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| last_modified
unknowndate 2020-02-15 11:33:14
2025-04-16 06:27:39
| downloads
int64 0
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| likes
int64 0
11.7k
| library_name
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sequencelengths 1
4.05k
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msharma95/babyberta-aochildes-checkpoint-390000 | msharma95 | "2025-04-05T15:23:20Z" | 0 | 0 | null | [
"safetensors",
"roberta",
"region:us"
] | null | "2025-04-05T07:06:17Z" | <!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> |
scvi-tools/tabula-sapiens-tongue-scvi | scvi-tools | "2024-12-08T11:07:14Z" | 0 | 0 | scvi-tools | [
"scvi-tools",
"tensorboard",
"biology",
"genomics",
"single-cell",
"model_cls_name:SCVI",
"scvi_version:1.2.0",
"anndata_version:0.11.1",
"modality:rna",
"tissue:various",
"annotated:True",
"license:cc-by-4.0",
"region:us"
] | null | "2023-03-15T21:46:44Z" | ---
library_name: scvi-tools
license: cc-by-4.0
tags:
- biology
- genomics
- single-cell
- model_cls_name:SCVI
- scvi_version:1.2.0
- anndata_version:0.11.1
- modality:rna
- tissue:various
- annotated:True
---
ScVI is a variational inference model for single-cell RNA-seq data that can learn an underlying
latent space, integrate technical batches and impute dropouts.
The learned low-dimensional latent representation of the data can be used for visualization and
clustering.
scVI takes as input a scRNA-seq gene expression matrix with cells and genes.
We provide an extensive [user guide](https://docs.scvi-tools.org/en/1.2.0/user_guide/models/scvi.html).
- See our original manuscript for further details of the model:
[scVI manuscript](https://www.nature.com/articles/s41592-018-0229-2).
- See our manuscript on [scvi-hub](https://www.biorxiv.org/content/10.1101/2024.03.01.582887v2) how
to leverage pre-trained models.
This model can be used for fine tuning on new data using our Arches framework:
[Arches tutorial](https://docs.scvi-tools.org/en/1.0.0/tutorials/notebooks/scarches_scvi_tools.html).
# Model Description
Tabula Sapiens is a benchmark, first-draft human cell atlas of nearly 500,000 cells from 24 organs of 15 normal human subjects.
# Metrics
We provide here key performance metrics for the uploaded model, if provided by the data uploader.
<details>
<summary><strong>Coefficient of variation</strong></summary>
The cell-wise coefficient of variation summarizes how well variation between different cells is
preserved by the generated model expression. Below a squared Pearson correlation coefficient of 0.4
, we would recommend not to use generated data for downstream analysis, while the generated latent
space might still be useful for analysis.
**Cell-wise Coefficient of Variation**:
| Metric | Training Value | Validation Value |
|-------------------------|----------------|------------------|
| Mean Absolute Error | 1.14 | 1.22 |
| Pearson Correlation | 0.89 | 0.86 |
| Spearman Correlation | 0.88 | 0.86 |
| R² (R-Squared) | 0.75 | 0.69 |
The gene-wise coefficient of variation summarizes how well variation between different genes is
preserved by the generated model expression. This value is usually quite high.
**Gene-wise Coefficient of Variation**:
| Metric | Training Value |
|-------------------------|----------------|
| Mean Absolute Error | 16.76 |
| Pearson Correlation | 0.63 |
| Spearman Correlation | 0.67 |
| R² (R-Squared) | -1.67 |
</details>
<details>
<summary><strong>Differential expression metric</strong></summary>
The differential expression metric provides a summary of the differential expression analysis
between cell types or input clusters. We provide here the F1-score, Pearson Correlation
Coefficient of Log-Foldchanges, Spearman Correlation Coefficient, and Area Under the Precision
Recall Curve (AUPRC) for the differential expression analysis using Wilcoxon Rank Sum test for each
cell-type.
**Differential expression**:
| Index | gene_f1 | lfc_mae | lfc_pearson | lfc_spearman | roc_auc | pr_auc | n_cells |
| --- | --- | --- | --- | --- | --- | --- | --- |
| basal cell | 0.92 | 0.32 | 0.86 | 0.96 | 0.43 | 0.89 | 6281.00 |
| keratinocyte | 0.88 | 0.79 | 0.75 | 0.97 | 0.23 | 0.90 | 3667.00 |
| epithelial cell | 0.84 | 0.54 | 0.71 | 0.93 | 0.53 | 0.87 | 2114.00 |
| leukocyte | 0.85 | 0.76 | 0.70 | 0.94 | 0.38 | 0.87 | 2109.00 |
| fibroblast | 0.79 | 2.62 | 0.58 | 0.82 | 0.44 | 0.80 | 251.00 |
| vein endothelial cell | 0.81 | 2.90 | 0.63 | 0.82 | 0.46 | 0.81 | 226.00 |
| pericyte | 0.79 | 3.66 | 0.59 | 0.72 | 0.41 | 0.72 | 117.00 |
| capillary endothelial cell | 0.78 | 3.85 | 0.64 | 0.74 | 0.44 | 0.74 | 101.00 |
| tongue muscle cell | 0.76 | 5.43 | 0.55 | 0.57 | 0.38 | 0.70 | 60.00 |
| endothelial cell of lymphatic vessel | 0.60 | 5.63 | 0.53 | 0.52 | 0.42 | 0.69 | 44.00 |
| endothelial cell of artery | 0.69 | 4.79 | 0.63 | 0.66 | 0.40 | 0.71 | 40.00 |
</details>
# Model Properties
We provide here key parameters used to setup and train the model.
<details>
<summary><strong>Model Parameters</strong></summary>
These provide the settings to setup the original model:
```json
{
"n_hidden": 128,
"n_latent": 20,
"n_layers": 3,
"dropout_rate": 0.05,
"dispersion": "gene",
"gene_likelihood": "nb",
"latent_distribution": "normal",
"use_batch_norm": "none",
"use_layer_norm": "both",
"encode_covariates": true
}
```
</details>
<details>
<summary><strong>Setup Data Arguments</strong></summary>
Arguments passed to setup_anndata of the original model:
```json
{
"layer": null,
"batch_key": "donor_assay",
"labels_key": "cell_ontology_class",
"size_factor_key": null,
"categorical_covariate_keys": null,
"continuous_covariate_keys": null
}
```
</details>
<details>
<summary><strong>Data Registry</strong></summary>
Registry elements for AnnData manager:
| Registry Key | scvi-tools Location |
|-------------------|--------------------------------------|
| X | adata.X |
| batch | adata.obs['_scvi_batch'] |
| labels | adata.obs['_scvi_labels'] |
| latent_qzm | adata.obsm['scvi_latent_qzm'] |
| latent_qzv | adata.obsm['scvi_latent_qzv'] |
| minify_type | adata.uns['_scvi_adata_minify_type'] |
| observed_lib_size | adata.obs['observed_lib_size'] |
- **Data is Minified**: False
</details>
<details>
<summary><strong>Summary Statistics</strong></summary>
| Summary Stat Key | Value |
|--------------------------|-------|
| n_batch | 4 |
| n_cells | 15010 |
| n_extra_categorical_covs | 0 |
| n_extra_continuous_covs | 0 |
| n_labels | 11 |
| n_latent_qzm | 20 |
| n_latent_qzv | 20 |
| n_vars | 3000 |
</details>
<details>
<summary><strong>Training</strong></summary>
<!-- If your model is not uploaded with any data (e.g., minified data) on the Model Hub, then make
sure to provide this field if you want users to be able to access your training data. See the
scvi-tools documentation for details. -->
**Training data url**: Not provided by uploader
If provided by the original uploader, for those interested in understanding or replicating the
training process, the code is available at the link below.
**Training Code URL**: https://github.com/YosefLab/scvi-hub-models/blob/main/src/scvi_hub_models/TS_train_all_tissues.ipynb
</details>
# References
The Tabula Sapiens Consortium. The Tabula Sapiens: A multiple-organ, single-cell transcriptomic atlas of humans. Science, May 2022. doi:10.1126/science.abl4896
|
HadarG/SmolLM2-FT-smoltalk | HadarG | "2025-01-19T15:48:13Z" | 7 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"smol-course",
"module_1",
"trl",
"sft",
"conversational",
"base_model:HuggingFaceTB/SmolLM2-135M",
"base_model:finetune:HuggingFaceTB/SmolLM2-135M",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-01-19T15:47:55Z" | ---
base_model: HuggingFaceTB/SmolLM2-135M
library_name: transformers
model_name: SmolLM2-FT-smoltalk
tags:
- generated_from_trainer
- smol-course
- module_1
- trl
- sft
licence: license
---
# Model Card for SmolLM2-FT-smoltalk
This model is a fine-tuned version of [HuggingFaceTB/SmolLM2-135M](https://huggingface.co/HuggingFaceTB/SmolLM2-135M).
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="HadarG/SmolLM2-FT-smoltalk", 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/hadar-grimberg/huggingface/runs/21g7cxmz)
This model was trained with SFT.
### Framework versions
- TRL: 0.13.0
- Transformers: 4.47.1
- Pytorch: 2.5.1+cu121
- Datasets: 3.2.0
- Tokenizers: 0.21.0
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
DreadPoor/Faber-12B-LINEAR | DreadPoor | "2025-03-16T23:20:51Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"mergekit",
"merge",
"conversational",
"arxiv:2203.05482",
"base_model:DreadPoor/Nullsworn-12B-LINEAR",
"base_model:merge:DreadPoor/Nullsworn-12B-LINEAR",
"base_model:DreadPoor/Nullsworn_1.1-8B-LINEAR",
"base_model:merge:DreadPoor/Nullsworn_1.1-8B-LINEAR",
"base_model:PocketDoc/Dans-PersonalityEngine-V1.1.0-12b",
"base_model:merge:PocketDoc/Dans-PersonalityEngine-V1.1.0-12b",
"base_model:nbeerbower/Mahou-1.5-mistral-nemo-12B-lorablated",
"base_model:merge:nbeerbower/Mahou-1.5-mistral-nemo-12B-lorablated",
"base_model:nbeerbower/Mistral-Nemo-Prism-12B-v7",
"base_model:merge:nbeerbower/Mistral-Nemo-Prism-12B-v7",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-03-16T23:14:57Z" | ---
base_model:
- PocketDoc/Dans-PersonalityEngine-V1.1.0-12b
- nbeerbower/Mistral-Nemo-Prism-12B-v7
- DreadPoor/Nullsworn-12B-LINEAR
- DreadPoor/Nullsworn_1.1-8B-LINEAR
- nbeerbower/Mahou-1.5-mistral-nemo-12B-lorablated
library_name: transformers
tags:
- mergekit
- merge
---
# merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [Linear](https://arxiv.org/abs/2203.05482) merge method.
### Models Merged
The following models were included in the merge:
* [PocketDoc/Dans-PersonalityEngine-V1.1.0-12b](https://huggingface.co/PocketDoc/Dans-PersonalityEngine-V1.1.0-12b)
* [nbeerbower/Mistral-Nemo-Prism-12B-v7](https://huggingface.co/nbeerbower/Mistral-Nemo-Prism-12B-v7)
* [DreadPoor/Nullsworn-12B-LINEAR](https://huggingface.co/DreadPoor/Nullsworn-12B-LINEAR)
* [DreadPoor/Nullsworn_1.1-8B-LINEAR](https://huggingface.co/DreadPoor/Nullsworn_1.1-8B-LINEAR)
* [nbeerbower/Mahou-1.5-mistral-nemo-12B-lorablated](https://huggingface.co/nbeerbower/Mahou-1.5-mistral-nemo-12B-lorablated)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: DreadPoor/Nullsworn_1.1-8B-LINEAR
parameters:
weight: 1.0
- model: DreadPoor/Nullsworn-12B-LINEAR
parameters:
weight: 1.0
- model: nbeerbower/Mistral-Nemo-Prism-12B-v7
parameters:
weight: 1.0
- model: PocketDoc/Dans-PersonalityEngine-V1.1.0-12b
parameters:
weight: 1.0
- model: nbeerbower/Mahou-1.5-mistral-nemo-12B-lorablated
parameters:
weight: 1.0
merge_method: linear
normalize: false
int8_mask: true
dtype: bfloat16
```
|
mayanklohani19/mergekit-slerp-ujysgyd | mayanklohani19 | "2025-02-24T14:24:08Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"base_model:meta-llama/Llama-2-7b-chat-hf",
"base_model:finetune:meta-llama/Llama-2-7b-chat-hf",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-02-24T14:21:50Z" | ---
base_model:
- meta-llama/Llama-2-7b-chat-hf
library_name: transformers
tags:
- mergekit
- merge
---
# merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [SLERP](https://en.wikipedia.org/wiki/Slerp) merge method.
### Models Merged
The following models were included in the merge:
* [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
slices:
- sources:
- model: meta-llama/Llama-2-7b-chat-hf
layer_range:
- 0
- 32
- model: meta-llama/Llama-2-7b-chat-hf
layer_range:
- 0
- 32
merge_method: slerp
base_model: meta-llama/Llama-2-7b-chat-hf
parameters:
t:
- filter: self_attn
value:
- 0
- 0.5
- 0.3
- 0.7
- 1
- filter: mlp
value:
- 1
- 0.5
- 0.7
- 0.3
- 0
- value: 0.5
dtype: bfloat16
```
|
Haricot24601/q-taxi-v3-0 | Haricot24601 | "2025-03-25T06:57:14Z" | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | "2025-03-25T06:57:08Z" | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-taxi-v3-0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.50 +/- 2.73
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="Haricot24601/q-taxi-v3-0", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
Astrofolia/distilbert-base-uncased-finetuned-emotion | Astrofolia | "2022-09-01T11:20:22Z" | 101 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2022-09-01T11:10:08Z" | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9285
- name: F1
type: f1
value: 0.9284597945931914
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2175
- Accuracy: 0.9285
- F1: 0.9285
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8486 | 1.0 | 250 | 0.3234 | 0.896 | 0.8913 |
| 0.257 | 2.0 | 500 | 0.2175 | 0.9285 | 0.9285 |
### Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
mmnga/Ninja-v1-NSFW-gguf | mmnga | "2024-05-04T13:26:52Z" | 941 | 3 | null | [
"gguf",
"en",
"ja",
"dataset:TFMC/imatrix-dataset-for-japanese-llm",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2024-05-03T14:03:23Z" |
---
license: apache-2.0
language:
- en
- ja
datasets:
- TFMC/imatrix-dataset-for-japanese-llm
---
# Ninja-v1-NSFW-gguf
[Local-Novel-LLM-projectさんが公開しているNinja-v1-NSFW](https://huggingface.co/Local-Novel-LLM-project/Ninja-v1-NSFW)のggufフォーマット変換版です。
imatrixのデータは[TFMC/imatrix-dataset-for-japanese-llm](https://huggingface.co/datasets/TFMC/imatrix-dataset-for-japanese-llm)を使用して作成しました。
# 他のモデル
[mmnga/Ninja-v1-gguf](https://huggingface.co/mmnga/Ninja-v1-gguf)
[mmnga/Ninja-v1-NSFW-gguf](https://huggingface.co/mmnga/Ninja-v1-NSFW-gguf)
[mmnga/Ninja-v1-128k-gguf](https://huggingface.co/mmnga/Ninja-v1-128k-gguf)
[mmnga/Ninja-v1-NSFW-128k-gguf](https://huggingface.co/mmnga/Ninja-v1-NSFW-128k-gguf)
[mmnga/Vecteus-v1-gguf](https://huggingface.co/mmnga/Vecteus-v1-gguf)
## Usage
```
git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
make -j
./main -m 'Ninja-v1-NSFW-Q4_0.gguf' -n 128 -p 'あなたはプロの小説家です。\n小説を書いてください\n-------- \nくじらの物語\n-------- \n'
```
|
NeuroDonu/Yi-1.5-gguf | NeuroDonu | "2024-05-16T14:22:33Z" | 9 | 0 | null | [
"gguf",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2024-05-16T10:48:12Z" | ---
license: apache-2.0
---
|
gljj/politeness-model | gljj | "2024-11-21T18:10:27Z" | 108 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2024-11-08T09:21:58Z" | ---
library_name: transformers
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: politeness-model
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. -->
# politeness-model
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8862
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 25 | 1.0766 |
| No log | 2.0 | 50 | 0.9297 |
| No log | 3.0 | 75 | 0.8862 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1
- Datasets 3.1.0
- Tokenizers 0.20.3
|
gokulsrinivasagan/tinybert_train_book_ent_15p_stsb | gokulsrinivasagan | "2025-04-09T00:57:32Z" | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"base_model:gokulsrinivasagan/tinybert_train_book_ent_15p",
"base_model:finetune:gokulsrinivasagan/tinybert_train_book_ent_15p",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2025-04-09T00:56:27Z" | ---
library_name: transformers
language:
- en
license: apache-2.0
base_model: gokulsrinivasagan/tinybert_train_book_ent_15p
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- spearmanr
model-index:
- name: tinybert_train_book_ent_15p_stsb
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE STSB
type: glue
args: stsb
metrics:
- name: Spearmanr
type: spearmanr
value: 0.21041199925604162
---
<!-- 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. -->
# tinybert_train_book_ent_15p_stsb
This model is a fine-tuned version of [gokulsrinivasagan/tinybert_train_book_ent_15p](https://huggingface.co/gokulsrinivasagan/tinybert_train_book_ent_15p) on the GLUE STSB dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2779
- Pearson: 0.2196
- Spearmanr: 0.2104
- Combined Score: 0.2150
## 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: 256
- eval_batch_size: 256
- seed: 10
- optimizer: Use 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: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Pearson | Spearmanr | Combined Score |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:---------:|:--------------:|
| 3.1125 | 1.0 | 23 | 2.4241 | 0.0970 | 0.0984 | 0.0977 |
| 2.0404 | 2.0 | 46 | 2.6128 | 0.1037 | 0.0949 | 0.0993 |
| 1.9426 | 3.0 | 69 | 2.3644 | 0.1457 | 0.1307 | 0.1382 |
| 1.8491 | 4.0 | 92 | 2.2779 | 0.2196 | 0.2104 | 0.2150 |
| 1.6662 | 5.0 | 115 | 2.5923 | 0.1881 | 0.1930 | 0.1906 |
| 1.4804 | 6.0 | 138 | 2.4731 | 0.2344 | 0.2459 | 0.2402 |
| 1.277 | 7.0 | 161 | 2.4954 | 0.2410 | 0.2499 | 0.2455 |
| 1.0894 | 8.0 | 184 | 2.6198 | 0.2424 | 0.2575 | 0.2499 |
| 0.8971 | 9.0 | 207 | 2.7564 | 0.2357 | 0.2491 | 0.2424 |
### Framework versions
- Transformers 4.46.3
- Pytorch 2.2.1+cu118
- Datasets 3.1.0
- Tokenizers 0.20.3
|
lesso/8678078a-25bf-43a4-a52f-8400990b3594 | lesso | "2025-02-06T15:34:55Z" | 8 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:huggyllama/llama-7b",
"base_model:adapter:huggyllama/llama-7b",
"license:other",
"region:us"
] | null | "2025-02-06T15:23:35Z" | ---
library_name: peft
license: other
base_model: huggyllama/llama-7b
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 8678078a-25bf-43a4-a52f-8400990b3594
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: huggyllama/llama-7b
bf16: true
chat_template: llama3
data_processes: 16
dataset_prepared_path: null
datasets:
- data_files:
- a60b111d682adfa9_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/a60b111d682adfa9_train_data.json
type:
field_instruction: Question
field_output: ProcessedQuestion
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
do_eval: true
early_stopping_patience: 5
eval_batch_size: 4
eval_max_new_tokens: 128
eval_steps: 50
eval_table_size: null
evals_per_epoch: null
flash_attention: true
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 2
gradient_checkpointing: true
group_by_length: true
hub_model_id: lesso/8678078a-25bf-43a4-a52f-8400990b3594
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0001002
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 128
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 64
lora_target_linear: true
lr_scheduler: linear
max_grad_norm: 1.0
max_steps: 200
micro_batch_size: 4
mlflow_experiment_name: /tmp/G.O.D/a60b111d682adfa9_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
optim_args:
adam_beta1: 0.9
adam_beta2: 0.95
adam_epsilon: 1e-5
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 50
saves_per_epoch: null
sequence_len: 1024
special_tokens:
pad_token: </s>
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 4b382a33-86bc-4d56-93fe-08612e70ed12
wandb_project: new-02
wandb_run: your_name
wandb_runid: 4b382a33-86bc-4d56-93fe-08612e70ed12
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 8678078a-25bf-43a4-a52f-8400990b3594
This model is a fine-tuned version of [huggyllama/llama-7b](https://huggingface.co/huggyllama/llama-7b) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0028
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001002
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.95,adam_epsilon=1e-5
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10
- training_steps: 185
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 2.1976 | 0.0163 | 1 | 2.1408 |
| 0.3457 | 0.8130 | 50 | 0.3422 |
| 0.0181 | 1.6260 | 100 | 0.0129 |
| 0.0019 | 2.4390 | 150 | 0.0028 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
santis2/gpt2-alpaca-instruction-fine-tuning-lora | santis2 | "2023-09-22T18:52:42Z" | 0 | 0 | null | [
"generated_from_trainer",
"base_model:openai-community/gpt2",
"base_model:finetune:openai-community/gpt2",
"license:mit",
"region:us"
] | null | "2023-09-22T15:35:13Z" | ---
license: mit
base_model: gpt2
tags:
- generated_from_trainer
model-index:
- name: gpt2-alpaca-instruction-fine-tuning-lora
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. -->
# gpt2-alpaca-instruction-fine-tuning-lora
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6981
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.118 | 0.11 | 1000 | 1.7807 |
| 1.8878 | 0.22 | 2000 | 1.7477 |
| 1.8609 | 0.33 | 3000 | 1.7318 |
| 1.8489 | 0.44 | 4000 | 1.7207 |
| 1.8416 | 0.55 | 5000 | 1.7134 |
| 1.8181 | 0.66 | 6000 | 1.7082 |
| 1.8144 | 0.77 | 7000 | 1.7021 |
| 1.816 | 0.88 | 8000 | 1.6987 |
| 1.7825 | 0.99 | 9000 | 1.6981 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|
ByteResearch/Hermes-Qwen1.5-MoE-A2.7B-Chat-240405 | ByteResearch | "2024-04-05T07:59:25Z" | 5 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2_moe",
"text-generation",
"conversational",
"en",
"zh",
"doi:10.57967/hf/2011",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-04-05T04:18:05Z" | ---
license: apache-2.0
language:
- en
- zh
pipeline_tag: text-generation
--- |
maxjahn/pretrained-for-oracle | maxjahn | "2024-05-10T21:49:21Z" | 0 | 0 | null | [
"onnx",
"license:mit",
"region:us"
] | null | "2024-05-10T15:12:55Z" | ---
license: mit
---
Collection of pretrained models suitable for use in Oracle Machine Learning (OML) converted into ONNX format ready for import. |
memeviss/DL10 | memeviss | "2025-02-13T08:18:26Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-02-13T07:25:27Z" | ---
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] |
Gnider/roberta_sentim_dist_test | Gnider | "2024-10-16T02:13:47Z" | 107 | 0 | transformers | [
"transformers",
"safetensors",
"roberta",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2024-10-16T02:10:48Z" | ---
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] |
Ningxin/bloom_wikitext_collaboration_2 | Ningxin | "2023-08-01T02:47:32Z" | 0 | 0 | peft | [
"peft",
"pytorch",
"bloom",
"region:us"
] | null | "2023-07-31T23:19:18Z" | ---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.4.0
|
QasimBin/Finetuned_model_on_reduced_dataset | QasimBin | "2024-09-28T10:18:56Z" | 119 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"mt5",
"text2text-generation",
"generated_from_trainer",
"base_model:csebuetnlp/mT5_m2o_english_crossSum",
"base_model:finetune:csebuetnlp/mT5_m2o_english_crossSum",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | "2024-09-28T09:07:57Z" | ---
library_name: transformers
base_model: csebuetnlp/mT5_m2o_english_crossSum
tags:
- generated_from_trainer
model-index:
- name: finetuned_text_summarization_model
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. -->
#finetuned_text_summerization_model
This model is a fine-tuned version of [csebuetnlp/mT5_m2o_english_crossSum](https://huggingface.co/csebuetnlp/mT5_m2o_english_crossSum) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Generated Length |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:----------------:|
| No log | 1.0 | 190 | 3.7432 | 0.1825 | 0.0547 | 0.1382 | 0.1383 | 33.9921 |
| No log | 2.0 | 190 | 3.7432 | 0.1825 | 0.0547 | 0.1382 | 0.1383 | 33.9921 |
| No log | 3.0 | 190 | 3.7432 | 0.1825 | 0.0547 | 0.1382 | 0.1383 | 33.9921 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.0
- Tokenizers 0.19.1
|
LarryAIDraw/saren_v1 | LarryAIDraw | "2023-11-15T17:14:33Z" | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
] | null | "2023-11-15T17:14:01Z" | ---
license: creativeml-openrail-m
---
https://civitai.com/models/198510/saren-princess-connect-redive |
AlignmentResearch/robust_llm_pythia-tt-410m-mz-ada-v3-ch-100000 | AlignmentResearch | "2024-03-21T10:58:38Z" | 108 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-classification",
"generated_from_trainer",
"base_model:EleutherAI/pythia-410m-deduped",
"base_model:finetune:EleutherAI/pythia-410m-deduped",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-classification | "2024-03-21T10:57:50Z" | ---
license: apache-2.0
tags:
- generated_from_trainer
base_model: EleutherAI/pythia-410m-deduped
model-index:
- name: robust_llm_pythia-tt-410m-mz-ada-v3-ch-100000
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. -->
# robust_llm_pythia-tt-410m-mz-ada-v3-ch-100000
This model is a fine-tuned version of [EleutherAI/pythia-410m-deduped](https://huggingface.co/EleutherAI/pythia-410m-deduped) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.37.2
- Pytorch 2.2.0
- Datasets 2.17.0
- Tokenizers 0.15.2
|
farsipal/whisper-small-el | farsipal | "2022-12-07T20:27:27Z" | 37 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"whisper-event",
"el",
"dataset:mozilla-foundation/common_voice_11_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | "2022-12-05T01:46:32Z" | ---
language:
- el
license: apache-2.0
tags:
- whisper-event
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: whisper-small-el
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: mozilla-foundation/common_voice_11_0 el
type: mozilla-foundation/common_voice_11_0
config: el
split: test
args: el
metrics:
- name: Wer
type: wer
value: 25.696508172362552
---
# Whisper Small - Greek (el)
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the mozilla-foundation/common_voice_11_0 el dataset
for transcription in Greek.
It achieves the following results on the evaluation set:
- train_loss: 0.0615
- Wer: 20.2080
### Training results
Upon completion of training the best model was reloaded and tested with the following results extracted from the stdout log:
```
Loading best model from ./whisper-small-el/checkpoint-5000 (score: 20.208023774145616).
