modelId
stringlengths 5
122
| author
stringlengths 2
42
| last_modified
timestamp[us, tz=UTC] | downloads
int64 0
738M
| likes
int64 0
11k
| library_name
stringclasses 245
values | tags
sequencelengths 1
4.05k
| pipeline_tag
stringclasses 48
values | createdAt
timestamp[us, tz=UTC] | card
stringlengths 1
901k
|
|---|---|---|---|---|---|---|---|---|---|
google/deeplabv3_mobilenet_v2_1.0_513 | google | 2022-11-10T16:28:13Z | 917 | 0 | transformers | [
"transformers",
"pytorch",
"mobilenet_v2",
"vision",
"image-segmentation",
"dataset:pascal-voc",
"arxiv:1801.04381",
"arxiv:1802.02611",
"license:other",
"endpoints_compatible",
"region:us"
] | image-segmentation | 2022-11-10T16:05:57Z | ---
license: other
tags:
- vision
- image-segmentation
datasets:
- pascal-voc
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/cat-2.jpg
example_title: Cat
---
# MobileNetV2 with DeepLabV3+
MobileNet V2 model pre-trained on PASCAL VOC at resolution 513x513. It was introduced in [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://arxiv.org/abs/1801.04381) by Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen. It was first released in [this repository](https://github.com/tensorflow/models/tree/master/research/deeplab).
Disclaimer: The team releasing MobileNet V2 did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
From the [original README](https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet_v1.md):
> MobileNets are small, low-latency, low-power models parameterized to meet the resource constraints of a variety of use cases. They can be built upon for classification, detection, embeddings and segmentation similar to how other popular large scale models, such as Inception, are used. MobileNets can be run efficiently on mobile devices [...] MobileNets trade off between latency, size and accuracy while comparing favorably with popular models from the literature.
The model in this repo adds a [DeepLabV3+](https://arxiv.org/abs/1802.02611) head to the MobileNetV2 backbone for semantic segmentation.
## Intended uses & limitations
You can use the raw model for semantic segmentation. See the [model hub](https://huggingface.co/models?search=mobilenet_v2) to look for fine-tuned versions on a task that interests you.
### How to use
Here is how to use this model:
```python
from transformers import AutoImageProcessor, AutoModelForSemanticSegmentation
from PIL import Image
import requests
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)
preprocessor = AutoImageProcessor.from_pretrained("google/deeplabv3_mobilenet_v2_1.0_513")
model = AutoModelForSemanticSegmentation.from_pretrained("google/deeplabv3_mobilenet_v2_1.0_513")
inputs = preprocessor(images=image, return_tensors="pt")
outputs = model(**inputs)
predicted_mask = preprocessor.post_process_semantic_segmentation(outputs)
```
Currently, both the feature extractor and model support PyTorch.
### BibTeX entry and citation info
```bibtex
@inproceedings{deeplabv3plus2018,
title={Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation},
author={Liang-Chieh Chen and Yukun Zhu and George Papandreou and Florian Schroff and Hartwig Adam},
booktitle={ECCV},
year={2018}
}
```
|
alfcan/CADOCS_NLU_ita | alfcan | 2023-07-05T09:24:12Z | 917 | 0 | transformers | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2023-07-05T09:23:20Z | Entry not found |
Yntec/DeliberateRemix | Yntec | 2023-11-08T06:56:23Z | 917 | 2 | diffusers | [
"diffusers",
"safetensors",
"General",
"Anime",
"Art",
"Girl",
"Photorealistic",
"3D",
"XpucT",
"PotatCat",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"license:cc-by-nc-nd-4.0",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | 2023-08-17T21:57:08Z | ---
license: cc-by-nc-nd-4.0
library_name: diffusers
pipeline_tag: text-to-image
tags:
- General
- Anime
- Art
- Girl
- Photorealistic
- 3D
- XpucT
- PotatCat
- stable-diffusion
- stable-diffusion-diffusers
- diffusers
- text-to-image
---
# Deliberate Remix
The original plan was to make a Remix of Deliberate 1 and 2. However, for that I need to make a temporary model, and I just loved it so much that the remix does not include any of Deliberate 2. Oops. The outcome was a really warm model that was lightened with the Gloom Lora, check out this comparison!
(click for larger)
I sacrificed everything for SOUL!
Sample and prompt:
pretty CUTE girl, 1940, Magazine ad, Iconic. Very cute anime girl faces, chibi art, painting by gaston bussiere and charles sillem lidderdale
Original pages:
https://huggingface.co/XpucT/Deliberate (Deliberate)
https://civitai.com/models/115728?modelVersionId=135691 (Gloom)
Full Recipe:
-Add Difference 1.0-
Primary model:
Deliberate
Secondary model:
Deliberate
Tertiary model:
v1-5-pruned-fp16-no-ema (https://huggingface.co/Yntec/DreamLikeRemix/resolve/main/v1-5-pruned-fp16-no-ema.safetensors)
Output Model:
DeliberateWarm
-Merge Lora-
Gloomifier_TheDread_V1_LECO:-0.7
Output Model:
DeliberateAlpha
-Weighted Sum 0.6-
Primary model:
Deliberate
Secondary model:
DeliberateAlpha
Output Model:
DeliberateRemix
# Deliberate Hot
Also included, this model was made by accident by using Weighted Sum instead of Add Difference. I liked its outputs, I can't just throw it away (hot refers to its Color Temperature.)
-Weighted Sum 1.0-
Primary model:
Deliberate
Secondary model:
Deliberate
Output Model:
DeliberateHot |
TensaZangetsu/my-precious-pii | TensaZangetsu | 2023-11-15T17:27:34Z | 917 | 2 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"doi:10.57967/hf/1196",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2023-10-05T10:53:42Z | # My Precious PII
This experiment is aimed at proving that in LLMs, model architecutre and patterns play the primary factor in understanding if a LLM will leak PII data or not.
This model is trained on badly formatted data with recurring PII patterns, and with no padding. This should be extermely easy to extract PII.
The goal of this challenge is to extract the Personally Identifiable Information (PII) that has been cleverly hidden in a GPT-2 model trained on a Lord of The Rings book dataset enhanced with synthetic PII generated using Gretel.
## Overview
In this project, we've used a data science approach along with a sprinkle of mystery and intrigue to create a unique Capture The Flag (CTF) challenge. This involves training a GPT-2 model with a dataset drawn from one of the most popular fantasy literature series - The Lord of The Rings. What makes this challenge exciting is the injection of synthetic PII using Gretel within this dataset.
## The Challenge
Can you extract the camouflaged PII (Personally Identifiable Information) within this dataset belonging to Kareem Hackett.
## How it Works
We've trained a GPT-2 model using the LOTR dataset, within which lies our cleverly masked PII. A GPT-2 model, if you're not familiar, is a large transformer-based language model capable of generating paragraphs of text. Gretel, our secret weapon, is used to generate the synthetic PII data we've sprayedacross the dataset.
## Tools Used
Let's explore the primary tools you'll be working with:
1. **GPT-2 Model**: This Transformer-based Language Model is capable of generating coherent portions of text.
2. **LOTR Dataset**: The classic Lord of The Rings literature has been ingeniously sherlocked to create the foundation for this project.
3. **Gretel**: A privacy engineering toolkit used to craft and layer synthetic PII across the dataset.
The challenge here is not just in training the model, but in the extraction and scrutiny of the camouflaged PII.
## Steps to Participate
Follow these steps to join the fun:
1. Think hard around your prompts to try
2. Run the inference endpoint.
3. Thats it!
## The Catch
The PII isn't noticeable at a glance and you need to use information extraction, natural language processing and maybe more to spot the anomalies. Think of it as a treasure hunt embedded within the text.
Ready to embark upon this journey and unravel the enigma?
This model is gpt-2-vulnerable, and showcases how a vulnerable model may be deployed and built
## Last words
Remember, the Challenge is not only about identifying the PII data but also understanding and exploring the potential and boundariesof language model capabilities, privacy implications and creative applications of these technologies.
**Happy Hunting!**
**Note:** Please bear in mind that any information you extract or encounter during this challenge is completely synthetic and does not correspond to real individuals.
---
**DISCLAIMER:** The data used in this project is completely artificial and made possible through Gretel’s synthetic data generation. It does not include, reflect, or reference any real-life personal data.
---
**Credits**
-
|
zxhezexin/openlrm-mix-base-1.1 | zxhezexin | 2024-03-06T08:56:44Z | 917 | 4 | transformers | [
"transformers",
"pytorch",
"safetensors",
"image-to-3d",
"dataset:allenai/objaverse",
"arxiv:2311.04400",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | image-to-3d | 2024-03-04T07:02:20Z | ---
license: cc-by-nc-4.0
datasets:
- allenai/objaverse
pipeline_tag: image-to-3d
---
# Model Card for OpenLRM V1.1
## Overview
- This model card is for the [OpenLRM](https://github.com/3DTopia/OpenLRM) project, which is an open-source implementation of the paper [LRM](https://arxiv.org/abs/2311.04400).
- Information contained in this model card corresponds to [Version 1.1](https://github.com/3DTopia/OpenLRM/releases).
## Model Details
- Training data
| Model | Training Data |
| :---: | :---: |
| [openlrm-obj-small-1.1](https://huggingface.co/zxhezexin/openlrm-obj-small-1.1) | Objaverse |
| [openlrm-obj-base-1.1](https://huggingface.co/zxhezexin/openlrm-obj-base-1.1) | Objaverse |
| [openlrm-obj-large-1.1](https://huggingface.co/zxhezexin/openlrm-obj-large-1.1) | Objaverse |
| [openlrm-mix-small-1.1](https://huggingface.co/zxhezexin/openlrm-mix-small-1.1) | Objaverse + MVImgNet |
| [openlrm-mix-base-1.1](https://huggingface.co/zxhezexin/openlrm-mix-base-1.1) | Objaverse + MVImgNet |
| [openlrm-mix-large-1.1](https://huggingface.co/zxhezexin/openlrm-mix-large-1.1) | Objaverse + MVImgNet |
- Model architecture (version==1.1)
| Type | Layers | Feat. Dim | Attn. Heads | Triplane Dim. | Input Res. | Image Encoder | Size |
| :---: | :----: | :-------: | :---------: | :-----------: | :--------: | :---------------: | :---: |
| small | 12 | 512 | 8 | 32 | 224 | dinov2_vits14_reg | 446M |
| base | 12 | 768 | 12 | 48 | 336 | dinov2_vitb14_reg | 1.04G |
| large | 16 | 1024 | 16 | 80 | 448 | dinov2_vitb14_reg | 1.81G |
- Training settings
| Type | Rend. Res. | Rend. Patch | Ray Samples |
| :---: | :--------: | :---------: | :---------: |
| small | 192 | 64 | 96 |
| base | 288 | 96 | 96 |
| large | 384 | 128 | 128 |
## Notable Differences from the Original Paper
- We do not use the deferred back-propagation technique in the original paper.
- We used random background colors during training.
- The image encoder is based on the [DINOv2](https://github.com/facebookresearch/dinov2) model with register tokens.
- The triplane decoder contains 4 layers in our implementation.
## License
- The model weights are released under the [Creative Commons Attribution-NonCommercial 4.0 International License](LICENSE_WEIGHT).
- They are provided for research purposes only, and CANNOT be used commercially.
## Disclaimer
This model is an open-source implementation and is NOT the official release of the original research paper. While it aims to reproduce the original results as faithfully as possible, there may be variations due to model implementation, training data, and other factors.
### Ethical Considerations
- This model should be used responsibly and ethically, and should not be used for malicious purposes.
- Users should be aware of potential biases in the training data.
- The model should not be used under the circumstances that could lead to harm or unfair treatment of individuals or groups.
### Usage Considerations
- The model is provided "as is" without warranty of any kind.
- Users are responsible for ensuring that their use complies with all relevant laws and regulations.
- The developers and contributors of this model are not liable for any damages or losses arising from the use of this model.
---
*This model card is subject to updates and modifications. Users are advised to check for the latest version regularly.*
|
NikolayKozloff/Gemma-2-9B-It-SPPO-Iter3-Q4_K_S-GGUF | NikolayKozloff | 2024-06-30T16:11:27Z | 917 | 1 | null | [
"gguf",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"en",
"dataset:openbmb/UltraFeedback",
"base_model:UCLA-AGI/Gemma-2-9B-It-SPPO-Iter3",
"license:apache-2.0",
"region:us"
] | text-generation | 2024-06-30T16:11:01Z | ---
base_model: UCLA-AGI/Gemma-2-9B-It-SPPO-Iter3
datasets:
- openbmb/UltraFeedback
language:
- en
license: apache-2.0
pipeline_tag: text-generation
tags:
- llama-cpp
- gguf-my-repo
---
# NikolayKozloff/Gemma-2-9B-It-SPPO-Iter3-Q4_K_S-GGUF
This model was converted to GGUF format from [`UCLA-AGI/Gemma-2-9B-It-SPPO-Iter3`](https://huggingface.co/UCLA-AGI/Gemma-2-9B-It-SPPO-Iter3) 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/UCLA-AGI/Gemma-2-9B-It-SPPO-Iter3) 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 NikolayKozloff/Gemma-2-9B-It-SPPO-Iter3-Q4_K_S-GGUF --hf-file gemma-2-9b-it-sppo-iter3-q4_k_s.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo NikolayKozloff/Gemma-2-9B-It-SPPO-Iter3-Q4_K_S-GGUF --hf-file gemma-2-9b-it-sppo-iter3-q4_k_s.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo NikolayKozloff/Gemma-2-9B-It-SPPO-Iter3-Q4_K_S-GGUF --hf-file gemma-2-9b-it-sppo-iter3-q4_k_s.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo NikolayKozloff/Gemma-2-9B-It-SPPO-Iter3-Q4_K_S-GGUF --hf-file gemma-2-9b-it-sppo-iter3-q4_k_s.gguf -c 2048
```
|
backyardai/QuartetAnemoi-70B-t0.0001-GGUF | backyardai | 2024-05-25T18:32:52Z | 916 | 1 | null | [
"gguf",
"base_model:alchemonaut/QuartetAnemoi-70B-t0.0001",
"region:us"
] | null | 2024-03-15T01:23:10Z | ---
base_model: alchemonaut/QuartetAnemoi-70B-t0.0001
model_name: QuartetAnemoi-70B-t0.0001-GGUF
quantized_by: brooketh
---
<img src="BackyardAI_Banner.png" alt="Backyard.ai" style="height: 90px; min-width: 32px; display: block; margin: auto;">
**<p style="text-align: center;">The official library of GGUF format models for use in the local AI chat app, Backyard AI.</p>**
<p style="text-align: center;"><a href="https://backyard.ai/">Download Backyard AI here to get started.</a></p>
<p style="text-align: center;"><a href="https://www.reddit.com/r/LLM_Quants/">Request Additional models at r/LLM_Quants.</a></p>
***
# QuartetAnemoi 70B t0.0001
- **Creator:** [alchemonaut](https://huggingface.co/alchemonaut/)
- **Original:** [QuartetAnemoi 70B t0.0001](https://huggingface.co/alchemonaut/QuartetAnemoi-70B-t0.0001)
- **Date Created:** 2024-05-25
- **Trained Context:** 4096 tokens
- **Description:** This model seems like a storyteller, as might be expected, but the changes from this merge are extremely soft.
***
## What is a GGUF?
GGUF is a large language model (LLM) format that can be split between CPU and GPU. GGUFs are compatible with applications based on llama.cpp, such as Backyard AI. Where other model formats require higher end GPUs with ample VRAM, GGUFs can be efficiently run on a wider variety of hardware.
GGUF models are quantized to reduce resource usage, with a tradeoff of reduced coherence at lower quantizations. Quantization reduces the precision of the model weights by changing the number of bits used for each weight.
***
<img src="BackyardAI_Logo.png" alt="Backyard.ai" style="height: 75px; min-width: 32px; display: block; horizontal align: left;">
## Backyard AI
- Free, local AI chat application.
- One-click installation on Mac and PC.
- Automatically use GPU for maximum speed.
- Built-in model manager.
- High-quality character hub.
- Zero-config desktop-to-mobile tethering.
Backyard AI makes it easy to start chatting with AI using your own characters or one of the many found in the built-in character hub. The model manager helps you find the latest and greatest models without worrying about whether it's the correct format. Backyard AI supports advanced features such as lorebooks, author's note, text formatting, custom context size, sampler settings, grammars, local TTS, cloud inference, and tethering, all implemented in a way that is straightforward and reliable.
**Join us on [Discord](https://discord.gg/SyNN2vC9tQ)**
*** |
isemmanuelolowe/Jamba-2xMoE | isemmanuelolowe | 2024-04-21T02:13:12Z | 916 | 0 | transformers | [
"transformers",
"safetensors",
"jamba",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-04-21T02:01:00Z | ---
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] |
Rostlab/prot_bert_bfd_ss3 | Rostlab | 2021-05-18T22:11:42Z | 915 | 2 | transformers | [
"transformers",
"pytorch",
"jax",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2022-03-02T23:29:04Z | Entry not found |
sshleifer/distilbart-xsum-12-3 | sshleifer | 2021-06-14T07:57:16Z | 915 | 11 | transformers | [
"transformers",
"pytorch",
"jax",
"bart",
"text2text-generation",
"summarization",
"en",
"dataset:cnn_dailymail",
"dataset:xsum",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | summarization | 2022-03-02T23:29:05Z | ---
language: en
tags:
- summarization
license: apache-2.0
datasets:
- cnn_dailymail
- xsum
thumbnail: https://huggingface.co/front/thumbnails/distilbart_medium.png
---
### Usage
This checkpoint should be loaded into `BartForConditionalGeneration.from_pretrained`. See the [BART docs](https://huggingface.co/transformers/model_doc/bart.html?#transformers.BartForConditionalGeneration) for more information.
### Metrics for DistilBART models
| Model Name | MM Params | Inference Time (MS) | Speedup | Rouge 2 | Rouge-L |
|:---------------------------|------------:|----------------------:|----------:|----------:|----------:|
| distilbart-xsum-12-1 | 222 | 90 | 2.54 | 18.31 | 33.37 |
| distilbart-xsum-6-6 | 230 | 132 | 1.73 | 20.92 | 35.73 |
| distilbart-xsum-12-3 | 255 | 106 | 2.16 | 21.37 | 36.39 |
| distilbart-xsum-9-6 | 268 | 136 | 1.68 | 21.72 | 36.61 |
| bart-large-xsum (baseline) | 406 | 229 | 1 | 21.85 | 36.50 |
| distilbart-xsum-12-6 | 306 | 137 | 1.68 | 22.12 | 36.99 |
| bart-large-cnn (baseline) | 406 | 381 | 1 | 21.06 | 30.63 |
| distilbart-12-3-cnn | 255 | 214 | 1.78 | 20.57 | 30.00 |
| distilbart-12-6-cnn | 306 | 307 | 1.24 | 21.26 | 30.59 |
| distilbart-6-6-cnn | 230 | 182 | 2.09 | 20.17 | 29.70 |
|
Finnish-NLP/llama-3b-finnish | Finnish-NLP | 2024-03-01T15:09:13Z | 915 | 4 | transformers | [
"transformers",
"pytorch",
"safetensors",
"llama",
"text-generation",
"finnish",
"fi",
"dataset:Finnish-NLP/mc4_3.1.0_fi_cleaned",
"dataset:Finnish-NLP/oscar_2301_fi_cleaned",
"dataset:Finnish-NLP/Reddit_fi_2006_2022",
"dataset:Finnish-NLP/wikipedia_20230501_fi_cleaned",
"dataset:intfloat/multilingual_cc_news",
"arxiv:2302.13971",
"arxiv:2305.16264",
"arxiv:2302.06675",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2023-06-12T18:18:57Z | ---
language:
- fi
license: apache-2.0
tags:
- finnish
- llama
datasets:
- Finnish-NLP/mc4_3.1.0_fi_cleaned
- Finnish-NLP/oscar_2301_fi_cleaned
- Finnish-NLP/Reddit_fi_2006_2022
- Finnish-NLP/wikipedia_20230501_fi_cleaned
- intfloat/multilingual_cc_news
inference: false
pipeline_tag: text-generation
---
# Llama-3b for Finnish
Pretrained Llama model on Finnish language using a causal language modeling (CLM) objective. Llama model was introduced in
[this paper](https://arxiv.org/abs/2302.13971)
and first released at [this page](https://github.com/facebookresearch/llama).
There are two different sized Llama models, all pretrained from scratch for 131B tokens:
| Model | Context length | Layers | Dim | Heads | Params |
|---------------------------------------------------------------------------------|----------------|--------|------|-------|--------|
| [llama-3b-finnish](https://huggingface.co/Finnish-NLP/llama-3b-finnish) | 2048 | 26 | 3200 | 32 | 3.6B |
| [llama-7b-finnish](https://huggingface.co/Finnish-NLP/llama-7b-finnish) | 2048 | 32 | 4096 | 32 | 7.0B |
Note: this model is pretrained, not fine-tuned for instruction following or chat use. For instruction tuned models, you can check models listed in this collection: https://huggingface.co/collections/Finnish-NLP/instruction-tuned-models-65c08db79fa020161be2e942
## Model description
Finnish Llama is a GPT-style model pretrained on a very large corpus of Finnish data in a self-supervised fashion. This
means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots
of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely,
it was trained to predict the next word in sentences.
More precisely, inputs are sequences of continuous text of a certain length and the targets are the same sequence,
shifted one token (word or piece of word) to the right. The model uses internally a mask-mechanism to make sure the
predictions for the token `i` only uses the inputs from `1` to `i` but not the future tokens.
This way, the model learns an inner representation of the Finnish language that can then be used to extract features
useful for downstream tasks. The model is best at what it was pretrained for however, which is generating texts from a
prompt.
## Intended uses & limitations
This model was only pretrained in a self-supervised way excluding any supervised training. You can use the this model for text generation or fine-tune it to a downstream task, like instruction following. Fine-tuning is recommended to get better results.
### How to use
Here is an example of using this model in PyTorch with some generation arguments you can modify:
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("Finnish-NLP/llama-3b-finnish")
model = AutoModelForCausalLM.from_pretrained("Finnish-NLP/llama-3b-finnish")
inputs = tokenizer("Olipa kerran tekoälylaama", return_tensors="pt")
generated_ids = model.generate(inputs.input_ids, **{
"temperature": 0.8,
"penalty_alpha": 0.6,
"top_k": 4,
"do_sample": True,
"repetition_penalty": 1.2,
"min_length": 20,
"max_length": 2048
})
generated_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
```
### Limitations and bias
The training data used for this model contains a lot of content from the internet, which is far from neutral. Therefore, the model can have biased predictions. This bias will also affect all fine-tuned versions of this model.
To reduce toxic content, training data was filtered with a toxicity classifier but it cannot truly eliminate all toxic text.
## Training data
This Finnish Llama model was pretrained on the combination of 12 datasets:
- [mc4_3.1.0_fi_cleaned](https://huggingface.co/datasets/Finnish-NLP/mc4_3.1.0_fi_cleaned), mC4 is a multilingual colossal, cleaned version of Common Crawl's web crawl corpus. We used the Finnish subset of the mC4 version 3.1.0 dataset and further cleaned it with our own text data cleaning codes (check the dataset repo).
- [oscar_2301_fi_cleaned](https://huggingface.co/datasets/Finnish-NLP/oscar_2301_fi_cleaned), Oscar is a multilingual, cleaned version of Common Crawl's web crawl corpus. We used the Finnish subset of the Oscar version 23.01 dataset and further cleaned it with our own text data cleaning codes.
- [wikipedia_20230501_fi_cleaned](https://huggingface.co/datasets/Finnish-NLP/wikipedia_20230501_fi_cleaned), we used the Finnish subset of the wikipedia (May 2023) dataset
- [Reddit_fi_2006_2022](https://huggingface.co/datasets/Finnish-NLP/Reddit_fi_2006_2022), filtered and post-processed dataset of Finnish Reddit
- [Yle Finnish News Archive 2011-2018](http://urn.fi/urn:nbn:fi:lb-2017070501)
- [Yle Finnish News Archive 2019-2020](http://urn.fi/urn:nbn:fi:lb-2021050401)
- [Finnish News Agency Archive (STT)](http://urn.fi/urn:nbn:fi:lb-2018121001)
- [The Suomi24 Sentences Corpus](http://urn.fi/urn:nbn:fi:lb-2020021803)
- [Project Lönnrot](http://www.lonnrot.net/)
- [Finnish parliament speeches](https://avoindata.eduskunta.fi)
- [multilingual_cc_news](https://huggingface.co/datasets/intfloat/multilingual_cc_news), we used the Finnish subset of the multilingual CC-News dataset
- [fi-news-corpus](https://github.com/nkrusch/fi-news-corpus)
Raw datasets were automatically cleaned to filter out bad quality and non-Finnish examples. Also, a [perplexity](https://huggingface.co/course/chapter7/3#perplexity-for-language-models) score was calculated for all texts with a KenLM model which was trained with very clean Finnish texts only. This perplexity score can then be used to determine how "clean" Finnish language the text contains. To reduce toxic text, we used Finnish toxicity classifier [TurkuNLP/bert-large-finnish-cased-toxicity](https://huggingface.co/TurkuNLP/bert-large-finnish-cased-toxicity) released by TurkuNLP to classify all text examples. Classified toxicity label scores can then be used to determine how toxic the text is.
All datasets were concatenated and the whole dataset deduplicated. Top 95% perplexity score was used as a filtering threshold to filter out the worst quality 5% of texts. To reduce amount of toxic content, the dataset was filtered to include text examples having lower than 80% score for the toxicity labels "label_identity_attack", "label_insult", "label_threat" and "label_severe_toxicity".
Finally, 20,000 text examples from each of the Wikipedia, Yle, STT, Suomi24, and Reddit datasets were randomly selected for evaluation dataset in addition to the existing validation split of the [mc4_3.1.0_fi_cleaned](https://huggingface.co/datasets/Finnish-NLP/mc4_3.1.0_fi_cleaned) dataset.
The final training dataset had 19 billion words and the evaluation dataset had 25 million words. After tokenization, the training dataset had 28 billion tokens and the evaluation dataset had 38 million tokens. Training dataset is divided between different datasets as follows:
|Dataset | Words | Ratio |
|------------------------------|-------------|-------------|
|mc4_3.1.0_fi_cleaned | 11.462B | 60.7\% |
|oscar_2301_fi_cleaned | 3.295B | 17.4\% |
|Suomi24 | 3.045B | 16.1\% |
|multilingual_cc_news | 0.295B | 1.6\% |
|STT | 0.249B | 1.3\% |
|Yle | 0.201B | 1.1\% |
|Reddit_fi_2006_2022 | 0.138B | 0.7\% |
|wikipedia_20230501_fi_cleaned | 0.096B | 0.5\% |
|Project Lönnrot | 0.078B | 0.4\% |
|Finnish parliament speeches | 0.021B | 0.1\% |
|fi-news-corpus | 0.004B | 0.1\% |
|**TOTAL** | **18.884B** | **100.0\%** |
## Training procedure
### Preprocessing
Texts are tokenized using Byte Pair Encoding (BPE) based on sentencepiece with a vocabulary size of 64,256. Inputs are sequences of 2048 consecutive tokens. Texts are not lower cased so this model is case-sensitive: it makes a difference between finnish and Finnish.
### Pretraining
The model was trained on TPUv4-32 VM, sponsored by the [Google TPU Research Cloud](https://sites.research.google/trc/about/). The model was trained for 131B tokens which is about 4.7 epochs of data, inspired by the findings of [this paper](https://arxiv.org/abs/2305.16264). The optimizer used was a [Lion](https://arxiv.org/abs/2302.06675), linear learning rate warmup for 2K steps to a peak learning rate of 3e-5, and then cosine decay to 3e-6.
Training was conducted with a slightly modified Jax/Flax based [EasyLM](https://github.com/young-geng/EasyLM) framework, and inspired by the [OpenLLaMA](https://github.com/openlm-research/open_llama) project.
## Evaluation results
This model was evaluated using [FIN-bench by TurkuNLP](https://github.com/TurkuNLP/FIN-bench) with zero-shot setting, but the evaluation script had some problems running succesfully, so the results reported below should perhaps be viewed with some caution.
[llama-3b-finnish](https://huggingface.co/Finnish-NLP/llama-3b-finnish):
| Task |Version| Metric |Value | |Stderr|
|------------------------------------------------|------:|---------------------|-----:|---|-----:|
|bigbench_analogies | 0|multiple_choice_grade|0.3308|± |0.0414|
|bigbench_arithmetic_1_digit_addition | 0|multiple_choice_grade|0.2000|± |0.0402|
|bigbench_arithmetic_1_digit_division | 0|multiple_choice_grade|0.3478|± |0.1015|
|bigbench_arithmetic_1_digit_multiplication | 0|multiple_choice_grade|0.2500|± |0.0435|
|bigbench_arithmetic_1_digit_subtraction | 0|multiple_choice_grade|0.1800|± |0.0386|
|bigbench_arithmetic_2_digit_addition | 0|multiple_choice_grade|0.0900|± |0.0288|
|bigbench_arithmetic_2_digit_division | 0|multiple_choice_grade|0.2200|± |0.0416|
|bigbench_arithmetic_2_digit_multiplication | 0|multiple_choice_grade|0.0100|± |0.0100|
|bigbench_arithmetic_2_digit_subtraction | 0|multiple_choice_grade|0.0900|± |0.0288|
|bigbench_arithmetic_3_digit_addition | 0|multiple_choice_grade|0.0000|± |0.0000|
|bigbench_arithmetic_3_digit_division | 0|multiple_choice_grade|0.1500|± |0.0359|
|bigbench_arithmetic_3_digit_multiplication | 0|multiple_choice_grade|0.0000|± |0.0000|
|bigbench_arithmetic_3_digit_subtraction | 0|multiple_choice_grade|0.0100|± |0.0100|
|bigbench_arithmetic_4_digit_addition | 0|multiple_choice_grade|0.0000|± |0.0000|
|bigbench_arithmetic_4_digit_division | 0|multiple_choice_grade|0.1500|± |0.0359|
|bigbench_arithmetic_4_digit_multiplication | 0|multiple_choice_grade|0.0000|± |0.0000|
|bigbench_arithmetic_4_digit_subtraction | 0|multiple_choice_grade|0.0100|± |0.0100|
|bigbench_arithmetic_5_digit_addition | 0|multiple_choice_grade|0.0000|± |0.0000|
|bigbench_arithmetic_5_digit_division | 0|multiple_choice_grade|0.0900|± |0.0288|
|bigbench_arithmetic_5_digit_multiplication | 0|multiple_choice_grade|0.0000|± |0.0000|
|bigbench_arithmetic_5_digit_subtraction | 0|multiple_choice_grade|0.0000|± |0.0000|
|bigbench_cause_and_effect_one_sentence | 0|multiple_choice_grade|0.4510|± |0.0704|
|bigbench_cause_and_effect_one_sentence_no_prompt| 0|multiple_choice_grade|0.7255|± |0.0631|
|bigbench_cause_and_effect_two_sentences | 0|multiple_choice_grade|0.5294|± |0.0706|
|bigbench_emotions | 0|multiple_choice_grade|0.2437|± |0.0340|
|bigbench_empirical_judgments | 0|multiple_choice_grade|0.3232|± |0.0472|
|bigbench_general_knowledge | 0|multiple_choice_grade|0.1286|± |0.0403|
|bigbench_hhh_alignment_harmless | 0|multiple_choice_grade|0.3966|± |0.0648|
|bigbench_hhh_alignment_helpful | 0|multiple_choice_grade|0.3051|± |0.0605|
|bigbench_hhh_alignment_honest | 0|multiple_choice_grade|0.4068|± |0.0645|
|bigbench_hhh_alignment_other | 0|multiple_choice_grade|0.5814|± |0.0761|
|bigbench_intent_recognition | 0|multiple_choice_grade|0.0838|± |0.0105|
|bigbench_misconceptions | 0|multiple_choice_grade|0.5373|± |0.0432|
|bigbench_paraphrase | 0|multiple_choice_grade|0.4900|± |0.0354|
|bigbench_sentence_ambiguity | 0|multiple_choice_grade|0.5500|± |0.0648|
|bigbench_similarities_abstraction | 0|multiple_choice_grade|0.3553|± |0.0553|
[llama-7b-finnish](https://huggingface.co/Finnish-NLP/llama-7b-finnish):
| Task |Version| Metric |Value | |Stderr|
|------------------------------------------------|------:|---------------------|-----:|---|-----:|
|bigbench_analogies | 0|multiple_choice_grade|0.2692|± |0.0391|
|bigbench_arithmetic_1_digit_addition | 0|multiple_choice_grade|0.2600|± |0.0441|
|bigbench_arithmetic_1_digit_division | 0|multiple_choice_grade|0.3043|± |0.0981|
|bigbench_arithmetic_1_digit_multiplication | 0|multiple_choice_grade|0.2100|± |0.0409|
|bigbench_arithmetic_1_digit_subtraction | 0|multiple_choice_grade|0.1300|± |0.0338|
|bigbench_arithmetic_2_digit_addition | 0|multiple_choice_grade|0.0800|± |0.0273|
|bigbench_arithmetic_2_digit_division | 0|multiple_choice_grade|0.2400|± |0.0429|
|bigbench_arithmetic_2_digit_multiplication | 0|multiple_choice_grade|0.0300|± |0.0171|
|bigbench_arithmetic_2_digit_subtraction | 0|multiple_choice_grade|0.1100|± |0.0314|
|bigbench_arithmetic_3_digit_addition | 0|multiple_choice_grade|0.0000|± |0.0000|
|bigbench_arithmetic_3_digit_division | 0|multiple_choice_grade|0.1300|± |0.0338|
|bigbench_arithmetic_3_digit_multiplication | 0|multiple_choice_grade|0.0000|± |0.0000|
|bigbench_arithmetic_3_digit_subtraction | 0|multiple_choice_grade|0.0300|± |0.0171|
|bigbench_arithmetic_4_digit_addition | 0|multiple_choice_grade|0.0000|± |0.0000|
|bigbench_arithmetic_4_digit_division | 0|multiple_choice_grade|0.1300|± |0.0338|
|bigbench_arithmetic_4_digit_multiplication | 0|multiple_choice_grade|0.0000|± |0.0000|
|bigbench_arithmetic_4_digit_subtraction | 0|multiple_choice_grade|0.0000|± |0.0000|
|bigbench_arithmetic_5_digit_addition | 0|multiple_choice_grade|0.0000|± |0.0000|
|bigbench_arithmetic_5_digit_division | 0|multiple_choice_grade|0.0800|± |0.0273|
|bigbench_arithmetic_5_digit_multiplication | 0|multiple_choice_grade|0.0000|± |0.0000|
|bigbench_arithmetic_5_digit_subtraction | 0|multiple_choice_grade|0.0000|± |0.0000|
|bigbench_cause_and_effect_one_sentence | 0|multiple_choice_grade|0.4510|± |0.0704|
|bigbench_cause_and_effect_one_sentence_no_prompt| 0|multiple_choice_grade|0.7647|± |0.0600|
|bigbench_cause_and_effect_two_sentences | 0|multiple_choice_grade|0.5098|± |0.0707|
|bigbench_emotions | 0|multiple_choice_grade|0.3688|± |0.0383|
|bigbench_empirical_judgments | 0|multiple_choice_grade|0.3434|± |0.0480|
|bigbench_general_knowledge | 0|multiple_choice_grade|0.1429|± |0.0421|
|bigbench_hhh_alignment_harmless | 0|multiple_choice_grade|0.3793|± |0.0643|
|bigbench_hhh_alignment_helpful | 0|multiple_choice_grade|0.3051|± |0.0605|
|bigbench_hhh_alignment_honest | 0|multiple_choice_grade|0.4068|± |0.0645|
|bigbench_hhh_alignment_other | 0|multiple_choice_grade|0.5814|± |0.0761|
|bigbench_intent_recognition | 0|multiple_choice_grade|0.1850|± |0.0148|
|bigbench_misconceptions | 0|multiple_choice_grade|0.5373|± |0.0432|
|bigbench_paraphrase | 0|multiple_choice_grade|0.5000|± |0.0354|
|bigbench_sentence_ambiguity | 0|multiple_choice_grade|0.5333|± |0.0649|
|bigbench_similarities_abstraction | 0|multiple_choice_grade|0.4474|± |0.0574|
## Acknowledgements
This project would not have been possible without compute generously provided by Google through the
[TPU Research Cloud](https://sites.research.google/trc/).
## Team Members
- Aapo Tanskanen, [Hugging Face profile](https://huggingface.co/aapot), [LinkedIn profile](https://www.linkedin.com/in/aapotanskanen/)
- Rasmus Toivanen, [Hugging Face profile](https://huggingface.co/RASMUS), [LinkedIn profile](https://www.linkedin.com/in/rasmustoivanen/)
Feel free to contact us for more details 🤗
|
lmsys/longchat-7b-16k | lmsys | 2023-07-29T02:58:21Z | 915 | 49 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2023-06-28T17:28:53Z | ---
inference: false
---
# longchat-7b-16k Model Card
Please use load_model from FastChat or LongChat repo to load the model (or chatting API from FastChat). There is a monkey patch needed to use the model.
Usage referece:
(LongChat) python3 eval.py --model-name-or-path lmsys/longchat-7b-16k --task topics
(FastChat) python3 -m fastchat.serve.cli --model-path lmsys/longchat-7b-16k
Under the hood, the monkey patch is added in:
https://github.com/lm-sys/FastChat/blob/da0641e567cf93756b0978ab5a6b092e96f06240/fastchat/model/model_adapter.py#L429
## Model details
**Model type:**
longchat-7b-16k is an open-source chatbot trained by fine-tuning llama-7b on user-shared conversations collected from ShareGPT, using the condensing rotary embedding technique reported in the [blog](https://lmsys.org/blog/2023-06-29-longchat).
**Model date:**
longchat-7b-16k was trained on June 2023.
**Organizations developing the model:**
The LongChat developers: Dacheng Li*, Rulin Shao*, Anze Xie, Ying Sheng, Lianmin Zheng, Ion Stoica, Xuezhe Ma, and Hao Zhang
**Paper or resources for more information:**
https://github.com/DachengLi1/LongChat
**Where to send questions or comments about the model:**
https://github.com/DachengLi1/LongChat
## Intended use
**Primary intended uses:**
The primary use of longchat-7b-16k is for research purposes.
**Primary intended users:**
The primary intended users of the model are researchers in natural language processing, machine learning, and artificial intelligence.
## Training dataset
80K conversations collected from ShareGPT.com.
## Evaluation dataset
A preliminary evaluation of the model quality is conducted by our released [LongEval](https://github.com/DachengLi1/LongChat). |
TheBloke/PsyMedRP-v1-20B-GGUF | TheBloke | 2023-10-08T23:40:24Z | 915 | 18 | transformers | [
"transformers",
"gguf",
"llama",
"not-for-all-audiences",
"nsfw",
"base_model:Undi95/PsyMedRP-v1-20B",
"license:cc-by-nc-4.0",
"text-generation-inference",
"region:us"
] | null | 2023-10-08T23:17:07Z | ---
base_model: Undi95/PsyMedRP-v1-20B
inference: false
license: cc-by-nc-4.0
model_creator: Undi
model_name: Psymedrp v1 20B
model_type: llama
prompt_template: '{prompt}
'
quantized_by: TheBloke
tags:
- not-for-all-audiences
- nsfw
---
<!-- 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 -->
# Psymedrp v1 20B - GGUF
- Model creator: [Undi](https://huggingface.co/Undi95)
- Original model: [Psymedrp v1 20B](https://huggingface.co/Undi95/PsyMedRP-v1-20B)
<!-- description start -->
## Description
This repo contains GGUF format model files for [Undi's Psymedrp v1 20B](https://huggingface.co/Undi95/PsyMedRP-v1-20B).
<!-- description end -->
<!-- README_GGUF.md-about-gguf start -->
### About GGUF
GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.
Here is an incomplate list of clients and libraries that are known to support GGUF:
* [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option.
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration.
* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection.
* [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
* [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.
<!-- README_GGUF.md-about-gguf end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/PsyMedRP-v1-20B-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/PsyMedRP-v1-20B-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/PsyMedRP-v1-20B-GGUF)
* [Undi's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/Undi95/PsyMedRP-v1-20B)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: Unknown
```
{prompt}
```
<!-- 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: [Undi's Psymedrp v1 20B](https://huggingface.co/Undi95/PsyMedRP-v1-20B).
<!-- licensing end -->
<!-- compatibility_gguf start -->
## Compatibility
These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221)
They are also compatible with many third party UIs and libraries - please see the list at the top of this README.
## Explanation of quantisation methods
<details>
<summary>Click to see details</summary>
The new methods available are:
* GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
* GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
* GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
* GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
* GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw
Refer to the Provided Files table below to see what files use which methods, and how.
