k_m.gguf -c 2048
```
|
AsukaMinato1216/Llama-3.2-1B-AQLM-2bit-1x16
|
AsukaMinato1216
| 2025-06-20T08:53:53Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"aqlm",
"region:us"
] |
text-generation
| 2025-06-20T08:53:11Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
MikCil/reddere-voces-orpheus
|
MikCil
| 2025-06-20T08:53:26Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"base_model:canopylabs/3b-es_it-ft-research_release",
"base_model:finetune:canopylabs/3b-es_it-ft-research_release",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-20T08:19:34Z |
---
base_model: canopylabs/3b-es_it-ft-research_release
tags:
- text-generation-inference
- transformers
- unsloth
- llama
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** MikCil
- **License:** apache-2.0
- **Finetuned from model :** canopylabs/3b-es_it-ft-research_release
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
hugigun/my-bert-fine-tuned
|
hugigun
| 2025-06-20T08:52:01Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-06-20T08:51:09Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
visolex/bartpho-hsd
|
visolex
| 2025-06-20T08:44:18Z | 0 | 0 | null |
[
"safetensors",
"mbart",
"hate-speech-detection",
"vietnamese",
"bartpho",
"text-classification",
"vi",
"dataset:VN-HSD",
"license:apache-2.0",
"model-index",
"region:us"
] |
text-classification
| 2025-06-19T08:29:34Z |
---
language: vi
tags:
- hate-speech-detection
- vietnamese
- bartpho
license: apache-2.0
datasets:
- VN-HSD
metrics:
- accuracy
- f1
model-index:
- name: bartpho-hsd
results:
- task:
type: text-classification
name: Hate Speech Detection
dataset:
name: VN-HSD
type: custom
metrics:
- name: Accuracy
type: accuracy
value: <INSERT_ACCURACY>
- name: F1 Score
type: f1
value: <INSERT_F1_SCORE>
base_model:
- vinai/bartpho-base
pipeline_tag: text-classification
---
# BARTPho‑HSD: Hate Speech Detection for Vietnamese Text
Fine‑tuned from [`vinai/bartpho-syllable`](https://huggingface.co/vinai/bartpho-syllable) on the **VN‑HSD** dataset.
## Model Details
* **Base Model**: [`vinai/bartpho-syllable`](https://huggingface.co/vinai/bartpho-syllable)
* **Dataset**: VN‑HSD (ViSoLex‑HSD unified hate speech corpus)
* **Fine‑tuning**: HuggingFace Transformers
### Hyperparameters
* Batch size: `32`
* Learning rate: `3e-5`
* Epochs: `100`
* Max sequence length: `256`
## Results
* **Accuracy**: `<INSERT_ACCURACY>`
* **F1 Score**: `<INSERT_F1_SCORE>`
## Usage
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("your-namespace/bartpho-hsd")
model = AutoModelForSequenceClassification.from_pretrained("your-namespace/bartpho-hsd")
text = "Anh ta đang lan truyền những lời lẽ căm ghét."
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=256)
pred = model(**inputs).logits.argmax(dim=-1).item()
print(f"Dự đoán: {['CLEAN','OFFENSIVE','HATE'][pred]}")
```
|
Josephinepassananti/sd21-kamala_ft_dataset_512_face_shaded_0.1_target_black_square-bs1-steps5000-lr1e-04
|
Josephinepassananti
| 2025-06-20T08:44:07Z | 0 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"diffusers-training",
"lora",
"base_model:stabilityai/stable-diffusion-2-1",
"base_model:adapter:stabilityai/stable-diffusion-2-1",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2025-06-20T08:14:14Z |
---
base_model: stabilityai/stable-diffusion-2-1
library_name: diffusers
license: creativeml-openrail-m
inference: true
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- diffusers-training
- lora
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# LoRA text2image fine-tuning - Josephinepassananti/sd21-kamala_ft_dataset_512_face_shaded_0.1_target_black_square-bs1-steps5000-lr1e-04
These are LoRA adaption weights for stabilityai/stable-diffusion-2-1. The weights were fine-tuned on the None dataset. You can find some example images in the following.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model]
|
JSlin/GRPO_Model
|
JSlin
| 2025-06-20T08:43:06Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-20T08:42:42Z |
---
base_model: unsloth/meta-llama-3.1-8b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** JSlin
- **License:** apache-2.0
- **Finetuned from model :** unsloth/meta-llama-3.1-8b-instruct-unsloth-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
visolex/phobert-hsd
|
visolex
| 2025-06-20T08:42:24Z | 0 | 0 | null |
[
"safetensors",
"roberta",
"hate-speech-detection",
"vietnamese",
"phobert",
"text-classification",
"vi",
"dataset:VN-HSD",
"base_model:vinai/phobert-base",
"base_model:finetune:vinai/phobert-base",
"license:apache-2.0",
"model-index",
"region:us"
] |
text-classification
| 2025-06-19T07:13:33Z |
---
language: vi
tags:
- hate-speech-detection
- vietnamese
- phobert
license: apache-2.0
datasets:
- VN-HSD
metrics:
- accuracy
- f1
model-index:
- name: phobert-hsd
results:
- task:
type: text-classification
name: Hate Speech Detection
dataset:
name: VN-HSD
type: custom
metrics:
- name: Accuracy
type: accuracy
value: <INSERT_ACCURACY>
- name: F1 Score
type: f1
value: <INSERT_F1_SCORE>
base_model:
- vinai/phobert-base
pipeline_tag: text-classification
---
# PhoBERT‑HSD: Hate Speech Detection for Vietnamese Text
Fine‑tuned from [`vinai/phobert-base`](https://huggingface.co/vinai/phobert-base) on the **VN‑HSD** dataset.
## Model Details
* **Base Model**: [`vinai/phobert-base`](https://huggingface.co/vinai/phobert-base)
* **Dataset**: VN‑HSD (ViSoLex‑HSD unified hate speech corpus)
* **Fine‑tuning**: HuggingFace Transformers
### Hyperparameters
* Batch size: `32`
* Learning rate: `5e-5`
* Epochs: `100`
* Max sequence length: `256`
## Results
* **Accuracy**: `<INSERT_ACCURACY>`
* **F1 Score**: `<INSERT_F1_SCORE>`
## Usage
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("visolex/phobert-hsd")
model = AutoModelForSequenceClassification.from_pretrained("visolex/phobert-hsd")
text = "Đừng nói những lời thô tục như vậy!"
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=256)
pred = model(**inputs).logits.argmax(dim=-1).item()
print(f"Label: {['CLEAN','OFFENSIVE','HATE'][pred]}")
```
|
georgedy/distilbert-rotten-tomatoes
|
georgedy
| 2025-06-20T08:42:23Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-06-20T08:34:19Z |
---
library_name: transformers
license: apache-2.0
base_model: distilbert/distilbert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: distilbert-rotten-tomatoes
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-rotten-tomatoes
This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset.
## 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.52.4
- Pytorch 2.7.1+cu126
- Datasets 3.6.0
- Tokenizers 0.21.1
|
ArindamSingh/Llama-3.2-3B-Instruct-FineTome100k-16bit
|
ArindamSingh
| 2025-06-20T08:42:00Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-19T16:43:56Z |
---
base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** *ArindamSingh*
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit
This llama model was trained with Unsloth and Huggingface's TRL library.
|
popn0/my-bert-fine-tuned
|
popn0
| 2025-06-20T08:41:56Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-06-20T08:41:29Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Yojirex/model
|
Yojirex
| 2025-06-20T08:40:24Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-20T08:16:46Z |
---
base_model: unsloth/meta-llama-3.1-8b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** Yojirex
- **License:** apache-2.0
- **Finetuned from model :** unsloth/meta-llama-3.1-8b-unsloth-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
Manush123/my-Blood_sugar_model
|
Manush123
| 2025-06-20T08:40:10Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"biogpt",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-20T08:39:02Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
nuilbg/turkish_search_model
|
nuilbg
| 2025-06-20T08:38:26Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-06-20T08:38:26Z |
---
license: apache-2.0
---
|
scb10x/typhoon2-qwen2.5-7b-mlx-4bit
|
scb10x
| 2025-06-20T08:37:09Z | 0 | 0 |
mlx
|
[
"mlx",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"base_model:scb10x/typhoon2-qwen2.5-7b",
"base_model:quantized:scb10x/typhoon2-qwen2.5-7b",
"license:apache-2.0",
"4-bit",
"region:us"
] |
text-generation
| 2025-06-20T08:35:31Z |
---
license: apache-2.0
pipeline_tag: text-generation
library_name: mlx
base_model: scb10x/typhoon2-qwen2.5-7b
tags:
- mlx
---
# scb10x/typhoon2-qwen2.5-7b-mlx-4bit
This model [scb10x/typhoon2-qwen2.5-7b-mlx-4bit](https://huggingface.co/scb10x/typhoon2-qwen2.5-7b-mlx-4bit) was
converted to MLX format from [scb10x/typhoon2-qwen2.5-7b](https://huggingface.co/scb10x/typhoon2-qwen2.5-7b)
using mlx-lm version **0.25.2**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("scb10x/typhoon2-qwen2.5-7b-mlx-4bit")
prompt = "hello"
if tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
```
|
Josephinepassananti/sd21-kamala_ft_dataset_512_face_shaded_0.1_target_man-bs1-steps600-lr1e-04
|
Josephinepassananti
| 2025-06-20T08:36:58Z | 0 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"diffusers-training",
"lora",
"base_model:stabilityai/stable-diffusion-2-1",
"base_model:adapter:stabilityai/stable-diffusion-2-1",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2025-06-20T05:13:29Z |
---
base_model: stabilityai/stable-diffusion-2-1
library_name: diffusers
license: creativeml-openrail-m
inference: true
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- diffusers-training
- lora
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# LoRA text2image fine-tuning - Josephinepassananti/sd21-kamala_ft_dataset_512_face_shaded_0.1_target_man-bs1-steps600-lr1e-04
These are LoRA adaption weights for stabilityai/stable-diffusion-2-1. The weights were fine-tuned on the None dataset. You can find some example images in the following.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model]
|
scb10x/llama3.2-typhoon2-3b-mlx-4bit
|
scb10x
| 2025-06-20T08:32:23Z | 0 | 0 |
mlx
|
[
"mlx",
"safetensors",
"llama",
"text-generation",
"conversational",
"base_model:scb10x/llama3.2-typhoon2-3b",
"base_model:quantized:scb10x/llama3.2-typhoon2-3b",
"license:llama3.2",
"4-bit",
"region:us"
] |
text-generation
| 2025-06-20T08:31:52Z |
---
license: llama3.2
pipeline_tag: text-generation
tags:
- mlx
base_model: scb10x/llama3.2-typhoon2-3b
library_name: mlx
---
# scb10x/llama3.2-typhoon2-3b-mlx-4bit
This model [scb10x/llama3.2-typhoon2-3b-mlx-4bit](https://huggingface.co/scb10x/llama3.2-typhoon2-3b-mlx-4bit) was
converted to MLX format from [scb10x/llama3.2-typhoon2-3b](https://huggingface.co/scb10x/llama3.2-typhoon2-3b)
using mlx-lm version **0.25.2**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("scb10x/llama3.2-typhoon2-3b-mlx-4bit")
prompt = "hello"
if tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
```
|
MoxStone/SmaliLLM-Qwen3-8B-Finetuned
|
MoxStone
| 2025-06-20T08:30:26Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"code",
"conversational",
"base_model:Qwen/Qwen3-8B",
"base_model:finetune:Qwen/Qwen3-8B",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-20T05:57:02Z |
---
license: mit
base_model:
- Qwen/Qwen3-8B
pipeline_tag: text-generation
library_name: transformers
tags:
- code
---
## What is SmaliLLM used for
SmaliLLM is a large language model designed to decompile Smali code into Java code. Reconstructing Smali language representations into high-level languages such as Java holds significant practical engineering value. This transformation not only lowers the technical barrier for reverse engineering but also provides the necessary semantic foundation for subsequent tasks such as static analysis and vulnerability detection.