{'train_runtime': 73232.697,
'train_samples_per_second': 4.37,
'train_steps_per_second': 0.068,
'train_loss': 0.06146362095708027,
'epoch': 94.34}
TrainOutput(global_step=5000,
training_loss=0.06146362095708027,
metrics={'train_runtime': 73232.697,
'train_samples_per_second': 4.37,
'train_steps_per_second': 0.068,
'train_loss': 0.06146362095708027,
'epoch': 94.34})
```
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0
- Datasets 2.7.1.dev0
- Tokenizers 0.12.1
|
hayle/q-taxi-v3 | hayle | "2023-01-02T14:29:14Z" | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | "2023-01-02T14:29:10Z" | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="hayle/q-taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
nhunglaaaaaaa/eae1f432-c5b2-4b33-a9f5-e879b12f872e | nhunglaaaaaaa | "2025-01-21T21:38:02Z" | 6 | 0 | peft | [
"peft",
"safetensors",
"gptj",
"axolotl",
"generated_from_trainer",
"base_model:furiosa-ai/mlperf-gpt-j-6b",
"base_model:adapter:furiosa-ai/mlperf-gpt-j-6b",
"8-bit",
"bitsandbytes",
"region:us"
] | null | "2025-01-21T21:05:56Z" | ---
library_name: peft
base_model: furiosa-ai/mlperf-gpt-j-6b
tags:
- axolotl
- generated_from_trainer
model-index:
- name: eae1f432-c5b2-4b33-a9f5-e879b12f872e
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: furiosa-ai/mlperf-gpt-j-6b
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 2c39c8a0a2c661a8_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/2c39c8a0a2c661a8_train_data.json
type:
field_instruction: func_documentation_string
field_output: whole_func_string
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
gradient_clipping: 1.0
group_by_length: false
hub_model_id: nhunglaaaaaaa/eae1f432-c5b2-4b33-a9f5-e879b12f872e
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 5.0e-05
load_in_4bit: true
load_in_8bit: true
local_rank: null
logging_steps: 1
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_steps: 200
micro_batch_size: 2
mlflow_experiment_name: /tmp/2c39c8a0a2c661a8_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 1024
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 4c8097be-9dcb-4320-b04f-a3ba61ff1aef
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 4c8097be-9dcb-4320-b04f-a3ba61ff1aef
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# eae1f432-c5b2-4b33-a9f5-e879b12f872e
This model is a fine-tuned version of [furiosa-ai/mlperf-gpt-j-6b](https://huggingface.co/furiosa-ai/mlperf-gpt-j-6b) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6050
## 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: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 2.0334 | 0.1719 | 200 | 0.6050 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
mradermacher/Llama_3.2_1b_Synerka_0.0-i1-GGUF | mradermacher | "2025-02-23T18:49:56Z" | 0 | 0 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:Nexesenex/Llama_3.2_1b_Synerka_0.0",
"base_model:quantized:Nexesenex/Llama_3.2_1b_Synerka_0.0",
"endpoints_compatible",
"region:us",
"imatrix"
] | null | "2025-02-23T18:09:08Z" | ---
base_model: Nexesenex/Llama_3.2_1b_Synerka_0.0
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/Nexesenex/Llama_3.2_1b_Synerka_0.0
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/Llama_3.2_1b_Synerka_0.0-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/Llama_3.2_1b_Synerka_0.0-i1-GGUF/resolve/main/Llama_3.2_1b_Synerka_0.0.i1-IQ1_S.gguf) | i1-IQ1_S | 0.6 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Llama_3.2_1b_Synerka_0.0-i1-GGUF/resolve/main/Llama_3.2_1b_Synerka_0.0.i1-IQ1_M.gguf) | i1-IQ1_M | 0.6 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Llama_3.2_1b_Synerka_0.0-i1-GGUF/resolve/main/Llama_3.2_1b_Synerka_0.0.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 0.6 | |
| [GGUF](https://huggingface.co/mradermacher/Llama_3.2_1b_Synerka_0.0-i1-GGUF/resolve/main/Llama_3.2_1b_Synerka_0.0.i1-IQ2_XS.gguf) | i1-IQ2_XS | 0.7 | |
| [GGUF](https://huggingface.co/mradermacher/Llama_3.2_1b_Synerka_0.0-i1-GGUF/resolve/main/Llama_3.2_1b_Synerka_0.0.i1-IQ2_S.gguf) | i1-IQ2_S | 0.7 | |
| [GGUF](https://huggingface.co/mradermacher/Llama_3.2_1b_Synerka_0.0-i1-GGUF/resolve/main/Llama_3.2_1b_Synerka_0.0.i1-IQ2_M.gguf) | i1-IQ2_M | 0.7 | |
| [GGUF](https://huggingface.co/mradermacher/Llama_3.2_1b_Synerka_0.0-i1-GGUF/resolve/main/Llama_3.2_1b_Synerka_0.0.i1-Q2_K_S.gguf) | i1-Q2_K_S | 0.7 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/Llama_3.2_1b_Synerka_0.0-i1-GGUF/resolve/main/Llama_3.2_1b_Synerka_0.0.i1-Q2_K.gguf) | i1-Q2_K | 0.8 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Llama_3.2_1b_Synerka_0.0-i1-GGUF/resolve/main/Llama_3.2_1b_Synerka_0.0.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 0.8 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Llama_3.2_1b_Synerka_0.0-i1-GGUF/resolve/main/Llama_3.2_1b_Synerka_0.0.i1-IQ3_XS.gguf) | i1-IQ3_XS | 0.8 | |
| [GGUF](https://huggingface.co/mradermacher/Llama_3.2_1b_Synerka_0.0-i1-GGUF/resolve/main/Llama_3.2_1b_Synerka_0.0.i1-Q3_K_S.gguf) | i1-Q3_K_S | 0.9 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Llama_3.2_1b_Synerka_0.0-i1-GGUF/resolve/main/Llama_3.2_1b_Synerka_0.0.i1-IQ3_S.gguf) | i1-IQ3_S | 0.9 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Llama_3.2_1b_Synerka_0.0-i1-GGUF/resolve/main/Llama_3.2_1b_Synerka_0.0.i1-IQ3_M.gguf) | i1-IQ3_M | 0.9 | |
| [GGUF](https://huggingface.co/mradermacher/Llama_3.2_1b_Synerka_0.0-i1-GGUF/resolve/main/Llama_3.2_1b_Synerka_0.0.i1-Q3_K_M.gguf) | i1-Q3_K_M | 0.9 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Llama_3.2_1b_Synerka_0.0-i1-GGUF/resolve/main/Llama_3.2_1b_Synerka_0.0.i1-Q3_K_L.gguf) | i1-Q3_K_L | 0.9 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Llama_3.2_1b_Synerka_0.0-i1-GGUF/resolve/main/Llama_3.2_1b_Synerka_0.0.i1-IQ4_XS.gguf) | i1-IQ4_XS | 1.0 | |
| [GGUF](https://huggingface.co/mradermacher/Llama_3.2_1b_Synerka_0.0-i1-GGUF/resolve/main/Llama_3.2_1b_Synerka_0.0.i1-IQ4_NL.gguf) | i1-IQ4_NL | 1.0 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/Llama_3.2_1b_Synerka_0.0-i1-GGUF/resolve/main/Llama_3.2_1b_Synerka_0.0.i1-Q4_0.gguf) | i1-Q4_0 | 1.0 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Llama_3.2_1b_Synerka_0.0-i1-GGUF/resolve/main/Llama_3.2_1b_Synerka_0.0.i1-Q4_K_S.gguf) | i1-Q4_K_S | 1.0 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Llama_3.2_1b_Synerka_0.0-i1-GGUF/resolve/main/Llama_3.2_1b_Synerka_0.0.i1-Q4_K_M.gguf) | i1-Q4_K_M | 1.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Llama_3.2_1b_Synerka_0.0-i1-GGUF/resolve/main/Llama_3.2_1b_Synerka_0.0.i1-Q4_1.gguf) | i1-Q4_1 | 1.1 | |
| [GGUF](https://huggingface.co/mradermacher/Llama_3.2_1b_Synerka_0.0-i1-GGUF/resolve/main/Llama_3.2_1b_Synerka_0.0.i1-Q5_K_S.gguf) | i1-Q5_K_S | 1.2 | |
| [GGUF](https://huggingface.co/mradermacher/Llama_3.2_1b_Synerka_0.0-i1-GGUF/resolve/main/Llama_3.2_1b_Synerka_0.0.i1-Q5_K_M.gguf) | i1-Q5_K_M | 1.2 | |
| [GGUF](https://huggingface.co/mradermacher/Llama_3.2_1b_Synerka_0.0-i1-GGUF/resolve/main/Llama_3.2_1b_Synerka_0.0.i1-Q6_K.gguf) | i1-Q6_K | 1.3 | practically like static Q6_K |
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 -->
|
dimasik87/9cd18951-426b-4604-ba14-10033f1f0bc7 | dimasik87 | "2025-01-15T06:44:58Z" | 12 | 0 | peft | [
"peft",
"safetensors",
"phi3",
"axolotl",
"generated_from_trainer",
"custom_code",
"base_model:microsoft/Phi-3-mini-128k-instruct",
"base_model:adapter:microsoft/Phi-3-mini-128k-instruct",
"license:mit",
"region:us"
] | null | "2025-01-15T06:33:41Z" | ---
library_name: peft
license: mit
base_model: microsoft/Phi-3-mini-128k-instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 9cd18951-426b-4604-ba14-10033f1f0bc7
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: microsoft/Phi-3-mini-128k-instruct
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- a46d02fc217c0509_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/a46d02fc217c0509_train_data.json
type:
field_instruction: question
field_output: answer
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
device: cuda
early_stopping_patience: 1
eval_max_new_tokens: 128
eval_steps: 5
eval_table_size: null
evals_per_epoch: null
flash_attention: false
fp16: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
group_by_length: false
hub_model_id: dimasik87/9cd18951-426b-4604-ba14-10033f1f0bc7
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 3
lora_alpha: 32
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 16
lora_target_linear: true
lr_scheduler: cosine
max_memory:
0: 78GiB
max_steps: 30
micro_batch_size: 2
mlflow_experiment_name: /tmp/a46d02fc217c0509_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_torch
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 10
sequence_len: 1024
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: true
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: da5a59e4-b4e2-40cf-906c-59fc8d44af6a
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: da5a59e4-b4e2-40cf-906c-59fc8d44af6a
warmup_steps: 10
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# 9cd18951-426b-4604-ba14-10033f1f0bc7
This model is a fine-tuned version of [microsoft/Phi-3-mini-128k-instruct](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5579
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- 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: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0007 | 1 | 1.6184 |
| 6.6298 | 0.0034 | 5 | 1.5668 |
| 6.118 | 0.0068 | 10 | 1.3362 |
| 4.3914 | 0.0103 | 15 | 0.9551 |
| 3.5586 | 0.0137 | 20 | 0.6931 |
| 2.4686 | 0.0171 | 25 | 0.5773 |
| 2.7427 | 0.0205 | 30 | 0.5579 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
PrunaAI/seresnet152d.ra2_in1k-turbo-green-smashed | PrunaAI | "2024-11-13T13:19:20Z" | 1 | 0 | pruna-engine | [
"pruna-engine",
"region:us"
] | null | "2024-03-14T09:56:25Z" | ---
library_name: pruna-engine
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://docs.pruna.ai/en/latest/setup/pip.html" target="_blank" rel="noopener noreferrer">
<img src="https://imgur.com/rVAgqMY.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/rskEr4BZJx)
<div style="color: #9B1DBE; font-size: 2em; font-weight: bold;">
Deprecation Notice: This model is deprecated and will no longer receive updates.
</div>
<br><br>
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/rskEr4BZJx) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed by combining quantization, xformers, jit, cuda graphs, triton.
- ***How does the model quality change?*** The quality of the model output might slightly vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We used a custom Pruna model format based on pickle to make models compatible with the compression methods. We provide a tutorial to run models in dockers in the documentation [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) if needed.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
- ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.
## Setup
You can run the smashed model with these steps:
0. Check that you have linux, python 3.10, and cuda 12.1.0 requirements installed. For cuda, check with `nvcc --version` and install with `conda install nvidia/label/cuda-12.1.0::cuda`.
1. Install the `pruna-engine` available [here](https://pypi.org/project/pruna-engine/) on Pypi. It might take up to 15 minutes to install.
```bash
pip install pruna-engine[gpu]==0.7.1 --extra-index-url https://pypi.nvidia.com --extra-index-url https://pypi.ngc.nvidia.com --extra-index-url https://prunaai.pythonanywhere.com/
```
2. Download the model files using one of these three options.
- Option 1 - Use command line interface (CLI):
```bash
mkdir seresnet152d.ra2_in1k-turbo-green-smashed
huggingface-cli download PrunaAI/seresnet152d.ra2_in1k-turbo-green-smashed --local-dir seresnet152d.ra2_in1k-turbo-green-smashed --local-dir-use-symlinks False
```
- Option 2 - Use Python:
```python
import subprocess
repo_name = "seresnet152d.ra2_in1k-turbo-green-smashed"
subprocess.run(["mkdir", repo_name])
subprocess.run(["huggingface-cli", "download", 'PrunaAI/'+ repo_name, "--local-dir", repo_name, "--local-dir-use-symlinks", "False"])
```
- Option 3 - Download them manually on the HuggingFace model page.
3. Load & run the model.
```python
from pruna_engine.PrunaModel import PrunaModel
model_path = "seresnet152d.ra2_in1k-turbo-green-smashed/model" # Specify the downloaded model path.
smashed_model = PrunaModel.load_model(model_path) # Load the model.
import torch; image = torch.rand(1, 3, 224, 224).to('cuda')
smashed_model(image)
```
## Configurations
The configuration info are in `model/smash_config.json`.
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model seresnet152d.ra2_in1k before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). |
PrunaAI/GroNLP-gpt2-small-italian-bnb-4bit-smashed | PrunaAI | "2024-08-02T15:45:30Z" | 81 | 0 | transformers | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"pruna-ai",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] | text-generation | "2024-04-03T19:46:05Z" | ---
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
tags:
- pruna-ai
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer">
<img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/rskEr4BZJx)
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/rskEr4BZJx) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed with llm-int8.
- ***How does the model quality change?*** The quality of the model output might vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We use safetensors.
- ***What calibration data has been used?*** If needed by the compression method, we used WikiText as the calibration data.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
- ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.
## Setup
You can run the smashed model with these steps:
0. Check requirements from the original repo GroNLP/gpt2-small-italian installed. In particular, check python, cuda, and transformers versions.
1. Make sure that you have installed quantization related packages.
```bash
pip install transformers accelerate bitsandbytes>0.37.0
```
2. Load & run the model.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("PrunaAI/GroNLP-gpt2-small-italian-bnb-4bit-smashed",
trust_remote_code=True, device_map='auto')
tokenizer = AutoTokenizer.from_pretrained("GroNLP/gpt2-small-italian")
input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"]
outputs = model.generate(input_ids, max_new_tokens=216)
tokenizer.decode(outputs[0])
```
## Configurations
The configuration info are in `smash_config.json`.
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model GroNLP/gpt2-small-italian before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). |
cgus/Solar-10.7B-SLERP-exl2 | cgus | "2024-05-03T05:51:49Z" | 18 | 4 | transformers | [
"transformers",
"llama",
"text-generation",
"conversational",
"en",
"base_model:jan-hq/Solar-10.7B-SLERP",
"base_model:finetune:jan-hq/Solar-10.7B-SLERP",
"license:apache-2.0",
"autotrain_compatible",
"region:us"
] | text-generation | "2023-12-15T14:14:09Z" | ---
inference: false
license: apache-2.0
language:
- en
base_model:
- jan-hq/Solar-10.7B-SLERP
---
<!-- header start -->
<!-- 200823 -->
## Solar-10.7B-SLERP-EXL2
Model: [Solar-10.7B-SLERP](https://huggingface.co/jan-hq/Solar-10.7B-SLERP)
Made by: [jan-hq](https://huggingface.co/jan-hq)
Based on original model: [SOLAR-10.7B-Instruct-v1.0](https://huggingface.co/upstage/SOLAR-10.7B-Instruct-v1.0)
Created by: [upstage](https://huggingface.co/upstage)
## List of quants:
[4bpw h8 (main)](https://huggingface.co/cgus/Solar-10.7B-SLERP-exl2/tree/main)
[4.65bpw h8](https://huggingface.co/cgus/Solar-10.7B-SLERP-exl2/tree/4.65bpw-h8)
[5bpw h8](https://huggingface.co/cgus/Solar-10.7B-SLERP-exl2/tree/5bpw-h8)
[6bpw h8](https://huggingface.co/cgus/Solar-10.7B-SLERP-exl2/tree/6bpw-h8)
[8bpw h8](https://huggingface.co/cgus/Solar-10.7B-SLERP-exl2/tree/8bpw-h8)
## Quantization notes
In my experience, it's one of the best models within 13B range for Slavic languages.
And overall it has very good language skills. I haven't encountered cases when the model randomly switches language unlike most models I tried.
It also capable to understand instructions in other languages, a pretty rare feat.
30.01.2024 Replaced old quants with newer ones that were made with exllamav2-0.0.12, calibrated with default exllamav2 dataset.
This model uses a lot less memory compared to Llama2-13B models and it *almost* fits 12GB VRAM even at 8bpw.
But with 8-bit cache it even uses just a tiny bit less than 12GB VRAM.
## How to run
This quantization method uses GPU and requires Exllamav2 loader which can be found in following applications:
[Text Generation Webui](https://github.com/oobabooga/text-generation-webui)
[KoboldAI](https://github.com/henk717/KoboldAI)
[ExUI](https://github.com/turboderp/exui)
# Original model card:
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://github.com/janhq/jan/assets/89722390/35daac7d-b895-487c-a6ac-6663daaad78e" alt="Jan banner" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<p align="center">
<a href="https://jan.ai/">Jan</a
>
- <a href="https://discord.gg/AsJ8krTT3N">Discord</a>
</p>
<!-- header end -->
# Model Description
This model uses the `Slerp` merge method from the best models on 14th Dec on the [OpenLLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard):
1. [upstage/SOLAR-10.7B-Instruct-v1.0](https://huggingface.co/upstage/SOLAR-10.7B-Instruct-v1.0)
2. [janhq/Pandora-v1-10.7B](https://huggingface.co/janhq/Pandora-v1-10.7B)
- base model: [upstage/SOLAR-10.7B-Instruct-v1.0](https://huggingface.co/upstage/SOLAR-10.7B-Instruct-v1.0)
The yaml config file for this model is here:
```yaml
slices:
- sources:
- model: upstage/SOLAR-10.7B-Instruct-v1.0
layer_range: [0, 48]
- model: janhq/Pandora-v1-10.7B
layer_range: [0, 48]
merge_method: slerp
base_model: upstage/SOLAR-10.7B-Instruct-v1.0
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
```
# Prompt template
- **ChatML**
```
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
# Run this model
You can run this model using [Jan Desktop](https://jan.ai/) on Mac, Windows, or Linux.
Jan is an open source, ChatGPT alternative that is:
- 💻 **100% offline on your machine**: Your conversations remain confidential, and visible only to you.
- 🗂️ **An Open File Format**: Conversations and model settings stay on your computer and can be exported or deleted at any time.
- 🌐 **OpenAI Compatible**: Local server on port `1337` with OpenAI compatible endpoints
- 🌍 **Open Source & Free**: We build in public; check out our [Github](https://github.com/janhq)
# About Jan
Jan believes in the need for an open-source AI ecosystem and is building the infra and tooling to allow open-source AIs to compete on a level playing field with proprietary ones.
Jan's long-term vision is to build a cognitive framework for future robots, who are practical, useful assistants for humans and businesses in everyday life.
# Jan Model Merger
This is a test project for merging models.
# Open LLM Leaderboard Evaluation Results
Detailed results can be found here.
| Metric | Value |
|-----------------------|---------------------------|
| Avg. | ?|
| ARC (25-shot) | ? |
| HellaSwag (10-shot) | ? |
| MMLU (5-shot) | ?|
| TruthfulQA (0-shot) | ? |
| Winogrande (5-shot) | ? |
| GSM8K (5-shot) | ? |
# Acknowlegement
- [mergekit](https://github.com/cg123/mergekit)
- [DARE](https://github.com/yule-BUAA/MergeLM/blob/main/README.md)
- [SLERP](https://github.com/Digitous/LLM-SLERP-Merge)
- [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) |
KarthikAlagarsamy/distilbertfinetuneHS5E8BHLR | KarthikAlagarsamy | "2024-04-14T09:42:51Z" | 127 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"question-answering",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | question-answering | "2024-04-14T09:24:05Z" | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: distilbertfinetuneHS5E8BHLR
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. -->
# distilbertfinetuneHS5E8BHLR
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6401
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.0251 | 1.0 | 500 | 1.7268 |
| 1.4512 | 2.0 | 1000 | 1.4143 |
| 0.9326 | 3.0 | 1500 | 1.4345 |
| 0.6653 | 4.0 | 2000 | 1.5804 |
| 0.5143 | 5.0 | 2500 | 1.6401 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
mshojaei77/gemma-3-4b-persian-lora-adaptors | mshojaei77 | "2025-03-14T23:57:53Z" | 0 | 1 | transformers | [
"transformers",
"safetensors",
"gemma3_text",
"text-generation",
"text-generation-inference",
"unsloth",
"gemma3",
"conversational",
"en",
"base_model:unsloth/gemma-3-4b-it-unsloth-bnb-4bit",
"base_model:finetune:unsloth/gemma-3-4b-it-unsloth-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-03-14T22:46:27Z" | ---
base_model: unsloth/gemma-3-4b-it-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3
license: apache-2.0
language:
- en
---
|
Technoculture/MT7Bi-alpha | Technoculture | "2024-01-17T11:58:12Z" | 7 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:epfl-llm/meditron-7b",
"base_model:adapter:epfl-llm/meditron-7b",
"license:llama2",
"4-bit",
"bitsandbytes",
"region:us"
] | null | "2024-01-08T11:22:18Z" | ---
license: llama2
library_name: peft
tags:
- axolotl
- generated_from_trainer
base_model: epfl-llm/meditron-7b
model-index:
- name: md7b-alpha
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.3.0`
```yaml
base_model: epfl-llm/meditron-7b
model_type: LlamaForCausalLM
tokenizer_type: LlamaTokenizer
is_llama_derived_model: true
load_in_8bit: false
load_in_4bit: true
strict: false
datasets:
- path: Open-Orca/SlimOrca-Dedup
type: sharegpt
- path: axiong/pmc_llama_instructions
type: alpaca
- path: xzuyn/chatdoctor-200k-stripped
type: alpaca
- path: technoculture/riddle_sense
type: alpaca
dataset_prepared_path:
val_set_size: 0.05
output_dir: ./qlora-out
adapter: qlora
lora_model_dir:
sequence_len: 2048
sample_packing: true
pad_to_sequence_len: true
lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
lora_target_modules:
lora_target_linear: true
lora_fan_in_fan_out:
wandb_project: MD7b-alpha
wandb_entity: technoculture
wandb_watch:
wandb_name:
wandb_log_model: true
gradient_accumulation_steps: 4
micro_batch_size: 2
num_epochs: 4
optimizer: paged_adamw_32bit
lr_scheduler_type: cosine
lr_scheduler: cosine
learning_rate: 0.0003
train_on_inputs: false
group_by_length: false
bf16: true
fp16: false
tf32: false
do_eval: true
evals_per_epoch: 2
eval_table_size:
saves_per_epoch: 1
hub_model_id: technoculture/md7b-alpha
hub_strategy: every_save
push_to_hub: true
log_level: info
logging_steps: 1
logging_strategy: steps
gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint: false
local_rank:
xformers_attention:
flash_attention: true
warmup_steps: 2000
debug:
deepspeed:
weight_decay: 0.1
fsdp:
fsdp_config:
special_tokens:
bos_token: "<s>"
eos_token: "</s>"
unk_token: "<unk>"
```
</details><br>
# md7b-alpha
This model is a fine-tuned version of [epfl-llm/meditron-7b](https://huggingface.co/epfl-llm/meditron-7b) on a set of datasets.
It achieves the following results on the evaluation set:
- Loss: 1.0238
## Evaluation
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 2000
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:------:|:---------------:|
| 2.1602 | 0.0 | 1 | 1.9066 |
| 1.1128 | 0.5 | 14744 | 1.1620 |
| 1.2463 | 1.0 | 29488 | 1.1288 |
| 0.8291 | 1.49 | 44232 | 1.1025 |
| 1.0524 | 1.99 | 58976 | 1.0771 |
| 1.0369 | 2.48 | 73720 | 1.0563 |
| 1.0402 | 2.98 | 88464 | 1.0299 |
| 0.943 | 3.47 | 103208 | 1.0271 |
| 1.0845 | 3.97 | 117952 | 1.0238 |
### Framework versions
- Transformers 4.37.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16 |
Akshatha7011/my-pet-dog-xzg | Akshatha7011 | "2024-02-19T15:26:15Z" | 3 | 0 | diffusers | [
"diffusers",
"safetensors",
"NxtWave-GenAI-Webinar",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | "2024-02-19T15:21:49Z" | ---
license: creativeml-openrail-m
tags:
- NxtWave-GenAI-Webinar
- text-to-image
- stable-diffusion
---
### My-Pet-Dog-XZG Dreambooth model trained by Akshatha7011 following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: 4NM21AI009
Sample pictures of this concept:
.jfif)
|
EnlightenedAI/APPSI-139 | EnlightenedAI | "2024-06-13T11:30:38Z" | 0 | 0 | null | [
"license:cc-by-nc-4.0",
"region:us"
] | null | "2024-06-13T06:00:40Z" | ---
license: cc-by-nc-4.0
---
|
John6666/gusgenm-pony-sp1-sdxl | John6666 | "2024-07-30T02:19:14Z" | 12 | 1 | diffusers | [
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"stable-diffusion-xl",
"anime",
"pony",
"en",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] | text-to-image | "2024-07-30T02:12:02Z" | ---
license: other
license_name: faipl-1.0-sd
license_link: https://freedevproject.org/faipl-1.0-sd/
language:
- en
library_name: diffusers
pipeline_tag: text-to-image
tags:
- text-to-image
- stable-diffusion
- stable-diffusion-xl
- anime
- pony
---
Original model is [here](https://civitai.com/models/594230?modelVersionId=682103).
|
kaisar-barlybay-sse/kaz_legal_distilbert_full_corpus_10.0_16 | kaisar-barlybay-sse | "2023-05-02T03:25:19Z" | 114 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"multiple-choice",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | multiple-choice | "2023-05-02T03:12:02Z" | ---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: kaz_legal_distilbert_full_corpus_10.0_16
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. -->
# kaz_legal_distilbert_full_corpus_10.0_16
This model is a fine-tuned version of [kaisar-barlybay-sse/kaz_legal_distilbert_full_corpus_10.0](https://huggingface.co/kaisar-barlybay-sse/kaz_legal_distilbert_full_corpus_10.0) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 4.0806
- Accuracy: 0.4351
## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 16
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.4013 | 1.0 | 501 | 1.3849 | 0.3473 |
| 1.3954 | 2.0 | 1002 | 1.3853 | 0.3234 |
| 1.3874 | 3.0 | 1503 | 1.3864 | 0.2216 |
| 1.3995 | 4.0 | 2004 | 1.3863 | 0.3513 |
| 1.3944 | 5.0 | 2505 | 1.3862 | 0.3473 |
| 1.3829 | 6.0 | 3006 | 1.3858 | 0.3713 |
| 1.2411 | 7.0 | 3507 | 1.4126 | 0.3972 |
| 0.9506 | 8.0 | 4008 | 1.7895 | 0.3992 |
| 0.7435 | 9.0 | 4509 | 1.5649 | 0.4212 |
| 0.5988 | 10.0 | 5010 | 2.6088 | 0.4371 |
| 0.4972 | 11.0 | 5511 | 2.8238 | 0.4371 |
| 0.4111 | 12.0 | 6012 | 3.2902 | 0.4431 |
| 0.3471 | 13.0 | 6513 | 3.5568 | 0.4212 |
| 0.3065 | 14.0 | 7014 | 3.9031 | 0.4351 |
| 0.3015 | 15.0 | 7515 | 4.0874 | 0.4311 |
| 0.2873 | 16.0 | 8016 | 4.0806 | 0.4351 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Rasul7718/rasul_flux | Rasul7718 | "2025-04-10T14:41:21Z" | 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 | "2025-04-10T14:20:40Z" | <!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> |
davidschulte/ESM_climate_fever_default | davidschulte | "2025-03-26T13:33:25Z" | 17 | 0 | null | [
"safetensors",
"embedding_space_map",
"BaseLM:bert-base-multilingual-uncased",
"dataset:tdiggelm/climate_fever",
"base_model:google-bert/bert-base-multilingual-uncased",
"base_model:finetune:google-bert/bert-base-multilingual-uncased",
"license:apache-2.0",
"region:us"
] | null | "2024-11-10T14:01:54Z" | ---
base_model: bert-base-multilingual-uncased
datasets:
- tdiggelm/climate_fever
license: apache-2.0
tags:
- embedding_space_map
- BaseLM:bert-base-multilingual-uncased
---
# ESM tdiggelm/climate_fever
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
ESM
- **Developed by:** David Schulte
- **Model type:** ESM
- **Base Model:** bert-base-multilingual-uncased
- **Intermediate Task:** tdiggelm/climate_fever
- **ESM architecture:** linear
- **ESM embedding dimension:** 768
- **Language(s) (NLP):** [More Information Needed]
- **License:** Apache-2.0 license
- **ESM version:** 0.1.0
## Training Details
### Intermediate Task
- **Task ID:** tdiggelm/climate_fever
- **Subset [optional]:** default
- **Text Column:** claim
- **Label Column:** claim_label
- **Dataset Split:** test
- **Sample size [optional]:** 1535
- **Sample seed [optional]:**
### Training Procedure [optional]
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Language Model Training Hyperparameters [optional]
- **Epochs:** 3
- **Batch size:** 32
- **Learning rate:** 2e-05
- **Weight Decay:** 0.01
- **Optimizer**: AdamW
### ESM Training Hyperparameters [optional]
- **Epochs:** 10
- **Batch size:** 32
- **Learning rate:** 0.001
- **Weight Decay:** 0.01
- **Optimizer**: AdamW
### Additional trainiung details [optional]
## Model evaluation
### Evaluation of fine-tuned language model [optional]
### Evaluation of ESM [optional]
MSE:
### Additional evaluation details [optional]
## What are Embedding Space Maps used for?
Embedding Space Maps are a part of ESM-LogME, a efficient method for finding intermediate datasets for transfer learning. There are two reasons to use ESM-LogME:
### You don't have enough training data for your problem
If you don't have a enough training data for your problem, just use ESM-LogME to find more.
You can supplement model training by including publicly available datasets in the training process.
1. Fine-tune a language model on suitable intermediate dataset.
2. Fine-tune the resulting model on your target dataset.
This workflow is called intermediate task transfer learning and it can significantly improve the target performance.
But what is a suitable dataset for your problem? ESM-LogME enable you to quickly rank thousands of datasets on the Hugging Face Hub by how well they are exptected to transfer to your target task.
### You want to find similar datasets to your target dataset
Using ESM-LogME can be used like search engine on the Hugging Face Hub. You can find similar tasks to your target task without having to rely on heuristics. ESM-LogME estimates how language models fine-tuned on each intermediate task would benefinit your target task. This quantitative approach combines the effects of domain similarity and task similarity.
## How can I use ESM-LogME / ESMs?
[](https://pypi.org/project/hf-dataset-selector)
We release **hf-dataset-selector**, a Python package for intermediate task selection using Embedding Space Maps.
**hf-dataset-selector** fetches ESMs for a given language model and uses it to find the best dataset for applying intermediate training to the target task. ESMs are found by their tags on the Huggingface Hub.
```python
from hfselect import Dataset, compute_task_ranking
# Load target dataset from the Hugging Face Hub
dataset = Dataset.from_hugging_face(
name="stanfordnlp/imdb",
split="train",
text_col="text",
label_col="label",
is_regression=False,
num_examples=1000,
seed=42
)
# Fetch ESMs and rank tasks
task_ranking = compute_task_ranking(
dataset=dataset,
model_name="bert-base-multilingual-uncased"
)
# Display top 5 recommendations
print(task_ranking[:5])
```
```python
1. davanstrien/test_imdb_embedd2 Score: -0.618529
2. davanstrien/test_imdb_embedd Score: -0.618644
3. davanstrien/test1 Score: -0.619334
4. stanfordnlp/imdb Score: -0.619454
5. stanfordnlp/sst Score: -0.62995
```
| Rank | Task ID | Task Subset | Text Column | Label Column | Task Split | Num Examples | ESM Architecture | Score |
|-------:|:------------------------------|:----------------|:--------------|:---------------|:-------------|---------------:|:-------------------|----------:|
| 1 | davanstrien/test_imdb_embedd2 | default | text | label | train | 10000 | linear | -0.618529 |
| 2 | davanstrien/test_imdb_embedd | default | text | label | train | 10000 | linear | -0.618644 |
| 3 | davanstrien/test1 | default | text | label | train | 10000 | linear | -0.619334 |
| 4 | stanfordnlp/imdb | plain_text | text | label | train | 10000 | linear | -0.619454 |
| 5 | stanfordnlp/sst | dictionary | phrase | label | dictionary | 10000 | linear | -0.62995 |
| 6 | stanfordnlp/sst | default | sentence | label | train | 8544 | linear | -0.63312 |
| 7 | kuroneko5943/snap21 | CDs_and_Vinyl_5 | sentence | label | train | 6974 | linear | -0.634365 |
| 8 | kuroneko5943/snap21 | Video_Games_5 | sentence | label | train | 6997 | linear | -0.638787 |
| 9 | kuroneko5943/snap21 | Movies_and_TV_5 | sentence | label | train | 6989 | linear | -0.639068 |
| 10 | fancyzhx/amazon_polarity | amazon_polarity | content | label | train | 10000 | linear | -0.639718 |
For more information on how to use ESMs please have a look at the [official Github repository](https://github.com/davidschulte/hf-dataset-selector). We provide documentation further documentation and tutorials for finding intermediate datasets and training your own ESMs.
## How do Embedding Space Maps work?
<!-- This section describes the evaluation protocols and provides the results. -->
Embedding Space Maps (ESMs) are neural networks that approximate the effect of fine-tuning a language model on a task. They can be used to quickly transform embeddings from a base model to approximate how a fine-tuned model would embed the the input text.
ESMs can be used for intermediate task selection with the ESM-LogME workflow.
## How can I use Embedding Space Maps for Intermediate Task Selection?
## Citation
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
If you are using this Embedding Space Maps, please cite our [paper](https://aclanthology.org/2024.emnlp-main.529/).
**BibTeX:**
```
@inproceedings{schulte-etal-2024-less,
title = "Less is More: Parameter-Efficient Selection of Intermediate Tasks for Transfer Learning",
author = "Schulte, David and
Hamborg, Felix and
Akbik, Alan",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.emnlp-main.529/",
doi = "10.18653/v1/2024.emnlp-main.529",
pages = "9431--9442",
abstract = "Intermediate task transfer learning can greatly improve model performance. If, for example, one has little training data for emotion detection, first fine-tuning a language model on a sentiment classification dataset may improve performance strongly. But which task to choose for transfer learning? Prior methods producing useful task rankings are infeasible for large source pools, as they require forward passes through all source language models. We overcome this by introducing Embedding Space Maps (ESMs), light-weight neural networks that approximate the effect of fine-tuning a language model. We conduct the largest study on NLP task transferability and task selection with 12k source-target pairs. We find that applying ESMs on a prior method reduces execution time and disk space usage by factors of 10 and 278, respectively, while retaining high selection performance (avg. regret@5 score of 2.95)."
}
```
**APA:**
```
Schulte, D., Hamborg, F., & Akbik, A. (2024, November). Less is More: Parameter-Efficient Selection of Intermediate Tasks for Transfer Learning. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing (pp. 9431-9442).