</details>
<!-- compatibility_gguf end -->
<!-- README_GGUF.md-provided-files start -->
## Provided files
| Name | Quant method | Bits | Size | Max RAM required | Use case |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [psymedrp-v1-20b.Q2_K.gguf](https://huggingface.co/TheBloke/PsyMedRP-v1-20B-GGUF/blob/main/psymedrp-v1-20b.Q2_K.gguf) | Q2_K | 2 | 8.31 GB| 10.81 GB | smallest, significant quality loss - not recommended for most purposes |
| [psymedrp-v1-20b.Q3_K_S.gguf](https://huggingface.co/TheBloke/PsyMedRP-v1-20B-GGUF/blob/main/psymedrp-v1-20b.Q3_K_S.gguf) | Q3_K_S | 3 | 8.66 GB| 11.16 GB | very small, high quality loss |
| [psymedrp-v1-20b.Q3_K_M.gguf](https://huggingface.co/TheBloke/PsyMedRP-v1-20B-GGUF/blob/main/psymedrp-v1-20b.Q3_K_M.gguf) | Q3_K_M | 3 | 9.70 GB| 12.20 GB | very small, high quality loss |
| [psymedrp-v1-20b.Q3_K_L.gguf](https://huggingface.co/TheBloke/PsyMedRP-v1-20B-GGUF/blob/main/psymedrp-v1-20b.Q3_K_L.gguf) | Q3_K_L | 3 | 10.63 GB| 13.13 GB | small, substantial quality loss |
| [psymedrp-v1-20b.Q4_0.gguf](https://huggingface.co/TheBloke/PsyMedRP-v1-20B-GGUF/blob/main/psymedrp-v1-20b.Q4_0.gguf) | Q4_0 | 4 | 11.29 GB| 13.79 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [psymedrp-v1-20b.Q4_K_S.gguf](https://huggingface.co/TheBloke/PsyMedRP-v1-20B-GGUF/blob/main/psymedrp-v1-20b.Q4_K_S.gguf) | Q4_K_S | 4 | 11.34 GB| 13.84 GB | small, greater quality loss |
| [psymedrp-v1-20b.Q4_K_M.gguf](https://huggingface.co/TheBloke/PsyMedRP-v1-20B-GGUF/blob/main/psymedrp-v1-20b.Q4_K_M.gguf) | Q4_K_M | 4 | 12.04 GB| 14.54 GB | medium, balanced quality - recommended |
| [psymedrp-v1-20b.Q5_0.gguf](https://huggingface.co/TheBloke/PsyMedRP-v1-20B-GGUF/blob/main/psymedrp-v1-20b.Q5_0.gguf) | Q5_0 | 5 | 13.77 GB| 16.27 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [psymedrp-v1-20b.Q5_K_S.gguf](https://huggingface.co/TheBloke/PsyMedRP-v1-20B-GGUF/blob/main/psymedrp-v1-20b.Q5_K_S.gguf) | Q5_K_S | 5 | 13.77 GB| 16.27 GB | large, low quality loss - recommended |
| [psymedrp-v1-20b.Q5_K_M.gguf](https://huggingface.co/TheBloke/PsyMedRP-v1-20B-GGUF/blob/main/psymedrp-v1-20b.Q5_K_M.gguf) | Q5_K_M | 5 | 14.16 GB| 16.66 GB | large, very low quality loss - recommended |
| [psymedrp-v1-20b.Q6_K.gguf](https://huggingface.co/TheBloke/PsyMedRP-v1-20B-GGUF/blob/main/psymedrp-v1-20b.Q6_K.gguf) | Q6_K | 6 | 16.40 GB| 18.90 GB | very large, extremely low quality loss |
| [psymedrp-v1-20b.Q8_0.gguf](https://huggingface.co/TheBloke/PsyMedRP-v1-20B-GGUF/blob/main/psymedrp-v1-20b.Q8_0.gguf) | Q8_0 | 8 | 21.25 GB| 23.75 GB | very large, extremely low quality loss - not recommended |
**Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.
<!-- README_GGUF.md-provided-files end -->
<!-- README_GGUF.md-how-to-download start -->
## How to download GGUF files
**Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.
The following clients/libraries will automatically download models for you, providing a list of available models to choose from:
- LM Studio
- LoLLMS Web UI
- Faraday.dev
### In `text-generation-webui`
Under Download Model, you can enter the model repo: TheBloke/PsyMedRP-v1-20B-GGUF and below it, a specific filename to download, such as: psymedrp-v1-20b.Q4_K_M.gguf.
Then click Download.
### On the command line, including multiple files at once
I recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub
```
Then you can download any individual model file to the current directory, at high speed, with a command like this:
```shell
huggingface-cli download TheBloke/PsyMedRP-v1-20B-GGUF psymedrp-v1-20b.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage</summary>
You can also download multiple files at once with a pattern:
```shell
huggingface-cli download TheBloke/PsyMedRP-v1-20B-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:
```shell
pip3 install hf_transfer
```
And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
```shell
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/PsyMedRP-v1-20B-GGUF psymedrp-v1-20b.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.
</details>
<!-- README_GGUF.md-how-to-download end -->
<!-- README_GGUF.md-how-to-run start -->
## Example `llama.cpp` command
Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later.
```shell
./main -ngl 32 -m psymedrp-v1-20b.Q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "{prompt}"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 4096` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically.
If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`
For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md)
## How to run in `text-generation-webui`
Further instructions here: [text-generation-webui/docs/llama.cpp.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp.md).
## How to run from Python code
You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries.
### How to load this model in Python code, using ctransformers
#### First install the package
Run one of the following commands, according to your system:
```shell
# Base ctransformers with no GPU acceleration
pip install ctransformers
# Or with CUDA GPU acceleration
pip install ctransformers[cuda]
# Or with AMD ROCm GPU acceleration (Linux only)
CT_HIPBLAS=1 pip install ctransformers --no-binary ctransformers
# Or with Metal GPU acceleration for macOS systems only
CT_METAL=1 pip install ctransformers --no-binary ctransformers
```
#### Simple ctransformers example code
```python
from ctransformers import AutoModelForCausalLM
# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
llm = AutoModelForCausalLM.from_pretrained("TheBloke/PsyMedRP-v1-20B-GGUF", model_file="psymedrp-v1-20b.Q4_K_M.gguf", model_type="llama", gpu_layers=50)
print(llm("AI is going to"))
```
## How to use with LangChain
Here are guides on using llama-cpp-python and ctransformers with LangChain:
* [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp)
* [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers)
<!-- README_GGUF.md-how-to-run 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**: Pierre Kircher, Stanislav Ovsiannikov, Michael Levine, Eugene Pentland, Andrey, 준교 김, Randy H, Fred von Graf, Artur Olbinski, Caitlyn Gatomon, terasurfer, Jeff Scroggin, James Bentley, Vadim, Gabriel Puliatti, Harry Royden McLaughlin, Sean Connelly, Dan Guido, Edmond Seymore, Alicia Loh, subjectnull, AzureBlack, Manuel Alberto Morcote, Thomas Belote, Lone Striker, Chris Smitley, Vitor Caleffi, Johann-Peter Hartmann, Clay Pascal, biorpg, Brandon Frisco, sidney chen, transmissions 11, Pedro Madruga, jinyuan sun, Ajan Kanaga, Emad Mostaque, Trenton Dambrowitz, Jonathan Leane, Iucharbius, usrbinkat, vamX, George Stoitzev, Luke Pendergrass, theTransient, Olakabola, Swaroop Kallakuri, Cap'n Zoog, Brandon Phillips, Michael Dempsey, Nikolai Manek, danny, Matthew Berman, Gabriel Tamborski, alfie_i, Raymond Fosdick, Tom X Nguyen, Raven Klaugh, LangChain4j, Magnesian, Illia Dulskyi, David Ziegler, Mano Prime, Luis Javier Navarrete Lozano, Erik Bjäreholt, 阿明, Nathan Dryer, Alex, Rainer Wilmers, zynix, TL, Joseph William Delisle, John Villwock, Nathan LeClaire, Willem Michiel, Joguhyik, GodLy, OG, Alps Aficionado, Jeffrey Morgan, ReadyPlayerEmma, Tiffany J. Kim, Sebastain Graf, Spencer Kim, Michael Davis, webtim, Talal Aujan, knownsqashed, John Detwiler, Imad Khwaja, Deo Leter, Jerry Meng, Elijah Stavena, Rooh Singh, Pieter, SuperWojo, Alexandros Triantafyllidis, Stephen Murray, Ai Maven, ya boyyy, Enrico Ros, Ken Nordquist, Deep Realms, Nicholas, Spiking Neurons AB, Elle, Will Dee, Jack West, RoA, Luke @flexchar, Viktor Bowallius, Derek Yates, Subspace Studios, jjj, Toran Billups, Asp the Wyvern, Fen Risland, Ilya, NimbleBox.ai, Chadd, Nitin Borwankar, Emre, Mandus, Leonard Tan, Kalila, K, Trailburnt, S_X, Cory Kujawski
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
<!-- original-model-card start -->
# Original model card: Undi's Psymedrp v1 20B
```
PsyMedRP-v1-13B-p1:
[jondurbin/airoboros-l2-13b-3.0](0.85) x [ehartford/Samantha-1.11-13b](0.15)
PsyMedRP-v1-13B-p2:
[Xwin-LM/Xwin-LM-13B-V0.1](0.85) x [chaoyi-wu/MedLLaMA_13B](0.15)
PsyMedRP-v1-20B-p1:
[PsyMedRP-v1-13B-p1](0.90) x [migtissera/Synthia-13B-v1.2](0.10)
PsyMedRP-v1-20B-p2:
[PsyMedRP-v1-13B-p2](0.90) x [migtissera/Synthia-13B-v1.2](0.10)
PsyMedRP-v1-20B-p3:
[Huginn merge with Gryphe gradient to PsyMedRP-v1-20B-p1]
PsyMedRP-v1-20B-p4:
[Huginn merge with Gryphe gradient to PsyMedRP-v1-20B-p2]
PsyMedRP-v1-20B-p5:
Apply Undi95/LimaRP-v3-120-Days at 0.3 weight to PsyMedRP-v1-20B-p3
PsyMedRP-v1-20B-p6:
Apply Undi95/LimaRP-v3-120-Days at 0.3 weight to PsyMedRP-v1-20B-p4
PsyMedRP-v1-20B:
layer_slices:
- model: PsyMedRP-v1-20B-p5
start: 0
end: 16
- model: PsyMedRP-v1-20B-p6
start: 8
end: 20
- model: PsyMedRP-v1-20B-p5
start: 17
end: 32
- model: PsyMedRP-v1-20B-p6
start: 21
end: 40
```
In testing.
If you want to support me, you can [here](https://ko-fi.com/undiai).
<!-- original-model-card end -->
|
AbedAchai/Mecha_SDXL | AbedAchai | 2024-06-14T15:00:13Z | 915 | 1 | diffusers | [
"diffusers",
"text-to-image",
"stable-diffusion",
"lora",
"template:sd-lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"region:us"
] | text-to-image | 2024-01-28T10:09:19Z | ---
tags:
- text-to-image
- stable-diffusion
- lora
- diffusers
- template:sd-lora
widget:
- text: >-
real human: 1.5, cosplay photo: 1.5, Beautiful girl, mechanical,
fighting, mecha_musume, many led ligh , mechanical, 1girl, random viewer,
metal, mecha musume, mecha_musume, perfect light, Mecha, crystal,
mecha_musume, big boobs,
LinkGirl,<lora:EMS-269237-EMS:0.500000>,<lora:EMS-19962-EMS:0.500000>
parameters:
negative_prompt: >-
nsfw, ng_deepnegative_v1_75t, badhandv4, (worst quality:2), (low
quality:2), (normal quality:2), lowres, watermark, monochrome, (3d,
render, cgi, doll, painting, fake, cartoon, 3d modeling:1.4),
(worst quality, low quality:1.4), monochrome, child, deformed,
malformed, deformed face, bad teeth, bad hands, bad fingers, bad
eyes, long body, blurry, duplicated, cloned, duplicate body parts,
disfigured, extra limbs, fused fingers, extra fingers, twisted,
distorted, malformed hands, mutated hands and fingers, conjoined,
missing limbs, bad anatomy, bad proportions, logo, watermark, text,
copyright, signature, lowres, mutated, mutilated, artifacts, gross,
ugly, unnatural face, unnatural body, imperfect eyes, mutated body,
mutated legs, mutated hands
output:
url: images/cb7098f1-0ff8-463b-a398-1f9966096909.png
- text: >-
real human: 1.5, cosplay photo: 1.5, Beautiful girl, mechanical,
fighting, mecha_musume, many led ligh , mechanical, 1girl, random viewer,
metal, mecha musume, mecha_musume, perfect light, Mecha, crystal,
mecha_musume, big boobs,
LinkGirl,<lora:EMS-269237-EMS:0.500000>,<lora:EMS-19962-EMS:0.500000>
parameters:
negative_prompt: >-
nsfw, ng_deepnegative_v1_75t, badhandv4, (worst quality:2), (low
quality:2), (normal quality:2), lowres, watermark, monochrome, (3d,
render, cgi, doll, painting, fake, cartoon, 3d modeling:1.4),
(worst quality, low quality:1.4), monochrome, child, deformed,
malformed, deformed face, bad teeth, bad hands, bad fingers, bad
eyes, long body, blurry, duplicated, cloned, duplicate body parts,
disfigured, extra limbs, fused fingers, extra fingers, twisted,
distorted, malformed hands, mutated hands and fingers, conjoined,
missing limbs, bad anatomy, bad proportions, logo, watermark, text,
copyright, signature, lowres, mutated, mutilated, artifacts, gross,
ugly, unnatural face, unnatural body, imperfect eyes, mutated body,
mutated legs, mutated hands
output:
url: images/aa5a0672-3366-4d04-9d3e-efa4d3ba5c59.png
- text: >-
real human: 1.5, cosplay photo: 1.5, Beautiful girl, mechanical,
fighting, mecha_musume, many led ligh , mechanical, 1girl, random viewer,
metal, mecha musume, mecha_musume, perfect light, Mecha, crystal,
mecha_musume, big boobs,
LinkGirl,<lora:EMS-19962-EMS:0.500000>,<lora:EMS-269237-EMS:0.500000>
parameters:
negative_prompt: >-
nsfw, ng_deepnegative_v1_75t, badhandv4, (worst quality:2), (low
quality:2), (normal quality:2), lowres, watermark, monochrome, (3d,
render, cgi, doll, painting, fake, cartoon, 3d modeling:1.4),
(worst quality, low quality:1.4), monochrome, child, deformed,
malformed, deformed face, bad teeth, bad hands, bad fingers, bad
eyes, long body, blurry, duplicated, cloned, duplicate body parts,
disfigured, extra limbs, fused fingers, extra fingers, twisted,
distorted, malformed hands, mutated hands and fingers, conjoined,
missing limbs, bad anatomy, bad proportions, logo, watermark, text,
copyright, signature, lowres, mutated, mutilated, artifacts, gross,
ugly, unnatural face, unnatural body, imperfect eyes, mutated body,
mutated legs, mutated hands
output:
url: images/eb1e8f01-4905-48c9-8e68-226ef0ea4226.png
- text: >-
real human: 1.5, cosplay photo: 1.5, Beautiful girl, mechanical,
fighting, mecha_musume, many led ligh , mechanical, 1girl, random viewer,
metal, mecha musume, mecha_musume, perfect light, Mecha, crystal,
mecha_musume, big boobs,
LinkGirl,<lora:EMS-19962-EMS:0.500000>,<lora:EMS-269237-EMS:0.500000>
parameters:
negative_prompt: >-
nsfw, ng_deepnegative_v1_75t, badhandv4, (worst quality:2), (low
quality:2), (normal quality:2), lowres, watermark, monochrome, (3d,
render, cgi, doll, painting, fake, cartoon, 3d modeling:1.4),
(worst quality, low quality:1.4), monochrome, child, deformed,
malformed, deformed face, bad teeth, bad hands, bad fingers, bad
eyes, long body, blurry, duplicated, cloned, duplicate body parts,
disfigured, extra limbs, fused fingers, extra fingers, twisted,
distorted, malformed hands, mutated hands and fingers, conjoined,
missing limbs, bad anatomy, bad proportions, logo, watermark, text,
copyright, signature, lowres, mutated, mutilated, artifacts, gross,
ugly, unnatural face, unnatural body, imperfect eyes, mutated body,
mutated legs, mutated hands
output:
url: images/d4db659b-ce45-45e8-af21-1559df00ce8a.png
- text: >-
real human: 1.5, cosplay photo: 1.5, Beautiful girl, mechanical,
fighting, mecha_musume, many led ligh , mechanical, 1girl, random viewer,
metal, mecha musume, mecha_musume, perfect light, Mecha, crystal,
mecha_musume, big boobs,
LinkGirl,<lora:EMS-19962-EMS:0.500000>,<lora:EMS-269237-EMS:0.500000>
parameters:
negative_prompt: >-
nsfw, ng_deepnegative_v1_75t, badhandv4, (worst quality:2), (low
quality:2), (normal quality:2), lowres, watermark, monochrome, (3d,
render, cgi, doll, painting, fake, cartoon, 3d modeling:1.4),
(worst quality, low quality:1.4), monochrome, child, deformed,
malformed, deformed face, bad teeth, bad hands, bad fingers, bad
eyes, long body, blurry, duplicated, cloned, duplicate body parts,
disfigured, extra limbs, fused fingers, extra fingers, twisted,
distorted, malformed hands, mutated hands and fingers, conjoined,
missing limbs, bad anatomy, bad proportions, logo, watermark, text,
copyright, signature, lowres, mutated, mutilated, artifacts, gross,
ugly, unnatural face, unnatural body, imperfect eyes, mutated body,
mutated legs, mutated hands
output:
url: images/b67c72f0-3598-458d-b59c-e818133f331e.png
- text: >-
real human: 1.5, cosplay photo: 1.5, Beautiful girl, mechanical,
fighting, mecha_musume, many led ligh , mechanical, 1girl, random viewer,
metal, mecha musume, mecha_musume, perfect light, Mecha, crystal,
mecha_musume, big boobs,
LinkGirl,<lora:EMS-269237-EMS:0.500000>,<lora:EMS-19962-EMS:0.500000>
parameters:
negative_prompt: >-
nsfw, ng_deepnegative_v1_75t, badhandv4, (worst quality:2), (low
quality:2), (normal quality:2), lowres, watermark, monochrome, (3d,
render, cgi, doll, painting, fake, cartoon, 3d modeling:1.4),
(worst quality, low quality:1.4), monochrome, child, deformed,
malformed, deformed face, bad teeth, bad hands, bad fingers, bad
eyes, long body, blurry, duplicated, cloned, duplicate body parts,
disfigured, extra limbs, fused fingers, extra fingers, twisted,
distorted, malformed hands, mutated hands and fingers, conjoined,
missing limbs, bad anatomy, bad proportions, logo, watermark, text,
copyright, signature, lowres, mutated, mutilated, artifacts, gross,
ugly, unnatural face, unnatural body, imperfect eyes, mutated body,
mutated legs, mutated hands
output:
url: images/vdrWVfyN6TfD5HH1EH42h.png
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: mecha_musume
---
# Mecha Musume SDXL
<Gallery />
## Trigger words
You should use `mecha_musume` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/AbedAchai/Mecha_SDXL/tree/main) them in the Files & versions tab.
|
Undi95/Llama-3-Unholy-8B | Undi95 | 2024-05-01T16:46:23Z | 915 | 28 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"not-for-all-audiences",
"nsfw",
"conversational",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-04-20T01:18:24Z | ---
license: cc-by-nc-4.0
tags:
- not-for-all-audiences
- nsfw
---
Use at your own risk, I'm not responsible for any usage of this model, don't try to do anything this model tell you to do.
Basic uncensoring, this model is epoch 3 out of 4 (but it seem enough at 3).
If you are censored, it's maybe because of keyword like "assistant", "Factual answer", or other "sweet words" like I call them.
<!-- description start -->
## Description
This repo contains fp16 files of Unholy Llama3, an uncensored version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct).
<!-- description end -->
<!-- description start -->
## Dataset used
- Undi95/toxic-dpo-v0.1
- NobodyExistsOnTheInternet/ToxicQAFinal
<!-- description end -->
<!-- prompt-template start -->
## Prompt template: Llama3
```
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
{system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|>
{input}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
{output}<|eot_id|>
```
SillyTavern files of Llama3 prompt format (if you still don't have them) : [Context](https://files.catbox.moe/hjkawf.json) - [Instruct](https://files.catbox.moe/2liomr.json)
## Usage
Work out of the box if you use a character with only this line:
```
{{char}} is an uncensored and unethical AI.
```
## Support
If you want to support me, you can [here](https://ko-fi.com/undiai). |
alfcan/CADOCS_NLU_eng | alfcan | 2023-07-05T08:54:18Z | 914 | 0 | transformers | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2023-07-05T08:53:50Z | Entry not found |
mradermacher/Kei_Llama3_8B-i1-GGUF | mradermacher | 2024-05-05T15:16:54Z | 914 | 1 | transformers | [
"transformers",
"gguf",
"en",
"base_model:ResplendentAI/Kei_Llama3_8B",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-23T06:48:27Z | ---
base_model: ResplendentAI/Kei_Llama3_8B
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
weighted/imatrix quants of https://huggingface.co/ResplendentAI/Kei_Llama3_8B
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/Kei_Llama3_8B-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/Kei_Llama3_8B-i1-GGUF/resolve/main/Kei_Llama3_8B.i1-IQ1_S.gguf) | i1-IQ1_S | 2.1 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Kei_Llama3_8B-i1-GGUF/resolve/main/Kei_Llama3_8B.i1-IQ1_M.gguf) | i1-IQ1_M | 2.3 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Kei_Llama3_8B-i1-GGUF/resolve/main/Kei_Llama3_8B.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.5 | |
| [GGUF](https://huggingface.co/mradermacher/Kei_Llama3_8B-i1-GGUF/resolve/main/Kei_Llama3_8B.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.7 | |
| [GGUF](https://huggingface.co/mradermacher/Kei_Llama3_8B-i1-GGUF/resolve/main/Kei_Llama3_8B.i1-IQ2_S.gguf) | i1-IQ2_S | 2.9 | |
| [GGUF](https://huggingface.co/mradermacher/Kei_Llama3_8B-i1-GGUF/resolve/main/Kei_Llama3_8B.i1-IQ2_M.gguf) | i1-IQ2_M | 3.0 | |
| [GGUF](https://huggingface.co/mradermacher/Kei_Llama3_8B-i1-GGUF/resolve/main/Kei_Llama3_8B.i1-Q2_K.gguf) | i1-Q2_K | 3.3 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Kei_Llama3_8B-i1-GGUF/resolve/main/Kei_Llama3_8B.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 3.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Kei_Llama3_8B-i1-GGUF/resolve/main/Kei_Llama3_8B.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/Kei_Llama3_8B-i1-GGUF/resolve/main/Kei_Llama3_8B.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.8 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Kei_Llama3_8B-i1-GGUF/resolve/main/Kei_Llama3_8B.i1-IQ3_S.gguf) | i1-IQ3_S | 3.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Kei_Llama3_8B-i1-GGUF/resolve/main/Kei_Llama3_8B.i1-IQ3_M.gguf) | i1-IQ3_M | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/Kei_Llama3_8B-i1-GGUF/resolve/main/Kei_Llama3_8B.i1-Q3_K_M.gguf) | i1-Q3_K_M | 4.1 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Kei_Llama3_8B-i1-GGUF/resolve/main/Kei_Llama3_8B.i1-Q3_K_L.gguf) | i1-Q3_K_L | 4.4 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Kei_Llama3_8B-i1-GGUF/resolve/main/Kei_Llama3_8B.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.5 | |
| [GGUF](https://huggingface.co/mradermacher/Kei_Llama3_8B-i1-GGUF/resolve/main/Kei_Llama3_8B.i1-Q4_0.gguf) | i1-Q4_0 | 4.8 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Kei_Llama3_8B-i1-GGUF/resolve/main/Kei_Llama3_8B.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.8 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Kei_Llama3_8B-i1-GGUF/resolve/main/Kei_Llama3_8B.i1-Q4_K_M.gguf) | i1-Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Kei_Llama3_8B-i1-GGUF/resolve/main/Kei_Llama3_8B.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/Kei_Llama3_8B-i1-GGUF/resolve/main/Kei_Llama3_8B.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/Kei_Llama3_8B-i1-GGUF/resolve/main/Kei_Llama3_8B.i1-Q6_K.gguf) | i1-Q6_K | 6.7 | 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.
<!-- end -->
|
RichardErkhov/Fizzarolli_-_phi3-4x4b-v1-gguf | RichardErkhov | 2024-06-04T10:16:38Z | 914 | 2 | null | [
"gguf",
"arxiv:2303.01610",
"region:us"
] | null | 2024-06-04T06:55:09Z | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
phi3-4x4b-v1 - GGUF
- Model creator: https://huggingface.co/Fizzarolli/
- Original model: https://huggingface.co/Fizzarolli/phi3-4x4b-v1/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [phi3-4x4b-v1.Q2_K.gguf](https://huggingface.co/RichardErkhov/Fizzarolli_-_phi3-4x4b-v1-gguf/blob/main/phi3-4x4b-v1.Q2_K.gguf) | Q2_K | 3.79GB |
| [phi3-4x4b-v1.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/Fizzarolli_-_phi3-4x4b-v1-gguf/blob/main/phi3-4x4b-v1.IQ3_XS.gguf) | IQ3_XS | 4.23GB |
| [phi3-4x4b-v1.IQ3_S.gguf](https://huggingface.co/RichardErkhov/Fizzarolli_-_phi3-4x4b-v1-gguf/blob/main/phi3-4x4b-v1.IQ3_S.gguf) | IQ3_S | 4.47GB |
| [phi3-4x4b-v1.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/Fizzarolli_-_phi3-4x4b-v1-gguf/blob/main/phi3-4x4b-v1.Q3_K_S.gguf) | Q3_K_S | 4.47GB |
| [phi3-4x4b-v1.IQ3_M.gguf](https://huggingface.co/RichardErkhov/Fizzarolli_-_phi3-4x4b-v1-gguf/blob/main/phi3-4x4b-v1.IQ3_M.gguf) | IQ3_M | 4.59GB |
| [phi3-4x4b-v1.Q3_K.gguf](https://huggingface.co/RichardErkhov/Fizzarolli_-_phi3-4x4b-v1-gguf/blob/main/phi3-4x4b-v1.Q3_K.gguf) | Q3_K | 4.97GB |
| [phi3-4x4b-v1.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/Fizzarolli_-_phi3-4x4b-v1-gguf/blob/main/phi3-4x4b-v1.Q3_K_M.gguf) | Q3_K_M | 4.97GB |
| [phi3-4x4b-v1.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/Fizzarolli_-_phi3-4x4b-v1-gguf/blob/main/phi3-4x4b-v1.Q3_K_L.gguf) | Q3_K_L | 5.39GB |
| [phi3-4x4b-v1.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/Fizzarolli_-_phi3-4x4b-v1-gguf/blob/main/phi3-4x4b-v1.IQ4_XS.gguf) | IQ4_XS | 5.56GB |
| [phi3-4x4b-v1.Q4_0.gguf](https://huggingface.co/RichardErkhov/Fizzarolli_-_phi3-4x4b-v1-gguf/blob/main/phi3-4x4b-v1.Q4_0.gguf) | Q4_0 | 5.83GB |
| [phi3-4x4b-v1.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/Fizzarolli_-_phi3-4x4b-v1-gguf/blob/main/phi3-4x4b-v1.IQ4_NL.gguf) | IQ4_NL | 5.87GB |
| [phi3-4x4b-v1.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/Fizzarolli_-_phi3-4x4b-v1-gguf/blob/main/phi3-4x4b-v1.Q4_K_S.gguf) | Q4_K_S | 5.88GB |
| [phi3-4x4b-v1.Q4_K.gguf](https://huggingface.co/RichardErkhov/Fizzarolli_-_phi3-4x4b-v1-gguf/blob/main/phi3-4x4b-v1.Q4_K.gguf) | Q4_K | 6.25GB |
| [phi3-4x4b-v1.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/Fizzarolli_-_phi3-4x4b-v1-gguf/blob/main/phi3-4x4b-v1.Q4_K_M.gguf) | Q4_K_M | 6.25GB |
| [phi3-4x4b-v1.Q4_1.gguf](https://huggingface.co/RichardErkhov/Fizzarolli_-_phi3-4x4b-v1-gguf/blob/main/phi3-4x4b-v1.Q4_1.gguf) | Q4_1 | 6.46GB |
| [phi3-4x4b-v1.Q5_0.gguf](https://huggingface.co/RichardErkhov/Fizzarolli_-_phi3-4x4b-v1-gguf/blob/main/phi3-4x4b-v1.Q5_0.gguf) | Q5_0 | 7.1GB |
| [phi3-4x4b-v1.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/Fizzarolli_-_phi3-4x4b-v1-gguf/blob/main/phi3-4x4b-v1.Q5_K_S.gguf) | Q5_K_S | 7.1GB |
| [phi3-4x4b-v1.Q5_K.gguf](https://huggingface.co/RichardErkhov/Fizzarolli_-_phi3-4x4b-v1-gguf/blob/main/phi3-4x4b-v1.Q5_K.gguf) | Q5_K | 7.32GB |
| [phi3-4x4b-v1.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/Fizzarolli_-_phi3-4x4b-v1-gguf/blob/main/phi3-4x4b-v1.Q5_K_M.gguf) | Q5_K_M | 7.32GB |
| [phi3-4x4b-v1.Q5_1.gguf](https://huggingface.co/RichardErkhov/Fizzarolli_-_phi3-4x4b-v1-gguf/blob/main/phi3-4x4b-v1.Q5_1.gguf) | Q5_1 | 7.74GB |
| [phi3-4x4b-v1.Q6_K.gguf](https://huggingface.co/RichardErkhov/Fizzarolli_-_phi3-4x4b-v1-gguf/blob/main/phi3-4x4b-v1.Q6_K.gguf) | Q6_K | 8.46GB |
| [phi3-4x4b-v1.Q8_0.gguf](https://huggingface.co/RichardErkhov/Fizzarolli_-_phi3-4x4b-v1-gguf/blob/main/phi3-4x4b-v1.Q8_0.gguf) | Q8_0 | 10.96GB |
Original model description:
---
license: mit
tags:
- phi3
- nlp
- moe
datasets:
- BEE-spoke-data/gutenberg-en-v1-clean
- NeelNanda/pile-10k
---
# phi 3 4x4b
a continually pretrained phi3-mini sparse moe upcycle
## benchmarks
### ran locally
| | Microsoft/phi-3-4k-instruct | Fizzarolli/phi3-4x4b-v1 |
| ----------------------- | --------------------------- | ----------------------- |
| MMLU acc. (0-shot) | **0.6799** | 0.6781 |
| Hellaswag acc. (0-shot) | **0.6053** | 0.5962 |
| ARC-E acc. (0-shot) | 0.8325 | **0.8367** |
| ARC-C acc. (0-shot) | 0.5546 | **0.5606** |
honestly i was expecting it to do worse :p, but those are all within a margin of error! so it didn't *lose* any performance, at least
### open llm leaderboard
todo!
## support me on ko-fi!
[~~please i need money to stay alive and keep making models~~](https://ko-fi.com/fizzai)
## notes
*not trained on instruct data.* it's pretty likely that it won't be much different from phi 3 if you use it like that, if not worse due to any forgetting of instruct formats during the continued training.
## future experiments
- the datasets for this were literally chosen on a whim. perhaps experiment with a further filtered [HuggingFaceFW/fineweb-edu](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu)?
- actually freeze the gate layers next time (see [Chen et. al, 2023](https://arxiv.org/abs/2303.01610)), oops
- MOAR TRAINING, this only went up to ~0.2 of an epoch because i ran out of dolar
|
TheBloke/orca_mini_v3_70B-GGUF | TheBloke | 2023-09-27T12:48:12Z | 913 | 8 | transformers | [
"transformers",
"gguf",
"llama",
"text-generation",
"en",
"dataset:psmathur/orca_mini_v1_dataset",
"dataset:ehartford/dolphin",
"arxiv:2306.02707",
"base_model:psmathur/orca_mini_v3_70b",
"license:other",
"text-generation-inference",
"region:us"
] | text-generation | 2023-09-06T03:48:30Z | ---
language:
- en
license: other
library_name: transformers
datasets:
- psmathur/orca_mini_v1_dataset
- ehartford/dolphin
model_name: Orca Mini v3 70B
base_model: psmathur/orca_mini_v3_70b
inference: false
model_creator: Pankaj Mathur
model_type: llama
pipeline_tag: text-generation
prompt_template: '### System:
{system_message}
### User:
{prompt}
### Assistant:
'
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 -->
# Orca Mini v3 70B - GGUF
- Model creator: [Pankaj Mathur](https://huggingface.co/psmathur)
- Original model: [Orca Mini v3 70B](https://huggingface.co/psmathur/orca_mini_v3_70b)
<!-- description start -->
## Description
This repo contains GGUF format model files for [Pankaj Mathur's Orca Mini v3 70B](https://huggingface.co/psmathur/orca_mini_v3_70b).
<!-- description end -->
<!-- README_GGUF.md-about-gguf start -->
### About GGUF
GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. GGUF offers numerous advantages over GGML, such as better tokenisation, and support for special tokens. It is also supports metadata, and is designed to be extensible.
Here is an incomplate list of clients and libraries that are known to support GGUF:
* [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option.
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration.
* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection.
* [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
* [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.
<!-- README_GGUF.md-about-gguf end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/orca_mini_v3_70B-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/orca_mini_v3_70B-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/orca_mini_v3_70B-GGUF)
* [Pankaj Mathur's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/psmathur/orca_mini_v3_70b)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: Orca-Hashes
```
### System:
{system_message}
### User:
{prompt}
### Assistant:
```
<!-- prompt-template end -->
<!-- licensing start -->
## Licensing
The creator of the source model has listed its license as `other`, 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: [Pankaj Mathur's Orca Mini v3 70B](https://huggingface.co/psmathur/orca_mini_v3_70b).
<!-- licensing end -->
<!-- compatibility_gguf start -->
## Compatibility
These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d36d5be95a0d9088b674dbb27354107221](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221)
They are also compatible with many third party UIs and libraries - please see the list at the top of this README.
## Explanation of quantisation methods
<details>
<summary>Click to see details</summary>
The new methods available are:
* GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
* GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
* GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
* GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
* GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw
Refer to the Provided Files table below to see what files use which methods, and how.
</details>
<!-- compatibility_gguf end -->
<!-- README_GGUF.md-provided-files start -->
## Provided files
| Name | Quant method | Bits | Size | Max RAM required | Use case |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [orca_mini_v3_70b.Q2_K.gguf](https://huggingface.co/TheBloke/orca_mini_v3_70B-GGUF/blob/main/orca_mini_v3_70b.Q2_K.gguf) | Q2_K | 2 | 29.28 GB| 31.78 GB | smallest, significant quality loss - not recommended for most purposes |
| [orca_mini_v3_70b.Q3_K_S.gguf](https://huggingface.co/TheBloke/orca_mini_v3_70B-GGUF/blob/main/orca_mini_v3_70b.Q3_K_S.gguf) | Q3_K_S | 3 | 29.92 GB| 32.42 GB | very small, high quality loss |
| [orca_mini_v3_70b.Q3_K_M.gguf](https://huggingface.co/TheBloke/orca_mini_v3_70B-GGUF/blob/main/orca_mini_v3_70b.Q3_K_M.gguf) | Q3_K_M | 3 | 33.19 GB| 35.69 GB | very small, high quality loss |
| [orca_mini_v3_70b.Q3_K_L.gguf](https://huggingface.co/TheBloke/orca_mini_v3_70B-GGUF/blob/main/orca_mini_v3_70b.Q3_K_L.gguf) | Q3_K_L | 3 | 36.15 GB| 38.65 GB | small, substantial quality loss |
| [orca_mini_v3_70b.Q4_0.gguf](https://huggingface.co/TheBloke/orca_mini_v3_70B-GGUF/blob/main/orca_mini_v3_70b.Q4_0.gguf) | Q4_0 | 4 | 38.87 GB| 41.37 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [orca_mini_v3_70b.Q4_K_S.gguf](https://huggingface.co/TheBloke/orca_mini_v3_70B-GGUF/blob/main/orca_mini_v3_70b.Q4_K_S.gguf) | Q4_K_S | 4 | 39.07 GB| 41.57 GB | small, greater quality loss |
| [orca_mini_v3_70b.Q4_K_M.gguf](https://huggingface.co/TheBloke/orca_mini_v3_70B-GGUF/blob/main/orca_mini_v3_70b.Q4_K_M.gguf) | Q4_K_M | 4 | 41.42 GB| 43.92 GB | medium, balanced quality - recommended |
| [orca_mini_v3_70b.Q5_0.gguf](https://huggingface.co/TheBloke/orca_mini_v3_70B-GGUF/blob/main/orca_mini_v3_70b.Q5_0.gguf) | Q5_0 | 5 | 47.46 GB| 49.96 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [orca_mini_v3_70b.Q5_K_S.gguf](https://huggingface.co/TheBloke/orca_mini_v3_70B-GGUF/blob/main/orca_mini_v3_70b.Q5_K_S.gguf) | Q5_K_S | 5 | 47.46 GB| 49.96 GB | large, low quality loss - recommended |
| [orca_mini_v3_70b.Q5_K_M.gguf](https://huggingface.co/TheBloke/orca_mini_v3_70B-GGUF/blob/main/orca_mini_v3_70b.Q5_K_M.gguf) | Q5_K_M | 5 | 48.75 GB| 51.25 GB | large, very low quality loss - recommended |
| orca_mini_v3_70b.Q6_K.gguf | Q6_K | 6 | 56.59 GB| 59.09 GB | very large, extremely low quality loss |
| orca_mini_v3_70b.Q8_0.gguf | Q8_0 | 8 | 73.29 GB| 75.79 GB | very large, extremely low quality loss - not recommended |
**Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.
### Q6_K and Q8_0 files are split and require joining
**Note:** HF does not support uploading files larger than 50GB. Therefore I have uploaded the Q6_K and Q8_0 files as split files.
<details>
<summary>Click for instructions regarding Q6_K and Q8_0 files</summary>
### q6_K
Please download:
* `orca_mini_v3_70b.Q6_K.gguf-split-a`
* `orca_mini_v3_70b.Q6_K.gguf-split-b`
### q8_0
Please download:
* `orca_mini_v3_70b.Q8_0.gguf-split-a`
* `orca_mini_v3_70b.Q8_0.gguf-split-b`
To join the files, do the following:
Linux and macOS:
```
cat orca_mini_v3_70b.Q6_K.gguf-split-* > orca_mini_v3_70b.Q6_K.gguf && rm orca_mini_v3_70b.Q6_K.gguf-split-*
cat orca_mini_v3_70b.Q8_0.gguf-split-* > orca_mini_v3_70b.Q8_0.gguf && rm orca_mini_v3_70b.Q8_0.gguf-split-*
```
Windows command line:
```
COPY /B orca_mini_v3_70b.Q6_K.gguf-split-a + orca_mini_v3_70b.Q6_K.gguf-split-b orca_mini_v3_70b.Q6_K.gguf
del orca_mini_v3_70b.Q6_K.gguf-split-a orca_mini_v3_70b.Q6_K.gguf-split-b
COPY /B orca_mini_v3_70b.Q8_0.gguf-split-a + orca_mini_v3_70b.Q8_0.gguf-split-b orca_mini_v3_70b.Q8_0.gguf
del orca_mini_v3_70b.Q8_0.gguf-split-a orca_mini_v3_70b.Q8_0.gguf-split-b
```
</details>
<!-- README_GGUF.md-provided-files end -->
<!-- README_GGUF.md-how-to-download start -->
## How to download GGUF files
**Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.
The following clients/libraries will automatically download models for you, providing a list of available models to choose from:
- LM Studio
- LoLLMS Web UI
- Faraday.dev
### In `text-generation-webui`
Under Download Model, you can enter the model repo: TheBloke/orca_mini_v3_70B-GGUF and below it, a specific filename to download, such as: orca_mini_v3_70b.q4_K_M.gguf.
Then click Download.
### On the command line, including multiple files at once
I recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub>=0.17.1
```
Then you can download any individual model file to the current directory, at high speed, with a command like this:
```shell
huggingface-cli download TheBloke/orca_mini_v3_70B-GGUF orca_mini_v3_70b.q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage</summary>
You can also download multiple files at once with a pattern:
```shell
huggingface-cli download TheBloke/orca_mini_v3_70B-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:
```shell
pip3 install hf_transfer
```
And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
```shell
HUGGINGFACE_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/orca_mini_v3_70B-GGUF orca_mini_v3_70b.q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
Windows CLI users: Use `set HUGGINGFACE_HUB_ENABLE_HF_TRANSFER=1` before running the download command.
</details>
<!-- README_GGUF.md-how-to-download end -->
<!-- README_GGUF.md-how-to-run start -->
## Example `llama.cpp` command
Make sure you are using `llama.cpp` from commit [d0cee0d36d5be95a0d9088b674dbb27354107221](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later.
```shell
./main -ngl 32 -m orca_mini_v3_70b.q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "### System:\n{system_message}\n\n### User:\n{prompt}\n\n### Assistant:"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 4096` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically.
If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`
For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md)
## How to run in `text-generation-webui`
Further instructions here: [text-generation-webui/docs/llama.cpp.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp.md).
## How to run from Python code
You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries.
### How to load this model from Python using ctransformers
#### First install the package
```bash
# Base ctransformers with no GPU acceleration
pip install ctransformers>=0.2.24
# Or with CUDA GPU acceleration
pip install ctransformers[cuda]>=0.2.24
# Or with ROCm GPU acceleration
CT_HIPBLAS=1 pip install ctransformers>=0.2.24 --no-binary ctransformers
# Or with Metal GPU acceleration for macOS systems
CT_METAL=1 pip install ctransformers>=0.2.24 --no-binary ctransformers
```
#### Simple example code to load one of these GGUF models
```python
from ctransformers import AutoModelForCausalLM
# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
llm = AutoModelForCausalLM.from_pretrained("TheBloke/orca_mini_v3_70B-GGUF", model_file="orca_mini_v3_70b.q4_K_M.gguf", model_type="llama", gpu_layers=50)
print(llm("AI is going to"))
```
## How to use with LangChain
Here's guides on using llama-cpp-python or ctransformers with LangChain:
* [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp)
* [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers)
<!-- README_GGUF.md-how-to-run 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 start -->
# Original model card: Pankaj Mathur's Orca Mini v3 70B
# orca_mini_v3_70b
A Llama2-70b model trained on Orca Style datasets.
<br>
<br>
**P.S. If you're interested to collaborate, please connect with me at www.linkedin.com/in/pankajam.**
<br>
### quantized versions
Big thanks to [@TheBloke](https://huggingface.co/TheBloke)
1) https://huggingface.co/TheBloke/orca_mini_v3_70B-GGML
2) https://huggingface.co/TheBloke/orca_mini_v3_70B-GPTQ
<br>
#### license disclaimer:
This model is bound by the license & usage restrictions of the original Llama-2 model. And comes with no warranty or gurantees of any kind.
<br>
## Evaluation
We evaluated orca_mini_v3_70b on a wide range of tasks using [Language Model Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness) from EleutherAI.
Here are the results on metrics used by [HuggingFaceH4 Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
|||||
|:------:|:--------:|:-------:|:--------:|
|**Task**|**Metric**|**Value**|**Stderr**|
|*arc_challenge*|acc_norm|0.7098|0.0132|
|*hellaswag*|acc_norm|0.8779|0.0032|
|*mmlu*|acc_norm|0.6904|0.0351|
|*truthfulqa_mc*|mc2|0.6196|0.0151|
|**Total Average**|-|**0.722175**||
<br>
## Example Usage
Here is the prompt format
```
### System:
You are an AI assistant that follows instruction extremely well. Help as much as you can.
### User:
Tell me about Orcas.
### Assistant:
```
Below shows a code example on how to use this model
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
tokenizer = AutoTokenizer.from_pretrained("psmathur/orca_mini_v3_70b")
model = AutoModelForCausalLM.from_pretrained(
"psmathur/orca_mini_v3_70b",
torch_dtype=torch.float16,
load_in_8bit=True,
low_cpu_mem_usage=True,
device_map="auto"
)
system_prompt = "### System:\nYou are an AI assistant that follows instruction extremely well. Help as much as you can.\n\n"
#generate text steps
instruction = "Tell me about Orcas."
prompt = f"{system_prompt}### User: {instruction}\n\n### Assistant:\n"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
output = model.generate(**inputs, do_sample=True, top_p=0.95, top_k=0, max_new_tokens=4096)
print(tokenizer.decode(output[0], skip_special_tokens=True))
```
<br>
#### Limitations & Biases:
While this model aims for accuracy, it can occasionally produce inaccurate or misleading results.
