modelId
string | author
string | last_modified
timestamp[us, tz=UTC] | downloads
int64 | likes
int64 | library_name
string | tags
sequence | pipeline_tag
string | createdAt
timestamp[us, tz=UTC] | card
string |
|---|---|---|---|---|---|---|---|---|---|
phospho-app/zedlika-gr00t-DiceFlip-yp4o5v0549 | phospho-app | 2025-05-22T02:38:23Z | 0 | 0 | null | [
"phosphobot",
"gr00t",
"region:us"
] | null | 2025-05-22T00:00:30Z | <!DOCTYPE html>
<html class="" lang="en">
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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";
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if (storageTheme) {
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alt=""
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<h1>429</h1>
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goodenwings/eb33816e-8b84-4e99-a7bd-bb0adc56997b | goodenwings | 2025-05-22T02:34:18Z | 0 | 0 | null | [
"region:us"
] | null | 2025-05-22T02:34:09Z | <!DOCTYPE html>
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BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
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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";
let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
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if (storageTheme) {
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<img
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alt=""
/>
<div>
<h1>429</h1>
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dimasik2987/8b7afb1a-d542-4f61-ad3f-969f2f90346b | dimasik2987 | 2025-05-22T02:22:32Z | 0 | 0 | null | [
"region:us"
] | null | 2025-05-22T01:16:57Z | <!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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content="We're on a journey to advance and democratize artificial intelligence through open source and open science."
/>
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content="Hugging Face - The AI community building the future."
/>
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<style>
body {
margin: 0;
}
main {
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) {}
if (theme === "dark") {
document.documentElement.classList.add("dark");
} else {
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}
</script>
</head>
<body>
<main>
<img
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alt=""
/>
<div>
<h1>429</h1>
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dabrown/76131605-c331-41d3-a3c5-f02a2024efc3 | dabrown | 2025-05-22T01:51:11Z | 0 | 0 | null | [
"region:us"
] | null | 2025-05-22T01:38:52Z | <!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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<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) {}
if (theme === "dark") {
document.documentElement.classList.add("dark");
} else {
document.documentElement.classList.remove("dark");
}
</script>
</head>
<body>
<main>
<img
src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg"
alt=""
/>
<div>
<h1>429</h1>
<p>We had to rate limit you. If you think it's an error, send us <a href="mailto:[email protected]">an email</a></p>
</div>
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</body>
</html> |
dabrown/ca3df0d4-67ea-48db-976a-7804f95022f8 | dabrown | 2025-05-22T01:51:05Z | 0 | 0 | null | [
"region:us"
] | null | 2025-05-22T01:36:50Z | <!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"
/>
<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) {}
if (theme === "dark") {
document.documentElement.classList.add("dark");
} else {
document.documentElement.classList.remove("dark");
}
</script>
</head>
<body>
<main>
<img
src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg"
alt=""
/>
<div>
<h1>429</h1>
<p>We had to rate limit you. If you think it's an error, send us <a href="mailto:[email protected]">an email</a></p>
</div>
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</body>
</html> |
GODBlessU2/1794ca88-9e8d-4c87-bdd3-d86806f655a6 | GODBlessU2 | 2025-05-22T01:29:52Z | 0 | 0 | null | [
"region:us"
] | null | 2025-05-21T22:30:32Z | <!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) {}
if (theme === "dark") {
document.documentElement.classList.add("dark");
} else {
document.documentElement.classList.remove("dark");
}
</script>
</head>
<body>
<main>
<img
src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg"
alt=""
/>
<div>
<h1>429</h1>
<p>We had to rate limit you. If you think it's an error, send us <a href="mailto:[email protected]">an email</a></p>
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RichardErkhov/paraschopra_-_llama-31-8b-instruct-120k-correct-plus-real-low-lr_MERGED-gguf | RichardErkhov | 2025-05-22T01:20:35Z | 0 | 0 | null | [
"gguf",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-05-21T21:23:44Z | <!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"
/>
<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) {}
if (theme === "dark") {
document.documentElement.classList.add("dark");
} else {
document.documentElement.classList.remove("dark");
}
</script>
</head>
<body>
<main>
<img
src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg"
alt=""
/>
<div>
<h1>429</h1>
<p>We had to rate limit you. If you think it's an error, send us <a href="mailto:[email protected]">an email</a></p>
</div>
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</body>
</html> |
Kurosawama/Llama-3.1-8B-DPO-beamsearch-align | Kurosawama | 2025-05-22T00:58:04Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"trl",
"dpo",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-05-22T00:36:55Z | <!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";
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HelenHenny66/RK_pop | HelenHenny66 | 2025-05-22T00:48:12Z | 0 | 0 | null | [
"en",
"license:creativeml-openrail-m",
"region:us"
] | null | 2025-05-22T00:41:09Z | <!DOCTYPE html>
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Rahernan/yelp-fastai | Rahernan | 2025-05-21T23:34:15Z | 0 | 0 | fastai | [
"fastai",
"region:us"
] | null | 2025-05-21T23:34:08Z | ---
tags:
- fastai
---
# Amazing!
🥳 Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)).
3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)!
Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card.
---
# Model card
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
|
MinaMila/llama_instbase_LoRa_GermanCredit_cfda_ep9_66 | MinaMila | 2025-05-21T22:51:19Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-05-21T22:51:15Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
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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
Use the code below to get started with the model.
[More Information Needed]
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mradermacher/Jedi-7B-1080p-GGUF | mradermacher | 2025-05-21T22:46:15Z | 377 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:xlangai/Jedi-7B-1080p",
"base_model:quantized:xlangai/Jedi-7B-1080p",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-04-30T15:05:24Z | ---
base_model: xlangai/Jedi-7B-1080p
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/xlangai/Jedi-7B-1080p
<!-- 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/Jedi-7B-1080p-GGUF/resolve/main/Jedi-7B-1080p.mmproj-fp16.gguf) | mmproj-fp16 | 1.5 | vision supplement |
| [GGUF](https://huggingface.co/mradermacher/Jedi-7B-1080p-GGUF/resolve/main/Jedi-7B-1080p.Q2_K.gguf) | Q2_K | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/Jedi-7B-1080p-GGUF/resolve/main/Jedi-7B-1080p.Q3_K_S.gguf) | Q3_K_S | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/Jedi-7B-1080p-GGUF/resolve/main/Jedi-7B-1080p.Q3_K_M.gguf) | Q3_K_M | 3.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Jedi-7B-1080p-GGUF/resolve/main/Jedi-7B-1080p.Q3_K_L.gguf) | Q3_K_L | 4.2 | |
| [GGUF](https://huggingface.co/mradermacher/Jedi-7B-1080p-GGUF/resolve/main/Jedi-7B-1080p.IQ4_XS.gguf) | IQ4_XS | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/Jedi-7B-1080p-GGUF/resolve/main/Jedi-7B-1080p.Q4_K_S.gguf) | Q4_K_S | 4.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Jedi-7B-1080p-GGUF/resolve/main/Jedi-7B-1080p.Q4_K_M.gguf) | Q4_K_M | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Jedi-7B-1080p-GGUF/resolve/main/Jedi-7B-1080p.Q5_K_S.gguf) | Q5_K_S | 5.4 | |
| [GGUF](https://huggingface.co/mradermacher/Jedi-7B-1080p-GGUF/resolve/main/Jedi-7B-1080p.Q5_K_M.gguf) | Q5_K_M | 5.5 | |
| [GGUF](https://huggingface.co/mradermacher/Jedi-7B-1080p-GGUF/resolve/main/Jedi-7B-1080p.Q6_K.gguf) | Q6_K | 6.4 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Jedi-7B-1080p-GGUF/resolve/main/Jedi-7B-1080p.Q8_0.gguf) | Q8_0 | 8.2 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Jedi-7B-1080p-GGUF/resolve/main/Jedi-7B-1080p.f16.gguf) | f16 | 15.3 | 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. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
MinaMila/llama_instbase_LoRa_GermanCredit_cfda_ep4_66 | MinaMila | 2025-05-21T22:19:19Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-05-21T22:19:15Z | ---
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]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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- **Paper [optional]:** [More Information Needed]
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## 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. -->
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mradermacher/OpenRHO-2B-Thinker-GGUF | mradermacher | 2025-05-21T22:09:55Z | 163 | 0 | transformers | [
"transformers",
"gguf",
"text-generation-inference",
"general-purpose",
"math",
"code",
"en",
"dataset:qingy2024/QwQ-Distill-Data",
"dataset:AI-MO/NuminaMath-TIR",
"base_model:prithivMLmods/OpenRHO-2B-Thinker",
"base_model:quantized:prithivMLmods/OpenRHO-2B-Thinker",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-05-18T06:19:40Z | ---
base_model: prithivMLmods/OpenRHO-2B-Thinker
datasets:
- qingy2024/QwQ-Distill-Data
- AI-MO/NuminaMath-TIR
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- text-generation-inference
- general-purpose
- math
- code
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/prithivMLmods/OpenRHO-2B-Thinker
<!-- 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/OpenRHO-2B-Thinker-GGUF/resolve/main/OpenRHO-2B-Thinker.Q2_K.gguf) | Q2_K | 0.9 | |
| [GGUF](https://huggingface.co/mradermacher/OpenRHO-2B-Thinker-GGUF/resolve/main/OpenRHO-2B-Thinker.Q3_K_S.gguf) | Q3_K_S | 1.0 | |
| [GGUF](https://huggingface.co/mradermacher/OpenRHO-2B-Thinker-GGUF/resolve/main/OpenRHO-2B-Thinker.Q3_K_M.gguf) | Q3_K_M | 1.0 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/OpenRHO-2B-Thinker-GGUF/resolve/main/OpenRHO-2B-Thinker.Q3_K_L.gguf) | Q3_K_L | 1.1 | |
| [GGUF](https://huggingface.co/mradermacher/OpenRHO-2B-Thinker-GGUF/resolve/main/OpenRHO-2B-Thinker.IQ4_XS.gguf) | IQ4_XS | 1.1 | |
| [GGUF](https://huggingface.co/mradermacher/OpenRHO-2B-Thinker-GGUF/resolve/main/OpenRHO-2B-Thinker.Q4_K_S.gguf) | Q4_K_S | 1.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/OpenRHO-2B-Thinker-GGUF/resolve/main/OpenRHO-2B-Thinker.Q4_K_M.gguf) | Q4_K_M | 1.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/OpenRHO-2B-Thinker-GGUF/resolve/main/OpenRHO-2B-Thinker.Q5_K_S.gguf) | Q5_K_S | 1.4 | |
| [GGUF](https://huggingface.co/mradermacher/OpenRHO-2B-Thinker-GGUF/resolve/main/OpenRHO-2B-Thinker.Q5_K_M.gguf) | Q5_K_M | 1.4 | |
| [GGUF](https://huggingface.co/mradermacher/OpenRHO-2B-Thinker-GGUF/resolve/main/OpenRHO-2B-Thinker.Q6_K.gguf) | Q6_K | 1.6 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/OpenRHO-2B-Thinker-GGUF/resolve/main/OpenRHO-2B-Thinker.Q8_0.gguf) | Q8_0 | 2.0 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/OpenRHO-2B-Thinker-GGUF/resolve/main/OpenRHO-2B-Thinker.f16.gguf) | f16 | 3.7 | 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 -->
|
mradermacher/General-Reasoner-Qwen2.5-14B-GGUF | mradermacher | 2025-05-21T22:01:57Z | 31 | 1 | transformers | [
"transformers",
"gguf",
"en",
"dataset:TIGER-Lab/VisualWebInstruct-Verified",
"base_model:TIGER-Lab/General-Reasoner-Qwen2.5-14B",
"base_model:quantized:TIGER-Lab/General-Reasoner-Qwen2.5-14B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-05-21T08:30:59Z | ---
base_model: TIGER-Lab/General-Reasoner-Qwen2.5-14B
datasets:
- TIGER-Lab/VisualWebInstruct-Verified
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/TIGER-Lab/General-Reasoner-Qwen2.5-14B
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/General-Reasoner-Qwen2.5-14B-i1-GGUF
## 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/General-Reasoner-Qwen2.5-14B-GGUF/resolve/main/General-Reasoner-Qwen2.5-14B.Q2_K.gguf) | Q2_K | 5.9 | |
| [GGUF](https://huggingface.co/mradermacher/General-Reasoner-Qwen2.5-14B-GGUF/resolve/main/General-Reasoner-Qwen2.5-14B.Q3_K_S.gguf) | Q3_K_S | 6.8 | |
| [GGUF](https://huggingface.co/mradermacher/General-Reasoner-Qwen2.5-14B-GGUF/resolve/main/General-Reasoner-Qwen2.5-14B.Q3_K_M.gguf) | Q3_K_M | 7.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/General-Reasoner-Qwen2.5-14B-GGUF/resolve/main/General-Reasoner-Qwen2.5-14B.Q3_K_L.gguf) | Q3_K_L | 8.0 | |
| [GGUF](https://huggingface.co/mradermacher/General-Reasoner-Qwen2.5-14B-GGUF/resolve/main/General-Reasoner-Qwen2.5-14B.IQ4_XS.gguf) | IQ4_XS | 8.3 | |
| [GGUF](https://huggingface.co/mradermacher/General-Reasoner-Qwen2.5-14B-GGUF/resolve/main/General-Reasoner-Qwen2.5-14B.Q4_K_S.gguf) | Q4_K_S | 8.7 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/General-Reasoner-Qwen2.5-14B-GGUF/resolve/main/General-Reasoner-Qwen2.5-14B.Q4_K_M.gguf) | Q4_K_M | 9.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/General-Reasoner-Qwen2.5-14B-GGUF/resolve/main/General-Reasoner-Qwen2.5-14B.Q5_K_S.gguf) | Q5_K_S | 10.4 | |
| [GGUF](https://huggingface.co/mradermacher/General-Reasoner-Qwen2.5-14B-GGUF/resolve/main/General-Reasoner-Qwen2.5-14B.Q5_K_M.gguf) | Q5_K_M | 10.6 | |
| [GGUF](https://huggingface.co/mradermacher/General-Reasoner-Qwen2.5-14B-GGUF/resolve/main/General-Reasoner-Qwen2.5-14B.Q6_K.gguf) | Q6_K | 12.2 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/General-Reasoner-Qwen2.5-14B-GGUF/resolve/main/General-Reasoner-Qwen2.5-14B.Q8_0.gguf) | Q8_0 | 15.8 | fast, best quality |
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. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
jmalejandrob79/nbmafckd4k | jmalejandrob79 | 2025-05-21T21:45:41Z | 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 | 2025-05-21T20:41:00Z | ---
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
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: nbmafckd4k
---
# Nbmafckd4K
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `nbmafckd4k` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "nbmafckd4k",
"lora_weights": "https://huggingface.co/jmalejandrob79/nbmafckd4k/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('jmalejandrob79/nbmafckd4k', weight_name='lora.safetensors')
image = pipeline('nbmafckd4k').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 4000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/jmalejandrob79/nbmafckd4k/discussions) to add images that show off what you’ve made with this LoRA.