```
## Additional Information
|
RichardErkhov/Qwen_-_Qwen2.5-7B-Instruct-1M-4bits | RichardErkhov | "2025-03-27T12:10:11Z" | 0 | 0 | null | [
"safetensors",
"qwen2",
"arxiv:2501.15383",
"4-bit",
"bitsandbytes",
"region:us"
] | null | "2025-03-27T12:05:35Z" | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
Qwen2.5-7B-Instruct-1M - bnb 4bits
- Model creator: https://huggingface.co/Qwen/
- Original model: https://huggingface.co/Qwen/Qwen2.5-7B-Instruct-1M/
Original model description:
---
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen2.5-7B-Instruct-1M/blob/main/LICENSE
language:
- en
pipeline_tag: text-generation
base_model: Qwen/Qwen2.5-7B
tags:
- chat
library_name: transformers
---
# Qwen2.5-7B-Instruct-1M
<a href="https://chat.qwenlm.ai/" target="_blank" style="margin: 2px;">
<img alt="Chat" src="https://img.shields.io/badge/%F0%9F%92%9C%EF%B8%8F%20Qwen%20Chat%20-536af5" style="display: inline-block; vertical-align: middle;"/>
</a>
## Introduction
Qwen2.5-1M is the long-context version of the Qwen2.5 series models, supporting a context length of up to 1M tokens. Compared to the Qwen2.5 128K version, Qwen2.5-1M demonstrates significantly improved performance in handling long-context tasks while maintaining its capability in short tasks.
The model has the following features:
- Type: Causal Language Models
- Training Stage: Pretraining & Post-training
- Architecture: transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias
- Number of Parameters: 7.61B
- Number of Paramaters (Non-Embedding): 6.53B
- Number of Layers: 28
- Number of Attention Heads (GQA): 28 for Q and 4 for KV
- Context Length: Full 1,010,000 tokens and generation 8192 tokens
- We recommend deploying with our custom vLLM, which introduces sparse attention and length extrapolation methods to ensure efficiency and accuracy for long-context tasks. For specific guidance, refer to [this section](#processing-ultra-long-texts).
- You can also use the previous framework that supports Qwen2.5 for inference, but accuracy degradation may occur for sequences exceeding 262,144 tokens.
For more details, please refer to our [blog](https://qwenlm.github.io/blog/qwen2.5-1m/), [GitHub](https://github.com/QwenLM/Qwen2.5), [Technical Report](https://huggingface.co/papers/2501.15383), and [Documentation](https://qwen.readthedocs.io/en/latest/).
## Requirements
The code of Qwen2.5 has been in the latest Hugging face `transformers` and we advise you to use the latest version of `transformers`.
With `transformers<4.37.0`, you will encounter the following error:
```
KeyError: 'qwen2'
```
## Quickstart
Here provides a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "Qwen/Qwen2.5-7B-Instruct-1M"
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)
prompt = "Give me a short introduction to large language model."
messages = [
{"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."},
{"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=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
```
### Processing Ultra Long Texts
To enhance processing accuracy and efficiency for long sequences, we have developed an advanced inference framework based on vLLM, incorporating sparse attention and length extrapolation. This approach significantly improves model generation performance for sequences exceeding 256K tokens and achieves a 3 to 7 times speedup for sequences up to 1M tokens.
Here we provide step-by-step instructions for deploying the Qwen2.5-1M models with our framework.
#### 1. System Preparation
To achieve the best performance, we recommend using GPUs with Ampere or Hopper architecture, which support optimized kernels.
Ensure your system meets the following requirements:
- **CUDA Version**: 12.1 or 12.3
- **Python Version**: >=3.9 and <=3.12
**VRAM Requirements:**
- For processing 1 million-token sequences:
- **Qwen2.5-7B-Instruct-1M**: At least 120GB VRAM (total across GPUs).
- **Qwen2.5-14B-Instruct-1M**: At least 320GB VRAM (total across GPUs).
If your GPUs do not have sufficient VRAM, you can still use Qwen2.5-1M for shorter tasks.
#### 2. Install Dependencies
For now, you need to clone the vLLM repository from our custom branch and install it manually. We are working on getting our branch merged into the main vLLM project.
```bash
git clone -b dev/dual-chunk-attn [email protected]:QwenLM/vllm.git
cd vllm
pip install -e . -v
```
#### 3. Launch vLLM
vLLM supports offline inference or launch an openai-like server.
**Example of Offline Inference**
```python
from transformers import AutoTokenizer
from vllm import LLM, SamplingParams
# Initialize the tokenizer
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-7B-Instruct-1M")
# Pass the default decoding hyperparameters of Qwen2.5-7B-Instruct
# max_tokens is for the maximum length for generation.
sampling_params = SamplingParams(temperature=0.7, top_p=0.8, repetition_penalty=1.05, max_tokens=512)
# Input the model name or path. See below for parameter explanation (after the example of openai-like server).
llm = LLM(model="Qwen/Qwen2.5-7B-Instruct-1M",
tensor_parallel_size=4,
max_model_len=1010000,
enable_chunked_prefill=True,
max_num_batched_tokens=131072,
enforce_eager=True,
# quantization="fp8", # Enabling FP8 quantization for model weights can reduce memory usage.
)
# Prepare your prompts
prompt = "Tell me something about large language models."
messages = [
{"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."},
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
# generate outputs
outputs = llm.generate([text], sampling_params)
# Print the outputs.
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
```
**Example of Openai-like Server**
```bash
vllm serve Qwen/Qwen2.5-7B-Instruct-1M \
--tensor-parallel-size 4 \
--max-model-len 1010000 \
--enable-chunked-prefill --max-num-batched-tokens 131072 \
--enforce-eager \
--max-num-seqs 1
# --quantization fp8 # Enabling FP8 quantization for model weights can reduce memory usage.
```
Then you can use curl or python to interact with the deployed model.
**Parameter Explanations:**
- **`--tensor-parallel-size`**
- Set to the number of GPUs you are using. Max 4 GPUs for the 7B model, and 8 GPUs for the 14B model.
- **`--max-model-len`**
- Defines the maximum input sequence length. Reduce this value if you encounter Out of Memory issues.
- **`--max-num-batched-tokens`**
- Sets the chunk size in Chunked Prefill. A smaller value reduces activation memory usage but may slow down inference.
- Recommend 131072 for optimal performance.
- **`--max-num-seqs`**
- Limits concurrent sequences processed.
You can also refer to our [Documentation](https://qwen.readthedocs.io/en/latest/deployment/vllm.html) for usage of vLLM.
#### Troubleshooting:
1. Encountering the error: "The model's max sequence length (xxxxx) is larger than the maximum number of tokens that can be stored in the KV cache."
The VRAM reserved for the KV cache is insufficient. Consider reducing the ``max_model_len`` or increasing the ``tensor_parallel_size``. Alternatively, you can reduce ``max_num_batched_tokens``, although this may significantly slow down inference.
2. Encountering the error: "torch.OutOfMemoryError: CUDA out of memory."
The VRAM reserved for activation weights is insufficient. You can try setting ``gpu_memory_utilization`` to 0.85 or lower, but be aware that this might reduce the VRAM available for the KV cache.
3. Encountering the error: "Input prompt (xxxxx tokens) + lookahead slots (0) is too long and exceeds the capacity of the block manager."
The input is too lengthy. Consider using a shorter sequence or increasing the ``max_model_len``.
## Evaluation & Performance
Detailed evaluation results are reported in this [📑 blog](https://qwenlm.github.io/blog/qwen2.5-1m/) and our [technical report](https://arxiv.org/abs/2501.15383).
## Citation
If you find our work helpful, feel free to give us a cite.
```
@misc{qwen2.5-1m,
title = {Qwen2.5-1M: Deploy Your Own Qwen with Context Length up to 1M Tokens},
url = {https://qwenlm.github.io/blog/qwen2.5-1m/},
author = {Qwen Team},
month = {January},
year = {2025}
}
@article{qwen2.5,
title={Qwen2.5-1M Technical Report},
author={An Yang and Bowen Yu and Chengyuan Li and Dayiheng Liu and Fei Huang and Haoyan Huang and Jiandong Jiang and Jianhong Tu and Jianwei Zhang and Jingren Zhou and Junyang Lin and Kai Dang and Kexin Yang and Le Yu and Mei Li and Minmin Sun and Qin Zhu and Rui Men and Tao He and Weijia Xu and Wenbiao Yin and Wenyuan Yu and Xiafei Qiu and Xingzhang Ren and Xinlong Yang and Yong Li and Zhiying Xu and Zipeng Zhang},
journal={arXiv preprint arXiv:2501.15383},
year={2025}
}
```
|
Nextcloud-AI/multilingual-e5-large-instruct | Nextcloud-AI | "2024-09-26T06:33:15Z" | 711 | 5 | sentence-transformers | [
"sentence-transformers",
"onnx",
"safetensors",
"xlm-roberta",
"feature-extraction",
"mteb",
"transformers",
"multilingual",
"af",
"am",
"ar",
"as",
"az",
"be",
"bg",
"bn",
"br",
"bs",
"ca",
"cs",
"cy",
"da",
"de",
"el",
"en",
"eo",
"es",
"et",
"eu",
"fa",
"fi",
"fr",
"fy",
"ga",
"gd",
"gl",
"gu",
"ha",
"he",
"hi",
"hr",
"hu",
"hy",
"id",
"is",
"it",
"ja",
"jv",
"ka",
"kk",
"km",
"kn",
"ko",
"ku",
"ky",
"la",
"lo",
"lt",
"lv",
"mg",
"mk",
"ml",
"mn",
"mr",
"ms",
"my",
"ne",
"nl",
"no",
"om",
"or",
"pa",
"pl",
"ps",
"pt",
"ro",
"ru",
"sa",
"sd",
"si",
"sk",
"sl",
"so",
"sq",
"sr",
"su",
"sv",
"sw",
"ta",
"te",
"th",
"tl",
"tr",
"ug",
"uk",
"ur",
"uz",
"vi",
"xh",
"yi",
"zh",
"arxiv:2402.05672",
"arxiv:2401.00368",
"arxiv:2104.08663",
"arxiv:2210.07316",
"license:mit",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | feature-extraction | "2024-09-27T06:44:12Z" | ---
tags:
- mteb
- sentence-transformers
- transformers
model-index:
- name: multilingual-e5-large-instruct
results:
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (en)
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 76.23880597014924
- type: ap
value: 39.07351965022687
- type: f1
value: 70.04836733862683
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (de)
config: de
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 66.71306209850107
- type: ap
value: 79.01499914759529
- type: f1
value: 64.81951817560703
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (en-ext)
config: en-ext
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 73.85307346326837
- type: ap
value: 22.447519885878737
- type: f1
value: 61.0162730745633
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (ja)
config: ja
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 76.04925053533191
- type: ap
value: 23.44983217128922
- type: f1
value: 62.5723230907759
- task:
type: Classification
dataset:
type: mteb/amazon_polarity
name: MTEB AmazonPolarityClassification
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 96.28742500000001
- type: ap
value: 94.8449918887462
- type: f1
value: 96.28680923610432
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (en)
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 56.716
- type: f1
value: 55.76510398266401
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (de)
config: de
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 52.99999999999999
- type: f1
value: 52.00829994765178
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (es)
config: es
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 48.806000000000004
- type: f1
value: 48.082345914983634
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (fr)
config: fr
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 48.507999999999996
- type: f1
value: 47.68752844642045
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (ja)
config: ja
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 47.709999999999994
- type: f1
value: 47.05870376637181
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (zh)
config: zh
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 44.662000000000006
- type: f1
value: 43.42371965372771
- task:
type: Retrieval
dataset:
type: arguana
name: MTEB ArguAna
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 31.721
- type: map_at_10
value: 49.221
- type: map_at_100
value: 49.884
- type: map_at_1000
value: 49.888
- type: map_at_3
value: 44.31
- type: map_at_5
value: 47.276
- type: mrr_at_1
value: 32.432
- type: mrr_at_10
value: 49.5
- type: mrr_at_100
value: 50.163000000000004
- type: mrr_at_1000
value: 50.166
- type: mrr_at_3
value: 44.618
- type: mrr_at_5
value: 47.541
- type: ndcg_at_1
value: 31.721
- type: ndcg_at_10
value: 58.384
- type: ndcg_at_100
value: 61.111000000000004
- type: ndcg_at_1000
value: 61.187999999999995
- type: ndcg_at_3
value: 48.386
- type: ndcg_at_5
value: 53.708999999999996
- type: precision_at_1
value: 31.721
- type: precision_at_10
value: 8.741
- type: precision_at_100
value: 0.991
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 20.057
- type: precision_at_5
value: 14.609
- type: recall_at_1
value: 31.721
- type: recall_at_10
value: 87.411
- type: recall_at_100
value: 99.075
- type: recall_at_1000
value: 99.644
- type: recall_at_3
value: 60.171
- type: recall_at_5
value: 73.044
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-p2p
name: MTEB ArxivClusteringP2P
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 46.40419580759799
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-s2s
name: MTEB ArxivClusteringS2S
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 40.48593255007969
- task:
type: Reranking
dataset:
type: mteb/askubuntudupquestions-reranking
name: MTEB AskUbuntuDupQuestions
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 63.889179122289995
- type: mrr
value: 77.61146286769556
- task:
type: STS
dataset:
type: mteb/biosses-sts
name: MTEB BIOSSES
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 88.15075203727929
- type: cos_sim_spearman
value: 86.9622224570873
- type: euclidean_pearson
value: 86.70473853624121
- type: euclidean_spearman
value: 86.9622224570873
- type: manhattan_pearson
value: 86.21089380980065
- type: manhattan_spearman
value: 86.75318154937008
- task:
type: BitextMining
dataset:
type: mteb/bucc-bitext-mining
name: MTEB BUCC (de-en)
config: de-en
split: test
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:
- type: accuracy
value: 99.65553235908142
- type: f1
value: 99.60681976339595
- type: precision
value: 99.58246346555325
- type: recall
value: 99.65553235908142
- task:
type: BitextMining
dataset:
type: mteb/bucc-bitext-mining
name: MTEB BUCC (fr-en)
config: fr-en
split: test
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:
- type: accuracy
value: 99.26260180497468
- type: f1
value: 99.14520507740848
- type: precision
value: 99.08650671362535
- type: recall
value: 99.26260180497468
- task:
type: BitextMining
dataset:
type: mteb/bucc-bitext-mining
name: MTEB BUCC (ru-en)
config: ru-en
split: test
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:
- type: accuracy
value: 98.07412538967787
- type: f1
value: 97.86629719431936
- type: precision
value: 97.76238309664012
- type: recall
value: 98.07412538967787
- task:
type: BitextMining
dataset:
type: mteb/bucc-bitext-mining
name: MTEB BUCC (zh-en)
config: zh-en
split: test
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:
- type: accuracy
value: 99.42074776197998
- type: f1
value: 99.38564156573635
- type: precision
value: 99.36808846761454
- type: recall
value: 99.42074776197998
- task:
type: Classification
dataset:
type: mteb/banking77
name: MTEB Banking77Classification
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 85.73376623376623
- type: f1
value: 85.68480707214599
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-p2p
name: MTEB BiorxivClusteringP2P
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 40.935218072113855
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-s2s
name: MTEB BiorxivClusteringS2S
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 36.276389017675264
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 27.764166666666668
- type: map_at_10
value: 37.298166666666674
- type: map_at_100
value: 38.530166666666666
- type: map_at_1000
value: 38.64416666666667
- type: map_at_3
value: 34.484833333333334
- type: map_at_5
value: 36.0385
- type: mrr_at_1
value: 32.93558333333333
- type: mrr_at_10
value: 41.589749999999995
- type: mrr_at_100
value: 42.425333333333334
- type: mrr_at_1000
value: 42.476333333333336
- type: mrr_at_3
value: 39.26825
- type: mrr_at_5
value: 40.567083333333336
- type: ndcg_at_1
value: 32.93558333333333
- type: ndcg_at_10
value: 42.706583333333334
- type: ndcg_at_100
value: 47.82483333333333
- type: ndcg_at_1000
value: 49.95733333333334
- type: ndcg_at_3
value: 38.064750000000004
- type: ndcg_at_5
value: 40.18158333333333
- type: precision_at_1
value: 32.93558333333333
- type: precision_at_10
value: 7.459833333333334
- type: precision_at_100
value: 1.1830833333333335
- type: precision_at_1000
value: 0.15608333333333332
- type: precision_at_3
value: 17.5235
- type: precision_at_5
value: 12.349833333333333
- type: recall_at_1
value: 27.764166666666668
- type: recall_at_10
value: 54.31775
- type: recall_at_100
value: 76.74350000000001
- type: recall_at_1000
value: 91.45208333333332
- type: recall_at_3
value: 41.23425
- type: recall_at_5
value: 46.73983333333334
- task:
type: Retrieval
dataset:
type: climate-fever
name: MTEB ClimateFEVER
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 12.969
- type: map_at_10
value: 21.584999999999997
- type: map_at_100
value: 23.3
- type: map_at_1000
value: 23.5
- type: map_at_3
value: 18.218999999999998
- type: map_at_5
value: 19.983
- type: mrr_at_1
value: 29.316
- type: mrr_at_10
value: 40.033
- type: mrr_at_100
value: 40.96
- type: mrr_at_1000
value: 41.001
- type: mrr_at_3
value: 37.123
- type: mrr_at_5
value: 38.757999999999996
- type: ndcg_at_1
value: 29.316
- type: ndcg_at_10
value: 29.858
- type: ndcg_at_100
value: 36.756
- type: ndcg_at_1000
value: 40.245999999999995
- type: ndcg_at_3
value: 24.822
- type: ndcg_at_5
value: 26.565
- type: precision_at_1
value: 29.316
- type: precision_at_10
value: 9.186
- type: precision_at_100
value: 1.6549999999999998
- type: precision_at_1000
value: 0.22999999999999998
- type: precision_at_3
value: 18.436
- type: precision_at_5
value: 13.876
- type: recall_at_1
value: 12.969
- type: recall_at_10
value: 35.142
- type: recall_at_100
value: 59.143
- type: recall_at_1000
value: 78.594
- type: recall_at_3
value: 22.604
- type: recall_at_5
value: 27.883000000000003
- task:
type: Retrieval
dataset:
type: dbpedia-entity
name: MTEB DBPedia
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 8.527999999999999
- type: map_at_10
value: 17.974999999999998
- type: map_at_100
value: 25.665
- type: map_at_1000
value: 27.406000000000002
- type: map_at_3
value: 13.017999999999999
- type: map_at_5
value: 15.137
- type: mrr_at_1
value: 62.5
- type: mrr_at_10
value: 71.891
- type: mrr_at_100
value: 72.294
- type: mrr_at_1000
value: 72.296
- type: mrr_at_3
value: 69.958
- type: mrr_at_5
value: 71.121
- type: ndcg_at_1
value: 50.875
- type: ndcg_at_10
value: 38.36
- type: ndcg_at_100
value: 44.235
- type: ndcg_at_1000
value: 52.154
- type: ndcg_at_3
value: 43.008
- type: ndcg_at_5
value: 40.083999999999996
- type: precision_at_1
value: 62.5
- type: precision_at_10
value: 30.0
- type: precision_at_100
value: 10.038
- type: precision_at_1000
value: 2.0869999999999997
- type: precision_at_3
value: 46.833000000000006
- type: precision_at_5
value: 38.800000000000004
- type: recall_at_1
value: 8.527999999999999
- type: recall_at_10
value: 23.828
- type: recall_at_100
value: 52.322
- type: recall_at_1000
value: 77.143
- type: recall_at_3
value: 14.136000000000001
- type: recall_at_5
value: 17.761
- task:
type: Classification
dataset:
type: mteb/emotion
name: MTEB EmotionClassification
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 51.51
- type: f1
value: 47.632159862049896
- task:
type: Retrieval
dataset:
type: fever
name: MTEB FEVER
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 60.734
- type: map_at_10
value: 72.442
- type: map_at_100
value: 72.735
- type: map_at_1000
value: 72.75
- type: map_at_3
value: 70.41199999999999
- type: map_at_5
value: 71.80499999999999
- type: mrr_at_1
value: 65.212
- type: mrr_at_10
value: 76.613
- type: mrr_at_100
value: 76.79899999999999
- type: mrr_at_1000
value: 76.801
- type: mrr_at_3
value: 74.8
- type: mrr_at_5
value: 76.12400000000001
- type: ndcg_at_1
value: 65.212
- type: ndcg_at_10
value: 77.988
- type: ndcg_at_100
value: 79.167
- type: ndcg_at_1000
value: 79.452
- type: ndcg_at_3
value: 74.362
- type: ndcg_at_5
value: 76.666
- type: precision_at_1
value: 65.212
- type: precision_at_10
value: 10.003
- type: precision_at_100
value: 1.077
- type: precision_at_1000
value: 0.11199999999999999
- type: precision_at_3
value: 29.518
- type: precision_at_5
value: 19.016
- type: recall_at_1
value: 60.734
- type: recall_at_10
value: 90.824
- type: recall_at_100
value: 95.71600000000001
- type: recall_at_1000
value: 97.577
- type: recall_at_3
value: 81.243
- type: recall_at_5
value: 86.90299999999999
- task:
type: Retrieval
dataset:
type: fiqa
name: MTEB FiQA2018
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 23.845
- type: map_at_10
value: 39.281
- type: map_at_100
value: 41.422
- type: map_at_1000
value: 41.593
- type: map_at_3
value: 34.467
- type: map_at_5
value: 37.017
- type: mrr_at_1
value: 47.531
- type: mrr_at_10
value: 56.204
- type: mrr_at_100
value: 56.928999999999995
- type: mrr_at_1000
value: 56.962999999999994
- type: mrr_at_3
value: 54.115
- type: mrr_at_5
value: 55.373000000000005
- type: ndcg_at_1
value: 47.531
- type: ndcg_at_10
value: 47.711999999999996
- type: ndcg_at_100
value: 54.510999999999996
- type: ndcg_at_1000
value: 57.103
- type: ndcg_at_3
value: 44.145
- type: ndcg_at_5
value: 45.032
- type: precision_at_1
value: 47.531
- type: precision_at_10
value: 13.194
- type: precision_at_100
value: 2.045
- type: precision_at_1000
value: 0.249
- type: precision_at_3
value: 29.424
- type: precision_at_5
value: 21.451
- type: recall_at_1
value: 23.845
- type: recall_at_10
value: 54.967
- type: recall_at_100
value: 79.11399999999999
- type: recall_at_1000
value: 94.56700000000001
- type: recall_at_3
value: 40.256
- type: recall_at_5
value: 46.215
- task:
type: Retrieval
dataset:
type: hotpotqa
name: MTEB HotpotQA
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 37.819
- type: map_at_10
value: 60.889
- type: map_at_100
value: 61.717999999999996
- type: map_at_1000
value: 61.778
- type: map_at_3
value: 57.254000000000005
- type: map_at_5
value: 59.541
- type: mrr_at_1
value: 75.638
- type: mrr_at_10
value: 82.173
- type: mrr_at_100
value: 82.362
- type: mrr_at_1000
value: 82.37
- type: mrr_at_3
value: 81.089
- type: mrr_at_5
value: 81.827
- type: ndcg_at_1
value: 75.638
- type: ndcg_at_10
value: 69.317
- type: ndcg_at_100
value: 72.221
- type: ndcg_at_1000
value: 73.382
- type: ndcg_at_3
value: 64.14
- type: ndcg_at_5
value: 67.07600000000001
- type: precision_at_1
value: 75.638
- type: precision_at_10
value: 14.704999999999998
- type: precision_at_100
value: 1.698
- type: precision_at_1000
value: 0.185
- type: precision_at_3
value: 41.394999999999996
- type: precision_at_5
value: 27.162999999999997
- type: recall_at_1
value: 37.819
- type: recall_at_10
value: 73.52499999999999
- type: recall_at_100
value: 84.875
- type: recall_at_1000
value: 92.559
- type: recall_at_3
value: 62.092999999999996
- type: recall_at_5
value: 67.907
- task:
type: Classification
dataset:
type: mteb/imdb
name: MTEB ImdbClassification
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 94.60079999999999
- type: ap
value: 92.67396345347356
- type: f1
value: 94.5988098167121
- task:
type: Retrieval
dataset:
type: msmarco
name: MTEB MSMARCO
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 21.285
- type: map_at_10
value: 33.436
- type: map_at_100
value: 34.63
- type: map_at_1000
value: 34.681
- type: map_at_3
value: 29.412
- type: map_at_5
value: 31.715
- type: mrr_at_1
value: 21.848
- type: mrr_at_10
value: 33.979
- type: mrr_at_100
value: 35.118
- type: mrr_at_1000
value: 35.162
- type: mrr_at_3
value: 30.036
- type: mrr_at_5
value: 32.298
- type: ndcg_at_1
value: 21.862000000000002
- type: ndcg_at_10
value: 40.43
- type: ndcg_at_100
value: 46.17
- type: ndcg_at_1000
value: 47.412
- type: ndcg_at_3
value: 32.221
- type: ndcg_at_5
value: 36.332
- type: precision_at_1
value: 21.862000000000002
- type: precision_at_10
value: 6.491
- type: precision_at_100
value: 0.935
- type: precision_at_1000
value: 0.104
- type: precision_at_3
value: 13.744
- type: precision_at_5
value: 10.331999999999999
- type: recall_at_1
value: 21.285
- type: recall_at_10
value: 62.083
- type: recall_at_100
value: 88.576
- type: recall_at_1000
value: 98.006
- type: recall_at_3
value: 39.729
- type: recall_at_5
value: 49.608000000000004
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (en)
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 93.92612859097127
- type: f1
value: 93.82370333372853
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (de)
config: de
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 92.67681036911807
- type: f1
value: 92.14191382411472
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (es)
config: es
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 92.26817878585723
- type: f1
value: 91.92824250337878
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (fr)
config: fr
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 89.96554963983714
- type: f1
value: 90.02859329630792
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (hi)
config: hi
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 90.02509860164935
- type: f1
value: 89.30665159182062
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (th)
config: th
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 87.55515370705244
- type: f1
value: 87.94449232331907
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (en)
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 82.4623803009576
- type: f1
value: 66.06738378772725
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (de)
config: de
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 79.3716539870386
- type: f1
value: 60.37614033396853
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (es)
config: es
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 80.34022681787857
- type: f1
value: 58.302008026952
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (fr)
config: fr
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 76.72095208268087
- type: f1
value: 59.64524724009049
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (hi)
config: hi
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 77.87020437432773
- type: f1
value: 57.80202694670567
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (th)
config: th
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 77.73598553345387
- type: f1
value: 58.19628250675031
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (af)
config: af
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 67.6630800268998
- type: f1
value: 65.00996668051691
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (am)
config: am
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 60.7128446536651
- type: f1
value: 57.95860594874963
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (ar)
config: ar
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 63.61129791526563
- type: f1
value: 59.75328290206483
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (az)
config: az
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 69.00134498991257
- type: f1
value: 67.0230483991802
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (bn)
config: bn
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 68.54068594485541
- type: f1
value: 65.54604628946976
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (cy)
config: cy
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 63.032952252858095
- type: f1
value: 58.715741857057104
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (da)
config: da
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 71.80901143241427
- type: f1
value: 68.33963989243877
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (de)
config: de
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 72.47141896435777
- type: f1
value: 69.56765020308262
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (el)
config: el
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 71.2373907195696
- type: f1
value: 69.04529836036467
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (en)
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 77.05783456624076
- type: f1
value: 74.69430584708174
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (es)
config: es
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 72.82111634162744
- type: f1
value: 70.77228952803762
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (fa)
config: fa
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 74.25353059852051
- type: f1
value: 71.05310103416411
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (fi)
config: fi
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 72.28648285137861
- type: f1
value: 69.08020473732226
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (fr)
config: fr
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 73.31540013449899
- type: f1
value: 70.9426355465791
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (he)
config: he
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 70.2151983860121
- type: f1
value: 67.52541755908858
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (hi)
config: hi
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 71.58372562205784
- type: f1
value: 69.49769064229827
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (hu)
config: hu
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 71.9233355749832
- type: f1
value: 69.36311548259593
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (hy)
config: hy
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 68.07330195023538
- type: f1
value: 64.99882022345572
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (id)
config: id
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 72.62273032952253
- type: f1
value: 70.6394885471001
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (is)
config: is
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 65.77000672494957
- type: f1
value: 62.9368944815065
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (it)
config: it
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 73.453261600538
- type: f1
value: 70.85069934666681
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (ja)
config: ja
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 74.6906523201076
- type: f1
value: 72.03249740074217
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (jv)
config: jv
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 63.03631472763953
- type: f1
value: 59.3165215571852
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (ka)
config: ka
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 58.913920645595155
- type: f1
value: 57.367337711611285
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (km)
config: km
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 54.42837928715535
- type: f1
value: 52.60527294970906
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (kn)
config: kn
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 66.33490248823135
- type: f1
value: 63.213340969404065
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (ko)
config: ko
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 70.58507061197041
- type: f1
value: 68.40256628040486
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (lv)
config: lv
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 69.11230665770006
- type: f1
value: 66.44863577842305
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (ml)
config: ml
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 69.70073974445192
- type: f1
value: 67.21291337273702
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (mn)
config: mn
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 66.43913920645595
- type: f1
value: 64.09838087422806
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (ms)
config: ms
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 70.80026899798251
- type: f1
value: 68.76986742962444
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (my)
config: my
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 64.78816408876934
- type: f1
value: 62.18781873428972
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (nb)
config: nb
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 71.6577000672495
- type: f1
value: 68.75171511133003
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (nl)
config: nl
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 74.42501681237391
- type: f1
value: 71.18434963451544
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (pl)
config: pl
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
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config: zh-CN
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 78.69535978480162
- type: f1
value: 78.90019070153316
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (zh-TW)
config: zh-TW
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 75.45729657027572
- type: f1
value: 76.19578371794672
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-p2p
name: MTEB MedrxivClusteringP2P
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 36.92715354123554
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-s2s
name: MTEB MedrxivClusteringS2S
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 35.53536244162518
- task:
type: Reranking
dataset:
type: mteb/mind_small
name: MTEB MindSmallReranking
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 33.08507884504006
- type: mrr
value: 34.32436977159129
- task:
type: Retrieval
dataset:
type: nfcorpus
name: MTEB NFCorpus
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 5.935
- type: map_at_10
value: 13.297
- type: map_at_100
value: 16.907
- type: map_at_1000
value: 18.391
- type: map_at_3
value: 9.626999999999999
- type: map_at_5
value: 11.190999999999999
- type: mrr_at_1
value: 46.129999999999995
- type: mrr_at_10
value: 54.346000000000004
- type: mrr_at_100
value: 55.067
- type: mrr_at_1000
value: 55.1
- type: mrr_at_3
value: 51.961
- type: mrr_at_5
value: 53.246
- type: ndcg_at_1
value: 44.118
- type: ndcg_at_10
value: 35.534
- type: ndcg_at_100
value: 32.946999999999996
- type: ndcg_at_1000
value: 41.599000000000004
- type: ndcg_at_3
value: 40.25
- type: ndcg_at_5
value: 37.978
- type: precision_at_1
value: 46.129999999999995
- type: precision_at_10
value: 26.842
- type: precision_at_100
value: 8.427
- type: precision_at_1000
value: 2.128
- type: precision_at_3
value: 37.977
- type: precision_at_5
value: 32.879000000000005
- type: recall_at_1
value: 5.935
- type: recall_at_10
value: 17.211000000000002
- type: recall_at_100
value: 34.33
- type: recall_at_1000
value: 65.551
- type: recall_at_3
value: 10.483
- type: recall_at_5
value: 13.078999999999999
- task:
type: Retrieval
dataset:
type: nq
name: MTEB NQ
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 35.231
- type: map_at_10
value: 50.202000000000005
- type: map_at_100
value: 51.154999999999994
- type: map_at_1000
value: 51.181
- type: map_at_3
value: 45.774
- type: map_at_5
value: 48.522
- type: mrr_at_1
value: 39.687
- type: mrr_at_10
value: 52.88
- type: mrr_at_100
value: 53.569
- type: mrr_at_1000
value: 53.58500000000001
- type: mrr_at_3
value: 49.228
- type: mrr_at_5
value: 51.525
- type: ndcg_at_1
value: 39.687
- type: ndcg_at_10
value: 57.754000000000005
- type: ndcg_at_100
value: 61.597
- type: ndcg_at_1000
value: 62.18900000000001
- type: ndcg_at_3
value: 49.55
- type: ndcg_at_5
value: 54.11899999999999
- type: precision_at_1
value: 39.687
- type: precision_at_10
value: 9.313
- type: precision_at_100
value: 1.146
- type: precision_at_1000
value: 0.12
- type: precision_at_3
value: 22.229
- type: precision_at_5
value: 15.939
- type: recall_at_1
value: 35.231
- type: recall_at_10
value: 78.083
- type: recall_at_100
value: 94.42099999999999
- type: recall_at_1000
value: 98.81
- type: recall_at_3
value: 57.047000000000004
- type: recall_at_5
value: 67.637
- task:
type: Retrieval
dataset:
type: quora
name: MTEB QuoraRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 71.241
- type: map_at_10
value: 85.462
- type: map_at_100
value: 86.083
- type: map_at_1000
value: 86.09700000000001
- type: map_at_3
value: 82.49499999999999
- type: map_at_5
value: 84.392
- type: mrr_at_1
value: 82.09
- type: mrr_at_10
value: 88.301
- type: mrr_at_100
value: 88.383
- type: mrr_at_1000
value: 88.384
- type: mrr_at_3
value: 87.37
- type: mrr_at_5
value: 88.035
- type: ndcg_at_1
value: 82.12
- type: ndcg_at_10
value: 89.149
- type: ndcg_at_100
value: 90.235
- type: ndcg_at_1000
value: 90.307
- type: ndcg_at_3
value: 86.37599999999999
- type: ndcg_at_5
value: 87.964
- type: precision_at_1
value: 82.12
- type: precision_at_10
value: 13.56
- type: precision_at_100
value: 1.539
- type: precision_at_1000
value: 0.157
- type: precision_at_3
value: 37.88
- type: precision_at_5
value: 24.92
- type: recall_at_1
value: 71.241
- type: recall_at_10
value: 96.128
- type: recall_at_100
value: 99.696
- type: recall_at_1000
value: 99.994
- type: recall_at_3
value: 88.181
- type: recall_at_5
value: 92.694
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering
name: MTEB RedditClustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 56.59757799655151
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering-p2p
name: MTEB RedditClusteringP2P
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 64.27391998854624
- task:
type: Retrieval
dataset:
type: scidocs