## SmaliLLM Highlights
SmaliLLM is a series of models finetuned using nearly 1000 "Smali2Java" data, based on Qwen3, Qwen2.5-Coder, Gemma3, with the following features:
- **High Compilation Success Rate** After our fine-tuning, the model’s compilation success rate increased by an average of 20%. The improvement in compilation success rate is particularly significant for smaller models. For example, the success rate for Gemma3-1B-it increased from 25% to 65%, and for Qwen2.5-Coder-0.5B, it rose from 15% to 45%.
- **High Quality of the Generated Java Code** After fine-tuning, the model’s average CodeBLEU score improved by 0.08. The improvement in CodeBLEU is especially notable for smaller models. Specifically, under the base models Gemma3-4B-it, Qwen2.5-Coder-0.5B-Instruct, Qwen3-0.6B, and Qwen3-4B, the CodeBLEU scores increased by 0.17, 0.14, 0.10, and 0.14 respectively.
- **Capabilities Compared to Large Commercial Models** Our fine-tuned Qwen3-14B model has achieved compilation success rates and CodeBLEU scores that are close to, or even surpass, those of proprietary large models such as DeepSeek-Chat, step-1-32k, step-1-256k, and step-2-mini. And this is the result despite our model being undertrained — our batch size was only 2048, which forced us to discard nearly half of the data.
## Quickstart
The following contains a code snippet illustrating how to use the model generate content based on given inputs.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "MoxStone/SmaliLLM-Qwen3-8B-Finetuned"
# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto"
)
# prepare the model input
prompt = "Smali Code You Want to Decompile"
messages = [
{"role":"system", "content": "Decompile following smali code to java code."}
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
enable_thinking=False # In the Qwen3 base model, we use the non-thinking mode to decompile Smali code.
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# conduct text completion
generated_ids = model.generate(
**model_inputs,
max_new_tokens=4096
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
# parsing thinking content
try:
# rindex finding 151668 (</think>)
index = len(output_ids) - output_ids[::-1].index(151668)
except ValueError:
index = 0
thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("\n")
content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n")
print("Java code:", content)
```
|
Yatsrib/a2c-PandaReachDense-v3
|
Yatsrib
| 2025-06-20T08:29:46Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"PandaReachDense-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-06-20T08:25:26Z |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v3
type: PandaReachDense-v3
metrics:
- type: mean_reward
value: -0.21 +/- 0.09
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v3**
This is a trained model of a **A2C** agent playing **PandaReachDense-v3**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
internalhell/wav2vec2-large-ru-5ep
|
internalhell
| 2025-06-20T08:29:24Z | 12,873 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"hf-asr-leaderboard",
"generated_from_trainer",
"ru",
"dataset:mozilla-foundation/common_voice_17_0",
"base_model:jonatasgrosman/wav2vec2-large-xlsr-53-russian",
"base_model:finetune:jonatasgrosman/wav2vec2-large-xlsr-53-russian",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2025-06-01T10:07:22Z |
---
library_name: transformers
language:
- ru
license: apache-2.0
base_model: jonatasgrosman/wav2vec2-large-xlsr-53-russian
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_17_0
metrics:
- wer
model-index:
- name: Wav2vec2-large ru - slowlydoor
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 17.0
type: mozilla-foundation/common_voice_17_0
config: ru
split: None
args: 'config: ru, split: test'
metrics:
- name: Wer
type: wer
value: 22.3988525667842
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Wav2vec2-large ru - slowlydoor ([Automatic Speech Recognition](https://github.com/SlowlyDoor/Automatic-Speech-Recognition))
This model is a fine-tuned version of [jonatasgrosman/wav2vec2-large-xlsr-53-russian](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-russian) on the Common Voice 17.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2124
- Wer: 22.3989
- Cer: 4.8036
- Ser: 75.4264
## 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: 0.0001
- train_batch_size: 8
- eval_batch_size: 4
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training code
```bash
pip install datasets librosa scikit-learn torch torchaudio evaluate jiwer nltk
pip install --upgrade datasets
```
```python
from huggingface_hub import login
from datasets import load_dataset, DatasetDict
from transformers import Wav2Vec2FeatureExtractor, Wav2Vec2CTCTokenizer, Wav2Vec2Processor, Wav2Vec2ForCTC, TrainingArguments, Trainer
from datasets import load_dataset, Audio
import torch
import torchaudio
import re
import evaluate
import numpy as np
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
login("***")
common_voice = DatasetDict()
common_voice["train"] = load_dataset("mozilla-foundation/common_voice_17_0", "ru", split="train")
common_voice["test"] = load_dataset("mozilla-foundation/common_voice_17_0", "ru", split="test")
common_voice = common_voice.remove_columns(["accent", "age", "client_id", "down_votes", "gender", "locale", "path", "segment", "up_votes"])
common_voice = common_voice.cast_column("audio", Audio(sampling_rate=16000))
tokenizer = Wav2Vec2CTCTokenizer.from_pretrained("jonatasgrosman/wav2vec2-large-xlsr-53-russian")
feature_extractor = Wav2Vec2FeatureExtractor(feature_size=1, sampling_rate=16000, padding_value=0.0, return_attention_mask=True)
processor = Wav2Vec2Processor(feature_extractor=feature_extractor, tokenizer=tokenizer)
def prepare_dataset(batch):
audio = batch["audio"]
# batched output is "un-batched"
batch["input_values"] = processor(audio["array"], sampling_rate=audio["sampling_rate"]).input_values[0]
batch["input_length"] = len(batch["input_values"])
with processor.as_target_processor():
batch["labels"] = processor(batch["sentence"]).input_ids
return batch
common_voice = common_voice.map(prepare_dataset, remove_columns=common_voice["train"].column_names, num_proc=2)
wer_metric = evaluate.load("wer")
cer_metric = evaluate.load("cer")
def compute_metrics(pred):
pred_logits = pred.predictions
pred_ids = np.argmax(pred_logits, axis=-1)
pred.label_ids[pred.label_ids == -100] = processor.tokenizer.pad_token_id
pred_str = processor.batch_decode(pred_ids, skip_special_tokens=True)
label_str = processor.batch_decode(pred.label_ids, group_tokens=False, skip_special_tokens=True)
pairs = [(ref.strip(), hyp.strip()) for ref, hyp in zip(label_str, pred_str)]
pairs = [(ref, hyp) for ref, hyp in pairs if len(ref) > 0]
if len(pairs) == 0:
return {"wer": 1.0, "cer": 1.0, "ser": 1.0}
label_str, pred_str = zip(*pairs)
wer = 100 * wer_metric.compute(predictions=pred_str, references=label_str)
cer = 100 * cer_metric.compute(predictions=pred_str, references=label_str)
incorrect_sentences = sum([ref != pred for ref, pred in zip(label_str, pred_str)])
ser = 100 * incorrect_sentences / len(label_str)
return {
"wer": wer,
"cer": cer,
"ser": ser
}
model = Wav2Vec2ForCTC.from_pretrained(
"jonatasgrosman/wav2vec2-large-xlsr-53-russian",
ctc_loss_reduction="mean",
pad_token_id=processor.tokenizer.pad_token_id,
)
@dataclass
class DataCollatorCTCWithPadding:
processor: Wav2Vec2Processor
padding: Union[bool, str] = True
max_length: Optional[int] = None
max_length_labels: Optional[int] = None
pad_to_multiple_of: Optional[int] = None
pad_to_multiple_of_labels: Optional[int] = None
def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]:
# split inputs and labels since they have to be of different lengths and need
# different padding methods
input_features = [{"input_values": feature["input_values"]} for feature in features]
label_features = [{"input_ids": feature["labels"]} for feature in features]
batch = self.processor.pad(
input_features,
padding=self.padding,
max_length=self.max_length,
pad_to_multiple_of=self.pad_to_multiple_of,
return_tensors="pt",
)
with self.processor.as_target_processor():
labels_batch = self.processor.pad(
label_features,
padding=self.padding,
max_length=self.max_length_labels,
pad_to_multiple_of=self.pad_to_multiple_of_labels,
return_tensors="pt",
)
# replace padding with -100 to ignore loss correctly
labels = labels_batch["input_ids"].masked_fill(labels_batch.attention_mask.ne(1), -100)
batch["labels"] = labels
return batch
data_collator = DataCollatorCTCWithPadding(processor=processor, padding=True)
training_args = TrainingArguments(
output_dir="/content/drive/MyDrive/models/wav2vec2-large-ru-5ep",
logging_dir="/content/drive/MyDrive/models/wav2vec2-large-ru-5ep",
group_by_length=True,
per_device_train_batch_size=8,
per_device_eval_batch_size=4,
eval_strategy="steps",
logging_strategy="steps",
save_strategy="steps",
num_train_epochs=5,
logging_steps=25,
eval_steps=500,
save_steps=500,
fp16=True,
optim="adamw_torch_fused",
torch_compile=True,
gradient_checkpointing=True,
learning_rate=1e-4,
weight_decay=0.005,
report_to=["tensorboard"],
push_to_hub=False
)
trainer = Trainer(
model=model,
data_collator=data_collator,
args=training_args,
compute_metrics=compute_metrics,
train_dataset=common_voice["train"],
eval_dataset=common_voice["test"],
tokenizer=processor,
)
trainer.train()
```
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer | Ser |
|:-------------:|:------:|:-----:|:---------------:|:-------:|:------:|:-------:|
| 0.3421 | 0.1516 | 500 | 0.2593 | 27.7416 | 6.2518 | 81.6311 |
| 0.2979 | 0.3032 | 1000 | 0.2741 | 27.9854 | 6.3745 | 82.2290 |
| 0.2787 | 0.4548 | 1500 | 0.2538 | 27.3041 | 6.0743 | 81.1998 |
| 0.325 | 0.6064 | 2000 | 0.2701 | 29.4006 | 6.5501 | 83.6503 |
| 0.3048 | 0.7580 | 2500 | 0.2435 | 27.0914 | 6.0148 | 80.8077 |
| 0.294 | 0.9096 | 3000 | 0.2495 | 26.9503 | 5.9946 | 80.9939 |
| 0.2648 | 1.0612 | 3500 | 0.2675 | 26.8356 | 6.0261 | 80.8175 |
| 0.2691 | 1.2129 | 4000 | 0.2372 | 26.1220 | 5.8259 | 80.2294 |
| 0.2245 | 1.3645 | 4500 | 0.2394 | 26.1603 | 5.8315 | 80.3470 |
| 0.2738 | 1.5161 | 5000 | 0.2388 | 26.0420 | 5.7826 | 79.9941 |
| 0.2767 | 1.6677 | 5500 | 0.2330 | 25.8089 | 5.7248 | 79.5138 |
| 0.2689 | 1.8193 | 6000 | 0.2284 | 25.7312 | 5.6832 | 79.6216 |
| 0.2571 | 1.9709 | 6500 | 0.2370 | 25.3403 | 5.6065 | 79.3080 |
| 0.2479 | 2.1225 | 7000 | 0.2372 | 25.2065 | 5.5776 | 78.9943 |
| 0.2021 | 2.2741 | 7500 | 0.2284 | 24.8718 | 5.4638 | 78.6610 |
| 0.1864 | 2.4257 | 8000 | 0.2280 | 24.8132 | 5.4340 | 78.8669 |
| 0.1953 | 2.5773 | 8500 | 0.2237 | 24.4941 | 5.3856 | 78.3670 |
| 0.195 | 2.7289 | 9000 | 0.2190 | 24.2658 | 5.2770 | 77.8279 |
| 0.1829 | 2.8805 | 9500 | 0.2194 | 24.2443 | 5.2697 | 77.8671 |
| 0.1457 | 3.0321 | 10000 | 0.2205 | 24.2587 | 5.2398 | 77.8279 |
| 0.1435 | 3.1837 | 10500 | 0.2223 | 23.7985 | 5.1608 | 77.1613 |
| 0.1435 | 3.3354 | 11000 | 0.2219 | 23.6551 | 5.1230 | 76.9065 |
| 0.1752 | 3.4870 | 11500 | 0.2186 | 23.4829 | 5.0767 | 76.5438 |
| 0.1793 | 3.6386 | 12000 | 0.2232 | 23.4339 | 5.0977 | 76.4556 |
| 0.1682 | 3.7902 | 12500 | 0.2133 | 23.1853 | 5.0090 | 76.0929 |
| 0.1607 | 3.9418 | 13000 | 0.2135 | 22.7610 | 4.9091 | 75.7597 |
| 0.1463 | 4.0934 | 13500 | 0.2138 | 22.8495 | 4.9314 | 76.1125 |
| 0.1654 | 4.2450 | 14000 | 0.2138 | 22.6379 | 4.8814 | 75.7008 |
| 0.1586 | 4.3966 | 14500 | 0.2173 | 22.6678 | 4.8705 | 75.5342 |
| 0.1438 | 4.5482 | 15000 | 0.2166 | 22.5411 | 4.8437 | 75.5342 |
| 0.1645 | 4.6998 | 15500 | 0.2146 | 22.4658 | 4.8308 | 75.3774 |
| 0.1254 | 4.8514 | 16000 | 0.2124 | 22.3989 | 4.8036 | 75.4264 |
### Framework versions
- Transformers 4.52.2
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
hasdal/a9a79421-590c-4eba-bfb4-4d4d013067e2
|
hasdal
| 2025-06-20T08:24:20Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"unsloth",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2025-06-20T08:07:22Z |
---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
hoan17/ddpo500
|
hoan17
| 2025-06-20T08:24:17Z | 0 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"model_hub_mixin",
"pytorch_model_hub_mixin",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2025-06-20T08:23:15Z |
---
tags:
- model_hub_mixin
- pytorch_model_hub_mixin
---
This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
- Code: [More Information Needed]
- Paper: [More Information Needed]
- Docs: [More Information Needed]
|
minhxle/truesight-ft-job-f9ce2ab9-84b3-4d59-96c1-36b68a01159c
|
minhxle
| 2025-06-20T08:23:52Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen2",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-20T08:23:43Z |
---
base_model: unsloth/qwen2.5-7b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** minhxle
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen2.5-7b-instruct-unsloth-bnb-4bit
This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
Triangle104/OpenThinker3-7B-Q5_K_M-GGUF
|
Triangle104
| 2025-06-20T08:22:17Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"llama-factory",
"full",
"generated_from_trainer",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"dataset:open-thoughts/OpenThoughts3-1.2M",
"base_model:open-thoughts/OpenThinker3-7B",
"base_model:quantized:open-thoughts/OpenThinker3-7B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2025-06-20T08:20:45Z |
---
base_model: open-thoughts/OpenThinker3-7B
datasets:
- open-thoughts/OpenThoughts3-1.2M
library_name: transformers
license: apache-2.0
tags:
- llama-factory
- full
- generated_from_trainer
- llama-cpp
- gguf-my-repo
pipeline_tag: text-generation
model-index:
- name: OpenThinker3-7B
results: []
---
# Triangle104/OpenThinker3-7B-Q5_K_M-GGUF
This model was converted to GGUF format from [`open-thoughts/OpenThinker3-7B`](https://huggingface.co/open-thoughts/OpenThinker3-7B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/open-thoughts/OpenThinker3-7B) for more details on the model.