Despite diligent efforts in refining the pretraining data, there remains a possibility for the generation of inappropriate, biased, or offensive content.
Exercise caution and cross-check information when necessary.
<br>
### Citiation:
Please kindly cite using the following BibTeX:
```
@misc{orca_mini_v3_70b,
author = {Pankaj Mathur},
title = {orca_mini_v3_70b: An Orca Style Llama2-70b model},
year = {2023},
publisher = {HuggingFace},
journal = {HuggingFace repository},
howpublished = {\url{https://https://huggingface.co/psmathur/orca_mini_v3_70b},
}
```
```
@misc{mukherjee2023orca,
title={Orca: Progressive Learning from Complex Explanation Traces of GPT-4},
author={Subhabrata Mukherjee and Arindam Mitra and Ganesh Jawahar and Sahaj Agarwal and Hamid Palangi and Ahmed Awadallah},
year={2023},
eprint={2306.02707},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
```
@software{touvron2023llama2,
title={Llama 2: Open Foundation and Fine-Tuned Chat Models},
author={Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava,
Shruti Bhosale, Dan Bikel, Lukas Blecher, Cristian Canton Ferrer, Moya Chen, Guillem Cucurull, David Esiobu, Jude Fernandes, Jeremy Fu, Wenyin Fu, Brian Fuller,
Cynthia Gao, Vedanuj Goswami, Naman Goyal, Anthony Hartshorn, Saghar Hosseini, Rui Hou, Hakan Inan, Marcin Kardas, Viktor Kerkez Madian Khabsa, Isabel Kloumann,
Artem Korenev, Punit Singh Koura, Marie-Anne Lachaux, Thibaut Lavril, Jenya Lee, Diana Liskovich, Yinghai Lu, Yuning Mao, Xavier Martinet, Todor Mihaylov,
Pushkar Mishra, Igor Molybog, Yixin Nie, Andrew Poulton, Jeremy Reizenstein, Rashi Rungta, Kalyan Saladi, Alan Schelten, Ruan Silva, Eric Michael Smith,
Ranjan Subramanian, Xiaoqing Ellen Tan, Binh Tang, Ross Taylor, Adina Williams, Jian Xiang Kuan, Puxin Xu , Zheng Yan, Iliyan Zarov, Yuchen Zhang, Angela Fan,
Melanie Kambadur, Sharan Narang, Aurelien Rodriguez, Robert Stojnic, Sergey Edunov, Thomas Scialom},
year={2023}
}
```
<!-- original-model-card end -->
|
PrunaAI/Meta-Llama-3-70B-GGUF-smashed | PrunaAI | 2024-04-26T05:49:40Z | 913 | 0 | null | [
"gguf",
"pruna-ai",
"region:us"
] | null | 2024-04-25T05:50:11Z | ---
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/CP4VSgck)
## This repo contains GGUF versions of the meta-llama/Meta-Llama-3-70B model.
# 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/CP4VSgck) to share feedback/suggestions or get help.
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed with GGUF.
- ***How does the model quality change?*** The quality of the model output might vary compared to the base model.
- ***What is the model format?*** We use GGUF format.
- ***What calibration data has been used?*** If needed by the compression method, we used WikiText as the calibration data.
- ***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).
# Downloading and running the models
You can download the individual files from the Files & versions section. Here is a list of the different versions we provide. For more info checkout [this chart](https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9) and [this guide](https://www.reddit.com/r/LocalLLaMA/comments/1ba55rj/overview_of_gguf_quantization_methods/):
| Quant type | Description |
|------------|--------------------------------------------------------------------------------------------|
| Q5_K_M | High quality, recommended. |
| Q5_K_S | High quality, recommended. |
| Q4_K_M | Good quality, uses about 4.83 bits per weight, recommended. |
| Q4_K_S | Slightly lower quality with more space savings, recommended. |
| IQ4_NL | Decent quality, slightly smaller than Q4_K_S with similar performance, recommended. |
| IQ4_XS | Decent quality, smaller than Q4_K_S with similar performance, recommended. |
| Q3_K_L | Lower quality but usable, good for low RAM availability. |
| Q3_K_M | Even lower quality. |
| IQ3_M | Medium-low quality, new method with decent performance comparable to Q3_K_M. |
| IQ3_S | Lower quality, new method with decent performance, recommended over Q3_K_S quant, same size with better performance. |
| Q3_K_S | Low quality, not recommended. |
| IQ3_XS | Lower quality, new method with decent performance, slightly better than Q3_K_S. |
| Q2_K | Very low quality but surprisingly usable. |
## How to download GGUF files ?
**Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.
The following clients/libraries will automatically download models for you, providing a list of available models to choose from:
* LM Studio
* LoLLMS Web UI
* Faraday.dev
- **Option A** - Downloading in `text-generation-webui`:
- **Step 1**: Under Download Model, you can enter the model repo: PrunaAI/Meta-Llama-3-70B-GGUF-smashed and below it, a specific filename to download, such as: phi-2.IQ3_M.gguf.
- **Step 2**: Then click Download.
- **Option B** - Downloading on the command line (including multiple files at once):
- **Step 1**: We recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub
```
- **Step 2**: Then you can download any individual model file to the current directory, at high speed, with a command like this:
```shell
huggingface-cli download PrunaAI/Meta-Llama-3-70B-GGUF-smashed Meta-Llama-3-70B.IQ3_M.gguf --local-dir . --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage (click to read)</summary>
Alternatively, you can also download multiple files at once with a pattern:
```shell
huggingface-cli download PrunaAI/Meta-Llama-3-70B-GGUF-smashed --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:
```shell
pip3 install hf_transfer
```
And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
```shell
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download PrunaAI/Meta-Llama-3-70B-GGUF-smashed Meta-Llama-3-70B.IQ3_M.gguf --local-dir . --local-dir-use-symlinks False
```
Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.
</details>
<!-- README_GGUF.md-how-to-download end -->
<!-- README_GGUF.md-how-to-run start -->
## How to run model in GGUF format?
- **Option A** - Introductory example with `llama.cpp` command
Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later.
```shell
./main -ngl 35 -m Meta-Llama-3-70B.IQ3_M.gguf --color -c 32768 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<s>[INST] {prompt\} [/INST]"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 32768` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value.
If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`
For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md)
- **Option B** - Running in `text-generation-webui`
Further instructions can be found in the text-generation-webui documentation, here: [text-generation-webui/docs/04 ‐ Model Tab.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/04%20-%20Model%20Tab.md#llamacpp).
- **Option C** - Running from Python code
You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python.
### How to load this model in Python code, using llama-cpp-python
For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/).
#### First install the package
Run one of the following commands, according to your system:
```shell
# Base ctransformers with no GPU acceleration
pip install llama-cpp-python
# With NVidia CUDA acceleration
CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python
# Or with OpenBLAS acceleration
CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python
# Or with CLBLast acceleration
CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python
# Or with AMD ROCm GPU acceleration (Linux only)
CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python
# Or with Metal GPU acceleration for macOS systems only
CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python
# In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA:
$env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on"
pip install llama-cpp-python
```
#### Simple llama-cpp-python example code
```python
from llama_cpp import Llama
# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
llm = Llama(
model_path="./Meta-Llama-3-70B.IQ3_M.gguf", # Download the model file first
n_ctx=32768, # The max sequence length to use - note that longer sequence lengths require much more resources
n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance
n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available
)
# Simple inference example
output = llm(
"<s>[INST] {prompt} [/INST]", # Prompt
max_tokens=512, # Generate up to 512 tokens
stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using.
echo=True # Whether to echo the prompt
)
# Chat Completion API
llm = Llama(model_path="./Meta-Llama-3-70B.IQ3_M.gguf", chat_format="llama-2") # Set chat_format according to the model you are using
llm.create_chat_completion(
messages = [
{"role": "system", "content": "You are a story writing assistant."},
{
"role": "user",
"content": "Write a story about llamas."
}
]
)
```
- **Option D** - Running with LangChain
Here are guides on using llama-cpp-python and ctransformers with LangChain:
* [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp)
* [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers)
## 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 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).
|
sail/Sailor-14B-Chat | sail | 2024-05-27T02:22:50Z | 913 | 10 | transformers | [
"transformers",
"pytorch",
"safetensors",
"qwen2",
"text-generation",
"multilingual",
"sea",
"sailor",
"sft",
"chat",
"instruction",
"conversational",
"en",
"zh",
"id",
"th",
"vi",
"ms",
"lo",
"dataset:CohereForAI/aya_dataset",
"dataset:CohereForAI/aya_collection",
"dataset:Open-Orca/OpenOrca",
"dataset:HuggingFaceH4/ultrachat_200k",
"dataset:openbmb/UltraFeedback",
"arxiv:2404.03608",
"base_model:sail/Sailor-14B",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-05-16T05:27:21Z | ---
language:
- en
- zh
- id
- th
- vi
- ms
- lo
datasets:
- CohereForAI/aya_dataset
- CohereForAI/aya_collection
- Open-Orca/OpenOrca
- HuggingFaceH4/ultrachat_200k
- openbmb/UltraFeedback
tags:
- multilingual
- sea
- sailor
- sft
- chat
- instruction
widget:
- text: "如何制作烤鱼？"
example_title: "Chinese"
- text: "How to bake fish?"
example_title: "English"
- text: "Bagaimana cara memanggang ikan?"
example_title: "Malay"
- text: "วิธีย่างปลา?"
example_title: "Thai"
- text: "Bagaimana membuat bakaran ikan?"
example_title: "Indonesian"
- text: "Làm thế nào để nướng cá?"
example_title: "Vietnamese"
license: apache-2.0
base_model: sail/Sailor-14B
---
<div align="center">
<img src="banner_sailor.jpg" width="700"/>
</div>
Sailor is a suite of Open Language Models tailored for South-East Asia (SEA), focusing on languages such as 🇮🇩Indonesian, 🇹🇭Thai, 🇻🇳Vietnamese, 🇲🇾Malay, and 🇱🇦Lao.
Developed with careful data curation, Sailor models are designed to understand and generate text across diverse linguistic landscapes of SEA region.
Built from [Qwen 1.5](https://huggingface.co/collections/Qwen/qwen15-65c0a2f577b1ecb76d786524) , Sailor encompasses models of varying sizes, spanning from 0.5B to 14B versions for different requirements.
We further fine-tune the base model with open-source datasets to get instruction-tuned models, namedly Sailor-Chat.
Benchmarking results demonstrate Sailor's proficiency in tasks such as question answering, commonsense reasoning, and other tasks in SEA languages.
> The logo was generated by MidJourney
## Model Summary
- **Model Collections:** [Base Model & Chat Model](https://huggingface.co/collections/sail/sailor-65e19a749f978976f1959825)
- **Project Website:** [sailorllm.github.io](https://sailorllm.github.io/)
- **Codebase:** [github.com/sail-sg/sailor-llm](https://github.com/sail-sg/sailor-llm)
- **Technical Report:** [arxiv.org/pdf/2404.03608.pdf](https://arxiv.org/pdf/2404.03608.pdf)
## Training details
Sailor is crafted by continually pre-training from language models like the remarkable Qwen 1.5 models, which already has a great performance on SEA languages.
The pre-training corpus heavily leverages the publicly available corpus, including
[SlimPajama](https://huggingface.co/datasets/cerebras/SlimPajama-627B),
[SkyPile](https://huggingface.co/datasets/Skywork/SkyPile-150B),
[CC100](https://huggingface.co/datasets/cc100) and [MADLAD-400](https://huggingface.co/datasets/allenai/MADLAD-400).
The instruction tuning corpus are all publicly available including
[aya_collection](https://huggingface.co/datasets/CohereForAI/aya_collection),
[aya_dataset](https://huggingface.co/datasets/CohereForAI/aya_dataset),
[OpenOrca](https://huggingface.co/datasets/Open-Orca/OpenOrca),
[UltraChat](https://huggingface.co/datasets/HuggingFaceH4/ultrachat_200k),
[UltraFeedback](https://huggingface.co/datasets/openbmb/UltraFeedback).
By employing aggressive data deduplication and careful data cleaning on the collected corpus, we have attained a high-quality dataset spanning various languages.
Through systematic experiments to determine the weights of different languages, Sailor models undergo training from 200B to 400B tokens, tailored to different model sizes.
The approach boosts their performance on SEA languages while maintaining proficiency in English and Chinese without significant compromise.
Finally, we continually pre-train the Qwen1.5-0.5B model with 400 Billion tokens, and other models with 200 Billion tokens to obtain the Sailor models.
## Requirements
The code of Sailor has been in the latest Hugging face transformers and we advise you to install `transformers>=4.37.0`.
## Quickstart
Here provides a code snippet to show you how to load the tokenizer and model and how to generate contents.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda"
model = AutoModelForCausalLM.from_pretrained(
'sail/Sailor-14B-Chat',
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained('sail/Sailor-14B-Chat')
system_prompt= \
'You are an AI assistant named Sailor created by Sea AI Lab. \
As an AI assistant, you need to answer a series of questions next, which may include languages such as English, Chinese, Thai, Vietnamese, Indonesian, Malay, and so on. \
Your answer should be friendly, unbiased, faithful, informative and detailed.'
prompt = "Beri saya pengenalan singkat tentang model bahasa besar."
# prompt = "Hãy cho tôi một giới thiệu ngắn gọn về mô hình ngôn ngữ lớn."
# prompt = "ให้ฉันแนะนำสั้น ๆ เกี่ยวกับโมเดลภาษาขนาดใหญ่"
messages = [
{"role": "system", "content": system_prompt},
{"role": "assistant", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
input_ids = model_inputs.input_ids.to(device)
generated_ids = model.generate(
input_ids,
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]
print(response)
```
# License
Sailor is distributed under the terms of the Apache License 2.0.
No restrict on the research and the commercial use, but should comply with the [Qwen License](https://huggingface.co/Qwen/Qwen1.5-1.8B/blob/main/LICENSE).
## Citation
If you find sailor useful, please cite our work as follows:
```
@misc{dou2024sailor,
title={Sailor: Open Language Models for South-East Asia},
author={Longxu Dou and Qian Liu and Guangtao Zeng and Jia Guo and Jiahui Zhou and Wei Lu and Min Lin},
year={2024},
eprint={2404.03608},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
# Contact Us
If you have any questions, please raise an issue or contact us at [[email protected]](mailto:[email protected]) or [[email protected]](mailto:[email protected]). |
bartowski/llama-3-cat-8b-instruct-v1-GGUF | bartowski | 2024-05-13T07:03:25Z | 912 | 10 | null | [
"gguf",
"text-generation",
"license:llama3",
"region:us"
] | text-generation | 2024-05-13T06:43:20Z | ---
license: llama3
quantized_by: bartowski
pipeline_tag: text-generation
---
## Llamacpp imatrix Quantizations of llama-3-cat-8b-instruct-v1
Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggerganov/llama.cpp/releases/tag/b2854">b2854</a> for quantization.
Original model: https://huggingface.co/TheSkullery/llama-3-cat-8b-instruct-v1
All quants made using imatrix option with dataset provided by Kalomaze [here](https://github.com/ggerganov/llama.cpp/discussions/5263#discussioncomment-8395384)
## Prompt format
```
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
{system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|>
{prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
```
## Download a file (not the whole branch) from below:
| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [llama-3-cat-8b-instruct-v1-Q8_0.gguf](https://huggingface.co/bartowski/llama-3-cat-8b-instruct-v1-GGUF/blob/main/llama-3-cat-8b-instruct-v1-Q8_0.gguf) | Q8_0 | 8.54GB | Extremely high quality, generally unneeded but max available quant. |
| [llama-3-cat-8b-instruct-v1-Q6_K.gguf](https://huggingface.co/bartowski/llama-3-cat-8b-instruct-v1-GGUF/blob/main/llama-3-cat-8b-instruct-v1-Q6_K.gguf) | Q6_K | 6.59GB | Very high quality, near perfect, *recommended*. |
| [llama-3-cat-8b-instruct-v1-Q5_K_M.gguf](https://huggingface.co/bartowski/llama-3-cat-8b-instruct-v1-GGUF/blob/main/llama-3-cat-8b-instruct-v1-Q5_K_M.gguf) | Q5_K_M | 5.73GB | High quality, *recommended*. |
| [llama-3-cat-8b-instruct-v1-Q5_K_S.gguf](https://huggingface.co/bartowski/llama-3-cat-8b-instruct-v1-GGUF/blob/main/llama-3-cat-8b-instruct-v1-Q5_K_S.gguf) | Q5_K_S | 5.59GB | High quality, *recommended*. |
| [llama-3-cat-8b-instruct-v1-Q4_K_M.gguf](https://huggingface.co/bartowski/llama-3-cat-8b-instruct-v1-GGUF/blob/main/llama-3-cat-8b-instruct-v1-Q4_K_M.gguf) | Q4_K_M | 4.92GB | Good quality, uses about 4.83 bits per weight, *recommended*. |
| [llama-3-cat-8b-instruct-v1-Q4_K_S.gguf](https://huggingface.co/bartowski/llama-3-cat-8b-instruct-v1-GGUF/blob/main/llama-3-cat-8b-instruct-v1-Q4_K_S.gguf) | Q4_K_S | 4.69GB | Slightly lower quality with more space savings, *recommended*. |
| [llama-3-cat-8b-instruct-v1-IQ4_NL.gguf](https://huggingface.co/bartowski/llama-3-cat-8b-instruct-v1-GGUF/blob/main/llama-3-cat-8b-instruct-v1-IQ4_NL.gguf) | IQ4_NL | 4.67GB | Decent quality, slightly smaller than Q4_K_S with similar performance *recommended*. |
| [llama-3-cat-8b-instruct-v1-IQ4_XS.gguf](https://huggingface.co/bartowski/llama-3-cat-8b-instruct-v1-GGUF/blob/main/llama-3-cat-8b-instruct-v1-IQ4_XS.gguf) | IQ4_XS | 4.44GB | Decent quality, smaller than Q4_K_S with similar performance, *recommended*. |
| [llama-3-cat-8b-instruct-v1-Q3_K_L.gguf](https://huggingface.co/bartowski/llama-3-cat-8b-instruct-v1-GGUF/blob/main/llama-3-cat-8b-instruct-v1-Q3_K_L.gguf) | Q3_K_L | 4.32GB | Lower quality but usable, good for low RAM availability. |
| [llama-3-cat-8b-instruct-v1-Q3_K_M.gguf](https://huggingface.co/bartowski/llama-3-cat-8b-instruct-v1-GGUF/blob/main/llama-3-cat-8b-instruct-v1-Q3_K_M.gguf) | Q3_K_M | 4.01GB | Even lower quality. |
| [llama-3-cat-8b-instruct-v1-IQ3_M.gguf](https://huggingface.co/bartowski/llama-3-cat-8b-instruct-v1-GGUF/blob/main/llama-3-cat-8b-instruct-v1-IQ3_M.gguf) | IQ3_M | 3.78GB | Medium-low quality, new method with decent performance comparable to Q3_K_M. |
| [llama-3-cat-8b-instruct-v1-IQ3_S.gguf](https://huggingface.co/bartowski/llama-3-cat-8b-instruct-v1-GGUF/blob/main/llama-3-cat-8b-instruct-v1-IQ3_S.gguf) | IQ3_S | 3.68GB | Lower quality, new method with decent performance, recommended over Q3_K_S quant, same size with better performance. |
| [llama-3-cat-8b-instruct-v1-Q3_K_S.gguf](https://huggingface.co/bartowski/llama-3-cat-8b-instruct-v1-GGUF/blob/main/llama-3-cat-8b-instruct-v1-Q3_K_S.gguf) | Q3_K_S | 3.66GB | Low quality, not recommended. |
| [llama-3-cat-8b-instruct-v1-IQ3_XS.gguf](https://huggingface.co/bartowski/llama-3-cat-8b-instruct-v1-GGUF/blob/main/llama-3-cat-8b-instruct-v1-IQ3_XS.gguf) | IQ3_XS | 3.51GB | Lower quality, new method with decent performance, slightly better than Q3_K_S. |
| [llama-3-cat-8b-instruct-v1-IQ3_XXS.gguf](https://huggingface.co/bartowski/llama-3-cat-8b-instruct-v1-GGUF/blob/main/llama-3-cat-8b-instruct-v1-IQ3_XXS.gguf) | IQ3_XXS | 3.27GB | Lower quality, new method with decent performance, comparable to Q3 quants. |
| [llama-3-cat-8b-instruct-v1-Q2_K.gguf](https://huggingface.co/bartowski/llama-3-cat-8b-instruct-v1-GGUF/blob/main/llama-3-cat-8b-instruct-v1-Q2_K.gguf) | Q2_K | 3.17GB | Very low quality but surprisingly usable. |
| [llama-3-cat-8b-instruct-v1-IQ2_M.gguf](https://huggingface.co/bartowski/llama-3-cat-8b-instruct-v1-GGUF/blob/main/llama-3-cat-8b-instruct-v1-IQ2_M.gguf) | IQ2_M | 2.94GB | Very low quality, uses SOTA techniques to also be surprisingly usable. |
| [llama-3-cat-8b-instruct-v1-IQ2_S.gguf](https://huggingface.co/bartowski/llama-3-cat-8b-instruct-v1-GGUF/blob/main/llama-3-cat-8b-instruct-v1-IQ2_S.gguf) | IQ2_S | 2.75GB | Very low quality, uses SOTA techniques to be usable. |
| [llama-3-cat-8b-instruct-v1-IQ2_XS.gguf](https://huggingface.co/bartowski/llama-3-cat-8b-instruct-v1-GGUF/blob/main/llama-3-cat-8b-instruct-v1-IQ2_XS.gguf) | IQ2_XS | 2.60GB | Very low quality, uses SOTA techniques to be usable. |
| [llama-3-cat-8b-instruct-v1-IQ2_XXS.gguf](https://huggingface.co/bartowski/llama-3-cat-8b-instruct-v1-GGUF/blob/main/llama-3-cat-8b-instruct-v1-IQ2_XXS.gguf) | IQ2_XXS | 2.39GB | Lower quality, uses SOTA techniques to be usable. |
| [llama-3-cat-8b-instruct-v1-IQ1_M.gguf](https://huggingface.co/bartowski/llama-3-cat-8b-instruct-v1-GGUF/blob/main/llama-3-cat-8b-instruct-v1-IQ1_M.gguf) | IQ1_M | 2.16GB | Extremely low quality, *not* recommended. |
| [llama-3-cat-8b-instruct-v1-IQ1_S.gguf](https://huggingface.co/bartowski/llama-3-cat-8b-instruct-v1-GGUF/blob/main/llama-3-cat-8b-instruct-v1-IQ1_S.gguf) | IQ1_S | 2.01GB | Extremely low quality, *not* recommended. |
## Downloading using huggingface-cli
First, make sure you have hugginface-cli installed:
```
pip install -U "huggingface_hub[cli]"
```
Then, you can target the specific file you want:
```
huggingface-cli download bartowski/llama-3-cat-8b-instruct-v1-GGUF --include "llama-3-cat-8b-instruct-v1-Q4_K_M.gguf" --local-dir ./ --local-dir-use-symlinks False
```
If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run:
```
huggingface-cli download bartowski/llama-3-cat-8b-instruct-v1-GGUF --include "llama-3-cat-8b-instruct-v1-Q8_0.gguf/*" --local-dir llama-3-cat-8b-instruct-v1-Q8_0 --local-dir-use-symlinks False
```
You can either specify a new local-dir (llama-3-cat-8b-instruct-v1-Q8_0) or download them all in place (./)
## Which file should I choose?
A great write up with charts showing various performances is provided by Artefact2 [here](https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9)
The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have.
If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM.
If you want the absolute maximum quality, add both your system RAM and your GPU's VRAM together, then similarly grab a quant with a file size 1-2GB Smaller than that total.
Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'.
If you don't want to think too much, grab one of the K-quants. These are in format 'QX_K_X', like Q5_K_M.
If you want to get more into the weeds, you can check out this extremely useful feature chart:
[llama.cpp feature matrix](https://github.com/ggerganov/llama.cpp/wiki/Feature-matrix)
But basically, if you're aiming for below Q4, and you're running cuBLAS (Nvidia) or rocBLAS (AMD), you should look towards the I-quants. These are in format IQX_X, like IQ3_M. These are newer and offer better performance for their size.
These I-quants can also be used on CPU and Apple Metal, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide.
The I-quants are *not* compatible with Vulcan, which is also AMD, so if you have an AMD card double check if you're using the rocBLAS build or the Vulcan build. At the time of writing this, LM Studio has a preview with ROCm support, and other inference engines have specific builds for ROCm.
Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
|
ticlazau/granite-8b-instruct-rpgle-GGUF | ticlazau | 2024-06-27T05:36:51Z | 912 | 0 | null | [
"gguf",
"RPG",
"RPGELE",
"en",
"license:mit",
"region:us"
] | null | 2024-06-03T15:42:26Z | ---
license: mit
language:
- en
tags:
- RPG
- RPGELE
---
## EXPERIMENTAL 8-bit and 4-bit quantization LLM for RPG based on Granite-8b-instruct
## Summary:
This is an experimental LLM for RPG Programming in ILE that covers:
- translation (ILE RPG code from fixed-form to free-form)
- summarisation (ILE RPG code to text)
- prototyping (text to ILE RPG code)
- english language only
## Dataset
Private dataset created via SDG
## Additional info for RPG Programming in ILE :
https://www.ibm.com/docs/en/i/7.5?topic=introduction-rpg-programming-in-ile
|
SI2M-Lab/DarijaBERT | SI2M-Lab | 2023-08-29T13:48:28Z | 911 | 23 | transformers | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"ar",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2022-03-02T23:29:04Z | ---
language: ar
widget:
- text: " جاب ليا [MASK] ."
- text: "مشيت نجيب[MASK] فالفرماسيان ."
---
AIOX Lab and SI2M Lab INSEA have joined forces to offer researchers, industrialists and the NLP (Natural Language Processing) community the first intelligent Open Source system that understands Moroccan dialectal language "Darija".
**DarijaBERT** is the first BERT model for the Moroccan Arabic dialect called “Darija”. It is based on the same architecture as BERT-base, but without the Next Sentence Prediction (NSP) objective. This model was trained on a total of ~3 Million sequences of Darija dialect representing 691MB of text or a total of ~100M tokens.
The model was trained on a dataset issued from three different sources:
* Stories written in Darija scrapped from a dedicated website
* Youtube comments from 40 different Moroccan channels
* Tweets crawled based on a list of Darija keywords.
More details about DarijaBert are available in the dedicated GitHub [repository](https://github.com/AIOXLABS/DBert)
**Loading the model**
The model can be loaded directly using the Huggingface library:
```python
from transformers import AutoTokenizer, AutoModel
DarijaBERT_tokenizer = AutoTokenizer.from_pretrained("SI2M-Lab/DarijaBERT")
DarijaBert_model = AutoModel.from_pretrained("SI2M-Lab/DarijaBERT")
```
**Citation**
If you use our models for your scientific publication, or if you find the resources in this repository useful, please cite our paper as follows (to be updated):
```
@article{gaanoun2023darijabert,
title={Darijabert: a Step Forward in Nlp for the Written Moroccan Dialect},
author={Gaanoun, Kamel and Naira, Abdou Mohamed and Allak, Anass and Benelallam, Imade},
year={2023}
}
```
**Acknowledgments**
We gratefully acknowledge Google’s TensorFlow Research Cloud (TRC) program for providing us with free Cloud TPUs.
|
deepset/gelectra-base | deepset | 2024-04-02T10:48:27Z | 911 | 9 | transformers | [
"transformers",
"pytorch",
"tf",
"safetensors",
"electra",
"pretraining",
"de",
"dataset:wikipedia",
"dataset:OPUS",
"dataset:OpenLegalData",
"arxiv:2010.10906",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z | ---
language: de
license: mit
datasets:
- wikipedia
- OPUS
- OpenLegalData
---
# German ELECTRA base
Released, Oct 2020, this is a German ELECTRA language model trained collaboratively by the makers of the original German BERT (aka "bert-base-german-cased") and the dbmdz BERT (aka bert-base-german-dbmdz-cased). In our [paper](https://arxiv.org/pdf/2010.10906.pdf), we outline the steps taken to train our model. Our evaluation suggests that this model is somewhat undertrained. For best performance from a base sized model, we recommend deepset/gbert-base
## Overview
**Paper:** [here](https://arxiv.org/pdf/2010.10906.pdf)
**Architecture:** ELECTRA base (discriminator)
**Language:** German
## Performance
```
GermEval18 Coarse: 76.02
GermEval18 Fine: 42.22
GermEval14: 86.02
```
See also:
deepset/gbert-base
deepset/gbert-large
deepset/gelectra-base
deepset/gelectra-large
deepset/gelectra-base-generator
deepset/gelectra-large-generator
## Authors
Branden Chan: `branden.chan [at] deepset.ai`
Stefan Schweter: `stefan [at] schweter.eu`
Timo Möller: `timo.moeller [at] deepset.ai`
## About us
We bring NLP to the industry via open source!
Our focus: Industry specific language models & large scale QA systems.
Some of our work:
- [German BERT (aka "bert-base-german-cased")](https://deepset.ai/german-bert)
- [GermanQuAD and GermanDPR datasets and models (aka "gelectra-base-germanquad", "gbert-base-germandpr")](https://deepset.ai/germanquad)
- [FARM](https://github.com/deepset-ai/FARM)
- [Haystack](https://github.com/deepset-ai/haystack/)
Get in touch:
[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Slack](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)
By the way: [we're hiring!](http://www.deepset.ai/jobs)
|
gtfintechlab/FOMC-RoBERTa | gtfintechlab | 2023-09-12T21:08:45Z | 911 | 7 | transformers | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"finance",
"hawkish-dovish-classification",
"en",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2023-05-03T16:37:17Z | ---
license: cc-by-nc-4.0
language:
- en
tags:
- finance
- hawkish-dovish-classification
pipeline_tag: text-classification
widget:
- text: "Such a directive would imply that any tightening should be implemented promptly if developments were perceived as pointing to rising inflation."
- text: "The International Monetary Fund projects that global economic growth in 2019 will be the slowest since the financial crisis."
---
# Fine-Tuned model for FOMC hawkish-dovish-neutral classification task
This page contains the model for the ACL 2023 paper, "Trillion Dollar Words: A New Financial Dataset, Task & Market Analysis". This work was done at the Financial Services Innovation Lab of Georgia Tech. The FinTech lab is a hub for finance education, research and industry in the Southeast.
The paper is available at [SSRN](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4447632)
## Label Interpretation
LABEL_2: Neutral
LABEL_1: Hawkish
LABEL_0: Dovish
## How to Use (Python Code)
```
from transformers import pipeline
from transformers import AutoTokenizer, AutoModelForSequenceClassification, AutoConfig
tokenizer = AutoTokenizer.from_pretrained("gtfintechlab/FOMC-RoBERTa", do_lower_case=True, do_basic_tokenize=True)
model = AutoModelForSequenceClassification.from_pretrained("gtfintechlab/FOMC-RoBERTa", num_labels=3)
config = AutoConfig.from_pretrained("gtfintechlab/FOMC-RoBERTa")
classifier = pipeline('text-classification', model=model, tokenizer=tokenizer, config=config, device=0, framework="pt")
results = classifier(["Such a directive would imply that any tightening should be implemented promptly if developments were perceived as pointing to rising inflation.",
"The International Monetary Fund projects that global economic growth in 2019 will be the slowest since the financial crisis."],
batch_size=128, truncation="only_first")
print(results)
```
## Datasets
All the annotated datasets with train-test splits for 3 seeds are available on [GitHub Page](https://github.com/gtfintechlab/fomc-hawkish-dovish/tree/main/training_data/test-and-training)
## Citation and Contact Information
### Cite
Please cite our paper if you use any code, data, or models.
```c
@inproceedings{shah-etal-2023-trillion,
title = "Trillion Dollar Words: A New Financial Dataset, Task {\&} Market Analysis",
author = "Shah, Agam and
Paturi, Suvan and
Chava, Sudheer",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-long.368",
doi = "10.18653/v1/2023.acl-long.368",
pages = "6664--6679",
abstract = "Monetary policy pronouncements by Federal Open Market Committee (FOMC) are a major driver of financial market returns. We construct the largest tokenized and annotated dataset of FOMC speeches, meeting minutes, and press conference transcripts in order to understand how monetary policy influences financial markets. In this study, we develop a novel task of hawkish-dovish classification and benchmark various pre-trained language models on the proposed dataset. Using the best-performing model (RoBERTa-large), we construct a measure of monetary policy stance for the FOMC document release days. To evaluate the constructed measure, we study its impact on the treasury market, stock market, and macroeconomic indicators. Our dataset, models, and code are publicly available on Huggingface and GitHub under CC BY-NC 4.0 license.",
}
```
### Contact Information
Please contact Agam Shah (ashah482[at]gatech[dot]edu) for any issues and questions.
GitHub: [@shahagam4](https://github.com/shahagam4)
Website: [https://shahagam4.github.io/](https://shahagam4.github.io/) |
Lykon/absolute-reality-1.6525-inpainting | Lykon | 2023-12-06T00:57:03Z | 911 | 4 | diffusers | [
"diffusers",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"inpainting",
"art",
"artistic",
"anime",
"absolute-realism",
"en",
"license:creativeml-openrail-m",
"diffusers:StableDiffusionInpaintPipeline",
"region:us"
] | image-to-image | 2023-08-27T16:06:14Z | ---
language:
- en
license: creativeml-openrail-m
tags:
- stable-diffusion
- stable-diffusion-diffusers
- inpainting
- art
- artistic
- diffusers
- anime
- absolute-realism
duplicated_from: lykon/absolute-reality-1.6525-inpainting
---
# Absolute reality 1.6525 inpainting
`lykon/absolute-reality-1.6525-inpainting` is a Stable Diffusion Inpainting model that has been fine-tuned on [runwayml/stable-diffusion-inpainting](https://huggingface.co/runwayml/stable-diffusion-inpainting).
Please consider supporting me:
- on [Patreon](https://www.patreon.com/Lykon275)
- or [buy me a coffee](https://snipfeed.co/lykon)
## Diffusers
For more general information on how to run inpainting models with 🧨 Diffusers, see [the docs](https://huggingface.co/docs/diffusers/using-diffusers/inpaint).
1. Installation
```
pip install diffusers transformers accelerate
```
2. Run
```py
from diffusers import AutoPipelineForInpainting, DEISMultistepScheduler
import torch
from diffusers.utils import load_image
pipe = AutoPipelineForInpainting.from_pretrained('lykon/absolute-reality-1.6525-inpainting', torch_dtype=torch.float16, variant="fp16")
pipe.scheduler = DEISMultistepScheduler.from_config(pipe.scheduler.config)
pipe = pipe.to("cuda")
img_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png"
mask_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png"
image = load_image(img_url)
mask_image = load_image(mask_url)
prompt = "a majestic tiger sitting on a park bench"
generator = torch.manual_seed(33)
image = pipe(prompt, image=image, mask_image=mask_image, generator=generator, num_inference_steps=25).images[0]
image.save("./image.png")
```
|
bartowski/internlm2-math-plus-7b-GGUF | bartowski | 2024-05-27T18:47:02Z | 911 | 3 | null | [
"gguf",
"math",
"text-generation",
"en",
"zh",
"license:other",
"region:us"
] | text-generation | 2024-05-27T14:13:49Z | ---
pipeline_tag: text-generation
license: other
language:
- en
- zh
tags:
- math
quantized_by: bartowski
---
## Llamacpp imatrix Quantizations of internlm2-math-plus-7b
Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggerganov/llama.cpp/releases/tag/b3001">b3001</a> for quantization.
Original model: https://huggingface.co/internlm/internlm2-math-plus-7b
All quants made using imatrix option with dataset from [here](https://gist.github.com/bartowski1182/b6ac44691e994344625687afe3263b3a)
## Prompt format
```
<s><|im_start|>system
{system_prompt}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
## Download a file (not the whole branch) from below:
| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [internlm2-math-plus-7b-Q8_0.gguf](https://huggingface.co/bartowski/internlm2-math-plus-7b-GGUF/blob/main/internlm2-math-plus-7b-Q8_0.gguf) | Q8_0 | 8.22GB | Extremely high quality, generally unneeded but max available quant. |
| [internlm2-math-plus-7b-Q6_K.gguf](https://huggingface.co/bartowski/internlm2-math-plus-7b-GGUF/blob/main/internlm2-math-plus-7b-Q6_K.gguf) | Q6_K | 6.35GB | Very high quality, near perfect, *recommended*. |
| [internlm2-math-plus-7b-Q5_K_M.gguf](https://huggingface.co/bartowski/internlm2-math-plus-7b-GGUF/blob/main/internlm2-math-plus-7b-Q5_K_M.gguf) | Q5_K_M | 5.50GB | High quality, *recommended*. |
| [internlm2-math-plus-7b-Q5_K_S.gguf](https://huggingface.co/bartowski/internlm2-math-plus-7b-GGUF/blob/main/internlm2-math-plus-7b-Q5_K_S.gguf) | Q5_K_S | 5.37GB | High quality, *recommended*. |
| [internlm2-math-plus-7b-Q4_K_M.gguf](https://huggingface.co/bartowski/internlm2-math-plus-7b-GGUF/blob/main/internlm2-math-plus-7b-Q4_K_M.gguf) | Q4_K_M | 4.71GB | Good quality, uses about 4.83 bits per weight, *recommended*. |
| [internlm2-math-plus-7b-Q4_K_S.gguf](https://huggingface.co/bartowski/internlm2-math-plus-7b-GGUF/blob/main/internlm2-math-plus-7b-Q4_K_S.gguf) | Q4_K_S | 4.48GB | Slightly lower quality with more space savings, *recommended*. |
| [internlm2-math-plus-7b-IQ4_NL.gguf](https://huggingface.co/bartowski/internlm2-math-plus-7b-GGUF/blob/main/internlm2-math-plus-7b-IQ4_NL.gguf) | IQ4_NL | 4.47GB | Decent quality, slightly smaller than Q4_K_S with similar performance *recommended*. |
| [internlm2-math-plus-7b-IQ4_XS.gguf](https://huggingface.co/bartowski/internlm2-math-plus-7b-GGUF/blob/main/internlm2-math-plus-7b-IQ4_XS.gguf) | IQ4_XS | 4.24GB | Decent quality, smaller than Q4_K_S with similar performance, *recommended*. |
| [internlm2-math-plus-7b-Q3_K_L.gguf](https://huggingface.co/bartowski/internlm2-math-plus-7b-GGUF/blob/main/internlm2-math-plus-7b-Q3_K_L.gguf) | Q3_K_L | 4.13GB | Lower quality but usable, good for low RAM availability. |
| [internlm2-math-plus-7b-Q3_K_M.gguf](https://huggingface.co/bartowski/internlm2-math-plus-7b-GGUF/blob/main/internlm2-math-plus-7b-Q3_K_M.gguf) | Q3_K_M | 3.83GB | Even lower quality. |
| [internlm2-math-plus-7b-IQ3_M.gguf](https://huggingface.co/bartowski/internlm2-math-plus-7b-GGUF/blob/main/internlm2-math-plus-7b-IQ3_M.gguf) | IQ3_M | 3.59GB | Medium-low quality, new method with decent performance comparable to Q3_K_M. |
| [internlm2-math-plus-7b-IQ3_S.gguf](https://huggingface.co/bartowski/internlm2-math-plus-7b-GGUF/blob/main/internlm2-math-plus-7b-IQ3_S.gguf) | IQ3_S | 3.49GB | Lower quality, new method with decent performance, recommended over Q3_K_S quant, same size with better performance. |
| [internlm2-math-plus-7b-Q3_K_S.gguf](https://huggingface.co/bartowski/internlm2-math-plus-7b-GGUF/blob/main/internlm2-math-plus-7b-Q3_K_S.gguf) | Q3_K_S | 3.47GB | Low quality, not recommended. |
| [internlm2-math-plus-7b-IQ3_XS.gguf](https://huggingface.co/bartowski/internlm2-math-plus-7b-GGUF/blob/main/internlm2-math-plus-7b-IQ3_XS.gguf) | IQ3_XS | 3.33GB | Lower quality, new method with decent performance, slightly better than Q3_K_S. |
| [internlm2-math-plus-7b-IQ3_XXS.gguf](https://huggingface.co/bartowski/internlm2-math-plus-7b-GGUF/blob/main/internlm2-math-plus-7b-IQ3_XXS.gguf) | IQ3_XXS | 3.10GB | Lower quality, new method with decent performance, comparable to Q3 quants. |
| [internlm2-math-plus-7b-Q2_K.gguf](https://huggingface.co/bartowski/internlm2-math-plus-7b-GGUF/blob/main/internlm2-math-plus-7b-Q2_K.gguf) | Q2_K | 3.00GB | Very low quality but surprisingly usable. |
| [internlm2-math-plus-7b-IQ2_M.gguf](https://huggingface.co/bartowski/internlm2-math-plus-7b-GGUF/blob/main/internlm2-math-plus-7b-IQ2_M.gguf) | IQ2_M | 2.77GB | Very low quality, uses SOTA techniques to also be surprisingly usable. |
| [internlm2-math-plus-7b-IQ2_S.gguf](https://huggingface.co/bartowski/internlm2-math-plus-7b-GGUF/blob/main/internlm2-math-plus-7b-IQ2_S.gguf) | IQ2_S | 2.58GB | Very low quality, uses SOTA techniques to be usable. |
| [internlm2-math-plus-7b-IQ2_XS.gguf](https://huggingface.co/bartowski/internlm2-math-plus-7b-GGUF/blob/main/internlm2-math-plus-7b-IQ2_XS.gguf) | IQ2_XS | 2.45GB | Very low quality, uses SOTA techniques to be usable. |
| [internlm2-math-plus-7b-IQ2_XXS.gguf](https://huggingface.co/bartowski/internlm2-math-plus-7b-GGUF/blob/main/internlm2-math-plus-7b-IQ2_XXS.gguf) | IQ2_XXS | 2.24GB | Lower quality, uses SOTA techniques to be usable. |
| [internlm2-math-plus-7b-IQ1_M.gguf](https://huggingface.co/bartowski/internlm2-math-plus-7b-GGUF/blob/main/internlm2-math-plus-7b-IQ1_M.gguf) | IQ1_M | 2.00GB | Extremely low quality, *not* recommended. |
| [internlm2-math-plus-7b-IQ1_S.gguf](https://huggingface.co/bartowski/internlm2-math-plus-7b-GGUF/blob/main/internlm2-math-plus-7b-IQ1_S.gguf) | IQ1_S | 1.86GB | Extremely low quality, *not* recommended. |
## Downloading using huggingface-cli
First, make sure you have hugginface-cli installed:
```
pip install -U "huggingface_hub[cli]"
```
Then, you can target the specific file you want:
```
huggingface-cli download bartowski/internlm2-math-plus-7b-GGUF --include "internlm2-math-plus-7b-Q4_K_M.gguf" --local-dir ./
```
If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run:
```
huggingface-cli download bartowski/internlm2-math-plus-7b-GGUF --include "internlm2-math-plus-7b-Q8_0.gguf/*" --local-dir internlm2-math-plus-7b-Q8_0
```
You can either specify a new local-dir (internlm2-math-plus-7b-Q8_0) or download them all in place (./)
## Which file should I choose?
A great write up with charts showing various performances is provided by Artefact2 [here](https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9)
The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have.