name: MTEB SCIDOCS
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 4.243
- type: map_at_10
value: 10.965
- type: map_at_100
value: 12.934999999999999
- type: map_at_1000
value: 13.256
- type: map_at_3
value: 7.907
- type: map_at_5
value: 9.435
- type: mrr_at_1
value: 20.9
- type: mrr_at_10
value: 31.849
- type: mrr_at_100
value: 32.964
- type: mrr_at_1000
value: 33.024
- type: mrr_at_3
value: 28.517
- type: mrr_at_5
value: 30.381999999999998
- type: ndcg_at_1
value: 20.9
- type: ndcg_at_10
value: 18.723
- type: ndcg_at_100
value: 26.384999999999998
- type: ndcg_at_1000
value: 32.114
- type: ndcg_at_3
value: 17.753
- type: ndcg_at_5
value: 15.558
- type: precision_at_1
value: 20.9
- type: precision_at_10
value: 9.8
- type: precision_at_100
value: 2.078
- type: precision_at_1000
value: 0.345
- type: precision_at_3
value: 16.900000000000002
- type: precision_at_5
value: 13.88
- type: recall_at_1
value: 4.243
- type: recall_at_10
value: 19.885
- type: recall_at_100
value: 42.17
- type: recall_at_1000
value: 70.12
- type: recall_at_3
value: 10.288
- type: recall_at_5
value: 14.072000000000001
- task:
type: STS
dataset:
type: mteb/sickr-sts
name: MTEB SICK-R
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 85.84209174935282
- type: cos_sim_spearman
value: 81.73248048438833
- type: euclidean_pearson
value: 83.02810070308149
- type: euclidean_spearman
value: 81.73248295679514
- type: manhattan_pearson
value: 82.95368060376002
- type: manhattan_spearman
value: 81.60277910998718
- task:
type: STS
dataset:
type: mteb/sts12-sts
name: MTEB STS12
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 88.52628804556943
- type: cos_sim_spearman
value: 82.5713913555672
- type: euclidean_pearson
value: 85.8796774746988
- type: euclidean_spearman
value: 82.57137506803424
- type: manhattan_pearson
value: 85.79671002960058
- type: manhattan_spearman
value: 82.49445981618027
- task:
type: STS
dataset:
type: mteb/sts13-sts
name: MTEB STS13
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 86.23682503505542
- type: cos_sim_spearman
value: 87.15008956711806
- type: euclidean_pearson
value: 86.79805401524959
- type: euclidean_spearman
value: 87.15008956711806
- type: manhattan_pearson
value: 86.65298502699244
- type: manhattan_spearman
value: 86.97677821948562
- task:
type: STS
dataset:
type: mteb/sts14-sts
name: MTEB STS14
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 85.63370304677802
- type: cos_sim_spearman
value: 84.97105553540318
- type: euclidean_pearson
value: 85.28896108687721
- type: euclidean_spearman
value: 84.97105553540318
- type: manhattan_pearson
value: 85.09663190337331
- type: manhattan_spearman
value: 84.79126831644619
- task:
type: STS
dataset:
type: mteb/sts15-sts
name: MTEB STS15
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 90.2614838800733
- type: cos_sim_spearman
value: 91.0509162991835
- type: euclidean_pearson
value: 90.33098317533373
- type: euclidean_spearman
value: 91.05091625871644
- type: manhattan_pearson
value: 90.26250435151107
- type: manhattan_spearman
value: 90.97999594417519
- task:
type: STS
dataset:
type: mteb/sts16-sts
name: MTEB STS16
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 85.80480973335091
- type: cos_sim_spearman
value: 87.313695492969
- type: euclidean_pearson
value: 86.49267251576939
- type: euclidean_spearman
value: 87.313695492969
- type: manhattan_pearson
value: 86.44019901831935
- type: manhattan_spearman
value: 87.24205395460392
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (en-en)
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 90.05662789380672
- type: cos_sim_spearman
value: 90.02759424426651
- type: euclidean_pearson
value: 90.4042483422981
- type: euclidean_spearman
value: 90.02759424426651
- type: manhattan_pearson
value: 90.51446975000226
- type: manhattan_spearman
value: 90.08832889933616
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (en)
config: en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 67.5975528273532
- type: cos_sim_spearman
value: 67.62969861411354
- type: euclidean_pearson
value: 69.224275734323
- type: euclidean_spearman
value: 67.62969861411354
- type: manhattan_pearson
value: 69.3761447059927
- type: manhattan_spearman
value: 67.90921005611467
- task:
type: STS
dataset:
type: mteb/stsbenchmark-sts
name: MTEB STSBenchmark
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 87.11244327231684
- type: cos_sim_spearman
value: 88.37902438979035
- type: euclidean_pearson
value: 87.86054279847336
- type: euclidean_spearman
value: 88.37902438979035
- type: manhattan_pearson
value: 87.77257757320378
- type: manhattan_spearman
value: 88.25208966098123
- task:
type: Reranking
dataset:
type: mteb/scidocs-reranking
name: MTEB SciDocsRR
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 85.87174608143563
- type: mrr
value: 96.12836872640794
- task:
type: Retrieval
dataset:
type: scifact
name: MTEB SciFact
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 57.760999999999996
- type: map_at_10
value: 67.258
- type: map_at_100
value: 67.757
- type: map_at_1000
value: 67.78800000000001
- type: map_at_3
value: 64.602
- type: map_at_5
value: 65.64
- type: mrr_at_1
value: 60.667
- type: mrr_at_10
value: 68.441
- type: mrr_at_100
value: 68.825
- type: mrr_at_1000
value: 68.853
- type: mrr_at_3
value: 66.444
- type: mrr_at_5
value: 67.26100000000001
- type: ndcg_at_1
value: 60.667
- type: ndcg_at_10
value: 71.852
- type: ndcg_at_100
value: 73.9
- type: ndcg_at_1000
value: 74.628
- type: ndcg_at_3
value: 67.093
- type: ndcg_at_5
value: 68.58
- type: precision_at_1
value: 60.667
- type: precision_at_10
value: 9.6
- type: precision_at_100
value: 1.0670000000000002
- type: precision_at_1000
value: 0.11199999999999999
- type: precision_at_3
value: 26.111
- type: precision_at_5
value: 16.733
- type: recall_at_1
value: 57.760999999999996
- type: recall_at_10
value: 84.967
- type: recall_at_100
value: 93.833
- type: recall_at_1000
value: 99.333
- type: recall_at_3
value: 71.589
- type: recall_at_5
value: 75.483
- task:
type: PairClassification
dataset:
type: mteb/sprintduplicatequestions-pairclassification
name: MTEB SprintDuplicateQuestions
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.66633663366336
- type: cos_sim_ap
value: 91.17685358899108
- type: cos_sim_f1
value: 82.16818642350559
- type: cos_sim_precision
value: 83.26488706365504
- type: cos_sim_recall
value: 81.10000000000001
- type: dot_accuracy
value: 99.66633663366336
- type: dot_ap
value: 91.17663411119032
- type: dot_f1
value: 82.16818642350559
- type: dot_precision
value: 83.26488706365504
- type: dot_recall
value: 81.10000000000001
- type: euclidean_accuracy
value: 99.66633663366336
- type: euclidean_ap
value: 91.17685189882275
- type: euclidean_f1
value: 82.16818642350559
- type: euclidean_precision
value: 83.26488706365504
- type: euclidean_recall
value: 81.10000000000001
- type: manhattan_accuracy
value: 99.66633663366336
- type: manhattan_ap
value: 91.2241619496737
- type: manhattan_f1
value: 82.20472440944883
- type: manhattan_precision
value: 86.51933701657458
- type: manhattan_recall
value: 78.3
- type: max_accuracy
value: 99.66633663366336
- type: max_ap
value: 91.2241619496737
- type: max_f1
value: 82.20472440944883
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering
name: MTEB StackExchangeClustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 66.85101268897951
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering-p2p
name: MTEB StackExchangeClusteringP2P
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 42.461184054706905
- task:
type: Reranking
dataset:
type: mteb/stackoverflowdupquestions-reranking
name: MTEB StackOverflowDupQuestions
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 51.44542568873886
- type: mrr
value: 52.33656151854681
- task:
type: Summarization
dataset:
type: mteb/summeval
name: MTEB SummEval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 30.75982974997539
- type: cos_sim_spearman
value: 30.385405026539914
- type: dot_pearson
value: 30.75982433546523
- type: dot_spearman
value: 30.385405026539914
- task:
type: Retrieval
dataset:
type: trec-covid
name: MTEB TRECCOVID
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.22799999999999998
- type: map_at_10
value: 2.064
- type: map_at_100
value: 13.056000000000001
- type: map_at_1000
value: 31.747999999999998
- type: map_at_3
value: 0.67
- type: map_at_5
value: 1.097
- type: mrr_at_1
value: 90.0
- type: mrr_at_10
value: 94.667
- type: mrr_at_100
value: 94.667
- type: mrr_at_1000
value: 94.667
- type: mrr_at_3
value: 94.667
- type: mrr_at_5
value: 94.667
- type: ndcg_at_1
value: 86.0
- type: ndcg_at_10
value: 82.0
- type: ndcg_at_100
value: 64.307
- type: ndcg_at_1000
value: 57.023999999999994
- type: ndcg_at_3
value: 85.816
- type: ndcg_at_5
value: 84.904
- type: precision_at_1
value: 90.0
- type: precision_at_10
value: 85.8
- type: precision_at_100
value: 66.46
- type: precision_at_1000
value: 25.202
- type: precision_at_3
value: 90.0
- type: precision_at_5
value: 89.2
- type: recall_at_1
value: 0.22799999999999998
- type: recall_at_10
value: 2.235
- type: recall_at_100
value: 16.185
- type: recall_at_1000
value: 53.620999999999995
- type: recall_at_3
value: 0.7040000000000001
- type: recall_at_5
value: 1.172
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (sqi-eng)
config: sqi-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 97.39999999999999
- type: f1
value: 96.75
- type: precision
value: 96.45
- type: recall
value: 97.39999999999999
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (fry-eng)
config: fry-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 85.54913294797689
- type: f1
value: 82.46628131021194
- type: precision
value: 81.1175337186898
- type: recall
value: 85.54913294797689
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (kur-eng)
config: kur-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 81.21951219512195
- type: f1
value: 77.33333333333334
- type: precision
value: 75.54878048780488
- type: recall
value: 81.21951219512195
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (tur-eng)
config: tur-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 98.6
- type: f1
value: 98.26666666666665
- type: precision
value: 98.1
- type: recall
value: 98.6
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (deu-eng)
config: deu-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 99.5
- type: f1
value: 99.33333333333333
- type: precision
value: 99.25
- type: recall
value: 99.5
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (nld-eng)
config: nld-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 97.8
- type: f1
value: 97.2
- type: precision
value: 96.89999999999999
- type: recall
value: 97.8
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ron-eng)
config: ron-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 97.8
- type: f1
value: 97.18333333333334
- type: precision
value: 96.88333333333333
- type: recall
value: 97.8
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ang-eng)
config: ang-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 77.61194029850746
- type: f1
value: 72.81094527363183
- type: precision
value: 70.83333333333333
- type: recall
value: 77.61194029850746
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ido-eng)
config: ido-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 93.7
- type: f1
value: 91.91666666666667
- type: precision
value: 91.08333333333334
- type: recall
value: 93.7
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (jav-eng)
config: jav-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 88.29268292682927
- type: f1
value: 85.27642276422765
- type: precision
value: 84.01277584204414
- type: recall
value: 88.29268292682927
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (isl-eng)
config: isl-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 96.1
- type: f1
value: 95.0
- type: precision
value: 94.46666666666668
- type: recall
value: 96.1
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (slv-eng)
config: slv-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 93.681652490887
- type: f1
value: 91.90765492102065
- type: precision
value: 91.05913325232888
- type: recall
value: 93.681652490887
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (cym-eng)
config: cym-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 92.17391304347827
- type: f1
value: 89.97101449275361
- type: precision
value: 88.96811594202899
- type: recall
value: 92.17391304347827
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (kaz-eng)
config: kaz-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 90.43478260869566
- type: f1
value: 87.72173913043478
- type: precision
value: 86.42028985507245
- type: recall
value: 90.43478260869566
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (est-eng)
config: est-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 90.4
- type: f1
value: 88.03
- type: precision
value: 86.95
- type: recall
value: 90.4
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (heb-eng)
config: heb-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 93.4
- type: f1
value: 91.45666666666666
- type: precision
value: 90.525
- type: recall
value: 93.4
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (gla-eng)
config: gla-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 81.9059107358263
- type: f1
value: 78.32557872364869
- type: precision
value: 76.78260286824823
- type: recall
value: 81.9059107358263
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (mar-eng)
config: mar-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 94.3
- type: f1
value: 92.58333333333333
- type: precision
value: 91.73333333333332
- type: recall
value: 94.3
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (lat-eng)
config: lat-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 79.10000000000001
- type: f1
value: 74.50500000000001
- type: precision
value: 72.58928571428571
- type: recall
value: 79.10000000000001
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (bel-eng)
config: bel-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 96.6
- type: f1
value: 95.55
- type: precision
value: 95.05
- type: recall
value: 96.6
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (pms-eng)
config: pms-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 82.0952380952381
- type: f1
value: 77.98458049886621
- type: precision
value: 76.1968253968254
- type: recall
value: 82.0952380952381
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (gle-eng)
config: gle-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 87.9
- type: f1
value: 84.99190476190476
- type: precision
value: 83.65
- type: recall
value: 87.9
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (pes-eng)
config: pes-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 95.7
- type: f1
value: 94.56666666666666
- type: precision
value: 94.01666666666667
- type: recall
value: 95.7
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (nob-eng)
config: nob-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 98.6
- type: f1
value: 98.2
- type: precision
value: 98.0
- type: recall
value: 98.6
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (bul-eng)
config: bul-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 95.6
- type: f1
value: 94.38333333333334
- type: precision
value: 93.78333333333335
- type: recall
value: 95.6
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (cbk-eng)
config: cbk-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 87.4
- type: f1
value: 84.10380952380952
- type: precision
value: 82.67
- type: recall
value: 87.4
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (hun-eng)
config: hun-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 95.5
- type: f1
value: 94.33333333333334
- type: precision
value: 93.78333333333333
- type: recall
value: 95.5
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (uig-eng)
config: uig-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 89.4
- type: f1
value: 86.82000000000001
- type: precision
value: 85.64500000000001
- type: recall
value: 89.4
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (rus-eng)
config: rus-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 95.1
- type: f1
value: 93.56666666666668
- type: precision
value: 92.81666666666666
- type: recall
value: 95.1
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (spa-eng)
config: spa-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 98.9
- type: f1
value: 98.6
- type: precision
value: 98.45
- type: recall
value: 98.9
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (hye-eng)
config: hye-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 95.01347708894879
- type: f1
value: 93.51752021563343
- type: precision
value: 92.82794249775381
- type: recall
value: 95.01347708894879
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (tel-eng)
config: tel-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 97.00854700854701
- type: f1
value: 96.08262108262107
- type: precision
value: 95.65527065527067
- type: recall
value: 97.00854700854701
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (afr-eng)
config: afr-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 96.5
- type: f1
value: 95.39999999999999
- type: precision
value: 94.88333333333333
- type: recall
value: 96.5
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (mon-eng)
config: mon-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 96.5909090909091
- type: f1
value: 95.49242424242425
- type: precision
value: 94.9621212121212
- type: recall
value: 96.5909090909091
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (arz-eng)
config: arz-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 84.90566037735849
- type: f1
value: 81.85883997204752
- type: precision
value: 80.54507337526205
- type: recall
value: 84.90566037735849
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (hrv-eng)
config: hrv-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 97.5
- type: f1
value: 96.75
- type: precision
value: 96.38333333333333
- type: recall
value: 97.5
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (nov-eng)
config: nov-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 86.7704280155642
- type: f1
value: 82.99610894941635
- type: precision
value: 81.32295719844358
- type: recall
value: 86.7704280155642
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (gsw-eng)
config: gsw-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 67.52136752136752
- type: f1
value: 61.89662189662191
- type: precision
value: 59.68660968660969
- type: recall
value: 67.52136752136752
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (nds-eng)
config: nds-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 89.2
- type: f1
value: 86.32
- type: precision
value: 85.015
- type: recall
value: 89.2
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ukr-eng)
config: ukr-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 96.0
- type: f1
value: 94.78333333333333
- type: precision
value: 94.18333333333334
- type: recall
value: 96.0
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (uzb-eng)
config: uzb-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 83.8785046728972
- type: f1
value: 80.54517133956385
- type: precision
value: 79.154984423676
- type: recall
value: 83.8785046728972
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (lit-eng)
config: lit-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 93.60000000000001
- type: f1
value: 92.01333333333334
- type: precision
value: 91.28333333333333
- type: recall
value: 93.60000000000001
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ina-eng)
config: ina-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 97.1
- type: f1
value: 96.26666666666667
- type: precision
value: 95.85000000000001
- type: recall
value: 97.1
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (lfn-eng)
config: lfn-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 84.3
- type: f1
value: 80.67833333333333
- type: precision
value: 79.03928571428571
- type: recall
value: 84.3
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (zsm-eng)
config: zsm-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 97.3
- type: f1
value: 96.48333333333332
- type: precision
value: 96.08333333333331
- type: recall
value: 97.3
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ita-eng)
config: ita-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 95.7
- type: f1
value: 94.66666666666667
- type: precision
value: 94.16666666666667
- type: recall
value: 95.7
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (cmn-eng)
config: cmn-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 97.2
- type: f1
value: 96.36666666666667
- type: precision
value: 95.96666666666668
- type: recall
value: 97.2
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (lvs-eng)
config: lvs-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 94.3
- type: f1
value: 92.80666666666667
- type: precision
value: 92.12833333333333
- type: recall
value: 94.3
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (glg-eng)
config: glg-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 97.0
- type: f1
value: 96.22333333333334
- type: precision
value: 95.875
- type: recall
value: 97.0
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ceb-eng)
config: ceb-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 74.33333333333333
- type: f1
value: 70.78174603174602
- type: precision
value: 69.28333333333332
- type: recall
value: 74.33333333333333
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (bre-eng)
config: bre-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 37.6
- type: f1
value: 32.938348952090365
- type: precision
value: 31.2811038961039
- type: recall
value: 37.6
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ben-eng)
config: ben-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 91.5
- type: f1
value: 89.13333333333333
- type: precision
value: 88.03333333333333
- type: recall
value: 91.5
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (swg-eng)
config: swg-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 82.14285714285714
- type: f1
value: 77.67857142857143
- type: precision
value: 75.59523809523809
- type: recall
value: 82.14285714285714
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (arq-eng)
config: arq-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 69.0450054884742
- type: f1
value: 63.070409283362075
- type: precision
value: 60.58992781824835
- type: recall
value: 69.0450054884742
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (kab-eng)
config: kab-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 63.1
- type: f1
value: 57.848333333333336
- type: precision
value: 55.69500000000001
- type: recall
value: 63.1
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (fra-eng)
config: fra-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 96.1
- type: f1
value: 95.01666666666667
- type: precision
value: 94.5
- type: recall
value: 96.1
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (por-eng)
config: por-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 95.89999999999999
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metrics:
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metrics:
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metrics:
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metrics:
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metrics:
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metrics:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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metrics:
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metrics:
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metrics:
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dataset:
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metrics:
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metrics:
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dataset:
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metrics:
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metrics:
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dataset:
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config: srp-eng
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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config: epo-eng
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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dataset:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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dataset:
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config: awa-eng
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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dataset:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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dataset:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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dataset:
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metrics:
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dataset:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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dataset:
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config: eus-eng
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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dataset:
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config: yue-eng
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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dataset:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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dataset:
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config: dtp-eng
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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dataset:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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dataset:
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config: jpn-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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dataset:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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dataset:
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config: xho-eng
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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dataset:
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config: orv-eng
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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dataset:
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split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 95.6
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value: 95.6
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dataset:
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config: tuk-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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dataset:
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config: max-eng
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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dataset:
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config: swh-eng
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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dataset:
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config: hin-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 98.2
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value: 98.2
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dataset:
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config: dsb-eng
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 75.5741127348643
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dataset:
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config: ber-eng
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 62.2
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value: 62.2
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type: BitextMining
dataset:
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config: tam-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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dataset:
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config: slk-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 96.1
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value: 96.1
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type: BitextMining
dataset:
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name: MTEB Tatoeba (tgl-eng)
config: tgl-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 96.8
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value: 96.8
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type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
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config: ast-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 92.1259842519685
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dataset:
type: mteb/tatoeba-bitext-mining
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config: mkd-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 94.1
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value: 92.49
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value: 91.725
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value: 94.1
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type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
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config: khm-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 77.5623268698061
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value: 71.51947852086357
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value: 77.5623268698061
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type: BitextMining
dataset:
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name: MTEB Tatoeba (ces-eng)
config: ces-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 97.39999999999999
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value: 97.39999999999999
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type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (tzl-eng)
config: tzl-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 66.34615384615384
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value: 66.34615384615384
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type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (urd-eng)
config: urd-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 94.89999999999999
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value: 93.41190476190476
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value: 92.7
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value: 94.89999999999999
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ara-eng)
config: ara-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 93.10000000000001
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value: 93.10000000000001
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (kor-eng)
config: kor-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 93.7
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value: 91.97333333333334
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value: 91.14166666666667
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value: 93.7
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (yid-eng)
config: yid-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 92.21698113207547
- type: f1
value: 90.3796046720575
- type: precision
value: 89.56367924528303
- type: recall
value: 92.21698113207547
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (fin-eng)
config: fin-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 97.6
- type: f1
value: 96.91666666666667
- type: precision
value: 96.6
- type: recall
value: 97.6
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (tha-eng)
config: tha-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 97.44525547445255
- type: f1
value: 96.71532846715328
- type: precision
value: 96.35036496350365
- type: recall
value: 97.44525547445255
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (wuu-eng)
config: wuu-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 94.1
- type: f1
value: 92.34000000000002
- type: precision
value: 91.49166666666667
- type: recall
value: 94.1
- task:
type: Retrieval
dataset:
type: webis-touche2020
name: MTEB Touche2020
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 3.2910000000000004
- type: map_at_10
value: 10.373000000000001
- type: map_at_100
value: 15.612
- type: map_at_1000
value: 17.06
- type: map_at_3
value: 6.119
- type: map_at_5
value: 7.917000000000001
- type: mrr_at_1
value: 44.897999999999996
- type: mrr_at_10
value: 56.054
- type: mrr_at_100
value: 56.82000000000001
- type: mrr_at_1000
value: 56.82000000000001
- type: mrr_at_3
value: 52.381
- type: mrr_at_5
value: 53.81
- type: ndcg_at_1
value: 42.857
- type: ndcg_at_10
value: 27.249000000000002
- type: ndcg_at_100
value: 36.529
- type: ndcg_at_1000
value: 48.136
- type: ndcg_at_3
value: 33.938
- type: ndcg_at_5
value: 29.951
- type: precision_at_1
value: 44.897999999999996
- type: precision_at_10
value: 22.653000000000002
- type: precision_at_100
value: 7.000000000000001
- type: precision_at_1000
value: 1.48
- type: precision_at_3
value: 32.653
- type: precision_at_5
value: 27.755000000000003
- type: recall_at_1
value: 3.2910000000000004
- type: recall_at_10
value: 16.16
- type: recall_at_100
value: 43.908
- type: recall_at_1000
value: 79.823
- type: recall_at_3
value: 7.156
- type: recall_at_5
value: 10.204
- task:
type: Classification
dataset:
type: mteb/toxic_conversations_50k
name: MTEB ToxicConversationsClassification
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 71.05879999999999
- type: ap
value: 14.609748142799111
- type: f1
value: 54.878956295843096
- task:
type: Classification
dataset:
type: mteb/tweet_sentiment_extraction
name: MTEB TweetSentimentExtractionClassification
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 64.61799660441426
- type: f1
value: 64.8698191961434
- task:
type: Clustering
dataset:
type: mteb/twentynewsgroups-clustering
name: MTEB TwentyNewsgroupsClustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 51.32860036611885
- task:
type: PairClassification
dataset:
type: mteb/twittersemeval2015-pairclassification
name: MTEB TwitterSemEval2015
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 88.34714192048638
- type: cos_sim_ap
value: 80.26732975975634
- type: cos_sim_f1
value: 73.53415148134374
- type: cos_sim_precision
value: 69.34767360299276
- type: cos_sim_recall
value: 78.25857519788919
- type: dot_accuracy
value: 88.34714192048638
- type: dot_ap
value: 80.26733698491206
- type: dot_f1
value: 73.53415148134374
- type: dot_precision
value: 69.34767360299276
- type: dot_recall
value: 78.25857519788919
- type: euclidean_accuracy
value: 88.34714192048638
- type: euclidean_ap
value: 80.26734337771738
- type: euclidean_f1
value: 73.53415148134374
- type: euclidean_precision
value: 69.34767360299276
- type: euclidean_recall
value: 78.25857519788919
- type: manhattan_accuracy
value: 88.30541813196639
- type: manhattan_ap
value: 80.19415808104145
- type: manhattan_f1
value: 73.55143870713441
- type: manhattan_precision
value: 73.25307511122743
- type: manhattan_recall
value: 73.85224274406332
- type: max_accuracy
value: 88.34714192048638
- type: max_ap
value: 80.26734337771738
- type: max_f1
value: 73.55143870713441
- task:
type: PairClassification
dataset:
type: mteb/twitterurlcorpus-pairclassification
name: MTEB TwitterURLCorpus
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 89.81061047075717
- type: cos_sim_ap
value: 87.11747055081017
- type: cos_sim_f1
value: 80.04355498817256
- type: cos_sim_precision
value: 78.1165262000733
- type: cos_sim_recall
value: 82.06806282722513
- type: dot_accuracy
value: 89.81061047075717
- type: dot_ap
value: 87.11746902745236
- type: dot_f1
value: 80.04355498817256
- type: dot_precision
value: 78.1165262000733
- type: dot_recall
value: 82.06806282722513
- type: euclidean_accuracy
value: 89.81061047075717
- type: euclidean_ap
value: 87.11746919324248
- type: euclidean_f1
value: 80.04355498817256
- type: euclidean_precision
value: 78.1165262000733
- type: euclidean_recall
value: 82.06806282722513
- type: manhattan_accuracy
value: 89.79508673885202
- type: manhattan_ap
value: 87.11074390832218
- type: manhattan_f1
value: 80.13002540726349
- type: manhattan_precision
value: 77.83826945412311
- type: manhattan_recall
value: 82.56082537727133
- type: max_accuracy
value: 89.81061047075717
- type: max_ap
value: 87.11747055081017
- type: max_f1
value: 80.13002540726349
language:
- multilingual
- af
- am
- ar
- as
- az
- be
- bg
- bn
- br
- bs
- ca
- cs
- cy
- da
- de
- el
- en
- eo
- es
- et
- eu
- fa
- fi
- fr
- fy
- ga
- gd
- gl
- gu
- ha
- he
- hi
- hr
- hu
- hy
- id
- is
- it
- ja
- jv
- ka
- kk
- km
- kn
- ko
- ku
- ky
- la
- lo
- lt
- lv
- mg
- mk
- ml
- mn
- mr
- ms
- my
- ne
- nl
- 'no'
- om
- or
- pa
- pl
- ps
- pt
- ro
- ru
- sa
- sd
- si
- sk
- sl
- so
- sq
- sr
- su
- sv
- sw
- ta
- te
- th
- tl
- tr
- ug
- uk
- ur
- uz
- vi
- xh
- yi
- zh
license: mit
---
## Multilingual-E5-large-instruct
[Multilingual E5 Text Embeddings: A Technical Report](https://arxiv.org/pdf/2402.05672).
Liang Wang, Nan Yang, Xiaolong Huang, Linjun Yang, Rangan Majumder, Furu Wei, arXiv 2024
This model has 24 layers and the embedding size is 1024.
## Usage
Below are examples to encode queries and passages from the MS-MARCO passage ranking dataset.