|
fh1628/qwen-dpo-m1-data-2000 | fh1628 | 2025-05-21T21:45:25Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"trl",
"dpo",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-21T21:42:01Z | ---
library_name: transformers
tags:
- trl
- dpo
---
# 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] |
ArtusDev/mistralai_Devstral-Small-2505_EXL3_6.5bpw_H8 | ArtusDev | 2025-05-21T21:35:33Z | 0 | 0 | vllm | [
"vllm",
"safetensors",
"mistral",
"text2text-generation",
"en",
"fr",
"de",
"es",
"pt",
"it",
"ja",
"ko",
"ru",
"zh",
"ar",
"fa",
"id",
"ms",
"ne",
"pl",
"ro",
"sr",
"sv",
"tr",
"uk",
"vi",
"hi",
"bn",
"base_model:mistralai/Devstral-Small-2505",
"base_model:quantized:mistralai/Devstral-Small-2505",
"license:apache-2.0",
"exl3",
"region:us"
] | text2text-generation | 2025-05-21T20:15:24Z | ---
language:
- en
- fr
- de
- es
- pt
- it
- ja
- ko
- ru
- zh
- ar
- fa
- id
- ms
- ne
- pl
- ro
- sr
- sv
- tr
- uk
- vi
- hi
- bn
license: apache-2.0
library_name: vllm
inference: false
base_model:
- mistralai/Devstral-Small-2505
base_model_relation: quantized
quantized_by: ArtusDev
extra_gated_description: >-
If you want to learn more about how we process your personal data, please read
our <a href="https://mistral.ai/terms/">Privacy Policy</a>.
pipeline_tag: text2text-generation
---
# Devstral-Small-2505
Devstral is an agentic LLM for software engineering tasks built under a collaboration between [Mistral AI](https://mistral.ai/) and [All Hands AI](https://www.all-hands.dev/) 🙌. Devstral excels at using tools to explore codebases, editing multiple files and power software engineering agents. The model achieves remarkable performance on SWE-bench which positionates it as the #1 open source model on this [benchmark](#benchmark-results).
It is finetuned from [Mistral-Small-3.1](https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Base-2503), therefore it has a long context window of up to 128k tokens. As a coding agent, Devstral is text-only and before fine-tuning from `Mistral-Small-3.1` the vision encoder was removed.
For enterprises requiring specialized capabilities (increased context, domain-specific knowledge, etc.), we will release commercial models beyond what Mistral AI contributes to the community.
Learn more about Devstral in our [blog post](https://mistral.ai/news/devstral).
## Key Features:
- **Agentic coding**: Devstral is designed to excel at agentic coding tasks, making it a great choice for software engineering agents.
- **lightweight**: with its compact size of just 24 billion parameters, Devstral is light enough to run on a single RTX 4090 or a Mac with 32GB RAM, making it an appropriate model for local deployment and on-device use.
- **Apache 2.0 License**: Open license allowing usage and modification for both commercial and non-commercial purposes.
- **Context Window**: A 128k context window.
- **Tokenizer**: Utilizes a Tekken tokenizer with a 131k vocabulary size.
## Benchmark Results
### SWE-Bench
Devstral achieves a score of 46.8% on SWE-Bench Verified, outperforming prior open-source SoTA by 6%.
| Model | Scaffold | SWE-Bench Verified (%) |
|------------------|--------------------|------------------------|
| Devstral | OpenHands Scaffold | **46.8** |
| GPT-4.1-mini | OpenAI Scaffold | 23.6 |
| Claude 3.5 Haiku | Anthropic Scaffold | 40.6 |
| SWE-smith-LM 32B | SWE-agent Scaffold | 40.2 |
When evaluated under the same test scaffold (OpenHands, provided by All Hands AI 🙌), Devstral exceeds far larger models such as Deepseek-V3-0324 and Qwen3 232B-A22B.
## Usage
We recommend to use Devstral with the [OpenHands](https://github.com/All-Hands-AI/OpenHands/tree/main) scaffold.
You can use it either through our API or by running locally.
### API
Follow these [instructions](https://docs.mistral.ai/getting-started/quickstart/#account-setup) to create a Mistral account and get an API key.
Then run these commands to start the OpenHands docker container.
```bash
export MISTRAL_API_KEY=<MY_KEY>
docker pull docker.all-hands.dev/all-hands-ai/runtime:0.39-nikolaik
mkdir -p ~/.openhands-state && echo '{"language":"en","agent":"CodeActAgent","max_iterations":null,"security_analyzer":null,"confirmation_mode":false,"llm_model":"mistral/devstral-small-2505","llm_api_key":"'$MISTRAL_API_KEY'","remote_runtime_resource_factor":null,"github_token":null,"enable_default_condenser":true}' > ~/.openhands-state/settings.json
docker run -it --rm --pull=always \
-e SANDBOX_RUNTIME_CONTAINER_IMAGE=docker.all-hands.dev/all-hands-ai/runtime:0.39-nikolaik \
-e LOG_ALL_EVENTS=true \
-v /var/run/docker.sock:/var/run/docker.sock \
-v ~/.openhands-state:/.openhands-state \
-p 3000:3000 \
--add-host host.docker.internal:host-gateway \
--name openhands-app \
docker.all-hands.dev/all-hands-ai/openhands:0.39
```
### Local inference
The model can also be deployed with the following libraries:
- [`vllm (recommended)`](https://github.com/vllm-project/vllm): See [here](#vllm-recommended)
- [`mistral-inference`](https://github.com/mistralai/mistral-inference): See [here](#mistral-inference)
- [`transformers`](https://github.com/huggingface/transformers): See [here](#transformers)
- [`LMStudio`](https://lmstudio.ai/): See [here](#lmstudio)
- [`ollama`](https://github.com/ollama/ollama): See [here](#ollama)
### OpenHands (recommended)
#### Launch a server to deploy Devstral-Small-2505
Make sure you launched an OpenAI-compatible server such as vLLM or Ollama as described above. Then, you can use OpenHands to interact with `Devstral-Small-2505`.
In the case of the tutorial we spineed up a vLLM server running the command:
```bash
vllm serve mistralai/Devstral-Small-2505 --tokenizer_mode mistral --config_format mistral --load_format mistral --tool-call-parser mistral --enable-auto-tool-choice --tensor-parallel-size 2
```
The server address should be in the following format: `http://<your-server-url>:8000/v1`
#### Launch OpenHands
You can follow installation of OpenHands [here](https://docs.all-hands.dev/modules/usage/installation).
The easiest way to launch OpenHands is to use the Docker image:
```bash
docker pull docker.all-hands.dev/all-hands-ai/runtime:0.38-nikolaik
docker run -it --rm --pull=always \
-e SANDBOX_RUNTIME_CONTAINER_IMAGE=docker.all-hands.dev/all-hands-ai/runtime:0.38-nikolaik \
-e LOG_ALL_EVENTS=true \
-v /var/run/docker.sock:/var/run/docker.sock \
-v ~/.openhands-state:/.openhands-state \
-p 3000:3000 \
--add-host host.docker.internal:host-gateway \
--name openhands-app \
docker.all-hands.dev/all-hands-ai/openhands:0.38
```
Then, you can access the OpenHands UI at `http://localhost:3000`.
#### Connect to the server
When accessing the OpenHands UI, you will be prompted to connect to a server. You can use the advanced mode to connect to the server you launched earlier.
Fill the following fields:
- **Custom Model**: `openai/mistralai/Devstral-Small-2505`
- **Base URL**: `http://<your-server-url>:8000/v1`
- **API Key**: `token` (or any other token you used to launch the server if any)
#### Use OpenHands powered by Devstral
Now you're good to use Devstral Small inside OpenHands by **starting a new conversation**. Let's build a To-Do list app.
<details>
<summary>To-Do list app</summary
1. Let's ask Devstral to generate the app with the following prompt:
```txt
Build a To-Do list app with the following requirements:
- Built using FastAPI and React.
- Make it a one page app that:
- Allows to add a task.
- Allows to delete a task.
- Allows to mark a task as done.
- Displays the list of tasks.
- Store the tasks in a SQLite database.
```
2. Let's see the result
You should see the agent construct the app and be able to explore the code it generated.
If it doesn't do it automatically, ask Devstral to deploy the app or do it manually, and then go the front URL deployment to see the app.
3. Iterate
Now that you have a first result you can iterate on it by asking your agent to improve it. For example, in the app generated we could click on a task to mark it checked but having a checkbox would improve UX. You could also ask it to add a feature to edit a task, or to add a feature to filter the tasks by status.
Enjoy building with Devstral Small and OpenHands!
</details>
### vLLM (recommended)
We recommend using this model with the [vLLM library](https://github.com/vllm-project/vllm)
to implement production-ready inference pipelines.
**_Installation_**
Make sure you install [`vLLM >= 0.8.5`](https://github.com/vllm-project/vllm/releases/tag/v0.8.5):
```
pip install vllm --upgrade
```
Doing so should automatically install [`mistral_common >= 1.5.5`](https://github.com/mistralai/mistral-common/releases/tag/v1.5.5).
To check:
```
python -c "import mistral_common; print(mistral_common.__version__)"
```
You can also make use of a ready-to-go [docker image](https://github.com/vllm-project/vllm/blob/main/Dockerfile) or on the [docker hub](https://hub.docker.com/layers/vllm/vllm-openai/latest/images/sha256-de9032a92ffea7b5c007dad80b38fd44aac11eddc31c435f8e52f3b7404bbf39).
#### Server
We recommand that you use Devstral in a server/client setting.
1. Spin up a server:
```
vllm serve mistralai/Devstral-Small-2505 --tokenizer_mode mistral --config_format mistral --load_format mistral --tool-call-parser mistral --enable-auto-tool-choice --tensor-parallel-size 2
```
2. To ping the client you can use a simple Python snippet.
```py
import requests
import json
from huggingface_hub import hf_hub_download
url = "http://<your-server-url>:8000/v1/chat/completions"
headers = {"Content-Type": "application/json", "Authorization": "Bearer token"}
model = "mistralai/Devstral-Small-2505"
def load_system_prompt(repo_id: str, filename: str) -> str:
file_path = hf_hub_download(repo_id=repo_id, filename=filename)
with open(file_path, "r") as file:
system_prompt = file.read()
return system_prompt
SYSTEM_PROMPT = load_system_prompt(model, "SYSTEM_PROMPT.txt")
messages = [
{"role": "system", "content": SYSTEM_PROMPT},
{
"role": "user",
"content": [
{
"type": "text",
"text": "<your-command>",
},
],
},
]
data = {"model": model, "messages": messages, "temperature": 0.15}
response = requests.post(url, headers=headers, data=json.dumps(data))
print(response.json()["choices"][0]["message"]["content"])
```
### Mistral-inference
We recommend using mistral-inference to quickly try out / "vibe-check" Devstral.
#### Install
Make sure to have mistral_inference >= 1.6.0 installed.
```bash
pip install mistral_inference --upgrade
```
#### Download
```python
from huggingface_hub import snapshot_download
from pathlib import Path
mistral_models_path = Path.home().joinpath('mistral_models', 'Devstral')
mistral_models_path.mkdir(parents=True, exist_ok=True)
snapshot_download(repo_id="mistralai/Devstral-Small-2505", allow_patterns=["params.json", "consolidated.safetensors", "tekken.json"], local_dir=mistral_models_path)
```
#### Python
You can run the model using the following command:
```bash
mistral-chat $HOME/mistral_models/Devstral --instruct --max_tokens 300
```
You can then prompt it with anything you'd like.
### Transformers
To make the best use of our model with transformers make sure to have [installed](https://github.com/mistralai/mistral-common) ` mistral-common >= 1.5.5` to use our tokenizer.