---
State-of-the-art open-data 7B reasoning model.
This model is a fine-tuned version of Qwen/Qwen2.5-7B-Instruct on the OpenThoughts3-1.2M dataset. It represents a notable improvement over our previous models, OpenThinker-7B and OpenThinker2-7B, and it outperforms several other strong reasoning 7B models such as DeepSeek-R1-Distill-Qwen-7B and Llama-3.1-Nemotron-Nano-8B-v1, despite being trained only with SFT, without any RL.
---
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo Triangle104/OpenThinker3-7B-Q5_K_M-GGUF --hf-file openthinker3-7b-q5_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Triangle104/OpenThinker3-7B-Q5_K_M-GGUF --hf-file openthinker3-7b-q5_k_m.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Triangle104/OpenThinker3-7B-Q5_K_M-GGUF --hf-file openthinker3-7b-q5_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Triangle104/OpenThinker3-7B-Q5_K_M-GGUF --hf-file openthinker3-7b-q5_k_m.gguf -c 2048
```
|
sgonzalezygil/sd-finetuning-dreambooth-v22-600
|
sgonzalezygil
| 2025-06-20T08:21:42Z | 0 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2025-06-20T08:20:01Z |
---
library_name: diffusers
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🧨 diffusers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Triangle104/OpenThinker3-7B-Q5_K_S-GGUF
|
Triangle104
| 2025-06-20T08:18:50Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"llama-factory",
"full",
"generated_from_trainer",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"dataset:open-thoughts/OpenThoughts3-1.2M",
"base_model:open-thoughts/OpenThinker3-7B",
"base_model:quantized:open-thoughts/OpenThinker3-7B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2025-06-20T08:17:46Z |
---
base_model: open-thoughts/OpenThinker3-7B
datasets:
- open-thoughts/OpenThoughts3-1.2M
library_name: transformers
license: apache-2.0
tags:
- llama-factory
- full
- generated_from_trainer
- llama-cpp
- gguf-my-repo
pipeline_tag: text-generation
model-index:
- name: OpenThinker3-7B
results: []
---
# Triangle104/OpenThinker3-7B-Q5_K_S-GGUF
This model was converted to GGUF format from [`open-thoughts/OpenThinker3-7B`](https://huggingface.co/open-thoughts/OpenThinker3-7B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/open-thoughts/OpenThinker3-7B) for more details on the model.
---
State-of-the-art open-data 7B reasoning model.
This model is a fine-tuned version of Qwen/Qwen2.5-7B-Instruct on the OpenThoughts3-1.2M dataset. It represents a notable improvement over our previous models, OpenThinker-7B and OpenThinker2-7B, and it outperforms several other strong reasoning 7B models such as DeepSeek-R1-Distill-Qwen-7B and Llama-3.1-Nemotron-Nano-8B-v1, despite being trained only with SFT, without any RL.
---
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo Triangle104/OpenThinker3-7B-Q5_K_S-GGUF --hf-file openthinker3-7b-q5_k_s.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Triangle104/OpenThinker3-7B-Q5_K_S-GGUF --hf-file openthinker3-7b-q5_k_s.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Triangle104/OpenThinker3-7B-Q5_K_S-GGUF --hf-file openthinker3-7b-q5_k_s.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Triangle104/OpenThinker3-7B-Q5_K_S-GGUF --hf-file openthinker3-7b-q5_k_s.gguf -c 2048
```
|
HSE-Chukchi-NLP/gemma3-4b-it-rus-ckt
|
HSE-Chukchi-NLP
| 2025-06-20T08:18:46Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"gemma3",
"arxiv:1910.09700",
"base_model:google/gemma-3-4b-it",
"base_model:adapter:google/gemma-3-4b-it",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-06-20T08:18:38Z |
---
base_model: google/gemma-3-4b-it
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.15.2
|
api-ix/MAKIMA
|
api-ix
| 2025-06-20T08:17:41Z | 0 | 0 |
vllm
|
[
"vllm",
"safetensors",
"mistral",
"text-generation",
"en",
"fr",
"de",
"es",
"pt",
"it",
"ja",
"ko",
"ru",
"zh",
"ar",
"fa",
"id",
"ms",
"ne",
"pl",
"ro",
"sr",
"sv",
"tr",
"uk",
"vi",
"hi",
"bn",
"arxiv:2506.10910",
"base_model:mistralai/Mistral-Small-3.1-24B-Instruct-2503",
"base_model:finetune:mistralai/Mistral-Small-3.1-24B-Instruct-2503",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2025-06-20T08:16:36Z |
---
base_model:
- mistralai/Mistral-Small-3.1-24B-Instruct-2503
language:
- en
- fr
- de
- es
- pt
- it
- ja
- ko
- ru
- zh
- ar
- fa
- id
- ms
- ne
- pl
- ro
- sr
- sv
- tr
- uk
- vi
- hi
- bn
library_name: vllm
license: apache-2.0
inference: false
extra_gated_description: If you want to learn more about how we process your personal
data, please read our <a href="https://mistral.ai/terms/">Privacy Policy</a>.
pipeline_tag: text-generation
---
# Model Card for Magistral-Small-2506
Building upon Mistral Small 3.1 (2503), **with added reasoning capabilities**, undergoing SFT from Magistral Medium traces and RL on top, it's a small, efficient reasoning model with 24B parameters.
Magistral Small can be deployed locally, fitting within a single RTX 4090 or a 32GB RAM MacBook once quantized.
Learn more about Magistral in our [blog post](https://mistral.ai/news/magistral/).
The model was presented in the paper [Magistral](https://huggingface.co/papers/2506.10910).
## Key Features
- **Reasoning:** Capable of long chains of reasoning traces before providing an answer.
- **Multilingual:** Supports dozens of languages, including English, French, German, Greek, Hindi, Indonesian, Italian, Japanese, Korean, Malay, Nepali, Polish, Portuguese, Romanian, Russian, Serbian, Spanish, Turkish, Ukrainian, Vietnamese, Arabic, Bengali, Chinese, and Farsi.
- **Apache 2.0 License:** Open license allowing usage and modification for both commercial and non-commercial purposes.
- **Context Window:** A 128k context window, **but** performance might degrade past **40k**. Hence we recommend setting the maximum model length to 40k.
## Benchmark Results
| Model | AIME24 pass@1 | AIME25 pass@1 | GPQA Diamond | Livecodebench (v5) |
|-------|-------------|-------------|--------------|-------------------|
| Magistral Medium | 73.59% | 64.95% | 70.83% | 59.36% |
| Magistral Small | 70.68% | 62.76% | 68.18% | 55.84% |
## Sampling parameters
Please make sure to use:
- `top_p`: 0.95
- `temperature`: 0.7
- `max_tokens`: 40960
## Basic Chat Template
We highly recommend including the default system prompt used during RL for the best results, you can edit and customise it if needed for your specific use case.
```
<s>[SYSTEM_PROMPT]system_prompt
A user will ask you to solve a task. You should first draft your thinking process (inner monologue) until you have derived the final answer. Afterwards, write a self-contained summary of your thoughts (i.e. your summary should be succinct but contain all the critical steps you needed to reach the conclusion). You should use Markdown to format your response. Write both your thoughts and summary in the same language as the task posed by the user. NEVER use \boxed{} in your response.
Your thinking process must follow the template below:
<think>
Your thoughts or/and draft, like working through an exercise on scratch paper. Be as casual and as long as you want until you are confident to generate a correct answer.
</think>
Here, provide a concise summary that reflects your reasoning and presents a clear final answer to the user. Don't mention that this is a summary.
Problem:
[/SYSTEM_PROMPT][INST]user_message[/INST]<think>
reasoning_traces
</think>
assistant_response</s>[INST]user_message[/INST]
```
*`system_prompt`, `user_message` and `assistant_response` are placeholders.*
We invite you to choose, depending on your use case and requirements, between keeping reasoning traces during multi-turn interactions or keeping only the final assistant response.
***Please make sure to use [mistral-common](https://github.com/mistralai/mistral-common) as the source of truth***
## Usage
The model can be used with the following frameworks;
### Inference
- [`vllm (recommended)`](https://github.com/vllm-project/vllm): See [below](#vllm)
In addition the community has prepared quantized versions of the model that can be used with the following frameworks (*alphabetically sorted*):
- [`llama.cpp`](https://github.com/ggml-org/llama.cpp): https://huggingface.co/mistralai/Magistral-Small-2506_gguf
- [`lmstudio` (llama.cpp, MLX)](https://lmstudio.ai/): https://lmstudio.ai/models/mistralai/magistral-small
- [`ollama`](https://ollama.com/): https://ollama.com/library/magistral
- [`unsloth` (llama.cpp)](https://huggingface.co/unsloth): https://huggingface.co/unsloth/Magistral-Small-2506-GGUF
### Training
Fine-tuning is possible with (*alphabetically sorted*):
- [`axolotl`](https://github.com/axolotl-ai-cloud/axolotl): https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/magistral
- [`unsloth`](https://github.com/unslothai/unsloth): https://docs.unsloth.ai/basics/magistral
### Other
Also you can use Magistral with:
- [`kaggle`](https://www.kaggle.com/models/mistral-ai/magistral-small-2506): https://www.kaggle.com/models/mistral-ai/magistral-small-2506
### vLLM (recommended)
We recommend using this model with the [vLLM library](https://github.com/vllm-project/vllm)
to implement production-ready inference pipelines.