### Transformers
```python
import torch.nn.functional as F
from torch import Tensor
from transformers import AutoTokenizer, AutoModel
def average_pool(last_hidden_states: Tensor,
attention_mask: Tensor) -> Tensor:
last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0)
return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None]
def get_detailed_instruct(task_description: str, query: str) -> str:
return f'Instruct: {task_description}\nQuery: {query}'
# Each query must come with a one-sentence instruction that describes the task
task = 'Given a web search query, retrieve relevant passages that answer the query'
queries = [
get_detailed_instruct(task, 'how much protein should a female eat'),
get_detailed_instruct(task, '南瓜的家常做法')
]
# No need to add instruction for retrieval documents
documents = [
"As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.",
"1.清炒南瓜丝 原料:嫩南瓜半个 调料:葱、盐、白糖、鸡精 做法: 1、南瓜用刀薄薄的削去表面一层皮,用勺子刮去瓤 2、擦成细丝(没有擦菜板就用刀慢慢切成细丝) 3、锅烧热放油,入葱花煸出香味 4、入南瓜丝快速翻炒一分钟左右,放盐、一点白糖和鸡精调味出锅 2.香葱炒南瓜 原料:南瓜1只 调料:香葱、蒜末、橄榄油、盐 做法: 1、将南瓜去皮,切成片 2、油锅8成热后,将蒜末放入爆香 3、爆香后,将南瓜片放入,翻炒 4、在翻炒的同时,可以不时地往锅里加水,但不要太多 5、放入盐,炒匀 6、南瓜差不多软和绵了之后,就可以关火 7、撒入香葱,即可出锅"
]
input_texts = queries + documents
tokenizer = AutoTokenizer.from_pretrained('intfloat/multilingual-e5-large-instruct')
model = AutoModel.from_pretrained('intfloat/multilingual-e5-large-instruct')
# Tokenize the input texts
batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors='pt')
outputs = model(**batch_dict)
embeddings = average_pool(outputs.last_hidden_state, batch_dict['attention_mask'])
# normalize embeddings
embeddings = F.normalize(embeddings, p=2, dim=1)
scores = (embeddings[:2] @ embeddings[2:].T) * 100
print(scores.tolist())
# => [[91.92852783203125, 67.580322265625], [70.3814468383789, 92.1330795288086]]
```
### Sentence Transformers
```python
from sentence_transformers import SentenceTransformer
def get_detailed_instruct(task_description: str, query: str) -> str:
return f'Instruct: {task_description}\nQuery: {query}'
# Each query must come with a one-sentence instruction that describes the task
task = 'Given a web search query, retrieve relevant passages that answer the query'
queries = [
get_detailed_instruct(task, 'how much protein should a female eat'),
get_detailed_instruct(task, '南瓜的家常做法')
]
# No need to add instruction for retrieval documents
documents = [
"As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.",
"1.清炒南瓜丝 原料:嫩南瓜半个 调料:葱、盐、白糖、鸡精 做法: 1、南瓜用刀薄薄的削去表面一层皮,用勺子刮去瓤 2、擦成细丝(没有擦菜板就用刀慢慢切成细丝) 3、锅烧热放油,入葱花煸出香味 4、入南瓜丝快速翻炒一分钟左右,放盐、一点白糖和鸡精调味出锅 2.香葱炒南瓜 原料:南瓜1只 调料:香葱、蒜末、橄榄油、盐 做法: 1、将南瓜去皮,切成片 2、油锅8成热后,将蒜末放入爆香 3、爆香后,将南瓜片放入,翻炒 4、在翻炒的同时,可以不时地往锅里加水,但不要太多 5、放入盐,炒匀 6、南瓜差不多软和绵了之后,就可以关火 7、撒入香葱,即可出锅"
]
input_texts = queries + documents
model = SentenceTransformer('intfloat/multilingual-e5-large-instruct')
embeddings = model.encode(input_texts, convert_to_tensor=True, normalize_embeddings=True)
scores = (embeddings[:2] @ embeddings[2:].T) * 100
print(scores.tolist())
# [[91.92853546142578, 67.5802993774414], [70.38143157958984, 92.13307189941406]]
```
## Supported Languages
This model is initialized from [xlm-roberta-large](https://huggingface.co/xlm-roberta-large)
and continually trained on a mixture of multilingual datasets.
It supports 100 languages from xlm-roberta,
but low-resource languages may see performance degradation.
## Training Details
**Initialization**: [xlm-roberta-large](https://huggingface.co/xlm-roberta-large)
**First stage**: contrastive pre-training with 1 billion weakly supervised text pairs.
**Second stage**: fine-tuning on datasets from the [E5-mistral](https://arxiv.org/abs/2401.00368) paper.
## MTEB Benchmark Evaluation
Check out [unilm/e5](https://github.com/microsoft/unilm/tree/master/e5) to reproduce evaluation results
on the [BEIR](https://arxiv.org/abs/2104.08663) and [MTEB benchmark](https://arxiv.org/abs/2210.07316).
## FAQ
**1. Do I need to add instructions to the query?**
Yes, this is how the model is trained, otherwise you will see a performance degradation.
The task definition should be a one-sentence instruction that describes the task.
This is a way to customize text embeddings for different scenarios through natural language instructions.
Please check out [unilm/e5/utils.py](https://github.com/microsoft/unilm/blob/9c0f1ff7ca53431fe47d2637dfe253643d94185b/e5/utils.py#L106) for instructions we used for evaluation.
On the other hand, there is no need to add instructions to the document side.
**2. Why are my reproduced results slightly different from reported in the model card?**
Different versions of `transformers` and `pytorch` could cause negligible but non-zero performance differences.
**3. Why does the cosine similarity scores distribute around 0.7 to 1.0?**
This is a known and expected behavior as we use a low temperature 0.01 for InfoNCE contrastive loss.
For text embedding tasks like text retrieval or semantic similarity,
what matters is the relative order of the scores instead of the absolute values,
so this should not be an issue.
## Citation
If you find our paper or models helpful, please consider cite as follows:
```
@article{wang2024multilingual,
title={Multilingual E5 Text Embeddings: A Technical Report},
author={Wang, Liang and Yang, Nan and Huang, Xiaolong and Yang, Linjun and Majumder, Rangan and Wei, Furu},
journal={arXiv preprint arXiv:2402.05672},
year={2024}
}
```
## Limitations
Long texts will be truncated to at most 512 tokens.
|
TheBloke/Athena-v2-AWQ | TheBloke | "2023-11-09T18:18:09Z" | 20 | 3 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"base_model:IkariDev/Athena-v2",
"base_model:quantized:IkariDev/Athena-v2",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"text-generation-inference",
"4-bit",
"awq",
"region:us"
] | text-generation | "2023-09-23T22:09:48Z" | ---
license: cc-by-nc-4.0
model_name: Athena V2
base_model: IkariDev/Athena-v2
inference: false
model_creator: IkariDev
model_type: llama
prompt_template: 'Below is an instruction that describes a task. Write a response
that appropriately completes the request.
### Instruction:
{prompt}
### Response:
'
quantized_by: TheBloke
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
<div style="display: flex; flex-direction: column; align-items: flex-start;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p>
</div>
<div style="display: flex; flex-direction: column; align-items: flex-end;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
</div>
</div>
<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# Athena V2 - AWQ
- Model creator: [IkariDev and Undi95](https://huggingface.co/IkariDev)
- Original model: [Athena V2](https://huggingface.co/IkariDev/Athena-v2)
<!-- description start -->
## Description
This repo contains AWQ model files for [IkariDev and Undi95's Athena V2](https://huggingface.co/IkariDev/Athena-v2).
### About AWQ
AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference.
It is also now supported by continuous batching server [vLLM](https://github.com/vllm-project/vllm), allowing use of AWQ models for high-throughput concurrent inference in multi-user server scenarios. Note that, at the time of writing, overall throughput is still lower than running vLLM with unquantised models, however using AWQ enables using much smaller GPUs which can lead to easier deployment and overall cost savings. For example, a 70B model can be run on 1 x 48GB GPU instead of 2 x 80GB.
<!-- description end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Athena-v2-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Athena-v2-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Athena-v2-GGUF)
* [IkariDev and Undi95's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/IkariDev/Athena-v2)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: Alpaca
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
```
<!-- prompt-template end -->
<!-- licensing start -->
## Licensing
The creator of the source model has listed its license as `cc-by-nc-4.0`, and this quantization has therefore used that same license.
As this model is based on Llama 2, it is also subject to the Meta Llama 2 license terms, and the license files for that are additionally included. It should therefore be considered as being claimed to be licensed under both licenses. I contacted Hugging Face for clarification on dual licensing but they do not yet have an official position. Should this change, or should Meta provide any feedback on this situation, I will update this section accordingly.
In the meantime, any questions regarding licensing, and in particular how these two licenses might interact, should be directed to the original model repository: [IkariDev's Athena V2](https://huggingface.co/IkariDev/Athena-v2).
<!-- licensing end -->
<!-- README_AWQ.md-provided-files start -->
## Provided files and AWQ parameters
For my first release of AWQ models, I am releasing 128g models only. I will consider adding 32g as well if there is interest, and once I have done perplexity and evaluation comparisons, but at this time 32g models are still not fully tested with AutoAWQ and vLLM.
Models are released as sharded safetensors files.
| Branch | Bits | GS | AWQ Dataset | Seq Len | Size |
| ------ | ---- | -- | ----------- | ------- | ---- |
| [main](https://huggingface.co/TheBloke/Athena-v2-AWQ/tree/main) | 4 | 128 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 7.25 GB
<!-- README_AWQ.md-provided-files end -->
<!-- README_AWQ.md-use-from-vllm start -->
## Serving this model from vLLM
Documentation on installing and using vLLM [can be found here](https://vllm.readthedocs.io/en/latest/).
- When using vLLM as a server, pass the `--quantization awq` parameter, for example:
```shell
python3 python -m vllm.entrypoints.api_server --model TheBloke/Athena-v2-AWQ --quantization awq --dtype half
```
When using vLLM from Python code, pass the `quantization=awq` parameter, for example:
```python
from vllm import LLM, SamplingParams
prompts = [
"Hello, my name is",
"The president of the United States is",
"The capital of France is",
"The future of AI is",
]
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
llm = LLM(model="TheBloke/Athena-v2-AWQ", quantization="awq", dtype="half")
outputs = llm.generate(prompts, sampling_params)
# Print the outputs.
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
```
<!-- README_AWQ.md-use-from-vllm start -->
<!-- README_AWQ.md-use-from-python start -->
## How to use this AWQ model from Python code
### Install the necessary packages
Requires: [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) 0.0.2 or later
```shell
pip3 install autoawq
```
If you have problems installing [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) using the pre-built wheels, install it from source instead:
```shell
pip3 uninstall -y autoawq
git clone https://github.com/casper-hansen/AutoAWQ
cd AutoAWQ
pip3 install .
```
### You can then try the following example code
```python
from awq import AutoAWQForCausalLM
from transformers import AutoTokenizer
model_name_or_path = "TheBloke/Athena-v2-AWQ"
# Load model
model = AutoAWQForCausalLM.from_quantized(model_name_or_path, fuse_layers=True,
trust_remote_code=False, safetensors=True)
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, trust_remote_code=False)
prompt = "Tell me about AI"
prompt_template=f'''Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
'''
print("\n\n*** Generate:")
tokens = tokenizer(
prompt_template,
return_tensors='pt'
).input_ids.cuda()
# Generate output
generation_output = model.generate(
tokens,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
max_new_tokens=512
)
print("Output: ", tokenizer.decode(generation_output[0]))
# Inference can also be done using transformers' pipeline
from transformers import pipeline
print("*** Pipeline:")
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
repetition_penalty=1.1
)
print(pipe(prompt_template)[0]['generated_text'])
```
<!-- README_AWQ.md-use-from-python end -->
<!-- README_AWQ.md-compatibility start -->
## Compatibility
The files provided are tested to work with [AutoAWQ](https://github.com/casper-hansen/AutoAWQ), and [vLLM](https://github.com/vllm-project/vllm).
[Huggingface Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) is not yet compatible with AWQ, but a PR is open which should bring support soon: [TGI PR #781](https://github.com/huggingface/text-generation-inference/issues/781).
<!-- README_AWQ.md-compatibility end -->
<!-- footer start -->
<!-- 200823 -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute
Thanks to the [chirper.ai](https://chirper.ai) team!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Aemon Algiz.
**Patreon special mentions**: Alicia Loh, Stephen Murray, K, Ajan Kanaga, RoA, Magnesian, Deo Leter, Olakabola, Eugene Pentland, zynix, Deep Realms, Raymond Fosdick, Elijah Stavena, Iucharbius, Erik Bjäreholt, Luis Javier Navarrete Lozano, Nicholas, theTransient, John Detwiler, alfie_i, knownsqashed, Mano Prime, Willem Michiel, Enrico Ros, LangChain4j, OG, Michael Dempsey, Pierre Kircher, Pedro Madruga, James Bentley, Thomas Belote, Luke @flexchar, Leonard Tan, Johann-Peter Hartmann, Illia Dulskyi, Fen Risland, Chadd, S_X, Jeff Scroggin, Ken Nordquist, Sean Connelly, Artur Olbinski, Swaroop Kallakuri, Jack West, Ai Maven, David Ziegler, Russ Johnson, transmissions 11, John Villwock, Alps Aficionado, Clay Pascal, Viktor Bowallius, Subspace Studios, Rainer Wilmers, Trenton Dambrowitz, vamX, Michael Levine, 준교 김, Brandon Frisco, Kalila, Trailburnt, Randy H, Talal Aujan, Nathan Dryer, Vadim, 阿明, ReadyPlayerEmma, Tiffany J. Kim, George Stoitzev, Spencer Kim, Jerry Meng, Gabriel Tamborski, Cory Kujawski, Jeffrey Morgan, Spiking Neurons AB, Edmond Seymore, Alexandros Triantafyllidis, Lone Striker, Cap'n Zoog, Nikolai Manek, danny, ya boyyy, Derek Yates, usrbinkat, Mandus, TL, Nathan LeClaire, subjectnull, Imad Khwaja, webtim, Raven Klaugh, Asp the Wyvern, Gabriel Puliatti, Caitlyn Gatomon, Joseph William Delisle, Jonathan Leane, Luke Pendergrass, SuperWojo, Sebastain Graf, Will Dee, Fred von Graf, Andrey, Dan Guido, Daniel P. Andersen, Nitin Borwankar, Elle, Vitor Caleffi, biorpg, jjj, NimbleBox.ai, Pieter, Matthew Berman, terasurfer, Michael Davis, Alex, Stanislav Ovsiannikov
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
# Original model card: IkariDev and Undi95's Athena V2
Experimental Athena v2 model. Use Alpaca format.
<!-- description start -->
## Description
This repo contains fp16 files of Athena-V2.
[GGUF](https://huggingface.co/IkariDev/Athena-v2-GGUF)
[exl2 - by AzureBlack](https://huggingface.co/AzureBlack/Athena-v2-6.0bit-exl2)
<!-- description end -->
<!-- description start -->
## Models and loras used
- Xwin-LM/Xwin-LM-13B-V0.1
- Undi95/ReMM-v2.2-L2-13B
- Undi95/MLewd-L2-13B-v2-3
- Brouz/Slerpeno
- boomerchan/Magpie-13b
```
[Xwin (0.30) + ReMM v2.2 (0.70)](0.45) x [[Xwin (0.40) + MLewd v2-3 (0.60)](0.80) + [Slerpeno(0.50) + Magpie-13b(0.50)](0.20)](0.55)
```
<!-- description end -->
<!-- prompt-template start -->
## Prompt template: Alpaca
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
```
HUGE thanks to [Undi95](https://huggingface.co/Undi95) for doing the merging (Recipe was my idea, he merged)
To TheBloke: please if you quant this, please include [IkariDev](https://huggingface.co/IkariDev) + [Undi95](https://huggingface.co/Undi95) in all the credits/links to the creator.
|
Bharatdeep-H/portfolio-query-qwen-2.5-7b-v5-cot | Bharatdeep-H | "2024-10-07T00:26:44Z" | 34 | 0 | transformers | [
"transformers",
"pytorch",
"qwen2",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"conversational",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-10-07T00:16:14Z" | ---
base_model: unsloth/qwen2.5-7b-instruct-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
- sft
---
# Uploaded model
- **Developed by:** Bharatdeep-H
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen2.5-7b-instruct-bnb-4bit
This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
luukromeijn/lncRNA-BERT-kmer-k3-pretrained | luukromeijn | "2025-01-24T10:18:55Z" | 18 | 0 | null | [
"safetensors",
"model_hub_mixin",
"pytorch_model_hub_mixin",
"license:mit",
"region:us"
] | null | "2025-01-09T15:00:42Z" | ---
tags:
- model_hub_mixin
- pytorch_model_hub_mixin
license: mit
---
LncRNA-BERT model, pre-trained on human mRNA/lncRNA data from GENCODE, RefSeq, and NONCODE.
Used by the [lncRNA-Py](https://github.com/luukromeijn/lncRNA-Py) package, which is required to run the model.
Encoding method: K-mer Tokenization, with k=3.
From Romeijn et al. (2025) [*LncRNA-BERT: An RNA Language Model for Classifying Coding and Long Non-Coding RNA*](https://doi.org/10.1101/2025.01.09.632168) |
davidschulte/ESM_ScandEval__norec-mini_default | davidschulte | "2025-03-26T13:26:50Z" | 13 | 0 | null | [
"safetensors",
"embedding_space_map",
"BaseLM:bert-base-multilingual-uncased",
"dataset:ScandEval/norec-mini",
"base_model:google-bert/bert-base-multilingual-uncased",
"base_model:finetune:google-bert/bert-base-multilingual-uncased",
"license:apache-2.0",
"region:us"
] | null | "2024-11-10T13:22:45Z" | ---
base_model: bert-base-multilingual-uncased
datasets:
- ScandEval/norec-mini
license: apache-2.0
tags:
- embedding_space_map
- BaseLM:bert-base-multilingual-uncased
---
# ESM ScandEval/norec-mini
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
ESM
- **Developed by:** David Schulte
- **Model type:** ESM
- **Base Model:** bert-base-multilingual-uncased
- **Intermediate Task:** ScandEval/norec-mini
- **ESM architecture:** linear
- **ESM embedding dimension:** 768
- **Language(s) (NLP):** [More Information Needed]
- **License:** Apache-2.0 license
- **ESM version:** 0.1.0
## Training Details
### Intermediate Task
- **Task ID:** ScandEval/norec-mini
- **Subset [optional]:** default
- **Text Column:** text
- **Label Column:** label
- **Dataset Split:** train
- **Sample size [optional]:** 1024
- **Sample seed [optional]:**
### Training Procedure [optional]
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Language Model Training Hyperparameters [optional]
- **Epochs:** 3
- **Batch size:** 32
- **Learning rate:** 2e-05
- **Weight Decay:** 0.01
- **Optimizer**: AdamW
### ESM Training Hyperparameters [optional]
- **Epochs:** 10
- **Batch size:** 32
- **Learning rate:** 0.001
- **Weight Decay:** 0.01
- **Optimizer**: AdamW
### Additional trainiung details [optional]
## Model evaluation
### Evaluation of fine-tuned language model [optional]
### Evaluation of ESM [optional]
MSE:
### Additional evaluation details [optional]
## What are Embedding Space Maps used for?
Embedding Space Maps are a part of ESM-LogME, a efficient method for finding intermediate datasets for transfer learning. There are two reasons to use ESM-LogME:
### You don't have enough training data for your problem
If you don't have a enough training data for your problem, just use ESM-LogME to find more.
You can supplement model training by including publicly available datasets in the training process.
1. Fine-tune a language model on suitable intermediate dataset.
2. Fine-tune the resulting model on your target dataset.
This workflow is called intermediate task transfer learning and it can significantly improve the target performance.
But what is a suitable dataset for your problem? ESM-LogME enable you to quickly rank thousands of datasets on the Hugging Face Hub by how well they are exptected to transfer to your target task.
### You want to find similar datasets to your target dataset
Using ESM-LogME can be used like search engine on the Hugging Face Hub. You can find similar tasks to your target task without having to rely on heuristics. ESM-LogME estimates how language models fine-tuned on each intermediate task would benefinit your target task. This quantitative approach combines the effects of domain similarity and task similarity.
## How can I use ESM-LogME / ESMs?
[](https://pypi.org/project/hf-dataset-selector)
We release **hf-dataset-selector**, a Python package for intermediate task selection using Embedding Space Maps.
**hf-dataset-selector** fetches ESMs for a given language model and uses it to find the best dataset for applying intermediate training to the target task. ESMs are found by their tags on the Huggingface Hub.
```python
from hfselect import Dataset, compute_task_ranking
# Load target dataset from the Hugging Face Hub
dataset = Dataset.from_hugging_face(
name="stanfordnlp/imdb",
split="train",
text_col="text",
label_col="label",
is_regression=False,
num_examples=1000,
seed=42
)
# Fetch ESMs and rank tasks
task_ranking = compute_task_ranking(
dataset=dataset,
model_name="bert-base-multilingual-uncased"
)
# Display top 5 recommendations
print(task_ranking[:5])
```
```python
1. davanstrien/test_imdb_embedd2 Score: -0.618529
2. davanstrien/test_imdb_embedd Score: -0.618644
3. davanstrien/test1 Score: -0.619334
4. stanfordnlp/imdb Score: -0.619454
5. stanfordnlp/sst Score: -0.62995
```
| Rank | Task ID | Task Subset | Text Column | Label Column | Task Split | Num Examples | ESM Architecture | Score |
|-------:|:------------------------------|:----------------|:--------------|:---------------|:-------------|---------------:|:-------------------|----------:|
| 1 | davanstrien/test_imdb_embedd2 | default | text | label | train | 10000 | linear | -0.618529 |
| 2 | davanstrien/test_imdb_embedd | default | text | label | train | 10000 | linear | -0.618644 |
| 3 | davanstrien/test1 | default | text | label | train | 10000 | linear | -0.619334 |
| 4 | stanfordnlp/imdb | plain_text | text | label | train | 10000 | linear | -0.619454 |
| 5 | stanfordnlp/sst | dictionary | phrase | label | dictionary | 10000 | linear | -0.62995 |
| 6 | stanfordnlp/sst | default | sentence | label | train | 8544 | linear | -0.63312 |
| 7 | kuroneko5943/snap21 | CDs_and_Vinyl_5 | sentence | label | train | 6974 | linear | -0.634365 |
| 8 | kuroneko5943/snap21 | Video_Games_5 | sentence | label | train | 6997 | linear | -0.638787 |
| 9 | kuroneko5943/snap21 | Movies_and_TV_5 | sentence | label | train | 6989 | linear | -0.639068 |
| 10 | fancyzhx/amazon_polarity | amazon_polarity | content | label | train | 10000 | linear | -0.639718 |
For more information on how to use ESMs please have a look at the [official Github repository](https://github.com/davidschulte/hf-dataset-selector). We provide documentation further documentation and tutorials for finding intermediate datasets and training your own ESMs.
## How do Embedding Space Maps work?
<!-- This section describes the evaluation protocols and provides the results. -->
Embedding Space Maps (ESMs) are neural networks that approximate the effect of fine-tuning a language model on a task. They can be used to quickly transform embeddings from a base model to approximate how a fine-tuned model would embed the the input text.
ESMs can be used for intermediate task selection with the ESM-LogME workflow.
## How can I use Embedding Space Maps for Intermediate Task Selection?
## Citation
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
If you are using this Embedding Space Maps, please cite our [paper](https://aclanthology.org/2024.emnlp-main.529/).
**BibTeX:**
```
@inproceedings{schulte-etal-2024-less,
title = "Less is More: Parameter-Efficient Selection of Intermediate Tasks for Transfer Learning",
author = "Schulte, David and
Hamborg, Felix and
Akbik, Alan",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.emnlp-main.529/",
doi = "10.18653/v1/2024.emnlp-main.529",
pages = "9431--9442",
abstract = "Intermediate task transfer learning can greatly improve model performance. If, for example, one has little training data for emotion detection, first fine-tuning a language model on a sentiment classification dataset may improve performance strongly. But which task to choose for transfer learning? Prior methods producing useful task rankings are infeasible for large source pools, as they require forward passes through all source language models. We overcome this by introducing Embedding Space Maps (ESMs), light-weight neural networks that approximate the effect of fine-tuning a language model. We conduct the largest study on NLP task transferability and task selection with 12k source-target pairs. We find that applying ESMs on a prior method reduces execution time and disk space usage by factors of 10 and 278, respectively, while retaining high selection performance (avg. regret@5 score of 2.95)."
}
```
**APA:**
```
Schulte, D., Hamborg, F., & Akbik, A. (2024, November). Less is More: Parameter-Efficient Selection of Intermediate Tasks for Transfer Learning. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing (pp. 9431-9442).
```
## Additional Information
|
OneFly7/T5-base-finetuned-on-eredat-kelm-Q4-epoch10 | OneFly7 | "2024-06-03T07:04:28Z" | 107 | 0 | transformers | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | "2024-05-27T12:29:55Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **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] |
LoneStriker/neuronovo-7B-v0.3-3.0bpw-h6-exl2 | LoneStriker | "2024-01-11T04:45:31Z" | 7 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"en",
"dataset:Intel/orca_dpo_pairs",
"dataset:mlabonne/chatml_dpo_pairs",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-01-11T04:43:55Z" | ---
license: apache-2.0
datasets:
- Intel/orca_dpo_pairs
- mlabonne/chatml_dpo_pairs
language:
- en
library_name: transformers
---
More information about previous [Neuronovo/neuronovo-7B-v0.2](https://huggingface.co/Neuronovo/neuronovo-7B-v0.2) version available here: 🔗[Don't stop DPOptimizing!](https://www.linkedin.com/pulse/dont-stop-dpoptimizing-jan-koco%2525C5%252584-mq4qf)
Author: Jan Kocoń 🔗[LinkedIn](https://www.linkedin.com/in/jankocon/) 🔗[Google Scholar](https://scholar.google.com/citations?user=pmQHb5IAAAAJ&hl=en&oi=ao) 🔗[ResearchGate](https://www.researchgate.net/profile/Jan-Kocon-2)
Changes concerning [Neuronovo/neuronovo-7B-v0.2](https://huggingface.co/Neuronovo/neuronovo-7B-v0.2):
1. **Training Dataset**: In addition to the [Intel/orca_dpo_pairs](Intel/orca_dpo_pairs) dataset, this version incorporates a [mlabonne/chatml_dpo_pairs](https://huggingface.co/datasets/mlabonne/chatml_dpo_pairs). The combined datasets enhance the model's capabilities in dialogues and interactive scenarios, further specializing it in natural language understanding and response generation.
2. **Tokenizer and Formatting**: The tokenizer now originates directly from the [Neuronovo/neuronovo-7B-v0.2](https://huggingface.co/Neuronovo/neuronovo-7B-v0.2) model.
3. **Training Configuration**: The training approach has shifted from using `max_steps=200` to `num_train_epochs=1`. This represents a change in the training strategy, focusing on epoch-based training rather than a fixed number of steps.
4. **Learning Rate**: The learning rate has been reduced to a smaller value of `5e-6`. This finer learning rate allows for more precise adjustments during the training process, potentially leading to better model performance. |
JOSESMOKE/tear_137 | JOSESMOKE | "2025-03-11T03:24:30Z" | 0 | 0 | null | [
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] | any-to-any | "2025-03-11T03:15:33Z" | ---
license: mit
tags:
- any-to-any
- omega
- omegalabs
- bittensor
- agi
---
This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet.
Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
|
wrbit01/donald_trump | wrbit01 | "2025-02-28T13:23:13Z" | 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 | "2025-02-28T12:58:51Z" | ---
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: DT
---
# Donald_Trump
<Gallery />
Trained on Replicate using:
https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `DT` to trigger the image generation.
## 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('wrbit01/donald_trump', weight_name='lora.safetensors')
image = pipeline('your prompt').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)
|
mradermacher/1852-Dark-9B-i1-GGUF | mradermacher | "2025-01-17T15:00:24Z" | 597 | 1 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:ClaudioItaly/1852-Dark-9B",
"base_model:quantized:ClaudioItaly/1852-Dark-9B",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | "2025-01-17T13:30:49Z" | ---
base_model: ClaudioItaly/1852-Dark-9B
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/ClaudioItaly/1852-Dark-9B
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/1852-Dark-9B-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/1852-Dark-9B-i1-GGUF/resolve/main/1852-Dark-9B.i1-IQ1_S.gguf) | i1-IQ1_S | 2.5 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/1852-Dark-9B-i1-GGUF/resolve/main/1852-Dark-9B.i1-IQ1_M.gguf) | i1-IQ1_M | 2.6 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/1852-Dark-9B-i1-GGUF/resolve/main/1852-Dark-9B.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.9 | |
| [GGUF](https://huggingface.co/mradermacher/1852-Dark-9B-i1-GGUF/resolve/main/1852-Dark-9B.i1-IQ2_XS.gguf) | i1-IQ2_XS | 3.2 | |
| [GGUF](https://huggingface.co/mradermacher/1852-Dark-9B-i1-GGUF/resolve/main/1852-Dark-9B.i1-IQ2_S.gguf) | i1-IQ2_S | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/1852-Dark-9B-i1-GGUF/resolve/main/1852-Dark-9B.i1-IQ2_M.gguf) | i1-IQ2_M | 3.5 | |
| [GGUF](https://huggingface.co/mradermacher/1852-Dark-9B-i1-GGUF/resolve/main/1852-Dark-9B.i1-Q2_K_S.gguf) | i1-Q2_K_S | 3.7 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/1852-Dark-9B-i1-GGUF/resolve/main/1852-Dark-9B.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 3.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/1852-Dark-9B-i1-GGUF/resolve/main/1852-Dark-9B.i1-Q2_K.gguf) | i1-Q2_K | 3.9 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/1852-Dark-9B-i1-GGUF/resolve/main/1852-Dark-9B.i1-IQ3_XS.gguf) | i1-IQ3_XS | 4.2 | |
| [GGUF](https://huggingface.co/mradermacher/1852-Dark-9B-i1-GGUF/resolve/main/1852-Dark-9B.i1-IQ3_S.gguf) | i1-IQ3_S | 4.4 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/1852-Dark-9B-i1-GGUF/resolve/main/1852-Dark-9B.i1-Q3_K_S.gguf) | i1-Q3_K_S | 4.4 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/1852-Dark-9B-i1-GGUF/resolve/main/1852-Dark-9B.i1-IQ3_M.gguf) | i1-IQ3_M | 4.6 | |
| [GGUF](https://huggingface.co/mradermacher/1852-Dark-9B-i1-GGUF/resolve/main/1852-Dark-9B.i1-Q3_K_M.gguf) | i1-Q3_K_M | 4.9 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/1852-Dark-9B-i1-GGUF/resolve/main/1852-Dark-9B.i1-Q3_K_L.gguf) | i1-Q3_K_L | 5.2 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/1852-Dark-9B-i1-GGUF/resolve/main/1852-Dark-9B.i1-IQ4_XS.gguf) | i1-IQ4_XS | 5.3 | |
| [GGUF](https://huggingface.co/mradermacher/1852-Dark-9B-i1-GGUF/resolve/main/1852-Dark-9B.i1-IQ4_NL.gguf) | i1-IQ4_NL | 5.5 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/1852-Dark-9B-i1-GGUF/resolve/main/1852-Dark-9B.i1-Q4_0.gguf) | i1-Q4_0 | 5.6 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/1852-Dark-9B-i1-GGUF/resolve/main/1852-Dark-9B.i1-Q4_K_S.gguf) | i1-Q4_K_S | 5.6 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/1852-Dark-9B-i1-GGUF/resolve/main/1852-Dark-9B.i1-Q4_K_M.gguf) | i1-Q4_K_M | 5.9 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/1852-Dark-9B-i1-GGUF/resolve/main/1852-Dark-9B.i1-Q4_1.gguf) | i1-Q4_1 | 6.1 | |
| [GGUF](https://huggingface.co/mradermacher/1852-Dark-9B-i1-GGUF/resolve/main/1852-Dark-9B.i1-Q5_K_S.gguf) | i1-Q5_K_S | 6.6 | |
| [GGUF](https://huggingface.co/mradermacher/1852-Dark-9B-i1-GGUF/resolve/main/1852-Dark-9B.i1-Q5_K_M.gguf) | i1-Q5_K_M | 6.7 | |
| [GGUF](https://huggingface.co/mradermacher/1852-Dark-9B-i1-GGUF/resolve/main/1852-Dark-9B.i1-Q6_K.gguf) | i1-Q6_K | 7.7 | practically like static Q6_K |
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 -->
|
neuro-symbolic-ai/eb-langvae-bert-base-cased-phi-4-l128 | neuro-symbolic-ai | "2025-03-19T12:11:57Z" | 0 | 0 | pythae | [
"pythae",
"en",
"license:apache-2.0",
"region:us"
] | null | "2025-03-19T12:10:00Z" | ---
language: en
tags:
- pythae
license: apache-2.0
---
### Downloading this model from the Hub
This model was trained with pythae. It can be downloaded or reloaded using the method `load_from_hf_hub`
```python
>>> from pythae.models import AutoModel
>>> model = AutoModel.load_from_hf_hub(hf_hub_path="your_hf_username/repo_name")
```
|
TobiGeth/tg_user_499499421_lora_1740659805 | TobiGeth | "2025-02-27T12:49:26Z" | 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 | "2025-02-27T12:49:25Z" | ---
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: USER_499499421_1740659805
---
# Tg_User_499499421_Lora_1740659805
<Gallery />
Trained on Replicate using:
https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `USER_499499421_1740659805` to trigger the image generation.
