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open-unlearning/unlearn_tofu_Llama-3.2-1B-Instruct_forget10_SimNPO_lr5e-05_b3.5_a1_d1_g0.25_ep5 | open-unlearning | 2025-05-24T19:14:52Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-24T19:13:47Z | <!DOCTYPE html>
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margin: 0 auto 1rem;
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}
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color: rgb(209, 213, 219);
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open-unlearning/unlearn_tofu_Llama-3.2-1B-Instruct_forget10_SimNPO_lr5e-05_b3.5_a1_d1_g0.25_ep10 | open-unlearning | 2025-05-24T19:13:44Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-24T18:53:09Z | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
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/>
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width: 6rem;
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margin: 0 auto 1rem;
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h1 {
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font-weight: 700;
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line-height: 1.75rem;
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box-sizing: border-box;
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}
.dark main {
background-color: rgb(11, 15, 25);
}
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color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
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TexX/NewModels | TexX | 2025-05-24T19:13:28Z | 0 | 0 | null | [
"license:openrail",
"region:us"
] | null | 2025-05-20T00:00:04Z | <!DOCTYPE html>
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BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
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p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
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ahmeterdempmk/Qwen3-4B-Cancer-Reasoning-SFT | ahmeterdempmk | 2025-05-24T19:12:51Z | 0 | 1 | transformers | [
"transformers",
"safetensors",
"qwen3",
"feature-extraction",
"text-generation-inference",
"unsloth",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | feature-extraction | 2025-05-24T19:12:38Z | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
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/>
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/>
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<style>
body {
margin: 0;
}
main {
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padding: 7rem 1rem 8rem 1rem;
text-align: center;
font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system,
BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
? "dark"
: "light";
try {
const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme;
if (storageTheme) {
theme = storageTheme === "dark" ? "dark" : "light";
}
} catch (e) {}
if (theme === "dark") {
document.documentElement.classList.add("dark");
} else {
document.documentElement.classList.remove("dark");
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<main>
<img
src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg"
alt=""
/>
<div>
<h1>429</h1>
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GODFirezz/59e681b3-5457-4ac8-a5bd-e7c65db25bd6 | GODFirezz | 2025-05-24T19:12:44Z | 0 | 0 | null | [
"region:us"
] | null | 2025-05-24T18:51:28Z | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
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name="description"
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/>
<meta property="fb:app_id" content="1321688464574422" />
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margin: 0;
}
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BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
? "dark"
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if (storageTheme) {
theme = storageTheme === "dark" ? "dark" : "light";
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open-unlearning/unlearn_tofu_Llama-3.2-1B-Instruct_forget10_GradDiff_lr1e-05_alpha5_epoch10 | open-unlearning | 2025-05-24T19:12:00Z | 1 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-15T16:50:45Z | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
<meta
name="description"
content="We're on a journey to advance and democratize artificial intelligence through open source and open science."
/>
<meta property="fb:app_id" content="1321688464574422" />
<meta name="twitter:card" content="summary_large_image" />
<meta name="twitter:site" content="@huggingface" />
<meta
property="og:title"
content="Hugging Face - The AI community building the future."
/>
<meta property="og:type" content="website" />
<title>Hugging Face - The AI community building the future.</title>
<style>
body {
margin: 0;
}
main {
background-color: white;
min-height: 100vh;
padding: 7rem 1rem 8rem 1rem;
text-align: center;
font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system,
BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
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if (storageTheme) {
theme = storageTheme === "dark" ? "dark" : "light";
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<img
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alt=""
/>
<div>
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BilateralBusiness/perma_chef_mandil_texano_vino_dama_1_20250524_1840 | BilateralBusiness | 2025-05-24T19:11:30Z | 0 | 0 | diffusers | [
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | 2025-05-24T18:57:54Z | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
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/>
<meta property="fb:app_id" content="1321688464574422" />
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<meta
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content="Hugging Face - The AI community building the future."
/>
<meta property="og:type" content="website" />
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body {
margin: 0;
}
main {
background-color: white;
min-height: 100vh;
padding: 7rem 1rem 8rem 1rem;
text-align: center;
font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system,
BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
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if (storageTheme) {
theme = storageTheme === "dark" ? "dark" : "light";
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<img
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alt=""
/>
<div>
<h1>429</h1>
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</div>
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RayneAmes/ri_v1 | RayneAmes | 2025-05-24T19:11:20Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"parler_tts",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2025-05-24T18:59:06Z | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
<meta
name="description"
content="We're on a journey to advance and democratize artificial intelligence through open source and open science."
/>
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<meta
property="og:title"
content="Hugging Face - The AI community building the future."
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zktmp/2rpp-dof-r16u8-math-step240 | zktmp | 2025-05-24T19:11:15Z | 0 | 0 | null | [
"safetensors",
"qwen2",
"region:us"
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Levichannn/ec90d576-14cb-4a8c-8d5e-d4a3def4e639 | Levichannn | 2025-05-24T19:09:13Z | 0 | 0 | null | [
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erpankajgupta1/clinical-t5-medical-dictation-formatter | erpankajgupta1 | 2025-05-24T19:08:49Z | 0 | 0 | null | [
"safetensors",
"t5",
"region:us"
] | null | 2025-05-24T18:50:12Z | <!DOCTYPE html>
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margin: 0 auto 1rem;
}
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font-size: 3.75rem;
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background-color: rgb(11, 15, 25);
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</style>
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open-unlearning/unlearn_tofu_Llama-3.2-1B-Instruct_forget10_SimNPO_lr1e-05_b4.5_a1_d1_g0.125_ep10 | open-unlearning | 2025-05-24T19:05:16Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-24T19:04:01Z | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
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content="width=device-width, initial-scale=1.0, user-scalable=no"
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font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system,
BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
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color: rgb(156, 163, 175);
}
</style>
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OmBhandwalkar/roberta-large-peft-lora | OmBhandwalkar | 2025-05-24T19:05:06Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-05-24T19:04:53Z | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
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content="width=device-width, initial-scale=1.0, user-scalable=no"
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}
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padding: 7rem 1rem 8rem 1rem;
text-align: center;
font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system,
BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
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caroprese/nova-psico | caroprese | 2025-05-24T19:05:00Z | 0 | 0 | null | [
"region:us"
] | null | 2025-05-24T19:04:58Z | <!DOCTYPE html>
<html class="" lang="en">
<head>
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<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
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BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
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D1224/solitera_model | D1224 | 2025-05-24T19:04:48Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-05-05T09:15:22Z | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
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name="description"
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/>
<meta property="fb:app_id" content="1321688464574422" />
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<meta
property="og:title"
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/>
<meta property="og:type" content="website" />
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background-color: white;