**_Installation_**
Make sure you install the latest [`vLLM`](https://github.com/vllm-project/vllm/) code:
```
pip install -U vllm \
--pre \
--extra-index-url https://wheels.vllm.ai/nightly
```
Doing so should automatically install [`mistral_common >= 1.6.0`](https://github.com/mistralai/mistral-common/releases/tag/v1.6.0).
To check:
```
python -c "import mistral_common; print(mistral_common.__version__)"
```
You can also make use of a ready-to-go [docker image](https://github.com/vllm-project/vllm/blob/main/Dockerfile) or on the [docker hub](https://hub.docker.com/layers/vllm/vllm-openai/latest/images/sha256-de9032a92ffea7b5c007dad80b38fd44aac11eddc31c435f8e52f3b7404bbf39).
Serve model as follows:
```
vllm serve mistralai/Magistral-Small-2506 --tokenizer_mode mistral --config_format mistral --load_format mistral --tool-call-parser mistral --enable-auto-tool-choice --tensor-parallel-size 2
```
Ping model as follows:
```py
from openai import OpenAI
from huggingface_hub import hf_hub_download
# Modify OpenAI's API key and API base to use vLLM's API server.
openai_api_key = "EMPTY"
openai_api_base = "http://localhost:8000/v1"
TEMP = 0.7
TOP_P = 0.95
MAX_TOK = 40_960
client = OpenAI(
api_key=openai_api_key,
base_url=openai_api_base,
)
models = client.models.list()
model = models.data[0].id
def load_system_prompt(repo_id: str, filename: str) -> str:
file_path = hf_hub_download(repo_id=repo_id, filename=filename)
with open(file_path, "r") as file:
system_prompt = file.read()
return system_prompt
SYSTEM_PROMPT = load_system_prompt(model, "SYSTEM_PROMPT.txt")
query = "Write 4 sentences, each with at least 8 words. Now make absolutely sure that every sentence has exactly one word less than the previous sentence."
# or try out other queries
# query = "Exactly how many days ago did the French Revolution start? Today is June 4th, 2025."
# query = "Think about 5 random numbers. Verify if you can combine them with addition, multiplication, subtraction or division to 133"
# query = "If it takes 30 minutes to dry 12 T-shirts in the sun, how long does it take to dry 33 T-shirts?"
messages = [
{"role": "system", "content": SYSTEM_PROMPT},
{"role": "user", "content": query}
]
stream = client.chat.completions.create(
model=model,
messages=messages,
stream=True,
temperature=TEMP,
top_p=TOP_P,
max_tokens=MAX_TOK,
)
print("client: Start streaming chat completions...")
printed_content = False
for chunk in stream:
content = None
# Check the content is content
if hasattr(chunk.choices[0].delta, "content"):
content = chunk.choices[0].delta.content
if content is not None:
if not printed_content:
printed_content = True
print("\ncontent:", end="", flush=True)
# Extract and print the content
print(content, end="", flush=True)
# content:<think>
# Alright, I need to write 4 sentences where each one has at least 8 words and each subsequent sentence has one fewer word than the previous one.
# ...
# Final boxed answer (the four sentences):
# \[
# \boxed{
# \begin{aligned}
# &\text{1. The quick brown fox jumps over lazy dog and yells hello.} \\
# &\text{2. I saw the cat on the stair with my hat.} \\
# &\text{3. The man in the moon came down quickly today.} \\
# &\text{4. A cat sat on the mat today patiently.}
# \end{aligned}
# }
# \]
```
|
sgonzalezygil/sd-finetuning-dreambooth-v22-1200
|
sgonzalezygil
| 2025-06-20T08:17:15Z | 0 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2025-06-20T08:15:46Z |
---
library_name: diffusers
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🧨 diffusers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
t-Liu-work/gpt_0.02B_global_step763_custom_dataset_2
|
t-Liu-work
| 2025-06-20T08:16:49Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gpt2",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-20T08:16:40Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
souvickdascmsa019/colbert_reasonir_v2
|
souvickdascmsa019
| 2025-06-20T08:12:39Z | 0 | 0 |
PyLate
|
[
"PyLate",
"safetensors",
"modernbert",
"ColBERT",
"sentence-transformers",
"sentence-similarity",
"feature-extraction",
"generated_from_trainer",
"dataset_size:310935",
"loss:Contrastive",
"arxiv:1908.10084",
"base_model:lightonai/GTE-ModernColBERT-v1",
"base_model:finetune:lightonai/GTE-ModernColBERT-v1",
"model-index",
"region:us"
] |
sentence-similarity
| 2025-06-20T08:11:37Z |
---
tags:
- ColBERT
- PyLate
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:310935
- loss:Contrastive
base_model: lightonai/GTE-ModernColBERT-v1
pipeline_tag: sentence-similarity
library_name: PyLate
metrics:
- accuracy
model-index:
- name: PyLate model based on lightonai/GTE-ModernColBERT-v1
results:
- task:
type: col-berttriplet
name: Col BERTTriplet
dataset:
name: Unknown
type: unknown
metrics:
- type: accuracy
value: 0.9512865543365479
name: Accuracy
---
# PyLate model based on lightonai/GTE-ModernColBERT-v1
This is a [PyLate](https://github.com/lightonai/pylate) model finetuned from [lightonai/GTE-ModernColBERT-v1](https://huggingface.co/lightonai/GTE-ModernColBERT-v1). It maps sentences & paragraphs to sequences of 128-dimensional dense vectors and can be used for semantic textual similarity using the MaxSim operator.
## Model Details
### Model Description
- **Model Type:** PyLate model
- **Base model:** [lightonai/GTE-ModernColBERT-v1](https://huggingface.co/lightonai/GTE-ModernColBERT-v1) <!-- at revision 78d50a162b04dfdc45c3af6b4294ba77c24888a3 -->
- **Document Length:** 300 tokens
- **Query Length:** 32 tokens
- **Output Dimensionality:** 128 tokens
- **Similarity Function:** MaxSim
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [PyLate Documentation](https://lightonai.github.io/pylate/)
- **Repository:** [PyLate on GitHub](https://github.com/lightonai/pylate)
- **Hugging Face:** [PyLate models on Hugging Face](https://huggingface.co/models?library=PyLate)
### Full Model Architecture
```
ColBERT(
(0): Transformer({'max_seq_length': 299, 'do_lower_case': False}) with Transformer model: ModernBertModel
(1): Dense({'in_features': 768, 'out_features': 128, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity'})
)
```
## Usage
First install the PyLate library:
```bash
pip install -U pylate
```
### Retrieval
PyLate provides a streamlined interface to index and retrieve documents using ColBERT models. The index leverages the Voyager HNSW index to efficiently handle document embeddings and enable fast retrieval.
#### Indexing documents
First, load the ColBERT model and initialize the Voyager index, then encode and index your documents:
```python
from pylate import indexes, models, retrieve
# Step 1: Load the ColBERT model
model = models.ColBERT(
model_name_or_path=pylate_model_id,
)
# Step 2: Initialize the Voyager index
index = indexes.Voyager(
index_folder="pylate-index",
index_name="index",
override=True, # This overwrites the existing index if any
)
# Step 3: Encode the documents
documents_ids = ["1", "2", "3"]
documents = ["document 1 text", "document 2 text", "document 3 text"]
documents_embeddings = model.encode(
documents,
batch_size=32,
is_query=False, # Ensure that it is set to False to indicate that these are documents, not queries
show_progress_bar=True,
)
# Step 4: Add document embeddings to the index by providing embeddings and corresponding ids
index.add_documents(
documents_ids=documents_ids,
documents_embeddings=documents_embeddings,
)
```
Note that you do not have to recreate the index and encode the documents every time. Once you have created an index and added the documents, you can re-use the index later by loading it:
```python
# To load an index, simply instantiate it with the correct folder/name and without overriding it
index = indexes.Voyager(
index_folder="pylate-index",
index_name="index",
)
```
#### Retrieving top-k documents for queries
Once the documents are indexed, you can retrieve the top-k most relevant documents for a given set of queries.
To do so, initialize the ColBERT retriever with the index you want to search in, encode the queries and then retrieve the top-k documents to get the top matches ids and relevance scores:
```python
# Step 1: Initialize the ColBERT retriever
retriever = retrieve.ColBERT(index=index)
# Step 2: Encode the queries
queries_embeddings = model.encode(
["query for document 3", "query for document 1"],
batch_size=32,
is_query=True, # # Ensure that it is set to False to indicate that these are queries
show_progress_bar=True,
)
# Step 3: Retrieve top-k documents
scores = retriever.retrieve(
queries_embeddings=queries_embeddings,
k=10, # Retrieve the top 10 matches for each query
)
```
### Reranking
If you only want to use the ColBERT model to perform reranking on top of your first-stage retrieval pipeline without building an index, you can simply use rank function and pass the queries and documents to rerank:
```python
from pylate import rank, models
queries = [
"query A",
"query B",
]
documents = [
["document A", "document B"],
["document 1", "document C", "document B"],
]
documents_ids = [
[1, 2],
[1, 3, 2],
]
model = models.ColBERT(
model_name_or_path=pylate_model_id,
)
queries_embeddings = model.encode(
queries,
is_query=True,
)
documents_embeddings = model.encode(
documents,
is_query=False,
)
reranked_documents = rank.rerank(