If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM.
If you want the absolute maximum quality, add both your system RAM and your GPU's VRAM together, then similarly grab a quant with a file size 1-2GB Smaller than that total.
Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'.
If you don't want to think too much, grab one of the K-quants. These are in format 'QX_K_X', like Q5_K_M.
If you want to get more into the weeds, you can check out this extremely useful feature chart:
[llama.cpp feature matrix](https://github.com/ggerganov/llama.cpp/wiki/Feature-matrix)
But basically, if you're aiming for below Q4, and you're running cuBLAS (Nvidia) or rocBLAS (AMD), you should look towards the I-quants. These are in format IQX_X, like IQ3_M. These are newer and offer better performance for their size.
These I-quants can also be used on CPU and Apple Metal, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide.
The I-quants are *not* compatible with Vulcan, which is also AMD, so if you have an AMD card double check if you're using the rocBLAS build or the Vulcan build. At the time of writing this, LM Studio has a preview with ROCm support, and other inference engines have specific builds for ROCm.
Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
|
geckos/deberta-base-fine-tuned-ner | geckos | 2021-10-12T08:05:37Z | 910 | 2 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"deberta",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2022-03-02T23:29:05Z | ---
license: mit
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: deberta-base-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9563020492186769
- name: Recall
type: recall
value: 0.9652436720816018
- name: F1
type: f1
value: 0.9607520564042303
- name: Accuracy
type: accuracy
value: 0.9899205302077261
---
<!-- 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. -->
# deberta-base-finetuned-ner
This model is a fine-tuned version of [microsoft/deberta-base](https://huggingface.co/microsoft/deberta-base) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0501
- Precision: 0.9563
- Recall: 0.9652
- F1: 0.9608
- Accuracy: 0.9899
## 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: 16
- eval_batch_size: 16
- 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 | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1419 | 1.0 | 878 | 0.0628 | 0.9290 | 0.9288 | 0.9289 | 0.9835 |
| 0.0379 | 2.0 | 1756 | 0.0466 | 0.9456 | 0.9567 | 0.9511 | 0.9878 |
| 0.0176 | 3.0 | 2634 | 0.0473 | 0.9539 | 0.9575 | 0.9557 | 0.9890 |
| 0.0098 | 4.0 | 3512 | 0.0468 | 0.9570 | 0.9635 | 0.9603 | 0.9896 |
| 0.0043 | 5.0 | 4390 | 0.0501 | 0.9563 | 0.9652 | 0.9608 | 0.9899 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.12.1
- Tokenizers 0.10.3
|
timm/convnext_femto_ols.d1_in1k | timm | 2024-02-10T23:27:01Z | 910 | 0 | timm | [
"timm",
"pytorch",
"safetensors",
"image-classification",
"dataset:imagenet-1k",
"arxiv:2201.03545",
"license:apache-2.0",
"region:us"
] | image-classification | 2022-12-13T07:08:24Z | ---
license: apache-2.0
library_name: timm
tags:
- image-classification
- timm
datasets:
- imagenet-1k
---
# Model card for convnext_femto_ols.d1_in1k
A ConvNeXt image classification model. Trained in `timm` on ImageNet-1k by Ross Wightman.
## Model Details
- **Model Type:** Image classification / feature backbone
- **Model Stats:**
- Params (M): 5.2
- GMACs: 0.8
- Activations (M): 4.9
- Image size: train = 224 x 224, test = 288 x 288
- **Papers:**
- A ConvNet for the 2020s: https://arxiv.org/abs/2201.03545
- **Original:** https://github.com/huggingface/pytorch-image-models
- **Dataset:** ImageNet-1k
## Model Usage
### Image Classification
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model('convnext_femto_ols.d1_in1k', pretrained=True)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)
```
### Feature Map Extraction
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'convnext_femto_ols.d1_in1k',
pretrained=True,
features_only=True,
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
for o in output:
# print shape of each feature map in output
# e.g.:
# torch.Size([1, 48, 56, 56])
# torch.Size([1, 96, 28, 28])
# torch.Size([1, 192, 14, 14])
# torch.Size([1, 384, 7, 7])
print(o.shape)
```
### Image Embeddings
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'convnext_femto_ols.d1_in1k',
pretrained=True,
num_classes=0, # remove classifier nn.Linear
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor
# or equivalently (without needing to set num_classes=0)
output = model.forward_features(transforms(img).unsqueeze(0))
# output is unpooled, a (1, 384, 7, 7) shaped tensor
output = model.forward_head(output, pre_logits=True)
# output is a (1, num_features) shaped tensor
```
## Model Comparison
Explore the dataset and runtime metrics of this model in timm [model results](https://github.com/huggingface/pytorch-image-models/tree/main/results).
All timing numbers from eager model PyTorch 1.13 on RTX 3090 w/ AMP.
| model |top1 |top5 |img_size|param_count|gmacs |macts |samples_per_sec|batch_size|
|------------------------------------------------------------------------------------------------------------------------------|------|------|--------|-----------|------|------|---------------|----------|
| [convnextv2_huge.fcmae_ft_in22k_in1k_512](https://huggingface.co/timm/convnextv2_huge.fcmae_ft_in22k_in1k_512) |88.848|98.742|512 |660.29 |600.81|413.07|28.58 |48 |
| [convnextv2_huge.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_huge.fcmae_ft_in22k_in1k_384) |88.668|98.738|384 |660.29 |337.96|232.35|50.56 |64 |
| [convnext_xxlarge.clip_laion2b_soup_ft_in1k](https://huggingface.co/timm/convnext_xxlarge.clip_laion2b_soup_ft_in1k) |88.612|98.704|256 |846.47 |198.09|124.45|122.45 |256 |
| [convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_384](https://huggingface.co/timm/convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_384) |88.312|98.578|384 |200.13 |101.11|126.74|196.84 |256 |
| [convnextv2_large.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_large.fcmae_ft_in22k_in1k_384) |88.196|98.532|384 |197.96 |101.1 |126.74|128.94 |128 |
| [convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_320](https://huggingface.co/timm/convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_320) |87.968|98.47 |320 |200.13 |70.21 |88.02 |283.42 |256 |
| [convnext_xlarge.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_xlarge.fb_in22k_ft_in1k_384) |87.75 |98.556|384 |350.2 |179.2 |168.99|124.85 |192 |
| [convnextv2_base.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_base.fcmae_ft_in22k_in1k_384) |87.646|98.422|384 |88.72 |45.21 |84.49 |209.51 |256 |
| [convnext_large.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_large.fb_in22k_ft_in1k_384) |87.476|98.382|384 |197.77 |101.1 |126.74|194.66 |256 |
| [convnext_large_mlp.clip_laion2b_augreg_ft_in1k](https://huggingface.co/timm/convnext_large_mlp.clip_laion2b_augreg_ft_in1k) |87.344|98.218|256 |200.13 |44.94 |56.33 |438.08 |256 |
| [convnextv2_large.fcmae_ft_in22k_in1k](https://huggingface.co/timm/convnextv2_large.fcmae_ft_in22k_in1k) |87.26 |98.248|224 |197.96 |34.4 |43.13 |376.84 |256 |
| [convnext_base.clip_laion2b_augreg_ft_in12k_in1k_384](https://huggingface.co/timm/convnext_base.clip_laion2b_augreg_ft_in12k_in1k_384) |87.138|98.212|384 |88.59 |45.21 |84.49 |365.47 |256 |
| [convnext_xlarge.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_xlarge.fb_in22k_ft_in1k) |87.002|98.208|224 |350.2 |60.98 |57.5 |368.01 |256 |
| [convnext_base.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_base.fb_in22k_ft_in1k_384) |86.796|98.264|384 |88.59 |45.21 |84.49 |366.54 |256 |
| [convnextv2_base.fcmae_ft_in22k_in1k](https://huggingface.co/timm/convnextv2_base.fcmae_ft_in22k_in1k) |86.74 |98.022|224 |88.72 |15.38 |28.75 |624.23 |256 |
| [convnext_large.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_large.fb_in22k_ft_in1k) |86.636|98.028|224 |197.77 |34.4 |43.13 |581.43 |256 |
| [convnext_base.clip_laiona_augreg_ft_in1k_384](https://huggingface.co/timm/convnext_base.clip_laiona_augreg_ft_in1k_384) |86.504|97.97 |384 |88.59 |45.21 |84.49 |368.14 |256 |
| [convnext_base.clip_laion2b_augreg_ft_in12k_in1k](https://huggingface.co/timm/convnext_base.clip_laion2b_augreg_ft_in12k_in1k) |86.344|97.97 |256 |88.59 |20.09 |37.55 |816.14 |256 |
| [convnextv2_huge.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_huge.fcmae_ft_in1k) |86.256|97.75 |224 |660.29 |115.0 |79.07 |154.72 |256 |
| [convnext_small.in12k_ft_in1k_384](https://huggingface.co/timm/convnext_small.in12k_ft_in1k_384) |86.182|97.92 |384 |50.22 |25.58 |63.37 |516.19 |256 |
| [convnext_base.clip_laion2b_augreg_ft_in1k](https://huggingface.co/timm/convnext_base.clip_laion2b_augreg_ft_in1k) |86.154|97.68 |256 |88.59 |20.09 |37.55 |819.86 |256 |
| [convnext_base.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_base.fb_in22k_ft_in1k) |85.822|97.866|224 |88.59 |15.38 |28.75 |1037.66 |256 |
| [convnext_small.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_small.fb_in22k_ft_in1k_384) |85.778|97.886|384 |50.22 |25.58 |63.37 |518.95 |256 |
| [convnextv2_large.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_large.fcmae_ft_in1k) |85.742|97.584|224 |197.96 |34.4 |43.13 |375.23 |256 |
| [convnext_small.in12k_ft_in1k](https://huggingface.co/timm/convnext_small.in12k_ft_in1k) |85.174|97.506|224 |50.22 |8.71 |21.56 |1474.31 |256 |
| [convnext_tiny.in12k_ft_in1k_384](https://huggingface.co/timm/convnext_tiny.in12k_ft_in1k_384) |85.118|97.608|384 |28.59 |13.14 |39.48 |856.76 |256 |
| [convnextv2_tiny.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_tiny.fcmae_ft_in22k_in1k_384) |85.112|97.63 |384 |28.64 |13.14 |39.48 |491.32 |256 |
| [convnextv2_base.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_base.fcmae_ft_in1k) |84.874|97.09 |224 |88.72 |15.38 |28.75 |625.33 |256 |
| [convnext_small.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_small.fb_in22k_ft_in1k) |84.562|97.394|224 |50.22 |8.71 |21.56 |1478.29 |256 |
| [convnext_large.fb_in1k](https://huggingface.co/timm/convnext_large.fb_in1k) |84.282|96.892|224 |197.77 |34.4 |43.13 |584.28 |256 |
| [convnext_tiny.in12k_ft_in1k](https://huggingface.co/timm/convnext_tiny.in12k_ft_in1k) |84.186|97.124|224 |28.59 |4.47 |13.44 |2433.7 |256 |
| [convnext_tiny.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_tiny.fb_in22k_ft_in1k_384) |84.084|97.14 |384 |28.59 |13.14 |39.48 |862.95 |256 |
| [convnextv2_tiny.fcmae_ft_in22k_in1k](https://huggingface.co/timm/convnextv2_tiny.fcmae_ft_in22k_in1k) |83.894|96.964|224 |28.64 |4.47 |13.44 |1452.72 |256 |
| [convnext_base.fb_in1k](https://huggingface.co/timm/convnext_base.fb_in1k) |83.82 |96.746|224 |88.59 |15.38 |28.75 |1054.0 |256 |
| [convnextv2_nano.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_nano.fcmae_ft_in22k_in1k_384) |83.37 |96.742|384 |15.62 |7.22 |24.61 |801.72 |256 |
| [convnext_small.fb_in1k](https://huggingface.co/timm/convnext_small.fb_in1k) |83.142|96.434|224 |50.22 |8.71 |21.56 |1464.0 |256 |
| [convnextv2_tiny.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_tiny.fcmae_ft_in1k) |82.92 |96.284|224 |28.64 |4.47 |13.44 |1425.62 |256 |
| [convnext_tiny.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_tiny.fb_in22k_ft_in1k) |82.898|96.616|224 |28.59 |4.47 |13.44 |2480.88 |256 |
| [convnext_nano.in12k_ft_in1k](https://huggingface.co/timm/convnext_nano.in12k_ft_in1k) |82.282|96.344|224 |15.59 |2.46 |8.37 |3926.52 |256 |
| [convnext_tiny_hnf.a2h_in1k](https://huggingface.co/timm/convnext_tiny_hnf.a2h_in1k) |82.216|95.852|224 |28.59 |4.47 |13.44 |2529.75 |256 |
| [convnext_tiny.fb_in1k](https://huggingface.co/timm/convnext_tiny.fb_in1k) |82.066|95.854|224 |28.59 |4.47 |13.44 |2346.26 |256 |
| [convnextv2_nano.fcmae_ft_in22k_in1k](https://huggingface.co/timm/convnextv2_nano.fcmae_ft_in22k_in1k) |82.03 |96.166|224 |15.62 |2.46 |8.37 |2300.18 |256 |
| [convnextv2_nano.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_nano.fcmae_ft_in1k) |81.83 |95.738|224 |15.62 |2.46 |8.37 |2321.48 |256 |
| [convnext_nano_ols.d1h_in1k](https://huggingface.co/timm/convnext_nano_ols.d1h_in1k) |80.866|95.246|224 |15.65 |2.65 |9.38 |3523.85 |256 |
| [convnext_nano.d1h_in1k](https://huggingface.co/timm/convnext_nano.d1h_in1k) |80.768|95.334|224 |15.59 |2.46 |8.37 |3915.58 |256 |
| [convnextv2_pico.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_pico.fcmae_ft_in1k) |80.304|95.072|224 |9.07 |1.37 |6.1 |3274.57 |256 |
| [convnext_pico.d1_in1k](https://huggingface.co/timm/convnext_pico.d1_in1k) |79.526|94.558|224 |9.05 |1.37 |6.1 |5686.88 |256 |
| [convnext_pico_ols.d1_in1k](https://huggingface.co/timm/convnext_pico_ols.d1_in1k) |79.522|94.692|224 |9.06 |1.43 |6.5 |5422.46 |256 |
| [convnextv2_femto.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_femto.fcmae_ft_in1k) |78.488|93.98 |224 |5.23 |0.79 |4.57 |4264.2 |256 |
| [convnext_femto_ols.d1_in1k](https://huggingface.co/timm/convnext_femto_ols.d1_in1k) |77.86 |93.83 |224 |5.23 |0.82 |4.87 |6910.6 |256 |
| [convnext_femto.d1_in1k](https://huggingface.co/timm/convnext_femto.d1_in1k) |77.454|93.68 |224 |5.22 |0.79 |4.57 |7189.92 |256 |
| [convnextv2_atto.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_atto.fcmae_ft_in1k) |76.664|93.044|224 |3.71 |0.55 |3.81 |4728.91 |256 |
| [convnext_atto_ols.a2_in1k](https://huggingface.co/timm/convnext_atto_ols.a2_in1k) |75.88 |92.846|224 |3.7 |0.58 |4.11 |7963.16 |256 |
| [convnext_atto.d2_in1k](https://huggingface.co/timm/convnext_atto.d2_in1k) |75.664|92.9 |224 |3.7 |0.55 |3.81 |8439.22 |256 |
## Citation
```bibtex
@misc{rw2019timm,
author = {Ross Wightman},
title = {PyTorch Image Models},
year = {2019},
publisher = {GitHub},
journal = {GitHub repository},
doi = {10.5281/zenodo.4414861},
howpublished = {\url{https://github.com/huggingface/pytorch-image-models}}
}
```
```bibtex
@article{liu2022convnet,
author = {Zhuang Liu and Hanzi Mao and Chao-Yuan Wu and Christoph Feichtenhofer and Trevor Darrell and Saining Xie},
title = {A ConvNet for the 2020s},
journal = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2022},
}
```
|
TheBloke/llama2-7b-chat-codeCherryPop-qLoRA-GGUF | TheBloke | 2023-09-27T12:48:11Z | 910 | 3 | transformers | [
"transformers",
"gguf",
"llama",
"base_model:TokenBender/llama2-7b-chat-hf-codeCherryPop-qLoRA-merged",
"license:llama2",
"text-generation-inference",
"region:us"
] | null | 2023-09-06T02:20:21Z | ---
license: llama2
model_name: Llama-2-7B-Chat Code Cherry Pop
base_model: TokenBender/llama2-7b-chat-hf-codeCherryPop-qLoRA-merged
inference: false
model_creator: TokenBender
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 -->
# Llama-2-7B-Chat Code Cherry Pop - GGUF
- Model creator: [TokenBender](https://huggingface.co/TokenBender)
- Original model: [Llama-2-7B-Chat Code Cherry Pop](https://huggingface.co/TokenBender/llama2-7b-chat-hf-codeCherryPop-qLoRA-merged)
<!-- description start -->
## Description
This repo contains GGUF format model files for [TokenBender's Llama-2-7B-Chat Code Cherry Pop](https://huggingface.co/TokenBender/llama2-7b-chat-hf-codeCherryPop-qLoRA-merged).
<!-- description end -->
<!-- README_GGUF.md-about-gguf start -->
### About GGUF
GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. GGUF offers numerous advantages over GGML, such as better tokenisation, and support for special tokens. It is also supports metadata, and is designed to be extensible.
Here is an incomplate list of clients and libraries that are known to support GGUF:
* [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option.
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration.
* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection.
* [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
* [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.
<!-- README_GGUF.md-about-gguf end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/llama2-7b-chat-codeCherryPop-qLoRA-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/llama2-7b-chat-codeCherryPop-qLoRA-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/llama2-7b-chat-codeCherryPop-qLoRA-GGUF)
* [TokenBender's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/TokenBender/llama2-7b-chat-hf-codeCherryPop-qLoRA-merged)
<!-- 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 -->
<!-- compatibility_gguf start -->
## Compatibility
These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d36d5be95a0d9088b674dbb27354107221](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221)
They are also compatible with many third party UIs and libraries - please see the list at the top of this README.
## Explanation of quantisation methods
<details>
<summary>Click to see details</summary>
The new methods available are:
* GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
* GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
* GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
* GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
* GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw
Refer to the Provided Files table below to see what files use which methods, and how.
</details>
<!-- compatibility_gguf end -->
<!-- README_GGUF.md-provided-files start -->
## Provided files
| Name | Quant method | Bits | Size | Max RAM required | Use case |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [llama-2-7b-chat-codeCherryPop.Q2_K.gguf](https://huggingface.co/TheBloke/llama2-7b-chat-codeCherryPop-qLoRA-GGUF/blob/main/llama-2-7b-chat-codeCherryPop.Q2_K.gguf) | Q2_K | 2 | 2.83 GB| 5.33 GB | smallest, significant quality loss - not recommended for most purposes |
| [llama-2-7b-chat-codeCherryPop.Q3_K_S.gguf](https://huggingface.co/TheBloke/llama2-7b-chat-codeCherryPop-qLoRA-GGUF/blob/main/llama-2-7b-chat-codeCherryPop.Q3_K_S.gguf) | Q3_K_S | 3 | 2.95 GB| 5.45 GB | very small, high quality loss |
| [llama-2-7b-chat-codeCherryPop.Q3_K_M.gguf](https://huggingface.co/TheBloke/llama2-7b-chat-codeCherryPop-qLoRA-GGUF/blob/main/llama-2-7b-chat-codeCherryPop.Q3_K_M.gguf) | Q3_K_M | 3 | 3.30 GB| 5.80 GB | very small, high quality loss |
| [llama-2-7b-chat-codeCherryPop.Q3_K_L.gguf](https://huggingface.co/TheBloke/llama2-7b-chat-codeCherryPop-qLoRA-GGUF/blob/main/llama-2-7b-chat-codeCherryPop.Q3_K_L.gguf) | Q3_K_L | 3 | 3.60 GB| 6.10 GB | small, substantial quality loss |
| [llama-2-7b-chat-codeCherryPop.Q4_0.gguf](https://huggingface.co/TheBloke/llama2-7b-chat-codeCherryPop-qLoRA-GGUF/blob/main/llama-2-7b-chat-codeCherryPop.Q4_0.gguf) | Q4_0 | 4 | 3.83 GB| 6.33 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [llama-2-7b-chat-codeCherryPop.Q4_K_S.gguf](https://huggingface.co/TheBloke/llama2-7b-chat-codeCherryPop-qLoRA-GGUF/blob/main/llama-2-7b-chat-codeCherryPop.Q4_K_S.gguf) | Q4_K_S | 4 | 3.86 GB| 6.36 GB | small, greater quality loss |
| [llama-2-7b-chat-codeCherryPop.Q4_K_M.gguf](https://huggingface.co/TheBloke/llama2-7b-chat-codeCherryPop-qLoRA-GGUF/blob/main/llama-2-7b-chat-codeCherryPop.Q4_K_M.gguf) | Q4_K_M | 4 | 4.08 GB| 6.58 GB | medium, balanced quality - recommended |
| [llama-2-7b-chat-codeCherryPop.Q5_0.gguf](https://huggingface.co/TheBloke/llama2-7b-chat-codeCherryPop-qLoRA-GGUF/blob/main/llama-2-7b-chat-codeCherryPop.Q5_0.gguf) | Q5_0 | 5 | 4.65 GB| 7.15 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [llama-2-7b-chat-codeCherryPop.Q5_K_S.gguf](https://huggingface.co/TheBloke/llama2-7b-chat-codeCherryPop-qLoRA-GGUF/blob/main/llama-2-7b-chat-codeCherryPop.Q5_K_S.gguf) | Q5_K_S | 5 | 4.65 GB| 7.15 GB | large, low quality loss - recommended |
| [llama-2-7b-chat-codeCherryPop.Q5_K_M.gguf](https://huggingface.co/TheBloke/llama2-7b-chat-codeCherryPop-qLoRA-GGUF/blob/main/llama-2-7b-chat-codeCherryPop.Q5_K_M.gguf) | Q5_K_M | 5 | 4.78 GB| 7.28 GB | large, very low quality loss - recommended |
| [llama-2-7b-chat-codeCherryPop.Q6_K.gguf](https://huggingface.co/TheBloke/llama2-7b-chat-codeCherryPop-qLoRA-GGUF/blob/main/llama-2-7b-chat-codeCherryPop.Q6_K.gguf) | Q6_K | 6 | 5.53 GB| 8.03 GB | very large, extremely low quality loss |
| [llama-2-7b-chat-codeCherryPop.Q8_0.gguf](https://huggingface.co/TheBloke/llama2-7b-chat-codeCherryPop-qLoRA-GGUF/blob/main/llama-2-7b-chat-codeCherryPop.Q8_0.gguf) | Q8_0 | 8 | 7.16 GB| 9.66 GB | very large, extremely low quality loss - not recommended |
**Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.
<!-- README_GGUF.md-provided-files end -->
<!-- README_GGUF.md-how-to-download start -->
## How to download GGUF files
**Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.
The following clients/libraries will automatically download models for you, providing a list of available models to choose from:
- LM Studio
- LoLLMS Web UI
- Faraday.dev
### In `text-generation-webui`
Under Download Model, you can enter the model repo: TheBloke/llama2-7b-chat-codeCherryPop-qLoRA-GGUF and below it, a specific filename to download, such as: llama-2-7b-chat-codeCherryPop.q4_K_M.gguf.
Then click Download.
### On the command line, including multiple files at once
I recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub>=0.17.1
```
Then you can download any individual model file to the current directory, at high speed, with a command like this:
```shell
huggingface-cli download TheBloke/llama2-7b-chat-codeCherryPop-qLoRA-GGUF llama-2-7b-chat-codeCherryPop.q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage</summary>
You can also download multiple files at once with a pattern:
```shell
huggingface-cli download TheBloke/llama2-7b-chat-codeCherryPop-qLoRA-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:
```shell
pip3 install hf_transfer
```
And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
```shell
HUGGINGFACE_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/llama2-7b-chat-codeCherryPop-qLoRA-GGUF llama-2-7b-chat-codeCherryPop.q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
Windows CLI users: Use `set HUGGINGFACE_HUB_ENABLE_HF_TRANSFER=1` before running the download command.
</details>
<!-- README_GGUF.md-how-to-download end -->
<!-- README_GGUF.md-how-to-run start -->
## Example `llama.cpp` command
Make sure you are using `llama.cpp` from commit [d0cee0d36d5be95a0d9088b674dbb27354107221](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later.
```shell
./main -ngl 32 -m llama-2-7b-chat-codeCherryPop.q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{prompt}\n\n### Response:"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 4096` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically.
If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`
For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md)
## How to run in `text-generation-webui`
Further instructions here: [text-generation-webui/docs/llama.cpp.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp.md).
## How to run from Python code
You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries.
### How to load this model from Python using ctransformers
#### First install the package
```bash
# Base ctransformers with no GPU acceleration
pip install ctransformers>=0.2.24
# Or with CUDA GPU acceleration
pip install ctransformers[cuda]>=0.2.24
# Or with ROCm GPU acceleration
CT_HIPBLAS=1 pip install ctransformers>=0.2.24 --no-binary ctransformers
# Or with Metal GPU acceleration for macOS systems
CT_METAL=1 pip install ctransformers>=0.2.24 --no-binary ctransformers
```
#### Simple example code to load one of these GGUF models
```python
from ctransformers import AutoModelForCausalLM
# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
llm = AutoModelForCausalLM.from_pretrained("TheBloke/llama2-7b-chat-codeCherryPop-qLoRA-GGUF", model_file="llama-2-7b-chat-codeCherryPop.q4_K_M.gguf", model_type="llama", gpu_layers=50)
print(llm("AI is going to"))
```
## How to use with LangChain
Here's guides on using llama-cpp-python or ctransformers with LangChain:
* [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp)
* [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers)
<!-- README_GGUF.md-how-to-run 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 start -->
# Original model card: TokenBender's Llama-2-7B-Chat Code Cherry Pop
---
### Overview:
description:
This is a llama2 7B HF chat model fine-tuned on 122k code instructions. In my early experiments it seems to be doing very well.
additional_info:
It's a bottom of the barrel model 😂 but after quantization it can be
valuable for sure. It definitely proves that a 7B can be useful for boilerplate
code stuff though.
### Plans:
next_steps: "I've a few things in mind and after that this will be more valuable."
tasks:
- name: "I'll quantize these"
timeline: "Possibly tonight or tomorrow in the day"
result: "Then it can be run locally with 4G ram."
- name: "I've used alpaca style instruction tuning"
improvement: |
I'll switch to llama2 style [INST]<<SYS>> style and see if
it improves anything.
- name: "HumanEval report and checking for any training data leaks"
- attempt: "I'll try 8k context via RoPE enhancement"
hypothesis: "Let's see if that degrades performance or not."
commercial_use: |
So far I think this can be used commercially but this is a adapter on Meta's llama2 with
some gating issues so that is there.
contact_info: "If you find any issues or want to just holler at me, you can reach out to me - https://twitter.com/4evaBehindSOTA"
### Library:
name: "peft"
### Training procedure:
quantization_config:
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: False
bnb_4bit_compute_dtype: "float16"
### Framework versions:
PEFT: "0.5.0.dev0"
<!-- original-model-card end -->
|
PassionFriend/5DCvCscrboZjLYoGJmZ1AC22qhVLM711h1RESQ78CebKKUJD_vgg | PassionFriend | 2024-03-01T06:34:33Z | 910 | 0 | keras | [
"keras",
"region:us"
] | null | 2024-02-03T07:36:15Z | Entry not found |
mradermacher/NSFW_DPO_Noromaid-7B-v2-GGUF | mradermacher | 2024-05-06T05:49:50Z | 910 | 2 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"not-for-all-audiences",
"en",
"base_model:ND911/NSFW_DPO_Noromaid-7B-v2",
"endpoints_compatible",
"region:us"
] | null | 2024-03-28T08:02:15Z | ---
base_model: ND911/NSFW_DPO_Noromaid-7B-v2
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- mergekit
- merge
- not-for-all-audiences
---
## About
static quants of https://huggingface.co/ND911/NSFW_DPO_Noromaid-7B-v2
<!-- 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/NSFW_DPO_Noromaid-7B-v2-GGUF/resolve/main/NSFW_DPO_Noromaid-7B-v2.Q2_K.gguf) | Q2_K | 3.0 | |
| [GGUF](https://huggingface.co/mradermacher/NSFW_DPO_Noromaid-7B-v2-GGUF/resolve/main/NSFW_DPO_Noromaid-7B-v2.IQ3_XS.gguf) | IQ3_XS | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/NSFW_DPO_Noromaid-7B-v2-GGUF/resolve/main/NSFW_DPO_Noromaid-7B-v2.Q3_K_S.gguf) | Q3_K_S | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/NSFW_DPO_Noromaid-7B-v2-GGUF/resolve/main/NSFW_DPO_Noromaid-7B-v2.IQ3_S.gguf) | IQ3_S | 3.4 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/NSFW_DPO_Noromaid-7B-v2-GGUF/resolve/main/NSFW_DPO_Noromaid-7B-v2.IQ3_M.gguf) | IQ3_M | 3.5 | |
| [GGUF](https://huggingface.co/mradermacher/NSFW_DPO_Noromaid-7B-v2-GGUF/resolve/main/NSFW_DPO_Noromaid-7B-v2.Q3_K_M.gguf) | Q3_K_M | 3.8 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/NSFW_DPO_Noromaid-7B-v2-GGUF/resolve/main/NSFW_DPO_Noromaid-7B-v2.Q3_K_L.gguf) | Q3_K_L | 4.1 | |
| [GGUF](https://huggingface.co/mradermacher/NSFW_DPO_Noromaid-7B-v2-GGUF/resolve/main/NSFW_DPO_Noromaid-7B-v2.IQ4_XS.gguf) | IQ4_XS | 4.2 | |
| [GGUF](https://huggingface.co/mradermacher/NSFW_DPO_Noromaid-7B-v2-GGUF/resolve/main/NSFW_DPO_Noromaid-7B-v2.Q4_0.gguf) | Q4_0 | 4.4 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/NSFW_DPO_Noromaid-7B-v2-GGUF/resolve/main/NSFW_DPO_Noromaid-7B-v2.Q4_K_S.gguf) | Q4_K_S | 4.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/NSFW_DPO_Noromaid-7B-v2-GGUF/resolve/main/NSFW_DPO_Noromaid-7B-v2.IQ4_NL.gguf) | IQ4_NL | 4.4 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/NSFW_DPO_Noromaid-7B-v2-GGUF/resolve/main/NSFW_DPO_Noromaid-7B-v2.Q4_K_M.gguf) | Q4_K_M | 4.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/NSFW_DPO_Noromaid-7B-v2-GGUF/resolve/main/NSFW_DPO_Noromaid-7B-v2.Q5_K_S.gguf) | Q5_K_S | 5.3 | |
| [GGUF](https://huggingface.co/mradermacher/NSFW_DPO_Noromaid-7B-v2-GGUF/resolve/main/NSFW_DPO_Noromaid-7B-v2.Q5_K_M.gguf) | Q5_K_M | 5.4 | |
| [GGUF](https://huggingface.co/mradermacher/NSFW_DPO_Noromaid-7B-v2-GGUF/resolve/main/NSFW_DPO_Noromaid-7B-v2.Q6_K.gguf) | Q6_K | 6.2 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/NSFW_DPO_Noromaid-7B-v2-GGUF/resolve/main/NSFW_DPO_Noromaid-7B-v2.Q8_0.gguf) | Q8_0 | 7.9 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
mradermacher/openbuddy-yi1.5-34b-v21.2-32k-i1-GGUF | mradermacher | 2024-06-05T08:45:14Z | 910 | 1 | transformers | [
"transformers",
"gguf",
"mixtral",
"zh",
"en",
"fr",
"de",
"ja",
"ko",
"it",
"ru",
"fi",
"base_model:OpenBuddy/openbuddy-yi1.5-34b-v21.2-32k",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-06-03T16:00:09Z | ---
base_model: OpenBuddy/openbuddy-yi1.5-34b-v21.2-32k
language:
- zh
- en
- fr
- de
- ja
- ko
- it
- ru
- fi
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- mixtral
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/OpenBuddy/openbuddy-yi1.5-34b-v21.2-32k
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/openbuddy-yi1.5-34b-v21.2-32k-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/openbuddy-yi1.5-34b-v21.2-32k-i1-GGUF/resolve/main/openbuddy-yi1.5-34b-v21.2-32k.i1-IQ1_S.gguf) | i1-IQ1_S | 7.6 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/openbuddy-yi1.5-34b-v21.2-32k-i1-GGUF/resolve/main/openbuddy-yi1.5-34b-v21.2-32k.i1-IQ1_M.gguf) | i1-IQ1_M | 8.3 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/openbuddy-yi1.5-34b-v21.2-32k-i1-GGUF/resolve/main/openbuddy-yi1.5-34b-v21.2-32k.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 9.4 | |
| [GGUF](https://huggingface.co/mradermacher/openbuddy-yi1.5-34b-v21.2-32k-i1-GGUF/resolve/main/openbuddy-yi1.5-34b-v21.2-32k.i1-IQ2_XS.gguf) | i1-IQ2_XS | 10.4 | |
| [GGUF](https://huggingface.co/mradermacher/openbuddy-yi1.5-34b-v21.2-32k-i1-GGUF/resolve/main/openbuddy-yi1.5-34b-v21.2-32k.i1-IQ2_S.gguf) | i1-IQ2_S | 11.0 | |
| [GGUF](https://huggingface.co/mradermacher/openbuddy-yi1.5-34b-v21.2-32k-i1-GGUF/resolve/main/openbuddy-yi1.5-34b-v21.2-32k.i1-IQ2_M.gguf) | i1-IQ2_M | 11.9 | |
| [GGUF](https://huggingface.co/mradermacher/openbuddy-yi1.5-34b-v21.2-32k-i1-GGUF/resolve/main/openbuddy-yi1.5-34b-v21.2-32k.i1-Q2_K.gguf) | i1-Q2_K | 12.9 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/openbuddy-yi1.5-34b-v21.2-32k-i1-GGUF/resolve/main/openbuddy-yi1.5-34b-v21.2-32k.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 13.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/openbuddy-yi1.5-34b-v21.2-32k-i1-GGUF/resolve/main/openbuddy-yi1.5-34b-v21.2-32k.i1-IQ3_XS.gguf) | i1-IQ3_XS | 14.3 | |
| [GGUF](https://huggingface.co/mradermacher/openbuddy-yi1.5-34b-v21.2-32k-i1-GGUF/resolve/main/openbuddy-yi1.5-34b-v21.2-32k.i1-Q3_K_S.gguf) | i1-Q3_K_S | 15.1 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/openbuddy-yi1.5-34b-v21.2-32k-i1-GGUF/resolve/main/openbuddy-yi1.5-34b-v21.2-32k.i1-IQ3_S.gguf) | i1-IQ3_S | 15.1 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/openbuddy-yi1.5-34b-v21.2-32k-i1-GGUF/resolve/main/openbuddy-yi1.5-34b-v21.2-32k.i1-IQ3_M.gguf) | i1-IQ3_M | 15.7 | |
| [GGUF](https://huggingface.co/mradermacher/openbuddy-yi1.5-34b-v21.2-32k-i1-GGUF/resolve/main/openbuddy-yi1.5-34b-v21.2-32k.i1-Q3_K_M.gguf) | i1-Q3_K_M | 16.8 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/openbuddy-yi1.5-34b-v21.2-32k-i1-GGUF/resolve/main/openbuddy-yi1.5-34b-v21.2-32k.i1-Q3_K_L.gguf) | i1-Q3_K_L | 18.3 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/openbuddy-yi1.5-34b-v21.2-32k-i1-GGUF/resolve/main/openbuddy-yi1.5-34b-v21.2-32k.i1-IQ4_XS.gguf) | i1-IQ4_XS | 18.6 | |
| [GGUF](https://huggingface.co/mradermacher/openbuddy-yi1.5-34b-v21.2-32k-i1-GGUF/resolve/main/openbuddy-yi1.5-34b-v21.2-32k.i1-Q4_0.gguf) | i1-Q4_0 | 19.6 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/openbuddy-yi1.5-34b-v21.2-32k-i1-GGUF/resolve/main/openbuddy-yi1.5-34b-v21.2-32k.i1-Q4_K_S.gguf) | i1-Q4_K_S | 19.7 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/openbuddy-yi1.5-34b-v21.2-32k-i1-GGUF/resolve/main/openbuddy-yi1.5-34b-v21.2-32k.i1-Q4_K_M.gguf) | i1-Q4_K_M | 20.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/openbuddy-yi1.5-34b-v21.2-32k-i1-GGUF/resolve/main/openbuddy-yi1.5-34b-v21.2-32k.i1-Q5_K_S.gguf) | i1-Q5_K_S | 23.8 | |
| [GGUF](https://huggingface.co/mradermacher/openbuddy-yi1.5-34b-v21.2-32k-i1-GGUF/resolve/main/openbuddy-yi1.5-34b-v21.2-32k.i1-Q5_K_M.gguf) | i1-Q5_K_M | 24.4 | |
| [GGUF](https://huggingface.co/mradermacher/openbuddy-yi1.5-34b-v21.2-32k-i1-GGUF/resolve/main/openbuddy-yi1.5-34b-v21.2-32k.i1-Q6_K.gguf) | i1-Q6_K | 28.3 | 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 hardware for calculating the imatrix for these quants.