```bash
pip install mistral-common --upgrade
```
Then load our tokenizer along with the model and generate:
```python
import torch
from mistral_common.protocol.instruct.messages import (
SystemMessage, UserMessage
)
from mistral_common.protocol.instruct.request import ChatCompletionRequest
from mistral_common.tokens.tokenizers.mistral import MistralTokenizer
from mistral_common.tokens.tokenizers.tekken import SpecialTokenPolicy
from huggingface_hub import hf_hub_download
from transformers import AutoModelForCausalLM
def load_system_prompt(repo_id: str, filename: str) -> str:
file_path = hf_hub_download(repo_id=repo_id, filename=filename)
with open(file_path, "r") as file:
system_prompt = file.read()
return system_prompt
model_id = "mistralai/Devstral-Small-2505"
tekken_file = hf_hub_download(repo_id=model_id, filename="tekken.json")
SYSTEM_PROMPT = load_system_prompt(model_id, "SYSTEM_PROMPT.txt")
tokenizer = MistralTokenizer.from_file(tekken_file)
model = AutoModelForCausalLM.from_pretrained(model_id)
tokenized = tokenizer.encode_chat_completion(
ChatCompletionRequest(
messages=[
SystemMessage(content=SYSTEM_PROMPT),
UserMessage(content="<your-command>"),
],
)
)
output = model.generate(
input_ids=torch.tensor([tokenized.tokens]),
max_new_tokens=1000,
)[0]
decoded_output = tokenizer.decode(output[len(tokenized.tokens):])
print(decoded_output)
```
### LMStudio
Download the weights from huggingface:
```
pip install -U "huggingface_hub[cli]"
huggingface-cli download \
"mistralai/Devstral-Small-2505_gguf" \
--include "devstralQ4_K_M.gguf" \
--local-dir "mistralai/Devstral-Small-2505_gguf/"
```
You can serve the model locally with [LMStudio](https://lmstudio.ai/).
* Download [LM Studio](https://lmstudio.ai/) and install it
* Install `lms cli ~/.lmstudio/bin/lms bootstrap`
* In a bash terminal, run `lms import devstralQ4_K_M.gguf` in the directory where you've downloaded the model checkpoint (e.g. `mistralai/Devstral-Small-2505_gguf`)
* Open the LMStudio application, click the terminal icon to get into the developer tab. Click select a model to load and select Devstral Q4 K M. Toggle the status button to start the model, in setting toggle Serve on Local Network to be on.
* On the right tab, you will see an API identifier which should be devstralq4_k_m and an api address under API Usage. Keep note of this address, we will use it in the next step.
Launch Openhands
You can now interact with the model served from LM Studio with openhands. Start the openhands server with the docker
```bash
docker pull docker.all-hands.dev/all-hands-ai/runtime:0.38-nikolaik
docker run -it --rm --pull=always \
-e SANDBOX_RUNTIME_CONTAINER_IMAGE=docker.all-hands.dev/all-hands-ai/runtime:0.38-nikolaik \
-e LOG_ALL_EVENTS=true \
-v /var/run/docker.sock:/var/run/docker.sock \
-v ~/.openhands-state:/.openhands-state \
-p 3000:3000 \
--add-host host.docker.internal:host-gateway \
--name openhands-app \
docker.all-hands.dev/all-hands-ai/openhands:0.38
```
Click “see advanced setting” on the second line.
In the new tab, toggle advanced to on. Set the custom model to be mistral/devstralq4_k_m and Base URL the api address we get from the last step in LM Studio. Set API Key to dummy. Click save changes.
### Ollama
You can run Devstral using the [Ollama](https://ollama.ai/) CLI.
```bash
ollama run devstral
``` |
ArtusDev/mistralai_Devstral-Small-2505_EXL3_3.5bpw_H6 | ArtusDev | 2025-05-21T21:33:10Z | 0 | 0 | vllm | [
"vllm",
"safetensors",
"mistral",
"text2text-generation",
"en",
"fr",
"de",
"es",
"pt",
"it",
"ja",
"ko",
"ru",
"zh",
"ar",
"fa",
"id",
"ms",
"ne",
"pl",
"ro",
"sr",
"sv",
"tr",
"uk",
"vi",
"hi",
"bn",
"base_model:mistralai/Devstral-Small-2505",
"base_model:quantized:mistralai/Devstral-Small-2505",
"license:apache-2.0",
"exl3",
"region:us"
] | text2text-generation | 2025-05-21T18:03:22Z | ---
language:
- en
- fr
- de
- es
- pt
- it
- ja
- ko
- ru
- zh
- ar
- fa
- id
- ms
- ne
- pl
- ro
- sr
- sv
- tr
- uk
- vi
- hi
- bn
license: apache-2.0
library_name: vllm
inference: false
base_model:
- mistralai/Devstral-Small-2505
base_model_relation: quantized
quantized_by: ArtusDev
extra_gated_description: >-
If you want to learn more about how we process your personal data, please read
our <a href="https://mistral.ai/terms/">Privacy Policy</a>.
pipeline_tag: text2text-generation
---
# Devstral-Small-2505
Devstral is an agentic LLM for software engineering tasks built under a collaboration between [Mistral AI](https://mistral.ai/) and [All Hands AI](https://www.all-hands.dev/) 🙌. Devstral excels at using tools to explore codebases, editing multiple files and power software engineering agents. The model achieves remarkable performance on SWE-bench which positionates it as the #1 open source model on this [benchmark](#benchmark-results).
It is finetuned from [Mistral-Small-3.1](https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Base-2503), therefore it has a long context window of up to 128k tokens. As a coding agent, Devstral is text-only and before fine-tuning from `Mistral-Small-3.1` the vision encoder was removed.
For enterprises requiring specialized capabilities (increased context, domain-specific knowledge, etc.), we will release commercial models beyond what Mistral AI contributes to the community.
Learn more about Devstral in our [blog post](https://mistral.ai/news/devstral).
## Key Features:
- **Agentic coding**: Devstral is designed to excel at agentic coding tasks, making it a great choice for software engineering agents.
- **lightweight**: with its compact size of just 24 billion parameters, Devstral is light enough to run on a single RTX 4090 or a Mac with 32GB RAM, making it an appropriate model for local deployment and on-device use.
- **Apache 2.0 License**: Open license allowing usage and modification for both commercial and non-commercial purposes.
- **Context Window**: A 128k context window.
- **Tokenizer**: Utilizes a Tekken tokenizer with a 131k vocabulary size.
## Benchmark Results
### SWE-Bench
Devstral achieves a score of 46.8% on SWE-Bench Verified, outperforming prior open-source SoTA by 6%.
| Model | Scaffold | SWE-Bench Verified (%) |
|------------------|--------------------|------------------------|
| Devstral | OpenHands Scaffold | **46.8** |
| GPT-4.1-mini | OpenAI Scaffold | 23.6 |
| Claude 3.5 Haiku | Anthropic Scaffold | 40.6 |
| SWE-smith-LM 32B | SWE-agent Scaffold | 40.2 |
When evaluated under the same test scaffold (OpenHands, provided by All Hands AI 🙌), Devstral exceeds far larger models such as Deepseek-V3-0324 and Qwen3 232B-A22B.
## Usage
We recommend to use Devstral with the [OpenHands](https://github.com/All-Hands-AI/OpenHands/tree/main) scaffold.
You can use it either through our API or by running locally.
### API
Follow these [instructions](https://docs.mistral.ai/getting-started/quickstart/#account-setup) to create a Mistral account and get an API key.
Then run these commands to start the OpenHands docker container.
```bash
export MISTRAL_API_KEY=<MY_KEY>
docker pull docker.all-hands.dev/all-hands-ai/runtime:0.39-nikolaik
mkdir -p ~/.openhands-state && echo '{"language":"en","agent":"CodeActAgent","max_iterations":null,"security_analyzer":null,"confirmation_mode":false,"llm_model":"mistral/devstral-small-2505","llm_api_key":"'$MISTRAL_API_KEY'","remote_runtime_resource_factor":null,"github_token":null,"enable_default_condenser":true}' > ~/.openhands-state/settings.json
docker run -it --rm --pull=always \
-e SANDBOX_RUNTIME_CONTAINER_IMAGE=docker.all-hands.dev/all-hands-ai/runtime:0.39-nikolaik \
-e LOG_ALL_EVENTS=true \
-v /var/run/docker.sock:/var/run/docker.sock \
-v ~/.openhands-state:/.openhands-state \
-p 3000:3000 \
--add-host host.docker.internal:host-gateway \
--name openhands-app \
docker.all-hands.dev/all-hands-ai/openhands:0.39
```
### Local inference
The model can also be deployed with the following libraries:
- [`vllm (recommended)`](https://github.com/vllm-project/vllm): See [here](#vllm-recommended)
- [`mistral-inference`](https://github.com/mistralai/mistral-inference): See [here](#mistral-inference)
- [`transformers`](https://github.com/huggingface/transformers): See [here](#transformers)
- [`LMStudio`](https://lmstudio.ai/): See [here](#lmstudio)
- [`ollama`](https://github.com/ollama/ollama): See [here](#ollama)
### OpenHands (recommended)
#### Launch a server to deploy Devstral-Small-2505
Make sure you launched an OpenAI-compatible server such as vLLM or Ollama as described above. Then, you can use OpenHands to interact with `Devstral-Small-2505`.
In the case of the tutorial we spineed up a vLLM server running the command:
```bash
vllm serve mistralai/Devstral-Small-2505 --tokenizer_mode mistral --config_format mistral --load_format mistral --tool-call-parser mistral --enable-auto-tool-choice --tensor-parallel-size 2
```
The server address should be in the following format: `http://<your-server-url>:8000/v1`
#### Launch OpenHands
You can follow installation of OpenHands [here](https://docs.all-hands.dev/modules/usage/installation).
The easiest way to launch OpenHands is to use the Docker image:
```bash
docker pull docker.all-hands.dev/all-hands-ai/runtime:0.38-nikolaik
docker run -it --rm --pull=always \
-e SANDBOX_RUNTIME_CONTAINER_IMAGE=docker.all-hands.dev/all-hands-ai/runtime:0.38-nikolaik \
-e LOG_ALL_EVENTS=true \
-v /var/run/docker.sock:/var/run/docker.sock \
-v ~/.openhands-state:/.openhands-state \
-p 3000:3000 \
--add-host host.docker.internal:host-gateway \
--name openhands-app \
docker.all-hands.dev/all-hands-ai/openhands:0.38
```
Then, you can access the OpenHands UI at `http://localhost:3000`.
#### Connect to the server
When accessing the OpenHands UI, you will be prompted to connect to a server. You can use the advanced mode to connect to the server you launched earlier.
Fill the following fields:
- **Custom Model**: `openai/mistralai/Devstral-Small-2505`
- **Base URL**: `http://<your-server-url>:8000/v1`
- **API Key**: `token` (or any other token you used to launch the server if any)
#### Use OpenHands powered by Devstral
Now you're good to use Devstral Small inside OpenHands by **starting a new conversation**. Let's build a To-Do list app.
<details>
<summary>To-Do list app</summary
1. Let's ask Devstral to generate the app with the following prompt:
```txt
Build a To-Do list app with the following requirements:
- Built using FastAPI and React.
- Make it a one page app that:
- Allows to add a task.
- Allows to delete a task.
- Allows to mark a task as done.
- Displays the list of tasks.
- Store the tasks in a SQLite database.
```
2. Let's see the result
You should see the agent construct the app and be able to explore the code it generated.
If it doesn't do it automatically, ask Devstral to deploy the app or do it manually, and then go the front URL deployment to see the app.
3. Iterate
Now that you have a first result you can iterate on it by asking your agent to improve it. For example, in the app generated we could click on a task to mark it checked but having a checkbox would improve UX. You could also ask it to add a feature to edit a task, or to add a feature to filter the tasks by status.
Enjoy building with Devstral Small and OpenHands!
</details>
### vLLM (recommended)
We recommend using this model with the [vLLM library](https://github.com/vllm-project/vllm)
to implement production-ready inference pipelines.
**_Installation_**
Make sure you install [`vLLM >= 0.8.5`](https://github.com/vllm-project/vllm/releases/tag/v0.8.5):
```
pip install vllm --upgrade
```
Doing so should automatically install [`mistral_common >= 1.5.5`](https://github.com/mistralai/mistral-common/releases/tag/v1.5.5).
To check:
```
python -c "import mistral_common; print(mistral_common.__version__)"
```
You can also make use of a ready-to-go [docker image](https://github.com/vllm-project/vllm/blob/main/Dockerfile) or on the [docker hub](https://hub.docker.com/layers/vllm/vllm-openai/latest/images/sha256-de9032a92ffea7b5c007dad80b38fd44aac11eddc31c435f8e52f3b7404bbf39).
#### Server
We recommand that you use Devstral in a server/client setting.
1. Spin up a server:
```
vllm serve mistralai/Devstral-Small-2505 --tokenizer_mode mistral --config_format mistral --load_format mistral --tool-call-parser mistral --enable-auto-tool-choice --tensor-parallel-size 2
```
2. To ping the client you can use a simple Python snippet.
```py
import requests
import json
from huggingface_hub import hf_hub_download
url = "http://<your-server-url>:8000/v1/chat/completions"
headers = {"Content-Type": "application/json", "Authorization": "Bearer token"}
model = "mistralai/Devstral-Small-2505"
def load_system_prompt(repo_id: str, filename: str) -> str:
file_path = hf_hub_download(repo_id=repo_id, filename=filename)
with open(file_path, "r") as file:
system_prompt = file.read()
return system_prompt
SYSTEM_PROMPT = load_system_prompt(model, "SYSTEM_PROMPT.txt")
messages = [
{"role": "system", "content": SYSTEM_PROMPT},
{
"role": "user",
"content": [
{
"type": "text",
"text": "<your-command>",
},
],
},
]
data = {"model": model, "messages": messages, "temperature": 0.15}
response = requests.post(url, headers=headers, data=json.dumps(data))
print(response.json()["choices"][0]["message"]["content"])
```
### Mistral-inference
We recommend using mistral-inference to quickly try out / "vibe-check" Devstral.
#### Install
Make sure to have mistral_inference >= 1.6.0 installed.
```bash
pip install mistral_inference --upgrade
```
#### Download
```python
from huggingface_hub import snapshot_download
from pathlib import Path
mistral_models_path = Path.home().joinpath('mistral_models', 'Devstral')
mistral_models_path.mkdir(parents=True, exist_ok=True)
snapshot_download(repo_id="mistralai/Devstral-Small-2505", allow_patterns=["params.json", "consolidated.safetensors", "tekken.json"], local_dir=mistral_models_path)
```
#### Python
You can run the model using the following command:
```bash
mistral-chat $HOME/mistral_models/Devstral --instruct --max_tokens 300
```
You can then prompt it with anything you'd like.