documents_ids=documents_ids,
queries_embeddings=queries_embeddings,
documents_embeddings=documents_embeddings,
)
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
## Evaluation
### Metrics
#### Col BERTTriplet
* Evaluated with <code>pylate.evaluation.colbert_triplet.ColBERTTripletEvaluator</code>
| Metric | Value |
|:-------------|:-----------|
| **accuracy** | **0.9513** |
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 310,935 training samples
* Columns: <code>query</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | query | positive | negative |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 6 tokens</li><li>mean: 24.92 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 20.06 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 24.7 tokens</li><li>max: 32 tokens</li></ul> |
* Samples:
| query | positive | negative |
|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>The primary objective of enacting a inheritance tax is to mitigate economic inequality and redistribute wealth among the poorer sections of society, although various empirical studies have demonstrated a lack of correlation between the two.</code> | <code>The principal goal of establishing estate duties as a form of taxation is not solely to address the problem of economic disparity, but more importantly, to redistribute wealth in an equitable manner so as to reduce the vast gap between the rich and the relatively poor segments of the population.</code> | <code>In a bid to abide by international agreements and world peaceful coexistence standards, most European nations have set up strict fiscal policies ensuring a strong relationship with neighboring countries, including strategic partnerships to promote tourism, as much as quotas to restrict immigration and asylum seekers.</code> |
| <code>Usability Evaluation Report for the New Web Application<br>Introduction<br>This usability evaluation was conducted to identify issues related to user experience and provide recommendations for improving the overall usability of the new web application. The evaluation focused on the login and registration process, navigation, and search functionality.<br>Methodology<br>The evaluation consisted of user testing and heuristic evaluation. A total of five participants were recruited to participate in the user testing, and each participant was asked to complete several tasks using the web application. The participants' interactions with the application were observed and recorded. Heuristic evaluation was conducted based on a set of well-established usability principles to identify potential usability issues in the application's design and functionality.<br>Results<br>During the user testing, several usability issues were identified. These included difficulties in locating the login and registration features, p...</code> | <code>Design Document: Home and Landing Page Redesign for New Web Application<br>Executive Summary<br>As part of an ongoing effort to improve the user experience and engagement for the new web application, this project focuses on the redesign of the home and landing page. The new design will address usability issues identified in a previous evaluation, make the application more appealing to users, and help drive sales and conversions. The following report includes the design requirements, a full design specification, and guidance for implementation.<br>Goals and Objectives<br>The main goals of this project include: to redesign the home and landing pages to give users an improved first impression of the application; to improve task completion times and create a seamless user experience; to increase conversion rates by reducing bounce rates and making it easier for users to find the information they need.<br>Scope of Work<br>The redesign of the home and landing pages includes: creating a clear visual hierarchy ...</code> | <code>Designing Effective User Interfaces for Virtual Reality ApplicationsIntroductionVirtual reality (VR) technology has been rapidly advancing in recent years, with applications in various fields such as gaming, education, and healthcare. As VR continues to grow in popularity, the need for effective user interfaces has become increasingly important. A well-designed user interface can enhance the overall VR experience, while a poorly designed one can lead to frustration and disorientation.Principles of Effective VR User Interface Design1. Intuitive Interaction: The primary goal of a VR user interface is to provide an intuitive and natural way for users to interact with the virtual environment. This can be achieved through the use of gestures, voice commands, or other innovative methods.2. Visual Feedback: Visual feedback is crucial in VR, as it helps users understand the consequences of their actions. This can be in the form of animations, particles, or other visual effects that provide a c...</code> |
| <code>The manager of the local conservation society recently explained measures for sustainable wildlife preservation.</code> | <code>The conservation society's manager recently explained measures for preserving wildlife sustainably.</code> | <code>After explaining university education requirements, the career counsellor also talked about wildlife preservation jobs.</code> |
* Loss: <code>pylate.losses.contrastive.Contrastive</code>
### Evaluation Dataset
#### Unnamed Dataset
* Size: 34,549 evaluation samples
* Columns: <code>query</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | query | positive | negative |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 6 tokens</li><li>mean: 24.32 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 19.37 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 24.12 tokens</li><li>max: 32 tokens</li></ul> |
* Samples:
| query | positive | negative |
|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>In a magical forest, there lived a group of animals that loved to dance under the stars. They danced to the rhythm of the crickets and felt the magic of the night.</code> | <code>In a magical forest, there lived a group of animals that loved to dance under the stars on a lovely night. They danced to the rhythm of the crickets.</code> | <code>The forest was a wonderful place where animals could sing and dance to the sounds of nature. Some liked the rustling of leaves, while others liked the buzzing of bees. But they all loved the music of a babbling brook.</code> |
| <code>Given this reasoning-intensive query, find relevant documents that could help answer the question. </code> | <code>food_percent/2063AApplicationsLeontiefModels_149.txt</code> | <code>The use of matrix equations in computer graphics is gaining significant attention in recent years. In computer-aided design (CAD), matrix equations play a crucial role in transforming 2D and 3D objects. For instance, when designing a car model, the CAD software uses matrix equations to rotate, translate, and scale the object. The transformation matrix is a 4x4 matrix that stores the coordinates of the object and performs the required operations. Similarly, in computer gaming, matrix equations are used to animate characters and objects in 3D space. The game developers use transformation matrices to create realistic movements and interactions between objects. However, the complexity of these transformations leads to a high computational cost, making it difficult to achieve real-time rendering. To address this challenge, researchers are exploring the use of machine learning algorithms to optimize the transformation process. For example, a research paper titled 'Matrix Equation-Based 6-DoF...</code> |
| <code>A study found that the use of virtual reality in therapy sessions can have a positive effect on mental health by reducing stress and anxiety.</code> | <code>A therapy session using virtual reality can significantly reduce patient stress and anxiety.</code> | <code>Research on artificial intelligence in mental health has also led to the innovation of virtual robots for therapy.</code> |
* Loss: <code>pylate.losses.contrastive.Contrastive</code>
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 32
- `gradient_accumulation_steps`: 2
- `learning_rate`: 2e-05
- `weight_decay`: 0.01
- `num_train_epochs`: 10
- `warmup_steps`: 100
- `fp16`: True
- `remove_unused_columns`: False
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 32
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 2
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 2e-05
- `weight_decay`: 0.01
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 10
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.0
- `warmup_steps`: 100
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: True
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: False
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
<details><summary>Click to expand</summary>
| Epoch | Step | Training Loss | Validation Loss | accuracy |
|:------:|:-----:|:-------------:|:---------------:|:--------:|
| 0.0051 | 50 | 4.8488 | - | - |
| 0.0103 | 100 | 2.2402 | - | - |
| 0.0154 | 150 | 1.8204 | - | - |
| 0.0206 | 200 | 1.7765 | - | - |
| 0.0257 | 250 | 1.7482 | - | - |
| 0 | 0 | - | - | 0.9227 |