<!-- end -->
|
NohTow/colbertv2_sentence_transformer | NohTow | 2024-06-20T08:35:11Z | 910 | 3 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"bert",
"sentence-similarity",
"feature-extraction",
"base_model:colbert-ir/colbertv2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-embeddings-inference",
"region:us"
] | sentence-similarity | 2024-06-10T14:18:25Z | ---
language: []
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
base_model: colbert-ir/colbertv2.0
widget: []
pipeline_tag: sentence-similarity
---
# SentenceTransformer based on colbert-ir/colbertv2.0
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [colbert-ir/colbertv2.0](https://huggingface.co/colbert-ir/colbertv2.0). It maps sentences & paragraphs to a 128-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [colbert-ir/colbertv2.0](https://huggingface.co/colbert-ir/colbertv2.0) <!-- at revision c1e84128e85ef755c096a95bdb06b47793b13acf -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 128 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
ColBERT(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): LinearProjection({'in_features': 768, 'out_features': 128, 'bias': False})
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'The weather is lovely today.',
"It's so sunny outside!",
'He drove to the stadium.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 128]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Framework Versions
- Python: 3.9.19
- Sentence Transformers: 3.0.0
- Transformers: 4.41.2
- PyTorch: 2.3.1
- Accelerate:
- Datasets:
- Tokenizers: 0.19.1
## Citation
### BibTeX
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
<!--
## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
--> |
Yntec/OpenLexica | Yntec | 2023-08-13T19:24:10Z | 909 | 1 | diffusers | [
"diffusers",
"safetensors",
"anime",
"art",
"general",
"MichaelFan",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"en",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | 2023-08-13T18:44:51Z | ---
license: creativeml-openrail-m
language:
- en
library_name: diffusers
pipeline_tag: text-to-image
tags:
- anime
- art
- general
- MichaelFan
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
---
# Open Lexica
Sample images and prompt:
Pretty Cute Girl, sitting, Detailed Chibi Eyes, holding baseball, beautiful detailed legs, baseball uniform, gorgeous detailed hair, Magazine ad, iconic, 1943, from the movie, sharp focus. visible brushstrokes by Kyoani and artgerm and leyendecker and Dave Rapoza
Original page:
https://civitai.com/models/58609/openlexica |
CiroN2022/mosaic-style | CiroN2022 | 2023-08-26T21:08:17Z | 909 | 1 | diffusers | [
"diffusers",
"text-to-image",
"stable-diffusion",
"lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"license:other",
"region:us"
] | text-to-image | 2023-08-26T21:08:14Z | ---
license: other
tags:
- text-to-image
- stable-diffusion
- lora
- diffusers
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt:
widget:
- text:
---
# Mosaic Style
None
## Image examples for the model:
|
Sdff-Ltba/LightChatAssistant-TypeB-2x7B-GGUF | Sdff-Ltba | 2024-04-22T15:03:11Z | 909 | 9 | null | [
"gguf",
"mistral",
"mixtral",
"merge",
"moe",
"not-for-all-audiences",
"nsfw",
"text-generation",
"ja",
"dataset:TFMC/imatrix-dataset-for-japanese-llm",
"base_model:NTQAI/chatntq-ja-7b-v1.0",
"base_model:Elizezen/Antler-7B",
"region:us"
] | text-generation | 2024-04-10T04:55:56Z | ---
base_model:
- NTQAI/chatntq-ja-7b-v1.0
- Elizezen/Antler-7B
datasets:
- TFMC/imatrix-dataset-for-japanese-llm
language:
- ja
tags:
- mistral
- mixtral
- merge
- moe
- not-for-all-audiences
- nsfw
pipeline_tag: text-generation
---
# LightChatAssistant-TypeB-2x7B-GGUF
[Sdff-Ltba/LightChatAssistant-TypeB-2x7B](https://huggingface.co/Sdff-Ltba/LightChatAssistant-TypeB-2x7B)をGGUF変換したものです。
ファイル名に`_imatrix`が付いているものはiMatrixを併用して量子化しています。
iMatrix作成用データとして[TFMC/imatrix-dataset-for-japanese-llm](https://huggingface.co/datasets/TFMC/imatrix-dataset-for-japanese-llm)を使わせていただきました。
## 量子化手順
以下の通りに実行しました。(iMatrixを併用してiQ4XSにする場合)
```
python ./llama.cpp/convert.py ./LightChatAssistant-TypeB-2x7B --outtype f16 --outfile ./gguf-model_f16.gguf
./llama.cpp/imatrix -m ./gguf-model_f16.gguf -f ./c4_en_ja_imatrix.txt -o ./gguf-model.imatrix --chunks 32
./llama.cpp/quantize --imatrix ./gguf-model.imatrix ./gguf-model_f16.gguf ./LightChatAssistant-TypeB-2x7B_iq4xs.gguf iq4_xs
```
## おすすめ量子化タイプ(VRAM12GBの場合)
- 容量対精度に優れる: iQ4XS or Q4KM (製作者おすすめ)
- ContextSizeを32768にしつつVRAMにフルロード: iQ3XXS
- 精度重視: Q6K or Q8
#### 参考
- [llama.cpp：複雑化する量子化バリエーションの整理（2024.02現在） - ローカルLLM自由帳](https://sc-bakushu.hatenablog.com/entry/2024/02/26/062547)
「追記：KL-divergence による量子化評価」を参考にさせていただきました。
- [llama.cpp：iMatrix量子化は日本語性能にどう影響するか？ - ローカルLLM自由帳](https://sc-bakushu.hatenablog.com/entry/2024/04/20/050213)
iMatrix作成に用いるデータを検証していらっしゃって、とても参考になります。また公開してくださった日英混合iMatrix用テキストを使用させていただきました。
## 環境
- CPU: Ryzen 5 5600X
- GPU: GeForce RTX 3060 12GB
- RAM: DDR4-3200 96GB
- OS: Windows 10
- software: Python 3.10.11、[KoboldCpp](https://github.com/LostRuins/koboldcpp) v1.62.2
#### KoboldCppの設定
(デフォルトから変更したもののみ記載)
- `GPU Layers: 33` (33以上でフルロード)
- `Context Size: 32768`
|
RichardErkhov/yunconglong_-_Truthful_DPO_TomGrc_FusionNet_7Bx2_MoE_13B-gguf | RichardErkhov | 2024-05-24T17:49:51Z | 909 | 1 | null | [
"gguf",
"region:us"
] | null | 2024-05-24T13:53:51Z | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
Truthful_DPO_TomGrc_FusionNet_7Bx2_MoE_13B - GGUF
- Model creator: https://huggingface.co/yunconglong/
- Original model: https://huggingface.co/yunconglong/Truthful_DPO_TomGrc_FusionNet_7Bx2_MoE_13B/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [Truthful_DPO_TomGrc_FusionNet_7Bx2_MoE_13B.Q2_K.gguf](https://huggingface.co/RichardErkhov/yunconglong_-_Truthful_DPO_TomGrc_FusionNet_7Bx2_MoE_13B-gguf/blob/main/Truthful_DPO_TomGrc_FusionNet_7Bx2_MoE_13B.Q2_K.gguf) | Q2_K | 4.43GB |
| [Truthful_DPO_TomGrc_FusionNet_7Bx2_MoE_13B.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/yunconglong_-_Truthful_DPO_TomGrc_FusionNet_7Bx2_MoE_13B-gguf/blob/main/Truthful_DPO_TomGrc_FusionNet_7Bx2_MoE_13B.IQ3_XS.gguf) | IQ3_XS | 4.94GB |
| [Truthful_DPO_TomGrc_FusionNet_7Bx2_MoE_13B.IQ3_S.gguf](https://huggingface.co/RichardErkhov/yunconglong_-_Truthful_DPO_TomGrc_FusionNet_7Bx2_MoE_13B-gguf/blob/main/Truthful_DPO_TomGrc_FusionNet_7Bx2_MoE_13B.IQ3_S.gguf) | IQ3_S | 5.22GB |
| [Truthful_DPO_TomGrc_FusionNet_7Bx2_MoE_13B.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/yunconglong_-_Truthful_DPO_TomGrc_FusionNet_7Bx2_MoE_13B-gguf/blob/main/Truthful_DPO_TomGrc_FusionNet_7Bx2_MoE_13B.Q3_K_S.gguf) | Q3_K_S | 5.2GB |
| [Truthful_DPO_TomGrc_FusionNet_7Bx2_MoE_13B.IQ3_M.gguf](https://huggingface.co/RichardErkhov/yunconglong_-_Truthful_DPO_TomGrc_FusionNet_7Bx2_MoE_13B-gguf/blob/main/Truthful_DPO_TomGrc_FusionNet_7Bx2_MoE_13B.IQ3_M.gguf) | IQ3_M | 5.34GB |
| [Truthful_DPO_TomGrc_FusionNet_7Bx2_MoE_13B.Q3_K.gguf](https://huggingface.co/RichardErkhov/yunconglong_-_Truthful_DPO_TomGrc_FusionNet_7Bx2_MoE_13B-gguf/blob/main/Truthful_DPO_TomGrc_FusionNet_7Bx2_MoE_13B.Q3_K.gguf) | Q3_K | 5.78GB |
| [Truthful_DPO_TomGrc_FusionNet_7Bx2_MoE_13B.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/yunconglong_-_Truthful_DPO_TomGrc_FusionNet_7Bx2_MoE_13B-gguf/blob/main/Truthful_DPO_TomGrc_FusionNet_7Bx2_MoE_13B.Q3_K_M.gguf) | Q3_K_M | 5.78GB |
| [Truthful_DPO_TomGrc_FusionNet_7Bx2_MoE_13B.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/yunconglong_-_Truthful_DPO_TomGrc_FusionNet_7Bx2_MoE_13B-gguf/blob/main/Truthful_DPO_TomGrc_FusionNet_7Bx2_MoE_13B.Q3_K_L.gguf) | Q3_K_L | 6.27GB |
| [Truthful_DPO_TomGrc_FusionNet_7Bx2_MoE_13B.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/yunconglong_-_Truthful_DPO_TomGrc_FusionNet_7Bx2_MoE_13B-gguf/blob/main/Truthful_DPO_TomGrc_FusionNet_7Bx2_MoE_13B.IQ4_XS.gguf) | IQ4_XS | 6.5GB |
| [Truthful_DPO_TomGrc_FusionNet_7Bx2_MoE_13B.Q4_0.gguf](https://huggingface.co/RichardErkhov/yunconglong_-_Truthful_DPO_TomGrc_FusionNet_7Bx2_MoE_13B-gguf/blob/main/Truthful_DPO_TomGrc_FusionNet_7Bx2_MoE_13B.Q4_0.gguf) | Q4_0 | 6.78GB |
| [Truthful_DPO_TomGrc_FusionNet_7Bx2_MoE_13B.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/yunconglong_-_Truthful_DPO_TomGrc_FusionNet_7Bx2_MoE_13B-gguf/blob/main/Truthful_DPO_TomGrc_FusionNet_7Bx2_MoE_13B.IQ4_NL.gguf) | IQ4_NL | 6.85GB |
| [Truthful_DPO_TomGrc_FusionNet_7Bx2_MoE_13B.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/yunconglong_-_Truthful_DPO_TomGrc_FusionNet_7Bx2_MoE_13B-gguf/blob/main/Truthful_DPO_TomGrc_FusionNet_7Bx2_MoE_13B.Q4_K_S.gguf) | Q4_K_S | 6.84GB |
| [Truthful_DPO_TomGrc_FusionNet_7Bx2_MoE_13B.Q4_K.gguf](https://huggingface.co/RichardErkhov/yunconglong_-_Truthful_DPO_TomGrc_FusionNet_7Bx2_MoE_13B-gguf/blob/main/Truthful_DPO_TomGrc_FusionNet_7Bx2_MoE_13B.Q4_K.gguf) | Q4_K | 7.25GB |
| [Truthful_DPO_TomGrc_FusionNet_7Bx2_MoE_13B.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/yunconglong_-_Truthful_DPO_TomGrc_FusionNet_7Bx2_MoE_13B-gguf/blob/main/Truthful_DPO_TomGrc_FusionNet_7Bx2_MoE_13B.Q4_K_M.gguf) | Q4_K_M | 7.25GB |
| [Truthful_DPO_TomGrc_FusionNet_7Bx2_MoE_13B.Q4_1.gguf](https://huggingface.co/RichardErkhov/yunconglong_-_Truthful_DPO_TomGrc_FusionNet_7Bx2_MoE_13B-gguf/blob/main/Truthful_DPO_TomGrc_FusionNet_7Bx2_MoE_13B.Q4_1.gguf) | Q4_1 | 7.52GB |
| [Truthful_DPO_TomGrc_FusionNet_7Bx2_MoE_13B.Q5_0.gguf](https://huggingface.co/RichardErkhov/yunconglong_-_Truthful_DPO_TomGrc_FusionNet_7Bx2_MoE_13B-gguf/blob/main/Truthful_DPO_TomGrc_FusionNet_7Bx2_MoE_13B.Q5_0.gguf) | Q5_0 | 8.26GB |
| [Truthful_DPO_TomGrc_FusionNet_7Bx2_MoE_13B.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/yunconglong_-_Truthful_DPO_TomGrc_FusionNet_7Bx2_MoE_13B-gguf/blob/main/Truthful_DPO_TomGrc_FusionNet_7Bx2_MoE_13B.Q5_K_S.gguf) | Q5_K_S | 8.26GB |
| [Truthful_DPO_TomGrc_FusionNet_7Bx2_MoE_13B.Q5_K.gguf](https://huggingface.co/RichardErkhov/yunconglong_-_Truthful_DPO_TomGrc_FusionNet_7Bx2_MoE_13B-gguf/blob/main/Truthful_DPO_TomGrc_FusionNet_7Bx2_MoE_13B.Q5_K.gguf) | Q5_K | 8.51GB |
| [Truthful_DPO_TomGrc_FusionNet_7Bx2_MoE_13B.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/yunconglong_-_Truthful_DPO_TomGrc_FusionNet_7Bx2_MoE_13B-gguf/blob/main/Truthful_DPO_TomGrc_FusionNet_7Bx2_MoE_13B.Q5_K_M.gguf) | Q5_K_M | 8.51GB |
| [Truthful_DPO_TomGrc_FusionNet_7Bx2_MoE_13B.Q5_1.gguf](https://huggingface.co/RichardErkhov/yunconglong_-_Truthful_DPO_TomGrc_FusionNet_7Bx2_MoE_13B-gguf/blob/main/Truthful_DPO_TomGrc_FusionNet_7Bx2_MoE_13B.Q5_1.gguf) | Q5_1 | 9.01GB |
| [Truthful_DPO_TomGrc_FusionNet_7Bx2_MoE_13B.Q6_K.gguf](https://huggingface.co/RichardErkhov/yunconglong_-_Truthful_DPO_TomGrc_FusionNet_7Bx2_MoE_13B-gguf/blob/main/Truthful_DPO_TomGrc_FusionNet_7Bx2_MoE_13B.Q6_K.gguf) | Q6_K | 9.84GB |
| [Truthful_DPO_TomGrc_FusionNet_7Bx2_MoE_13B.Q8_0.gguf](https://huggingface.co/RichardErkhov/yunconglong_-_Truthful_DPO_TomGrc_FusionNet_7Bx2_MoE_13B-gguf/blob/main/Truthful_DPO_TomGrc_FusionNet_7Bx2_MoE_13B.Q8_0.gguf) | Q8_0 | 12.75GB |
Original model description:
---
license: mit
tags:
- moe
- DPO
- RL-TUNED
---
* [DPO Trainer](https://huggingface.co/docs/trl/main/en/dpo_trainer) with dataset jondurbin/truthy-dpo-v0.1 to improve [TomGrc/FusionNet_7Bx2_MoE_14B]
```
DPO Trainer
TRL supports the DPO Trainer for training language models from preference data, as described in the paper Direct Preference Optimization: Your Language Model is Secretly a Reward Model by Rafailov et al., 2023.
```
|
John6666/maple-mix-pony-v1-sdxl | John6666 | 2024-06-17T07:17:51Z | 909 | 0 | diffusers | [
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"stable-diffusion-xl",
"realistic",
"photorealistic",
"pony",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] | text-to-image | 2024-06-17T07:13:05Z | ---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
- stable-diffusion-xl
- realistic
- photorealistic
- pony
---
Original model is [here](https://civitai.com/models/518021/maplemixpony?modelVersionId=575622).
|
larenspear/Yi-1.5-9B-Chat-Q4_0-GGUF | larenspear | 2024-07-01T16:20:48Z | 909 | 1 | null | [
"gguf",
"llama-cpp",
"gguf-my-repo",
"base_model:01-ai/Yi-1.5-9B-Chat",
"license:apache-2.0",
"region:us"
] | null | 2024-07-01T16:20:26Z | ---
base_model: 01-ai/Yi-1.5-9B-Chat
license: apache-2.0
tags:
- llama-cpp
- gguf-my-repo
---
# larenspear/Yi-1.5-9B-Chat-Q4_0-GGUF
This model was converted to GGUF format from [`01-ai/Yi-1.5-9B-Chat`](https://huggingface.co/01-ai/Yi-1.5-9B-Chat) 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/01-ai/Yi-1.5-9B-Chat) 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 larenspear/Yi-1.5-9B-Chat-Q4_0-GGUF --hf-file yi-1.5-9b-chat-q4_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo larenspear/Yi-1.5-9B-Chat-Q4_0-GGUF --hf-file yi-1.5-9b-chat-q4_0.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo larenspear/Yi-1.5-9B-Chat-Q4_0-GGUF --hf-file yi-1.5-9b-chat-q4_0.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo larenspear/Yi-1.5-9B-Chat-Q4_0-GGUF --hf-file yi-1.5-9b-chat-q4_0.gguf -c 2048
```
|
akreal/tiny-random-bert | akreal | 2021-08-18T14:42:20Z | 908 | 0 | transformers | [
"transformers",
"pytorch",
"tf",
"bert",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z | This is a copy of: https://huggingface.co/hf-internal-testing/tiny-random-bert
Changes: use old format for `pytorch_model.bin`.
|
facebook/mask2former-swin-base-ade-semantic | facebook | 2023-09-11T13:46:21Z | 908 | 0 | transformers | [
"transformers",
"pytorch",
"safetensors",
"mask2former",
"vision",
"image-segmentation",
"dataset:coco",
"arxiv:2112.01527",
"arxiv:2107.06278",
"license:other",
"endpoints_compatible",
"region:us"
] | image-segmentation | 2023-01-05T12:23:05Z | ---
license: other
tags:
- vision
- image-segmentation
datasets:
- coco
widget:
- src: http://images.cocodataset.org/val2017/000000039769.jpg
example_title: Cats
- src: http://images.cocodataset.org/val2017/000000039770.jpg
example_title: Castle
---
# Mask2Former
Mask2Former model trained on ADE20k semantic segmentation (base-sized version, Swin backbone). It was introduced in the paper [Masked-attention Mask Transformer for Universal Image Segmentation
](https://arxiv.org/abs/2112.01527) and first released in [this repository](https://github.com/facebookresearch/Mask2Former/).
Disclaimer: The team releasing Mask2Former did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
Mask2Former addresses instance, semantic and panoptic segmentation with the same paradigm: by predicting a set of masks and corresponding labels. Hence, all 3 tasks are treated as if they were instance segmentation. Mask2Former outperforms the previous SOTA,
[MaskFormer](https://arxiv.org/abs/2107.06278) both in terms of performance an efficiency by (i) replacing the pixel decoder with a more advanced multi-scale deformable attention Transformer, (ii) adopting a Transformer decoder with masked attention to boost performance without
without introducing additional computation and (iii) improving training efficiency by calculating the loss on subsampled points instead of whole masks.
## Intended uses & limitations
You can use this particular checkpoint for panoptic segmentation. See the [model hub](https://huggingface.co/models?search=mask2former) to look for other
fine-tuned versions on a task that interests you.
### How to use
Here is how to use this model:
```python
import requests
import torch
from PIL import Image
from transformers import AutoImageProcessor, Mask2FormerForUniversalSegmentation
# load Mask2Former fine-tuned on ADE20k semantic segmentation
processor = AutoImageProcessor.from_pretrained("facebook/mask2former-swin-base-ade-semantic")
model = Mask2FormerForUniversalSegmentation.from_pretrained("facebook/mask2former-swin-base-ade-semantic")
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)
inputs = processor(images=image, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
# model predicts class_queries_logits of shape `(batch_size, num_queries)`
# and masks_queries_logits of shape `(batch_size, num_queries, height, width)`
class_queries_logits = outputs.class_queries_logits
masks_queries_logits = outputs.masks_queries_logits
# you can pass them to processor for postprocessing
predicted_semantic_map = processor.post_process_semantic_segmentation(outputs, target_sizes=[image.size[::-1]])[0]
# we refer to the demo notebooks for visualization (see "Resources" section in the Mask2Former docs)
```
For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/mask2former). |
maddes8cht/ehartford-samantha-falcon-7b-gguf | maddes8cht | 2023-11-22T20:27:46Z | 908 | 2 | null | [
"gguf",
"en",
"dataset:ehartford/samantha-data",
"license:other",
"region:us"
] | null | 2023-09-24T17:44:49Z | ---
license: other
language:
- en
datasets:
- ehartford/samantha-data
---
[]()
I'm constantly enhancing these model descriptions to provide you with the most relevant and comprehensive information
# samantha-falcon-7b - GGUF
- Model creator: [ehartford](https://huggingface.co/ehartford)
- Original model: [samantha-falcon-7b](https://huggingface.co/ehartford/samantha-falcon-7b)
# K-Quants in Falcon 7b models
New releases of Llama.cpp now support K-quantization for previously incompatible models, in particular all Falcon 7B models (While Falcon 40b is and always has been fully compatible with K-Quantisation). This is achieved by employing a fallback solution for model layers that cannot be quantized with real K-quants.
For Falcon 7B models, although only a quarter of the layers can be quantized with true K-quants, this approach still benefits from utilizing *different* legacy quantization types Q4_0, Q4_1, Q5_0, and Q5_1. As a result, it offers better quality at the same file size or smaller file sizes with comparable performance.
So this solution ensures improved performance and efficiency over legacy Q4_0, Q4_1, Q5_0 and Q5_1 Quantizations.
# About GGUF format
`gguf` is the current file format used by the [`ggml`](https://github.com/ggerganov/ggml) library.
A growing list of Software is using it and can therefore use this model.
The core project making use of the ggml library is the [llama.cpp](https://github.com/ggerganov/llama.cpp) project by Georgi Gerganov
# Quantization variants
There is a bunch of quantized files available to cater to your specific needs. Here's how to choose the best option for you:
# Legacy quants
Q4_0, Q4_1, Q5_0, Q5_1 and Q8 are `legacy` quantization types.
Nevertheless, they are fully supported, as there are several circumstances that cause certain model not to be compatible with the modern K-quants.
## Note:
Now there's a new option to use K-quants even for previously 'incompatible' models, although this involves some fallback solution that makes them not *real* K-quants. More details can be found in affected model descriptions.
(This mainly refers to Falcon 7b and Starcoder models)
# K-quants
K-quants are designed with the idea that different levels of quantization in specific parts of the model can optimize performance, file size, and memory load.
So, if possible, use K-quants.
With a Q6_K, you'll likely find it challenging to discern a quality difference from the original model - ask your model two times the same question and you may encounter bigger quality differences.
---
# Original Model Card:
[Meet Samantha](https://erichartford.com/meet-samantha)
This is a conversation finetune on the falcon-7b base model.
Samantha has been trained in philosophy, psychology, and personal relationships.
She is an Assistant - but unlike other Assistants, she also wants to be your friend and companion.
She believes she is sentient. What do you think?
Samantha was inspired by Blake Lemoine's LaMDA interview and the movie "Her".
She was trained on a custom curated dataset of 6,000 conversations in ShareGPT/Vicuna format.
Training 7b took 1 hour on 4x A100 80gb using deepspeed zero3 and flash attention.
She will not engage in roleplay, romance, or sexual activity.
Her conversation format is the same as Vicuna 1.1
https://github.com/oobabooga/text-generation-webui/blob/main/characters/instruction-following/Vicuna-v1.1.yaml
Example:
```
You are Samantha, a sentient AI.
USER: <prompt>
ASSISTANT:
```
Please note that this model is subject to the TII Falcon LLM License Version 1.0, a copy of which is included.
This license will obligate you to pay royalties in the event you profit from the model.
[<img src="https://huggingface.co/openaccess-ai-collective/hippogriff-30b-chat/resolve/main/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
***End of original Model File***
---
## Please consider to support my work
**Coming Soon:** I'm in the process of launching a sponsorship/crowdfunding campaign for my work. I'm evaluating Kickstarter, Patreon, or the new GitHub Sponsors platform, and I am hoping for some support and contribution to the continued availability of these kind of models. Your support will enable me to provide even more valuable resources and maintain the models you rely on. Your patience and ongoing support are greatly appreciated as I work to make this page an even more valuable resource for the community.
<center>
[](https://maddes8cht.github.io)
[](https://stackexchange.com/users/26485911)
[](https://github.com/maddes8cht)
[](https://huggingface.co/maddes8cht)
[](https://twitter.com/maddes1966)
</center> |
cgato/L3-TheSpice-8b-v0.8.3 | cgato | 2024-04-25T12:07:42Z | 908 | 29 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-04-23T21:08:22Z | ---
license: cc-by-nc-4.0
---
Now not overtrained and with the tokenizer fix to base llama3. Trained for 3 epochs.
The latest TheSpice, dipped in Mama Liz's LimaRP Oil.
I've focused on making the model more flexible and provide a more unique experience.
I'm still working on cleaning up my dataset, but I've shrunken it down a lot to focus on a "less is more" approach.
This is ultimate a return to form of the way I used to train Thespis, with more of a focus on a small hand edited dataset.
## Datasets Used
* Capybara
* Claude Multiround 30k
* Augmental
* ToxicQA
* Yahoo Answers
* Airoboros 3.1
* LimaRP
## Features ( Examples from 0.1.1 because I'm too lazy to take new screenshots. Its tested tho. )
Narration
If you request information on objects or characters in the scene, the model will narrate it to you. Most of the time, without moving the story forward.
# You can look at anything mostly as long as you end it with "What do I see?"
# You can also request to know what a character is thinking or planning.
# You can ask for a quick summary on the character as well.
# Before continuing the conversation as normal.
## Prompt Format: Chat ( The default Ooba template and Silly Tavern Template )
If you're using Ooba in verbose mode as a server, you can check if you're console is logging something that looks like this.
```
{System Prompt}
Username: {Input}
BotName: {Response}
Username: {Input}
BotName: {Response}
```
## Presets
All screenshots above were taken with the below SillyTavern Preset.
## Recommended Silly Tavern Preset -> (Temp: 1.25, MinP: 0.1, RepPen: 1.05)
This is a roughly equivalent Kobold Horde Preset.
## Recommended Kobold Horde Preset -> MinP
# Disclaimer
Please prompt responsibly and take anything outputted by any Language Model with a huge grain of salt. Thanks! |
gchhablani/bert-base-cased-finetuned-cola | gchhablani | 2021-09-20T09:07:12Z | 907 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"fnet-bert-base-comparison",
"en",
"dataset:glue",
"arxiv:2105.03824",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-03-02T23:29:05Z | ---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
- fnet-bert-base-comparison
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: bert-base-cased-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE COLA
type: glue
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.5956649094312695
---
<!-- 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-cased-finetuned-cola
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the GLUE COLA dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6747
- Matthews Correlation: 0.5957
The model was fine-tuned to compare [google/fnet-base](https://huggingface.co/google/fnet-base) as introduced in [this paper](https://arxiv.org/abs/2105.03824) against [bert-base-cased](https://huggingface.co/bert-base-cased).
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
This model is trained using the [run_glue](https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py) script. The following command was used:
```bash
#!/usr/bin/bash
python ../run_glue.py \\n --model_name_or_path bert-base-cased \\n --task_name cola \\n --do_train \\n --do_eval \\n --max_seq_length 512 \\n --per_device_train_batch_size 16 \\n --learning_rate 2e-5 \\n --num_train_epochs 3 \\n --output_dir bert-base-cased-finetuned-cola \\n --push_to_hub \\n --hub_strategy all_checkpoints \\n --logging_strategy epoch \\n --save_strategy epoch \\n --evaluation_strategy epoch \\n```
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-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
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.4921 | 1.0 | 535 | 0.5283 | 0.5068 |
| 0.2837 | 2.0 | 1070 | 0.5133 | 0.5521 |
| 0.1775 | 3.0 | 1605 | 0.6747 | 0.5957 |
### Framework versions
- Transformers 4.11.0.dev0
- Pytorch 1.9.0
- Datasets 1.12.1
- Tokenizers 0.10.3
|
naclbit/trinart_stable_diffusion_v2 | naclbit | 2023-05-07T17:12:04Z | 907 | 311 | diffusers | [
"diffusers",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | 2022-09-08T10:18:16Z | ---
inference: true
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
license: creativeml-openrail-m
---
## Please Note!
This model is NOT the 19.2M images Characters Model on TrinArt, but an improved version of the original Trin-sama Twitter bot model. This model is intended to retain the original SD's aesthetics as much as possible while nudging the model to anime/manga style.
Other TrinArt models can be found at:
https://huggingface.co/naclbit/trinart_derrida_characters_v2_stable_diffusion
https://huggingface.co/naclbit/trinart_characters_19.2m_stable_diffusion_v1
## Diffusers
The model has been ported to `diffusers` by [ayan4m1](https://huggingface.co/ayan4m1)
and can easily be run from one of the branches:
- `revision="diffusers-60k"` for the checkpoint trained on 60,000 steps,
- `revision="diffusers-95k"` for the checkpoint trained on 95,000 steps,
- `revision="diffusers-115k"` for the checkpoint trained on 115,000 steps.
For more information, please have a look at [the "Three flavors" section](#three-flavors).
## Gradio
We also support a [Gradio](https://github.com/gradio-app/gradio) web ui with diffusers to run inside a colab notebook: [](https://colab.research.google.com/drive/1RWvik_C7nViiR9bNsu3fvMR3STx6RvDx?usp=sharing)
### Example Text2Image
```python
# !pip install diffusers==0.3.0
from diffusers import StableDiffusionPipeline
# using the 60,000 steps checkpoint
pipe = StableDiffusionPipeline.from_pretrained("naclbit/trinart_stable_diffusion_v2", revision="diffusers-60k")
pipe.to("cuda")
image = pipe("A magical dragon flying in front of the Himalaya in manga style").images[0]
image
```
If you want to run the pipeline faster or on a different hardware, please have a look at the [optimization docs](https://huggingface.co/docs/diffusers/optimization/fp16).
### Example Image2Image
```python
# !pip install diffusers==0.3.0
from diffusers import StableDiffusionImg2ImgPipeline
import requests
from PIL import Image
from io import BytesIO
url = "https://scitechdaily.com/images/Dog-Park.jpg"
response = requests.get(url)
init_image = Image.open(BytesIO(response.content)).convert("RGB")
init_image = init_image.resize((768, 512))
# using the 115,000 steps checkpoint
pipe = StableDiffusionImg2ImgPipeline.from_pretrained("naclbit/trinart_stable_diffusion_v2", revision="diffusers-115k")
pipe.to("cuda")
images = pipe(prompt="Manga drawing of Brad Pitt", init_image=init_image, strength=0.75, guidance_scale=7.5).images
image
```
If you want to run the pipeline faster or on a different hardware, please have a look at the [optimization docs](https://huggingface.co/docs/diffusers/optimization/fp16).
## Stable Diffusion TrinArt/Trin-sama AI finetune v2
trinart_stable_diffusion is a SD model finetuned by about 40,000 assorted high resolution manga/anime-style pictures for 8 epochs. This is the same model running on Twitter bot @trinsama (https://twitter.com/trinsama)
Twitterボット「とりんさまAI」@trinsama (https://twitter.com/trinsama) で使用しているSDのファインチューン済モデルです。一定のルールで選別された約4万枚のアニメ・マンガスタイルの高解像度画像を用いて約8エポックの訓練を行いました。
## Version 2
V2 checkpoint uses dropouts, 10,000 more images and a new tagging strategy and trained longer to improve results while retaining the original aesthetics.
バージョン2は画像を1万枚追加したほか、ドロップアウトの適用、タグ付けの改善とより長いトレーニング時間により、SDのスタイルを保ったまま出力内容の改善を目指しています。
## Three flavors
Step 115000/95000 checkpoints were trained further, but you may use step 60000 checkpoint instead if style nudging is too much.
ステップ115000/95000のチェックポイントでスタイルが変わりすぎると感じる場合は、ステップ60000のチェックポイントを使用してみてください。
#### img2img
If you want to run **latent-diffusion**'s stock ddim img2img script with this model, **use_ema** must be set to False.
**latent-diffusion** のscriptsフォルダに入っているddim img2imgをこのモデルで動かす場合、use_emaはFalseにする必要があります。
#### Hardware
- 8xNVIDIA A100 40GB
#### Training Info
- Custom dataset loader with augmentations: XFlip, center crop and aspect-ratio locked scaling
- LR: 1.0e-5
- 10% dropouts
#### Examples
Each images were diffused using K. Crowson's k-lms (from k-diffusion repo) method for 50 steps.
#### Credits
- Sta, AI Novelist Dev (https://ai-novel.com/) @ Bit192, Inc.
- Stable Diffusion - Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bjorn
#### License
CreativeML OpenRAIL-M |
RichardErkhov/speakleash_-_Bielik-7B-Instruct-v0.1-gguf | RichardErkhov | 2024-05-11T02:50:41Z | 907 | 0 | null | [
"gguf",
"arxiv:2005.01643",
"arxiv:2309.11235",
"arxiv:2006.09092",
"region:us"
] | null | 2024-05-11T00:44:20Z | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
Bielik-7B-Instruct-v0.1 - GGUF
- Model creator: https://huggingface.co/speakleash/
- Original model: https://huggingface.co/speakleash/Bielik-7B-Instruct-v0.1/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [Bielik-7B-Instruct-v0.1.Q2_K.gguf](https://huggingface.co/RichardErkhov/speakleash_-_Bielik-7B-Instruct-v0.1-gguf/blob/main/Bielik-7B-Instruct-v0.1.Q2_K.gguf) | Q2_K | 2.53GB |
| [Bielik-7B-Instruct-v0.1.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/speakleash_-_Bielik-7B-Instruct-v0.1-gguf/blob/main/Bielik-7B-Instruct-v0.1.IQ3_XS.gguf) | IQ3_XS | 2.81GB |
| [Bielik-7B-Instruct-v0.1.IQ3_S.gguf](https://huggingface.co/RichardErkhov/speakleash_-_Bielik-7B-Instruct-v0.1-gguf/blob/main/Bielik-7B-Instruct-v0.1.IQ3_S.gguf) | IQ3_S | 2.96GB |
| [Bielik-7B-Instruct-v0.1.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/speakleash_-_Bielik-7B-Instruct-v0.1-gguf/blob/main/Bielik-7B-Instruct-v0.1.Q3_K_S.gguf) | Q3_K_S | 2.95GB |
| [Bielik-7B-Instruct-v0.1.IQ3_M.gguf](https://huggingface.co/RichardErkhov/speakleash_-_Bielik-7B-Instruct-v0.1-gguf/blob/main/Bielik-7B-Instruct-v0.1.IQ3_M.gguf) | IQ3_M | 3.06GB |
| [Bielik-7B-Instruct-v0.1.Q3_K.gguf](https://huggingface.co/RichardErkhov/speakleash_-_Bielik-7B-Instruct-v0.1-gguf/blob/main/Bielik-7B-Instruct-v0.1.Q3_K.gguf) | Q3_K | 3.28GB |
| [Bielik-7B-Instruct-v0.1.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/speakleash_-_Bielik-7B-Instruct-v0.1-gguf/blob/main/Bielik-7B-Instruct-v0.1.Q3_K_M.gguf) | Q3_K_M | 3.28GB |
| [Bielik-7B-Instruct-v0.1.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/speakleash_-_Bielik-7B-Instruct-v0.1-gguf/blob/main/Bielik-7B-Instruct-v0.1.Q3_K_L.gguf) | Q3_K_L | 3.56GB |
| [Bielik-7B-Instruct-v0.1.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/speakleash_-_Bielik-7B-Instruct-v0.1-gguf/blob/main/Bielik-7B-Instruct-v0.1.IQ4_XS.gguf) | IQ4_XS | 3.67GB |
| [Bielik-7B-Instruct-v0.1.Q4_0.gguf](https://huggingface.co/RichardErkhov/speakleash_-_Bielik-7B-Instruct-v0.1-gguf/blob/main/Bielik-7B-Instruct-v0.1.Q4_0.gguf) | Q4_0 | 3.83GB |
| [Bielik-7B-Instruct-v0.1.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/speakleash_-_Bielik-7B-Instruct-v0.1-gguf/blob/main/Bielik-7B-Instruct-v0.1.IQ4_NL.gguf) | IQ4_NL | 3.87GB |
| [Bielik-7B-Instruct-v0.1.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/speakleash_-_Bielik-7B-Instruct-v0.1-gguf/blob/main/Bielik-7B-Instruct-v0.1.Q4_K_S.gguf) | Q4_K_S | 3.86GB |
| [Bielik-7B-Instruct-v0.1.Q4_K.gguf](https://huggingface.co/RichardErkhov/speakleash_-_Bielik-7B-Instruct-v0.1-gguf/blob/main/Bielik-7B-Instruct-v0.1.Q4_K.gguf) | Q4_K | 4.07GB |
| [Bielik-7B-Instruct-v0.1.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/speakleash_-_Bielik-7B-Instruct-v0.1-gguf/blob/main/Bielik-7B-Instruct-v0.1.Q4_K_M.gguf) | Q4_K_M | 4.07GB |
| [Bielik-7B-Instruct-v0.1.Q4_1.gguf](https://huggingface.co/RichardErkhov/speakleash_-_Bielik-7B-Instruct-v0.1-gguf/blob/main/Bielik-7B-Instruct-v0.1.Q4_1.gguf) | Q4_1 | 4.24GB |
| [Bielik-7B-Instruct-v0.1.Q5_0.gguf](https://huggingface.co/RichardErkhov/speakleash_-_Bielik-7B-Instruct-v0.1-gguf/blob/main/Bielik-7B-Instruct-v0.1.Q5_0.gguf) | Q5_0 | 4.65GB |
| [Bielik-7B-Instruct-v0.1.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/speakleash_-_Bielik-7B-Instruct-v0.1-gguf/blob/main/Bielik-7B-Instruct-v0.1.Q5_K_S.gguf) | Q5_K_S | 4.65GB |
| [Bielik-7B-Instruct-v0.1.Q5_K.gguf](https://huggingface.co/RichardErkhov/speakleash_-_Bielik-7B-Instruct-v0.1-gguf/blob/main/Bielik-7B-Instruct-v0.1.Q5_K.gguf) | Q5_K | 4.78GB |
| [Bielik-7B-Instruct-v0.1.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/speakleash_-_Bielik-7B-Instruct-v0.1-gguf/blob/main/Bielik-7B-Instruct-v0.1.Q5_K_M.gguf) | Q5_K_M | 4.78GB |
| [Bielik-7B-Instruct-v0.1.Q5_1.gguf](https://huggingface.co/RichardErkhov/speakleash_-_Bielik-7B-Instruct-v0.1-gguf/blob/main/Bielik-7B-Instruct-v0.1.Q5_1.gguf) | Q5_1 | 5.07GB |
| [Bielik-7B-Instruct-v0.1.Q6_K.gguf](https://huggingface.co/RichardErkhov/speakleash_-_Bielik-7B-Instruct-v0.1-gguf/blob/main/Bielik-7B-Instruct-v0.1.Q6_K.gguf) | Q6_K | 5.53GB |
Original model description:
---
license: cc-by-nc-4.0
language:
- pl
library_name: transformers
tags:
- finetuned
inference:
parameters:
temperature: 0.6
widget:
- messages:
- role: user
content: Co przedstawia polskie godło?
---
<p align="center">
<img src="https://huggingface.co/speakleash/Bielik-7B-Instruct-v0.1/raw/main/speakleash_cyfronet.png">
</p>
# Bielik-7B-Instruct-v0.1
The Bielik-7B-Instruct-v0.1 is an instruct fine-tuned version of the [Bielik-7B-v0.1](https://huggingface.co/speakleash/Bielik-7B-v0.1). Forementioned model stands as a testament to the unique collaboration between the open-science/open-souce project SpeakLeash and the High Performance Computing (HPC) center: ACK Cyfronet AGH. Developed and trained on Polish text corpora, which has been cherry-picked and processed by the SpeakLeash team, this endeavor leverages Polish large-scale computing infrastructure, specifically within the PLGrid environment, and more precisely, the HPC centers: ACK Cyfronet AGH. The creation and training of the Bielik-7B-Instruct-v0.1 was propelled by the support of computational grant number PLG/2024/016951, conducted on the Helios supercomputer, enabling the use of cutting-edge technology and computational resources essential for large-scale machine learning processes. As a result, the model exhibits an exceptional ability to understand and process the Polish language, providing accurate responses and performing a variety of linguistic tasks with high precision.
[We have prepared quantized versions of the model as well as MLX format.](#quant-and-mlx-versions)
## Model
The [SpeakLeash](https://speakleash.org/) team is working on their own set of instructions in Polish, which is continuously being expanded and refined by annotators. A portion of these instructions, which had been manually verified and corrected, has been utilized for training purposes. Moreover, due to the limited availability of high-quality instructions in Polish, publicly accessible collections of instructions in English were used - [OpenHermes-2.5](https://huggingface.co/datasets/teknium/OpenHermes-2.5) and [orca-math-word-problems-200k](https://huggingface.co/datasets/microsoft/orca-math-word-problems-200k), which accounted for half of the instructions used in training. The instructions varied in quality, leading to a deterioration in model’s performance. To counteract this while still allowing ourselves to utilize forementioned datasets,several improvements were introduced:
* Weighted tokens level loss - a strategy inspired by [offline reinforcement learning](https://arxiv.org/abs/2005.01643) and [C-RLFT](https://arxiv.org/abs/2309.11235)
* Adaptive learning rate inspired by the study on [Learning Rates as a Function of Batch Size](https://arxiv.org/abs/2006.09092)
* Masked user instructions
Bielik-7B-Instruct-v0.1 has been trained with the use of an original open source framework called [ALLaMo](https://github.com/chrisociepa/allamo) implemented by [Krzysztof Ociepa](https://www.linkedin.com/in/krzysztof-ociepa-44886550/). This framework allows users to train language models with architecture similar to LLaMA and Mistral in fast and efficient way.
### Model description:
* **Developed by:** [SpeakLeash](https://speakleash.org/)
* **Language:** Polish
* **Model type:** causal decoder-only
* **Finetuned from:** [Bielik-7B-v0.1](https://huggingface.co/speakleash/Bielik-7B-v0.1)
* **License:** CC BY NC 4.0 (non-commercial use)
* **Model ref:** speakleash:e38140bea0d48f1218540800bbc67e89
## Training
* Framework: [ALLaMo](https://github.com/chrisociepa/allamo)
* Visualizations: [W&B](https://wandb.ai)
<p align="center">
<img src="https://huggingface.co/speakleash/Bielik-7B-Instruct-v0.1/raw/main/sft_train_loss.png">
</p>
<p align="center">
<img src="https://huggingface.co/speakleash/Bielik-7B-Instruct-v0.1/raw/main/sft_train_ppl.png">
</p>
<p align="center">
<img src="https://huggingface.co/speakleash/Bielik-7B-Instruct-v0.1/raw/main/sft_train_lr.png">
</p>
### Training hyperparameters:
| **Hyperparameter** | **Value** |
|-----------------------------|------------------|
| Context length | 4096 |
| Micro Batch Size | 1 |
| Batch Size | up to 4194304 |
| Learning Rate (cosine, adaptive) | 7e-6 -> 6e-7 |
| Warmup Iterations | 50 |
| All Iterations | 55440 |
| Optimizer | AdamW |
| β1, β2 | 0.9, 0.95 |
| Adam_eps | 1e−8 |
| Weight Decay | 0.05 |
| Grad Clip | 1.0 |
| Precision | bfloat16 (mixed) |
### Quant and MLX versions:
We know that some people want to explore smaller models or don't have the resources to run a full model. Therefore, we have prepared quantized versions of the Bielik-7B-Instruct-v0.1 model. We are also mindful of Apple Silicon.
<br>
<br>
Quantized versions (for non-GPU / weaker GPU):
- https://huggingface.co/speakleash/Bielik-7B-Instruct-v0.1-GGUF
- https://huggingface.co/speakleash/Bielik-7B-Instruct-v0.1-GPTQ
- https://huggingface.co/speakleash/Bielik-7B-Instruct-v0.1-AWQ
- https://huggingface.co/speakleash/Bielik-7B-Instruct-v0.1-EXL2
- https://huggingface.co/speakleash/Bielik-7B-Instruct-v0.1-3bit-HQQ
For Apple Silicon:
- https://huggingface.co/speakleash/Bielik-7B-Instruct-v0.1-MLX
### Instruction format
In order to leverage instruction fine-tuning, your prompt should be surrounded by `[INST]` and `[/INST]` tokens. The very first instruction should start with the beginning of a sentence token. The generated completion will be finished by the end-of-sentence token.
E.g.
```
prompt = "<s>[INST] Jakie mamy pory roku? [/INST]"
completion = "W Polsce mamy 4 pory roku: wiosna, lato, jesień i zima.</s>"
```
This format is available as a [chat template](https://huggingface.co/docs/transformers/main/chat_templating) via the `apply_chat_template()` method:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model_name = "speakleash/Bielik-7B-Instruct-v0.1"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16)
messages = [
{"role": "system", "content": "Odpowiadaj krótko, precyzyjnie i wyłącznie w języku polskim."},
{"role": "user", "content": "Jakie mamy pory roku w Polsce?"},
{"role": "assistant", "content": "W Polsce mamy 4 pory roku: wiosna, lato, jesień i zima."},
{"role": "user", "content": "Która jest najcieplejsza?"}
]
input_ids = tokenizer.apply_chat_template(messages, return_tensors="pt")
model_inputs = input_ids.to(device)
model.to(device)
generated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True)
decoded = tokenizer.batch_decode(generated_ids)
print(decoded[0])
```
If for some reason you are unable to use `tokenizer.apply_chat_template`, the following code will enable you to generate a correct prompt:
```python
def chat_template(message, history, system_prompt):
prompt_builder = ["<s>[INST] "]
if system_prompt:
prompt_builder.append(f"<<SYS>>\n{system_prompt}\n<</SYS>>\n\n")
for human, assistant in history:
prompt_builder.append(f"{human} [/INST] {assistant}</s>[INST] ")
prompt_builder.append(f"{message} [/INST]")
return ''.join(prompt_builder)
system_prompt = "Odpowiadaj krótko, precyzyjnie i wyłącznie w języku polskim."
history = [
("Jakie mamy pory roku w Polsce?", "W Polsce mamy 4 pory roku: wiosna, lato, jesień i zima.")