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padding: 7rem 1rem 8rem 1rem;
text-align: center;
font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system,
BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
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if (storageTheme) {
theme = storageTheme === "dark" ? "dark" : "light";
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stokemctoke/civitai_celeb-lora-archive | stokemctoke | 2025-05-24T19:04:27Z | 0 | 2 | null | [
"text-to-image",
"stable-diffusion",
"lora",
"civitai",
"celebrity",
"archive",
"safetensors",
"license:other",
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] | text-to-image | 2025-05-23T23:04:55Z | <!DOCTYPE html>
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.dark main {
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open-unlearning/unlearn_tofu_Llama-3.2-1B-Instruct_forget10_SimNPO_lr5e-05_b3.5_a1_d0_g0.25_ep10 | open-unlearning | 2025-05-24T19:02:36Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-24T19:01:29Z | <!DOCTYPE html>
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content="width=device-width, initial-scale=1.0, user-scalable=no"
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}
img {
width: 6rem;
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margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
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font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
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}
</style>
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vermoney/360644b1-7400-45d5-a1ae-aead7837641c | vermoney | 2025-05-24T19:02:21Z | 0 | 0 | null | [
"region:us"
] | null | 2025-05-24T18:59:21Z | <!DOCTYPE html>
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width: 6rem;
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font-size: 3.75rem;
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p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
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namfuentesganti/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-silky_lightfooted_ape | namfuentesganti | 2025-05-24T19:01:54Z | 11 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am silky lightfooted ape",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-04T13:25:35Z | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
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/>
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margin: 0;
}
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padding: 7rem 1rem 8rem 1rem;
text-align: center;
font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system,
BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
? "dark"
: "light";
try {
const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme;
if (storageTheme) {
theme = storageTheme === "dark" ? "dark" : "light";
}
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alt=""
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aleegis/7c9f271b-30dd-4fba-b2a3-2c97f6e25836 | aleegis | 2025-05-24T19:01:52Z | 0 | 0 | null | [
"region:us"
] | null | 2025-05-24T18:57:44Z | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
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font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system,
BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
? "dark"
: "light";
try {
const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme;
if (storageTheme) {
theme = storageTheme === "dark" ? "dark" : "light";
}
} catch (e) {}
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dimasik87/b04d7291-3078-471e-aefa-80694e5efce9 | dimasik87 | 2025-05-24T19:00:38Z | 0 | 0 | null | [
"region:us"
] | null | 2025-05-24T18:57:58Z | <!DOCTYPE html>
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BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
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open-unlearning/unlearn_tofu_Llama-3.2-1B-Instruct_forget10_SimNPO_lr2e-05_b4.5_a1_d1_g0.25_ep5 | open-unlearning | 2025-05-24T19:00:19Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-24T18:59:09Z | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
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/>
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<style>
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margin: 0;
}
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background-color: white;
min-height: 100vh;
padding: 7rem 1rem 8rem 1rem;
text-align: center;
font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system,
BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
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theme = storageTheme === "dark" ? "dark" : "light";
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alt=""
/>
<div>
<h1>429</h1>
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k-Video-do-gotinha-no-iml-portal-zacarias/Video-do-gotinha-no-iml-portal-zacarias.news.Social.Media.X | k-Video-do-gotinha-no-iml-portal-zacarias | 2025-05-24T18:59:28Z | 0 | 0 | null | [
"region:us"
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content="Hugging Face - The AI community building the future."
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padding: 7rem 1rem 8rem 1rem;
text-align: center;
font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system,
BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
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eddg/Qwen2-0.5B-SFT-full | eddg | 2025-05-24T18:59:23Z | 0 | 0 | null | [
"tensorboard",
"safetensors",
"qwen2",
"region:us"
] | null | 2025-05-21T05:43:51Z | <!DOCTYPE html>
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}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
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p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
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.dark main {
background-color: rgb(11, 15, 25);
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open-unlearning/unlearn_tofu_Llama-3.2-1B-Instruct_forget10_SimNPO_lr1e-05_b3.5_a1_d1_g0.25_ep10 | open-unlearning | 2025-05-24T18:59:06Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-24T18:58:02Z | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
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BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
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}
</style>
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FelipeRivas1/modelo-Lesson1-2 | FelipeRivas1 | 2025-05-24T18:58:18Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2025-05-24T18:56:51Z | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
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content="width=device-width, initial-scale=1.0, user-scalable=no"
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Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
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zktmp/2rpp-doh-r8u4b256-step120 | zktmp | 2025-05-24T18:56:06Z | 0 | 0 | null | [
"safetensors",
"qwen2",
"region:us"
] | null | 2025-05-24T18:54:28Z | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
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/>
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font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system,
BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
? "dark"
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try {
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if (storageTheme) {
theme = storageTheme === "dark" ? "dark" : "light";
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huskyhong/noname-ai-v3.2-gguf | huskyhong | 2025-05-24T18:55:57Z | 0 | 0 | null | [
"gguf",
"region:us"
] | null | 2025-05-19T08:01:54Z | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
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name="description"
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/>
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content="Hugging Face - The AI community building the future."
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body {
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min-height: 100vh;
padding: 7rem 1rem 8rem 1rem;
text-align: center;
font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system,
BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
? "dark"
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if (storageTheme) {
theme = storageTheme === "dark" ? "dark" : "light";
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Saptarshi1234/starcoder2-3b-finetuned-fix | Saptarshi1234 | 2025-05-24T18:55:50Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-05-24T18:48:07Z | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
<meta
name="description"
content="We're on a journey to advance and democratize artificial intelligence through open source and open science."
/>
<meta property="fb:app_id" content="1321688464574422" />
<meta name="twitter:card" content="summary_large_image" />
<meta name="twitter:site" content="@huggingface" />
<meta
property="og:title"
content="Hugging Face - The AI community building the future."
/>
<meta property="og:type" content="website" />
<title>Hugging Face - The AI community building the future.</title>
<style>
body {
margin: 0;
}
main {
background-color: white;
min-height: 100vh;
padding: 7rem 1rem 8rem 1rem;
text-align: center;
font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system,
BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
? "dark"
: "light";
try {
const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme;
if (storageTheme) {
theme = storageTheme === "dark" ? "dark" : "light";
}
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alt=""
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open-unlearning/unlearn_tofu_Llama-3.2-1B-Instruct_forget10_SimNPO_lr1e-05_b3.5_a1_d0_g0.25_ep10 | open-unlearning | 2025-05-24T18:55:41Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-24T18:54:30Z | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
<meta
name="description"
content="We're on a journey to advance and democratize artificial intelligence through open source and open science."
/>
<meta property="fb:app_id" content="1321688464574422" />
<meta name="twitter:card" content="summary_large_image" />
<meta name="twitter:site" content="@huggingface" />
<meta
property="og:title"
content="Hugging Face - The AI community building the future."