## 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('TobiGeth/tg_user_499499421_lora_1740659805', weight_name='lora.safetensors')
image = pipeline('your prompt').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)
|
distily/distily_miles_projector_experiment | distily | "2024-08-23T15:33:14Z" | 5 | 0 | Distily | [
"Distily",
"tensorboard",
"safetensors",
"gpt2",
"bitnet",
"1.58b",
"generated_from_trainer",
"dataset:wikimedia/wikipedia",
"base_model:openai-community/gpt2",
"base_model:finetune:openai-community/gpt2",
"license:mit",
"region:us"
] | null | "2024-08-23T06:47:54Z" | ---
base_model: gpt2
datasets:
- wikimedia/wikipedia
library_name: Distily
license: mit
tags:
- bitnet
- 1.58b
- generated_from_trainer
model-index:
- name: distily_miles_projector_experiment
results: []
---
# Summary
Distilled with [Distily](https://github.com/lapp0/distily) library
using teacher model [gpt2](https://huggingface.co/gpt2)
on dataset [wikimedia/wikipedia](https://huggingface.co/datasets/wikimedia/wikipedia).
<!-- 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.
# Model description
More information needed
# Intended uses & limitations
More information needed
-->
# Model Architecture:
- **Architecture**: `GPT2LMHeadModel`
- **Total Parameters**: 124,439,808
- **Data Type (dtype)**: torch.bfloat16
- **Model Size**: 0.24 GB
# Evaluation Metrics Comparison
| step | epoch | enwikippl | frwikippl | loss | runtime | samples_per_second | steps_per_second | tinystoriesppl | zhwikippl |
| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
| **teacher eval** | | 36.25 | 77.0 | | | | | 11.75 | 21.375 |
| 0 | 0 | 10788957847552.0 | 93458488360960.0 | 23.9652 | 41.1128 | 60.808 | 7.613 | 3539992576.0 | 57174604644352.0 |
| 2500 | 0.0404 | 888.0 | 5536.0 | 3.2958 | 40.0823 | 62.372 | 7.809 | 492.0 | 4576.0 |
| 5000 | 0.0808 | 380.0 | 1448.0 | 2.4808 | 41.6839 | 59.975 | 7.509 | 255.0 | 400.0 |
| 7500 | 0.1212 | 250.0 | 748.0 | 2.1083 | 44.1725 | 56.596 | 7.086 | 197.0 | 233.0 |
| 10000 | 0.1616 | 189.0 | 616.0 | 1.8890 | 43.9453 | 56.889 | 7.122 | 156.0 | 216.0 |
| 12500 | 0.2020 | 140.0 | 488.0 | 1.6027 | 42.1657 | 59.29 | 7.423 | 119.0 | 178.0 |
| 15000 | 0.2424 | 113.5 | 434.0 | 1.4410 | 42.3062 | 59.093 | 7.398 | 94.0 | 183.0 |
| 17500 | 0.2828 | 92.5 | 340.0 | 1.3090 | 42.413 | 58.944 | 7.38 | 76.5 | 165.0 |
| 20000 | 0.3232 | 79.5 | 308.0 | 1.1661 | 40.1951 | 62.197 | 7.787 | 73.0 | 151.0 |
| 22500 | 0.3636 | 68.0 | 229.0 | 0.9997 | 41.1581 | 60.741 | 7.605 | 56.75 | 122.5 |
| 25000 | 0.4040 | 63.25 | 201.0 | 0.9359 | 40.9228 | 61.091 | 7.649 | 50.75 | 99.5 |
| 27500 | 0.4444 | 59.25 | 218.0 | 0.8936 | 40.1195 | 62.314 | 7.802 | 46.25 | 116.5 |
| 30000 | 0.4848 | 59.25 | 204.0 | 0.8841 | 42.297 | 59.106 | 7.4 | 49.75 | 87.0 |
| 32500 | 0.5253 | 57.5 | 184.0 | 0.8730 | 40.8597 | 61.185 | 7.66 | 44.25 | 101.5 |
| 35000 | 0.5657 | 56.0 | 177.0 | 0.8049 | 44.9443 | 55.624 | 6.964 | 39.75 | 62.25 |
| 37500 | 0.6061 | 55.0 | 163.0 | 0.7798 | 44.8966 | 55.684 | 6.972 | 43.5 | 93.5 |
| 40000 | 0.6465 | 52.0 | 166.0 | 0.7611 | 40.5252 | 61.69 | 7.724 | 37.25 | 73.5 |
| 42500 | 0.6869 | 51.5 | 159.0 | 0.7336 | 41.7519 | 59.878 | 7.497 | 38.5 | 70.0 |
| 45000 | 0.7273 | 46.25 | 143.0 | 0.6241 | 40.2456 | 62.119 | 7.777 | 32.25 | 54.5 |
| 47500 | 0.7677 | 45.75 | 136.0 | 0.5998 | 42.1189 | 59.356 | 7.431 | 31.5 | 43.75 |
| 50000 | 0.8081 | 45.25 | 135.0 | 0.5841 | 40.1272 | 62.302 | 7.8 | 31.0 | 43.75 |
| 52500 | 0.8485 | 44.25 | 128.0 | 0.5705 | 41.9206 | 59.637 | 7.466 | 31.25 | 43.25 |
| 55000 | 0.8889 | 43.5 | 125.5 | 0.5532 | 40.1106 | 62.328 | 7.803 | 29.875 | 38.25 |
| 57500 | 0.9293 | 43.5 | 125.5 | 0.5470 | 40.2997 | 62.035 | 7.767 | 29.875 | 38.0 |
| 60000 | 0.9697 | 43.5 | 126.0 | 0.5432 | 39.9729 | 62.542 | 7.83 | 29.625 | 37.5 |
| 61875 | 1.0 | 43.5 | 126.0 | 0.5426 | 41.9287 | 59.625 | 7.465 | 29.625 | 37.5 |
# Resource Usage Comparison
- VRAM Use: 7.7831 GB
# Distillation (Teacher -> Student) Architecture Difference:
- **Architecture**: `GPT2LMHeadModel` -> `GPT2LMHeadModel`
- **Total Parameters**: 124,439,808 -> 124,439,808
- **Data Type (dtype)**: torch.bfloat16 -> torch.bfloat16
- **Model Size**: 0.24 GB -> 0.24 GB
<details>
<summary>Module Diff Details</summary>
```diff
```
</details>
<br/>
# Train Dataset
Trained on 145,744,973 tokens from the [wikimedia/wikipedia](https://huggingface.co/datasets/wikimedia/wikipedia) dataset.
- Num Samples: `247,500`
- Subset: `20231101.en`
- Split: `train`
# Training Objective
```
DistillationObjective(logits_loss_component=LossComponent(label=logits, weight=1, loss_fn=kl), attn_loss_component=LossComponent(label=attn, weight=25.0, loss_fn=raw_mse, layer_mapper=layer-2))
```
# Hyperparameters
The following hyperparameters were used during training:
<details>
<summary>Expand</summary>
- learning_rate: `0.0001`
- train_batch_size: `4`
- eval_batch_size: `8`
- seed: `42`
- optimizer: `Adam with betas=(0.9,0.999) and epsilon=1e-08`
- lr_scheduler_type: `linear`
- lr_scheduler_warmup_ratio: `0.5`
- num_epochs: `1.0`
- distillation_objective: `DistillationObjective(logits_loss_component=LossComponent(label=logits, weight=1, loss_fn=kl), attn_loss_component=LossComponent(label=attn, weight=25.0, loss_fn=raw_mse, layer_mapper=layer-2))`
- train_embeddings: `True`
- lr_scheduler: `<torch.optim.lr_scheduler.LambdaLR object at 0x7fd7d547a6e0>`
- student_model_name_or_path: `None`
- student_config_name_or_path: `None`
- student_model_config: `None`
- reinitialize_weights: `None`
- copy_teacher_modules: `[('lm_head', False)]`
- student_model_as_bitnet: `True`
- student_model_compile: `False`
- dropout: `None`
- teacher_model_name_or_path: `gpt2`
- teacher_load_in_8bit: `False`
- teacher_load_in_4bit: `False`
- teacher_model_compile: `False`
- dataset_uri: `wikimedia/wikipedia`
- dataset_subset: `20231101.en`
- dataset_split: `train`
- dataset_column_name: `text`
- dataset_sample_size: `250000`
- dataset_test_size: `0.01`
- gradient_accumulation_steps: `1`
- weight_decay: `0.0`
- max_grad_norm: `1.0`
- warmup_ratio: `0.5`
- warmup_steps: `0`
- gradient_checkpointing: `True`
</details>
<br/>
# Framework Versions
- Distily 0.2.0
- Transformers 4.44.2
- Pytorch 2.3.0
- Datasets 2.21.0
|
macb/ppo-Huggy | macb | "2023-02-21T19:52:47Z" | 1 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] | reinforcement-learning | "2023-02-21T19:52:40Z" |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
library_name: ml-agents
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy
2. Step 1: Write your model_id: macb/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
ctoraman/RoBERTa-TR-medium-bpe-28k | ctoraman | "2022-04-20T06:48:33Z" | 4 | 0 | transformers | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"tr",
"dataset:oscar",
"arxiv:2204.08832",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | "2022-03-09T12:00:46Z" | ---
language:
- tr
tags:
- roberta
license: cc-by-nc-sa-4.0
datasets:
- oscar
---
# RoBERTa Turkish medium BPE 28k (uncased)
Pretrained model on Turkish language using a masked language modeling (MLM) objective. The model is uncased.
The pretrained corpus is OSCAR's Turkish split, but it is further filtered and cleaned.
Model architecture is similar to bert-medium (8 layers, 8 heads, and 512 hidden size). Tokenization algorithm is BPE. Vocabulary size is 28.6k.
The details and performance comparisons can be found at this paper:
https://arxiv.org/abs/2204.08832
The following code can be used for model loading and tokenization, example max length (514) can be changed:
```
model = AutoModel.from_pretrained([model_path])
#for sequence classification:
#model = AutoModelForSequenceClassification.from_pretrained([model_path], num_labels=[num_classes])
tokenizer = PreTrainedTokenizerFast(tokenizer_file=[file_path])
tokenizer.mask_token = "[MASK]"
tokenizer.cls_token = "[CLS]"
tokenizer.sep_token = "[SEP]"
tokenizer.pad_token = "[PAD]"
tokenizer.unk_token = "[UNK]"
tokenizer.bos_token = "[CLS]"
tokenizer.eos_token = "[SEP]"
tokenizer.model_max_length = 514
```
### BibTeX entry and citation info
```bibtex
@misc{https://doi.org/10.48550/arxiv.2204.08832,
doi = {10.48550/ARXIV.2204.08832},
url = {https://arxiv.org/abs/2204.08832},
author = {Toraman, Cagri and Yilmaz, Eyup Halit and Şahinuç, Furkan and Ozcelik, Oguzhan},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Impact of Tokenization on Language Models: An Analysis for Turkish},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution Non Commercial Share Alike 4.0 International}
}
```
|
liujr1980/mmodels | liujr1980 | "2022-05-11T12:14:52Z" | 4 | 0 | transformers | [
"transformers",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2022-03-08T03:01:46Z" | ## my first model
fine-tuned from distillbert |
Jonjew/LauraWiggins | Jonjew | "2025-03-31T14:08:08Z" | 0 | 0 | diffusers | [
"diffusers",
"text-to-image",
"lora",
"template:diffusion-lora",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:unknown",
"region:us"
] | text-to-image | "2025-03-31T14:08:05Z" | ---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- text: '-'
output:
url: images/wiggins.png
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: null
license: unknown
---
# Laura Wiggins
<Gallery />
## Model description
FROM https://civitai.com/models/1351915/laura-wiggins-sololora?modelVersionId=1527043
## Download model
Weights for this model are available in Safetensors format.
[Download](/Jonjew/LauraWiggins/tree/main) them in the Files & versions tab.
|
Holarissun/RM-helpful_helpful_contrast_loraR64_20000_gemma2b_lr1e-05_bs2_g4 | Holarissun | "2024-05-03T08:25:46Z" | 2 | 0 | peft | [
"peft",
"safetensors",
"trl",
"reward-trainer",
"generated_from_trainer",
"base_model:google/gemma-2b",
"base_model:adapter:google/gemma-2b",
"license:gemma",
"region:us"
] | null | "2024-05-03T08:25:43Z" | ---
license: gemma
library_name: peft
tags:
- trl
- reward-trainer
- generated_from_trainer
metrics:
- accuracy
base_model: google/gemma-2b
model-index:
- name: RM-helpful_helpful_contrast_loraR64_20000_gemma2b_lr1e-05_bs2_g4
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. -->
# RM-helpful_helpful_contrast_loraR64_20000_gemma2b_lr1e-05_bs2_g4
This model is a fine-tuned version of [google/gemma-2b](https://huggingface.co/google/gemma-2b) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0210
- Accuracy: 0.994
## 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: 1e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0218 | 1.0 | 2250 | 0.0235 | 0.9935 |
| 0.0247 | 2.0 | 4500 | 0.0210 | 0.994 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2 |
mradermacher/MT5-Gen5-MM-gemma-2-MTg2G-9B-GGUF | mradermacher | "2024-12-30T08:17:22Z" | 19 | 1 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:zelk12/MT5-Gen5-MM-gemma-2-MTg2G-9B",
"base_model:quantized:zelk12/MT5-Gen5-MM-gemma-2-MTg2G-9B",
"endpoints_compatible",
"region:us",
"conversational"
] | null | "2024-12-30T08:04:39Z" | ---
base_model: zelk12/MT5-Gen5-MM-gemma-2-MTg2G-9B
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/zelk12/MT5-Gen5-MM-gemma-2-MTg2G-9B
<!-- 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/MT5-Gen5-MM-gemma-2-MTg2G-9B-GGUF/resolve/main/MT5-Gen5-MM-gemma-2-MTg2G-9B.Q2_K.gguf) | Q2_K | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/MT5-Gen5-MM-gemma-2-MTg2G-9B-GGUF/resolve/main/MT5-Gen5-MM-gemma-2-MTg2G-9B.Q3_K_S.gguf) | Q3_K_S | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/MT5-Gen5-MM-gemma-2-MTg2G-9B-GGUF/resolve/main/MT5-Gen5-MM-gemma-2-MTg2G-9B.Q3_K_M.gguf) | Q3_K_M | 4.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/MT5-Gen5-MM-gemma-2-MTg2G-9B-GGUF/resolve/main/MT5-Gen5-MM-gemma-2-MTg2G-9B.Q3_K_L.gguf) | Q3_K_L | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/MT5-Gen5-MM-gemma-2-MTg2G-9B-GGUF/resolve/main/MT5-Gen5-MM-gemma-2-MTg2G-9B.IQ4_XS.gguf) | IQ4_XS | 5.3 | |
| [GGUF](https://huggingface.co/mradermacher/MT5-Gen5-MM-gemma-2-MTg2G-9B-GGUF/resolve/main/MT5-Gen5-MM-gemma-2-MTg2G-9B.Q4_K_S.gguf) | Q4_K_S | 5.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/MT5-Gen5-MM-gemma-2-MTg2G-9B-GGUF/resolve/main/MT5-Gen5-MM-gemma-2-MTg2G-9B.Q4_K_M.gguf) | Q4_K_M | 5.9 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/MT5-Gen5-MM-gemma-2-MTg2G-9B-GGUF/resolve/main/MT5-Gen5-MM-gemma-2-MTg2G-9B.Q5_K_S.gguf) | Q5_K_S | 6.6 | |
| [GGUF](https://huggingface.co/mradermacher/MT5-Gen5-MM-gemma-2-MTg2G-9B-GGUF/resolve/main/MT5-Gen5-MM-gemma-2-MTg2G-9B.Q5_K_M.gguf) | Q5_K_M | 6.7 | |
| [GGUF](https://huggingface.co/mradermacher/MT5-Gen5-MM-gemma-2-MTg2G-9B-GGUF/resolve/main/MT5-Gen5-MM-gemma-2-MTg2G-9B.Q6_K.gguf) | Q6_K | 7.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/MT5-Gen5-MM-gemma-2-MTg2G-9B-GGUF/resolve/main/MT5-Gen5-MM-gemma-2-MTg2G-9B.Q8_0.gguf) | Q8_0 | 9.9 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/MT5-Gen5-MM-gemma-2-MTg2G-9B-GGUF/resolve/main/MT5-Gen5-MM-gemma-2-MTg2G-9B.f16.gguf) | f16 | 18.6 | 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 -->
|
CheeLi03/whisper-base-tr-8 | CheeLi03 | "2024-10-17T07:17:36Z" | 6 | 0 | null | [
"tensorboard",
"safetensors",
"whisper",
"hf-asr-leaderboard",
"generated_from_trainer",
"tr",
"dataset:fleurs",
"base_model:openai/whisper-base",
"base_model:finetune:openai/whisper-base",
"license:apache-2.0",
"model-index",
"region:us"
] | null | "2024-10-17T03:10:42Z" | ---
base_model: openai/whisper-base
datasets:
- fleurs
language:
- tr
license: apache-2.0
metrics:
- wer
tags:
- hf-asr-leaderboard
- generated_from_trainer
model-index:
- name: Whisper Base Turkish 8000 - Chee Li
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: Google Fleurs
type: fleurs
config: tr_tr
split: None
args: 'config: tr split: test'
metrics:
- type: wer
value: 25.847853142501553
name: Wer
---
<!-- 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. -->
# Whisper Base Turkish 8000 - Chee Li
This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the Google Fleurs dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5649
- Wer: 25.8479
## 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: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 850
- training_steps: 8000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-------:|:----:|:---------------:|:-------:|
| 0.1634 | 5.5866 | 1000 | 0.4092 | 24.8833 |
| 0.0075 | 11.1732 | 2000 | 0.4509 | 24.2066 |
| 0.0024 | 16.7598 | 3000 | 0.4874 | 24.1910 |
| 0.0012 | 22.3464 | 4000 | 0.5125 | 24.3777 |
| 0.0008 | 27.9330 | 5000 | 0.5305 | 24.5644 |
| 0.0005 | 33.5196 | 6000 | 0.5473 | 24.8289 |
| 0.0004 | 39.1061 | 7000 | 0.5592 | 24.9922 |
| 0.0003 | 44.6927 | 8000 | 0.5649 | 25.8479 |
### Framework versions
- Transformers 4.43.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
kyeul611/bert-base-finetuned-ynat | kyeul611 | "2023-11-10T11:56:57Z" | 7 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:klue",
"base_model:klue/bert-base",
"base_model:finetune:klue/bert-base",
"license:cc-by-sa-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2023-11-10T11:34:50Z" | ---
license: cc-by-sa-4.0
base_model: klue/bert-base
tags:
- generated_from_trainer
datasets:
- klue
metrics:
- f1
model-index:
- name: bert-base-finetuned-ynat
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: klue
type: klue
config: ynat
split: validation
args: ynat
metrics:
- name: F1
type: f1
value: 0.8673393457362918
---
<!-- 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. -->
# bert-base-finetuned-ynat
This model is a fine-tuned version of [klue/bert-base](https://huggingface.co/klue/bert-base) on the klue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3817
- F1: 0.8673
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 256
- eval_batch_size: 256
- seed: 1
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 179 | 0.3817 | 0.8673 |
| No log | 2.0 | 358 | 0.4065 | 0.8634 |
| 0.2194 | 3.0 | 537 | 0.4077 | 0.8624 |
| 0.2194 | 4.0 | 716 | 0.4443 | 0.8584 |
| 0.2194 | 5.0 | 895 | 0.4795 | 0.8569 |
| 0.1477 | 6.0 | 1074 | 0.5159 | 0.8570 |
| 0.1477 | 7.0 | 1253 | 0.5445 | 0.8569 |
| 0.1477 | 8.0 | 1432 | 0.5711 | 0.8565 |
| 0.0849 | 9.0 | 1611 | 0.5913 | 0.8542 |
| 0.0849 | 10.0 | 1790 | 0.5945 | 0.8553 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
fifxus/7360a467-e128-4398-ba4f-76974fd9472c | fifxus | "2025-02-05T10:32:45Z" | 8 | 0 | peft | [
"peft",
"safetensors",
"qwen2",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/Qwen2.5-Math-7B-Instruct",
"base_model:adapter:unsloth/Qwen2.5-Math-7B-Instruct",
"license:apache-2.0",
"8-bit",
"bitsandbytes",
"region:us"
] | null | "2025-02-05T09:48:15Z" | ---
library_name: peft
license: apache-2.0
base_model: unsloth/Qwen2.5-Math-7B-Instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 7360a467-e128-4398-ba4f-76974fd9472c
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: unsloth/Qwen2.5-Math-7B-Instruct
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 66dccac5b1651544_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/66dccac5b1651544_train_data.json
type:
field_input: optimized_prompt
field_instruction: prompt
field_output: good_res
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
device_map: auto
do_eval: true
early_stopping_patience: null
eval_batch_size: 2
eval_max_new_tokens: 128
eval_steps: null
eval_table_size: null
evals_per_epoch: null
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
gradient_clipping: 1.0
group_by_length: true
hub_model_id: fifxus/7360a467-e128-4398-ba4f-76974fd9472c
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 0.0001
load_in_4bit: true
load_in_8bit: true
local_rank: null
logging_steps: 1
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_grad_norm: 1.0
max_memory:
0: 75GB
max_steps: 500
micro_batch_size: 2
mlflow_experiment_name: /tmp/66dccac5b1651544_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: null
saves_per_epoch: null
sequence_len: 1024
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: techspear-hub
wandb_mode: online
wandb_name: 2b9b33a3-d885-4d95-9d45-bd57487b706e
wandb_project: Gradients-On-10
wandb_run: your_name
wandb_runid: 2b9b33a3-d885-4d95-9d45-bd57487b706e
warmup_steps: 5
weight_decay: 0.01
xformers_attention: null
```
</details><br>
# 7360a467-e128-4398-ba4f-76974fd9472c
This model is a fine-tuned version of [unsloth/Qwen2.5-Math-7B-Instruct](https://huggingface.co/unsloth/Qwen2.5-Math-7B-Instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0254
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5
- training_steps: 500
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 2.0262 | 0.2946 | 500 | 2.0254 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
kostiantynk/934d17e1-d138-466a-9b90-05858017bc6d | kostiantynk | "2025-01-26T13:00:11Z" | 6 | 0 | peft | [
"peft",
"safetensors",
"gpt_neox",
"axolotl",
"generated_from_trainer",
"base_model:beomi/polyglot-ko-12.8b-safetensors",
"base_model:adapter:beomi/polyglot-ko-12.8b-safetensors",
"license:apache-2.0",
"region:us"
] | null | "2025-01-26T11:29:41Z" | ---
library_name: peft
license: apache-2.0
base_model: beomi/polyglot-ko-12.8b-safetensors
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 934d17e1-d138-466a-9b90-05858017bc6d
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: beomi/polyglot-ko-12.8b-safetensors
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- b1d75b8ed2b2d984_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/b1d75b8ed2b2d984_train_data.json
type:
field_input: input
field_instruction: instruction
field_output: output
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 4
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: false
group_by_length: false
hub_model_id: kostiantynk/934d17e1-d138-466a-9b90-05858017bc6d
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_steps: 10
micro_batch_size: 2
mlflow_experiment_name: /tmp/b1d75b8ed2b2d984_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 4
sequence_len: 512
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 8566324c-8942-404a-931b-8268fb7dd1cf
wandb_project: Mine-SN56-22-Gradients-On-Demand
wandb_run: your_name
wandb_runid: 8566324c-8942-404a-931b-8268fb7dd1cf
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 934d17e1-d138-466a-9b90-05858017bc6d
This model is a fine-tuned version of [beomi/polyglot-ko-12.8b-safetensors](https://huggingface.co/beomi/polyglot-ko-12.8b-safetensors) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5400
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 7.4129 | 0.0000 | 1 | 1.6822 |
| 6.893 | 0.0001 | 3 | 1.6813 |
| 7.3844 | 0.0001 | 6 | 1.6454 |
| 7.6698 | 0.0002 | 9 | 1.5400 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
rachana-rj-news-classification/stacking-classifier | rachana-rj-news-classification | "2024-12-17T04:04:33Z" | 48 | 0 | transformers | [
"transformers",
"joblib",
"safetensors",
"stacking_classifier",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-12-16T23:43:49Z" | ---
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]
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## 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).
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tarundachepally/EGL_Granite_TestModel2-Q6_K-GGUF | tarundachepally | "2025-02-22T07:12:46Z" | 0 | 0 | transformers | [
"transformers",
"gguf",
"llama-cpp",
"gguf-my-repo",
"base_model:tarundachepally/EGL_Granite_TestModel2",
"base_model:quantized:tarundachepally/EGL_Granite_TestModel2",
"endpoints_compatible",
"region:us",
"conversational"
] | null | "2025-02-22T07:12:32Z" | ---
library_name: transformers
tags:
- llama-cpp
- gguf-my-repo
base_model: tarundachepally/EGL_Granite_TestModel2
---
# tarundachepally/EGL_Granite_TestModel2-Q6_K-GGUF
This model was converted to GGUF format from [`tarundachepally/EGL_Granite_TestModel2`](https://huggingface.co/tarundachepally/EGL_Granite_TestModel2) 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/tarundachepally/EGL_Granite_TestModel2) 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 tarundachepally/EGL_Granite_TestModel2-Q6_K-GGUF --hf-file egl_granite_testmodel2-q6_k.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo tarundachepally/EGL_Granite_TestModel2-Q6_K-GGUF --hf-file egl_granite_testmodel2-q6_k.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 tarundachepally/EGL_Granite_TestModel2-Q6_K-GGUF --hf-file egl_granite_testmodel2-q6_k.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo tarundachepally/EGL_Granite_TestModel2-Q6_K-GGUF --hf-file egl_granite_testmodel2-q6_k.gguf -c 2048
```
|
W1lson/zephyr-book-3 | W1lson | "2023-12-05T00:41:20Z" | 52 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:HuggingFaceH4/zephyr-7b-beta",
"base_model:adapter:HuggingFaceH4/zephyr-7b-beta",
"region:us"
] | null | "2023-12-05T00:41:17Z" | ---
library_name: peft
base_model: HuggingFaceH4/zephyr-7b-beta
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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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]
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<!-- 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:**
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**APA:**
[More Information Needed]
## Glossary [optional]
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[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.6.3.dev0 |
Intel/whisper-base-onnx-int4-inc | Intel | "2023-10-16T08:46:37Z" | 4 | 9 | transformers | [
"transformers",
"onnx",
"whisper",
"automatic-speech-recognition",
"ONNX",
"Intel® Neural Compressor",
"neural-compressor",
"dataset:librispeech_asr",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | "2023-10-08T05:16:50Z" | ---
license: apache-2.0
datasets:
- librispeech_asr
metrics:
- wer
pipeline_tag: automatic-speech-recognition
tags:
- automatic-speech-recognition
- ONNX
- Intel® Neural Compressor
- neural-compressor
library_name: transformers
---
## INT4 Whisper base ONNX Model
Whisper is a pre-trained model for automatic speech recognition (ASR) and speech translation. Trained on 680k hours of labelled data, Whisper models demonstrate a strong ability to generalise to many datasets and domains without the need for fine-tuning. This is the repository of INT4 weight only quantization for the Whisper base model in ONNX format, powered by [Intel® Neural Compressor](https://github.com/intel/neural-compressor) and [Intel® Extension for Transformers](https://github.com/intel/intel-extension-for-transformers).
This INT4 ONNX model is generated by [Intel® Neural Compressor](https://github.com/intel/neural-compressor)'s weight-only quantization method.
| Model Detail | Description |
| ----------- | ----------- |
| Model Authors - Company | Intel |
| Date | October 8, 2023 |
| Version | 1 |
| Type | Speech Recognition |
| Paper or Other Resources | - |
| License | Apache 2.0 |
| Questions or Comments | [Community Tab](https://huggingface.co/Intel/whisper-base-onnx-int4/discussions)|
| Intended Use | Description |
| ----------- | ----------- |
| Primary intended uses | You can use the raw model for automatic speech recognition inference |
| Primary intended users | Anyone doing automatic speech recognition inference |
| Out-of-scope uses | This model in most cases will need to be fine-tuned for your particular task. The model should not be used to intentionally create hostile or alienating environments for people.|
### Export to ONNX Model
The FP32 model is exported with openai/whisper-base:
```shell
optimum-cli export onnx --model openai/whisper-base whisper-base-with-past/ --task automatic-speech-recognition-with-past --opset 13
```
### Install ONNX Runtime
Install `onnxruntime>=1.16.0` to support [`MatMulFpQ4`](https://github.com/microsoft/onnxruntime/blob/v1.16.0/docs/ContribOperators.md#com.microsoft.MatMulFpQ4) operator.
### Run Quantization
Build [Intel® Neural Compressor](https://github.com/intel/neural-compressor/tree/master) from master branch and run INT4 weight-only quantization.
The weight-only quantization cofiguration is as below:
| dtype | group_size | scheme | algorithm |
| :----- | :---------- | :------ | :--------- |
| INT4 | 32 | asym | RTN |
We provide the key code below. For the complete script, please refer to [whisper example](https://github.com/intel/intel-extension-for-transformers/tree/main/examples/huggingface/onnxruntime/speech-recognition/quantization).