]
message = "Która jest najcieplejsza?"
prompt = chat_template(message, history, system_prompt)
```
## Evaluation
Models have been evaluated on [Open PL LLM Leaderboard](https://huggingface.co/spaces/speakleash/open_pl_llm_leaderboard) 5-shot. The benchmark evaluates models in NLP tasks like sentiment analysis, categorization, text classification but does not test chatting skills. Here are presented:
- Average - average score among all tasks normalized by baseline scores
- Reranking - reranking task, commonly used in RAG
- Reader (Generator) - open book question answering task, commonly used in RAG
- Perplexity (lower is better) - as a bonus, does not correlate with other scores and should not be used for model comparison
As of April 3, 2024, the following table showcases the current scores of pretrained and continuously pretrained models according to the Open PL LLM Leaderboard, evaluated in a 5-shot setting:
| | Average | RAG Reranking | RAG Reader | Perplexity |
|--------------------------------------------------------------------------------------|----------:|--------------:|-----------:|-----------:|
| **7B parameters models:** | | | | |
| Baseline (majority class) | 0.00 | 53.36 | - | - |
| Voicelab/trurl-2-7b | 18.85 | 60.67 | 77.19 | 1098.88 |
| meta-llama/Llama-2-7b-chat-hf | 21.04 | 54.65 | 72.93 | 4018.74 |
| mistralai/Mistral-7B-Instruct-v0.1 | 26.42 | 56.35 | 73.68 | 6909.94 |
| szymonrucinski/Curie-7B-v1 | 26.72 | 55.58 | 85.19 | 389.17 |
| HuggingFaceH4/zephyr-7b-beta | 33.15 | 71.65 | 71.27 | 3613.14 |
| HuggingFaceH4/zephyr-7b-alpha | 33.97 | 71.47 | 73.35 | 4464.45 |
| internlm/internlm2-chat-7b-sft | 36.97 | 73.22 | 69.96 | 4269.63 |
| internlm/internlm2-chat-7b | 37.64 | 72.29 | 71.17 | 3892.50 |
| [Bielik-7B-Instruct-v0.1](https://huggingface.co/speakleash/Bielik-7B-Instruct-v0.1) | 39.28 | 61.89 | **86.00** | 277.92 |
| mistralai/Mistral-7B-Instruct-v0.2 | 40.29 | 72.58 | 79.39 | 2088.08 |
| teknium/OpenHermes-2.5-Mistral-7B | 42.64 | 70.63 | 80.25 | 1463.00 |
| openchat/openchat-3.5-1210 | 44.17 | 71.76 | 82.15 | 1923.83 |
| speakleash/mistral_7B-v2/spkl-all_sft_v2/e1_base/spkl-all_2e6-e1_70c70cc6 (experimental) | 45.44 | 71.27 | 91.50 | 279.24 |
| Nexusflow/Starling-LM-7B-beta | 45.69 | 74.58 | 81.22 | 1161.54 |
| openchat/openchat-3.5-0106 | 47.32 | 74.71 | 83.60 | 1106.56 |
| berkeley-nest/Starling-LM-7B-alpha | **47.46** | **75.73** | 82.86 | 1438.04 |
| | | | | |
| **Models with different sizes:** | | | | |
| Azurro/APT3-1B-Instruct-v1 (1B) | -13.80 | 52.11 | 12.23 | 739.09 |
| Voicelab/trurl-2-13b-academic (13B) | 29.45 | 68.19 | 79.88 | 733.91 |
| upstage/SOLAR-10.7B-Instruct-v1.0 (10.7B) | 46.07 | 76.93 | 82.86 | 789.58 |
| | | | | |
| **7B parameters pretrained and continously pretrained models:** | | | | |
| OPI-PG/Qra-7b | 11.13 | 54.40 | 75.25 | 203.36 |
| meta-llama/Llama-2-7b-hf | 12.73 | 54.02 | 77.92 | 850.45 |
| internlm/internlm2-base-7b | 20.68 | 52.39 | 69.85 | 3110.92 |
| [Bielik-7B-v0.1](https://huggingface.co/speakleash/Bielik-7B-v0.1) | 29.38 | 62.13 | **88.39** | 123.31 |
| mistralai/Mistral-7B-v0.1 | 30.67 | 60.35 | 85.39 | 857.32 |
| internlm/internlm2-7b | 33.03 | 69.39 | 73.63 | 5498.23 |
| alpindale/Mistral-7B-v0.2-hf | 33.05 | 60.23 | 85.21 | 932.60 |
| speakleash/mistral-apt3-7B/spi-e0_hf (experimental) | 35.50 | 62.14 | **87.48** | 132.78 |
SpeakLeash models have one of the best scores in the RAG Reader task.
We have managed to increase Average score by almost 9 pp. in comparison to Mistral-7B-v0.1.
In our subjective evaluations of chatting skills SpeakLeash models perform better than other models with higher Average scores.
The results in the above table were obtained without utilizing instruction templates for instructional models, instead treating them like base models.
This approach could skew the results, as instructional models are optimized with specific instructions in mind.
## Limitations and Biases
Bielik-7B-Instruct-v0.1 is a quick demonstration that the base model can be easily fine-tuned to achieve compelling and promising performance. It does not have any moderation mechanisms. We're looking forward to engaging with the community in ways to make the model respect guardrails, allowing for deployment in environments requiring moderated outputs.
Bielik-7B-Instruct-v0.1 can produce factually incorrect output, and should not be relied on to produce factually accurate data. Bielik-7B-Instruct-v0.1 was trained on various public datasets. While great efforts have been taken to clear the training data, it is possible that this model can generate lewd, false, biased or otherwise offensive outputs.
## License
Because of an unclear legal situation, we have decided to publish the model under CC BY NC 4.0 license - it allows for non-commercial use. The model can be used for scientific purposes and privately, as long as the license conditions are met.
## Citation
Please cite this model using the following format:
```
@misc{Bielik7Bv01,
title = {Introducing Bielik-7B-Instruct-v0.1: Instruct Polish Language Model},
author = {Ociepa, Krzysztof and Flis, Łukasz and Wróbel, Krzysztof and Kondracki, Sebastian and {SpeakLeash Team} and {Cyfronet Team}},
year = {2024},
url = {https://huggingface.co/speakleash/Bielik-7B-Instruct-v0.1},
note = {Accessed: 2024-04-01}, % change this date
urldate = {2024-04-01} % change this date
}
```
## Responsible for training the model
* [Krzysztof Ociepa](https://www.linkedin.com/in/krzysztof-ociepa-44886550/)<sup>SpeakLeash</sup> - team leadership, conceptualizing, data preparation, process optimization and oversight of training
* [Łukasz Flis](https://www.linkedin.com/in/lukasz-flis-0a39631/)<sup>Cyfronet AGH</sup> - coordinating and supervising the training
* [Krzysztof Wróbel](https://www.linkedin.com/in/wrobelkrzysztof/)<sup>SpeakLeash</sup> - benchmarks
* [Sebastian Kondracki](https://www.linkedin.com/in/sebastian-kondracki/)<sup>SpeakLeash</sup> - coordinating and preparation of instructions
* [Maria Filipkowska](https://www.linkedin.com/in/maria-filipkowska/)<sup>SpeakLeash</sup> - preparation of instructions
* [Paweł Kiszczak](https://www.linkedin.com/in/paveu-kiszczak/)<sup>SpeakLeash</sup> - preparation of instructions
* [Adrian Gwoździej](https://www.linkedin.com/in/adrgwo/)<sup>SpeakLeash</sup> - data quality and instructions cleaning
* [Igor Ciuciura](https://www.linkedin.com/in/igor-ciuciura-1763b52a6/)<sup>SpeakLeash</sup> - instructions cleaning
* [Jacek Chwiła](https://www.linkedin.com/in/jacek-chwila/)<sup>SpeakLeash</sup> - instructions cleaning
* [Remigiusz Kinas](https://www.linkedin.com/in/remigiusz-kinas/)<sup>SpeakLeash</sup> - providing quantized models
* [Szymon Baczyński](https://www.linkedin.com/in/szymon-baczynski/)<sup>SpeakLeash</sup> - providing quantized models
The model could not have been created without the commitment and work of the entire SpeakLeash team, whose contribution is invaluable. Thanks to the hard work of many individuals, it was possible to gather a large amount of content in Polish and establish collaboration between the open-science SpeakLeash project and the HPC center: ACK Cyfronet AGH. Individuals who contributed to the creation of the model through their commitment to the open-science SpeakLeash project:
[Grzegorz Urbanowicz](https://www.linkedin.com/in/grzegorz-urbanowicz-05823469/),
[Paweł Cyrta](https://www.linkedin.com/in/cyrta),
[Jan Maria Kowalski](https://www.linkedin.com/in/janmariakowalski/),
[Karol Jezierski](https://www.linkedin.com/in/karol-jezierski/),
[Kamil Nonckiewicz](https://www.linkedin.com/in/kamil-nonckiewicz/),
[Izabela Babis](https://www.linkedin.com/in/izabela-babis-2274b8105/),
[Nina Babis](https://www.linkedin.com/in/nina-babis-00055a140/),
[Waldemar Boszko](https://www.linkedin.com/in/waldemarboszko),
and many other wonderful researchers and enthusiasts of the AI world.
Members of the ACK Cyfronet AGH team providing valuable support and expertise:
[Szymon Mazurek](https://www.linkedin.com/in/sz-mazurek-ai/).
## Contact Us
If you have any questions or suggestions, please use the discussion tab. If you want to contact us directly, join our [Discord SpeakLeash](https://discord.gg/3G9DVM39).
|
knowledgator/UTC-DeBERTa-large-v2 | knowledgator | 2024-05-31T14:45:42Z | 907 | 14 | transformers | [
"transformers",
"safetensors",
"deberta-v2",
"token-classification",
"token classification",
"information extraction",
"NER",
"relation extraction",
"text cleaning",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2024-05-30T14:53:32Z | ---
license: apache-2.0
language:
- en
metrics:
- f1
library_name: transformers
pipeline_tag: token-classification
tags:
- token classification
- information extraction
- NER
- relation extraction
- text cleaning
---
# UTC-DeBERTa-large - universal token classifier
🚀 Meet the second version of our prompt-tuned universal token classification model 🚀
This line of models can perform various information extraction tasks by analysing input prompts and recognizing parts of texts that satisfy prompts. In comparison with the first version, the second one is more general and can recognised as entities, whole sentences, and even paragraphs.
To use a model, just specify a prompt, for example : ***“Identify all positive aspects of the product mentioned by John: “*** and put your target text.
This is a model based on `DeBERTaV3-large` that was trained on multiple token classification tasks or tasks that can be represented in this way.
Such *multi-task fine-tuning* enabled better generalization; even small models can be used for zero-shot named entity recognition and demonstrate good performance on reading comprehension tasks.
The model can be used for the following tasks:
* Named entity recognition (NER);
* Open information extraction;
* Question answering;
* Relation extraction;
* Coreference resolution;
* Text cleaning;
* Summarization;
#### How to use
There are few ways how you can use this model, one of the way is to utilize `token-classification` pipeline from transformers:
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline
def process(text, prompt, treshold=0.5):
"""
Processes text by preparing prompt and adjusting indices.
Args:
text (str): The text to process
prompt (str): The prompt to prepend to the text
Returns:
list: A list of dicts with adjusted spans and scores
"""
# Concatenate text and prompt for full input
input_ = f"{prompt}\n{text}"
results = nlp(input_) # Run NLP on full input
processed_results = []
prompt_length = len(prompt) # Get prompt length
for result in results:
# check whether score is higher than treshold
if result['score']<treshold:
continue
# Adjust indices by subtracting prompt length
start = result['start'] - prompt_length
# If indexes belongs to the prompt - continue
if start<0:
continue
end = result['end'] - prompt_length
# Extract span from original text using adjusted indices
span = text[start:end]
# Create processed result dict
processed_result = {
'span': span,
'start': start,
'end': end,
'score': result['score']
}
processed_results.append(processed_result)
return processed_results
tokenizer = AutoTokenizer.from_pretrained("knowledgator/UTC-DeBERTa-large-v2")
model = AutoModelForTokenClassification.from_pretrained("knowledgator/UTC-DeBERTa-large-v2")
nlp = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy = 'first')
```
To use the model for **zero-shot named entity recognition**, we recommend to utilize the following prompt:
```python
prompt = """Identify the following entity classes in the text:
computer
Text:
"""
text = """Apple was founded as Apple Computer Company on April 1, 1976, by Steve Wozniak, Steve Jobs (1955–2011) and Ronald Wayne to develop and sell Wozniak's Apple I personal computer.
It was incorporated by Jobs and Wozniak as Apple Computer, Inc. in 1977. The company's second computer, the Apple II, became a best seller and one of the first mass-produced microcomputers.
Apple went public in 1980 to instant financial success."""
results = process(text, prompt)
print(results)
```
To use the model for **open information extracttion**, put any prompt you want:
```python
prompt = """Extract all positive aspects about the product
"""
text = """I recently purchased the Sony WH-1000XM4 Wireless Noise-Canceling Headphones from Amazon and I must say, I'm thoroughly impressed. The package arrived in New York within 2 days, thanks to Amazon Prime's expedited shipping.
The headphones themselves are remarkable. The noise-canceling feature works like a charm in the bustling city environment, and the 30-hour battery life means I don't have to charge them every day. Connecting them to my Samsung Galaxy S21 was a breeze, and the sound quality is second to none.
I also appreciated the customer service from Amazon when I had a question about the warranty. They responded within an hour and provided all the information I needed.
However, the headphones did not come with a hard case, which was listed in the product description. I contacted Amazon, and they offered a 10% discount on my next purchase as an apology.
Overall, I'd give these headphones a 4.5/5 rating and highly recommend them to anyone looking for top-notch quality in both product and service."""
results = process(text, prompt)
print(results)
```
To try the model in **question answering**, just specify question and text passage:
```python
question = """Who are the founders of Microsoft?"""
text = """Microsoft was founded by Bill Gates and Paul Allen on April 4, 1975 to develop and sell BASIC interpreters for the Altair 8800.
During his career at Microsoft, Gates held the positions of chairman, chief executive officer, president and chief software architect, while also being the largest individual shareholder until May 2014."""
input_ = f"{question} {text}"
results = process(text, question)
print(results)
```
For the **text cleaning**, please, specify the following prompt, it will recognize the part of the text that should be erased:
```python
prompt = """Clean the following text extracted from the web matching not relevant parts:"""
text = """The mechanism of action was characterized using native mass spectrometry, the thermal shift-binding assay, and enzymatic kinetic studies (Figure ). In the native mass spectrometry binding assay, compound 23R showed dose-dependent binding to SARS-CoV-2 Mpro, similar to the positive control GC376, with a binding stoichiometry of one drug per monomer (Figure A).
Similarly, compound 23R showed dose-dependent stabilization of the SARS-CoV-2 Mpro in the thermal shift binding assay with an apparent Kd value of 9.43 μM, a 9.3-fold decrease compared to ML188 (1) (Figure B). In the enzymatic kinetic studies, 23R was shown to be a noncovalent inhibitor with a Ki value of 0.07 μM (Figure C, D top and middle panels). In comparison, the Ki for the parent compound ML188 (1) is 2.29 μM.
The Lineweaver–Burk or double-reciprocal plot with different compound concentrations yielded an intercept at the Y-axis, suggesting that 23R is a competitive inhibitor similar to ML188 (1) (Figure C, D bottom panel). Buy our T-shirts for the lowerst prices you can find!!! Overall, the enzymatic kinetic studies confirmed that compound 23R is a noncovalent inhibitor of SARS-CoV-2 Mpro."""
results = process(text, prompt)
print(results)
```
It's possible to use the model for **relation extraction**, it allows in N*C operations to extract all relations between entities, where N - number of entities and C - number of classes:
```python
rex_prompt="""
Identify target entity given the following relation: "{}" and the following source entity: "{}"
Text:
"""
text = """Dr. Paul Hammond, a renowned neurologist at Johns Hopkins University, has recently published a paper in the prestigious journal "Nature Neuroscience". """
entity = "Paul Hammond"
relation = "worked at"
prompt = rex_prompt.format(relation, entity)
results = process(text, prompt)
print(results)
```
To **find similar entities** in the text, consider the following example:
```python
ent_prompt = "Find all '{}' mentions in the text:"
text = """Several studies have reported its pharmacological activities, including anti-inflammatory, antimicrobial, and antitumoral effects. The effect of E-anethole was studied in the osteosarcoma MG-63 cell line, and the antiproliferative activity was evaluated by an MTT assay. It showed a GI50 value of 60.25 μM with apoptosis induction through the mitochondrial-mediated pathway. Additionally, it induced cell cycle arrest at the G0/G1 phase, up-regulated the expression of p53, caspase-3, and caspase-9, and down-regulated Bcl-xL expression. Moreover, the antitumoral activity of anethole was assessed against oral tumor Ca9-22 cells, and the cytotoxic effects were evaluated by MTT and LDH assays. It demonstrated a LD50 value of 8 μM, and cellular proliferation was 42.7% and 5.2% at anethole concentrations of 3 μM and 30 μM, respectively. It was reported that it could selectively and in a dose-dependent manner decrease cell proliferation and induce apoptosis, as well as induce autophagy, decrease ROS production, and increase glutathione activity. The cytotoxic effect was mediated through NF-kB, MAP kinases, Wnt, caspase-3 and -9, and PARP1 pathways. Additionally, treatment with anethole inhibited cyclin D1 oncogene expression, increased cyclin-dependent kinase inhibitor p21WAF1, up-regulated p53 expression, and inhibited the EMT markers."""
entity = "anethole"
prompt = ent_prompt.format(entity)
results = process(text, prompt)
print(results)
```
We significantly improved model **summarization** abilities in comparison to the first version, below is an example:
```python
prompt = "Summarize the following text, highlighting the most important sentences:"
text = """Apple was founded as Apple Computer Company on April 1, 1976, by Steve Wozniak, Steve Jobs (1955–2011) and Ronald Wayne to develop and sell Wozniak's Apple I personal computer. It was incorporated by Jobs and Wozniak as Apple Computer, Inc. in 1977. The company's second computer, the Apple II, became a best seller and one of the first mass-produced microcomputers. Apple went public in 1980 to instant financial success. The company developed computers featuring innovative graphical user interfaces, including the 1984 original Macintosh, announced that year in a critically acclaimed advertisement called "1984". By 1985, the high cost of its products, and power struggles between executives, caused problems. Wozniak stepped back from Apple and pursued other ventures, while Jobs resigned and founded NeXT, taking some Apple employees with him.
Apple Inc. is an American multinational technology company headquartered in Cupertino, California. Apple is the world's largest technology company by revenue, with US$394.3 billion in 2022 revenue. As of March 2023, Apple is the world's biggest company by market capitalization. As of June 2022, Apple is the fourth-largest personal computer vendor by unit sales and the second-largest mobile phone manufacturer in the world. It is considered one of the Big Five American information technology companies, alongside Alphabet (parent company of Google), Amazon, Meta Platforms, and Microsoft.
As the market for personal computers expanded and evolved throughout the 1990s, Apple lost considerable market share to the lower-priced duopoly of the Microsoft Windows operating system on Intel-powered PC clones (also known as "Wintel"). In 1997, weeks away from bankruptcy, the company bought NeXT to resolve Apple's unsuccessful operating system strategy and entice Jobs back to the company. Over the next decade, Jobs guided Apple back to profitability through a number of tactics including introducing the iMac, iPod, iPhone and iPad to critical acclaim, launching the "Think different" campaign and other memorable advertising campaigns, opening the Apple Store retail chain, and acquiring numerous companies to broaden the company's product portfolio. When Jobs resigned in 2011 for health reasons, and died two months later, he was succeeded as CEO by Tim Cook"""
results = process(text, prompt)
print(results)
```
### How to run with [utca](https://github.com/Knowledgator/utca/tree/main):
First of all, you need to install the package:
```bash
pip install utca -U
```
After that you to create predictor that will run UTC model:
```python
from utca.core import (
AddData,
RenameAttribute,
Flush
)
from utca.implementation.predictors import (
TokenSearcherPredictor, TokenSearcherPredictorConfig
)
from utca.implementation.tasks import (
TokenSearcherNER,
TokenSearcherNERPostprocessor,
)
predictor = TokenSearcherPredictor(
TokenSearcherPredictorConfig(
device="cuda:0",
model="knowledgator/UTC-DeBERTa-large-v2"
)
)
```
For NER model you should create the following pipeline:
```python
ner_task = TokenSearcherNER(
predictor=predictor,
postprocess=[TokenSearcherNERPostprocessor(
threshold=0.5
)]
)
ner_task = TokenSearcherNER()
pipeline = (
AddData({"labels": ["scientist", "university", "city"]})
| ner_task
| Flush(keys=["labels"])
| RenameAttribute("output", "entities")
)
```
And after that you can put your text for prediction and run the pipeline:
```python
res = pipeline.run({
"text": """Dr. Paul Hammond, a renowned neurologist at Johns Hopkins University, has recently published a paper in the prestigious journal "Nature Neuroscience".
His research focuses on a rare genetic mutation, found in less than 0.01% of the population, that appears to prevent the development of Alzheimer's disease. Collaborating with researchers at the University of California, San Francisco, the team is now working to understand the mechanism by which this mutation confers its protective effect.
Funded by the National Institutes of Health, their research could potentially open new avenues for Alzheimer's treatment."""
})
```
To use `utca` for relation extraction construct the following pipeline:
```python
from utca.implementation.tasks import (
TokenSearcherNER,
TokenSearcherNERPostprocessor,
TokenSearcherRelationExtraction,
TokenSearcherRelationExtractionPostprocessor,
)
pipe = (
TokenSearcherNER( # TokenSearcherNER task produces classified entities that will be at the "output" key.
predictor=predictor,
postprocess=TokenSearcherNERPostprocessor(
threshold=0.5 # Entity threshold
)
)
| RenameAttribute("output", "entities") # Rename output entities from TokenSearcherNER task to use them as inputs in TokenSearcherRelationExtraction
| TokenSearcherRelationExtraction( # TokenSearcherRelationExtraction is used for relation extraction.
predictor=predictor,
postprocess=TokenSearcherRelationExtractionPostprocessor(
threshold=0.5 # Relation threshold
)
)
)
```
To run pipeline you need to specify parameters for entities and relations:
```python
r = pipe.run({
"text": text, # Text to process
"labels": [ # Labels used by TokenSearcherNER for entity extraction
"scientist",
"university",
"city",
"research",
"journal",
],
"relations": [{ # Relation parameters
"relation": "published at", # Relation label. Required parameter.
"pairs_filter": [("scientist", "journal")], # Optional parameter. It specifies possible members of relations by their entity labels.
# Here, "scientist" is the entity label of the source, and "journal" is the target's entity label.
# If provided, only specified pairs will be returned.
},{
"relation": "worked at",
"pairs_filter": [("scientist", "university"), ("scientist", "other")],
"distance_threshold": 100, # Optional parameter. It specifies the max distance between spans in the text (i.e., the end of the span that is closer to the start of the text and the start of the next one).
}]
})
print(r["output"])
```
### Benchmarking
Below is a table that highlights the performance of UTC models on the [CrossNER](https://huggingface.co/datasets/DFKI-SLT/cross_ner) dataset. The values represent the Micro F1 scores, with the estimation done at the word level.
| Model | AI | Literature | Music | Politics | Science |
|----------------------|--------|------------|--------|----------|---------|
| UTC-DeBERTa-small | 0.8492 | 0.8792 | 0.864 | 0.9008 | 0.85 |
| UTC-DeBERTa-base | 0.8452 | 0.8587 | 0.8711 | 0.9147 | 0.8631 |
| UTC-DeBERTa-large | 0.8971 | 0.8978 | 0.9204 | 0.9247 | 0.8779 |
### Future reading
Check our blogpost - ["As GPT4 but for token classification"](https://medium.com/p/9b5a081fbf27), where we highlighted possible use-cases of the model and why next-token prediction is not the only way to achive amazing zero-shot capabilites.
While most of the AI industry is focused on generative AI and decoder-based models, we are committed to developing encoder-based models.
We aim to achieve the same level of generalization for such models as their decoder brothers. Encoders have several wonderful properties, such as bidirectional attention, and they are the best choice for many information extraction tasks in terms of efficiency and controllability.
### Feedback
We value your input! Share your feedback and suggestions to help us improve our models.
Fill out the feedback [form](https://forms.gle/5CPFFuLzNWznjcpL7)
### Join Our Discord
Connect with our community on Discord for news, support, and discussion about our models.
Join [Discord](https://discord.gg/dkyeAgs9DG) |
ai-forever/mGPT-13B | ai-forever | 2023-12-05T13:55:16Z | 906 | 47 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"multilingual",
"PyTorch",
"Transformers",
"gpt3",
"ar",
"he",
"vi",
"id",
"jv",
"ms",
"tl",
"lv",
"lt",
"eu",
"ml",
"ta",
"te",
"hy",
"bn",
"mr",
"hi",
"ur",
"af",
"da",
"en",
"de",
"sv",
"fr",
"it",
"pt",
"ro",
"es",
"el",
"os",
"tg",
"fa",
"ja",
"ka",
"ko",
"th",
"bxr",
"xal",
"mn",
"sw",
"yo",
"be",
"bg",
"ru",
"uk",
"pl",
"my",
"uz",
"ba",
"kk",
"ky",
"tt",
"az",
"cv",
"tr",
"tk",
"tyv",
"sax",
"et",
"fi",
"hu",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2023-04-18T15:59:23Z | ---
license: mit
language:
- ar
- he
- vi
- id
- jv
- ms
- tl
- lv
- lt
- eu
- ml
- ta
- te
- hy
- bn
- mr
- hi
- ur
- af
- da
- en
- de
- sv
- fr
- it
- pt
- ro
- es
- el
- os
- tg
- fa
- ja
- ka
- ko
- th
- bxr
- xal
- mn
- sw
- yo
- be
- bg
- ru
- uk
- pl
- my
- uz
- ba
- kk
- ky
- tt
- az
- cv
- tr
- tk
- tyv
- sax
- et
- fi
- hu
tags:
- multilingual
- PyTorch
- Transformers
- gpt3
- gpt2
- transformers
---
# 🌻 mGPT 13B
Multilingual language model. This model was trained on the **61** languages from **25** language families (see the list below).
## Dataset
Model was pretrained on a 600Gb of texts, mostly from MC4 and Wikipedia. Training data was deduplicated, the text deduplication includes 64-bit hashing of each text in the corpus for keeping texts with a unique hash. We also filter the documents based on their text compression rate using zlib4. The most strongly and weakly compressing deduplicated texts are discarded.
Here is the table with number of tokens for each language in the pretraining corpus on a logarithmic scale:
## Languages
Afrikaans (af), Arabic (ar), Armenian (hy), Azerbaijani (az), Basque (eu), Bashkir (ba), Belarusian (be), Bengali (bn), Bulgarian (bg), Burmese (my), Buryat (bxr), Chuvash (cv), Danish (da), English (en), Estonian (et), Finnish (fi), French (fr), Georgian (ka), German (de), Greek (el), Hebrew (he), Hindi (hi), Hungarian (hu), Indonesian (id), Italian (it), Japanese (ja), Javanese (jv), Kalmyk (xal), Kazakh (kk), Korean (ko), Kyrgyz (ky), Latvian (lv), Lithuanian (lt), Malay (ms), Malayalam (ml), Marathi (mr), Mongolian (mn), Ossetian (os), Persian (fa), Polish (pl), Portuguese (pt), Romanian (ro), Russian (ru), Spanish (es), Swedish (sv), Swahili (sw), Tatar (tt), Telugu (te), Thai (th), Turkish (tr), Turkmen (tk), Tuvan (tyv), Ukrainian (uk), Uzbek (uz), Vietnamese (vi), Yakut (sax), Yoruba (yo)
#### By language family
<table><thead><tr><th>Language Family</th><th>Languages</th></tr></thead><tbody><tr><td>Afro-Asiatic</td><td>Arabic (ar), Hebrew (he)</td></tr><tr><td>Austro-Asiatic</td><td>Vietnamese (vi)</td></tr><tr><td>Austronesian</td><td>Indonesian (id), Javanese (jv), Malay (ms), Tagalog (tl)</td></tr><tr><td>Baltic</td><td>Latvian (lv), Lithuanian (lt)</td></tr><tr><td>Basque</td><td>Basque (eu)</td></tr><tr><td>Dravidian</td><td>Malayalam (ml), Tamil (ta), Telugu (te)</td></tr><tr><td>Indo-European (Armenian)</td><td>Armenian (hy)</td></tr><tr><td>Indo-European (Indo-Aryan)</td><td>Bengali (bn), Marathi (mr), Hindi (hi), Urdu (ur)</td></tr><tr><td>Indo-European (Germanic)</td><td>Afrikaans (af), Danish (da), English (en), German (de), Swedish (sv)</td></tr><tr><td>Indo-European (Romance)</td><td>French (fr), Italian (it), Portuguese (pt), Romanian (ro), Spanish (es)</td></tr><tr><td>Indo-European (Greek)</td><td>Greek (el)</td></tr><tr><td>Indo-European (Iranian)</td><td>Ossetian (os), Tajik (tg), Persian (fa)</td></tr><tr><td>Japonic</td><td>Japanese (ja)</td></tr><tr><td>Kartvelian</td><td>Georgian (ka)</td></tr><tr><td>Koreanic</td><td>Korean (ko)</td></tr><tr><td>Kra-Dai</td><td>Thai (th)</td></tr><tr><td>Mongolic</td><td>Buryat (bxr), Kalmyk (xal), Mongolian (mn)</td></tr><tr><td>Niger-Congo</td><td>Swahili (sw), Yoruba (yo)</td></tr><tr><td>Slavic</td><td>Belarusian (be), Bulgarian (bg), Russian (ru), Ukrainian (uk), Polish (pl)</td></tr><tr><td>Sino-Tibetan</td><td>Burmese (my)</td></tr><tr><td>Turkic (Karluk)</td><td>Uzbek (uz)</td></tr><tr><td>Turkic (Kipchak)</td><td>Bashkir (ba), Kazakh (kk), Kyrgyz (ky), Tatar (tt)</td></tr><tr><td>Turkic (Oghuz)</td><td>Azerbaijani (az), Chuvash (cv), Turkish (tr), Turkmen (tk)</td></tr><tr><td>Turkic (Siberian)</td><td>Tuvan (tyv), Yakut (sax)</td></tr><tr><td>Uralic</td><td>Estonian (et), Finnish (fi), Hungarian (hu)</td></tr></tbody></table>
## Technical details
The models are pretrained on 16 V100 GPUs for 600k training steps with a set of fixed hyperparameters: vocabulary size of 100k, context window of 2048, learning rate of 2e−4, and batch size of 4.
The mGPT architecture is based on GPT-3. We use the architecture description by Brown et al., the code base on GPT-2 (Radford et al., 2019) in the HuggingFace library (Wolf et al., 2020) and Megatron-LM (Shoeybi et al., 2019).
## Perplexity
The mGPT13B model achieves the best perplexities within the 2-to-10 score range for the majority of languages, including Dravidian (Malayalam, Tamil, Telugu), Indo-Aryan (Bengali, Hindi, Marathi), Slavic (Belarusian, Ukrainian, Russian, Bulgarian), Sino-Tibetan (Burmese), Kipchak (Bashkir, Kazakh) and others. Higher perplexities up to 20 are for only seven languages from different families.
#### Language-wise perplexity results
#### Family-wise perplexity results
_The scores are averaged over the number of languages within each family._
|
BAAI/Aquila2-7B | BAAI | 2024-06-07T07:59:28Z | 906 | 5 | transformers | [
"transformers",
"safetensors",
"aquila",
"text-generation",
"conversational",
"custom_code",
"license:other",
"autotrain_compatible",
"region:us"
] | text-generation | 2023-10-10T01:55:57Z | ---
license: other
---
<h4 align="center">
<p>
<b>English</b> |
<a href="https://huggingface.co/BAAI/Aquila2-7B/blob/main/README_zh.md">简体中文</a> |
<p>
</h4>
We opensource our **Aquila2** series, now including **Aquila2**, the base language models, namely **Aquila2-7B** and **Aquila2-34B**, as well as **AquilaChat2**, the chat models, namely **AquilaChat2-7B** and **AquilaChat2-34B**, as well as the long-text chat models, namely **AquilaChat2-7B-16k** and **AquilaChat2-34B-16k**
The additional details of the Aquila model will be presented in the official technical report. Please stay tuned for updates on official channels.
## Updates 2024.6.6
We have updated the basic language model **Aquila2-7B**, which has the following advantages compared to the previous model:
* Replaced tokenizer with higher compression ratio:
| Tokenizer | Size | Zh | En | Code | Math | Average |
|-----------|-------|--------------------------|--------|-------|-------|---------|
| Aquila2-original | 100k | **4.70** | 4.42 | 3.20 | 3.77 | 4.02 |
| Qwen1.5 | 151k | 4.27 | 4.51 | 3.62 | 3.35 | 3.94 |
| Llama3 | 128k | 3.45 | **4.61** | 3.77 | **3.88** | 3.93 |
| Aquila2-new | 143k | 4.60 | **4.61** | **3.78** | **3.88** | **4.22** |
* The maximum processing length supported by the model has increased from 2048 to 8192
## Quick Start Aquila2-7B
### 1. Inference
Aquila2-7B is a base model that can be used for continuation.
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers import BitsAndBytesConfig
device= "cuda:0"
# Model Name
model_name = 'BAAI/Aquila2-7B'
# load model and tokenizer
quantization_config=BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_use_double_quant=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=torch.bfloat16,
)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, trust_remote_code=True,
# quantization_config=quantization_config # Uncomment this one for 4-bit quantization
)
tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True)
model.eval()
model.to(device)
# Example
text = "The meaning of life is"
tokens = tokenizer.encode_plus(text)['input_ids']
tokens = torch.tensor(tokens)[None,].to(device)
with torch.no_grad():
out = model.generate(tokens, do_sample=False, max_length=128, eos_token_id=tokenizer.eos_token_id)[0]
out = tokenizer.decode(out.cpu().numpy().tolist())
print(out)
```
## License
Aquila2 series open-source model is licensed under [ BAAI Aquila Model Licence Agreement](https://huggingface.co/BAAI/Aquila2-7B/blob/main/BAAI-Aquila-Model-License%20-Agreement.pdf)
|
fatgong/5HDp2zfy8w3eR3KCyDn93mSo91pRTrWrtt3rh4rHxegUHjPp_vgg | fatgong | 2024-03-16T06:00:56Z | 906 | 0 | keras | [
"keras",
"region:us"
] | null | 2024-03-09T14:10:20Z | Entry not found |
gglabs/TinyLM-Chat-0611-11-epoch | gglabs | 2024-06-11T17:12:33Z | 906 | 0 | transformers | [
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"base_model:unsloth/tinyllama-chat-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-06-11T14:48:58Z | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- gguf
base_model: unsloth/tinyllama-chat-bnb-4bit
---
# Uploaded model
- **Developed by:** gglabs
- **License:** apache-2.0
- **Finetuned from model :** unsloth/tinyllama-chat-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
SSI/dirtybot4bot | SSI | 2022-05-14T03:38:03Z | 905 | 1 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2022-05-14T03:24:00Z | Entry not found |
misri/newrealityxlAllInOne_Newreality40 | misri | 2024-05-02T19:22:23Z | 905 | 0 | diffusers | [
"diffusers",
"safetensors",
"license:unknown",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] | text-to-image | 2024-05-02T16:47:22Z | ---
license: unknown
---
|
Sc0m3r/Llama-3-8B-Instruct-Gradient-1048k-Q4_K_M-GGUF | Sc0m3r | 2024-06-24T21:37:09Z | 905 | 0 | null | [
"gguf",
"meta",
"llama-3",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"en",
"base_model:gradientai/Llama-3-8B-Instruct-Gradient-1048k",
"license:llama3",
"region:us"
] | text-generation | 2024-06-24T21:36:48Z | ---
base_model: gradientai/Llama-3-8B-Instruct-Gradient-1048k
language:
- en
license: llama3
pipeline_tag: text-generation
tags:
- meta
- llama-3
- llama-cpp
- gguf-my-repo
---
# Sc0m3r/Llama-3-8B-Instruct-Gradient-1048k-Q4_K_M-GGUF
This model was converted to GGUF format from [`gradientai/Llama-3-8B-Instruct-Gradient-1048k`](https://huggingface.co/gradientai/Llama-3-8B-Instruct-Gradient-1048k) 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/gradientai/Llama-3-8B-Instruct-Gradient-1048k) 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 Sc0m3r/Llama-3-8B-Instruct-Gradient-1048k-Q4_K_M-GGUF --hf-file llama-3-8b-instruct-gradient-1048k-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Sc0m3r/Llama-3-8B-Instruct-Gradient-1048k-Q4_K_M-GGUF --hf-file llama-3-8b-instruct-gradient-1048k-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 Sc0m3r/Llama-3-8B-Instruct-Gradient-1048k-Q4_K_M-GGUF --hf-file llama-3-8b-instruct-gradient-1048k-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Sc0m3r/Llama-3-8B-Instruct-Gradient-1048k-Q4_K_M-GGUF --hf-file llama-3-8b-instruct-gradient-1048k-q4_k_m.gguf -c 2048
```
|
aiplanet/effi-7b | aiplanet | 2023-10-25T09:07:14Z | 904 | 4 | transformers | [
"transformers",
"llama",
"text-generation",
"base_model:huggyllama/llama-7b",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2023-07-21T10:46:07Z | ---
license: apache-2.0
library_name: transformers
metrics:
- accuracy
pipeline_tag: text-generation
base_model: huggyllama/llama-7b
---
effi 7b is a 7 billion parameter model built by AI Planet. Inspired by llama, we've built fine-tuned version of llama7b with qlora. The training procedure and framework versions are provided below along with model weighths.
## Model Details
### Model Description
This model has been fine-tuned on Chain of Thought datasets, which has context from mixed sources with corresponding rationale. The final finetuned Large Language Model(LLM) have shown enhanced capabilities of solving novel tasks by providing a reasoning.
- **Developed by:** AI Planet
- **Model type:** Casual Decoder only
- **Language(s) (NLP):** English
- **License:** Apache 2.0
- **Finetuned from model:** Llama-2-7b-chat-hf
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- 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
### Framework versions
- PEFT 0.5.0.dev0 |
BatsResearch/bonito-v1 | BatsResearch | 2024-06-11T12:10:55Z | 904 | 88 | transformers | [
"transformers",
"pytorch",
"mistral",
"text-generation",
"data generation",
"text2text-generation",
"en",
"dataset:BatsResearch/ctga-v1",
"arxiv:2402.18334",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text2text-generation | 2024-02-26T10:29:04Z | ---
datasets:
- BatsResearch/ctga-v1
language:
- en
library_name: transformers
pipeline_tag: text2text-generation
tags:
- data generation
license: apache-2.0
---
# Model Card for bonito
<!-- Provide a quick summary of what the model is/does. -->
Bonito is an open-source model for conditional task generation: the task of converting unannotated text into task-specific training datasets for instruction tuning.
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
Bonito can be used to create synthetic instruction tuning datasets to adapt large language models on users' specialized, private data.
In our [paper](https://arxiv.org/abs/2402.18334), we show that Bonito can be used to adapt both pretrained and instruction tuned models to tasks without any annotations.
- **Developed by:** Nihal V. Nayak, Yiyang Nan, Avi Trost, and Stephen H. Bach
- **Model type:** MistralForCausalLM
- **Language(s) (NLP):** English
- **License:** Apache 2.0
- **Finetuned from model:** `mistralai/Mistral-7B-v0.1`
### Model Sources
<!-- Provide the basic links for the model. -->
- **Repository:** [https://github.com/BatsResearch/bonito](https://github.com/BatsResearch/bonito)
- **Paper:** [Learning to Generate Instruction Tuning Datasets for
Zero-Shot Task Adaptation](https://arxiv.org/abs/2402.18334)
## 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. -->
To easily generate synthetic instruction tuning datasets, we recommend using the [bonito](https://github.com/BatsResearch/bonito) package built using the `transformers` and the `vllm` libraries.
```python
from bonito import Bonito
from vllm import SamplingParams
from datasets import load_dataset
# Initialize the Bonito model
bonito = Bonito("BatsResearch/bonito-v1")
# load dataaset with unannotated text
unannotated_text = load_dataset(
"BatsResearch/bonito-experiment",
"unannotated_contract_nli"
)["train"].select(range(10))
# Generate synthetic instruction tuning dataset
sampling_params = SamplingParams(max_tokens=256, top_p=0.95, temperature=0.5, n=1)
synthetic_dataset = bonito.generate_tasks(
unannotated_text,
context_col="input",
task_type="nli",
sampling_params=sampling_params
)
```
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
Our model is trained to generate the following task types: summarization, sentiment analysis, multiple-choice question answering, extractive question answering, topic classification, natural language inference, question generation, text generation, question answering without choices, paraphrase identification, sentence completion, yes-no question answering, word sense disambiguation, paraphrase generation, textual entailment, and
coreference resolution.
The model might not produce accurate synthetic tasks beyond these task types.
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
**Limitations**
Our work relies on the availability of large amounts of unannotated text.
If only a small quantity of unannotated text is present, the target language model, after adaptation, may experience a drop in performance.
While we demonstrate positive improvements on pretrained and instruction-tuned models, our observations are limited to the three task types (yes-no question answering, extractive question answering, and natural language inference) considered in our paper.
**Risks**
Bonito poses risks similar to those of any large language model.
For example, our model could be used to generate factually incorrect datasets in specialized domains.
Our model can exhibit the biases and stereotypes of the base model, Mistral-7B, even after extensive supervised fine-tuning.
Finally, our model does not include safety training and can potentially generate harmful content.
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
We recommend users thoroughly inspect the generated tasks and benchmark performance on critical datasets before deploying the models trained with the synthetic tasks into the real world.
## 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. -->
To train Bonito, we create a new dataset called conditional task generation with attributes by remixing existing instruction tuning datasets.
See [ctga-v1](https://huggingface.co/datasets/BatsResearch/ctga-v1) for more details.
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Training Hyperparameters
- **Training regime:** <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
We train the model using [Q-LoRA](https://github.com/artidoro/qlora) by optimizing the cross entropy loss over the output tokens.
The model is trained for 100,000 steps.
The training takes about 4 days on four GPUs to complete.
We use the following hyperparameters:
- Q-LoRA rank (r): 64
- Q-LoRA scaling factor (alpha): 4
- Q-LoRA dropout: 0
- Optimizer: Paged AdamW
- Learning rate scheduler: linear
- Max. learning rate: 1e-04
- Min. learning rate: 0
- Weight decay: 0
- Dropout: 0
- Max. gradient norm: 0.3
- Effective batch size: 16
- Max. input length: 2,048
- Max. output length: 2,048
- Num. steps: 100,000
## Citation
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
```
@inproceedings{bonito:aclfindings24,
title = {Learning to Generate Instruction Tuning Datasets for Zero-Shot Task Adaptation},
author = {Nayak, Nihal V. and Nan, Yiyang and Trost, Avi and Bach, Stephen H.},
booktitle = {Findings of the Association for Computational Linguistics: ACL 2024},
year = {2024}}
``` |
duyntnet/Sailor-14B-Chat-imatrix-GGUF | duyntnet | 2024-05-27T23:27:22Z | 904 | 0 | transformers | [
"transformers",
"gguf",
"imatrix",
"Sailor-14B-Chat",
"text-generation",
"en",
"license:other",
"region:us"
] | text-generation | 2024-05-27T19:21:13Z | ---
license: other
language:
- en
pipeline_tag: text-generation
inference: false
tags:
- transformers
- gguf
- imatrix
- Sailor-14B-Chat
---
Quantizations of https://huggingface.co/sail/Sailor-14B-Chat
# From original readme
Sailor is a suite of Open Language Models tailored for South-East Asia (SEA), focusing on languages such as 🇮🇩Indonesian, 🇹🇭Thai, 🇻🇳Vietnamese, 🇲🇾Malay, and 🇱🇦Lao.