/>
<meta property="og:type" content="website" />
<title>Hugging Face - The AI community building the future.</title>
<style>
body {
margin: 0;
}
main {
background-color: white;
min-height: 100vh;
padding: 7rem 1rem 8rem 1rem;
text-align: center;
font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system,
BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
? "dark"
: "light";
try {
const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme;
if (storageTheme) {
theme = storageTheme === "dark" ? "dark" : "light";
}
} catch (e) {}
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document.documentElement.classList.add("dark");
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<img
src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg"
alt=""
/>
<div>
<h1>429</h1>
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tungdqzenai/a1635610-0370-4c6f-b8a3-0c6f8464ab52 | tungdqzenai | 2025-05-24T18:55:33Z | 0 | 0 | null | [
"region:us"
] | null | 2025-05-24T18:55:25Z | <!DOCTYPE html>
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<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
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/>
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N0v1k/QLora_legal | N0v1k | 2025-05-24T18:54:52Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-05-24T18:54:39Z | <!DOCTYPE html>
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pvwvs/gemma-3-3-4B-it-thinking-function_calling-V0 | pvwvs | 2025-05-24T18:53:58Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:google/gemma-3-4b-it",
"base_model:finetune:google/gemma-3-4b-it",
"endpoints_compatible",
"region:us"
] | null | 2025-05-24T18:47:06Z | <!DOCTYPE html>
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open-unlearning/unlearn_tofu_Llama-3.2-1B-Instruct_forget10_SimNPO_lr2e-05_b4.5_a1_d0_g0.25_ep5 | open-unlearning | 2025-05-24T18:53:06Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-24T18:52:01Z | <!DOCTYPE html>
<html class="" lang="en">
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ahmadmwali/mt0_small_Hausa | ahmadmwali | 2025-05-24T18:52:34Z | 0 | 0 | transformers | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-05-24T18:24:32Z | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
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line-height: 1;
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abhipa/gemma-product-description | abhipa | 2025-05-24T18:52:15Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:google/gemma-3-4b-pt",
"base_model:finetune:google/gemma-3-4b-pt",
"endpoints_compatible",
"region:us"
] | null | 2025-05-23T21:11:02Z | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
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/>
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sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
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color: rgba(107, 114, 128, 1);
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line-height: 1.75rem;
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margin: 0 auto;
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}
.dark h1 {
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}
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open-unlearning/unlearn_tofu_Llama-3.2-1B-Instruct_forget10_SimNPO_lr5e-05_b3.5_a1_d1_g0.125_ep10 | open-unlearning | 2025-05-24T18:50:47Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-24T18:49:41Z | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
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/>
<meta property="fb:app_id" content="1321688464574422" />
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BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
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font-size: 3.75rem;
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box-sizing: border-box;
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fakeid/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-scavenging_freckled_macaque | fakeid | 2025-05-24T18:50:08Z | 12 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am scavenging freckled macaque",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-24T07:57:18Z | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
<meta
name="description"
content="We're on a journey to advance and democratize artificial intelligence through open source and open science."
/>
<meta property="fb:app_id" content="1321688464574422" />
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<meta
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/>
<meta property="og:type" content="website" />
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margin: 0;
}
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background-color: white;
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padding: 7rem 1rem 8rem 1rem;
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BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
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MOhammmed111/deepseek-llm-7b-base-finetuned | MOhammmed111 | 2025-05-24T18:50:02Z | 0 | 0 | null | [
"region:us"
] | null | 2025-05-24T18:50:02Z | <!DOCTYPE html>
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Ibisbill/stage3_OpenThinker2-7B_lr_5e6 | Ibisbill | 2025-05-24T18:48:21Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-24T18:18:41Z | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
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/>
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Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
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box-sizing: border-box;
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p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
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margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
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}
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}
</style>
<script>
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open-unlearning/unlearn_tofu_Llama-3.2-1B-Instruct_forget10_AltPO_lr1e-05_beta0.1_alpha2_epoch10 | open-unlearning | 2025-05-24T18:47:33Z | 1 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-15T18:54:50Z | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
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/>
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Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
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wusize/minigpt4o-gen | wusize | 2025-05-24T18:46:43Z | 0 | 0 | null | [
"region:us"
] | null | 2025-05-21T07:58:01Z | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
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/>
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<style>
body {
margin: 0;
}
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padding: 7rem 1rem 8rem 1rem;
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font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system,
BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
? "dark"
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if (storageTheme) {
theme = storageTheme === "dark" ? "dark" : "light";
}
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cigan13/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-climbing_omnivorous_cobra | cigan13 | 2025-05-24T18:46:16Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am climbing omnivorous cobra",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-24T14:28:29Z | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
<meta
name="description"
content="We're on a journey to advance and democratize artificial intelligence through open source and open science."
/>
<meta property="fb:app_id" content="1321688464574422" />
<meta name="twitter:card" content="summary_large_image" />
<meta name="twitter:site" content="@huggingface" />
<meta
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content="Hugging Face - The AI community building the future."
/>
<meta property="og:type" content="website" />
<title>Hugging Face - The AI community building the future.</title>
<style>
body {
margin: 0;
}
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background-color: white;
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padding: 7rem 1rem 8rem 1rem;
text-align: center;
font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system,
BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
? "dark"
: "light";
try {
const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme;
if (storageTheme) {
theme = storageTheme === "dark" ? "dark" : "light";
}
} catch (e) {}
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alt=""
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imdatta0/qwen4b_eurus_matheval_lora_8k_openrs_unsloth | imdatta0 | 2025-05-24T18:46:05Z | 0 | 0 | null | [
"region:us"
] | null | 2025-05-24T18:46:05Z | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
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name="description"
content="We're on a journey to advance and democratize artificial intelligence through open source and open science."
/>
<meta property="fb:app_id" content="1321688464574422" />
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content="Hugging Face - The AI community building the future."
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<style>
body {
margin: 0;
}
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background-color: white;
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padding: 7rem 1rem 8rem 1rem;
text-align: center;
font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system,
BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
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open-unlearning/unlearn_tofu_Llama-3.2-1B-Instruct_forget10_SimNPO_lr5e-05_b4.5_a1_d0_g0.125_ep5 | open-unlearning | 2025-05-24T18:45:34Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-24T18:44:28Z | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
<meta
name="description"
content="We're on a journey to advance and democratize artificial intelligence through open source and open science."
/>
<meta property="fb:app_id" content="1321688464574422" />
<meta name="twitter:card" content="summary_large_image" />
<meta name="twitter:site" content="@huggingface" />
<meta
property="og:title"
content="Hugging Face - The AI community building the future."