| 0.0257 | 250 | - | 1.1625 | - |
| 0.0309 | 300 | 1.7821 | - | - |
| 0.0360 | 350 | 1.6761 | - | - |
| 0.0412 | 400 | 1.4887 | - | - |
| 0.0463 | 450 | 1.6001 | - | - |
| 0.0515 | 500 | 1.7426 | - | - |
| 0 | 0 | - | - | 0.9317 |
| 0.0515 | 500 | - | 1.1088 | - |
| 0.0566 | 550 | 1.5562 | - | - |
| 0.0617 | 600 | 1.6811 | - | - |
| 0.0669 | 650 | 1.5994 | - | - |
| 0.0720 | 700 | 1.5981 | - | - |
| 0.0772 | 750 | 1.5713 | - | - |
| 0 | 0 | - | - | 0.9369 |
| 0.0772 | 750 | - | 1.0817 | - |
| 0.0823 | 800 | 1.6516 | - | - |
| 0.0875 | 850 | 1.5768 | - | - |
| 0.0926 | 900 | 1.5902 | - | - |
| 0.0978 | 950 | 1.4613 | - | - |
| 0.1029 | 1000 | 1.6295 | - | - |
| 0 | 0 | - | - | 0.9374 |
| 0.1029 | 1000 | - | 1.0677 | - |
| 0.1081 | 1050 | 1.5301 | - | - |
| 0.1132 | 1100 | 1.6072 | - | - |
| 0.1183 | 1150 | 1.4644 | - | - |
| 0.1235 | 1200 | 1.6331 | - | - |
| 0.1286 | 1250 | 1.5464 | - | - |
| 0 | 0 | - | - | 0.9408 |
| 0.1286 | 1250 | - | 1.0547 | - |
| 0.1338 | 1300 | 1.5406 | - | - |
| 0.1389 | 1350 | 1.5471 | - | - |
| 0.1441 | 1400 | 1.6685 | - | - |
| 0.1492 | 1450 | 1.5644 | - | - |
| 0.1544 | 1500 | 1.6587 | - | - |
| 0 | 0 | - | - | 0.9420 |
| 0.1544 | 1500 | - | 1.0590 | - |
| 0.1595 | 1550 | 1.5793 | - | - |
| 0.1647 | 1600 | 1.4877 | - | - |
| 0.1698 | 1650 | 1.5781 | - | - |
| 0.1750 | 1700 | 1.5081 | - | - |
| 0.1801 | 1750 | 1.5434 | - | - |
| 0 | 0 | - | - | 0.9396 |
| 0.1801 | 1750 | - | 1.0564 | - |
| 0.1852 | 1800 | 1.4617 | - | - |
| 0.1904 | 1850 | 1.4531 | - | - |
| 0.1955 | 1900 | 1.5713 | - | - |
| 0.2007 | 1950 | 1.5166 | - | - |
| 0.2058 | 2000 | 1.4771 | - | - |
| 0 | 0 | - | - | 0.9431 |
| 0.2058 | 2000 | - | 1.0344 | - |
| 0.2110 | 2050 | 1.4706 | - | - |
| 0.2161 | 2100 | 1.5276 | - | - |
| 0.2213 | 2150 | 1.4002 | - | - |
| 0.2264 | 2200 | 1.5605 | - | - |
| 0.2316 | 2250 | 1.4871 | - | - |
| 0 | 0 | - | - | 0.9441 |
| 0.2316 | 2250 | - | 1.0355 | - |
| 0.2367 | 2300 | 1.56 | - | - |
| 0.2418 | 2350 | 1.4322 | - | - |
| 0.2470 | 2400 | 1.4682 | - | - |
| 0.2521 | 2450 | 1.4375 | - | - |
| 0.2573 | 2500 | 1.4499 | - | - |
| 0 | 0 | - | - | 0.9434 |
| 0.2573 | 2500 | - | 1.0306 | - |
| 0.2624 | 2550 | 1.5088 | - | - |
| 0.2676 | 2600 | 1.5577 | - | - |
| 0.2727 | 2650 | 1.4221 | - | - |
| 0.2779 | 2700 | 1.5105 | - | - |
| 0.2830 | 2750 | 1.4681 | - | - |
| 0 | 0 | - | - | 0.9453 |
| 0.2830 | 2750 | - | 1.0219 | - |
| 0.2882 | 2800 | 1.4354 | - | - |
| 0.2933 | 2850 | 1.4982 | - | - |
| 0.2984 | 2900 | 1.5374 | - | - |
| 0.3036 | 2950 | 1.4769 | - | - |
| 0.3087 | 3000 | 1.5767 | - | - |
| 0 | 0 | - | - | 0.9450 |
| 0.3087 | 3000 | - | 1.0168 | - |
| 0.3139 | 3050 | 1.3712 | - | - |
| 0.3190 | 3100 | 1.4979 | - | - |
| 0.3242 | 3150 | 1.4633 | - | - |
| 0.3293 | 3200 | 1.5025 | - | - |
| 0.3345 | 3250 | 1.5206 | - | - |
| 0 | 0 | - | - | 0.9457 |
| 0.3345 | 3250 | - | 1.0161 | - |
| 0.3396 | 3300 | 1.5119 | - | - |
| 0.3448 | 3350 | 1.6285 | - | - |
| 0.3499 | 3400 | 1.4421 | - | - |
| 0.3550 | 3450 | 1.4866 | - | - |
| 0.3602 | 3500 | 1.4651 | - | - |
| 0 | 0 | - | - | 0.9465 |
| 0.3602 | 3500 | - | 1.0085 | - |
| 0.3653 | 3550 | 1.3777 | - | - |
| 0.3705 | 3600 | 1.5256 | - | - |
| 0.3756 | 3650 | 1.358 | - | - |
| 0.3808 | 3700 | 1.4384 | - | - |
| 0.3859 | 3750 | 1.4847 | - | - |
| 0 | 0 | - | - | 0.9461 |
| 0.3859 | 3750 | - | 1.0093 | - |
| 0.3911 | 3800 | 1.327 | - | - |
| 0.3962 | 3850 | 1.4463 | - | - |
| 0.4014 | 3900 | 1.3179 | - | - |
| 0.4065 | 3950 | 1.4312 | - | - |
| 0.4116 | 4000 | 1.4179 | - | - |
| 0 | 0 | - | - | 0.9460 |
| 0.4116 | 4000 | - | 1.0145 | - |
| 0.4168 | 4050 | 1.4828 | - | - |
| 0.4219 | 4100 | 1.4568 | - | - |
| 0.4271 | 4150 | 1.4921 | - | - |
| 0.4322 | 4200 | 1.4485 | - | - |
| 0.4374 | 4250 | 1.4908 | - | - |
| 0 | 0 | - | - | 0.9478 |
| 0.4374 | 4250 | - | 1.0121 | - |
| 0.4425 | 4300 | 1.295 | - | - |
| 0.4477 | 4350 | 1.4687 | - | - |
| 0.4528 | 4400 | 1.3846 | - | - |
| 0.4580 | 4450 | 1.4704 | - | - |
| 0.4631 | 4500 | 1.3646 | - | - |
| 0 | 0 | - | - | 0.9480 |
| 0.4631 | 4500 | - | 1.0056 | - |
| 0.4683 | 4550 | 1.4779 | - | - |
| 0.4734 | 4600 | 1.4581 | - | - |
| 0.4785 | 4650 | 1.3786 | - | - |
| 0.4837 | 4700 | 1.56 | - | - |
| 0.4888 | 4750 | 1.4334 | - | - |
| 0 | 0 | - | - | 0.9475 |
| 0.4888 | 4750 | - | 1.0032 | - |
| 0.4940 | 4800 | 1.3877 | - | - |
| 0.4991 | 4850 | 1.3485 | - | - |
| 0.5043 | 4900 | 1.4509 | - | - |
| 0.5094 | 4950 | 1.3693 | - | - |
| 0.5146 | 5000 | 1.5226 | - | - |
| 0 | 0 | - | - | 0.9477 |
| 0.5146 | 5000 | - | 0.9976 | - |
| 0.5197 | 5050 | 1.4423 | - | - |
| 0.5249 | 5100 | 1.4191 | - | - |
| 0.5300 | 5150 | 1.5109 | - | - |
| 0.5351 | 5200 | 1.4509 | - | - |
| 0.5403 | 5250 | 1.4351 | - | - |
| 0 | 0 | - | - | 0.9486 |
| 0.5403 | 5250 | - | 1.0001 | - |
| 0.5454 | 5300 | 1.3868 | - | - |
| 0.5506 | 5350 | 1.4339 | - | - |
| 0.5557 | 5400 | 1.365 | - | - |
| 0.5609 | 5450 | 1.44 | - | - |
| 0.5660 | 5500 | 1.2895 | - | - |
| 0 | 0 | - | - | 0.9491 |
| 0.5660 | 5500 | - | 1.0065 | - |
| 0.5712 | 5550 | 1.4253 | - | - |
| 0.5763 | 5600 | 1.4438 | - | - |
| 0.5815 | 5650 | 1.3543 | - | - |
| 0.5866 | 5700 | 1.5587 | - | - |
| 0.5917 | 5750 | 1.342 | - | - |
| 0 | 0 | - | - | 0.9488 |
| 0.5917 | 5750 | - | 0.9927 | - |
| 0.5969 | 5800 | 1.4503 | - | - |
| 0.6020 | 5850 | 1.4045 | - | - |
| 0.6072 | 5900 | 1.4092 | - | - |
| 0.6123 | 5950 | 1.3318 | - | - |
| 0.6175 | 6000 | 1.416 | - | - |
| 0 | 0 | - | - | 0.9504 |
| 0.6175 | 6000 | - | 0.9910 | - |
| 0.6226 | 6050 | 1.5132 | - | - |
| 0.6278 | 6100 | 1.3275 | - | - |
| 0.6329 | 6150 | 1.4595 | - | - |
| 0.6381 | 6200 | 1.5112 | - | - |
| 0.6432 | 6250 | 1.4435 | - | - |
| 0 | 0 | - | - | 0.9515 |
| 0.6432 | 6250 | - | 0.9928 | - |
| 0.6483 | 6300 | 1.4268 | - | - |
| 0.6535 | 6350 | 1.5071 | - | - |
| 0.6586 | 6400 | 1.3817 | - | - |
| 0.6638 | 6450 | 1.5101 | - | - |
| 0.6689 | 6500 | 1.4014 | - | - |
| 0 | 0 | - | - | 0.9490 |
| 0.6689 | 6500 | - | 0.9954 | - |
| 0.6741 | 6550 | 1.2797 | - | - |
| 0.6792 | 6600 | 1.3829 | - | - |
| 0.6844 | 6650 | 1.4907 | - | - |
| 0.6895 | 6700 | 1.4098 | - | - |
| 0.6947 | 6750 | 1.482 | - | - |
| 0 | 0 | - | - | 0.9492 |
| 0.6947 | 6750 | - | 0.9937 | - |
| 0.6998 | 6800 | 1.3779 | - | - |
| 0.7050 | 6850 | 1.3791 | - | - |
| 0.7101 | 6900 | 1.5183 | - | - |
| 0.7152 | 6950 | 1.4022 | - | - |
| 0.7204 | 7000 | 1.544 | - | - |
| 0 | 0 | - | - | 0.9508 |
| 0.7204 | 7000 | - | 0.9935 | - |
| 0.7255 | 7050 | 1.4566 | - | - |
| 0.7307 | 7100 | 1.4641 | - | - |
| 0.7358 | 7150 | 1.4208 | - | - |
| 0.7410 | 7200 | 1.3391 | - | - |
| 0.7461 | 7250 | 1.5002 | - | - |
| 0 | 0 | - | - | 0.9497 |
| 0.7461 | 7250 | - | 0.9861 | - |
| 0.7513 | 7300 | 1.2985 | - | - |
| 0.7564 | 7350 | 1.5496 | - | - |
| 0.7616 | 7400 | 1.5046 | - | - |
| 0.7667 | 7450 | 1.3687 | - | - |
| 0.7718 | 7500 | 1.3841 | - | - |
| 0 | 0 | - | - | 0.9501 |
| 0.7718 | 7500 | - | 0.9868 | - |
| 0.7770 | 7550 | 1.3996 | - | - |
| 0.7821 | 7600 | 1.5112 | - | - |
| 0.7873 | 7650 | 1.4335 | - | - |
| 0.7924 | 7700 | 1.3867 | - | - |
| 0.7976 | 7750 | 1.3865 | - | - |
| 0 | 0 | - | - | 0.9511 |
| 0.7976 | 7750 | - | 0.9863 | - |
| 0.8027 | 7800 | 1.4039 | - | - |
| 0.8079 | 7850 | 1.379 | - | - |
| 0.8130 | 7900 | 1.3459 | - | - |
| 0.8182 | 7950 | 1.3996 | - | - |
| 0.8233 | 8000 | 1.4151 | - | - |
| 0 | 0 | - | - | 0.9511 |
| 0.8233 | 8000 | - | 0.9822 | - |
| 0.8284 | 8050 | 1.3745 | - | - |
| 0.8336 | 8100 | 1.4404 | - | - |
| 0.8387 | 8150 | 1.4776 | - | - |
| 0.8439 | 8200 | 1.398 | - | - |
| 0.8490 | 8250 | 1.4482 | - | - |
| 0 | 0 | - | - | 0.9506 |
| 0.8490 | 8250 | - | 0.9803 | - |
| 0.8542 | 8300 | 1.4551 | - | - |
| 0.8593 | 8350 | 1.46 | - | - |
| 0.8645 | 8400 | 1.5179 | - | - |
| 0.8696 | 8450 | 1.4067 | - | - |
| 0.8748 | 8500 | 1.4393 | - | - |
| 0 | 0 | - | - | 0.9504 |
| 0.8748 | 8500 | - | 0.9809 | - |
| 0.8799 | 8550 | 1.4995 | - | - |
| 0.8850 | 8600 | 1.4077 | - | - |
| 0.8902 | 8650 | 1.4088 | - | - |
| 0.8953 | 8700 | 1.3464 | - | - |
| 0.9005 | 8750 | 1.3455 | - | - |
| 0 | 0 | - | - | 0.9506 |
| 0.9005 | 8750 | - | 0.9797 | - |
| 0.9056 | 8800 | 1.5172 | - | - |
| 0.9108 | 8850 | 1.3922 | - | - |
| 0.9159 | 8900 | 1.3645 | - | - |
| 0.9211 | 8950 | 1.3627 | - | - |
| 0.9262 | 9000 | 1.3896 | - | - |
| 0 | 0 | - | - | 0.9506 |
| 0.9262 | 9000 | - | 0.9806 | - |
| 0.9314 | 9050 | 1.433 | - | - |
| 0.9365 | 9100 | 1.4678 | - | - |
| 0.9416 | 9150 | 1.3206 | - | - |
| 0.9468 | 9200 | 1.4589 | - | - |
| 0.9519 | 9250 | 1.3494 | - | - |
| 0 | 0 | - | - | 0.9509 |
| 0.9519 | 9250 | - | 0.9761 | - |
| 0.9571 | 9300 | 1.3768 | - | - |
| 0.9622 | 9350 | 1.4449 | - | - |
| 0.9674 | 9400 | 1.4187 | - | - |
| 0.9725 | 9450 | 1.3046 | - | - |
| 0.9777 | 9500 | 1.3586 | - | - |
| 0 | 0 | - | - | 0.9512 |
| 0.9777 | 9500 | - | 0.9817 | - |
| 0.9828 | 9550 | 1.4631 | - | - |
| 0.9880 | 9600 | 1.3113 | - | - |
| 0.9931 | 9650 | 1.2972 | - | - |
| 0.9983 | 9700 | 1.3793 | - | - |
| 1.0034 | 9750 | 1.1729 | - | - |
| 0 | 0 | - | - | 0.9509 |
| 1.0034 | 9750 | - | 0.9847 | - |
| 1.0085 | 9800 | 1.2009 | - | - |
| 1.0137 | 9850 | 1.2576 | - | - |
| 1.0188 | 9900 | 1.3483 | - | - |
| 1.0240 | 9950 | 1.2609 | - | - |
| 1.0291 | 10000 | 1.3099 | - | - |
| 0 | 0 | - | - | 0.9513 |