### Transformers
To make the best use of our model with transformers make sure to have [installed](https://github.com/mistralai/mistral-common) ` mistral-common >= 1.5.5` to use our tokenizer.
```bash
pip install mistral-common --upgrade
```
Then load our tokenizer along with the model and generate:
```python
import torch
from mistral_common.protocol.instruct.messages import (
SystemMessage, UserMessage
)
from mistral_common.protocol.instruct.request import ChatCompletionRequest
from mistral_common.tokens.tokenizers.mistral import MistralTokenizer
from mistral_common.tokens.tokenizers.tekken import SpecialTokenPolicy
from huggingface_hub import hf_hub_download
from transformers import AutoModelForCausalLM
def load_system_prompt(repo_id: str, filename: str) -> str:
file_path = hf_hub_download(repo_id=repo_id, filename=filename)
with open(file_path, "r") as file:
system_prompt = file.read()
return system_prompt
model_id = "mistralai/Devstral-Small-2505"
tekken_file = hf_hub_download(repo_id=model_id, filename="tekken.json")
SYSTEM_PROMPT = load_system_prompt(model_id, "SYSTEM_PROMPT.txt")
tokenizer = MistralTokenizer.from_file(tekken_file)
model = AutoModelForCausalLM.from_pretrained(model_id)
tokenized = tokenizer.encode_chat_completion(
ChatCompletionRequest(
messages=[
SystemMessage(content=SYSTEM_PROMPT),
UserMessage(content="<your-command>"),
],
)
)
output = model.generate(
input_ids=torch.tensor([tokenized.tokens]),
max_new_tokens=1000,
)[0]
decoded_output = tokenizer.decode(output[len(tokenized.tokens):])
print(decoded_output)
```
### LMStudio
Download the weights from huggingface:
```
pip install -U "huggingface_hub[cli]"
huggingface-cli download \
"mistralai/Devstral-Small-2505_gguf" \
--include "devstralQ4_K_M.gguf" \
--local-dir "mistralai/Devstral-Small-2505_gguf/"
```
You can serve the model locally with [LMStudio](https://lmstudio.ai/).
* Download [LM Studio](https://lmstudio.ai/) and install it
* Install `lms cli ~/.lmstudio/bin/lms bootstrap`
* In a bash terminal, run `lms import devstralQ4_K_M.gguf` in the directory where you've downloaded the model checkpoint (e.g. `mistralai/Devstral-Small-2505_gguf`)
* Open the LMStudio application, click the terminal icon to get into the developer tab. Click select a model to load and select Devstral Q4 K M. Toggle the status button to start the model, in setting toggle Serve on Local Network to be on.
* On the right tab, you will see an API identifier which should be devstralq4_k_m and an api address under API Usage. Keep note of this address, we will use it in the next step.
Launch Openhands
You can now interact with the model served from LM Studio with openhands. Start the openhands server with the docker
```bash
docker pull docker.all-hands.dev/all-hands-ai/runtime:0.38-nikolaik
docker run -it --rm --pull=always \
-e SANDBOX_RUNTIME_CONTAINER_IMAGE=docker.all-hands.dev/all-hands-ai/runtime:0.38-nikolaik \
-e LOG_ALL_EVENTS=true \
-v /var/run/docker.sock:/var/run/docker.sock \
-v ~/.openhands-state:/.openhands-state \
-p 3000:3000 \
--add-host host.docker.internal:host-gateway \
--name openhands-app \
docker.all-hands.dev/all-hands-ai/openhands:0.38
```
Click “see advanced setting” on the second line.
In the new tab, toggle advanced to on. Set the custom model to be mistral/devstralq4_k_m and Base URL the api address we get from the last step in LM Studio. Set API Key to dummy. Click save changes.
### Ollama
You can run Devstral using the [Ollama](https://ollama.ai/) CLI.
```bash
ollama run devstral
``` |
MinaMila/llama_instbase_LoRa_GermanCredit_cfda_ep1_55 | MinaMila | 2025-05-21T20:55:30Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-04-19T22:48:40Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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<!-- Provide the basic links for the model. -->
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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[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]
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<!-- 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
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[More Information Needed]
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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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- **Cloud Provider:** [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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[More Information Needed]
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## Model Card Contact
[More Information Needed] |
ErasureResearch/tv_van_gogh | ErasureResearch | 2025-05-21T20:32:34Z | 0 | 0 | diffusers | [
"diffusers",
"safetensors",
"diffusion",
"concept-erasure",
"stable-diffusion",
"tv",
"van_gogh",
"text-to-image",
"en",
"dataset:imagenet",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | 2025-05-21T20:29:28Z | <!DOCTYPE html>
<html class="" lang="en">
<head>
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name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
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/>
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<meta name="twitter:card" content="summary_large_image" />
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color: rgb(156, 163, 175);
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if (storageTheme) {
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</html> |
ErasureResearch/tv_airliner | ErasureResearch | 2025-05-21T20:29:26Z | 0 | 0 | diffusers | [
"diffusers",
"safetensors",
"diffusion",
"concept-erasure",
"stable-diffusion",
"tv",
"airliner",
"text-to-image",
"en",
"dataset:imagenet",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | 2025-05-21T20:26:59Z | <!DOCTYPE html>
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ChasingMidnightSun/6e6ce7cd-e27a-4198-9edf-aa8857612177 | ChasingMidnightSun | 2025-05-21T20:22:30Z | 0 | 0 | null | [
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fats-fme/4d338014-67fc-44c3-bf52-c76eb1331043 | fats-fme | 2025-05-21T20:19:08Z | 0 | 0 | null | [
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zktmp/2rpp-no_balance_batch-step40 | zktmp | 2025-05-21T20:18:45Z | 0 | 0 | null | [
"safetensors",
"qwen2",
"region:us"
] | null | 2025-05-21T20:16:23Z | <!DOCTYPE html>
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iTroned/self_iterative_v2_offensive_iteration_1 | iTroned | 2025-05-21T20:11:52Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2025-05-14T01:09:53Z | <!DOCTYPE html>
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GODFirezz/aa633f7b-0342-4045-b809-21e71811b792 | GODFirezz | 2025-05-21T19:56:03Z | 0 | 0 | null | [
"region:us"
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margin: 0 auto;
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rosieyzh/uf-dpo-llama3_1_8b-checkpoint_1875-seed_42 | rosieyzh | 2025-05-21T19:40:41Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-21T19:34:15Z | <!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 {
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.dark h1 {
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}
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mrferr3t/f0c28ba5-9767-444e-83f5-66bec271d6b7 | mrferr3t | 2025-05-21T19:36:07Z | 0 | 0 | null | [
"region:us"
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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);
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.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
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}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
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if (storageTheme) {
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ErasureResearch/esdx_van_gogh | ErasureResearch | 2025-05-21T19:28:32Z | 0 | 0 | diffusers | [
"diffusers",
"safetensors",
"diffusion",
"concept-erasure",
"stable-diffusion",
"esdx",
"van_gogh",
"text-to-image",
"en",
"dataset:imagenet",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | 2025-05-21T18:59:19Z | <!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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testrightnow/d85dc0bb-428d-41b4-adce-bc5a272eeff3 | testrightnow | 2025-05-21T19:03:12Z | 0 | 0 | null | [
"region:us"
] | null | 2025-05-21T19:02:38Z | <!DOCTYPE html>
<html class="" lang="en">
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name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
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letsfxxkingGo/a8be5120-c0d6-496f-baf4-6302ecff1c85 | letsfxxkingGo | 2025-05-21T18:49:37Z | 0 | 0 | null | [
"region:us"
] | null | 2025-05-21T18:49:28Z | <!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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Swissmountain/model-llama-3.2 | Swissmountain | 2025-05-21T18:20:56Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-05-21T18:20:16Z | ---
base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- gguf
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** Swissmountain
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
miaomiao64/bce-reranker-base_v1-openvino | miaomiao64 | 2025-05-21T17:24:19Z | 0 | 0 | sentence-transformers | [
"sentence-transformers",
"openvino",
"xlm-roberta",
"text-classification",
"transformers",
"openvino-export",
"en",
"zh",
"ja",
"ko",
"base_model:maidalun1020/bce-reranker-base_v1",
"base_model:finetune:maidalun1020/bce-reranker-base_v1",
"license:apache-2.0",
"region:us"
] | text-classification | 2025-05-21T17:24:09Z | ---
license: apache-2.0
pipeline_tag: text-classification
tags:
- transformers
- sentence-transformers
- openvino
- openvino-export
language:
- en
- zh
- ja
- ko
base_model: maidalun1020/bce-reranker-base_v1
---
This model was converted to OpenVINO from [`maidalun1020/bce-reranker-base_v1`](https://huggingface.co/maidalun1020/bce-reranker-base_v1) using [optimum-intel](https://github.com/huggingface/optimum-intel)
via the [export](https://huggingface.co/spaces/echarlaix/openvino-export) space.
First make sure you have optimum-intel installed:
```bash
pip install optimum[openvino]
```
To load your model you can do as follows:
```python
from optimum.intel import OVModelForSequenceClassification
model_id = "miaomiao64/bce-reranker-base_v1-openvino"
model = OVModelForSequenceClassification.from_pretrained(model_id)
```
|
rubricreward/R3-Qwen3-8B-14k | rubricreward | 2025-05-21T16:38:08Z | 30 | 1 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"en",
"dataset:rubricreward/R3-Dataset-14K",
"arxiv:2505.13388",
"base_model:Qwen/Qwen3-8B",
"base_model:finetune:Qwen/Qwen3-8B",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-14T09:09:06Z | ---
license: apache-2.0
language:
- en
datasets:
- rubricreward/R3-Dataset-14K
base_model:
- Qwen/Qwen3-8B
pipeline_tag: text-generation
library_name: transformers
---
<img alt="R3 Logo" src="https://cdn-avatars.huggingface.co/v1/production/uploads/651803f834c26962535eb022/hj3UEN9_9wlkmvMfUY1OL.png" width="150px">
# R3-Qwen3-8B-14k
R3-Qwen3-8B-14k is part of the R3 family, a series of **R**obust **R**ubric-Agnostic **R**eward Models.
We perform SFT on the Qwen3 model family on the 4B, 8B, and 14B scales as well as on Phi-4-reasoning plus.
Check out [our paper](https://arxiv.org/abs/2505.13388) for more information!
## Model description
- **Model type:** A reward model trained on a curated R3 dataset collected from 45 diverse sources that covers
tasks such as classification, preference optimization, and question answering. Each example in the dataset contains an instruction and task description, input, response(s),
evaluation rubrics, and a score along with the corresponding reasoning.
- **Language(s) (NLP):** English
- **License:** Apache 2.0
- **Finetuned from model:** Qwen/Qwen3-8B
### Model Sources
- **Project Page:** https://rubricreward.github.io
- **Repository:** https://github.com/rubricreward/r3
- **Paper:** https://arxiv.org/abs/2505.13388
## Using the Model
```python
from transformers import AutoTokenizer
from vllm import LLM, SamplingParams
model_path = "rubricreward/R3-Qwen3-8B-14k"
tokenizer = AutoTokenizer.from_pretrained(model_path)
sampling_params = SamplingParams(temperature=0.6, top_p=0.95, max_tokens=8192, min_p=0, top_k=20)
llm = LLM(
model=model_path,
dtype="bfloat16",
max_model_len=10000,
tensor_parallel_size=2,
gpu_memory_utilization=0.9,
enforce_eager=True,
)
messages: list[dict[str, str]] = [
{'content': "Evaluate the response based on the given task, input, response, and evaluation rubric. Provide a fair and detailed assessment following the rubric...", 'role': 'user'}
]
list_text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
enable_thinking=True # Switch between thinking and non-thinking modes.
)
outputs = llm.generate(list_text, sampling_params)
```
## License and use
R3 is licensed under the Apache 2.0 license.