/>
<meta property="og:type" content="website" />
<title>Hugging Face - The AI community building the future.</title>
<style>
body {
margin: 0;
}
main {
background-color: white;
min-height: 100vh;
padding: 7rem 1rem 8rem 1rem;
text-align: center;
font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system,
BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
? "dark"
: "light";
try {
const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme;
if (storageTheme) {
theme = storageTheme === "dark" ? "dark" : "light";
}
} catch (e) {}
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src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg"
alt=""
/>
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<h1>429</h1>
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open-unlearning/unlearn_tofu_Llama-3.2-1B-Instruct_forget10_AltPO_lr2e-05_beta0.05_alpha5_epoch10 | open-unlearning | 2025-05-24T18:44:20Z | 1 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-15T16:49:55Z | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
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open-unlearning/unlearn_tofu_Llama-3.2-1B-Instruct_forget10_RMU_lr2e-05_layer10_scoeff100_epoch5 | open-unlearning | 2025-05-24T18:43:55Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-15T17:48:18Z | <!DOCTYPE html>
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open-unlearning/unlearn_tofu_Llama-3.2-1B-Instruct_forget10_NPO_lr1e-05_beta0.5_alpha1_epoch10 | open-unlearning | 2025-05-24T18:43:29Z | 1 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-15T16:49:22Z | <!DOCTYPE html>
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open-unlearning/unlearn_tofu_Llama-3.2-1B-Instruct_forget10_SimNPO_lr1e-05_b3.5_a1_d0_g0.125_ep10 | open-unlearning | 2025-05-24T18:43:05Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-24T18:42:01Z | <!DOCTYPE html>
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Asbyx/MNLP_M2_quantized_model | Asbyx | 2025-05-24T18:41:12Z | 0 | 0 | null | [
"safetensors",
"qwen3",
"license:mit",
"8-bit",
"region:us"
] | null | 2025-05-24T18:37:44Z | <!DOCTYPE html>
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Iceed/5c60a781-f40b-4e05-aabf-76dc58940dde | Iceed | 2025-05-24T18:41:11Z | 0 | 0 | null | [
"region:us"
] | null | 2025-05-24T16:23:29Z | <!DOCTYPE html>
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NamoNam/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-giant_skittish_hamster | NamoNam | 2025-05-24T18:40:31Z | 1 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am giant skittish hamster",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-20T16:41:36Z | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
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Noto Color Emoji;
}
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margin: 0 auto 1rem;
}
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noneUsername/Harbinger-24B-W8A8 | noneUsername | 2025-05-24T18:39:32Z | 0 | 0 | null | [
"safetensors",
"mistral",
"base_model:LatitudeGames/Harbinger-24B",
"base_model:quantized:LatitudeGames/Harbinger-24B",
"8-bit",
"compressed-tensors",
"region:us"
] | null | 2025-05-24T18:19:15Z | <!DOCTYPE html>
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open-unlearning/unlearn_tofu_Llama-3.2-1B-Instruct_forget10_SimNPO_lr2e-05_b4.5_a1_d1_g0.125_ep10 | open-unlearning | 2025-05-24T18:39:27Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-24T17:56:01Z | <!DOCTYPE html>
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aarifahullah/LunarLander-v2_CleanRL | aarifahullah | 2025-05-24T18:37:57Z | 0 | 0 | null | [
"tensorboard",
"LunarLander-v2",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-course",
"model-index",
"region:us"
] | reinforcement-learning | 2025-05-24T18:34:31Z | <!DOCTYPE html>
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atena23/45d | atena23 | 2025-05-24T18:36:43Z | 0 | 0 | null | [
"region:us"
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dhintech/marian-id-en-finetuned | dhintech | 2025-05-24T18:36:24Z | 0 | 0 | null | [
"region:us"
] | null | 2025-05-24T18:36:24Z | <!DOCTYPE html>
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softdev629/scms-demo | softdev629 | 2025-05-24T18:35:50Z | 0 | 0 | null | [
"arxiv:2012.12624",
"region:us"
] | null | 2025-05-24T18:34:20Z | <!DOCTYPE html>
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Rahul39/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-tall_striped_frog | Rahul39 | 2025-05-24T18:34:55Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am tall striped frog",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-17T05:58:57Z | <!DOCTYPE html>
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<head>
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content="width=device-width, initial-scale=1.0, user-scalable=no"
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sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
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width: 6rem;
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margin: 0 auto 1rem;
}
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font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
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box-sizing: border-box;
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}
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BeckerAnas/swept-brook-197 | BeckerAnas | 2025-05-24T18:34:49Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"convnextv2",
"image-classification",
"generated_from_trainer",
"base_model:facebook/convnextv2-tiny-1k-224",
"base_model:finetune:facebook/convnextv2-tiny-1k-224",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | 2025-05-24T10:10:24Z | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
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name="description"
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/>
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BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
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VladimirFireBall/deberta-v3-base-finetuned | VladimirFireBall | 2025-05-24T18:34:17Z | 0 | 0 | null | [
"region:us"
] | null | 2025-05-24T18:34:17Z | <!DOCTYPE html>
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FormlessAI/6e9bbd1f-bdb6-4375-a1c9-ce129d7728de | FormlessAI | 2025-05-24T18:34:17Z | 0 | 0 | null | [
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Nika0411/my-lora-sdxl-model | Nika0411 | 2025-05-24T18:33:44Z | 0 | 0 | null | [
"region:us"
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AiBERTa/aiberta-d-2000M-random | AiBERTa | 2025-05-24T18:33:32Z | 0 | 0 | null | [
"safetensors",
"bert",
"license:mit",
"region:us"
] | null | 2025-05-24T18:19:09Z | <!DOCTYPE html>
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open-unlearning/unlearn_tofu_Llama-3.2-1B-Instruct_forget10_SimNPO_lr2e-05_b3.5_a1_d0_g0.25_ep5 | open-unlearning | 2025-05-24T18:32:59Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-24T18:31:55Z | <!DOCTYPE html>
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Noto Color Emoji;
}
img {
width: 6rem;
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margin: 0 auto 1rem;
}
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font-size: 3.75rem;
line-height: 1;
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font-weight: 700;
box-sizing: border-box;
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color: rgba(107, 114, 128, 1);
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}
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}
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open-unlearning/unlearn_tofu_Llama-3.2-1B-Instruct_forget10_AltPO_lr5e-05_beta0.1_alpha1_epoch5 | open-unlearning | 2025-05-24T18:31:25Z | 1 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-15T16:51:01Z | <!DOCTYPE html>
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BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
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color: rgb(156, 163, 175);
}
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// On page load or when changing themes, best to add inline in `head` to avoid FOUC
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starlineventures/outputs | starlineventures | 2025-05-24T18:30:31Z | 0 | 0 | peft | [
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:microsoft/phi-2",
"base_model:adapter:microsoft/phi-2",
"license:mit",
"region:us"
] | null | 2025-05-24T00:37:40Z | <!DOCTYPE html>
<html class="" lang="en">
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BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
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font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
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open-unlearning/unlearn_tofu_Llama-3.2-1B-Instruct_forget10_SimNPO_lr5e-05_b4.5_a1_d0_g0.25_ep10 | open-unlearning | 2025-05-24T18:29:51Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-24T18:28:46Z | <!DOCTYPE html>
<html class="" lang="en">
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<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
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}
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background-color: white;
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text-align: center;
font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system,
BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
? "dark"
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open-unlearning/unlearn_tofu_Llama-3.2-1B-Instruct_forget10_SimNPO_lr2e-05_b3.5_a1_d1_g0.25_ep5 | open-unlearning | 2025-05-24T18:28:42Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
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MISHANM/meta-llama-Llama-3.3-70B-Instruct-int8 | MISHANM | 2025-05-24T18:27:31Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-24T18:22:05Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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open-unlearning/unlearn_tofu_Llama-3.2-1B-Instruct_forget10_SimNPO_lr2e-05_b3.5_a1_d1_g0.25_ep10 | open-unlearning | 2025-05-24T18:27:31Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
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"region:us"
] | text-generation | 2025-05-24T18:26:04Z | ---
library_name: transformers
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vcollos/ricco | vcollos | 2025-05-24T18:27:08Z | 0 | 0 | pytorch | [
"pytorch",
"text-to-audio",
"pt",
"license:mit",
"region:us"
] | text-to-audio | 2025-05-24T17:58:13Z | ---
license: mit
language: pt
library_name: pytorch
pipeline_tag: text-to-audio
---
# 🎵 Ricco - Gerador de Trilhas Sonoras para TV
Modelo de IA treinado para gerar trilhas sonoras instrumentais em **20 estilos diferentes** para televisão.