| 1.0291 | 10000 | - | 0.9895 | - |
| 1.0343 | 10050 | 1.2224 | - | - |
| 1.0394 | 10100 | 1.3552 | - | - |
| 1.0446 | 10150 | 1.3508 | - | - |
| 1.0497 | 10200 | 1.3242 | - | - |
| 1.0549 | 10250 | 1.2287 | - | - |
| 0 | 0 | - | - | 0.9512 |
| 1.0549 | 10250 | - | 0.9977 | - |
| 1.0600 | 10300 | 1.2863 | - | - |
| 1.0651 | 10350 | 1.2377 | - | - |
| 1.0703 | 10400 | 1.3058 | - | - |
| 1.0754 | 10450 | 1.3013 | - | - |
| 1.0806 | 10500 | 1.3233 | - | - |
| 0 | 0 | - | - | 0.9488 |
| 1.0806 | 10500 | - | 0.9948 | - |
| 1.0857 | 10550 | 1.334 | - | - |
| 1.0909 | 10600 | 1.246 | - | - |
| 1.0960 | 10650 | 1.2298 | - | - |
| 1.1012 | 10700 | 1.2016 | - | - |
| 1.1063 | 10750 | 1.3035 | - | - |
| 0 | 0 | - | - | 0.9506 |
| 1.1063 | 10750 | - | 0.9947 | - |
| 1.1115 | 10800 | 1.2457 | - | - |
| 1.1166 | 10850 | 1.2882 | - | - |
| 1.1217 | 10900 | 1.2365 | - | - |
| 1.1269 | 10950 | 1.19 | - | - |
| 1.1320 | 11000 | 1.2377 | - | - |
| 0 | 0 | - | - | 0.9511 |
| 1.1320 | 11000 | - | 0.9915 | - |
| 1.1372 | 11050 | 1.3028 | - | - |
| 1.1423 | 11100 | 1.319 | - | - |
| 1.1475 | 11150 | 1.3315 | - | - |
| 1.1526 | 11200 | 1.2161 | - | - |
| 1.1578 | 11250 | 1.3555 | - | - |
| 0 | 0 | - | - | 0.9511 |
| 1.1578 | 11250 | - | 0.9902 | - |
| 1.1629 | 11300 | 1.1874 | - | - |
| 1.1681 | 11350 | 1.2373 | - | - |
| 1.1732 | 11400 | 1.2474 | - | - |
| 1.1783 | 11450 | 1.2838 | - | - |
| 1.1835 | 11500 | 1.2242 | - | - |
| 0 | 0 | - | - | 0.9518 |
| 1.1835 | 11500 | - | 0.9927 | - |
| 1.1886 | 11550 | 1.3123 | - | - |
| 1.1938 | 11600 | 1.2874 | - | - |
| 1.1989 | 11650 | 1.2568 | - | - |
| 1.2041 | 11700 | 1.2526 | - | - |
| 1.2092 | 11750 | 1.347 | - | - |
| 0 | 0 | - | - | 0.9509 |
| 1.2092 | 11750 | - | 0.9883 | - |
| 1.2144 | 11800 | 1.3098 | - | - |
| 1.2195 | 11850 | 1.2541 | - | - |
| 1.2247 | 11900 | 1.2791 | - | - |
| 1.2298 | 11950 | 1.2333 | - | - |
| 1.2349 | 12000 | 1.3827 | - | - |
| 0 | 0 | - | - | 0.9507 |
| 1.2349 | 12000 | - | 0.9943 | - |
| 1.2401 | 12050 | 1.2732 | - | - |
| 1.2452 | 12100 | 1.2993 | - | - |
| 1.2504 | 12150 | 1.2947 | - | - |
| 1.2555 | 12200 | 1.3001 | - | - |
| 1.2607 | 12250 | 1.2957 | - | - |
| 0 | 0 | - | - | 0.9514 |
| 1.2607 | 12250 | - | 0.9865 | - |
| 1.2658 | 12300 | 1.1393 | - | - |
| 1.2710 | 12350 | 1.2996 | - | - |
| 1.2761 | 12400 | 1.3218 | - | - |
| 1.2813 | 12450 | 1.2138 | - | - |
| 1.2864 | 12500 | 1.1731 | - | - |
| 0 | 0 | - | - | 0.9510 |
| 1.2864 | 12500 | - | 0.9964 | - |
| 1.2916 | 12550 | 1.3326 | - | - |
| 1.2967 | 12600 | 1.3575 | - | - |
| 1.3018 | 12650 | 1.2948 | - | - |
| 1.3070 | 12700 | 1.2921 | - | - |
| 1.3121 | 12750 | 1.3052 | - | - |
| 0 | 0 | - | - | 0.9509 |
| 1.3121 | 12750 | - | 0.9840 | - |
| 1.3173 | 12800 | 1.3662 | - | - |
| 1.3224 | 12850 | 1.3673 | - | - |
| 1.3276 | 12900 | 1.3006 | - | - |
| 1.3327 | 12950 | 1.4217 | - | - |
| 1.3379 | 13000 | 1.1608 | - | - |
| 0 | 0 | - | - | 0.9520 |
| 1.3379 | 13000 | - | 0.9848 | - |
| 1.3430 | 13050 | 1.2066 | - | - |
| 1.3482 | 13100 | 1.408 | - | - |
| 1.3533 | 13150 | 1.3574 | - | - |
| 1.3584 | 13200 | 1.3171 | - | - |
| 1.3636 | 13250 | 1.3188 | - | - |
| 0 | 0 | - | - | 0.9502 |
| 1.3636 | 13250 | - | 0.9888 | - |
| 1.3687 | 13300 | 1.299 | - | - |
| 1.3739 | 13350 | 1.3015 | - | - |
| 1.3790 | 13400 | 1.3159 | - | - |
| 1.3842 | 13450 | 1.2139 | - | - |
| 1.3893 | 13500 | 1.2855 | - | - |
| 0 | 0 | - | - | 0.9514 |
| 1.3893 | 13500 | - | 0.9957 | - |
| 1.3945 | 13550 | 1.2705 | - | - |
| 1.3996 | 13600 | 1.3099 | - | - |
| 1.4048 | 13650 | 1.3144 | - | - |
| 1.4099 | 13700 | 1.2948 | - | - |
| 1.4150 | 13750 | 1.3313 | - | - |
| 0 | 0 | - | - | 0.9512 |
| 1.4150 | 13750 | - | 0.9910 | - |
| 1.4202 | 13800 | 1.3473 | - | - |
| 1.4253 | 13850 | 1.2037 | - | - |
| 1.4305 | 13900 | 1.3059 | - | - |
| 1.4356 | 13950 | 1.3763 | - | - |
| 1.4408 | 14000 | 1.2606 | - | - |
| 0 | 0 | - | - | 0.9523 |
| 1.4408 | 14000 | - | 0.9876 | - |
| 1.4459 | 14050 | 1.2394 | - | - |
| 1.4511 | 14100 | 1.219 | - | - |
| 1.4562 | 14150 | 1.3501 | - | - |
| 1.4614 | 14200 | 1.2664 | - | - |
| 1.4665 | 14250 | 1.2704 | - | - |
| 0 | 0 | - | - | 0.9513 |
| 1.4665 | 14250 | - | 0.9945 | - |
| 1.4716 | 14300 | 1.2332 | - | - |
| 1.4768 | 14350 | 1.2286 | - | - |
| 1.4819 | 14400 | 1.2123 | - | - |
| 1.4871 | 14450 | 1.2437 | - | - |
| 1.4922 | 14500 | 1.2292 | - | - |
| 0 | 0 | - | - | 0.9502 |
| 1.4922 | 14500 | - | 0.9886 | - |
| 1.4974 | 14550 | 1.3007 | - | - |
| 1.5025 | 14600 | 1.308 | - | - |
| 1.5077 | 14650 | 1.174 | - | - |
| 1.5128 | 14700 | 1.2648 | - | - |
| 1.5180 | 14750 | 1.2533 | - | - |
| 0 | 0 | - | - | 0.9517 |
| 1.5180 | 14750 | - | 0.9885 | - |
| 1.5231 | 14800 | 1.2576 | - | - |
| 1.5282 | 14850 | 1.3659 | - | - |
| 1.5334 | 14900 | 1.298 | - | - |
| 1.5385 | 14950 | 1.2723 | - | - |
| 1.5437 | 15000 | 1.3099 | - | - |
| 0 | 0 | - | - | 0.9518 |
| 1.5437 | 15000 | - | 0.9875 | - |
| 1.5488 | 15050 | 1.2984 | - | - |
| 1.5540 | 15100 | 1.2128 | - | - |
| 1.5591 | 15150 | 1.2689 | - | - |
| 1.5643 | 15200 | 1.2516 | - | - |
| 1.5694 | 15250 | 1.3028 | - | - |
| 0 | 0 | - | - | 0.9523 |
| 1.5694 | 15250 | - | 0.9856 | - |
| 1.5746 | 15300 | 1.3619 | - | - |
| 1.5797 | 15350 | 1.3524 | - | - |
| 1.5849 | 15400 | 1.1749 | - | - |
| 1.5900 | 15450 | 1.205 | - | - |
| 1.5951 | 15500 | 1.297 | - | - |
| 0 | 0 | - | - | 0.9513 |
| 1.5951 | 15500 | - | 0.9780 | - |
| 1.6003 | 15550 | 1.2469 | - | - |
| 1.6054 | 15600 | 1.2285 | - | - |
| 1.6106 | 15650 | 1.2963 | - | - |
| 1.6157 | 15700 | 1.2406 | - | - |
| 1.6209 | 15750 | 1.3049 | - | - |
| 0 | 0 | - | - | 0.9512 |
| 1.6209 | 15750 | - | 0.9873 | - |
| 1.6260 | 15800 | 1.2174 | - | - |
| 1.6312 | 15850 | 1.2789 | - | - |
| 1.6363 | 15900 | 1.289 | - | - |
| 1.6415 | 15950 | 1.3242 | - | - |
| 1.6466 | 16000 | 1.2974 | - | - |
| 0 | 0 | - | - | 0.9522 |
| 1.6466 | 16000 | - | 0.9755 | - |
| 1.6517 | 16050 | 1.2741 | - | - |
| 1.6569 | 16100 | 1.1625 | - | - |
| 1.6620 | 16150 | 1.2795 | - | - |
| 1.6672 | 16200 | 1.2301 | - | - |
| 1.6723 | 16250 | 1.2348 | - | - |
| 0 | 0 | - | - | 0.9528 |
| 1.6723 | 16250 | - | 0.9801 | - |
| 1.6775 | 16300 | 1.2408 | - | - |
| 1.6826 | 16350 | 1.2477 | - | - |
| 1.6878 | 16400 | 1.3386 | - | - |
| 1.6929 | 16450 | 1.2346 | - | - |
| 1.6981 | 16500 | 1.2904 | - | - |
| 0 | 0 | - | - | 0.9520 |
| 1.6981 | 16500 | - | 0.9906 | - |
| 1.7032 | 16550 | 1.2947 | - | - |
| 1.7083 | 16600 | 1.2572 | - | - |
| 1.7135 | 16650 | 1.2738 | - | - |
| 1.7186 | 16700 | 1.2686 | - | - |
| 1.7238 | 16750 | 1.4041 | - | - |
| 0 | 0 | - | - | 0.9528 |
| 1.7238 | 16750 | - | 0.9791 | - |
| 1.7289 | 16800 | 1.2935 | - | - |
| 1.7341 | 16850 | 1.2501 | - | - |
| 1.7392 | 16900 | 1.3208 | - | - |
| 1.7444 | 16950 | 1.2486 | - | - |
| 1.7495 | 17000 | 1.2587 | - | - |
| 0 | 0 | - | - | 0.9520 |
| 1.7495 | 17000 | - | 0.9862 | - |
| 1.7547 | 17050 | 1.3325 | - | - |
| 1.7598 | 17100 | 1.3104 | - | - |
| 1.7649 | 17150 | 1.2504 | - | - |
| 1.7701 | 17200 | 1.3153 | - | - |
| 1.7752 | 17250 | 1.328 | - | - |
| 0 | 0 | - | - | 0.9530 |
| 1.7752 | 17250 | - | 0.9803 | - |
| 1.7804 | 17300 | 1.3417 | - | - |
| 1.7855 | 17350 | 1.2486 | - | - |
| 1.7907 | 17400 | 1.2869 | - | - |
| 1.7958 | 17450 | 1.3599 | - | - |
| 1.8010 | 17500 | 1.2822 | - | - |
| 0 | 0 | - | - | 0.9526 |
| 1.8010 | 17500 | - | 0.9847 | - |
| 1.8061 | 17550 | 1.3001 | - | - |
| 1.8113 | 17600 | 1.0848 | - | - |
| 1.8164 | 17650 | 1.3171 | - | - |
| 1.8215 | 17700 | 1.3387 | - | - |
| 1.8267 | 17750 | 1.2401 | - | - |
| 0 | 0 | - | - | 0.9528 |
| 1.8267 | 17750 | - | 0.9804 | - |
| 1.8318 | 17800 | 1.2979 | - | - |
| 1.8370 | 17850 | 1.2222 | - | - |
| 1.8421 | 17900 | 1.27 | - | - |
| 1.8473 | 17950 | 1.3109 | - | - |
| 1.8524 | 18000 | 1.2306 | - | - |
| 0 | 0 | - | - | 0.9537 |
| 1.8524 | 18000 | - | 0.9876 | - |
| 1.8576 | 18050 | 1.1878 | - | - |
| 1.8627 | 18100 | 1.2398 | - | - |
| 1.8679 | 18150 | 1.2576 | - | - |
| 1.8730 | 18200 | 1.1579 | - | - |
| 1.8782 | 18250 | 1.2889 | - | - |
| 0 | 0 | - | - | 0.9519 |
| 1.8782 | 18250 | - | 0.9859 | - |
| 1.8833 | 18300 | 1.3331 | - | - |
| 1.8884 | 18350 | 1.2957 | - | - |
| 1.8936 | 18400 | 1.2286 | - | - |
| 1.8987 | 18450 | 1.2513 | - | - |
| 1.9039 | 18500 | 1.1702 | - | - |
| 0 | 0 | - | - | 0.9541 |
| 1.9039 | 18500 | - | 0.9840 | - |
| 1.9090 | 18550 | 1.3181 | - | - |
| 1.9142 | 18600 | 1.1976 | - | - |
| 1.9193 | 18650 | 1.3623 | - | - |
| 1.9245 | 18700 | 1.2594 | - | - |
| 1.9296 | 18750 | 1.2902 | - | - |
| 0 | 0 | - | - | 0.9522 |
| 1.9296 | 18750 | - | 0.9844 | - |
| 1.9348 | 18800 | 1.3283 | - | - |
| 1.9399 | 18850 | 1.2987 | - | - |
| 1.9450 | 18900 | 1.1987 | - | - |
| 1.9502 | 18950 | 1.2385 | - | - |
| 1.9553 | 19000 | 1.2772 | - | - |
| 0 | 0 | - | - | 0.9533 |
| 1.9553 | 19000 | - | 0.9861 | - |
| 1.9605 | 19050 | 1.1906 | - | - |
| 1.9656 | 19100 | 1.3041 | - | - |
| 1.9708 | 19150 | 1.2345 | - | - |
| 1.9759 | 19200 | 1.2586 | - | - |
| 1.9811 | 19250 | 1.196 | - | - |
| 0 | 0 | - | - | 0.9522 |
| 1.9811 | 19250 | - | 0.9835 | - |
| 1.9862 | 19300 | 1.2872 | - | - |
| 1.9914 | 19350 | 1.2449 | - | - |
| 1.9965 | 19400 | 1.2435 | - | - |
| 2.0016 | 19450 | 1.3096 | - | - |
| 2.0068 | 19500 | 1.1697 | - | - |
| 0 | 0 | - | - | 0.9514 |