## Citation
```bibtex
@article{anugraha2025r3,
title={R3: Robust Rubric-Agnostic Reward Models},
author={Anugraha, David and Tang, Zilu and Miranda, Lester James V. and Zhao, Hanyang and Farhansyah, Mohammad Rifqi and Kuwanto, Garry and Wijaya, Derry and Winata, Genta Indra},
journal={arXiv preprint arXiv:2505.13388},
year={2025}
}
``` |
DanielNRU/pollen-ner-1450 | DanielNRU | 2025-05-21T16:08:09Z | 0 | 0 | peft | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:DeepPavlov/rubert-base-cased",
"base_model:adapter:DeepPavlov/rubert-base-cased",
"region:us"
] | null | 2025-05-21T16:00:06Z | ---
library_name: peft
base_model: DeepPavlov/rubert-base-cased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
model-index:
- name: pollen-ner-1450
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. -->
# pollen-ner-1450
This model is a fine-tuned version of [DeepPavlov/rubert-base-cased](https://huggingface.co/DeepPavlov/rubert-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1299
- Precision: 0.8752
- Recall: 0.9297
- F1: 0.9017
## 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-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|
| No log | 1.0 | 182 | 0.1380 | 0.8652 | 0.9277 | 0.8953 |
| No log | 2.0 | 364 | 0.1278 | 0.8776 | 0.9217 | 0.8991 |
| 0.2279 | 3.0 | 546 | 0.1265 | 0.8795 | 0.9237 | 0.9011 |
| 0.2279 | 4.0 | 728 | 0.1271 | 0.8762 | 0.9237 | 0.8993 |
| 0.2279 | 5.0 | 910 | 0.1299 | 0.8752 | 0.9297 | 0.9017 |
### Framework versions
- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.7.0+cu128
- Datasets 3.5.0
- Tokenizers 0.21.1 |
bigfish951/wav2vec2-base-timit-demo-colab | bigfish951 | 2025-05-21T16:02:47Z | 0 | 0 | transformers | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-05-21T16:02:46Z | ---
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] |
SpaceReii/Qwen3-4B-IQ3_XXS-GGUF | SpaceReii | 2025-05-21T15:41:54Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"base_model:Qwen/Qwen3-4B",
"base_model:quantized:Qwen/Qwen3-4B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | text-generation | 2025-05-21T15:41:43Z | ---
library_name: transformers
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen3-4B/blob/main/LICENSE
pipeline_tag: text-generation
base_model: Qwen/Qwen3-4B
tags:
- llama-cpp
- gguf-my-repo
---
# SpaceReii/Qwen3-4B-IQ3_XXS-GGUF
This model was converted to GGUF format from [`Qwen/Qwen3-4B`](https://huggingface.co/Qwen/Qwen3-4B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/Qwen/Qwen3-4B) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo SpaceReii/Qwen3-4B-IQ3_XXS-GGUF --hf-file qwen3-4b-iq3_xxs-imat.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo SpaceReii/Qwen3-4B-IQ3_XXS-GGUF --hf-file qwen3-4b-iq3_xxs-imat.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo SpaceReii/Qwen3-4B-IQ3_XXS-GGUF --hf-file qwen3-4b-iq3_xxs-imat.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo SpaceReii/Qwen3-4B-IQ3_XXS-GGUF --hf-file qwen3-4b-iq3_xxs-imat.gguf -c 2048
```
|
23ikram/llama3FinalFinal | 23ikram | 2025-05-21T14:23:31Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-05-21T14:23:13Z | ---
base_model: unsloth/meta-llama-3.1-8b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** 23ikram
- **License:** apache-2.0
- **Finetuned from model :** unsloth/meta-llama-3.1-8b-unsloth-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
marie000/linkedin-salary-2025-05-08_20.57.11 | marie000 | 2025-05-21T14:16:12Z | 16 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-09T00:57:13Z | ---
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] |
LouiSeHU/Qwen2.5-VL-7B-Instruct-Q8_0-GGUF | LouiSeHU | 2025-05-21T13:53:24Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"multimodal",
"llama-cpp",
"gguf-my-repo",
"image-text-to-text",
"en",
"base_model:Qwen/Qwen2.5-VL-7B-Instruct",
"base_model:quantized:Qwen/Qwen2.5-VL-7B-Instruct",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | image-text-to-text | 2025-05-21T13:52:50Z | ---
license: apache-2.0
language:
- en
pipeline_tag: image-text-to-text
tags:
- multimodal
- llama-cpp
- gguf-my-repo
library_name: transformers
base_model: Qwen/Qwen2.5-VL-7B-Instruct
---
# LouiSeHU/Qwen2.5-VL-7B-Instruct-Q8_0-GGUF
This model was converted to GGUF format from [`Qwen/Qwen2.5-VL-7B-Instruct`](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo LouiSeHU/Qwen2.5-VL-7B-Instruct-Q8_0-GGUF --hf-file qwen2.5-vl-7b-instruct-q8_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo LouiSeHU/Qwen2.5-VL-7B-Instruct-Q8_0-GGUF --hf-file qwen2.5-vl-7b-instruct-q8_0.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo LouiSeHU/Qwen2.5-VL-7B-Instruct-Q8_0-GGUF --hf-file qwen2.5-vl-7b-instruct-q8_0.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo LouiSeHU/Qwen2.5-VL-7B-Instruct-Q8_0-GGUF --hf-file qwen2.5-vl-7b-instruct-q8_0.gguf -c 2048
```
|
0xarchit/ai_teacher_2 | 0xarchit | 2025-05-21T13:16:12Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/tinyllama-bnb-4bit",
"base_model:finetune:unsloth/tinyllama-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-05-21T13:07:07Z | ---
base_model: unsloth/tinyllama-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** 0xarchit
- **License:** apache-2.0
- **Finetuned from model :** unsloth/tinyllama-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
shirro/Llama3.1_8B_SFT_GRPO_GGUF | shirro | 2025-05-21T13:15:02Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-05-21T13:10:14Z | ---
base_model: unsloth/meta-llama-3.1-8b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- gguf
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** shirro
- **License:** apache-2.0
- **Finetuned from model :** unsloth/meta-llama-3.1-8b-instruct-unsloth-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
SHTSRTgsre/awfe | SHTSRTgsre | 2025-05-21T12:41:08Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2025-05-21T12:41:08Z | <!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) {}
if (theme === "dark") {
document.documentElement.classList.add("dark");
} else {
document.documentElement.classList.remove("dark");
}
</script>
</head>
<body>
<main>
<img
src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg"
alt=""
/>
<div>
<h1>429</h1>
<p>We had to rate limit you. If you think it's an error, send us <a href="mailto:[email protected]">an email</a></p>
</div>
</main>
</body>
</html> |
trshrsjysj/ghdrthgfh | trshrsjysj | 2025-05-21T12:41:07Z | 0 | 0 | null | [
"region:us"
] | null | 2025-05-21T12:41:07Z | <!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";
}
} catch (e) {}
if (theme === "dark") {
document.documentElement.classList.add("dark");
} else {
document.documentElement.classList.remove("dark");
}
</script>
</head>
<body>
<main>
<img
src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg"
alt=""
/>
<div>
<h1>429</h1>
<p>We had to rate limit you. If you think it's an error, send us <a href="mailto:[email protected]">an email</a></p>
</div>
</main>
</body>
</html> |
LarryAIDraw/Shiunji_Ouka-60 | LarryAIDraw | 2025-05-21T11:50:58Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
] | null | 2025-05-21T08:41:59Z | ---
license: creativeml-openrail-m
---
https://civitai.com/models/1600595/shiunji-ouka-or-the-shiunji-family-children-or?modelVersionId=1811329 |
markus13/ENGORILE | markus13 | 2025-05-21T11:37:53Z | 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 | 2025-05-21T10:45:13Z | ---
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
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: ENGORILE
---
# Engorile
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `ENGORILE` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "ENGORILE",
"lora_weights": "https://huggingface.co/markus13/ENGORILE/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('markus13/ENGORILE', weight_name='lora.safetensors')
image = pipeline('ENGORILE').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 1000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/markus13/ENGORILE/discussions) to add images that show off what you’ve made with this LoRA.
|
rkutyu/gjyg | rkutyu | 2025-05-21T10:57:06Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2025-05-21T10:57:06Z | ---
license: apache-2.0
---
|
rtyjrtky/gjyg | rtyjrtky | 2025-05-21T10:57:06Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2025-05-21T10:57:06Z | ---
license: apache-2.0
---
|
TarhanE/sft-count-t5-small-mle0.5-ul0.5-tox1.0-e20 | TarhanE | 2025-05-21T10:48:55Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2025-05-21T10:34:31Z | ---
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
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[More Information Needed]
## Training Details
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#### Summary
## Model Examination [optional]
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## Environmental Impact
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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).
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xw17/Qwen2-1.5B-Instruct_finetuned_4_optimized1_task_grouping_off_FT | xw17 | 2025-05-21T10:32:23Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-21T10:28:50Z | ---
library_name: transformers
tags:
- trl
- sft
---
# 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]
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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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RedbeardNZ/ACE-Step-v1-3.5B | RedbeardNZ | 2025-05-21T09:55:41Z | 0 | 0 | diffusers | [
"diffusers",
"safetensors",
"music",
"text2music",
"text-to-audio",
"en",
"zh",
"de",
"fr",
"es",
"it",
"pt",
"pl",
"tr",
"ru",
"cs",
"nl",
"ar",
"ja",
"hu",
"ko",
"hi",
"license:apache-2.0",
"region:us"
] | text-to-audio | 2025-05-21T09:55:33Z | ---
license: apache-2.0
tags:
- music
- text2music
pipeline_tag: text-to-audio
language:
- en
- zh
- de
- fr
- es
- it
- pt
- pl
- tr
- ru
- cs
- nl
- ar
- ja
- hu
- ko
- hi
library_name: diffusers
---
# ACE-Step: A Step Towards Music Generation Foundation Model
## Model Description
ACE-Step is a novel open-source foundation model for music generation that overcomes key limitations of existing approaches through a holistic architectural design. It integrates diffusion-based generation with Sana's Deep Compression AutoEncoder (DCAE) and a lightweight linear transformer, achieving state-of-the-art performance in generation speed, musical coherence, and controllability.
**Key Features:**
- 15× faster than LLM-based baselines (20s for 4-minute music on A100)
- Superior musical coherence across melody, harmony, and rhythm
- full-song generation, duration control and accepts natural language descriptions
## Uses
### Direct Use
ACE-Step can be used for:
- Generating original music from text descriptions
- Music remixing and style transfer
- edit song lyrics
### Downstream Use
The model serves as a foundation for:
- Voice cloning applications
- Specialized music generation (rap, jazz, etc.)
- Music production tools
- Creative AI assistants
### Out-of-Scope Use
The model should not be used for:
- Generating copyrighted content without permission
- Creating harmful or offensive content
- Misrepresenting AI-generated music as human-created
## How to Get Started
see: https://github.com/ace-step/ACE-Step
## Hardware Performance
| Device | 27 Steps | 60 Steps |
|---------------|----------|----------|
| NVIDIA A100 | 27.27x | 12.27x |
| RTX 4090 | 34.48x | 15.63x |
| RTX 3090 | 12.76x | 6.48x |
| M2 Max | 2.27x | 1.03x |
*RTF (Real-Time Factor) shown - higher values indicate faster generation*
## Limitations
- Performance varies by language (top 10 languages perform best)
- Longer generations (>5 minutes) may lose structural coherence
- Rare instruments may not render perfectly
- Output Inconsistency: Highly sensitive to random seeds and input duration, leading to varied "gacha-style" results.
- Style-specific Weaknesses: Underperforms on certain genres (e.g. Chinese rap/zh_rap) Limited style adherence and musicality ceiling
- Continuity Artifacts: Unnatural transitions in repainting/extend operations
- Vocal Quality: Coarse vocal synthesis lacking nuance
- Control Granularity: Needs finer-grained musical parameter control
## Ethical Considerations
Users should:
- Verify originality of generated works
- Disclose AI involvement
- Respect cultural elements and copyrights
- Avoid harmful content generation
## Model Details
**Developed by:** ACE Studio and StepFun
**Model type:** Diffusion-based music generation with transformer conditioning
**License:** Apache 2.0
**Resources:**
- [Project Page](https://ace-step.github.io/)
- [Demo Space](https://huggingface.co/spaces/ACE-Step/ACE-Step)
- [GitHub Repository](https://github.com/ACE-Step/ACE-Step)
## Citation
```bibtex
@misc{gong2025acestep,
title={ACE-Step: A Step Towards Music Generation Foundation Model},
author={Junmin Gong, Wenxiao Zhao, Sen Wang, Shengyuan Xu, Jing Guo},
howpublished={\url{https://github.com/ace-step/ACE-Step}},
year={2025},
note={GitHub repository}
}
```
## Acknowledgements
This project is co-led by ACE Studio and StepFun. |
simmen/w2v2-libri-10min | simmen | 2025-05-21T08:56:00Z | 0 | 0 | transformers | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-05-21T08:45:58Z | ---
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]
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- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
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[More Information Needed]
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[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
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Mungert/AM-Thinking-v1-GGUF | Mungert | 2025-05-21T08:51:19Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"text-generation",
"arxiv:2505.08311",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | text-generation | 2025-05-20T12:09:47Z | ---
license: apache-2.0
pipeline_tag: text-generation
library_name: transformers
---
# <span style="color: #7FFF7F;">AM-Thinking-v1 GGUF Models</span>
## <span style="color: #7F7FFF;">Model Generation Details</span>
This model was generated using [llama.cpp](https://github.com/ggerganov/llama.cpp) at commit [`92ecdcc0`](https://github.com/ggerganov/llama.cpp/commit/92ecdcc06a4c405a415bcaa0cb772bc560aa23b1).
## <span style="color: #7FFF7F;">Ultra-Low-Bit Quantization with IQ-DynamicGate (1-2 bit)</span>
Our latest quantization method introduces **precision-adaptive quantization** for ultra-low-bit models (1-2 bit), with benchmark-proven improvements on **Llama-3-8B**. This approach uses layer-specific strategies to preserve accuracy while maintaining extreme memory efficiency.
### **Benchmark Context**
All tests conducted on **Llama-3-8B-Instruct** using:
- Standard perplexity evaluation pipeline
- 2048-token context window
- Same prompt set across all quantizations
### **Method**
- **Dynamic Precision Allocation**:
- First/Last 25% of layers → IQ4_XS (selected layers)
- Middle 50% → IQ2_XXS/IQ3_S (increase efficiency)
- **Critical Component Protection**:
- Embeddings/output layers use Q5_K
- Reduces error propagation by 38% vs standard 1-2bit
### **Quantization Performance Comparison (Llama-3-8B)**
| Quantization | Standard PPL | DynamicGate PPL | Δ PPL | Std Size | DG Size | Δ Size | Std Speed | DG Speed |
|--------------|--------------|------------------|---------|----------|---------|--------|-----------|----------|
| IQ2_XXS | 11.30 | 9.84 | -12.9% | 2.5G | 2.6G | +0.1G | 234s | 246s |
| IQ2_XS | 11.72 | 11.63 | -0.8% | 2.7G | 2.8G | +0.1G | 242s | 246s |
| IQ2_S | 14.31 | 9.02 | -36.9% | 2.7G | 2.9G | +0.2G | 238s | 244s |
| IQ1_M | 27.46 | 15.41 | -43.9% | 2.2G | 2.5G | +0.3G | 206s | 212s |
| IQ1_S | 53.07 | 32.00 | -39.7% | 2.1G | 2.4G | +0.3G | 184s | 209s |
**Key**:
- PPL = Perplexity (lower is better)
- Δ PPL = Percentage change from standard to DynamicGate
- Speed = Inference time (CPU avx2, 2048 token context)
- Size differences reflect mixed quantization overhead
**Key Improvements:**
- 🔥 **IQ1_M** shows massive 43.9% perplexity reduction (27.46 → 15.41)
- 🚀 **IQ2_S** cuts perplexity by 36.9% while adding only 0.2GB
- ⚡ **IQ1_S** maintains 39.7% better accuracy despite 1-bit quantization
**Tradeoffs:**
- All variants have modest size increases (0.1-0.3GB)
- Inference speeds remain comparable (<5% difference)
### **When to Use These Models**
📌 **Fitting models into GPU VRAM**
✔ **Memory-constrained deployments**
✔ **Cpu and Edge Devices** where 1-2bit errors can be tolerated
✔ **Research** into ultra-low-bit quantization
## **Choosing the Right Model Format**
Selecting the correct model format depends on your **hardware capabilities** and **memory constraints**.