## 🎯 Estilos Disponíveis
- **Ação** - trilha instrumental de estilo acao
- **Acústico** - trilha instrumental de estilo acustico
- **Drama** - trilha instrumental de estilo drama
- **Eletrônica** - trilha instrumental de estilo eletronica
- **Emocional** - trilha instrumental de estilo emocional
- **Épico** - trilha instrumental de estilo epico
- **Hip Hop** - trilha instrumental de estilo hip hop
- **House** - trilha instrumental de estilo house
- **Jornalístico** - trilha instrumental de estilo jornalistico
- **Mistério** - trilha instrumental de estilo misterio
- **Motivacional** - trilha instrumental de estilo motivacional
- **Oração** - trilha instrumental de estilo oracao
- **Pop** - trilha instrumental de estilo pop
- **Rock** - trilha instrumental de estilo rock
- **Suspense** - trilha instrumental de estilo suspense
- **Tenso** - trilha instrumental de estilo tenso
- **Trap** - trilha instrumental de estilo trap
- **Violão** - trilha instrumental de estilo violao
- **Xote** - trilha instrumental de estilo xote
## 🚀 Como Usar
```python
# Carregar modelo
from transformers import AutoModel
model = AutoModel.from_pretrained("vitorcollos/ricco", trust_remote_code=True)
# Gerar trilha
audio, sr = model.generate_audio("trilha instrumental de estilo epico") |
YujinPang/reasoning_model_2 | YujinPang | 2025-05-24T18:26:18Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-24T17:16:43Z | ---
library_name: transformers
tags: []
---
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SANGWARUGAMBA/mrbertin-lora | SANGWARUGAMBA | 2025-05-24T18:24:22Z | 0 | 0 | null | [
"license:other",
"region:us"
] | null | 2025-05-24T17:02:54Z | ---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
--- |
open-unlearning/unlearn_tofu_Llama-3.2-1B-Instruct_forget10_SimNPO_lr2e-05_b4.5_a1_d0_g0.125_ep10 | open-unlearning | 2025-05-24T18:23:32Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-24T18:22:23Z | ---
library_name: transformers
tags: []
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Anisa206/wav2vec_finetune_bengali_asr | Anisa206 | 2025-05-24T18:23:19Z | 0 | 0 | null | [
"safetensors",
"license:apache-2.0",
"region:us"
] | null | 2025-05-23T12:07:49Z | ---
license: apache-2.0
---
|
Cherran/medical_gemma_1b_sft | Cherran | 2025-05-24T18:22:09Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:unsloth/gemma-3-1b-it-unsloth-bnb-4bit",
"base_model:adapter:unsloth/gemma-3-1b-it-unsloth-bnb-4bit",
"region:us"
] | null | 2025-05-24T18:21:43Z | ---
base_model: unsloth/gemma-3-1b-it-unsloth-bnb-4bit
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 |
tifin-india/sarvam-m-24b-f16-gguf | tifin-india | 2025-05-24T18:20:01Z | 0 | 0 | null | [
"gguf",
"mistral",
"text-generation",
"llama.cpp",
"quantized",
"f16",
"conversational",
"base_model:sarvamai/sarvam-m",
"base_model:quantized:sarvamai/sarvam-m",
"license:apache-2.0",
"region:us"
] | text-generation | 2025-05-24T16:35:28Z | ---
license: apache-2.0
tags:
- text-generation
- llama.cpp
- gguf
- quantized
- f16
model_type: llama
inference: false
base_model:
- sarvamai/sarvam-m
---
# sarvam-m-24b - F16 GGUF
This repository contains the **F16** quantized version of sarvam-m-24b in GGUF format.
## Model Details
- **Quantization**: F16
- **File Size**: ~43.9GB
- **Description**: Full precision 16-bit model
- **Format**: GGUF (compatible with llama.cpp)
## Usage
### With llama.cpp
```bash
# Download the model
huggingface-cli download tifin-india/sarvam-m-24b-f16-gguf
# Run inference
./main -m sarvam-m-24b-F16.gguf -p "Your prompt here"
```
### With Python (llama-cpp-python)
```python
from llama_cpp import Llama
# Load the model
llm = Llama(
model_path="./sarvam-m-24b-F16.gguf",
n_ctx=2048, # Context length
n_gpu_layers=35, # Adjust based on your GPU
verbose=False
)
# Generate text
response = llm("Your prompt here", max_tokens=100)
print(response['choices'][0]['text'])
```
### With Transformers + AutoGGUF
```python
from transformers import AutoTokenizer
from auto_gptq import AutoGPTQForCausalLM
model_name = "tifin-india/sarvam-m-24b-f16-gguf"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoGPTQForCausalLM.from_quantized(model_name)
```
## Performance Characteristics
| Aspect | Rating |
|--------|--------|
| **Speed** | ⭐ |
| **Quality** | ⭐⭐⭐⭐⭐ |
| **Memory** | ⭐ |
## Original Model
This is a quantized version of the original model. For the full-precision version and more details, please refer to the original model repository.
## Quantization Details
This model was quantized using llama.cpp's quantization tools. The F16 format provides a good balance of model size, inference speed, and output quality for most use cases.
## License
This model follows the same license as the original model (Apache 2.0).