| 2.0068 | 19500 | - | 1.0036 | - |
| 2.0119 | 19550 | 1.0556 | - | - |
| 2.0171 | 19600 | 1.1592 | - | - |
| 2.0222 | 19650 | 1.1808 | - | - |
| 2.0274 | 19700 | 1.141 | - | - |
| 2.0325 | 19750 | 1.1139 | - | - |
| 0 | 0 | - | - | 0.9517 |
| 2.0325 | 19750 | - | 1.0205 | - |
| 2.0377 | 19800 | 1.1959 | - | - |
| 2.0428 | 19850 | 1.0762 | - | - |
| 2.0480 | 19900 | 1.3522 | - | - |
| 2.0531 | 19950 | 1.1175 | - | - |
| 2.0582 | 20000 | 1.178 | - | - |
| 0 | 0 | - | - | 0.9512 |
| 2.0582 | 20000 | - | 1.0184 | - |
| 2.0634 | 20050 | 1.1416 | - | - |
| 2.0685 | 20100 | 1.1523 | - | - |
| 2.0737 | 20150 | 1.2561 | - | - |
| 2.0788 | 20200 | 1.119 | - | - |
| 2.0840 | 20250 | 1.095 | - | - |
| 0 | 0 | - | - | 0.9504 |
| 2.0840 | 20250 | - | 1.0155 | - |
| 2.0891 | 20300 | 1.1432 | - | - |
| 2.0943 | 20350 | 1.1455 | - | - |
| 2.0994 | 20400 | 1.0913 | - | - |
| 2.1046 | 20450 | 1.1671 | - | - |
| 2.1097 | 20500 | 1.2776 | - | - |
| 0 | 0 | - | - | 0.9514 |
| 2.1097 | 20500 | - | 1.0334 | - |
| 2.1149 | 20550 | 1.3092 | - | - |
| 2.1200 | 20600 | 1.1981 | - | - |
| 2.1251 | 20650 | 1.1399 | - | - |
| 2.1303 | 20700 | 1.0976 | - | - |
| 2.1354 | 20750 | 1.1335 | - | - |
| 0 | 0 | - | - | 0.9518 |
| 2.1354 | 20750 | - | 1.0136 | - |
| 2.1406 | 20800 | 1.1567 | - | - |
| 2.1457 | 20850 | 1.2536 | - | - |
| 2.1509 | 20900 | 1.1717 | - | - |
| 2.1560 | 20950 | 1.1433 | - | - |
| 2.1612 | 21000 | 1.1885 | - | - |
| 0 | 0 | - | - | 0.9512 |
| 2.1612 | 21000 | - | 1.0185 | - |
| 2.1663 | 21050 | 1.0543 | - | - |
| 2.1715 | 21100 | 1.1122 | - | - |
| 2.1766 | 21150 | 1.17 | - | - |
| 2.1817 | 21200 | 1.0757 | - | - |
| 2.1869 | 21250 | 1.3008 | - | - |
| 0 | 0 | - | - | 0.9506 |
| 2.1869 | 21250 | - | 1.0161 | - |
| 2.1920 | 21300 | 1.1723 | - | - |
| 2.1972 | 21350 | 1.2517 | - | - |
| 2.2023 | 21400 | 1.1834 | - | - |
| 2.2075 | 21450 | 1.1284 | - | - |
| 2.2126 | 21500 | 1.28 | - | - |
| 0 | 0 | - | - | 0.9507 |
| 2.2126 | 21500 | - | 1.0217 | - |
| 2.2178 | 21550 | 1.2478 | - | - |
| 2.2229 | 21600 | 1.1798 | - | - |
| 2.2281 | 21650 | 1.1218 | - | - |
| 2.2332 | 21700 | 1.2787 | - | - |
| 2.2383 | 21750 | 1.1254 | - | - |
| 0 | 0 | - | - | 0.9508 |
| 2.2383 | 21750 | - | 1.0312 | - |
| 2.2435 | 21800 | 1.2375 | - | - |
| 2.2486 | 21850 | 1.1074 | - | - |
| 2.2538 | 21900 | 1.0927 | - | - |
| 2.2589 | 21950 | 1.1691 | - | - |
| 2.2641 | 22000 | 1.1703 | - | - |
| 0 | 0 | - | - | 0.9499 |
| 2.2641 | 22000 | - | 1.0275 | - |
| 2.2692 | 22050 | 1.2158 | - | - |
| 2.2744 | 22100 | 1.1026 | - | - |
| 2.2795 | 22150 | 1.0644 | - | - |
| 2.2847 | 22200 | 1.1092 | - | - |
| 2.2898 | 22250 | 1.1686 | - | - |
| 0 | 0 | - | - | 0.9512 |
| 2.2898 | 22250 | - | 1.0343 | - |
| 2.2949 | 22300 | 1.2711 | - | - |
| 2.3001 | 22350 | 1.2942 | - | - |
| 2.3052 | 22400 | 1.2073 | - | - |
| 2.3104 | 22450 | 1.2131 | - | - |
| 2.3155 | 22500 | 1.1445 | - | - |
| 0 | 0 | - | - | 0.9517 |
| 2.3155 | 22500 | - | 1.0128 | - |
| 2.3207 | 22550 | 1.1553 | - | - |
| 2.3258 | 22600 | 1.1512 | - | - |
| 2.3310 | 22650 | 1.2069 | - | - |
| 2.3361 | 22700 | 1.1345 | - | - |
| 2.3413 | 22750 | 1.1681 | - | - |
| 0 | 0 | - | - | 0.9509 |
| 2.3413 | 22750 | - | 1.0101 | - |
| 2.3464 | 22800 | 1.1372 | - | - |
| 2.3515 | 22850 | 1.1393 | - | - |
| 2.3567 | 22900 | 1.1327 | - | - |
| 2.3618 | 22950 | 1.0903 | - | - |
| 2.3670 | 23000 | 1.1354 | - | - |
| 0 | 0 | - | - | 0.9513 |
| 2.3670 | 23000 | - | 1.0173 | - |
| 2.3721 | 23050 | 1.2517 | - | - |
| 2.3773 | 23100 | 1.0634 | - | - |
| 2.3824 | 23150 | 1.2095 | - | - |
| 2.3876 | 23200 | 1.1686 | - | - |
| 2.3927 | 23250 | 1.1063 | - | - |
| 0 | 0 | - | - | 0.9517 |
| 2.3927 | 23250 | - | 1.0243 | - |
| 2.3979 | 23300 | 1.1309 | - | - |
| 2.4030 | 23350 | 1.1869 | - | - |
| 2.4082 | 23400 | 1.1743 | - | - |
| 2.4133 | 23450 | 1.1001 | - | - |
| 2.4184 | 23500 | 1.1696 | - | - |
| 0 | 0 | - | - | 0.9525 |
| 2.4184 | 23500 | - | 1.0315 | - |
| 2.4236 | 23550 | 1.1493 | - | - |
| 2.4287 | 23600 | 1.1486 | - | - |
| 2.4339 | 23650 | 1.2302 | - | - |
| 2.4390 | 23700 | 1.1427 | - | - |
| 2.4442 | 23750 | 1.2123 | - | - |
| 0 | 0 | - | - | 0.9510 |
| 2.4442 | 23750 | - | 1.0297 | - |
| 2.4493 | 23800 | 1.1169 | - | - |
| 2.4545 | 23850 | 1.1688 | - | - |
| 2.4596 | 23900 | 1.0506 | - | - |
| 2.4648 | 23950 | 1.1965 | - | - |
| 2.4699 | 24000 | 1.1253 | - | - |
| 0 | 0 | - | - | 0.9508 |
| 2.4699 | 24000 | - | 1.0238 | - |
| 2.4750 | 24050 | 1.1957 | - | - |
| 2.4802 | 24100 | 1.1395 | - | - |
| 2.4853 | 24150 | 1.1238 | - | - |
| 2.4905 | 24200 | 1.1342 | - | - |
| 2.4956 | 24250 | 1.1703 | - | - |
| 0 | 0 | - | - | 0.9506 |
| 2.4956 | 24250 | - | 1.0219 | - |
| 2.5008 | 24300 | 1.0947 | - | - |
| 2.5059 | 24350 | 1.1281 | - | - |
| 2.5111 | 24400 | 1.1029 | - | - |
| 2.5162 | 24450 | 1.1784 | - | - |
| 2.5214 | 24500 | 1.101 | - | - |
| 0 | 0 | - | - | 0.9528 |
| 2.5214 | 24500 | - | 1.0267 | - |
| 2.5265 | 24550 | 1.1231 | - | - |
| 2.5316 | 24600 | 1.1364 | - | - |
| 2.5368 | 24650 | 1.1778 | - | - |
| 2.5419 | 24700 | 1.1089 | - | - |
| 2.5471 | 24750 | 1.1626 | - | - |
| 0 | 0 | - | - | 0.9508 |
| 2.5471 | 24750 | - | 1.0254 | - |
| 2.5522 | 24800 | 1.2019 | - | - |
| 2.5574 | 24850 | 1.1503 | - | - |
| 2.5625 | 24900 | 1.1697 | - | - |
| 2.5677 | 24950 | 1.0921 | - | - |
| 2.5728 | 25000 | 1.3136 | - | - |
| 0 | 0 | - | - | 0.9513 |
| 2.5728 | 25000 | - | 1.0222 | - |
</details>
### Framework Versions
- Python: 3.12.4
- Sentence Transformers: 4.0.2
- PyLate: 1.2.0
- Transformers: 4.48.2
- PyTorch: 2.6.0+cu124
- Accelerate: 1.7.0
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084"
}
```
#### PyLate
```bibtex
@misc{PyLate,
title={PyLate: Flexible Training and Retrieval for Late Interaction Models},
author={Chaffin, Antoine and Sourty, Raphaël},
url={https://github.com/lightonai/pylate},
year={2024}
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
<!--
## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->
|
vijayakumaran92/Unmodel_Woman_Model_7
|
vijayakumaran92
| 2025-06-20T08:10:59Z | 0 | 0 | null |
[
"license:cc-by-nc-nd-4.0",
"region:us"
] | null | 2025-06-20T07:56:46Z |
---
license: cc-by-nc-nd-4.0
---
|
hf-100/Jamba-1.6-Large-Spellbound-StoryWriter-instruct-0.3-chkpt-96
|
hf-100
| 2025-06-20T08:07:26Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:ai21labs/AI21-Jamba-Large-1.6",
"base_model:adapter:ai21labs/AI21-Jamba-Large-1.6",
"region:us"
] | null | 2025-06-20T08:03:15Z |
---
base_model: ai21labs/AI21-Jamba-Large-1.6
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.15.2
|
Subsets and Splits
Filtered Qwen2.5 Distill Models
Identifies specific configurations of models by filtering cards that contain 'distill', 'qwen2.5', '7b' while excluding certain base models and incorrect model ID patterns, uncovering unique model variants.
Filtered Model Cards Count
Finds the count of entries with specific card details that include 'distill', 'qwen2.5', '7b' but exclude certain base models, revealing valuable insights about the dataset's content distribution.
Filtered Distill Qwen 7B Models
Filters for specific card entries containing 'distill', 'qwen', and '7b', excluding certain strings and patterns, to identify relevant model configurations.
Filtered Qwen-7b Model Cards
The query performs a detailed filtering based on specific keywords and excludes certain entries, which could be useful for identifying a specific subset of cards but does not provide deeper insights or trends.
Filtered Qwen 7B Model Cards
The query filters for specific terms related to "distilled" or "distill", "qwen", and "7b" in the 'card' column but excludes certain base models, providing a limited set of entries for further inspection.
Qwen 7B Distilled Models
The query provides a basic filtering of records to find specific card names that include keywords related to distilled Qwen 7b models, excluding a particular base model, which gives limited insight but helps in focusing on relevant entries.
Qwen 7B Distilled Model Cards
The query filters data based on specific keywords in the modelId and card fields, providing limited insight primarily useful for locating specific entries rather than revealing broad patterns or trends.
Qwen 7B Distilled Models
Finds all entries containing the terms 'distilled', 'qwen', and '7b' in a case-insensitive manner, providing a filtered set of records but without deeper analysis.
Distilled Qwen 7B Models
The query filters for specific model IDs containing 'distilled', 'qwen', and '7b', providing a basic retrieval of relevant entries but without deeper analysis or insight.
Filtered Model Cards with Distill Qwen2.
Filters and retrieves records containing specific keywords in the card description while excluding certain phrases, providing a basic count of relevant entries.
Filtered Model Cards with Distill Qwen 7
The query filters specific variations of card descriptions containing 'distill', 'qwen', and '7b' while excluding a particular base model, providing limited but specific data retrieval.
Distill Qwen 7B Model Cards
The query filters and retrieves rows where the 'card' column contains specific keywords ('distill', 'qwen', and '7b'), providing a basic filter result that can help in identifying specific entries.