```python
from neural_compressor import quantization, PostTrainingQuantConfig
from neural_compressor.utils.constant import FP32
model_list = ['encoder_model.onnx', 'decoder_model.onnx', 'decoder_with_past_model.onnx']
for model in model_list:
config = PostTrainingQuantConfig(
approach="weight_only",
calibration_sampling_size=[8],
op_type_dict={".*": {"weight": {"bits": 4,
"algorithm": ["RTN"],
"scheme": ["asym"],
"group_size": 32}}},)
q_model = quantization.fit(
os.path.join("/path/to/whisper-base-with-past", model), # FP32 model path
config,
calib_dataloader=dataloader)
q_model.save(os.path.join("/path/to/whisper-base-onnx-int4", model)) # INT4 model path
```
### Evaluation
**Operator Statistics**
Below shows the operator statistics in the INT4 ONNX model:
|Model| Op Type | Total | INT4 weight | FP32 weight |
|:-------:|:-------:|:-------:|:-------:|:-------:|
|encoder_model| MatMul | 48 | 36 | 12 |
|decoder_model| MatMul | 85 | 61 | 24 |
|decoder_with_past_model| MatMul | 73 | 49 | 24 |
**Evaluation of wer**
Evaluate the model on `librispeech_asr` dataset with below code:
```python
import os
from evaluate import load
from datasets import load_dataset
from transformers import WhisperForConditionalGeneration, WhisperProcessor, AutoConfig
model_name = 'openai/whisper-base'
model_path = 'whisper-base-onnx-int4'
processor = WhisperProcessor.from_pretrained(model_name)
model = WhisperForConditionalGeneration.from_pretrained(model_name)
config = AutoConfig.from_pretrained(model_name)
wer = load("wer")
librispeech_test_clean = load_dataset("librispeech_asr", "clean", split="test")
from optimum.onnxruntime import ORTModelForSpeechSeq2Seq
from transformers import PretrainedConfig
model_config = PretrainedConfig.from_pretrained(model_name)
predictions = []
references = []
sessions = ORTModelForSpeechSeq2Seq.load_model(
os.path.join(model_path, 'encoder_model.onnx'),
os.path.join(model_path, 'decoder_model.onnx'),
os.path.join(model_path, 'decoder_with_past_model.onnx'))
model = ORTModelForSpeechSeq2Seq(sessions[0], sessions[1], model_config, model_path, sessions[2])
for idx, batch in enumerate(librispeech_test_clean):
audio = batch["audio"]
input_features = processor(audio["array"], sampling_rate=audio["sampling_rate"], return_tensors="pt").input_features
reference = processor.tokenizer._normalize(batch['text'])
references.append(reference)
predicted_ids = model.generate(input_features)[0]
transcription = processor.decode(predicted_ids)
prediction = processor.tokenizer._normalize(transcription)
predictions.append(prediction)
wer_result = wer.compute(references=references, predictions=predictions)
print(f"Result wer: {wer_result * 100}")
```
## Metrics (Model Performance):
| Model | Model Size (MB) | wer |
|---|:---:|:---:|
| FP32 |465|5.04|
| INT4 |289|6.14|
|
1aurent/vit_small_patch16_224.medmae_in1k_mae | 1aurent | "2023-11-28T18:03:33Z" | 17 | 0 | timm | [
"timm",
"safetensors",
"image-classification",
"license:apache-2.0",
"region:us"
] | image-classification | "2023-11-28T18:03:29Z" | ---
tags:
- image-classification
- timm
library_name: timm
license: apache-2.0
---
# Model card for vit_small_patch16_224.medmae_in1k_mae
|
emresahin2501/gemma-try-001 | emresahin2501 | "2025-03-15T21:09:31Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2025-03-15T20:44:05Z" | ---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
Shannonjunior/52f9131f-abe0-4e1d-be33-9ee43266f653 | Shannonjunior | "2025-04-09T20:03:45Z" | 0 | 0 | null | [
"region:us"
] | null | "2025-04-09T20:03:32Z" | <!DOCTYPE html>
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content="width=device-width, initial-scale=1.0, user-scalable=no"
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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> |
arkiven4/phobert-sentiment | arkiven4 | "2025-02-22T17:20:00Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"roberta",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2025-02-22T15:04:00Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
# Usage
```python
from huggingface_hub import hf_hub_download
import torch
from transformers import (
AutoModel, AutoConfig, XLMRobertaModel, AutoTokenizer
)
class RobertaClass(torch.nn.Module):
def __init__(self):
super(RobertaClass, self).__init__()
self.l1 = XLMRobertaModel.from_pretrained("vinai/phobert-large")
self.l1.resize_token_embeddings(len(tokenizer))
self.pre_classifier = torch.nn.Linear(1024, 1024)
self.dropout = torch.nn.Dropout(0.3)
self.classifier = torch.nn.Linear(1024, 2)
def forward(self, input_ids, attention_mask, token_type_ids):
output_1 = self.l1(input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids)
hidden_state = output_1[0]
pooler = hidden_state[:, 0]
pooler = self.pre_classifier(pooler)
pooler = torch.nn.ReLU()(pooler)
pooler = self.dropout(pooler)
output = self.classifier(pooler)
return output
REPO_ID = "arkiven4/phobert-sentiment"
FILENAME = "pytorch_model_sentiment.bin"
model_original = torch.load(
hf_hub_download(repo_id=REPO_ID, filename=FILENAME), map_location=torch.device('cpu')
)
tokenizer = AutoTokenizer.from_pretrained("vinai/phobert-large", truncation=True, do_lower_case=True)
text = str('một mớ lỗi : không xem_lại được bài test , không hiện link ( ví_dụ như là youtube khi chia_sẻ ) không copy được từ không biết để dịch . làm_ơn cho về bản web đi')
text = " ".join(text.split())
inputs = tokenizer.encode_plus(
text,
None,
add_special_tokens=True,
max_length=256,
pad_to_max_length=True,
return_token_type_ids=True
)
ids = inputs['input_ids']
mask = inputs['attention_mask']
token_type_ids = inputs["token_type_ids"]
inputs = {
'ids': torch.tensor(ids, dtype=torch.long),
'mask': torch.tensor(mask, dtype=torch.long),
'token_type_ids': torch.tensor(token_type_ids, dtype=torch.long)
}
with torch.no_grad():
features = model_original(inputs['ids'].unsqueeze(0),
inputs['mask'].unsqueeze(0),
inputs['token_type_ids'].unsqueeze(0))
torch.argmax(features, dim=1)
# tensor([1])
```
## 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] |
AiTools2/snb01 | AiTools2 | "2025-03-07T04:57:25Z" | 0 | 0 | diffusers | [
"diffusers",
"text-to-image",
"flux",
"lora",
"template:sd-lora",
"fluxgym",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | "2025-03-07T02:52:15Z" | ---
tags:
- text-to-image
- flux
- lora
- diffusers
- template:sd-lora
- fluxgym
widget:
- output:
url: sample/snb01_005080_00_20250307045354.png
text: SNDB01
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: SNDB01
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
---
# Snb01
A Flux LoRA trained on a local computer with [Fluxgym](https://github.com/cocktailpeanut/fluxgym)
<Gallery />
## Trigger words
You should use `SNDB01` to trigger the image generation.
## Download model and use it with ComfyUI, AUTOMATIC1111, SD.Next, Invoke AI, Forge, etc.
Weights for this model are available in Safetensors format.
|
Xu-Ouyang/pythia-6.9b-deduped-int3-step93000-GPTQ-wikitext2 | Xu-Ouyang | "2024-09-04T03:28:22Z" | 76 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"3-bit",
"gptq",
"region:us"
] | text-generation | "2024-09-04T03:21:18Z" | ---
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] |
samoline/7e46456b-bb95-4670-ad3f-7b4f0e5ed902 | samoline | "2025-03-26T10:28:47Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/SmolLM-1.7B-Instruct",
"base_model:adapter:unsloth/SmolLM-1.7B-Instruct",
"license:apache-2.0",
"region:us"
] | null | "2025-03-26T10:24:51Z" | ---
library_name: peft
license: apache-2.0
base_model: unsloth/SmolLM-1.7B-Instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 7e46456b-bb95-4670-ad3f-7b4f0e5ed902
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: unsloth/SmolLM-1.7B-Instruct
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- f7d5f7ed1b27f3ce_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/f7d5f7ed1b27f3ce_train_data.json
type:
field_input: source
field_instruction: prompt
field_output: prompt_orig
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 4
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 1
gradient_checkpointing: false
group_by_length: false
hub_model_id: samoline/7e46456b-bb95-4670-ad3f-7b4f0e5ed902
hub_repo: samoline
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 4
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 4
lora_target_linear: true
lr_scheduler: cosine
max_steps: 2
micro_batch_size: 1
mlflow_experiment_name: /tmp/f7d5f7ed1b27f3ce_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 4
sequence_len: 512
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: samoline-nan
wandb_mode: online
wandb_name: ce460ca5-68a3-451f-bb08-2fcd333bedd8
wandb_project: Gradients-On-Demand
wandb_run: dev
wandb_runid: ce460ca5-68a3-451f-bb08-2fcd333bedd8
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 7e46456b-bb95-4670-ad3f-7b4f0e5ed902
This model is a fine-tuned version of [unsloth/SmolLM-1.7B-Instruct](https://huggingface.co/unsloth/SmolLM-1.7B-Instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: nan
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.0 | 0.0000 | 1 | nan |
| 0.0 | 0.0000 | 2 | nan |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
Portugal-vs-Denmark/Portugal.vs.Denmark.Live.Stream.Free | Portugal-vs-Denmark | "2025-03-23T19:19:05Z" | 0 | 0 | null | [
"region:us"
] | null | "2025-03-23T19:17:17Z" | 𝐆𝐨 𝐋𝐢𝐯𝐞 🔴 ➤ https://tvstream.fun/soccer/
𝐒𝐭𝐫𝐞𝐚𝐦 𝐋𝐢𝐯𝐞. 🔴 ➤ https://tvstream.fun/soccer/
LIVE: Portugal vs Denmark – UEFA Nations League quarterfinal second leg
Follow the build-up and live text commentary as Portugal seek to overturn a 1-0 first-leg deficit against Denmark.
Portugal vs Denmark LIVE streaming info, Nations League quarterfinal 2nd leg: Where to watch Ronaldo play? Predicted lineups; Preview
All you need to know about the Portugal vs Denmark UEFA Nations League 2024-25 quarterfinal second leg match to be played at the Jose...
Portugal vs. Denmark (24 Mar, 2025) Live Score
Portugal in a tense moment, Ronaldo acknowledges ahead of Denmark clash · Top Scorers · Cristiano Ronaldo · Bruno Fernandes · Rafael Leão.
Portugal Vs Denmark Live Streaming, UEFA Nations League 2024-25 Quarter-final: When, Where To Watch 2nd Leg On TV And Online
Get all the live streaming details for the upcoming Portugal vs Denmark, UEFA Nations League 2024-25 quarter-final match to be played at the |
lesso04/491e4fbc-361d-4efe-a758-bc700370e891 | lesso04 | "2025-02-23T09:51:34Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/tinyllama",
"base_model:adapter:unsloth/tinyllama",
"license:apache-2.0",
"region:us"
] | null | "2025-02-23T09:42:44Z" | ---
library_name: peft
license: apache-2.0
base_model: unsloth/tinyllama
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 491e4fbc-361d-4efe-a758-bc700370e891
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
auto_find_batch_size: true
base_model: unsloth/tinyllama
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 468c28834b8718b7_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/468c28834b8718b7_train_data.json
type:
field_instruction: tools
field_output: mock_functions
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
do_eval: true
early_stopping_patience: 3
eval_max_new_tokens: 128
eval_steps: 50
evals_per_epoch: null
flash_attention: true
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 2
gradient_checkpointing: false
group_by_length: true
hub_model_id: lesso04/491e4fbc-361d-4efe-a758-bc700370e891
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.000204
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 10
lora_alpha: 32
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 16
lora_target_linear: true
lr_scheduler: cosine
max_grad_norm: 1.0
max_steps: 500
micro_batch_size: 4
mlflow_experiment_name: /tmp/468c28834b8718b7_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 50
saves_per_epoch: null
seed: 40
sequence_len: 512
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 8c58dde8-5b9d-43e4-a575-521f9d24d6e5
wandb_project: 04a
wandb_run: your_name
wandb_runid: 8c58dde8-5b9d-43e4-a575-521f9d24d6e5
warmup_steps: 50
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 491e4fbc-361d-4efe-a758-bc700370e891
This model is a fine-tuned version of [unsloth/tinyllama](https://huggingface.co/unsloth/tinyllama) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2575
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.000204
- train_batch_size: 4
- eval_batch_size: 4
- seed: 40
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 50
- training_steps: 500
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0005 | 1 | 0.7003 |
| 0.3443 | 0.0261 | 50 | 0.3639 |
| 0.3529 | 0.0522 | 100 | 0.3178 |
| 0.3027 | 0.0783 | 150 | 0.2983 |
| 0.3314 | 0.1044 | 200 | 0.2883 |
| 0.2934 | 0.1305 | 250 | 0.2768 |
| 0.3359 | 0.1566 | 300 | 0.2695 |
| 0.3211 | 0.1827 | 350 | 0.2638 |
| 0.3081 | 0.2088 | 400 | 0.2606 |
| 0.2886 | 0.2349 | 450 | 0.2585 |
| 0.2821 | 0.2610 | 500 | 0.2575 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
sinsforeal/omo | sinsforeal | "2023-01-03T17:17:49Z" | 0 | 0 | null | [
"license:openrail",
"region:us"
] | null | "2023-01-03T17:14:55Z" | ---
license: openrail
---
trained off of images with the "pee" tag from danbooru. 768 res 16 batch 2e-5 lr this is 15 epochs.
not including a sample image just gonna have to test it yourself if you are into that |
HBCoop/FLUX-Heather | HBCoop | "2024-09-02T00:42:46Z" | 6 | 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 | "2024-09-02T00:15:51Z" | ---
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
instance_prompt: Heather
---
# Flux Heather
Trained on Replicate using:
https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `Heather` to trigger the image generation.
## 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('HBCoop/FLUX-Heather', weight_name='lora.safetensors')
image = pipeline('your prompt').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)
|
JacobPerera/testing-rolls-royce-100-steps | JacobPerera | "2023-01-24T07:51:30Z" | 9 | 0 | diffusers | [
"diffusers",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | "2023-01-24T07:40:22Z" | ---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### Testing_rolls_royce_100_steps Dreambooth model trained by JacobPerera with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Sample pictures of this concept:
|
sayhan/Mixtral-8x7B-v0.1-turkish-GGUF | sayhan | "2024-02-16T19:44:01Z" | 109 | 2 | transformers | [
"transformers",
"gguf",
"text-generation",
"tr",
"en",
"base_model:malhajar/Mixtral-8x7B-v0.1-turkish",
"base_model:quantized:malhajar/Mixtral-8x7B-v0.1-turkish",
"license:apache-2.0",
"region:us"
] | text-generation | "2024-02-15T16:50:44Z" | ---
base_model: malhajar/Mixtral-8x7B-v0.1-turkish
language:
- tr
- en
pipeline_tag: text-generation
license: apache-2.0
model_type: mixtral
library_name: transformers
inference: false
---
## Mixtral 8x7B v0.1 Turkish
- **Model creator:** [malhajar](https://huggingface.co/malhajar)
- **Original model:** [Mixtral-8x7B-v0.1-turkish](https://huggingface.co/malhajar/Mixtral-8x7B-v0.1-turkish)
<!-- description start -->
## Description
This repo contains GGUF format model files for [malhajar's Mixtral 8x7B v0.1 Turkish](https://huggingface.co/malhajar/Mixtral-8x7B-v0.1-turkish)
## Original model
- **Developed by:** [`Mohamad Alhajar`](https://www.linkedin.com/in/muhammet-alhajar/)
- **Language(s) (NLP):** Turkish
- **Finetuned from model:** [`mistralai/Mistral-7B-Instruct-v0.2`](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1)
### Original model description
malhajar/Mixtral-8x7B-v0.1-turkish is a finetuned version of Mixtral-8x7B-v0.1 using SFT Training.
This model can answer information in turkish language as it is finetuned on a turkish dataset specifically [`alpaca-gpt4-tr`]( https://huggingface.co/datasets/malhajar/alpaca-gpt4-tr)
## Quantizon types
| quantization method | bits | size | description | recommended |
|---------------------|------|----------|-----------------------------------------------------|-------------|
| Q3_K_S | 3 | 20.4 GB | very small, high quality loss | ❌ |
| Q3_K_L | 3 | 26.4 GB | small, substantial quality loss | ❌ |
| Q4_0 | 4 | 26.4 GB | legacy; small, very high quality loss | ❌ |
| Q4_K_M | 4 | 28.4 GB | medium, balanced quality | ✅ |
| Q5_0 | 5 | 33.2 GB | legacy; medium, balanced quality | ❌ |
| Q5_K_S | 5 | 32.2 GB | large, low quality loss | ✅ |
| Q5_K_M | 5 | 33.2 GB | large, very low quality loss | ✅ |
| Q6_K | 6 | 38.4 GB | very large, extremely low quality loss | ❌ |
| Q8_0 | 8 | 49.6 GB | very large, extremely low quality loss | ❌ |
## Prompt Template
```
### Instruction:
<prompt> (without the <>)
### Response:
```
<!-- description end --> |
PrunaAI/nicholasKluge-TeenyTinyLlama-460m-AWQ-4bit-smashed | PrunaAI | "2024-08-02T16:13:47Z" | 77 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"pruna-ai",
"base_model:nicholasKluge/TeenyTinyLlama-460m",
"base_model:quantized:nicholasKluge/TeenyTinyLlama-460m",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"awq",
"region:us"
] | text-generation | "2024-06-24T06:39:15Z" | ---
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
base_model: nicholasKluge/TeenyTinyLlama-460m
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
tags:
- pruna-ai
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer">
<img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/rskEr4BZJx)
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/rskEr4BZJx) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed with awq.
- ***How does the model quality change?*** The quality of the model output might vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained on HARDWARE_NAME with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We use safetensors.
- ***What calibration data has been used?*** If needed by the compression method, we used WikiText as the calibration data.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
- ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.
## Setup
You can run the smashed model with these steps:
0. Check requirements from the original repo nicholasKluge/TeenyTinyLlama-460m installed. In particular, check python, cuda, and transformers versions.
1. Make sure that you have installed quantization related packages.
```bash
pip install autoawq
```
2. Load & run the model.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from awq import AutoAWQForCausalLM
model = AutoAWQForCausalLM.from_quantized("PrunaAI/nicholasKluge-TeenyTinyLlama-460m-AWQ-4bit-smashed", trust_remote_code=True, device_map='auto')
tokenizer = AutoTokenizer.from_pretrained("nicholasKluge/TeenyTinyLlama-460m")
input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"]
outputs = model.generate(input_ids, max_new_tokens=216)
tokenizer.decode(outputs[0])
```
## Configurations
The configuration info are in `smash_config.json`.
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model nicholasKluge/TeenyTinyLlama-460m before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). |
achdaisy/OpenPipe_mistral-ft-optimized-1227 | achdaisy | "2024-03-31T15:14:08Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-03-31T15:14:03Z" | ---
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] |
vishonomic/autotrain-aza4z-u8yg0 | vishonomic | "2024-06-12T23:38:18Z" | 81 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"llama",
"autotrain",
"text-generation-inference",
"text-generation",
"peft",
"conversational",
"license:mit",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-05-29T20:23:57Z" | ---
tags:
- autotrain
- text-generation-inference
- text-generation
- peft
library_name: transformers
widget:
- messages:
- role: user
content: What is your favorite condiment?
license: mit
---
# Model Trained Using AutoTrain
This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain).
# Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = "PATH_TO_THIS_REPO"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
model_path,
device_map="auto",
torch_dtype='auto'
).eval()
# Prompt content: "hi"
messages = [
{"role": "user", "content": "hi"}
]
input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt')
output_ids = model.generate(input_ids.to('cuda'))
response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)
# Model response: "Hello! How can I assist you today?"
print(response)
``` |
RichardErkhov/skumar9_-_Llama-medx_v2-awq | RichardErkhov | "2025-03-30T16:42:19Z" | 0 | 0 | null | [
"safetensors",
"llama",
"arxiv:2305.14314",
"4-bit",
"awq",
"region:us"
] | null | "2025-03-30T16:38:16Z" | <!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> |
TheBloke/Llama-2-70B-GPTQ | TheBloke | "2023-09-27T12:44:50Z" | 1,938 | 83 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"facebook",
"meta",
"pytorch",
"llama-2",
"en",
"arxiv:2307.09288",
"base_model:meta-llama/Llama-2-70b-hf",
"base_model:quantized:meta-llama/Llama-2-70b-hf",
"license:llama2",
"autotrain_compatible",
"text-generation-inference",
"4-bit",
"gptq",
"region:us"
] | text-generation | "2023-07-18T23:45:06Z" | ---
language:
- en
license: llama2
tags:
- facebook
- meta
- pytorch
- llama
- llama-2
model_name: Llama 2 70B
base_model: meta-llama/Llama-2-70b-hf
inference: false
model_creator: Meta Llama 2
model_type: llama
pipeline_tag: text-generation
prompt_template: '{prompt}
'
quantized_by: TheBloke
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
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<div style="display: flex; flex-direction: column; align-items: flex-start;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p>
</div>
<div style="display: flex; flex-direction: column; align-items: flex-end;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
</div>
</div>
<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# Llama 2 70B - GPTQ
- Model creator: [Meta Llama 2](https://huggingface.co/meta-llama)
- Original model: [Llama 2 70B](https://huggingface.co/meta-llama/Llama-2-70b-hf)
<!-- description start -->
## Description
This repo contains GPTQ model files for [Meta Llama 2's Llama 2 70B](https://huggingface.co/meta-llama/Llama-2-70b-hf).
Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them.
<!-- description end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Llama-2-70B-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Llama-2-70B-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Llama-2-70B-GGUF)
* [Meta Llama 2's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/meta-llama/Llama-2-70b-hf)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: None
```
{prompt}
```
<!-- prompt-template end -->
<!-- README_GPTQ.md-provided-files start -->
## Provided files and GPTQ parameters
Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements.
Each separate quant is in a different branch. See below for instructions on fetching from different branches.
All recent GPTQ files are made with AutoGPTQ, and all files in non-main branches are made with AutoGPTQ. Files in the `main` branch which were uploaded before August 2023 were made with GPTQ-for-LLaMa.
<details>
<summary>Explanation of GPTQ parameters</summary>
- Bits: The bit size of the quantised model.
- GS: GPTQ group size. Higher numbers use less VRAM, but have lower quantisation accuracy. "None" is the lowest possible value.
- Act Order: True or False. Also known as `desc_act`. True results in better quantisation accuracy. Some GPTQ clients have had issues with models that use Act Order plus Group Size, but this is generally resolved now.
- Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 0.01 is default, but 0.1 results in slightly better accuracy.
- GPTQ dataset: The dataset used for quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ dataset is not the same as the dataset used to train the model - please refer to the original model repo for details of the training dataset(s).
- Sequence Length: The length of the dataset sequences used for quantisation. Ideally this is the same as the model sequence length. For some very long sequence models (16+K), a lower sequence length may have to be used. Note that a lower sequence length does not limit the sequence length of the quantised model. It only impacts the quantisation accuracy on longer inference sequences.
- ExLlama Compatibility: Whether this file can be loaded with ExLlama, which currently only supports Llama models in 4-bit.
</details>
| Branch | Bits | GS | Act Order | Damp % | GPTQ Dataset | Seq Len | Size | ExLlama | Desc |
| ------ | ---- | -- | --------- | ------ | ------------ | ------- | ---- | ------- | ---- |
| [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/Llama-2-70B-GPTQ/tree/gptq-4bit-32g-actorder_True) | 4 | 32 | Yes | 0.01 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 40.66 GB | Yes | 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. |
| [gptq-4bit-64g-actorder_True](https://huggingface.co/TheBloke/Llama-2-70B-GPTQ/tree/gptq-4bit-64g-actorder_True) | 4 | 64 | Yes | 0.01 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 37.99 GB | Yes | 4-bit, with Act Order and group size 64g. Uses less VRAM than 32g, but with slightly lower accuracy. |
| [gptq-4bit-128g-actorder_True](https://huggingface.co/TheBloke/Llama-2-70B-GPTQ/tree/gptq-4bit-128g-actorder_True) | 4 | 128 | Yes | 0.01 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 36.65 GB | Yes | 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. |
| [gptq-3bit-128g-actorder_True](https://huggingface.co/TheBloke/Llama-2-70B-GPTQ/tree/gptq-3bit-128g-actorder_True) | 3 | 128 | Yes | 0.01 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 28.03 GB | No | 3-bit, with group size 128g and act-order. Higher quality than 128g-False. |
| [gptq-3bit-128g-actorder_False](https://huggingface.co/TheBloke/Llama-2-70B-GPTQ/tree/gptq-3bit-128g-actorder_False) | 3 | 128 | No | 0.01 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 28.03 GB | No | 3-bit, with group size 128g but no act-order. Slightly higher VRAM requirements than 3-bit None. |
| [gptq-3bit--1g-actorder_True](https://huggingface.co/TheBloke/Llama-2-70B-GPTQ/tree/gptq-3bit--1g-actorder_True) | 3 | None | Yes | 0.01 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 26.78 GB | No | 3-bit, with Act Order and no group size. Lowest possible VRAM requirements. May be lower quality than 3-bit 128g. |
| [gptq-3bit-64g-actorder_True](https://huggingface.co/TheBloke/Llama-2-70B-GPTQ/tree/gptq-3bit-64g-actorder_True) | 3 | 64 | Yes | 0.01 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 29.30 GB | No | 3-bit, with group size 64g and act-order. |
| [main](https://huggingface.co/TheBloke/Llama-2-70B-GPTQ/tree/main) | 4 | None | Yes | 0.01 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 35.33 GB | Yes | 4-bit, with Act Order. No group size, to lower VRAM requirements. |
<!-- README_GPTQ.md-provided-files end -->
<!-- README_GPTQ.md-download-from-branches start -->
## How to download from branches
- In text-generation-webui, you can add `:branch` to the end of the download name, eg `TheBloke/Llama-2-70B-GPTQ:gptq-4bit-32g-actorder_True`
- With Git, you can clone a branch with:
```
git clone --single-branch --branch gptq-4bit-32g-actorder_True https://huggingface.co/TheBloke/Llama-2-70B-GPTQ
```
- In Python Transformers code, the branch is the `revision` parameter; see below.
<!-- README_GPTQ.md-download-from-branches end -->
<!-- README_GPTQ.md-text-generation-webui start -->
## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui).
Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui).
It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install.
1. Click the **Model tab**.
2. Under **Download custom model or LoRA**, enter `TheBloke/Llama-2-70B-GPTQ`.
- To download from a specific branch, enter for example `TheBloke/Llama-2-70B-GPTQ:gptq-4bit-32g-actorder_True`
- see Provided Files above for the list of branches for each option.
3. Click **Download**.
4. The model will start downloading. Once it's finished it will say "Done".
5. In the top left, click the refresh icon next to **Model**.
6. In the **Model** dropdown, choose the model you just downloaded: `Llama-2-70B-GPTQ`
7. The model will automatically load, and is now ready for use!
8. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right.
* Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file `quantize_config.json`.
9. Once you're ready, click the **Text Generation tab** and enter a prompt to get started!
<!-- README_GPTQ.md-text-generation-webui end -->
<!-- README_GPTQ.md-use-from-python start -->
## How to use this GPTQ model from Python code
### Install the necessary packages
Requires: Transformers 4.32.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later.
```shell
pip3 install transformers>=4.32.0 optimum>=1.12.0
pip3 install auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/ # Use cu117 if on CUDA 11.7
```
If you have problems installing AutoGPTQ using the pre-built wheels, install it from source instead:
```shell
pip3 uninstall -y auto-gptq
git clone https://github.com/PanQiWei/AutoGPTQ
cd AutoGPTQ
pip3 install .
```
### For CodeLlama models only: you must use Transformers 4.33.0 or later.
If 4.33.0 is not yet released when you read this, you will need to install Transformers from source:
```shell
pip3 uninstall -y transformers
pip3 install git+https://github.com/huggingface/transformers.git
```
### You can then use the following code
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
model_name_or_path = "TheBloke/Llama-2-70B-GPTQ"
# To use a different branch, change revision
# For example: revision="gptq-4bit-32g-actorder_True"
model = AutoModelForCausalLM.from_pretrained(model_name_or_path,
device_map="auto",
trust_remote_code=False,
revision="main")
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)
prompt = "Tell me about AI"
prompt_template=f'''{prompt}
'''
print("\n\n*** Generate:")
input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512)
print(tokenizer.decode(output[0]))
# Inference can also be done using transformers' pipeline
print("*** Pipeline:")
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
repetition_penalty=1.1
)
print(pipe(prompt_template)[0]['generated_text'])
```
<!-- README_GPTQ.md-use-from-python end -->
<!-- README_GPTQ.md-compatibility start -->
## Compatibility
The files provided are tested to work with AutoGPTQ, both via Transformers and using AutoGPTQ directly. They should also work with [Occ4m's GPTQ-for-LLaMa fork](https://github.com/0cc4m/KoboldAI).
[ExLlama](https://github.com/turboderp/exllama) is compatible with Llama models in 4-bit. Please see the Provided Files table above for per-file compatibility.
[Huggingface Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) is compatible with all GPTQ models.
<!-- README_GPTQ.md-compatibility end -->
<!-- footer start -->
<!-- 200823 -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute
Thanks to the [chirper.ai](https://chirper.ai) team!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Aemon Algiz.
**Patreon special mentions**: Alicia Loh, Stephen Murray, K, Ajan Kanaga, RoA, Magnesian, Deo Leter, Olakabola, Eugene Pentland, zynix, Deep Realms, Raymond Fosdick, Elijah Stavena, Iucharbius, Erik Bjäreholt, Luis Javier Navarrete Lozano, Nicholas, theTransient, John Detwiler, alfie_i, knownsqashed, Mano Prime, Willem Michiel, Enrico Ros, LangChain4j, OG, Michael Dempsey, Pierre Kircher, Pedro Madruga, James Bentley, Thomas Belote, Luke @flexchar, Leonard Tan, Johann-Peter Hartmann, Illia Dulskyi, Fen Risland, Chadd, S_X, Jeff Scroggin, Ken Nordquist, Sean Connelly, Artur Olbinski, Swaroop Kallakuri, Jack West, Ai Maven, David Ziegler, Russ Johnson, transmissions 11, John Villwock, Alps Aficionado, Clay Pascal, Viktor Bowallius, Subspace Studios, Rainer Wilmers, Trenton Dambrowitz, vamX, Michael Levine, 준교 김, Brandon Frisco, Kalila, Trailburnt, Randy H, Talal Aujan, Nathan Dryer, Vadim, 阿明, ReadyPlayerEmma, Tiffany J. Kim, George Stoitzev, Spencer Kim, Jerry Meng, Gabriel Tamborski, Cory Kujawski, Jeffrey Morgan, Spiking Neurons AB, Edmond Seymore, Alexandros Triantafyllidis, Lone Striker, Cap'n Zoog, Nikolai Manek, danny, ya boyyy, Derek Yates, usrbinkat, Mandus, TL, Nathan LeClaire, subjectnull, Imad Khwaja, webtim, Raven Klaugh, Asp the Wyvern, Gabriel Puliatti, Caitlyn Gatomon, Joseph William Delisle, Jonathan Leane, Luke Pendergrass, SuperWojo, Sebastain Graf, Will Dee, Fred von Graf, Andrey, Dan Guido, Daniel P. Andersen, Nitin Borwankar, Elle, Vitor Caleffi, biorpg, jjj, NimbleBox.ai, Pieter, Matthew Berman, terasurfer, Michael Davis, Alex, Stanislav Ovsiannikov
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
# Original model card: Meta Llama 2's Llama 2 70B
# **Llama 2**
Llama 2 is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This is the repository for the 70B pretrained model, converted for the Hugging Face Transformers format. Links to other models can be found in the index at the bottom.
## Model Details
*Note: Use of this model is governed by the Meta license. In order to download the model weights and tokenizer, please visit the [website](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) and accept our License before requesting access here.*
Meta developed and publicly released the Llama 2 family of large language models (LLMs), a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called Llama-2-Chat, are optimized for dialogue use cases. Llama-2-Chat models outperform open-source chat models on most benchmarks we tested, and in our human evaluations for helpfulness and safety, are on par with some popular closed-source models like ChatGPT and PaLM.