### **BF16 (Brain Float 16) – Use if BF16 acceleration is available**
- A 16-bit floating-point format designed for **faster computation** while retaining good precision.
- Provides **similar dynamic range** as FP32 but with **lower memory usage**.
- Recommended if your hardware supports **BF16 acceleration** (check your device's specs).
- Ideal for **high-performance inference** with **reduced memory footprint** compared to FP32.
📌 **Use BF16 if:**
✔ Your hardware has native **BF16 support** (e.g., newer GPUs, TPUs).
✔ You want **higher precision** while saving memory.
✔ You plan to **requantize** the model into another format.
📌 **Avoid BF16 if:**
❌ Your hardware does **not** support BF16 (it may fall back to FP32 and run slower).
❌ You need compatibility with older devices that lack BF16 optimization.
---
### **F16 (Float 16) – More widely supported than BF16**
- A 16-bit floating-point **high precision** but with less of range of values than BF16.
- Works on most devices with **FP16 acceleration support** (including many GPUs and some CPUs).
- Slightly lower numerical precision than BF16 but generally sufficient for inference.
📌 **Use F16 if:**
✔ Your hardware supports **FP16** but **not BF16**.
✔ You need a **balance between speed, memory usage, and accuracy**.
✔ You are running on a **GPU** or another device optimized for FP16 computations.
📌 **Avoid F16 if:**
❌ Your device lacks **native FP16 support** (it may run slower than expected).
❌ You have memory limitations.
---
### **Quantized Models (Q4_K, Q6_K, Q8, etc.) – For CPU & Low-VRAM Inference**
Quantization reduces model size and memory usage while maintaining as much accuracy as possible.
- **Lower-bit models (Q4_K)** → **Best for minimal memory usage**, may have lower precision.
- **Higher-bit models (Q6_K, Q8_0)** → **Better accuracy**, requires more memory.
📌 **Use Quantized Models if:**
✔ You are running inference on a **CPU** and need an optimized model.
✔ Your device has **low VRAM** and cannot load full-precision models.
✔ You want to reduce **memory footprint** while keeping reasonable accuracy.
📌 **Avoid Quantized Models if:**
❌ You need **maximum accuracy** (full-precision models are better for this).
❌ Your hardware has enough VRAM for higher-precision formats (BF16/F16).
---
### **Very Low-Bit Quantization (IQ3_XS, IQ3_S, IQ3_M, Q4_K, Q4_0)**
These models are optimized for **extreme memory efficiency**, making them ideal for **low-power devices** or **large-scale deployments** where memory is a critical constraint.
- **IQ3_XS**: Ultra-low-bit quantization (3-bit) with **extreme memory efficiency**.
- **Use case**: Best for **ultra-low-memory devices** where even Q4_K is too large.
- **Trade-off**: Lower accuracy compared to higher-bit quantizations.
- **IQ3_S**: Small block size for **maximum memory efficiency**.
- **Use case**: Best for **low-memory devices** where **IQ3_XS** is too aggressive.
- **IQ3_M**: Medium block size for better accuracy than **IQ3_S**.
- **Use case**: Suitable for **low-memory devices** where **IQ3_S** is too limiting.
- **Q4_K**: 4-bit quantization with **block-wise optimization** for better accuracy.
- **Use case**: Best for **low-memory devices** where **Q6_K** is too large.
- **Q4_0**: Pure 4-bit quantization, optimized for **ARM devices**.
- **Use case**: Best for **ARM-based devices** or **low-memory environments**.
---
### **Summary Table: Model Format Selection**
| Model Format | Precision | Memory Usage | Device Requirements | Best Use Case |
|--------------|------------|---------------|----------------------|---------------|
| **BF16** | Highest | High | BF16-supported GPU/CPUs | High-speed inference with reduced memory |
| **F16** | High | High | FP16-supported devices | GPU inference when BF16 isn't available |
| **Q4_K** | Medium Low | Low | CPU or Low-VRAM devices | Best for memory-constrained environments |
| **Q6_K** | Medium | Moderate | CPU with more memory | Better accuracy while still being quantized |
| **Q8_0** | High | Moderate | CPU or GPU with enough VRAM | Best accuracy among quantized models |
| **IQ3_XS** | Very Low | Very Low | Ultra-low-memory devices | Extreme memory efficiency and low accuracy |
| **Q4_0** | Low | Low | ARM or low-memory devices | llama.cpp can optimize for ARM devices |
---
## **Included Files & Details**
### `AM-Thinking-v1-bf16.gguf`
- Model weights preserved in **BF16**.
- Use this if you want to **requantize** the model into a different format.
- Best if your device supports **BF16 acceleration**.
### `AM-Thinking-v1-f16.gguf`
- Model weights stored in **F16**.
- Use if your device supports **FP16**, especially if BF16 is not available.
### `AM-Thinking-v1-bf16-q8_0.gguf`
- **Output & embeddings** remain in **BF16**.
- All other layers quantized to **Q8_0**.
- Use if your device supports **BF16** and you want a quantized version.
### `AM-Thinking-v1-f16-q8_0.gguf`
- **Output & embeddings** remain in **F16**.
- All other layers quantized to **Q8_0**.
### `AM-Thinking-v1-q4_k.gguf`
- **Output & embeddings** quantized to **Q8_0**.
- All other layers quantized to **Q4_K**.
- Good for **CPU inference** with limited memory.
### `AM-Thinking-v1-q4_k_s.gguf`
- Smallest **Q4_K** variant, using less memory at the cost of accuracy.
- Best for **very low-memory setups**.
### `AM-Thinking-v1-q6_k.gguf`
- **Output & embeddings** quantized to **Q8_0**.
- All other layers quantized to **Q6_K** .
### `AM-Thinking-v1-q8_0.gguf`
- Fully **Q8** quantized model for better accuracy.
- Requires **more memory** but offers higher precision.
### `AM-Thinking-v1-iq3_xs.gguf`
- **IQ3_XS** quantization, optimized for **extreme memory efficiency**.
- Best for **ultra-low-memory devices**.
### `AM-Thinking-v1-iq3_m.gguf`
- **IQ3_M** quantization, offering a **medium block size** for better accuracy.
- Suitable for **low-memory devices**.
### `AM-Thinking-v1-q4_0.gguf`
- Pure **Q4_0** quantization, optimized for **ARM devices**.
- Best for **low-memory environments**.
- Prefer IQ4_NL for better accuracy.
# <span id="testllm" style="color: #7F7FFF;">🚀 If you find these models useful</span>
❤ **Please click "Like" if you find this useful!**
Help me test my **AI-Powered Network Monitor Assistant** with **quantum-ready security checks**:
👉 [Free Network Monitor](https://readyforquantum.com/dashboard/?assistant=open)
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- `TurboLLM` (GPT-4o-mini)
- `HugLLM` (Hugginface Open-source)
- `TestLLM` (Experimental CPU-only)
### **What I’m Testing**
I’m pushing the limits of **small open-source models for AI network monitoring**, specifically:
- **Function calling** against live network services
- **How small can a model go** while still handling:
- Automated **Nmap scans**
- **Quantum-readiness checks**
- **Network Monitoring tasks**
🟡 **TestLLM** – Current experimental model (llama.cpp on 2 CPU threads):
- ✅ **Zero-configuration setup**
- ⏳ 30s load time (slow inference but **no API costs**)
- 🔧 **Help wanted!** If you’re into **edge-device AI**, let’s collaborate!
### **Other Assistants**
🟢 **TurboLLM** – Uses **gpt-4o-mini** for:
- **Create custom cmd processors to run .net code on Free Network Monitor Agents**
- **Real-time network diagnostics and monitoring**
- **Security Audits**
- **Penetration testing** (Nmap/Metasploit)
- 🔑 Get more tokens by logging in or [downloading our Free Network Monitor Agent with integrated AI Assistant](https://readyforquantum.com/download)
🔵 **HugLLM** – Latest Open-source models:
- 🌐 Runs on Hugging Face Inference API
### 💡 **Example commands to you could test**:
1. `"Give me info on my websites SSL certificate"`
2. `"Check if my server is using quantum safe encyption for communication"`
3. `"Run a comprehensive security audit on my server"`
4. '"Create a cmd processor to .. (what ever you want)" Note you need to install a Free Network Monitor Agent to run the .net code from. This is a very flexible and powerful feature. Use with caution!
# AM‑Thinking‑v1: Advancing the Frontier of Reasoning at 32B Scale
* 2025-05-10 · a-m‑team
<p align="center">
🤗 <a href="https://huggingface.co/a-m-team">Hugging Face</a>   |    📑 <a href="https://arxiv.org/abs/2505.08311"> Paper</a>    |    📑 <a href="https://a-m-team.github.io/am-thinking-v1/">Blog</a>   
</p>
## 🚀 Introduction
We release **AM-Thinking‑v1**, a 32B dense language model focused on enhancing reasoning capabilities.
Built on Qwen 2.5‑32B‑Base, AM-Thinking‑v1 shows strong performance on reasoning benchmarks, comparable to much larger MoE models like **DeepSeek‑R1**, **Qwen3‑235B‑A22B**, **Seed1.5-Thinking**, and larger dense model like **Nemotron-Ultra-253B-v1**.
<div style="text-align: center;">
<img src="assets/benchmark.png" alt="benchmark" style="width: 90%;">
</div>
## 🧩 Why Another 32B Reasoning Model Matters?
Large Mixture‑of‑Experts (MoE) models such as **DeepSeek‑R1** or **Qwen3‑235B‑A22B** dominate leaderboards—but they also demand clusters of high‑end GPUs. Many teams just need *the best dense model that fits on a single card*.
**AM‑Thinking‑v1** fills that gap **while remaining fully based on open-source components**:
* **Outperforms DeepSeek‑R1** on AIME’24/’25 & LiveCodeBench and **approaches Qwen3‑235B‑A22B** despite being 1/7‑th the parameter count.
* **Built on the publicly available Qwen 2.5‑32B‑Base**, as well as the RL training queries.
* Shows that with a **well‑designed post‑training pipeline** ( SFT + dual‑stage RL ) you can squeeze flagship‑level reasoning out of a 32 B dense model.
* **Deploys on one A100‑80 GB** with deterministic latency—no MoE routing overhead.
<div style="text-align: center;">
<img src="assets/param-aime2024.jpeg" alt="AIME 2024" style="width: 90%; margin-bottom: 20px;">
<img src="assets/param-lcb.jpeg" alt="LiveCodeBench" style="width: 90%;">
<div style="margin-top: 10px;">
<em>AM-Thinking-v1 achieves strong reasoning performance with significantly fewer parameters.</em>
</div>
</div>
## 🛠️ Use Cases
### 1) Code Generation
<pre style="font-family: 'Times New Roman', serif; font-size: 12px; border: 1px solid black; padding: 10px; font-style: italic;">
PROMPT :
write a python script for a bouncing red ball within a triangle, make sure to handle collision detection properly. make the triangle slowly rotate. implement it in python. make sure ball stays within the triangle
</pre>
<div style="text-align: center;">
<img src="assets/ball.gif" alt="Bouncing Red Ball" width="50%">
</div>
### 2) Logic
<div style="text-align: center;">
<img src="assets/diamond.png" alt="diamond" width="90%">
</div>
### 3) Writing
<div style="text-align: center;">
<img src="assets/writing.png" alt="sushi" width="90%">
</div>
## ⚡ Quick start
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "a-m-team/AM-Thinking-v1"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto"
)
prompt = "How can I find inner peace?"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
generated_ids = model.generate(
**model_inputs,
max_new_tokens=49152
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
response = tokenizer.decode(output_ids, skip_special_tokens=True)
think_content = response.split("<think>")[1].split("</think>")[0]
answer_content = response.split("<answer>")[1].split("</answer>")[0]
print (f"user prompt: {prompt}")
print (f"model thinking: {think_content}")
print (f"model answer: {answer_content}")
```
> Note: We have included the system prompt in the tokenizer configuration, as it was used during both the SFT and RL stages. To ensure consistent output quality, we recommend including the same system prompt during actual usage; otherwise, the model's responses may be significantly affected.
### Quantized versions for compact devices
A series of quantized versions for [AM-Thinking-v1](https://huggingface.co/a-m-team/AM-Thinking-v1-gguf) model.
For use with [llama.cpp](https://github.com/ggml-org/llama.cpp) and [Ollama](https://github.com/ollama/ollama)
is available at [AM-Thinking-v1-gguf](https://huggingface.co/a-m-team/AM-Thinking-v1-gguf).
## 🔧 Post-training pipeline
To achieve its strong reasoning ability, AM‑Thinking‑v1 goes through a carefully designed post-training pipeline.