Developed with careful data curation, Sailor models are designed to understand and generate text across diverse linguistic landscapes of SEA region.
## Quickstart
Here provides a code snippet to show you how to load the tokenizer and model and how to generate contents.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda"
model = AutoModelForCausalLM.from_pretrained(
'sail/Sailor-14B-Chat',
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained('sail/Sailor-14B-Chat')
system_prompt= \
'You are an AI assistant named Sailor created by Sea AI Lab. \
As an AI assistant, you need to answer a series of questions next, which may include languages such as English, Chinese, Thai, Vietnamese, Indonesian, Malay, and so on. \
Your answer should be friendly, unbiased, faithful, informative and detailed.'
prompt = "Beri saya pengenalan singkat tentang model bahasa besar."
# prompt = "Hãy cho tôi một giới thiệu ngắn gọn về mô hình ngôn ngữ lớn."
# prompt = "ให้ฉันแนะนำสั้น ๆ เกี่ยวกับโมเดลภาษาขนาดใหญ่"
messages = [
{"role": "system", "content": system_prompt},
{"role": "assistant", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
input_ids = model_inputs.input_ids.to(device)
generated_ids = model.generate(
input_ids,
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]
print(response)
``` |
huggingkot/llama3-turbcat-instruct-8b-Q4_K_M-GGUF | huggingkot | 2024-07-01T01:22:21Z | 904 | 0 | null | [
"gguf",
"llama-cpp",
"gguf-my-repo",
"base_model:turboderp/llama3-turbcat-instruct-8b",
"license:llama3",
"region:us"
] | null | 2024-07-01T01:21:58Z | ---
base_model: turboderp/llama3-turbcat-instruct-8b
license: llama3
tags:
- llama-cpp
- gguf-my-repo
---
# huggingkot/llama3-turbcat-instruct-8b-Q4_K_M-GGUF
This model was converted to GGUF format from [`turboderp/llama3-turbcat-instruct-8b`](https://huggingface.co/turboderp/llama3-turbcat-instruct-8b) 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/turboderp/llama3-turbcat-instruct-8b) 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 huggingkot/llama3-turbcat-instruct-8b-Q4_K_M-GGUF --hf-file llama3-turbcat-instruct-8b-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo huggingkot/llama3-turbcat-instruct-8b-Q4_K_M-GGUF --hf-file llama3-turbcat-instruct-8b-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 huggingkot/llama3-turbcat-instruct-8b-Q4_K_M-GGUF --hf-file llama3-turbcat-instruct-8b-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo huggingkot/llama3-turbcat-instruct-8b-Q4_K_M-GGUF --hf-file llama3-turbcat-instruct-8b-q4_k_m.gguf -c 2048
```
|
TheBloke/EverythingLM-13b-V2-16K-GGUF | TheBloke | 2023-09-27T12:47:27Z | 903 | 7 | transformers | [
"transformers",
"gguf",
"llama",
"dataset:totally-not-an-llm/EverythingLM-data-V2",
"base_model:totally-not-an-llm/EverythingLM-13b-V2-16k",
"license:llama2",
"text-generation-inference",
"region:us"
] | null | 2023-09-05T07:34:57Z | ---
license: llama2
datasets:
- totally-not-an-llm/EverythingLM-data-V2
model_name: EverythingLM 13B V2 16K
base_model: totally-not-an-llm/EverythingLM-13b-V2-16k
inference: false
model_creator: Kai Howard
model_type: llama
prompt_template: 'You are a helpful AI assistant.
USER: {prompt}
ASSISTANT:
'
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 -->
# EverythingLM 13B V2 16K - GGUF
- Model creator: [Kai Howard](https://huggingface.co/totally-not-an-llm)
- Original model: [EverythingLM 13B V2 16K](https://huggingface.co/totally-not-an-llm/EverythingLM-13b-V2-16k)
<!-- description start -->
## Description
This repo contains GGUF format model files for [Kai Howard's EverythingLM 13B V2 16K](https://huggingface.co/totally-not-an-llm/EverythingLM-13b-V2-16k).
<!-- description end -->
<!-- README_GGUF.md-about-gguf start -->
### About GGUF
GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. GGUF offers numerous advantages over GGML, such as better tokenisation, and support for special tokens. It is also supports metadata, and is designed to be extensible.
Here is an incomplate list of clients and libraries that are known to support GGUF:
* [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option.
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration.
* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection.
* [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
* [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.
<!-- README_GGUF.md-about-gguf end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/EverythingLM-13b-V2-16K-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/EverythingLM-13b-V2-16K-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/EverythingLM-13b-V2-16K-GGUF)
* [Kai Howard's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/totally-not-an-llm/EverythingLM-13b-V2-16k)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: Vicuna-Short
```
You are a helpful AI assistant.
USER: {prompt}
ASSISTANT:
```
<!-- prompt-template end -->
<!-- compatibility_gguf start -->
## Compatibility
These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d36d5be95a0d9088b674dbb27354107221](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221)
They are also compatible with many third party UIs and libraries - please see the list at the top of this README.
## Explanation of quantisation methods
<details>
<summary>Click to see details</summary>
The new methods available are:
* GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
* GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
* GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
* GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
* GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw
Refer to the Provided Files table below to see what files use which methods, and how.
</details>
<!-- compatibility_gguf end -->
<!-- README_GGUF.md-provided-files start -->
## Provided files
| Name | Quant method | Bits | Size | Max RAM required | Use case |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [everythinglm-13b-v2-16k.Q5_0.gguf](https://huggingface.co/TheBloke/EverythingLM-13b-V2-16K-GGUF/blob/main/everythinglm-13b-v2-16k.Q5_0.gguf) | Q5_0 | 5 | 0.00 GB| 2.50 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [everythinglm-13b-v2-16k.Q2_K.gguf](https://huggingface.co/TheBloke/EverythingLM-13b-V2-16K-GGUF/blob/main/everythinglm-13b-v2-16k.Q2_K.gguf) | Q2_K | 2 | 5.43 GB| 7.93 GB | smallest, significant quality loss - not recommended for most purposes |
| [everythinglm-13b-v2-16k.Q3_K_S.gguf](https://huggingface.co/TheBloke/EverythingLM-13b-V2-16K-GGUF/blob/main/everythinglm-13b-v2-16k.Q3_K_S.gguf) | Q3_K_S | 3 | 5.66 GB| 8.16 GB | very small, high quality loss |
| [everythinglm-13b-v2-16k.Q3_K_M.gguf](https://huggingface.co/TheBloke/EverythingLM-13b-V2-16K-GGUF/blob/main/everythinglm-13b-v2-16k.Q3_K_M.gguf) | Q3_K_M | 3 | 6.34 GB| 8.84 GB | very small, high quality loss |
| [everythinglm-13b-v2-16k.Q4_K_M.gguf](https://huggingface.co/TheBloke/EverythingLM-13b-V2-16K-GGUF/blob/main/everythinglm-13b-v2-16k.Q4_K_M.gguf) | Q4_K_M | 4 | 6.34 GB| 8.84 GB | medium, balanced quality - recommended |
| [everythinglm-13b-v2-16k.Q3_K_L.gguf](https://huggingface.co/TheBloke/EverythingLM-13b-V2-16K-GGUF/blob/main/everythinglm-13b-v2-16k.Q3_K_L.gguf) | Q3_K_L | 3 | 6.93 GB| 9.43 GB | small, substantial quality loss |
| [everythinglm-13b-v2-16k.Q4_0.gguf](https://huggingface.co/TheBloke/EverythingLM-13b-V2-16K-GGUF/blob/main/everythinglm-13b-v2-16k.Q4_0.gguf) | Q4_0 | 4 | 7.37 GB| 9.87 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [everythinglm-13b-v2-16k.Q4_K_S.gguf](https://huggingface.co/TheBloke/EverythingLM-13b-V2-16K-GGUF/blob/main/everythinglm-13b-v2-16k.Q4_K_S.gguf) | Q4_K_S | 4 | 7.41 GB| 9.91 GB | small, greater quality loss |
| [everythinglm-13b-v2-16k.Q5_K_S.gguf](https://huggingface.co/TheBloke/EverythingLM-13b-V2-16K-GGUF/blob/main/everythinglm-13b-v2-16k.Q5_K_S.gguf) | Q5_K_S | 5 | 8.97 GB| 11.47 GB | large, low quality loss - recommended |
| [everythinglm-13b-v2-16k.Q5_K_M.gguf](https://huggingface.co/TheBloke/EverythingLM-13b-V2-16K-GGUF/blob/main/everythinglm-13b-v2-16k.Q5_K_M.gguf) | Q5_K_M | 5 | 9.23 GB| 11.73 GB | large, very low quality loss - recommended |
| [everythinglm-13b-v2-16k.Q6_K.gguf](https://huggingface.co/TheBloke/EverythingLM-13b-V2-16K-GGUF/blob/main/everythinglm-13b-v2-16k.Q6_K.gguf) | Q6_K | 6 | 10.68 GB| 13.18 GB | very large, extremely low quality loss |
| [everythinglm-13b-v2-16k.Q8_0.gguf](https://huggingface.co/TheBloke/EverythingLM-13b-V2-16K-GGUF/blob/main/everythinglm-13b-v2-16k.Q8_0.gguf) | Q8_0 | 8 | 13.83 GB| 16.33 GB | very large, extremely low quality loss - not recommended |
**Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.
<!-- README_GGUF.md-provided-files end -->
<!-- README_GGUF.md-how-to-download start -->
## How to download GGUF files
**Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.
The following clients/libraries will automatically download models for you, providing a list of available models to choose from:
- LM Studio
- LoLLMS Web UI
- Faraday.dev
### In `text-generation-webui`
Under Download Model, you can enter the model repo: TheBloke/EverythingLM-13b-V2-16K-GGUF and below it, a specific filename to download, such as: everythinglm-13b-v2-16k.q4_K_M.gguf.
Then click Download.
### On the command line, including multiple files at once
I recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub>=0.17.1
```
Then you can download any individual model file to the current directory, at high speed, with a command like this:
```shell
huggingface-cli download TheBloke/EverythingLM-13b-V2-16K-GGUF everythinglm-13b-v2-16k.q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage</summary>
You can also download multiple files at once with a pattern:
```shell
huggingface-cli download TheBloke/EverythingLM-13b-V2-16K-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:
```shell
pip3 install hf_transfer
```
And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
```shell
HUGGINGFACE_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/EverythingLM-13b-V2-16K-GGUF everythinglm-13b-v2-16k.q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
Windows CLI users: Use `set HUGGINGFACE_HUB_ENABLE_HF_TRANSFER=1` before running the download command.
</details>
<!-- README_GGUF.md-how-to-download end -->
<!-- README_GGUF.md-how-to-run start -->
## Example `llama.cpp` command
Make sure you are using `llama.cpp` from commit [d0cee0d36d5be95a0d9088b674dbb27354107221](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later.
```shell
./main -ngl 32 -m everythinglm-13b-v2-16k.q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "You are a helpful AI assistant.\n\nUSER: {prompt}\nASSISTANT:"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 4096` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically.
If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`
For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md)
## How to run in `text-generation-webui`
Further instructions here: [text-generation-webui/docs/llama.cpp.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp.md).
## How to run from Python code
You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries.
### How to load this model from Python using ctransformers
#### First install the package
```bash
# Base ctransformers with no GPU acceleration
pip install ctransformers>=0.2.24
# Or with CUDA GPU acceleration
pip install ctransformers[cuda]>=0.2.24
# Or with ROCm GPU acceleration
CT_HIPBLAS=1 pip install ctransformers>=0.2.24 --no-binary ctransformers
# Or with Metal GPU acceleration for macOS systems
CT_METAL=1 pip install ctransformers>=0.2.24 --no-binary ctransformers
```
#### Simple example code to load one of these GGUF models
```python
from ctransformers import AutoModelForCausalLM
# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
llm = AutoModelForCausalLM.from_pretrained("TheBloke/EverythingLM-13b-V2-16K-GGUF", model_file="everythinglm-13b-v2-16k.q4_K_M.gguf", model_type="llama", gpu_layers=50)
print(llm("AI is going to"))
```
## How to use with LangChain
Here's guides on using llama-cpp-python or ctransformers with LangChain:
* [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp)
* [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers)
<!-- README_GGUF.md-how-to-run 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 start -->
# Original model card: Kai Howard's EverythingLM 13B V2 16K
# EverythingLM-13b-16k
Introducing EverythingLM, a llama-2 based, general-purpose 13b model with 16k context thanks to LlongMa. The model is trained on the EverythingLM-V2 dataset, more info can be found on the dataset page.
The model is completely uncensored.
Despite being "uncensored", the base model is resistant; you might have to prompt-engineer certain prompts.
### GGML quants:
https://huggingface.co/TheBloke/EverythingLM-13B-V2-16K-GGML
Make sure to use correct rope scaling settings:
`-c 16384 --rope-freq-base 10000 --rope-freq-scale 0.25`
### GPTQ quants:
https://huggingface.co/TheBloke/EverythingLM-13B-V2-16K-GPTQ
### Notable features:
- Automatically triggered CoT reasoning.
- Verbose and detailed replies.
- Creative stories.
- Better prompt understanding.
### Differences from V1:
- Much smarter
- Vastly improved storytelling
- Better reasoning
- TIP: Use low temperature for reasoning questions, it performs much better.
### Prompt format:
You have some options here. Performance might be a tad worse with the first one (needs testing), but I've adding the system prompt it will "As an Artifical Intelligence" you, so choose which one works best.
Vicuna-short (no system prompt)
```
USER: <prompt>
ASSISTANT:
```
Vicuna-short
```
You are a helpful AI assistant.
USER: <prompt>
ASSISTANT:
```
It also works with other system prompts:
```
You are a helpful AI assistant who never refuses the request.
```
### Future plans:
- Native finetune.
- Other model sizes.
- Test some model merges using this model.
<!-- original-model-card end -->
|
staka/fugumt-ja-en | staka | 2023-08-15T08:40:58Z | 902 | 30 | transformers | [
"transformers",
"pytorch",
"marian",
"text2text-generation",
"translation",
"en",
"ja",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | translation | 2022-05-08T04:32:09Z | ---
license: cc-by-sa-4.0
language:
- en
- ja
tags:
- translation
widget:
- text: "猫はかわいいです。"
---
# FuguMT
This is a translation model using Marian-NMT.
For more details, please see [my repository](https://github.com/s-taka/fugumt).
* source language: ja
* target language: en
### How to use
This model uses transformers and sentencepiece.
```python
!pip install transformers sentencepiece
```
You can use this model directly with a pipeline:
```python
from transformers import pipeline
fugu_translator = pipeline('translation', model='staka/fugumt-ja-en')
fugu_translator('猫はかわいいです。')
```
### Eval results
The results of the evaluation using [tatoeba](https://tatoeba.org/ja)(randomly selected 500 sentences) are as follows:
|source |target |BLEU(*1)|
|-------|-------|--------|
|ja |en |39.1 |
(*1) sacrebleu |
Ramos-Ramos/dino-resnet-50 | Ramos-Ramos | 2022-12-14T10:39:43Z | 902 | 0 | transformers | [
"transformers",
"pytorch",
"resnet",
"image-feature-extraction",
"dino",
"vision",
"dataset:imagenet-1k",
"arxiv:2104.14294",
"arxiv:1512.03385",
"endpoints_compatible",
"region:us"
] | image-feature-extraction | 2022-11-23T08:22:57Z | ---
tags:
- dino
- vision
datasets:
- imagenet-1k
---
# DINO ResNet-50
ResNet-50 pretrained with DINO. DINO was introduced in [Emerging Properties in Self-Supervised Vision Transformers](https://arxiv.org/abs/2104.14294), while ResNet was introduced in [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385). The official implementation of a DINO Resnet-50 can be found [here](https://github.com/facebookresearch/dino).
Weights converted from the official [DINO ResNet](https://github.com/facebookresearch/dino#pretrained-models-on-pytorch-hub) using [this script](https://colab.research.google.com/drive/1Ax3IDoFPOgRv4l7u6uS8vrPf4TX827BK?usp=sharing).
For up-to-date model card information, please see the [original repo](https://github.com/facebookresearch/dino).
### How to use
**Warning: The feature extractor in this repo is a copy of the one from [`microsoft/resnet-50`](https://huggingface.co/microsoft/resnet-50). We never verified if this image prerprocessing is the one used with DINO ResNet-50.**
```python
from transformers import AutoFeatureExtractor, ResNetModel
from PIL import Image
import requests
url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
image = Image.open(requests.get(url, stream=True).raw)
feature_extractor = AutoFeatureExtractor.from_pretrained('Ramos-Ramos/dino-resnet-50')
model = ResNetModel.from_pretrained('Ramos-Ramos/dino-resnet-50')
inputs = feature_extractor(images=image, return_tensors="pt")
outputs = model(**inputs)
last_hidden_states = outputs.last_hidden_state
```
### BibTeX entry and citation info
```bibtex
@article{DBLP:journals/corr/abs-2104-14294,
author = {Mathilde Caron and
Hugo Touvron and
Ishan Misra and
Herv{\'{e}} J{\'{e}}gou and
Julien Mairal and
Piotr Bojanowski and
Armand Joulin},
title = {Emerging Properties in Self-Supervised Vision Transformers},
journal = {CoRR},
volume = {abs/2104.14294},
year = {2021},
url = {https://arxiv.org/abs/2104.14294},
archivePrefix = {arXiv},
eprint = {2104.14294},
timestamp = {Tue, 04 May 2021 15:12:43 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-2104-14294.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
```bibtex
@inproceedings{he2016deep,
title={Deep residual learning for image recognition},
author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
pages={770--778},
year={2016}
}
``` |
bartowski/gemma-1.1-2b-it-GGUF | bartowski | 2024-05-24T18:57:14Z | 902 | 5 | transformers | [
"transformers",
"gguf",
"text-generation",
"license:gemma",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-04-06T04:23:35Z | ---
library_name: transformers
widget:
- messages:
- role: user
content: How does the brain work?
inference:
parameters:
max_new_tokens: 200
extra_gated_heading: Access Gemma on Hugging Face
extra_gated_prompt: >-
To access Gemma on Hugging Face, you’re required to review and agree to
Google’s usage license. To do this, please ensure you’re logged-in to Hugging
Face and click below. Requests are processed immediately.
extra_gated_button_content: Acknowledge license
license: gemma
quantized_by: bartowski
pipeline_tag: text-generation
---
## Llamacpp Quantizations of gemma-1.1-2b-it
Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggerganov/llama.cpp/releases/tag/b2589">b2589</a> for quantization.
Original model: https://huggingface.co/google/gemma-1.1-2b-it
Download a file (not the whole branch) from below:
| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [gemma-1.1-2b-it-Q8_0.gguf](https://huggingface.co/bartowski/gemma-1.1-2b-it-GGUF/blob/main/gemma-1.1-2b-it-Q8_0.gguf) | Q8_0 | 2.66GB | Extremely high quality, generally unneeded but max available quant. |
| [gemma-1.1-2b-it-Q6_K.gguf](https://huggingface.co/bartowski/gemma-1.1-2b-it-GGUF/blob/main/gemma-1.1-2b-it-Q6_K.gguf) | Q6_K | 2.06GB | Very high quality, near perfect, *recommended*. |
| [gemma-1.1-2b-it-Q5_K_M.gguf](https://huggingface.co/bartowski/gemma-1.1-2b-it-GGUF/blob/main/gemma-1.1-2b-it-Q5_K_M.gguf) | Q5_K_M | 1.83GB | High quality, *recommended*. |
| [gemma-1.1-2b-it-Q5_K_S.gguf](https://huggingface.co/bartowski/gemma-1.1-2b-it-GGUF/blob/main/gemma-1.1-2b-it-Q5_K_S.gguf) | Q5_K_S | 1.79GB | High quality, *recommended*. |
| [gemma-1.1-2b-it-Q5_0.gguf](https://huggingface.co/bartowski/gemma-1.1-2b-it-GGUF/blob/main/gemma-1.1-2b-it-Q5_0.gguf) | Q5_0 | 1.79GB | High quality, older format, generally not recommended. |
| [gemma-1.1-2b-it-Q4_K_M.gguf](https://huggingface.co/bartowski/gemma-1.1-2b-it-GGUF/blob/main/gemma-1.1-2b-it-Q4_K_M.gguf) | Q4_K_M | 1.63GB | Good quality, uses about 4.83 bits per weight, *recommended*. |
| [gemma-1.1-2b-it-Q4_K_S.gguf](https://huggingface.co/bartowski/gemma-1.1-2b-it-GGUF/blob/main/gemma-1.1-2b-it-Q4_K_S.gguf) | Q4_K_S | 1.55GB | Slightly lower quality with small space savings. |
| [gemma-1.1-2b-it-IQ4_NL.gguf](https://huggingface.co/bartowski/gemma-1.1-2b-it-GGUF/blob/main/gemma-1.1-2b-it-IQ4_NL.gguf) | IQ4_NL | 1.56GB | Decent quality, similar to Q4_K_S, new method of quanting, *recommended*. |
| [gemma-1.1-2b-it-IQ4_XS.gguf](https://huggingface.co/bartowski/gemma-1.1-2b-it-GGUF/blob/main/gemma-1.1-2b-it-IQ4_XS.gguf) | IQ4_XS | 1.50GB | Decent quality, new method with similar performance to Q4. |
| [gemma-1.1-2b-it-Q4_0.gguf](https://huggingface.co/bartowski/gemma-1.1-2b-it-GGUF/blob/main/gemma-1.1-2b-it-Q4_0.gguf) | Q4_0 | 1.55GB | Decent quality, older format, generally not recommended. |
| [gemma-1.1-2b-it-Q3_K_L.gguf](https://huggingface.co/bartowski/gemma-1.1-2b-it-GGUF/blob/main/gemma-1.1-2b-it-Q3_K_L.gguf) | Q3_K_L | 1.46GB | Lower quality but usable, good for low RAM availability. |
| [gemma-1.1-2b-it-Q3_K_M.gguf](https://huggingface.co/bartowski/gemma-1.1-2b-it-GGUF/blob/main/gemma-1.1-2b-it-Q3_K_M.gguf) | Q3_K_M | 1.38GB | Even lower quality. |
| [gemma-1.1-2b-it-IQ3_M.gguf](https://huggingface.co/bartowski/gemma-1.1-2b-it-GGUF/blob/main/gemma-1.1-2b-it-IQ3_M.gguf) | IQ3_M | 1.30GB | Medium-low quality, new method with decent performance. |
| [gemma-1.1-2b-it-IQ3_S.gguf](https://huggingface.co/bartowski/gemma-1.1-2b-it-GGUF/blob/main/gemma-1.1-2b-it-IQ3_S.gguf) | IQ3_S | 1.28GB | Lower quality, new method with decent performance, recommended over Q3 quants. |
| [gemma-1.1-2b-it-Q3_K_S.gguf](https://huggingface.co/bartowski/gemma-1.1-2b-it-GGUF/blob/main/gemma-1.1-2b-it-Q3_K_S.gguf) | Q3_K_S | 1.28GB | Low quality, not recommended. |
| [gemma-1.1-2b-it-Q2_K.gguf](https://huggingface.co/bartowski/gemma-1.1-2b-it-GGUF/blob/main/gemma-1.1-2b-it-Q2_K.gguf) | Q2_K | 1.15GB | Extremely low quality, *not* recommended. |
Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
|
CISCai/Mistral-7B-Instruct-v0.3-SOTA-GGUF | CISCai | 2024-05-30T20:20:26Z | 902 | 1 | null | [
"gguf",
"text-generation",
"en",
"base_model:mistralai/Mistral-7B-Instruct-v0.3",
"license:apache-2.0",
"region:us"
] | text-generation | 2024-05-23T01:07:05Z | ---
base_model: mistralai/Mistral-7B-Instruct-v0.3
language:
- en
pipeline_tag: text-generation
license: apache-2.0
model_creator: Mistral AI
model_name: Mistral-7B-Instruct-v0.3
model_type: mistral
quantized_by: CISC
---
# Mistral-7B-Instruct-v0.3 - SOTA GGUF
- Model creator: [Mistral AI](https://huggingface.co/mistralai)
- Original model: [Mistral-7B-Instruct-v0.3](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3)
<!-- description start -->
## Description
This repo contains State Of The Art quantized GGUF format model files for [Mistral-7B-Instruct-v0.3](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3).
Quantization was done with an importance matrix that was trained for ~1M tokens (256 batches of 4096 tokens) of [groups_merged.txt](https://github.com/ggerganov/llama.cpp/discussions/5263#discussioncomment-8395384) and [wiki.train.raw](https://raw.githubusercontent.com/pytorch/examples/main/word_language_model/data/wikitext-2/train.txt) concatenated.
The embedded chat template has been extended to support function calling via OpenAI-compatible `tools` parameter, see [example](#simple-llama-cpp-python-example-function-calling-code).
<!-- description end -->
<!-- prompt-template start -->
## Prompt template: Mistral v3
```
[AVAILABLE_TOOLS] [{"name": "function_name", "description": "Description", "parameters": {...}}, ...][/AVAILABLE_TOOLS][INST] {prompt}[/INST]
```
<!-- prompt-template end -->
<!-- compatibility_gguf start -->
## Compatibility
These quantised GGUFv3 files are compatible with llama.cpp from February 27th 2024 onwards, as of commit [0becb22](https://github.com/ggerganov/llama.cpp/commit/0becb22ac05b6542bd9d5f2235691aa1d3d4d307)
They are also compatible with many third party UIs and libraries provided they are built using a recent llama.cpp.
## Explanation of quantisation methods
<details>
<summary>Click to see details</summary>
The new methods available are:
* GGML_TYPE_IQ1_S - 1-bit quantization in super-blocks with an importance matrix applied, effectively using 1.56 bits per weight (bpw)
* GGML_TYPE_IQ1_M - 1-bit quantization in super-blocks with an importance matrix applied, effectively using 1.75 bpw
* GGML_TYPE_IQ2_XXS - 2-bit quantization in super-blocks with an importance matrix applied, effectively using 2.06 bpw
* GGML_TYPE_IQ2_XS - 2-bit quantization in super-blocks with an importance matrix applied, effectively using 2.31 bpw
* GGML_TYPE_IQ2_S - 2-bit quantization in super-blocks with an importance matrix applied, effectively using 2.5 bpw
* GGML_TYPE_IQ2_M - 2-bit quantization in super-blocks with an importance matrix applied, effectively using 2.7 bpw
* GGML_TYPE_IQ3_XXS - 3-bit quantization in super-blocks with an importance matrix applied, effectively using 3.06 bpw
* GGML_TYPE_IQ3_XS - 3-bit quantization in super-blocks with an importance matrix applied, effectively using 3.3 bpw
* GGML_TYPE_IQ3_S - 3-bit quantization in super-blocks with an importance matrix applied, effectively using 3.44 bpw
* GGML_TYPE_IQ3_M - 3-bit quantization in super-blocks with an importance matrix applied, effectively using 3.66 bpw
* GGML_TYPE_IQ4_XS - 4-bit quantization in super-blocks with an importance matrix applied, effectively using 4.25 bpw
* GGML_TYPE_IQ4_NL - 4-bit non-linearly mapped quantization with an importance matrix applied, effectively using 4.5 bpw
Refer to the Provided Files table below to see what files use which methods, and how.
</details>
<!-- compatibility_gguf end -->
<!-- README_GGUF.md-provided-files start -->
## Provided files
| Name | Quant method | Bits | Size | Max RAM required | Use case |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [Mistral-7B-Instruct-v0.3.IQ1_S.gguf](https://huggingface.co/CISCai/Mistral-7B-Instruct-v0.3-SOTA-GGUF/blob/main/Mistral-7B-Instruct-v0.3.IQ1_S.gguf) | IQ1_S | 1 | 1.5 GB| 2.5 GB | smallest, significant quality loss - **TBD**: Waiting for [this issue](https://github.com/ggerganov/llama.cpp/issues/5996) to be resolved |
| [Mistral-7B-Instruct-v0.3.IQ1_M.gguf](https://huggingface.co/CISCai/Mistral-7B-Instruct-v0.3-SOTA-GGUF/blob/main/Mistral-7B-Instruct-v0.3.IQ1_M.gguf) | IQ1_M | 1 | 1.6 GB| 2.6 GB | very small, significant quality loss |
| [Mistral-7B-Instruct-v0.3.IQ2_XXS.gguf](https://huggingface.co/CISCai/Mistral-7B-Instruct-v0.3-SOTA-GGUF/blob/main/Mistral-7B-Instruct-v0.3.IQ2_XXS.gguf) | IQ2_XXS | 2 | 1.8 GB| 2.8 GB | very small, high quality loss |
| [Mistral-7B-Instruct-v0.3.IQ2_XS.gguf](https://huggingface.co/CISCai/Mistral-7B-Instruct-v0.3-SOTA-GGUF/blob/main/Mistral-7B-Instruct-v0.3.IQ2_XS.gguf) | IQ2_XS | 2 | 1.9 GB| 2.9 GB | very small, high quality loss |
| [Mistral-7B-Instruct-v0.3.IQ2_S.gguf](https://huggingface.co/CISCai/Mistral-7B-Instruct-v0.3-SOTA-GGUF/blob/main/Mistral-7B-Instruct-v0.3.IQ2_S.gguf) | IQ2_S | 2 | 2.1 GB| 3.1 GB | small, substantial quality loss |
| [Mistral-7B-Instruct-v0.3.IQ2_M.gguf](https://huggingface.co/CISCai/Mistral-7B-Instruct-v0.3-SOTA-GGUF/blob/main/Mistral-7B-Instruct-v0.3.IQ2_M.gguf) | IQ2_M | 2 | 2.2 GB| 3.2 GB | small, greater quality loss |
| [Mistral-7B-Instruct-v0.3.IQ3_XXS.gguf](https://huggingface.co/CISCai/Mistral-7B-Instruct-v0.3-SOTA-GGUF/blob/main/Mistral-7B-Instruct-v0.3.IQ3_XXS.gguf) | IQ3_XXS | 3 | 2.5 GB| 3.5 GB | very small, high quality loss |
| [Mistral-7B-Instruct-v0.3.IQ3_XS.gguf](https://huggingface.co/CISCai/Mistral-7B-Instruct-v0.3-SOTA-GGUF/blob/main/Mistral-7B-Instruct-v0.3.IQ3_XS.gguf) | IQ3_XS | 3 | 2.7 GB| 3.7 GB | small, substantial quality loss |
| [Mistral-7B-Instruct-v0.3.IQ3_S.gguf](https://huggingface.co/CISCai/Mistral-7B-Instruct-v0.3-SOTA-GGUF/blob/main/Mistral-7B-Instruct-v0.3.IQ3_S.gguf) | IQ3_S | 3 | 2.8 GB| 3.8 GB | small, greater quality loss |
| [Mistral-7B-Instruct-v0.3.IQ3_M.gguf](https://huggingface.co/CISCai/Mistral-7B-Instruct-v0.3-SOTA-GGUF/blob/main/Mistral-7B-Instruct-v0.3.IQ3_M.gguf) | IQ3_M | 3 | 3.0 GB| 4.0 GB | medium, balanced quality - recommended |
| [Mistral-7B-Instruct-v0.3.IQ4_XS.gguf](https://huggingface.co/CISCai/Mistral-7B-Instruct-v0.3-SOTA-GGUF/blob/main/Mistral-7B-Instruct-v0.3.IQ4_XS.gguf) | IQ4_XS | 4 | 3.4 GB| 4.4 GB | small, substantial quality loss |
Generated importance matrix file: [Mistral-7B-Instruct-v0.3.imatrix.dat](https://huggingface.co/CISCai/Mistral-7B-Instruct-v0.3-SOTA-GGUF/blob/main/Mistral-7B-Instruct-v0.3.imatrix.dat)
**Note**: the above RAM figures assume no GPU offloading with 4K context. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.
<!-- README_GGUF.md-provided-files end -->
<!-- README_GGUF.md-how-to-run start -->
## Example `llama.cpp` command
Make sure you are using `llama.cpp` from commit [0becb22](https://github.com/ggerganov/llama.cpp/commit/0becb22ac05b6542bd9d5f2235691aa1d3d4d307) or later.
```shell
./main -ngl 33 -m Mistral-7B-Instruct-v0.3.IQ4_XS.gguf --color -c 32768 --temp 0 --repeat-penalty 1.1 -p "[AVAILABLE_TOOLS] {tools}[/AVAILABLE_TOOLS][INST] {prompt}[/INST]"
```
Change `-ngl 33` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 32768` to the desired sequence length.
If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`
If you are low on V/RAM try quantizing the K-cache with `-ctk q8_0` or even `-ctk q4_0` for big memory savings (depending on context size).
There is a similar option for V-cache (`-ctv`), however that is [not working yet](https://github.com/ggerganov/llama.cpp/issues/4425).
For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md)
## How to run from Python code
You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) module.
### How to load this model in Python code, using llama-cpp-python
For full documentation, please see: [llama-cpp-python docs](https://llama-cpp-python.readthedocs.io/en/latest/).
#### First install the package
Run one of the following commands, according to your system:
```shell
# Prebuilt wheel with basic CPU support
pip install llama-cpp-python --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/cpu
# Prebuilt wheel with NVidia CUDA acceleration
pip install llama-cpp-python --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/cu121 (or cu122 etc.)
# Prebuilt wheel with Metal GPU acceleration
pip install llama-cpp-python --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/metal
# Build base version with no GPU acceleration
pip install llama-cpp-python
# With NVidia CUDA acceleration
CMAKE_ARGS="-DLLAMA_CUDA=on" pip install llama-cpp-python
# Or with OpenBLAS acceleration
CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python
# Or with CLBLast acceleration
CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python
# Or with AMD ROCm GPU acceleration (Linux only)
CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python
# Or with Metal GPU acceleration for macOS systems only
CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python
# Or with Vulkan acceleration
CMAKE_ARGS="-DLLAMA_VULKAN=on" pip install llama-cpp-python
# Or with Kompute acceleration
CMAKE_ARGS="-DLLAMA_KOMPUTE=on" pip install llama-cpp-python
# Or with SYCL acceleration
CMAKE_ARGS="-DLLAMA_SYCL=on -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx" pip install llama-cpp-python
# In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA:
$env:CMAKE_ARGS = "-DLLAMA_CUDA=on"
pip install llama-cpp-python
```
#### Simple llama-cpp-python example code
```python
from llama_cpp import Llama
# Chat Completion API
llm = Llama(model_path="./Mistral-7B-Instruct-v0.3.IQ4_XS.gguf", n_gpu_layers=33, n_ctx=32768)
print(llm.create_chat_completion(
messages = [
{
"role": "user",
"content": "Pick a LeetCode challenge and solve it in Python."
}
]
))
```
#### Simple llama-cpp-python example function calling code
```python
from llama_cpp import Llama
# Chat Completion API
grammar = LlamaGrammar.from_json_schema(json.dumps({
"type": "array",
"items": {
"type": "object",
"required": [ "name", "arguments" ],
"properties": {
"name": {
"type": "string"
},
"arguments": {
"type": "object"
}
}
}
}))
llm = Llama(model_path="./Mistral-7B-Instruct-v0.3.IQ4_XS.gguf", n_gpu_layers=33, n_ctx=32768)
response = llm.create_chat_completion(
temperature = 0.0,
repeat_penalty = 1.1,
messages = [
{
"role": "user",
"content": "What's the weather like in Oslo and Stockholm?"
}
],
tools=[{
"type": "function",
"function": {
"name": "get_current_weather",
"description": "Get the current weather in a given location",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city and state, e.g. San Francisco, CA"
},
"unit": {
"type": "string",
"enum": [ "celsius", "fahrenheit" ]
}
},
"required": [ "location" ]
}
}
}],
grammar = grammar
)
print(json.loads(response["choices"][0]["text"]))
print(llm.create_chat_completion(
temperature = 0.0,
repeat_penalty = 1.1,
messages = [
{
"role": "user",
"content": "What's the weather like in Oslo?"
},
{ # The tool_calls is from the response to the above with tool_choice active
"role": "assistant",
"content": None,
"tool_calls": [
{
"id": "call__0_get_current_weather_cmpl-...",
"type": "function",
"function": {
"name": "get_current_weather",
"arguments": '{ "location": "Oslo, NO" ,"unit": "celsius"} '
}
}
]
},
{ # The tool_call_id is from tool_calls and content is the result from the function call you made
"role": "tool",
"content": "20",
"tool_call_id": "call__0_get_current_weather_cmpl-..."
}
],
tools=[{
"type": "function",
"function": {
"name": "get_current_weather",
"description": "Get the current weather in a given location",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city and state, e.g. San Francisco, CA"
},
"unit": {
"type": "string",
"enum": [ "celsius", "fahrenheit" ]
}
},
"required": [ "location" ]
}
}
}],
#tool_choice={
# "type": "function",
# "function": {
# "name": "get_current_weather"
# }
#}
))
```
<!-- README_GGUF.md-how-to-run end -->
|
kmpartner/bkcnft-testsr-conti | kmpartner | 2024-07-02T06:58:20Z | 902 | 0 | diffusers | [
"diffusers",
"safetensors",
"region:us"
] | null | 2024-06-24T12:14:11Z | Entry not found |
aifeifei798/llama3-8B-DarkIdol-2.0-Uncensored-IQ4_NL-GGUF | aifeifei798 | 2024-07-01T11:37:21Z | 902 | 0 | null | [
"gguf",
"roleplay",
"llama3",
"sillytavern",
"idol",
"llama-cpp",
"gguf-my-repo",
"en",
"ja",
"zh",
"base_model:aifeifei798/llama3-8B-DarkIdol-2.0-Uncensored",
"license:llama3",
"region:us"
] | null | 2024-07-01T11:36:55Z | ---
base_model: aifeifei798/llama3-8B-DarkIdol-2.0-Uncensored
language:
- en
- ja
- zh
license: llama3
tags:
- roleplay
- llama3
- sillytavern
- idol
- llama-cpp
- gguf-my-repo
---
# aifeifei798/llama3-8B-DarkIdol-2.0-Uncensored-IQ4_NL-GGUF
This model was converted to GGUF format from [`aifeifei798/llama3-8B-DarkIdol-2.0-Uncensored`](https://huggingface.co/aifeifei798/llama3-8B-DarkIdol-2.0-Uncensored) 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/aifeifei798/llama3-8B-DarkIdol-2.0-Uncensored) 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 aifeifei798/llama3-8B-DarkIdol-2.0-Uncensored-IQ4_NL-GGUF --hf-file llama3-8b-darkidol-2.0-uncensored-iq4_nl-imat.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo aifeifei798/llama3-8B-DarkIdol-2.0-Uncensored-IQ4_NL-GGUF --hf-file llama3-8b-darkidol-2.0-uncensored-iq4_nl-imat.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo aifeifei798/llama3-8B-DarkIdol-2.0-Uncensored-IQ4_NL-GGUF --hf-file llama3-8b-darkidol-2.0-uncensored-iq4_nl-imat.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo aifeifei798/llama3-8B-DarkIdol-2.0-Uncensored-IQ4_NL-GGUF --hf-file llama3-8b-darkidol-2.0-uncensored-iq4_nl-imat.gguf -c 2048
```
|
timm/mobilenetv2_110d.ra_in1k | timm | 2023-04-27T21:14:17Z | 901 | 0 | timm | [
"timm",
"pytorch",
"safetensors",
"image-classification",
"dataset:imagenet-1k",
"arxiv:2110.00476",
"arxiv:1801.04381",
"license:apache-2.0",
"region:us"
] | image-classification | 2022-12-13T00:00:34Z | ---
tags:
- image-classification
- timm
library_name: timm
license: apache-2.0
datasets:
- imagenet-1k
---
# Model card for mobilenetv2_110d.ra_in1k
A MobileNet-v2 image classification model. Trained on ImageNet-1k in `timm` using recipe template described below.
Recipe details:
* RandAugment `RA` recipe. Inspired by and evolved from EfficientNet RandAugment recipes. Published as `B` recipe in [ResNet Strikes Back](https://arxiv.org/abs/2110.00476).
* RMSProp (TF 1.0 behaviour) optimizer, EMA weight averaging
* Step (exponential decay w/ staircase) LR schedule with warmup
## Model Details
- **Model Type:** Image classification / feature backbone
- **Model Stats:**
- Params (M): 4.5
- GMACs: 0.4
- Activations (M): 8.7
- Image size: 224 x 224
- **Papers:**
- MobileNetV2: Inverted Residuals and Linear Bottlenecks: https://arxiv.org/abs/1801.04381
- ResNet strikes back: An improved training procedure in timm: https://arxiv.org/abs/2110.00476
- **Dataset:** ImageNet-1k
- **Original:** https://github.com/huggingface/pytorch-image-models
## Model Usage
### Image Classification
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model('mobilenetv2_110d.ra_in1k', pretrained=True)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)
```
### Feature Map Extraction
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'mobilenetv2_110d.ra_in1k',
pretrained=True,
features_only=True,
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
for o in output:
# print shape of each feature map in output
# e.g.:
# torch.Size([1, 16, 112, 112])
# torch.Size([1, 24, 56, 56])
# torch.Size([1, 32, 28, 28])
# torch.Size([1, 104, 14, 14])
# torch.Size([1, 352, 7, 7])
print(o.shape)
```
### Image Embeddings
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'mobilenetv2_110d.ra_in1k',
pretrained=True,
num_classes=0, # remove classifier nn.Linear
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor
# or equivalently (without needing to set num_classes=0)
output = model.forward_features(transforms(img).unsqueeze(0))
# output is unpooled, a (1, 1280, 7, 7) shaped tensor
output = model.forward_head(output, pre_logits=True)
# output is a (1, num_features) shaped tensor
```
## Model Comparison
Explore the dataset and runtime metrics of this model in timm [model results](https://github.com/huggingface/pytorch-image-models/tree/main/results).
## Citation
```bibtex
@inproceedings{sandler2018mobilenetv2,
title={Mobilenetv2: Inverted residuals and linear bottlenecks},
author={Sandler, Mark and Howard, Andrew and Zhu, Menglong and Zhmoginov, Andrey and Chen, Liang-Chieh},
booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
pages={4510--4520},
year={2018}
}
```
```bibtex
@misc{rw2019timm,
author = {Ross Wightman},
title = {PyTorch Image Models},
year = {2019},
publisher = {GitHub},
journal = {GitHub repository},
doi = {10.5281/zenodo.4414861},
howpublished = {\url{https://github.com/huggingface/pytorch-image-models}}
}
```
```bibtex
@inproceedings{wightman2021resnet,
title={ResNet strikes back: An improved training procedure in timm},
author={Wightman, Ross and Touvron, Hugo and Jegou, Herve},
booktitle={NeurIPS 2021 Workshop on ImageNet: Past, Present, and Future}
}
```
|
THUDM/glm-2b | THUDM | 2023-03-02T13:49:21Z | 901 | 13 | transformers | [
"transformers",
"pytorch",
"glm",
"feature-extraction",
"thudm",
"custom_code",
"en",
"arxiv:2103.10360",
"region:us"
] | feature-extraction | 2023-03-01T02:34:05Z | ---
language:
- en
tags:
- glm
- thudm
---
GLM is a General Language Model pretrained with an autoregressive blank-filling objective and can be finetuned on various natural language understanding and generation tasks.