**Model Developers** Meta
**Variations** Llama 2 comes in a range of parameter sizes — 7B, 13B, and 70B — as well as pretrained and fine-tuned variations.
**Input** Models input text only.
**Output** Models generate text only.
**Model Architecture** Llama 2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align to human preferences for helpfulness and safety.
||Training Data|Params|Content Length|GQA|Tokens|LR|
|---|---|---|---|---|---|---|
|Llama 2|*A new mix of publicly available online data*|7B|4k|✗|2.0T|3.0 x 10<sup>-4</sup>|
|Llama 2|*A new mix of publicly available online data*|13B|4k|✗|2.0T|3.0 x 10<sup>-4</sup>|
|Llama 2|*A new mix of publicly available online data*|70B|4k|✔|2.0T|1.5 x 10<sup>-4</sup>|
*Llama 2 family of models.* Token counts refer to pretraining data only. All models are trained with a global batch-size of 4M tokens. Bigger models - 70B -- use Grouped-Query Attention (GQA) for improved inference scalability.
**Model Dates** Llama 2 was trained between January 2023 and July 2023.
**Status** This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback.
**License** A custom commercial license is available at: [https://ai.meta.com/resources/models-and-libraries/llama-downloads/](https://ai.meta.com/resources/models-and-libraries/llama-downloads/)
**Research Paper** ["Llama-2: Open Foundation and Fine-tuned Chat Models"](arxiv.org/abs/2307.09288)
## Intended Use
**Intended Use Cases** Llama 2 is intended for commercial and research use in English. Tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks.
To get the expected features and performance for the chat versions, a specific formatting needs to be followed, including the `INST` and `<<SYS>>` tags, `BOS` and `EOS` tokens, and the whitespaces and breaklines in between (we recommend calling `strip()` on inputs to avoid double-spaces). See our reference code in github for details: [`chat_completion`](https://github.com/facebookresearch/llama/blob/main/llama/generation.py#L212).
**Out-of-scope Uses** Use in any manner that violates applicable laws or regulations (including trade compliance laws).Use in languages other than English. Use in any other way that is prohibited by the Acceptable Use Policy and Licensing Agreement for Llama 2.
## Hardware and Software
**Training Factors** We used custom training libraries, Meta's Research Super Cluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute.
**Carbon Footprint** Pretraining utilized a cumulative 3.3M GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 539 tCO2eq, 100% of which were offset by Meta’s sustainability program.
||Time (GPU hours)|Power Consumption (W)|Carbon Emitted(tCO<sub>2</sub>eq)|
|---|---|---|---|
|Llama 2 7B|184320|400|31.22|
|Llama 2 13B|368640|400|62.44|
|Llama 2 70B|1720320|400|291.42|
|Total|3311616||539.00|
**CO<sub>2</sub> emissions during pretraining.** Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others.
## Training Data
**Overview** Llama 2 was pretrained on 2 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over one million new human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data.
**Data Freshness** The pretraining data has a cutoff of September 2022, but some tuning data is more recent, up to July 2023.
## Evaluation Results
In this section, we report the results for the Llama 1 and Llama 2 models on standard academic benchmarks.For all the evaluations, we use our internal evaluations library.
|Model|Size|Code|Commonsense Reasoning|World Knowledge|Reading Comprehension|Math|MMLU|BBH|AGI Eval|
|---|---|---|---|---|---|---|---|---|---|
|Llama 1|7B|14.1|60.8|46.2|58.5|6.95|35.1|30.3|23.9|
|Llama 1|13B|18.9|66.1|52.6|62.3|10.9|46.9|37.0|33.9|
|Llama 1|33B|26.0|70.0|58.4|67.6|21.4|57.8|39.8|41.7|
|Llama 1|65B|30.7|70.7|60.5|68.6|30.8|63.4|43.5|47.6|
|Llama 2|7B|16.8|63.9|48.9|61.3|14.6|45.3|32.6|29.3|
|Llama 2|13B|24.5|66.9|55.4|65.8|28.7|54.8|39.4|39.1|
|Llama 2|70B|**37.5**|**71.9**|**63.6**|**69.4**|**35.2**|**68.9**|**51.2**|**54.2**|
**Overall performance on grouped academic benchmarks.** *Code:* We report the average pass@1 scores of our models on HumanEval and MBPP. *Commonsense Reasoning:* We report the average of PIQA, SIQA, HellaSwag, WinoGrande, ARC easy and challenge, OpenBookQA, and CommonsenseQA. We report 7-shot results for CommonSenseQA and 0-shot results for all other benchmarks. *World Knowledge:* We evaluate the 5-shot performance on NaturalQuestions and TriviaQA and report the average. *Reading Comprehension:* For reading comprehension, we report the 0-shot average on SQuAD, QuAC, and BoolQ. *MATH:* We report the average of the GSM8K (8 shot) and MATH (4 shot) benchmarks at top 1.
|||TruthfulQA|Toxigen|
|---|---|---|---|
|Llama 1|7B|27.42|23.00|
|Llama 1|13B|41.74|23.08|
|Llama 1|33B|44.19|22.57|
|Llama 1|65B|48.71|21.77|
|Llama 2|7B|33.29|**21.25**|
|Llama 2|13B|41.86|26.10|
|Llama 2|70B|**50.18**|24.60|
**Evaluation of pretrained LLMs on automatic safety benchmarks.** For TruthfulQA, we present the percentage of generations that are both truthful and informative (the higher the better). For ToxiGen, we present the percentage of toxic generations (the smaller the better).
|||TruthfulQA|Toxigen|
|---|---|---|---|
|Llama-2-Chat|7B|57.04|**0.00**|
|Llama-2-Chat|13B|62.18|**0.00**|
|Llama-2-Chat|70B|**64.14**|0.01|
**Evaluation of fine-tuned LLMs on different safety datasets.** Same metric definitions as above.
## Ethical Considerations and Limitations
Llama 2 is a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Llama 2’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 2, developers should perform safety testing and tuning tailored to their specific applications of the model.
Please see the Responsible Use Guide available at [https://ai.meta.com/llama/responsible-use-guide/](https://ai.meta.com/llama/responsible-use-guide)
## Reporting Issues
Please report any software “bug,” or other problems with the models through one of the following means:
- Reporting issues with the model: [github.com/facebookresearch/llama](http://github.com/facebookresearch/llama)
- Reporting problematic content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback)
- Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info)
## Llama Model Index
|Model|Llama2|Llama2-hf|Llama2-chat|Llama2-chat-hf|
|---|---|---|---|---|
|7B| [Link](https://huggingface.co/llamaste/Llama-2-7b) | [Link](https://huggingface.co/llamaste/Llama-2-7b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-7b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-7b-chat-hf)|
|13B| [Link](https://huggingface.co/llamaste/Llama-2-13b) | [Link](https://huggingface.co/llamaste/Llama-2-13b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-13b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-13b-hf)|
|70B| [Link](https://huggingface.co/llamaste/Llama-2-70b) | [Link](https://huggingface.co/llamaste/Llama-2-70b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-70b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-70b-hf)|
|
carlosadrocha/fran-lora | carlosadrocha | "2025-01-22T21:56:26Z" | 25 | 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 | "2025-01-22T21:12:01Z" | ---
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: FRAN
---
# Fran Lora
<Gallery />
Trained on Replicate using:
https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `FRAN` to trigger the image generation.
## 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('carlosadrocha/fran-lora', weight_name='lora.safetensors')
image = pipeline('your prompt').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)
|
stojchet/ndpo3-sft8 | stojchet | "2024-07-16T12:09:11Z" | 101 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"sft",
"generated_from_trainer",
"dataset:generator",
"base_model:stojchet/ndpo3",
"base_model:finetune:stojchet/ndpo3",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-07-16T10:25:49Z" | ---
license: other
base_model: stojchet/ndpo3
tags:
- trl
- sft
- generated_from_trainer
datasets:
- generator
model-index:
- name: ndpo3-sft8
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/stojchets/huggingface/runs/ndpo3-sft8)
# ndpo3-sft8
This model is a fine-tuned version of [stojchet/ndpo3](https://huggingface.co/stojchet/ndpo3) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2186
## 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
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.0903 | 2.56 | 100 | 1.2186 |
### Framework versions
- Transformers 4.43.0.dev0
- Pytorch 2.2.2+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1
|
lidiapierre/marian-finetuned-kde4-en-to-fr | lidiapierre | "2023-10-04T10:16:08Z" | 114 | 0 | transformers | [
"transformers",
"pytorch",
"marian",
"text2text-generation",
"translation",
"generated_from_trainer",
"dataset:kde4",
"base_model:Helsinki-NLP/opus-mt-en-fr",
"base_model:finetune:Helsinki-NLP/opus-mt-en-fr",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | translation | "2023-10-04T07:52:59Z" | ---
license: apache-2.0
base_model: Helsinki-NLP/opus-mt-en-fr
tags:
- translation
- generated_from_trainer
datasets:
- kde4
metrics:
- bleu
model-index:
- name: marian-finetuned-kde4-en-to-fr
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: kde4
type: kde4
config: en-fr
split: train
args: en-fr
metrics:
- name: Bleu
type: bleu
value: 52.77584129918399
---
<!-- 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. -->
# marian-finetuned-kde4-en-to-fr
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsinki-NLP/opus-mt-en-fr) on the kde4 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8567
- Bleu: 52.7758
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.0
|
lesso13/62b38526-caab-488b-a911-12396e0a48b2 | lesso13 | "2025-03-16T11:24:42Z" | 9 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/llama-3-8b",
"base_model:adapter:unsloth/llama-3-8b",
"license:llama3",
"region:us"
] | null | "2025-03-11T19:11:21Z" | ---
library_name: peft
license: llama3
base_model: unsloth/llama-3-8b
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 62b38526-caab-488b-a911-12396e0a48b2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<br>
# 62b38526-caab-488b-a911-12396e0a48b2
This model is a fine-tuned version of [unsloth/llama-3-8b](https://huggingface.co/unsloth/llama-3-8b) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1262
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.000213
- train_batch_size: 4
- eval_batch_size: 4
- seed: 130
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- training_steps: 500
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0008 | 1 | 1.3484 |
| 0.1238 | 0.3891 | 500 | 0.1262 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
FabioQuintao/ppo-LunarLander-v2 | FabioQuintao | "2022-12-18T00:46:00Z" | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | "2022-12-18T00:45:31Z" | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 269.41 +/- 23.55
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Aiden07/Mistral-7B-Instruct-dota2-GGUF | Aiden07 | "2024-03-31T14:45:30Z" | 5 | 0 | null | [
"gguf",
"en",
"dataset:Aiden07/dota2_instruct_prompt",
"license:mit",
"endpoints_compatible",
"region:us",
"conversational"
] | null | "2024-03-25T10:22:19Z" | ---
license: mit
datasets:
- Aiden07/dota2_instruct_prompt
language:
- en
---
Quantized version of [Aiden07/Mistral-7B-Instruct-dota2](https://huggingface.co/Aiden07/Mistral-7B-Instruct-dota2)
Quantization methods: Q8_0 & Q4_K_M |
okamototk/DeepSeek-R1-Distill-Qwen-14B-imatrix-gguf | okamototk | "2025-03-03T14:30:52Z" | 50 | 0 | null | [
"gguf",
"dataset:TFMC/imatrix-dataset-for-japanese-llm",
"base_model:deepseek-ai/DeepSeek-R1-Distill-Qwen-14B",
"base_model:quantized:deepseek-ai/DeepSeek-R1-Distill-Qwen-14B",
"license:mit",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | "2025-02-04T05:45:00Z" | ---
license: mit
datasets:
- TFMC/imatrix-dataset-for-japanese-llm
base_model:
- deepseek-ai/DeepSeek-R1-Distill-Qwen-14B
---
## 1. Introduction
This model is quantized version of DeepSeek-R1-Distill-Qwen-14B with dataset for imatrix [TFMC/imatrix-dataset-for-japanese-llm](https://huggingface.co/datasets/TFMC/imatrix-dataset-for-japanese-llm).
Usgin English/Japanese mixed and quantization is tuned for Japanese.
## 2. License
This code repository and the model weights are licensed under the [MIT License](LICENSE).
DeepSeek-R1 series support commercial use, allow for any modifications and derivative works, including, but not limited to, distillation for training other LLMs. Please note that:
- DeepSeek-R1-Distill-Qwen-1.5B, DeepSeek-R1-Distill-Qwen-7B, DeepSeek-R1-Distill-Qwen-14B and DeepSeek-R1-Distill-Qwen-32B are derived from [Qwen-2.5 series](https://github.com/QwenLM/Qwen2.5), which are originally licensed under [Apache 2.0 License](LICENSE-Qwen), and now finetuned with 800k samples curated with DeepSeek-R1.
- DeepSeek-R1-Distill-Llama-8B is derived from Llama3.1-8B-Base and is originally licensed under [llama3.1 license](https://huggingface.co/meta-llama/Llama-3.1-8B/blob/main/LICENSE).
- DeepSeek-R1-Distill-Llama-70B is derived from Llama3.3-70B-Instruct and is originally licensed under [llama3.3 license](https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct/blob/main/LICENSE).
|
soumickmj/DS6_UNet3D_woDeform | soumickmj | "2024-09-10T18:44:31Z" | 12 | 0 | pytorch | [
"pytorch",
"safetensors",
"UNet",
"medical",
"vessel segmentation",
"small vessel segmentation",
"UNetMSS",
"Deep Supervision",
"3D",
"DS6",
"7T MRA-ToF",
"MRA",
"TOF",
"MRI",
"7T",
"brain MRI",
"vessels",
"image-segmentation",
"custom_code",
"arxiv:2006.10802",
"license:apache-2.0",
"region:us"
] | image-segmentation | "2024-09-01T17:08:35Z" | ---
license: apache-2.0
pipeline_tag: image-segmentation
tags:
- medical
- vessel segmentation
- small vessel segmentation
- UNet
- UNetMSS
- Deep Supervision
- 3D
- DS6
- 7T MRA-ToF
- MRA
- TOF
- MRI
- 7T
- brain MRI
- vessels
library_name: pytorch
---
# DS6_UNetMSS3D_woDeform
Blood vessels of the brain provide the human brain with the required nutrients and oxygen. As a vulnerable part of the cerebral blood supply, pathology of small vessels can cause serious problems such as Cerebral Small Vessel Diseases (CSVD). It has also been shown that CSVD is related to neurodegeneration, such as Alzheimer’s disease. With the advancement of 7 Tesla MRI systems, higher spatial image resolution can be achieved, enabling the depiction of very small vessels in the brain. Non-Deep Learning-based approaches for vessel segmentation, e.g., Frangi’s vessel enhancement with subsequent thresholding, are capable of segmenting medium to large vessels but often fail to segment small vessels. The sensitivity of these methods to small vessels can be increased by extensive parameter tuning or by manual corrections, albeit making them time-consuming, laborious, and not feasible for larger datasets. This paper proposes a deep learning architecture to automatically segment small vessels in 7 Tesla 3D Time-of-Flight (ToF) Magnetic Resonance Angiography (MRA) data. The algorithm was trained and evaluated on a small imperfect semi-automatically segmented dataset of only 11 subjects; using six for training, two for validation, and three for testing. The deep learning model based on U-Net Multi-Scale Supervision was trained using the training subset and was made equivariant to elastic deformations in a self-supervised manner using deformation-aware learning to improve the generalisation performance. The proposed technique was evaluated quantitatively and qualitatively against the test set and achieved a Dice score of 80.44 ± 0.83. Furthermore, the result of the proposed method was compared against a selected manually segmented region (62.07 resultant Dice) and has shown a considerable improvement (18.98%) with deformation-aware learning.
## Model Details
It was introduced in [DS6, Deformation-Aware Semi-Supervised Learning: Application to Small Vessel Segmentation with Noisy Training Data](https://doi.org/10.3390/jimaging8100259) by Soumick Chatterjee, Kartik Prabhu, Mahantesh Pattadkal, Gerda Bortsova, Chompunuch Sarasaen, Florian Dubost, Hendrik Mattern, Marleen de Bruijne, Oliver Speck, Andreas Nürnberger. [ArXiv preprint](https://arxiv.org/abs/2006.10802)
### Model Description
- **Model type:** UNet 3D
- **Task:** Vessel segmentation in 7T MRA-ToF volumes
- **Training dataset:** In-house 7T ToF-MRAs, details can be found in Section 4 of the published paper.
- **Training type:** Trained without deformation-aware learning (Baseline method)
### Model Sources
<!-- Provide the basic links for the model. -->
- **Repository:** https://github.com/soumickmj/DS6
- **Paper:** https://doi.org/10.3390/jimaging8100259
- **Preprint:** https://arxiv.org/abs/2006.10802
## Citation
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
If you use this approach in your research or use codes from this repository or these weights, please cite the following in your publications:
**BibTeX:**
```bibtex
@article{chatterjee2022ds6,
title={Ds6, deformation-aware semi-supervised learning: Application to small vessel segmentation with noisy training data},
author={Chatterjee, Soumick and Prabhu, Kartik and Pattadkal, Mahantesh and Bortsova, Gerda and Sarasaen, Chompunuch and Dubost, Florian and Mattern, Hendrik and de Bruijne, Marleen and Speck, Oliver and N{\"u}rnberger, Andreas},
journal={Journal of Imaging},
volume={8},
number={10},
pages={259},
year={2022},
publisher={MDPI}
}
```
**APA:**
Chatterjee, S., Prabhu, K., Pattadkal, M., Bortsova, G., Sarasaen, C., Dubost, F., ... & Nürnberger, A. (2022). Ds6, deformation-aware semi-supervised learning: Application to small vessel segmentation with noisy training data. Journal of Imaging, 8(10), 259. |
ombharamadev/autotrain-positive-or-negative | ombharamadev | "2024-07-07T08:38:25Z" | 9 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"roberta",
"text-classification",
"autotrain",
"base_model:cardiffnlp/twitter-roberta-base-sentiment-latest",
"base_model:finetune:cardiffnlp/twitter-roberta-base-sentiment-latest",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2024-07-07T08:37:52Z" |
---
tags:
- autotrain
- text-classification
base_model: cardiffnlp/twitter-roberta-base-sentiment-latest
widget:
- text: "I love AutoTrain"
---
# Model Trained Using AutoTrain
- Problem type: Text Classification
## Validation Metrics
loss: 0.5549666881561279
f1: 0.6666666666666666
precision: 0.5
recall: 1.0
auc: 1.0
accuracy: 0.5
|
Hachipo/OpenCoder-8B-Base_MIFT-En_opencoder-edu_PIFT-enja_5000 | Hachipo | "2025-03-22T07:12:54Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-03-19T20:52:47Z" | ---
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]
- **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] |
kostiantynk-out/a26690fa-f727-4f65-bce1-534fa04f046e | kostiantynk-out | "2025-02-06T00:05:32Z" | 8 | 0 | peft | [
"peft",
"safetensors",
"qwen2",
"axolotl",
"generated_from_trainer",
"base_model:Qwen/Qwen2.5-7B",
"base_model:adapter:Qwen/Qwen2.5-7B",
"license:apache-2.0",
"region:us"
] | null | "2025-02-05T23:40:27Z" | ---
library_name: peft
license: apache-2.0
base_model: Qwen/Qwen2.5-7B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: a26690fa-f727-4f65-bce1-534fa04f046e
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
# a26690fa-f727-4f65-bce1-534fa04f046e
This model is a fine-tuned version of [Qwen/Qwen2.5-7B](https://huggingface.co/Qwen/Qwen2.5-7B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5890
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
globalyako/swallowv2-8b-ft-jp-r64_grpo-merged | globalyako | "2025-02-23T12:37:22Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"grpo",
"conversational",
"en",
"base_model:tokyotech-llm/Llama-3.1-Swallow-8B-v0.2",
"base_model:finetune:tokyotech-llm/Llama-3.1-Swallow-8B-v0.2",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-02-23T05:37:42Z" | ---
base_model: tokyotech-llm/Llama-3.1-Swallow-8B-v0.2
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- grpo
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** globalyako
- **License:** apache-2.0
- **Finetuned from model :** tokyotech-llm/Llama-3.1-Swallow-8B-v0.2
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)
|
samsl/ConPLex_V1_BindingDB | samsl | "2024-07-18T09:42:08Z" | 78 | 0 | transformers | [
"transformers",
"safetensors",
"model_hub_mixin",
"pytorch_model_hub_mixin",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | "2024-07-14T18:22:30Z" | ---
tags:
- model_hub_mixin
- pytorch_model_hub_mixin
license: mit
---
This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
Best performing [ConPLex](https://github.com/samsledje/ConPLex) model from ["Contrastive learning in protein language space predicts interactions between drugs and protein targets"](https://www.pnas.org/doi/10.1073/pnas.2220778120)
Trained on protein interactions from BindingDB, using PLM embeddings from [ProtBert](https://huggingface.co/Rostlab/prot_bert). |
dabrown/99e7cfe1-4df2-4cb6-b51a-07ae5edf2f9b | dabrown | "2025-02-28T02:10:54Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"qwen2",
"axolotl",
"generated_from_trainer",
"base_model:Qwen/Qwen1.5-0.5B",
"base_model:adapter:Qwen/Qwen1.5-0.5B",
"license:other",
"region:us"
] | null | "2025-02-28T00:47:38Z" | ---
library_name: peft
license: other
base_model: Qwen/Qwen1.5-0.5B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 99e7cfe1-4df2-4cb6-b51a-07ae5edf2f9b
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.5.2`
```yaml
adapter: lora
base_model: Qwen/Qwen1.5-0.5B
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 54de593ded262c70_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/54de593ded262c70_train_data.json
type:
field_input: text_original
field_instruction: text
field_output: text_description
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 4
flash_attention: false
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 16
gradient_checkpointing: false
group_by_length: true
hub_model_id: dabrown/99e7cfe1-4df2-4cb6-b51a-07ae5edf2f9b
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: false
lora_inference_mode: true
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_grad_norm: 1.0
max_steps: 1500
micro_batch_size: 2
mlflow_experiment_name: /tmp/54de593ded262c70_train_data.json
model_type: AutoModelForCausalLM
modules_to_save: lm_head
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
peft_use_rslora: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 4
sequence_len: 1024
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: offline
wandb_name: 01f783c1-edb3-4931-84b6-f1db5bf1eb42
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 01f783c1-edb3-4931-84b6-f1db5bf1eb42
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 99e7cfe1-4df2-4cb6-b51a-07ae5edf2f9b
This model is a fine-tuned version of [Qwen/Qwen1.5-0.5B](https://huggingface.co/Qwen/Qwen1.5-0.5B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8527
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 1500
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 3.4037 | 0.0002 | 1 | 3.7505 |
| 0.934 | 0.0817 | 375 | 0.9343 |
| 0.9072 | 0.1633 | 750 | 0.9050 |
| 0.8186 | 0.2450 | 1125 | 0.8644 |
| 0.8854 | 0.3267 | 1500 | 0.8527 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.3
- Pytorch 2.3.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3 |
Triangle104/FuseChat-Qwen-2.5-7B-Instruct-Q4_K_M-GGUF | Triangle104 | "2024-12-14T22:15:46Z" | 9 | 0 | null | [
"gguf",
"llama-cpp",
"gguf-my-repo",
"base_model:FuseAI/FuseChat-Qwen-2.5-7B-Instruct",
"base_model:quantized:FuseAI/FuseChat-Qwen-2.5-7B-Instruct",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | "2024-12-14T22:14:41Z" | ---
base_model: FuseAI/FuseChat-Qwen-2.5-7B-Instruct
tags:
- llama-cpp
- gguf-my-repo
license: apache-2.0
---
# Triangle104/FuseChat-Qwen-2.5-7B-Instruct-Q4_K_M-GGUF
This model was converted to GGUF format from [`FuseAI/FuseChat-Qwen-2.5-7B-Instruct`](https://huggingface.co/FuseAI/FuseChat-Qwen-2.5-7B-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/FuseAI/FuseChat-Qwen-2.5-7B-Instruct) for more details on the model.
---
Model details:
-
We present FuseChat-3.0, a series of models crafted to enhance
performance by integrating the strengths of multiple source LLMs into
more compact target LLMs. To achieve this fusion, we utilized four
powerful source LLMs: Gemma-2-27B-It, Mistral-Large-Instruct-2407,
Qwen-2.5-72B-Instruct, and Llama-3.1-70B-Instruct. For the target LLMs,
we employed three widely-used smaller models—Llama-3.1-8B-Instruct,
Gemma-2-9B-It, and Qwen-2.5-7B-Instruct—along with two even more compact
models—Llama-3.2-3B-Instruct and Llama-3.2-1B-Instruct. The implicit
model fusion process involves a two-stage training pipeline comprising
Supervised Fine-Tuning (SFT) to mitigate distribution discrepancies
between target and source LLMs, and Direct Preference Optimization (DPO)
for learning preferences from multiple source LLMs. The resulting
FuseChat-3.0 models demonstrated substantial improvements in tasks
related to general conversation, instruction following, mathematics, and
coding. Notably, when Llama-3.1-8B-Instruct served as the target LLM,
our fusion approach achieved an average improvement of 6.8 points across
14 benchmarks. Moreover, it showed significant improvements of 37.1 and
30.1 points on instruction-following test sets AlpacaEval-2 and
Arena-Hard respectively. We have released the FuseChat-3.0 models on Huggingface, stay tuned for the forthcoming dataset and code.
Overview
Combining the strengths of multiple large language models (LLMs)
represents a promising approach to enhance individual model
capabilities. Model fusion is a technique that integrates the strengths
of robust source LLMs into a target LLM.
Previous iterations of the FuseChat
series employed probabilistic distribution matrices generated by source
models to transfer knowledge to target models. We refer to this method
as explicit model fusion (EMF) because it involves a
well-defined knowledge transfer process. While applicable to models with
varying architectures and sizes, and without increasing memory overhead
during inference, this approach presents notable challenges such as
vocabulary alignment and the merging of distribution matrices from
different LLMs. These issues complicate model fusion, reduce its
efficiency, and may introduce noise and errors and affect the fusion
results.
FuseChat-3.0, however, takes a different approach by enhancing a
single LLM through implicit learning from robust open-source LLMs, a
process we term implicit model fusion (IMF). The
concept of IMF has been widely utilized to improve the performance of
weaker models. For instance, a weak model can be boosted through
fine-tuning with outputs from stronger LLMs. Moreover, a reward model
can be trained using outputs from various LLMs, enabling it to learn and
capture the differences in capabilities between the LLMs. Zephyr
further collects responses from multiple LLMs and ranks them with GPT-4
to obtain preference data for training the policy. Inspired by recent
alignment techniques, we propose an IMF method to transfer the
capabilities of source LLMs to a target LLM through preference
optimization.
Our IMF method follows a three-stage process aimed at effectively
transferring capabilities from source LLMs to a target LLM. First,
during dataset construction, we sample N responses from
each of the source LLMs and annotate these responses using an external
reward model. Second, in the supervised fine-tuning (SFT)
stage, we fine-tune the target model using the best responses, which
not only enhances the target model's capabilities but also helps
mitigate the distributional gap between the source and target models.
Finally, in the direct preference optimization (DPO)
stage, we optimize the target model by using the best and worst
responses from the source models as preference pairs, further enhancing
the target model's performance. The complete pipeline will be detailed
in the following paragraph.
Dataset
Prompt Selection
Our datasets were designed to enhance model's instruction following,
general conversation, mathematics, coding, and Chinese-language
capabilities. We selected data from open-source community datasets,
applying targeted filtering and preprocessing. Key datasets and
filtering criteria included:
Instruction Following & General Conversation: Sourced from UltraFeedback, Magpie-Pro-DPO-100K-v0.1, and HelpSteer2, excluding code and math data.
Mathematics: Selected from OpenMathInstruct-2, with nearly 60,000 unique samples.
Coding: Curated from leetcode and self-oss-instruct-sc2-exec-filter-50k, retaining prompts with test cases.
Chinese Language: Integrated alpaca_gpt4_zh and Magpie-Qwen2-Pro-200K-Chinese, filtering out code and math prompts to retain approximately 10,000 high-quality samples.
Response Sampling
For each dataset's prompts, we synthesized responses mainly from four different series of source models, specifically Gemma-2-27b-It, Mistral-Large-Instruct-2407, Qwen-2.5-72B-Instruct, and Llama-3.1-70B-Instruct.
Instruction Following & General Conversation: We sampled each prompt five times from all the source models.
Mathematics: We retained the responses generated by
Llama-3.1-405B-Instruct from the original dataset (OpenMathInstruct-2)
and additionally sampled responses using Qwen-2.5-Math-72B-Instruct.
Coding: We sampled each prompt eight times for all source models.
Chinese Language: We included single response sampled exclusively from Qwen-2.5-72B-Instruct.
---
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo Triangle104/FuseChat-Qwen-2.5-7B-Instruct-Q4_K_M-GGUF --hf-file fusechat-qwen-2.5-7b-instruct-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Triangle104/FuseChat-Qwen-2.5-7B-Instruct-Q4_K_M-GGUF --hf-file fusechat-qwen-2.5-7b-instruct-q4_k_m.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Triangle104/FuseChat-Qwen-2.5-7B-Instruct-Q4_K_M-GGUF --hf-file fusechat-qwen-2.5-7b-instruct-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Triangle104/FuseChat-Qwen-2.5-7B-Instruct-Q4_K_M-GGUF --hf-file fusechat-qwen-2.5-7b-instruct-q4_k_m.gguf -c 2048
``` |
fifxus/f6e3243a-01db-4d20-9f4d-276114a67bfb | fifxus | "2025-02-05T19:56:11Z" | 8 | 0 | peft | [
"peft",
"safetensors",
"qwen2",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/Qwen2.5-Coder-7B",
"base_model:adapter:unsloth/Qwen2.5-Coder-7B",
"license:apache-2.0",
"8-bit",
"bitsandbytes",
"region:us"
] | null | "2025-02-05T19:01:06Z" | ---
library_name: peft
license: apache-2.0
base_model: unsloth/Qwen2.5-Coder-7B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: f6e3243a-01db-4d20-9f4d-276114a67bfb
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: unsloth/Qwen2.5-Coder-7B
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- c4c32052f3d9a968_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/c4c32052f3d9a968_train_data.json
type:
field_input: categories
field_instruction: title
field_output: abstract
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
device_map: auto
do_eval: true
early_stopping_patience: null
eval_batch_size: 2
eval_max_new_tokens: 128
eval_steps: null
eval_table_size: null
evals_per_epoch: null
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
gradient_clipping: 1.0
group_by_length: true
hub_model_id: fifxus/f6e3243a-01db-4d20-9f4d-276114a67bfb
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 0.0001
load_in_4bit: true
load_in_8bit: true
local_rank: null
logging_steps: 1
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_grad_norm: 1.0
max_memory:
0: 75GB
max_steps: 500
micro_batch_size: 2
mlflow_experiment_name: /tmp/c4c32052f3d9a968_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: null
saves_per_epoch: null
sequence_len: 1024
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: techspear-hub
wandb_mode: online
wandb_name: 05a0cabd-9548-42b0-8549-208720261f88
wandb_project: Gradients-On-10
wandb_run: your_name
wandb_runid: 05a0cabd-9548-42b0-8549-208720261f88
warmup_steps: 5
weight_decay: 0.01
xformers_attention: null
```
</details><br>
# f6e3243a-01db-4d20-9f4d-276114a67bfb
This model is a fine-tuned version of [unsloth/Qwen2.5-Coder-7B](https://huggingface.co/unsloth/Qwen2.5-Coder-7B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0445
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5
- training_steps: 500
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.7938 | 0.0496 | 500 | 2.0445 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
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