Below we describe the key stages involved in turning a base model into a high-performing reasoner:
**Step 1 – Cold‑start SFT.**
We begin with the open-sourced **Qwen 2.5‑32B‑Base** and run a broad supervised fine‑tune on a blended training dataset of math, code and open‑domain chat. This endows the model with a "think‑then‑answer" behavioural pattern and equips it with an initial capacity for reasoning.
**Step 2 – Pass‑rate‑aware data curation.**
Before any RL, the SFT model is evaluated on every math‑ and code‑oriented training query. For each item we log a pass rate; only those with **0 < pass‑rate < 1** are kept. In effect we discard problems the model already masters and those it utterly fails, concentrating learning on genuinely informative cases.
**Step 3 – Reinforcement learning .**
We adopt a two‑stage GRPO scheme: Stage 1 trains only on math and code queries. Once it converges, stage 2 starts by removing every query the model answered 100% correctly in Stage 1 and adjusting key hyper‑parameters such as maximum generation length and learning rate.
## ⚠️ Limitations
While AM‑Thinking‑v1 excels at pure language reasoning and open‑domain chat, it has not yet been trained for structured function‑calling or tool‑use workflows, which restricts its usefulness in agent‑style applications that must act on external systems.
Improving the model's ability to follow complex instructions is also an important direction for our future work.
In addition, our safety alignment is still at an early stage, so more rigorous red‑teaming are required to reduce potential harms.
## 📚 Citation
The a-m-team is an internal team at Beike (Ke.com), dedicated to exploring AGI technology.
If you find our work helpful, feel free to give us a cite.
```
@misc{ji2025amthinkingv1advancingfrontierreasoning,
title={AM-Thinking-v1: Advancing the Frontier of Reasoning at 32B Scale},
author={Yunjie Ji and Xiaoyu Tian and Sitong Zhao and Haotian Wang and Shuaiting Chen and Yiping Peng and Han Zhao and Xiangang Li},
year={2025},
eprint={2505.08311},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2505.08311},
}
``` |
Eric1227/Dolphin-Mistral-24B-Venice-Edition-MLX-8bits | Eric1227 | 2025-05-21T08:16:49Z | 0 | 0 | mlx | [
"mlx",
"safetensors",
"mistral",
"text-generation",
"conversational",
"base_model:cognitivecomputations/Dolphin-Mistral-24B-Venice-Edition",
"base_model:quantized:cognitivecomputations/Dolphin-Mistral-24B-Venice-Edition",
"license:apache-2.0",
"8-bit",
"region:us"
] | text-generation | 2025-05-21T07:54:03Z | ---
license: apache-2.0
base_model: cognitivecomputations/Dolphin-Mistral-24B-Venice-Edition
pipeline_tag: text-generation
tags:
- mlx
library_name: mlx
---
|
tiiuae/Falcon-H1-1.5B-Instruct-GPTQ-Int4 | tiiuae | 2025-05-21T08:06:14Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"falcon_h1",
"text-generation",
"falcon-h1",
"conversational",
"base_model:tiiuae/Falcon-H1-1.5B-Instruct",
"base_model:quantized:tiiuae/Falcon-H1-1.5B-Instruct",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"4-bit",
"gptq",
"region:us"
] | text-generation | 2025-05-12T12:08:22Z | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
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content="width=device-width, initial-scale=1.0, user-scalable=no"
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width: 6rem;
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font-size: 1.125rem;
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max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.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);
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</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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Traviswang1997/blotz-dev | Traviswang1997 | 2025-05-21T07:39:27Z | 0 | 0 | null | [
"region:us"
] | null | 2025-05-21T07:39:26Z | <!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."
/>
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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;
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img {
width: 6rem;
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}
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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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;
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<h1>429</h1>
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princekramm/41b49806-efb1-44f3-a1e4-428bb001e7e4 | princekramm | 2025-05-21T06:57:22Z | 0 | 0 | null | [
"region:us"
] | null | 2025-05-21T06:34:28Z | <!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"
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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<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"
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const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme;
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<h1>429</h1>
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eljolahoyek/xcvbxcvb | eljolahoyek | 2025-05-21T06:49:59Z | 0 | 0 | null | [
"license:bigscience-bloom-rail-1.0",
"region:us"
] | null | 2025-05-21T06:49:59Z | <!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" />
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<style>
body {
margin: 0;
}
main {
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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);
}
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<h1>429</h1>
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rgn-la/rgn-rodg-lora-flux | rgn-la | 2025-05-21T06:22:35Z | 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 | 2025-05-21T06:02:06Z | ---
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
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: rodg
---
# Rgn Rodg Lora Flux
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `rodg` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "rodg",
"lora_weights": "https://huggingface.co/rgn-la/rgn-rodg-lora-flux/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('rgn-la/rgn-rodg-lora-flux', weight_name='lora.safetensors')
image = pipeline('rodg').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 1000
- Learning rate: 0.0004
- LoRA rank: 20
## Contribute your own examples
You can use the [community tab](https://huggingface.co/rgn-la/rgn-rodg-lora-flux/discussions) to add images that show off what you’ve made with this LoRA.
|
sergioalves/f82b3a65-5abc-43fc-96ed-a16e5fe62f68 | sergioalves | 2025-05-21T05:13:20Z | 0 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"axolotl",
"dpo",
"trl",
"unsloth",
"conversational",
"arxiv:2305.18290",
"base_model:unsloth/Qwen2-1.5B-Instruct",
"base_model:quantized:unsloth/Qwen2-1.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] | text-generation | 2025-05-21T05:01:17Z | ---
base_model: unsloth/Qwen2-1.5B-Instruct
library_name: transformers
model_name: f82b3a65-5abc-43fc-96ed-a16e5fe62f68
tags:
- generated_from_trainer
- axolotl
- dpo
- trl
- unsloth
licence: license
---
# Model Card for f82b3a65-5abc-43fc-96ed-a16e5fe62f68
This model is a fine-tuned version of [unsloth/Qwen2-1.5B-Instruct](https://huggingface.co/unsloth/Qwen2-1.5B-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="sergioalves/f82b3a65-5abc-43fc-96ed-a16e5fe62f68", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/dedok-yo/s56-7/runs/isifq0xp)
This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290).
### Framework versions
- TRL: 0.12.0.dev0
- Transformers: 4.46.0
- Pytorch: 2.5.0+cu124
- Datasets: 3.0.1
- Tokenizers: 0.20.1
## Citations
Cite DPO as:
```bibtex
@inproceedings{rafailov2023direct,
title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}},
author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn},
year = 2023,
booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023},
url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html},
editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine},
}
```
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 Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
the-acorn-ai/Qwen3-0.6B-OSP-ttt-fixed-gemini-step_00576 | the-acorn-ai | 2025-05-21T05:02:54Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-21T05:01:59Z | ---
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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[More Information Needed]
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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]
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<!-- 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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[More Information Needed]
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[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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Anawexler/Anawexler | Anawexler | 2025-05-21T04:44:56Z | 0 | 0 | null | [
"license:other",
"region:us"
] | null | 2025-05-21T04:42:45Z | ---
license: other
license_name: custom
license_link: LICENSE
---
|
AICU/Betamina-LoRA-AnimagineXL4 | AICU | 2025-05-21T04:23:07Z | 0 | 0 | diffusers | [
"diffusers",
"text-to-image",
"lora",
"template:diffusion-lora",
"dataset:AICU/Betamina-VRM100shots",
"base_model:cagliostrolab/animagine-xl-4.0",
"base_model:adapter:cagliostrolab/animagine-xl-4.0",
"license:creativeml-openrail-m",
"region:us"
] | text-to-image | 2025-05-21T03:11:48Z | ---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- text: >-
masterpiece, high score, great score, absurdres, Betamina, smile, on bike,
detailed
parameters:
negative_prompt: >-
lowres, bad anatomy, bad hands, text, error, missing finger, extra digits,
fewer digits, cropped, worst quality, low quality, low score, bad score,
average score, signature, watermark, username, blurry
output:
url: images/Betamina_00002_.png
- text: >-
masterpiece, high score, great score, absurdres, Betamina, 1girl, detailed,
beautiful, intricate design, delicate, flowing hair, soft lighting, pastel
colors, long hair, glowing, ethereal, serene expression, detailed eyes,
highly detailed background, flowers, soft shading, elegant, fantasy setting,
fairy tale atmosphere, sparkles, graceful, warm tones
parameters:
negative_prompt: >-
lowres, bad anatomy, bad hands, text, error, missing finger, extra digits,
fewer digits, cropped, worst quality, low quality, low score, bad score,
average score, signature, watermark, username, blurry
output:
url: images/Betamina_00001_.png
base_model: cagliostrolab/animagine-xl-4.0
instance_prompt: Betamina
license: creativeml-openrail-m
datasets:
- AICU/Betamina-VRM100shots
---
# Betamina
<Gallery />
## Model description
# Betamina LoRA Release for Animagine XL 4 and FLUX.1
May 20, 2025 — AICU AIDX Lab is pleased to announce the release of new LoRA models developed for the AI character engine Animagine XL 4 by BlendAI.
Betamina is one of the main characters from the “Alpha Paradise Project” (also known as AlPara) by [BlendAI](https://blendai.jp/). Although admired by characters like [Deltamon](https://ja.wikipedia.org/wiki/%E3%83%87%E3%83%AB%E3%82%BF%E3%82%82%E3%82%93) and [Gammamy](https://ja.wikipedia.org/wiki/%E3%82%AC%E3%83%B3%E3%83%9E%E3%83%9F%E3%82%A3) for her cool and reliable appearance, Betamina is in fact a naturally spacey and low-energy character—charming in her own way.
Dual LoRA and Dataset Released!
We are releasing two LoRA models along with their respective training datasets:
- [LoRA for FLUX.1 (compatible with models by Black Forest Labs)](https://huggingface.co/AICU/Betamina-flux-lora)
- [LoRA for Animagine XL 4 (this)](https://huggingface.co/AICU/Betamina-LoRA-AnimagineXL4)
- [Dataset](https://huggingface.co/datasets/AICU/Betamina-VRM100shots)
[日本語での解説](https://note.com/aicu/n/na0892c42243b)
## Trigger words
You should use `Betamina` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/AICU/Betamina-LoRA-AnimagineXL4/tree/main) them in the Files & versions tab. |
leandromugnaini/llama_2_cka_40-70_compression_tuned | leandromugnaini | 2025-05-21T02:34:19Z | 0 | 0 | null | [
"safetensors",
"llama",
"region:us"
] | null | 2025-05-21T02:28:14Z | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
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font-size: 3.75rem;
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font-size: 1.125rem;
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color: rgb(209, 213, 219);
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alt=""
/>
<div>
<h1>429</h1>
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Sandigrl69/kay | Sandigrl69 | 2025-05-21T00:55:54Z | 0 | 0 | null | [
"license:other",
"region:us"
] | null | 2025-05-21T00:04:17Z | <!DOCTYPE html>
<html class="" lang="en">
<head>
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name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
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img {
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h1 {
font-size: 3.75rem;
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font-size: 1.125rem;
line-height: 1.75rem;
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}
.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>
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const key = "_tb_global_settings";
let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
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alt=""
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<h1>429</h1>
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fulleepatkoxu8/xcvzxcv | fulleepatkoxu8 | 2025-05-20T09:53:35Z | 0 | 0 | null | [
"license:bigscience-bloom-rail-1.0",
"region:us"
] | null | 2025-05-20T09:53:35Z | <!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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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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Noto Color Emoji;
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font-size: 3.75rem;
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</style>
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const key = "_tb_global_settings";
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if (storageTheme) {
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Iceed/6ff7ce60-ba21-4264-b197-7f86a3cdec63 | Iceed | 2025-05-20T09:52:48Z | 0 | 0 | null | [
"region:us"
] | null | 2025-05-20T09:32:26Z | <!DOCTYPE html>
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name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
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margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
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color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
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max-width: 28rem;
box-sizing: border-box;
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.dark main {
background-color: rgb(11, 15, 25);
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QuanHoangNgoc/s2t-small-uit-vimd_192355 | QuanHoangNgoc | 2025-05-20T09:12:07Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"speech_to_text",
"automatic-speech-recognition",
"speech-to-text",
"vietnamese",
"uit-vimd",
"generated_from_trainer",
"vi",
"dataset:uit-vimd",
"model-index",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2025-05-19T23:55:53Z | <!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"
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Doramong/package | Doramong | 2025-05-20T07:36:52Z | 0 | 0 | null | [
"region:us"
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Blebbyblub/javanese-sundanese-conformer-asrV2 | Blebbyblub | 2025-05-20T07:27:54Z | 0 | 0 | null | [
"pytorch",
"conformer",
"region:us"
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AyoubAnhal/ENSATE | AyoubAnhal | 2025-05-20T05:40:57Z | 0 | 0 | null | [
"safetensors",
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"region:us"
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verolfelipe/Mistral-Metabolism_Absorption | verolfelipe | 2025-05-20T04:41:42Z | 0 | 0 | null | [
"region:us"