## Citation
If you use this model, please cite the original model authors and acknowledge the quantization. |
khuam/run_6 | khuam | 2025-05-24T18:20:00Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:Qwen/Qwen2.5-VL-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-VL-7B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-24T07:24:29Z | ---
base_model: Qwen/Qwen2.5-VL-7B-Instruct
library_name: transformers
model_name: run_6
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for run_6
This model is a fine-tuned version of [Qwen/Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="khuam/run_6", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.17.0
- Transformers: 4.52.3
- Pytorch: 2.8.0.dev20250518+cu126
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
nojedag/distilroberta-roberta-finetuned-financial-news-sentiment-analysis-european | nojedag | 2025-05-24T18:19:55Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilroberta-base",
"base_model:finetune:distilbert/distilroberta-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-05-24T18:19:16Z | ---
library_name: transformers
license: apache-2.0
base_model: distilbert/distilroberta-base
tags:
- generated_from_trainer
model-index:
- name: distilroberta-roberta-finetuned-financial-news-sentiment-analysis-european
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. -->
# distilroberta-roberta-finetuned-financial-news-sentiment-analysis-european
This model is a fine-tuned version of [distilbert/distilroberta-base](https://huggingface.co/distilbert/distilroberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.6637
- eval_model_preparation_time: 0.0015
- eval_accuracy: 0.7764
- eval_macro_precision: 0.7737
- eval_macro_recall: 0.7865
- eval_macro_f1: 0.7762
- eval_neutral_precision: 0.8569
- eval_neutral_recall: 0.7260
- eval_neutral_f1: 0.7860
- eval_positive_precision: 0.7815
- eval_positive_recall: 0.8178
- eval_positive_f1: 0.7992
- eval_negative_precision: 0.6827
- eval_negative_recall: 0.8157
- eval_negative_f1: 0.7433
- eval_runtime: 18.4835
- eval_samples_per_second: 449.589
- eval_steps_per_second: 28.133
- step: 0
## 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
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- 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
- lr_scheduler_warmup_steps: 846
- num_epochs: 7
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.51.3
- Pytorch 2.7.0+cu128
- Datasets 3.6.0
- Tokenizers 0.21.1
|
tifin-india/sarvam-m-24b-q5-1-gguf | tifin-india | 2025-05-24T18:19:32Z | 0 | 0 | null | [
"gguf",
"mistral",
"text-generation",
"llama.cpp",
"quantized",
"q5_1",
"conversational",
"base_model:sarvamai/sarvam-m",
"base_model:quantized:sarvamai/sarvam-m",
"license:apache-2.0",
"region:us"
] | text-generation | 2025-05-24T16:15:05Z | ---
license: apache-2.0
tags:
- text-generation
- llama.cpp
- gguf
- quantized
- q5_1
model_type: llama
inference: false
base_model:
- sarvamai/sarvam-m
---
# sarvam-m-24b - Q5_1 GGUF
This repository contains the **Q5_1** quantized version of sarvam-m-24b in GGUF format.
## Model Details
- **Quantization**: Q5_1
- **File Size**: ~16.5GB
- **Description**: Legacy Q5 format with very low quality loss
- **Format**: GGUF (compatible with llama.cpp)
## Usage
### With llama.cpp
```bash
# Download the model
huggingface-cli download tifin-india/sarvam-m-24b-q5_1-gguf
# Run inference
./main -m sarvam-m-24b-Q5_1.gguf -p "Your prompt here"
```
### With Python (llama-cpp-python)
```python
from llama_cpp import Llama
# Load the model
llm = Llama(
model_path="./sarvam-m-24b-Q5_1.gguf",
n_ctx=2048, # Context length
n_gpu_layers=35, # Adjust based on your GPU
verbose=False
)
# Generate text
response = llm("Your prompt here", max_tokens=100)
print(response['choices'][0]['text'])
```
### With Transformers + AutoGGUF
```python
from transformers import AutoTokenizer
from auto_gptq import AutoGPTQForCausalLM
model_name = "tifin-india/sarvam-m-24b-q5_1-gguf"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoGPTQForCausalLM.from_quantized(model_name)
```
## Performance Characteristics
| Aspect | Rating |
|--------|--------|
| **Speed** | ⭐⭐ |
| **Quality** | ⭐⭐⭐⭐ |
| **Memory** | ⭐⭐ |
## Original Model
This is a quantized version of the original model. For the full-precision version and more details, please refer to the original model repository.
## Quantization Details
This model was quantized using llama.cpp's quantization tools. The Q5_1 format provides a good balance of model size, inference speed, and output quality for most use cases.
## License
This model follows the same license as the original model (Apache 2.0).
## Citation
If you use this model, please cite the original model authors and acknowledge the quantization. |
open-unlearning/unlearn_tofu_Llama-3.2-1B-Instruct_forget10_SimNPO_lr1e-05_b3.5_a1_d1_g0.125_ep10 | open-unlearning | 2025-05-24T18:19:09Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-24T18:17:58Z | ---
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] |
fats-fme/509382f3-a000-464c-b986-359253cd5e4c | fats-fme | 2025-05-24T18:18:04Z | 0 | 0 | peft | [
"peft",
"safetensors",
"qwen2",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/Qwen2.5-Math-1.5B-Instruct",
"base_model:adapter:unsloth/Qwen2.5-Math-1.5B-Instruct",
"license:apache-2.0",
"region:us"
] | null | 2025-05-24T18:03:03Z | ---
library_name: peft
license: apache-2.0
base_model: unsloth/Qwen2.5-Math-1.5B-Instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 509382f3-a000-464c-b986-359253cd5e4c
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. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: unsloth/Qwen2.5-Math-1.5B-Instruct
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- ad0293a17a070f7c_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/
type:
field_instruction: instruct
field_output: output
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
device_map: auto
early_stopping_patience: 4
eval_max_new_tokens: 128
eval_sample_packing: false
eval_steps: 100
eval_table_size: null
evals_per_epoch: null
flash_attention: false
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 8
gradient_checkpointing: true
group_by_length: true
hub_model_id: fats-fme/509382f3-a000-464c-b986-359253cd5e4c
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 5.0e-06
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 10
lora_alpha: 64
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lora_target_modules:
- q_proj
- k_proj
- v_proj
- o_proj
- gate_proj
lr_scheduler: constant_with_warmup
max_memory:
0: 130GB
max_steps: 300
micro_batch_size: 4
mlflow_experiment_name: /tmp/ad0293a17a070f7c_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 5
optimizer: adamw_torch_fused
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: true
save_steps: 100
saves_per_epoch: null
sequence_len: 1024
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
use_scaled_dot_product_attention: false
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 1e017fb6-f8c8-4390-9333-cc59aac70178
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 1e017fb6-f8c8-4390-9333-cc59aac70178
warmup_steps: 200
weight_decay: 0.03
xformers_attention: null
```
</details><br>
# 509382f3-a000-464c-b986-359253cd5e4c
This model is a fine-tuned version of [unsloth/Qwen2.5-Math-1.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-Math-1.5B-Instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: nan
## 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: 5e-06
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- 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: constant_with_warmup
- lr_scheduler_warmup_steps: 200
- training_steps: 300
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0015 | 1 | nan |
| 0.0 | 0.1451 | 100 | nan |
| 0.0 | 0.2902 | 200 | nan |
| 0.0 | 0.4353 | 300 | nan |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
open-unlearning/unlearn_tofu_Llama-3.2-1B-Instruct_forget10_UNDIAL_lr0.0001_beta10_alpha2_epoch10 | open-unlearning | 2025-05-24T18:16:59Z | 1 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-15T16:51:06Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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## Model Details
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open-unlearning/unlearn_tofu_Llama-3.2-1B-Instruct_forget10_SimNPO_lr5e-05_b4.5_a1_d1_g0.125_ep10 | open-unlearning | 2025-05-24T18:16:30Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-24T18:15:22Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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tifin-india/sarvam-m-24b-q4-k-s-gguf | tifin-india | 2025-05-24T18:15:54Z | 0 | 0 | null | [
"gguf",
"mistral",
"text-generation",
"llama.cpp",
"quantized",
"q4_k_s",
"conversational",
"base_model:sarvamai/sarvam-m",
"base_model:quantized:sarvamai/sarvam-m",
"license:apache-2.0",
"region:us"
] | text-generation | 2025-05-24T17:36:06Z | ---
license: apache-2.0
tags:
- text-generation
- llama.cpp
- gguf
- quantized
- q4_k_s
model_type: llama
inference: false
base_model:
- sarvamai/sarvam-m
---
# sarvam-m-24b - Q4_K_S GGUF
This repository contains the **Q4_K_S** quantized version of sarvam-m-24b in GGUF format.