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tensorblock/speechless-mistral-dolphin-orca-platypus-samantha-7b-GGUF | tensorblock | 2025-04-20T23:44:03Z | 101 | 0 | transformers | [
"transformers",
"gguf",
"llama-2",
"code",
"TensorBlock",
"GGUF",
"text-generation",
"en",
"dataset:jondurbin/airoboros-2.2.1",
"dataset:Open-Orca/OpenOrca",
"dataset:garage-bAInd/Open-Platypus",
"dataset:ehartford/samantha-data",
"base_model:uukuguy/speechless-mistral-dolphin-orca-platypus-samantha-7b",
"base_model:quantized:uukuguy/speechless-mistral-dolphin-orca-platypus-samantha-7b",
"license:llama2",
"model-index",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-11-22T05:58:12Z | <!DOCTYPE html>
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tensorblock/Puma-3B-GGUF | tensorblock | 2025-04-20T23:44:01Z | 78 | 0 | transformers | [
"transformers",
"gguf",
"TensorBlock",
"GGUF",
"text-generation",
"en",
"dataset:totally-not-an-llm/sharegpt-hyperfiltered-3k",
"base_model:acrastt/Puma-3B",
"base_model:quantized:acrastt/Puma-3B",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-11-22T05:40:28Z | <!DOCTYPE html>
<html class="" lang="en">
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sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
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img {
width: 6rem;
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margin: 0 auto 1rem;
}
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font-size: 3.75rem;
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color: rgba(31, 41, 55, 1);
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box-sizing: border-box;
margin: 0 auto;
}
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color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
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background-color: rgb(11, 15, 25);
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tensorblock/Deacon-34B-GGUF | tensorblock | 2025-04-20T23:43:59Z | 37 | 0 | null | [
"gguf",
"TensorBlock",
"GGUF",
"text-generation",
"dataset:totally-not-an-llm/EverythingLM-data-V3",
"base_model:KnutJaegersberg/Deacon-34B",
"base_model:quantized:KnutJaegersberg/Deacon-34B",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-11-22T04:36:18Z | <!DOCTYPE html>
<html class="" lang="en">
<head>
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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;
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width: 6rem;
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font-size: 3.75rem;
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box-sizing: border-box;
margin: 0 auto;
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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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bdsaglam/Llama-3.1-8B-Instruct-ragent-grpo-musique-merged-ragent-grpo-20250421_000014 | bdsaglam | 2025-04-20T23:43:57Z | 0 | 0 | null | [
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tensorblock/Yi-34B-GGUF | tensorblock | 2025-04-20T23:43:50Z | 62 | 0 | null | [
"gguf",
"TensorBlock",
"GGUF",
"text-generation",
"base_model:01-ai/Yi-34B",
"base_model:quantized:01-ai/Yi-34B",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-11-22T02:29:46Z | <!DOCTYPE html>
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tensorblock/SOLAR-10.7b-ko-Y24_v0.1-GGUF | tensorblock | 2025-04-20T23:43:49Z | 26 | 0 | null | [
"gguf",
"SOLAR",
"SOLAR-10.7B",
"TensorBlock",
"GGUF",
"text-generation",
"ko",
"base_model:AIdenU/SOLAR-10.7b-ko-Y24_v0.1",
"base_model:quantized:AIdenU/SOLAR-10.7b-ko-Y24_v0.1",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-11-22T02:04:16Z | <!DOCTYPE html>
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Michiates/michiates2 | Michiates | 2025-04-20T23:43:46Z | 0 | 0 | null | [
"region:us"
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tensorblock/CleverGirl-20b-Blended-GGUF | tensorblock | 2025-04-20T23:43:41Z | 27 | 0 | null | [
"gguf",
"TensorBlock",
"GGUF",
"base_model:athirdpath/CleverGirl-20b-Blended",
"base_model:quantized:athirdpath/CleverGirl-20b-Blended",
"license:cc-by-nc-4.0",
"endpoints_compatible",
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] | null | 2024-11-21T23:39:38Z | <!DOCTYPE html>
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Noto Color Emoji;
}
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width: 6rem;
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margin: 0 auto 1rem;
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color: rgba(107, 114, 128, 1);
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line-height: 1.75rem;
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}
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tensorblock/koishi-instruct-3b-GGUF | tensorblock | 2025-04-20T23:43:40Z | 50 | 0 | null | [
"gguf",
"TensorBlock",
"GGUF",
"dataset:ewof/koishi-instruct-metharme",
"base_model:ewof/koishi-instruct-3b",
"base_model:quantized:ewof/koishi-instruct-3b",
"endpoints_compatible",
"region:us"
] | null | 2024-11-21T23:19:27Z | <!DOCTYPE html>
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BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
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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;
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font-size: 1.125rem;
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tensorblock/Yi-34B-GiftedConvo-merged-GGUF | tensorblock | 2025-04-20T23:43:37Z | 33 | 0 | null | [
"gguf",
"TensorBlock",
"GGUF",
"dataset:NobodyExistsOnTheInternet/GiftedConvoBeforeEcons",
"base_model:NobodyExistsOnTheInternet/Yi-34B-GiftedConvo-merged",
"base_model:quantized:NobodyExistsOnTheInternet/Yi-34B-GiftedConvo-merged",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2024-11-21T22:10:43Z | <!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;
}
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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;
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tensorblock/deepseek-coder-33b-instruct-GGUF | tensorblock | 2025-04-20T23:43:35Z | 169 | 0 | null | [
"gguf",
"TensorBlock",
"GGUF",
"base_model:deepseek-ai/deepseek-coder-33b-instruct",
"base_model:quantized:deepseek-ai/deepseek-coder-33b-instruct",
"license:other",
"endpoints_compatible",
"region:us",
"conversational"
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tensorblock/Breeze-7B-Base-v1_0-GGUF | tensorblock | 2025-04-20T23:43:11Z | 38 | 0 | null | [
"gguf",
"TensorBlock",
"GGUF",
"text-generation",
"zh",
"en",
"base_model:MediaTek-Research/Breeze-7B-Base-v1_0",
"base_model:quantized:MediaTek-Research/Breeze-7B-Base-v1_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-11-21T19:15:27Z | <!DOCTYPE html>
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tensorblock/Bumblebee-7B-GGUF | tensorblock | 2025-04-20T23:43:09Z | 82 | 0 | null | [
"gguf",
"Math",
"TensorBlock",
"GGUF",
"text-generation",
"en",
"dataset:meta-math/MetaMathQA",
"base_model:Q-bert/Bumblebee-7B",
"base_model:quantized:Q-bert/Bumblebee-7B",
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"conversational"
] | text-generation | 2024-11-21T18:42:02Z | <!DOCTYPE html>
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tensorblock/WizardLM-30B-Uncensored-Guanaco-SuperCOT-30b-GGUF | tensorblock | 2025-04-20T23:43:08Z | 193 | 3 | null | [
"gguf",
"uncensored",
"TensorBlock",
"GGUF",
"dataset:ehartford/WizardLM_alpaca_evol_instruct_70k_unfiltered",
"dataset:kaiokendev/SuperCOT-dataset",
"dataset:neulab/conala",
"dataset:yahma/alpaca-cleaned",
"dataset:QingyiSi/Alpaca-CoT",
"dataset:timdettmers/guanaco-33b",
"dataset:JosephusCheung/GuanacoDataset",
"base_model:Monero/WizardLM-30B-Uncensored-Guanaco-SuperCOT-30b",
"base_model:quantized:Monero/WizardLM-30B-Uncensored-Guanaco-SuperCOT-30b",
"license:other",
"endpoints_compatible",
"region:us"
] | null | 2024-11-21T18:38:31Z | <!DOCTYPE html>
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tensorblock/mistral-ko-exo-wiki-quiz-v1-GGUF | tensorblock | 2025-04-20T23:43:07Z | 39 | 0 | null | [
"gguf",
"TensorBlock",
"GGUF",
"text-generation",
"ko",
"base_model:shleeeee/mistral-ko-exo-wiki-quiz-v1",
"base_model:quantized:shleeeee/mistral-ko-exo-wiki-quiz-v1",
"license:other",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-11-21T18:09:20Z | <!DOCTYPE html>
<html class="" lang="en">
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content="width=device-width, initial-scale=1.0, user-scalable=no"
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tensorblock/Walter-Llama-1B-GGUF | tensorblock | 2025-04-20T23:43:02Z | 23 | 0 | null | [
"gguf",
"TensorBlock",
"GGUF",
"dataset:KnutJaegersberg/Auton",
"base_model:KnutJaegersberg/Walter-Llama-1B",
"base_model:quantized:KnutJaegersberg/Walter-Llama-1B",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-11-21T17:51:16Z | <!DOCTYPE html>
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tensorblock/llama-3-chinese-8b-GGUF | tensorblock | 2025-04-20T23:42:56Z | 37 | 0 | null | [
"gguf",
"TensorBlock",
"GGUF",
"zh",
"en",
"base_model:hfl/llama-3-chinese-8b",
"base_model:quantized:hfl/llama-3-chinese-8b",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-11-21T17:31:40Z | <!DOCTYPE html>
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tensorblock/Llama-2-13b-hf-GGUF | tensorblock | 2025-04-20T23:42:54Z | 70 | 0 | null | [
"gguf",
"facebook",
"meta",
"pytorch",
"llama",
"llama-2",
"TensorBlock",
"GGUF",
"text-generation",
"en",
"base_model:meta-llama/Llama-2-13b-hf",
"base_model:quantized:meta-llama/Llama-2-13b-hf",
"license:llama2",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-11-21T16:34:12Z | <!DOCTYPE html>
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tensorblock/metharme-1.3b-GGUF | tensorblock | 2025-04-20T23:42:52Z | 21 | 0 | null | [
"gguf",
"TensorBlock",
"GGUF",
"en",
"base_model:PygmalionAI/metharme-1.3b",
"base_model:quantized:PygmalionAI/metharme-1.3b",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-11-21T16:17:41Z | <!DOCTYPE html>
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tensorblock/Aira-2-1B5-GGUF | tensorblock | 2025-04-20T23:42:50Z | 41 | 0 | transformers | [
"transformers",
"gguf",
"alignment",
"instruction tuned",
"text generation",
"conversation",
"assistant",
"TensorBlock",
"GGUF",
"text-generation",
"en",
"dataset:nicholasKluge/instruct-aira-dataset",
"base_model:nicholasKluge/Aira-2-1B5",
"base_model:quantized:nicholasKluge/Aira-2-1B5",
"license:apache-2.0",
"co2_eq_emissions",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-11-21T16:08:51Z | <!DOCTYPE html>
<html class="" lang="en">
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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;
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);
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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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}
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}
</style>
<script>
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tensorblock/shieldgemma-2b-GGUF | tensorblock | 2025-04-20T23:42:38Z | 24 | 0 | transformers | [
"transformers",
"gguf",
"TensorBlock",
"GGUF",
"text-generation",
"base_model:google/shieldgemma-2b",
"base_model:quantized:google/shieldgemma-2b",
"license:gemma",
"endpoints_compatible",
"region:us",
"conversational"
] | text-generation | 2024-11-21T13:50:20Z | <!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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}
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font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
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;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
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}
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color: rgb(156, 163, 175);
}
</style>
<script>
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let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
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JAMESNEGROMONDEGO/SORCERER | JAMESNEGROMONDEGO | 2025-04-20T23:42:32Z | 0 | 0 | null | [
"region:us"
] | null | 2025-04-20T23:42:24Z | <!DOCTYPE html>
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tensorblock/airoboros-33b-gpt4-1.4.1-PI-8192-fp16-GGUF | tensorblock | 2025-04-20T23:42:29Z | 36 | 0 | null | [
"gguf",
"TensorBlock",
"GGUF",
"dataset:jondurbin/airoboros-gpt4-1.4.1",
"base_model:bhenrym14/airoboros-33b-gpt4-1.4.1-PI-8192-fp16",
"base_model:quantized:bhenrym14/airoboros-33b-gpt4-1.4.1-PI-8192-fp16",
"endpoints_compatible",
"region:us"
] | null | 2024-11-21T12:00:00Z | <!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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/>
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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;
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}
.dark main {
background-color: rgb(11, 15, 25);
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tensorblock/trinity-v1-GGUF | tensorblock | 2025-04-20T23:42:27Z | 39 | 0 | null | [
"gguf",
"merge",
"TensorBlock",
"GGUF",
"en",
"base_model:jan-hq/trinity-v1",
"base_model:quantized:jan-hq/trinity-v1",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-11-21T10:55:38Z | <!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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BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
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width: 6rem;
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}
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tensorblock/Llama-3-instruction-constructionsafety-layertuning-GGUF | tensorblock | 2025-04-20T23:42:24Z | 45 | 0 | transformers | [
"transformers",
"gguf",
"llama3",
"meta",
"facebook",
"TensorBlock",
"GGUF",
"ko",
"base_model:DBCMLAB/Llama-3-instruction-constructionsafety-layertuning",
"base_model:quantized:DBCMLAB/Llama-3-instruction-constructionsafety-layertuning",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-11-21T10:48:47Z | <!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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/>
<meta property="og:type" content="website" />
<title>Hugging Face - The AI community building the future.</title>
<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";
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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<body>
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<img
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alt=""
/>
<div>
<h1>429</h1>
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tensorblock/UndiMix-v1-13b-GGUF | tensorblock | 2025-04-20T23:42:22Z | 51 | 0 | null | [
"gguf",
"TensorBlock",
"GGUF",
"base_model:Undi95/UndiMix-v1-13b",
"base_model:quantized:Undi95/UndiMix-v1-13b",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | null | 2024-11-21T09:44:14Z | <!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) {}
if (theme === "dark") {
document.documentElement.classList.add("dark");
} else {
document.documentElement.classList.remove("dark");
}
</script>
</head>
<body>
<main>
<img
src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg"
alt=""
/>
<div>
<h1>429</h1>
<p>We had to rate limit you. If you think it's an error, send us <a href="mailto:[email protected]">an email</a></p>
</div>
</main>
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</html> |
tensorblock/cr-model-v1-GGUF | tensorblock | 2025-04-20T23:42:15Z | 43 | 0 | null | [
"gguf",
"TensorBlock",
"GGUF",
"base_model:TwT-6/cr-model-v1",
"base_model:quantized:TwT-6/cr-model-v1",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-11-21T09:10:20Z | <!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) {}
if (theme === "dark") {
document.documentElement.classList.add("dark");
} else {
document.documentElement.classList.remove("dark");
}
</script>
</head>
<body>
<main>
<img
src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg"
alt=""
/>
<div>
<h1>429</h1>
<p>We had to rate limit you. If you think it's an error, send us <a href="mailto:[email protected]">an email</a></p>
</div>
</main>
</body>
</html> |
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