## Model Details
- **Quantization**: Q4_K_S
- **File Size**: ~12.6GB
- **Description**: Small Q4 model with greater quality loss
- **Format**: GGUF (compatible with llama.cpp)
## Usage
### With llama.cpp
```bash
# Download the model
huggingface-cli download tifin-india/sarvam-m-24b-q4_k_s-gguf
# Run inference
./main -m sarvam-m-24b-Q4_K_S.gguf -p "Your prompt here"
```
### With Python (llama-cpp-python)
```python
from llama_cpp import Llama
# Load the model
llm = Llama(
model_path="./sarvam-m-24b-Q4_K_S.gguf",
n_ctx=2048, # Context length
n_gpu_layers=35, # Adjust based on your GPU
verbose=False
)
# Generate text
response = llm("Your prompt here", max_tokens=100)
print(response['choices'][0]['text'])
```
### With Transformers + AutoGGUF
```python
from transformers import AutoTokenizer
from auto_gptq import AutoGPTQForCausalLM
model_name = "tifin-india/sarvam-m-24b-q4_k_s-gguf"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoGPTQForCausalLM.from_quantized(model_name)
```
## Performance Characteristics
| Aspect | Rating |
|--------|--------|
| **Speed** | ⭐⭐⭐ |
| **Quality** | ⭐⭐⭐ |
| **Memory** | ⭐⭐⭐ |
## Original Model
This is a quantized version of the original model. For the full-precision version and more details, please refer to the original model repository.
## Quantization Details
This model was quantized using llama.cpp's quantization tools. The Q4_K_S format provides a good balance of model size, inference speed, and output quality for most use cases.
## License
This model follows the same license as the original model (Apache 2.0).
## Citation
If you use this model, please cite the original model authors and acknowledge the quantization. |
ahmadmwali/opus_Hausa | ahmadmwali | 2025-05-24T18:15:45Z | 0 | 0 | transformers | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-05-24T17:30:31Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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## Model Details
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open-unlearning/unlearn_tofu_Llama-3.2-1B-Instruct_forget10_SimNPO_lr5e-05_b4.5_a1_d1_g0.125_ep5 | open-unlearning | 2025-05-24T18:15:15Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-24T17:56:03Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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[More Information Needed]
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cdp57/MM_gemmaFT8.2 | cdp57 | 2025-05-24T18:14:58Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"gemma3",
"trl",
"en",
"base_model:unsloth/gemma-3-4b-it",
"base_model:finetune:unsloth/gemma-3-4b-it",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-05-24T18:13:51Z | ---
base_model: unsloth/gemma-3-4b-it
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** cdp57
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-3-4b-it
This gemma3 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)
|
GIAR-UTN/alium-cepa-classifier | GIAR-UTN | 2025-05-24T18:14:39Z | 0 | 0 | null | [
"region:us"
] | null | 2025-05-24T18:07:43Z | license: mit
# Alium cepa Cell Classifier Models
For more information, visit the [code repository](https://github.com/RawthiL/giar_ina_dev)
|
open-unlearning/unlearn_tofu_Llama-3.2-1B-Instruct_forget10_AltPO_lr5e-05_beta0.5_alpha1_epoch5 | open-unlearning | 2025-05-24T18:12:17Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-15T22:13:41Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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[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
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#### Preprocessing [optional]
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open-unlearning/unlearn_tofu_Llama-3.2-1B-Instruct_forget10_UNDIAL_lr1e-05_beta3_alpha1_epoch10 | open-unlearning | 2025-05-24T18:11:05Z | 1 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-15T16:50:56Z | ---
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.
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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
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[More Information Needed]
## Training Details
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#### Summary
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moraix/SentimentBot | moraix | 2025-05-24T18:10:54Z | 0 | 0 | null | [
"safetensors",
"bert",
"sentiment-analysis",
"pytorch",
"text-classification",
"license:mit",
"region:us"
] | text-classification | 2025-05-24T17:48:09Z | ---
tags:
- sentiment-analysis
- bert
- pytorch
- text-classification
license: mit
---
# SentimentBot: A Sentiment Analysis Chatbot with BERT 🤖
## Overview
SentimentBot is a fine-tuned BERT model (bert-base-uncased) designed for sentiment analysis, trained on the SST-2 (Stanford Sentiment Treebank) dataset. This model predicts whether a given text expresses a positive or negative sentiment with high accuracy, achieving 92.3% accuracy and 92.3% F1 score on the validation set after 2 epochs. It was developed as part of an AI Engineer internship project focusing on NLP.
## Model Details
- Base Model: bert-base-uncased
- Dataset: SST-2 (2 classes: 0 = negative, 1 = positive)
- Training: Fine-tuned for 2 epochs with a batch size of 16
- Performance:
Eval Loss: 0.311
Accuracy: 92.3%
F1 Score: 92.3%
## Usage
To use the SentimentBot model in your Python project, install the required libraries and load the model as follows:
```
pip install transformers torch
```
```
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
# Load the model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("moraix/SentimentBot")
model = AutoModelForSequenceClassification.from_pretrained("moraix/SentimentBot").to("cuda")
# Example input
text = "I love this movie so much!"
inputs = tokenizer(text, return_tensors="pt", padding="max_length", truncation=True, max_length=128).to("cuda")
# Predict sentiment
model.eval()
with torch.no_grad():
outputs = model(**inputs)
predictions = torch.softmax(outputs.logits, dim=1)
predicted_class = torch.argmax(predictions, dim=1).item()
confidence = predictions[0, predicted_class].item()
label_map = {0: "negative", 1: "positive"}
sentiment = label_map[predicted_class]
print(f"Text: {text}")
print(f"Sentiment: {sentiment} (Confidence: {confidence:.2f})")
```
## Intended Uses
- Primary Use: Analyzing sentiment in English text (e.g., movie reviews, social media posts).
- Out-of-Scope: Multi-class sentiment (e.g., neutral) or non-English text (requires further fine-tuning).
## Limitations
- Trained only on SST-2, so performance may vary on other datasets.
- Limited to binary classification (positive/negative).
## Future Improvements
- dd support for more complex sentiments (e.g., neutral, angry).
- Expand the dataset with custom data.
- Deploy the chatbot as a web app using Flask or Streamlit.
## Contributing
Feel free to fork this repository, open issues, or submit pull requests. Feedback is always welcome! Contact For questions, reach out via [email protected] or connect with me on LinkedIn. |
open-unlearning/unlearn_tofu_Llama-3.2-1B-Instruct_forget10_SimNPO_lr1e-05_b4.5_a1_d0_g0.125_ep5 | open-unlearning | 2025-05-24T18:09:01Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-24T18:07:44Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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jisu1002/assignment2-model | jisu1002 | 2025-05-24T18:08:51Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-05-24T18:08:35Z | ---
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.
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[More Information Needed]
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## How to Get Started with the Model
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[More Information Needed]
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#### Summary
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mradermacher/OLMo-1B-sft-GGUF | mradermacher | 2025-05-24T18:07:19Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:ShowMakerTAT/OLMo-1B-sft",
"base_model:quantized:ShowMakerTAT/OLMo-1B-sft",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-05-24T18:01:09Z | ---
base_model: ShowMakerTAT/OLMo-1B-sft
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/ShowMakerTAT/OLMo-1B-sft
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/OLMo-1B-sft-GGUF/resolve/main/OLMo-1B-sft.Q2_K.gguf) | Q2_K | 0.7 | |
| [GGUF](https://huggingface.co/mradermacher/OLMo-1B-sft-GGUF/resolve/main/OLMo-1B-sft.Q3_K_S.gguf) | Q3_K_S | 0.7 | |
| [GGUF](https://huggingface.co/mradermacher/OLMo-1B-sft-GGUF/resolve/main/OLMo-1B-sft.Q3_K_M.gguf) | Q3_K_M | 0.7 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/OLMo-1B-sft-GGUF/resolve/main/OLMo-1B-sft.Q3_K_L.gguf) | Q3_K_L | 0.8 | |
| [GGUF](https://huggingface.co/mradermacher/OLMo-1B-sft-GGUF/resolve/main/OLMo-1B-sft.IQ4_XS.gguf) | IQ4_XS | 0.8 | |
| [GGUF](https://huggingface.co/mradermacher/OLMo-1B-sft-GGUF/resolve/main/OLMo-1B-sft.Q4_K_S.gguf) | Q4_K_S | 0.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/OLMo-1B-sft-GGUF/resolve/main/OLMo-1B-sft.Q4_K_M.gguf) | Q4_K_M | 0.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/OLMo-1B-sft-GGUF/resolve/main/OLMo-1B-sft.Q5_K_S.gguf) | Q5_K_S | 0.9 | |
| [GGUF](https://huggingface.co/mradermacher/OLMo-1B-sft-GGUF/resolve/main/OLMo-1B-sft.Q5_K_M.gguf) | Q5_K_M | 0.9 | |
| [GGUF](https://huggingface.co/mradermacher/OLMo-1B-sft-GGUF/resolve/main/OLMo-1B-sft.Q6_K.gguf) | Q6_K | 1.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/OLMo-1B-sft-GGUF/resolve/main/OLMo-1B-sft.Q8_0.gguf) | Q8_0 | 1.3 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/OLMo-1B-sft-GGUF/resolve/main/OLMo-1B-sft.f16.gguf) | f16 | 2.3 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
chdany12/Taxi-v3 | chdany12 | 2025-05-24T18:07:00Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2025-05-24T18:06:53Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.50 +/- 2.74
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="chdany12/Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
javokhirraimov/birdclef2025-model | javokhirraimov | 2025-05-24T18:04:19Z | 0 | 0 | null | [
"license:mit",
"region:us"
] | null | 2025-05-24T17:22:55Z | ---
license: mit
---
# BirdCLEF 2025 Model
This repository hosts a PyTorch model trained for the BirdCLEF 2025 challenge, classifying bird species from audio spectrograms.
---
## Model Details
- **Architecture:** EfficientNetV2-S backbone (modified for single-channel mel-spectrogram input)
- **Input:** Log-mel spectrogram of audio clips (5 seconds, 32kHz, 128 mel bins)
- **Classes:** 206 bird species
- **Loss:** Improved Focal Loss with label smoothing
- **Data Augmentation:** SpecAugment (frequency and time masking)
- **Training:** Mixed precision and early stopping applied
---
## Usage
### 1. Install Dependencies
```bash
pip install torch torchaudio timm librosa numpy
import librosa
import numpy as np
import torch
# Audio preprocessing parameters
SAMPLE_RATE = 32000
DURATION = 5 # seconds
N_MELS = 128
HOP_LENGTH = 512
def extract_logmel(audio_path):
y, sr = librosa.load(audio_path, sr=SAMPLE_RATE, mono=True)
target_length = SAMPLE_RATE * DURATION
if len(y) < target_length:
y = np.pad(y, (0, target_length - len(y)))
else:
y = y[:target_length]
mel_spec = librosa.feature.melspectrogram(y=y, sr=sr, n_mels=N_MELS, hop_length=HOP_LENGTH)
log_mel_spec = librosa.power_to_db(mel_spec, ref=np.max)
return torch.tensor(log_mel_spec).unsqueeze(0).float() # shape: [1, mel_bins, time_frames]
# Load your model definition here
from model import BirdCLEFModel # ensure model.py is present
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = BirdCLEFModel(num_classes=206, backbone='efficientnet').to(device)
model.load_state_dict(torch.load("best_model.pth", map_location=device))
model.eval()
def predict(audio_path):
spec = extract_logmel(audio_path).unsqueeze(0).to(device) # add batch dimension: [1, 1, 128, T]
with torch.no_grad():
outputs = model(spec)
probs = torch.softmax(outputs, dim=1)
pred_label = torch.argmax(probs, dim=1).item()
return pred_label, probs.cpu().numpy()
# Example usage:
audio_file = "path/to/sample.ogg"
label_idx, probabilities = predict(audio_file)
print(f"Predicted label index: {label_idx}")
---
|
YujinPang/reasoning_model_1 | YujinPang | 2025-05-24T18:03:37Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-24T17:03:55Z | ---
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]
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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.
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### Training Data
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#### Preprocessing [optional]
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#### Testing Data
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[More Information Needed]
#### Metrics
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[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]
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## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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[More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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## Glossary [optional]
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## Model Card Contact
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khuam/run_5 | khuam | 2025-05-24T18:03:28Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:Qwen/Qwen2.5-VL-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-VL-7B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-24T07:12:47Z | ---
base_model: Qwen/Qwen2.5-VL-7B-Instruct
library_name: transformers
model_name: run_5
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for run_5
This model is a fine-tuned version of [Qwen/Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="khuam/run_5", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.17.0
- Transformers: 4.52.3
- Pytorch: 2.8.0.dev20250518+cu126
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
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