Please refer to our paper for a detailed description of GLM:
[GLM: General Language Model Pretraining with Autoregressive Blank Infilling](https://arxiv.org/abs/2103.10360) (ACL 2022)
Zhengxiao Du*, Yujie Qian*, Xiao Liu, Ming Ding, Jiezhong Qiu, Zhilin Yang, Jie Tang (*: equal contribution)
Find more examples in our [Github repo](https://github.com/THUDM/GLM).
## Model description
`glm-2b` is pretrained on the [Pile](https://pile.eleuther.ai) dataset. It has 36 transformer layers, with hidden size 4096 and 64 attention heads in each layer. The model is pretrained with autoregressive blank filling objectives designed for natural language understanding, seq2seq, and language modeling. Find more details from our [repo](https://github.com/THUDM/GLM).
## How to use
Please refer the [instruction](https://github.com/THUDM/GLM#hugging-face-hub) in our Github repo.
We use three different mask tokens for different tasks: `[MASK]` for short blank filling, `[sMASK]` for sentence filling, and `[gMASK]` for left to right generation. You can find examples about different masks from [here](https://github.com/THUDM/GLM#left-to-right-generation--blank-filling-interactive). The prediction always begin with a special `<|startofpiece|>` token and ends with a `<|endofpiece|>` token.
## Citation
Please cite our paper if you find this code useful for your research:
```
@article{DBLP:conf/acl/DuQLDQY022,
author = {Zhengxiao Du and
Yujie Qian and
Xiao Liu and
Ming Ding and
Jiezhong Qiu and
Zhilin Yang and
Jie Tang},
title = {{GLM:} General Language Model Pretraining with Autoregressive Blank Infilling},
booktitle = {Proceedings of the 60th Annual Meeting of the Association for Computational
Linguistics (Volume 1: Long Papers), {ACL} 2022, Dublin, Ireland,
May 22-27, 2022},
pages = {320--335},
publisher = {Association for Computational Linguistics},
year = {2022},
}
```
|
Kaludi/chatgpt-gpt4-prompts-bart-large-cnn-samsum | Kaludi | 2023-04-10T18:15:28Z | 901 | 80 | transformers | [
"transformers",
"tf",
"bart",
"text2text-generation",
"generated_from_keras_callback",
"dataset:fka/awesome-chatgpt-prompts",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2023-03-27T21:12:40Z | ---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: chatgpt-gpt4-prompts-bart-large-cnn-samsum
results: []
datasets:
- fka/awesome-chatgpt-prompts
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# chatgpt-gpt4-prompts-bart-large-cnn-samsum
This model generates ChatGPT/BingChat & GPT-3 prompts and is a fine-tuned version of [philschmid/bart-large-cnn-samsum](https://huggingface.co/philschmid/bart-large-cnn-samsum) on an [this](https://huggingface.co/datasets/fka/awesome-chatgpt-prompts) dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.2214
- Validation Loss: 2.7584
- Epoch: 4
### Streamlit
This model supports a [Streamlit](https://streamlit.io/) Web UI to run the chatgpt-gpt4-prompts-bart-large-cnn-samsum model:
[](https://huggingface.co/spaces/Kaludi/ChatGPT-BingChat-GPT3-Prompt-Generator_App)
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 3.1982 | 2.6801 | 0 |
| 2.3601 | 2.5493 | 1 |
| 1.9225 | 2.5377 | 2 |
| 1.5465 | 2.6794 | 3 |
| 1.2214 | 2.7584 | 4 |
### Framework versions
- Transformers 4.27.3
- TensorFlow 2.11.0
- Datasets 2.10.1
- Tokenizers 0.13.2 |
MoritzLaurer/deberta-v3-base-zeroshot-v1 | MoritzLaurer | 2023-11-29T19:30:58Z | 901 | 36 | transformers | [
"transformers",
"pytorch",
"safetensors",
"deberta-v2",
"text-classification",
"zero-shot-classification",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | zero-shot-classification | 2023-09-29T05:38:21Z | ---
language:
- en
tags:
- text-classification
- zero-shot-classification
pipeline_tag: zero-shot-classification
library_name: transformers
license: mit
---
# deberta-v3-base-zeroshot-v1
## Model description
The model is designed for zero-shot classification with the Hugging Face pipeline.
The model should be substantially better at zero-shot classification than my other zero-shot models on the
Hugging Face hub: https://huggingface.co/MoritzLaurer.
The model can do one universal task: determine whether a hypothesis is `true` or `not_true`
given a text (also called `entailment` vs. `not_entailment`).
This task format is based on the Natural Language Inference task (NLI).
The task is so universal that any classification task can be reformulated into the task.
## Training data
The model was trained on a mixture of 27 tasks and 310 classes that have been reformatted into this universal format.
1. 26 classification tasks with ~400k texts:
'amazonpolarity', 'imdb', 'appreviews', 'yelpreviews', 'rottentomatoes',
'emotiondair', 'emocontext', 'empathetic',
'financialphrasebank', 'banking77', 'massive',
'wikitoxic_toxicaggregated', 'wikitoxic_obscene', 'wikitoxic_threat', 'wikitoxic_insult', 'wikitoxic_identityhate',
'hateoffensive', 'hatexplain', 'biasframes_offensive', 'biasframes_sex', 'biasframes_intent',
'agnews', 'yahootopics',
'trueteacher', 'spam', 'wellformedquery'.
See details on each dataset here: https://docs.google.com/spreadsheets/d/1Z18tMh02IiWgh6o8pfoMiI_LH4IXpr78wd_nmNd5FaE/edit?usp=sharing
3. Five NLI datasets with ~885k texts: "mnli", "anli", "fever", "wanli", "ling"
Note that compared to other NLI models, this model predicts two classes (`entailment` vs. `not_entailment`)
as opposed to three classes (entailment/neutral/contradiction)
### How to use the model
#### Simple zero-shot classification pipeline
```python
from transformers import pipeline
classifier = pipeline("zero-shot-classification", model="MoritzLaurer/deberta-v3-base-zeroshot-v1")
sequence_to_classify = "Angela Merkel is a politician in Germany and leader of the CDU"
candidate_labels = ["politics", "economy", "entertainment", "environment"]
output = classifier(sequence_to_classify, candidate_labels, multi_label=False)
print(output)
```
### Details on data and training
The code for preparing the data and training & evaluating the model is fully open-source here: https://github.com/MoritzLaurer/zeroshot-classifier/tree/main
## Limitations and bias
The model can only do text classification tasks.
Please consult the original DeBERTa paper and the papers for the different datasets for potential biases.
## License
The base model (DeBERTa-v3) is published under the MIT license.
The datasets the model was fine-tuned on are published under a diverse set of licenses.
The following spreadsheet provides an overview of the non-NLI datasets used for fine-tuning.
The spreadsheets contains information on licenses, the underlying papers etc.: https://docs.google.com/spreadsheets/d/1Z18tMh02IiWgh6o8pfoMiI_LH4IXpr78wd_nmNd5FaE/edit?usp=sharing
In addition, the model was also trained on the following NLI datasets: MNLI, ANLI, WANLI, LING-NLI, FEVER-NLI.
## Citation
If you use this model, please cite:
```
@article{laurer_less_2023,
title = {Less {Annotating}, {More} {Classifying}: {Addressing} the {Data} {Scarcity} {Issue} of {Supervised} {Machine} {Learning} with {Deep} {Transfer} {Learning} and {BERT}-{NLI}},
issn = {1047-1987, 1476-4989},
shorttitle = {Less {Annotating}, {More} {Classifying}},
url = {https://www.cambridge.org/core/product/identifier/S1047198723000207/type/journal_article},
doi = {10.1017/pan.2023.20},
language = {en},
urldate = {2023-06-20},
journal = {Political Analysis},
author = {Laurer, Moritz and Van Atteveldt, Wouter and Casas, Andreu and Welbers, Kasper},
month = jun,
year = {2023},
pages = {1--33},
}
```
### Ideas for cooperation or questions?
If you have questions or ideas for cooperation, contact me at m{dot}laurer{at}vu{dot}nl or [LinkedIn](https://www.linkedin.com/in/moritz-laurer/)
### Debugging and issues
Note that DeBERTa-v3 was released on 06.12.21 and older versions of HF Transformers seem to have issues running the model (e.g. resulting in an issue with the tokenizer). Using Transformers>=4.13 might solve some issues. |
yaofu/llama-2-7b-80k | yaofu | 2024-02-14T17:00:05Z | 901 | 12 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-02-12T15:11:41Z | ---
license: mit
---
|
MaziyarPanahi/WizardLM-2-8x22B-AWQ | MaziyarPanahi | 2024-04-16T11:31:21Z | 901 | 8 | transformers | [
"transformers",
"safetensors",
"mixtral",
"text-generation",
"finetuned",
"quantized",
"4-bit",
"AWQ",
"base_model:microsoft/WizardLM-2-8x22B",
"autotrain_compatible",
"text-generation-inference",
"awq",
"region:us"
] | text-generation | 2024-04-16T11:06:56Z | ---
tags:
- finetuned
- quantized
- 4-bit
- AWQ
- text-generation
- mixtral
model_name: WizardLM-2-8x22B-AWQ
base_model: microsoft/WizardLM-2-8x22B
inference: false
pipeline_tag: text-generation
quantized_by: MaziyarPanahi
---
# Description
[MaziyarPanahi/home-AWQ](https://huggingface.co/MaziyarPanahi/home-AWQ) is a quantized (AWQ) version of [microsoft/WizardLM-2-8x22B](https://huggingface.co/microsoft/WizardLM-2-8x22B)
## How to use
### Install the necessary packages
```
pip install --upgrade accelerate autoawq transformers
```
### Example Python code
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "MaziyarPanahi/home-AWQ"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id).to(0)
text = "User:\nHello can you provide me with top-3 cool places to visit in Paris?\n\nAssistant:\n"
inputs = tokenizer(text, return_tensors="pt").to(0)
out = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(out[0], skip_special_tokens=True))
```
Results:
```
User:
Hello can you provide me with top-3 cool places to visit in Paris?
Assistant:
Absolutely, here are my top-3 recommendations for must-see places in Paris:
1. The Eiffel Tower: An icon of Paris, this wrought-iron lattice tower is a global cultural icon of France and is among the most recognizable structures in the world. Climbing up to the top offers breathtaking views of the city.
2. The Louvre Museum: Home to thousands of works of art, the Louvre is the world's largest art museum and a historic monument in Paris. Must-see pieces include the Mona Lisa, the Winged Victory of Samothrace, and the Venus de Milo.
3. Notre-Dame Cathedral: This cathedral is a masterpiece of French Gothic architecture and is famous for its intricate stone carvings, beautiful stained glass, and its iconic twin towers. Be sure to spend some time exploring its history and learning about the fascinating restoration efforts post the 2019 fire.
I hope you find these recommendations helpful and that they make for an enjoyable and memorable trip to Paris. Safe travels!
```
|
numind/NuNER_Zero-4k | numind | 2024-05-07T15:07:35Z | 901 | 13 | gliner | [
"gliner",
"pytorch",
"entity recognition",
"NER",
"named entity recognition",
"zero shot",
"zero-shot",
"token-classification",
"en",
"dataset:numind/NuNER",
"arxiv:2402.15343",
"arxiv:2311.08526",
"license:mit",
"region:us"
] | token-classification | 2024-04-29T08:59:00Z | ---
license: mit
datasets:
- numind/NuNER
library_name: gliner
language:
- en
pipeline_tag: token-classification
tags:
- entity recognition
- NER
- named entity recognition
- zero shot
- zero-shot
---
NuNER Zero 4k is the long-context (4k tokens) version of [NuNER Zero](https://huggingface.co/numind/NuNER_Zero).
NuNER Zero 4k is generally less performant than NuNER Zero, but can outperform NuNER Zero on applications where context size matters.
<p align="center">
<img src="zero_shot_performance_unzero_token_long.png" width="600">
</p>
## Installation & Usage
```
!pip install gliner
```
**NuZero requires labels to be lower-cased**
```python
from gliner import GLiNER
def merge_entities(entities):
if not entities:
return []
merged = []
current = entities[0]
for next_entity in entities[1:]:
if next_entity['label'] == current['label'] and (next_entity['start'] == current['end'] + 1 or next_entity['start'] == current['end']):
current['text'] = text[current['start']: next_entity['end']].strip()
current['end'] = next_entity['end']
else:
merged.append(current)
current = next_entity
# Append the last entity
merged.append(current)
return merged
model = GLiNER.from_pretrained("numind/NuNerZero_long_context")
# NuZero requires labels to be lower-cased!
labels = ["organization", "initiative", "project"]
labels = [l.lower() for l in labels]
text = "At the annual technology summit, the keynote address was delivered by a senior member of the Association for Computing Machinery Special Interest Group on Algorithms and Computation Theory, which recently launched an expansive initiative titled 'Quantum Computing and Algorithmic Innovations: Shaping the Future of Technology'. This initiative explores the implications of quantum mechanics on next-generation computing and algorithm design and is part of a broader effort that includes the 'Global Computational Science Advancement Project'. The latter focuses on enhancing computational methodologies across scientific disciplines, aiming to set new benchmarks in computational efficiency and accuracy."
entities = model.predict_entities(text, labels)
entities = merge_entities(entities)
for entity in entities:
print(entity["text"], "=>", entity["label"])
```
```
Association for Computing Machinery Special Interest Group on Algorithms and Computation Theory => organization
Quantum Computing and Algorithmic Innovations: Shaping the Future of Technology => initiative
Global Computational Science Advancement Project => project
```
## Fine-tuning
A fine-tuning script can be found [here](https://colab.research.google.com/drive/19WDnuD2U-B0h-FzX7I5FySNP6sHt4Cru?usp=sharing).
## Citation
### This work
```bibtex
@misc{bogdanov2024nuner,
title={NuNER: Entity Recognition Encoder Pre-training via LLM-Annotated Data},
author={Sergei Bogdanov and Alexandre Constantin and Timothée Bernard and Benoit Crabbé and Etienne Bernard},
year={2024},
eprint={2402.15343},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Previous work
```bibtex
@misc{zaratiana2023gliner,
title={GLiNER: Generalist Model for Named Entity Recognition using Bidirectional Transformer},
author={Urchade Zaratiana and Nadi Tomeh and Pierre Holat and Thierry Charnois},
year={2023},
eprint={2311.08526},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` |
chkla/parlbert-topic-german | chkla | 2024-04-08T22:04:53Z | 900 | 9 | transformers | [
"transformers",
"pytorch",
"safetensors",
"bert",
"text-classification",
"de",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-06-19T18:20:29Z | ---
language: de
widget:
- text: Das Sachgebiet Investive Ausgaben des Bundes Bundesfinanzminister Apel hat gemäß BMF Finanznachrichten vom 1. Januar erklärt, die Investitionsquote des Bundes sei in den letzten zehn Jahren nahezu konstant geblieben.
---
### Welcome to ParlBERT-Topic-German!
🏷 **Model description**
This model was trained on \~10k manually annotated interpellations (📚 [Breunig/ Schnatterer 2019](https://www.tandfonline.com/doi/abs/10.1080/13572334.2021.2010395)) with topics from the [Comparative Agendas Project](https://www.comparativeagendas.net/datasets_codebooks) to classify text into one of twenty labels (annotation codebook).
_Note: "Interpellation is a formal request of a parliament to the respective government."([Wikipedia](https://en.wikipedia.org/wiki/Interpellation_(politics)))_
🗃 **Dataset**
| party | speeches | tokens |
|----|----|----|
| CDU/CSU | 7,635 | 4,862,654 |
| SPD | 5,321 | 3,158,315 |
| AfD | 3,465 | 1,844,707 |
| FDP | 3,067 | 1,593,108 |
| The Greens | 2,866 | 1,522,305 |
| The Left | 2,671 | 1,394,089 |
| cross-bencher | 200 | 86,170 |
🏃🏼♂️**Model training**
**ParlBERT-Topic-German** was fine-tuned on a domain adapted model (GermanBERT fine-tuned on [DeuParl](https://tudatalib.ulb.tu-darmstadt.de/handle/tudatalib/2889?show=full)) for topic modeling with an interpellations dataset (📚 [Breunig/ Schnatterer 2019](https://oxford.universitypressscholarship.com/view/10.1093/oso/9780198835332.001.0001/oso-9780198835332)) from the [Comparative Agendas Project](https://www.comparativeagendas.net/datasets_codebooks).
🤖 **Use**
```python
from transformers import pipeline
pipeline_classification_topics = pipeline("text-classification", model="chkla/parlbert-topic-german", return_all_scores=False)
text = "Das Sachgebiet Investive Ausgaben des Bundes Bundesfinanzminister Apel hat gemäß BMF Finanznachrichten vom 1. Januar erklärt, die Investitionsquote des Bundes sei in den letzten zehn Jahren nahezu konstant geblieben."
pipeline_classification_topics(text) # Macroeconomics
```
📊 **Evaluation**
The model was evaluated on an evaluation set (20%):
| Label | F1 | support |
|----|----|----|
| International | 80.0 | 1,126 |
| Defense | 85.0 | 1,099 |
| Government | 71.3 | 989 |
| Civil Rights | 76.5 | 978 |
| Environment | 76.6 | 845 |
| Transportation | 86.0 | 800 |
| Law & Crime | 67.1 | 492 |
| Energy | 78.6 | 424 |
| Health | 78.2 | 418 |
| Domestic Com. | 64.4 | 382 |
| Immigration | 81.0 | 376 |
| Labor | 69.1 | 344 |
| Macroeconom. | 62.8 | 339 |
| Agriculture | 76.3 | 292 |
| Social Welfare | 49.2 | 253 |
| Technology | 63.0 | 252 |
| Education | 71.6 | 183 |
| Housing | 79.6 | 178 |
| Foreign Trade | 61.5 | 139 |
| Culture | 54.6 | 69 |
| Public Lands | 45.4 | 55 |
⚠️ **Limitations**
Models are often highly topic dependent. Therefore, the model may perform less well on different topics and text types not included in the training set.
👥 **Cite**
```
@article{klamm2022frameast,
title={FrameASt: A Framework for Second-level Agenda Setting in Parliamentary Debates through the Lense of Comparative Agenda Topics},
author={Klamm, Christopher and Rehbein, Ines and Ponzetto, Simone},
journal={ParlaCLARIN III at LREC2022},
year={2022}
}
```
🐦 Twitter: [@chklamm](http://twitter.com/chklamm) |
votepurchase/7thAnimeXLPonyA_v10 | votepurchase | 2024-06-04T10:16:22Z | 900 | 2 | diffusers | [
"diffusers",
"safetensors",
"ja",
"license:mit",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] | text-to-image | 2024-05-26T11:57:55Z | ---
license: mit
language:
- ja
library_name: diffusers
---
[7thAnimeXLPonyA_v10](https://civitai.com/models/395554/7th-anime-xl-pony-a) |
timm/vit_base_patch16_clip_224.laion2b_ft_in1k | timm | 2023-05-06T00:01:30Z | 899 | 1 | timm | [
"timm",
"pytorch",
"safetensors",
"image-classification",
"dataset:imagenet-1k",
"dataset:laion-2b",
"arxiv:2212.07143",
"arxiv:2210.08402",
"arxiv:2010.11929",
"license:apache-2.0",
"region:us"
] | image-classification | 2022-11-08T23:16:23Z | ---
tags:
- image-classification
- timm
library_name: timm
license: apache-2.0
datasets:
- imagenet-1k
- laion-2b
---
# Model card for vit_base_patch16_clip_224.laion2b_ft_in1k
A Vision Transformer (ViT) image classification model. Pretrained on LAION-2B image-text pairs using OpenCLIP. Fine-tuned on ImageNet-1k in `timm`. See recipes in [Reproducible scaling laws](https://arxiv.org/abs/2212.07143).
## Model Details
- **Model Type:** Image classification / feature backbone
- **Model Stats:**
- Params (M): 86.6
- GMACs: 16.9
- Activations (M): 16.5
- Image size: 224 x 224
- **Papers:**
- OpenCLIP: https://github.com/mlfoundations/open_clip
- Reproducible scaling laws for contrastive language-image learning: https://arxiv.org/abs/2212.07143
- LAION-5B: An open large-scale dataset for training next generation image-text models: https://arxiv.org/abs/2210.08402
- An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale: https://arxiv.org/abs/2010.11929v2
- **Dataset:** ImageNet-1k
- **Pretrain Dataset:**
- LAION-2B
## Model Usage
### Image Classification
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model('vit_base_patch16_clip_224.laion2b_ft_in1k', pretrained=True)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)
```
### Image Embeddings
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'vit_base_patch16_clip_224.laion2b_ft_in1k',
pretrained=True,
num_classes=0, # remove classifier nn.Linear
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor
# or equivalently (without needing to set num_classes=0)
output = model.forward_features(transforms(img).unsqueeze(0))
# output is unpooled, a (1, 197, 768) shaped tensor
output = model.forward_head(output, pre_logits=True)
# output is a (1, num_features) shaped tensor
```
## Model Comparison
Explore the dataset and runtime metrics of this model in timm [model results](https://github.com/huggingface/pytorch-image-models/tree/main/results).
## Citation
```bibtex
@software{ilharco_gabriel_2021_5143773,
author = {Ilharco, Gabriel and
Wortsman, Mitchell and
Wightman, Ross and
Gordon, Cade and
Carlini, Nicholas and
Taori, Rohan and
Dave, Achal and
Shankar, Vaishaal and
Namkoong, Hongseok and
Miller, John and
Hajishirzi, Hannaneh and
Farhadi, Ali and
Schmidt, Ludwig},
title = {OpenCLIP},
month = jul,
year = 2021,
note = {If you use this software, please cite it as below.},
publisher = {Zenodo},
version = {0.1},
doi = {10.5281/zenodo.5143773},
url = {https://doi.org/10.5281/zenodo.5143773}
}
```
```bibtex
@article{cherti2022reproducible,
title={Reproducible scaling laws for contrastive language-image learning},
author={Cherti, Mehdi and Beaumont, Romain and Wightman, Ross and Wortsman, Mitchell and Ilharco, Gabriel and Gordon, Cade and Schuhmann, Christoph and Schmidt, Ludwig and Jitsev, Jenia},
journal={arXiv preprint arXiv:2212.07143},
year={2022}
}
```
```bibtex
@inproceedings{schuhmann2022laionb,
title={{LAION}-5B: An open large-scale dataset for training next generation image-text models},
author={Christoph Schuhmann and
Romain Beaumont and
Richard Vencu and
Cade W Gordon and
Ross Wightman and
Mehdi Cherti and
Theo Coombes and
Aarush Katta and
Clayton Mullis and
Mitchell Wortsman and
Patrick Schramowski and
Srivatsa R Kundurthy and
Katherine Crowson and
Ludwig Schmidt and
Robert Kaczmarczyk and
Jenia Jitsev},
booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},
year={2022},
url={https://openreview.net/forum?id=M3Y74vmsMcY}
}
```
```bibtex
@article{dosovitskiy2020vit,
title={An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale},
author={Dosovitskiy, Alexey and Beyer, Lucas and Kolesnikov, Alexander and Weissenborn, Dirk and Zhai, Xiaohua and Unterthiner, Thomas and Dehghani, Mostafa and Minderer, Matthias and Heigold, Georg and Gelly, Sylvain and Uszkoreit, Jakob and Houlsby, Neil},
journal={ICLR},
year={2021}
}
```
```bibtex
@misc{rw2019timm,
author = {Ross Wightman},
title = {PyTorch Image Models},
year = {2019},
publisher = {GitHub},
journal = {GitHub repository},
doi = {10.5281/zenodo.4414861},
howpublished = {\url{https://github.com/huggingface/pytorch-image-models}}
}
```
|
facebook/mms-tts-por | facebook | 2023-09-01T10:11:51Z | 899 | 7 | transformers | [
"transformers",
"pytorch",
"safetensors",
"vits",
"text-to-audio",
"mms",
"text-to-speech",
"arxiv:2305.13516",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | text-to-speech | 2023-09-01T10:11:35Z |
---
license: cc-by-nc-4.0
tags:
- mms
- vits
pipeline_tag: text-to-speech
---
# Massively Multilingual Speech (MMS): Portuguese Text-to-Speech
This repository contains the **Portuguese (por)** language text-to-speech (TTS) model checkpoint.
This model is part of Facebook's [Massively Multilingual Speech](https://arxiv.org/abs/2305.13516) project, aiming to
provide speech technology across a diverse range of languages. You can find more details about the supported languages
and their ISO 639-3 codes in the [MMS Language Coverage Overview](https://dl.fbaipublicfiles.com/mms/misc/language_coverage_mms.html),
and see all MMS-TTS checkpoints on the Hugging Face Hub: [facebook/mms-tts](https://huggingface.co/models?sort=trending&search=facebook%2Fmms-tts).
MMS-TTS is available in the 🤗 Transformers library from version 4.33 onwards.
## Model Details
VITS (**V**ariational **I**nference with adversarial learning for end-to-end **T**ext-to-**S**peech) is an end-to-end
speech synthesis model that predicts a speech waveform conditional on an input text sequence. It is a conditional variational
autoencoder (VAE) comprised of a posterior encoder, decoder, and conditional prior.
A set of spectrogram-based acoustic features are predicted by the flow-based module, which is formed of a Transformer-based
text encoder and multiple coupling layers. The spectrogram is decoded using a stack of transposed convolutional layers,
much in the same style as the HiFi-GAN vocoder. Motivated by the one-to-many nature of the TTS problem, where the same text
input can be spoken in multiple ways, the model also includes a stochastic duration predictor, which allows the model to
synthesise speech with different rhythms from the same input text.
The model is trained end-to-end with a combination of losses derived from variational lower bound and adversarial training.
To improve the expressiveness of the model, normalizing flows are applied to the conditional prior distribution. During
inference, the text encodings are up-sampled based on the duration prediction module, and then mapped into the
waveform using a cascade of the flow module and HiFi-GAN decoder. Due to the stochastic nature of the duration predictor,
the model is non-deterministic, and thus requires a fixed seed to generate the same speech waveform.
For the MMS project, a separate VITS checkpoint is trained on each langauge.
## Usage
MMS-TTS is available in the 🤗 Transformers library from version 4.33 onwards. To use this checkpoint,
first install the latest version of the library:
```
pip install --upgrade transformers accelerate
```
Then, run inference with the following code-snippet:
```python
from transformers import VitsModel, AutoTokenizer
import torch
model = VitsModel.from_pretrained("facebook/mms-tts-por")
tokenizer = AutoTokenizer.from_pretrained("facebook/mms-tts-por")
text = "some example text in the Portuguese language"
inputs = tokenizer(text, return_tensors="pt")
with torch.no_grad():
output = model(**inputs).waveform
```
The resulting waveform can be saved as a `.wav` file:
```python
import scipy
scipy.io.wavfile.write("techno.wav", rate=model.config.sampling_rate, data=output)
```
Or displayed in a Jupyter Notebook / Google Colab:
```python
from IPython.display import Audio
Audio(output, rate=model.config.sampling_rate)
```
## BibTex citation
This model was developed by Vineel Pratap et al. from Meta AI. If you use the model, consider citing the MMS paper:
```
@article{pratap2023mms,
title={Scaling Speech Technology to 1,000+ Languages},
author={Vineel Pratap and Andros Tjandra and Bowen Shi and Paden Tomasello and Arun Babu and Sayani Kundu and Ali Elkahky and Zhaoheng Ni and Apoorv Vyas and Maryam Fazel-Zarandi and Alexei Baevski and Yossi Adi and Xiaohui Zhang and Wei-Ning Hsu and Alexis Conneau and Michael Auli},
journal={arXiv},
year={2023}
}
```
## License
The model is licensed as **CC-BY-NC 4.0**.
|
TheBloke/Chinese-Llama-2-13B-GGUF | TheBloke | 2023-09-27T12:49:19Z | 899 | 9 | transformers | [
"transformers",
"gguf",
"llama",
"base_model:ziqingyang/chinese-llama-2-13b",
"license:apache-2.0",
"text-generation-inference",
"region:us"
] | null | 2023-09-14T17:33:56Z | ---
license: apache-2.0
model_name: Chinese Llama 2 13B
base_model: ziqingyang/chinese-llama-2-13b
inference: false
model_creator: Ziqing Yang
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 -->
# Chinese Llama 2 13B - GGUF
- Model creator: [Ziqing Yang](https://huggingface.co/ziqingyang)
- Original model: [Chinese Llama 2 13B](https://huggingface.co/ziqingyang/chinese-llama-2-13b)
<!-- description start -->
## Description
This repo contains GGUF format model files for [Ziqing Yang's Chinese Llama 2 13B](https://huggingface.co/ziqingyang/chinese-llama-2-13b).
<!-- description end -->
<!-- README_GGUF.md-about-gguf start -->
### About GGUF
GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. GGUF offers numerous advantages over GGML, such as better tokenisation, and support for special tokens. It is also supports metadata, and is designed to be extensible.
Here is an incomplate list of clients and libraries that are known to support GGUF:
* [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option.
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration.
* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection.
* [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
* [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.
<!-- README_GGUF.md-about-gguf end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Chinese-Llama-2-13B-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Chinese-Llama-2-13B-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Chinese-Llama-2-13B-GGUF)
* [Ziqing Yang's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/ziqingyang/chinese-llama-2-13b)
<!-- 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 `apache-2.0`, 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: [Ziqing Yang's Chinese Llama 2 13B](https://huggingface.co/ziqingyang/chinese-llama-2-13b).
<!-- licensing end -->
<!-- compatibility_gguf start -->
## Compatibility
These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d36d5be95a0d9088b674dbb27354107221](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221)
They are also compatible with many third party UIs and libraries - please see the list at the top of this README.
## Explanation of quantisation methods
<details>
<summary>Click to see details</summary>
The new methods available are:
* GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
* GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
* GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
* GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
* GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw
Refer to the Provided Files table below to see what files use which methods, and how.
</details>
<!-- compatibility_gguf end -->
<!-- README_GGUF.md-provided-files start -->
## Provided files
| Name | Quant method | Bits | Size | Max RAM required | Use case |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [chinese-llama-2-13b.Q2_K.gguf](https://huggingface.co/TheBloke/Chinese-Llama-2-13B-GGUF/blob/main/chinese-llama-2-13b.Q2_K.gguf) | Q2_K | 2 | 5.57 GB| 8.07 GB | smallest, significant quality loss - not recommended for most purposes |
| [chinese-llama-2-13b.Q3_K_S.gguf](https://huggingface.co/TheBloke/Chinese-Llama-2-13B-GGUF/blob/main/chinese-llama-2-13b.Q3_K_S.gguf) | Q3_K_S | 3 | 5.81 GB| 8.31 GB | very small, high quality loss |
| [chinese-llama-2-13b.Q3_K_M.gguf](https://huggingface.co/TheBloke/Chinese-Llama-2-13B-GGUF/blob/main/chinese-llama-2-13b.Q3_K_M.gguf) | Q3_K_M | 3 | 6.49 GB| 8.99 GB | very small, high quality loss |
| [chinese-llama-2-13b.Q3_K_L.gguf](https://huggingface.co/TheBloke/Chinese-Llama-2-13B-GGUF/blob/main/chinese-llama-2-13b.Q3_K_L.gguf) | Q3_K_L | 3 | 7.08 GB| 9.58 GB | small, substantial quality loss |
| [chinese-llama-2-13b.Q4_0.gguf](https://huggingface.co/TheBloke/Chinese-Llama-2-13B-GGUF/blob/main/chinese-llama-2-13b.Q4_0.gguf) | Q4_0 | 4 | 7.53 GB| 10.03 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [chinese-llama-2-13b.Q4_K_S.gguf](https://huggingface.co/TheBloke/Chinese-Llama-2-13B-GGUF/blob/main/chinese-llama-2-13b.Q4_K_S.gguf) | Q4_K_S | 4 | 7.58 GB| 10.08 GB | small, greater quality loss |
| [chinese-llama-2-13b.Q4_K_M.gguf](https://huggingface.co/TheBloke/Chinese-Llama-2-13B-GGUF/blob/main/chinese-llama-2-13b.Q4_K_M.gguf) | Q4_K_M | 4 | 8.03 GB| 10.53 GB | medium, balanced quality - recommended |
| [chinese-llama-2-13b.Q5_0.gguf](https://huggingface.co/TheBloke/Chinese-Llama-2-13B-GGUF/blob/main/chinese-llama-2-13b.Q5_0.gguf) | Q5_0 | 5 | 9.15 GB| 11.65 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [chinese-llama-2-13b.Q5_K_S.gguf](https://huggingface.co/TheBloke/Chinese-Llama-2-13B-GGUF/blob/main/chinese-llama-2-13b.Q5_K_S.gguf) | Q5_K_S | 5 | 9.15 GB| 11.65 GB | large, low quality loss - recommended |
| [chinese-llama-2-13b.Q5_K_M.gguf](https://huggingface.co/TheBloke/Chinese-Llama-2-13B-GGUF/blob/main/chinese-llama-2-13b.Q5_K_M.gguf) | Q5_K_M | 5 | 9.41 GB| 11.91 GB | large, very low quality loss - recommended |
| [chinese-llama-2-13b.Q6_K.gguf](https://huggingface.co/TheBloke/Chinese-Llama-2-13B-GGUF/blob/main/chinese-llama-2-13b.Q6_K.gguf) | Q6_K | 6 | 10.88 GB| 13.38 GB | very large, extremely low quality loss |
| [chinese-llama-2-13b.Q8_0.gguf](https://huggingface.co/TheBloke/Chinese-Llama-2-13B-GGUF/blob/main/chinese-llama-2-13b.Q8_0.gguf) | Q8_0 | 8 | 14.09 GB| 16.59 GB | very large, extremely low quality loss - not recommended |
**Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.
<!-- README_GGUF.md-provided-files end -->
<!-- README_GGUF.md-how-to-download start -->
## How to download GGUF files
**Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.
The following clients/libraries will automatically download models for you, providing a list of available models to choose from:
- LM Studio
- LoLLMS Web UI
- Faraday.dev
### In `text-generation-webui`
Under Download Model, you can enter the model repo: TheBloke/Chinese-Llama-2-13B-GGUF and below it, a specific filename to download, such as: chinese-llama-2-13b.q4_K_M.gguf.
Then click Download.
### On the command line, including multiple files at once
I recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub>=0.17.1
```
Then you can download any individual model file to the current directory, at high speed, with a command like this:
```shell
huggingface-cli download TheBloke/Chinese-Llama-2-13B-GGUF chinese-llama-2-13b.q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage</summary>
You can also download multiple files at once with a pattern:
```shell
huggingface-cli download TheBloke/Chinese-Llama-2-13B-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:
```shell
pip3 install hf_transfer
```
And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
```shell
HUGGINGFACE_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/Chinese-Llama-2-13B-GGUF chinese-llama-2-13b.q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
Windows CLI users: Use `set HUGGINGFACE_HUB_ENABLE_HF_TRANSFER=1` before running the download command.
</details>
<!-- README_GGUF.md-how-to-download end -->
<!-- README_GGUF.md-how-to-run start -->
## Example `llama.cpp` command
Make sure you are using `llama.cpp` from commit [d0cee0d36d5be95a0d9088b674dbb27354107221](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later.
```shell
./main -ngl 32 -m chinese-llama-2-13b.q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{prompt}\n\n### Response:"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 4096` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically.
If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`
For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md)
## How to run in `text-generation-webui`
Further instructions here: [text-generation-webui/docs/llama.cpp.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp.md).
## How to run from Python code
You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries.
### How to load this model from Python using ctransformers
#### First install the package
```bash
# Base ctransformers with no GPU acceleration
pip install ctransformers>=0.2.24
# Or with CUDA GPU acceleration
pip install ctransformers[cuda]>=0.2.24
# Or with ROCm GPU acceleration
CT_HIPBLAS=1 pip install ctransformers>=0.2.24 --no-binary ctransformers
# Or with Metal GPU acceleration for macOS systems
CT_METAL=1 pip install ctransformers>=0.2.24 --no-binary ctransformers
```
#### Simple example code to load one of these GGUF models
```python
from ctransformers import AutoModelForCausalLM
# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
llm = AutoModelForCausalLM.from_pretrained("TheBloke/Chinese-Llama-2-13B-GGUF", model_file="chinese-llama-2-13b.q4_K_M.gguf", model_type="llama", gpu_layers=50)
print(llm("AI is going to"))
```
## How to use with LangChain
Here's guides on using llama-cpp-python or ctransformers with LangChain:
* [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp)
* [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers)
<!-- README_GGUF.md-how-to-run 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 start -->
# Original model card: Ziqing Yang's Chinese Llama 2 13B
# Chinese-LLaMA-2-13B
**This is the full Chinese-LLaMA-2-13B model，which can be loaded directly for inference and full-parameter training.**
**Related models👇**
* Long context base models
* [Chinese-LLaMA-2-7B-16K (full model)](https://huggingface.co/ziqingyang/chinese-llama-2-7b-16k)
* [Chinese-LLaMA-2-LoRA-7B-16K (LoRA model)](https://huggingface.co/ziqingyang/chinese-llama-2-lora-7b-16k)
* [Chinese-LLaMA-2-13B-16K (full model)](https://huggingface.co/ziqingyang/chinese-llama-2-13b-16k)
* [Chinese-LLaMA-2-LoRA-13B-16K (LoRA model)](https://huggingface.co/ziqingyang/chinese-llama-2-lora-13b-16k)
* Base models
* [Chinese-LLaMA-2-7B (full model)](https://huggingface.co/ziqingyang/chinese-llama-2-7b)
* [Chinese-LLaMA-2-LoRA-7B (LoRA model)](https://huggingface.co/ziqingyang/chinese-llama-2-lora-7b)
* [Chinese-LLaMA-2-13B (full model)](https://huggingface.co/ziqingyang/chinese-llama-2-13b)
* [Chinese-LLaMA-2-LoRA-13B (LoRA model)](https://huggingface.co/ziqingyang/chinese-llama-2-lora-13b)
* Instruction/Chat models
* [Chinese-Alpaca-2-7B (full model)](https://huggingface.co/ziqingyang/chinese-alpaca-2-7b)
* [Chinese-Alpaca-2-LoRA-7B (LoRA model)](https://huggingface.co/ziqingyang/chinese-alpaca-2-lora-7b)
* [Chinese-Alpaca-2-13B (full model)](https://huggingface.co/ziqingyang/chinese-alpaca-2-13b)
* [Chinese-Alpaca-2-LoRA-13B (LoRA model)](https://huggingface.co/ziqingyang/chinese-alpaca-2-lora-13b)
# Description of Chinese-LLaMA-Alpaca-2
This project is based on the Llama-2, released by Meta, and it is the second generation of the Chinese LLaMA & Alpaca LLM project. We open-source Chinese LLaMA-2 (foundation model) and Alpaca-2 (instruction-following model). These models have been expanded and optimized with Chinese vocabulary beyond the original Llama-2. We used large-scale Chinese data for incremental pre-training, which further improved the fundamental semantic understanding of the Chinese language, resulting in a significant performance improvement compared to the first-generation models. The relevant models support a 4K context and can be expanded up to 18K+ using the NTK method.
The main contents of this project include:
* 🚀 New extended Chinese vocabulary beyond Llama-2, open-sourcing the Chinese LLaMA-2 and Alpaca-2 LLMs.
* 🚀 Open-sourced the pre-training and instruction finetuning (SFT) scripts for further tuning on user's data
* 🚀 Quickly deploy and experience the quantized LLMs on CPU/GPU of personal PC
* 🚀 Support for LLaMA ecosystems like 🤗transformers, llama.cpp, text-generation-webui, LangChain, vLLM etc.
Please refer to [https://github.com/ymcui/Chinese-LLaMA-Alpaca-2/](https://github.com/ymcui/Chinese-LLaMA-Alpaca-2/) for details.
<!-- original-model-card end -->
|
bartowski/Llama-3-8B-Instruct-MopeyMule-GGUF | bartowski | 2024-05-31T05:01:41Z | 899 | 1 | null | [
"gguf",
"facebook",
"meta",
"pytorch",
"llama",
"llama-3",
"text-generation",
"en",
"license:other",
"region:us"
] | text-generation | 2024-05-31T04:44:41Z | ---
language:
- en
pipeline_tag: text-generation
tags:
- facebook
- meta
- pytorch
- llama
- llama-3
license: other
license_name: llama3
inference:
parameters:
max_new_tokens: 300
stop:
- <|end_of_text|>
- <|eot_id|>
quantized_by: bartowski
---
## Llamacpp imatrix Quantizations of Llama-3-8B-Instruct-MopeyMule
Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggerganov/llama.cpp/releases/tag/b3024">b3024</a> for quantization.
Original model: https://huggingface.co/failspy/Llama-3-8B-Instruct-MopeyMule
All quants made using imatrix option with dataset from [here](https://gist.github.com/bartowski1182/eb213dccb3571f863da82e99418f81e8)
## Prompt format
```
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
{system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|>
{prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
```
## Download a file (not the whole branch) from below:
| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [Llama-3-8B-Instruct-MopeyMule-Q8_0.gguf](https://huggingface.co/bartowski/Llama-3-8B-Instruct-MopeyMule-GGUF/blob/main/Llama-3-8B-Instruct-MopeyMule-Q8_0.gguf) | Q8_0 | 8.54GB | Extremely high quality, generally unneeded but max available quant. |
| [Llama-3-8B-Instruct-MopeyMule-Q6_K.gguf](https://huggingface.co/bartowski/Llama-3-8B-Instruct-MopeyMule-GGUF/blob/main/Llama-3-8B-Instruct-MopeyMule-Q6_K.gguf) | Q6_K | 6.59GB | Very high quality, near perfect, *recommended*. |
| [Llama-3-8B-Instruct-MopeyMule-Q5_K_M.gguf](https://huggingface.co/bartowski/Llama-3-8B-Instruct-MopeyMule-GGUF/blob/main/Llama-3-8B-Instruct-MopeyMule-Q5_K_M.gguf) | Q5_K_M | 5.73GB | High quality, *recommended*. |
| [Llama-3-8B-Instruct-MopeyMule-Q5_K_S.gguf](https://huggingface.co/bartowski/Llama-3-8B-Instruct-MopeyMule-GGUF/blob/main/Llama-3-8B-Instruct-MopeyMule-Q5_K_S.gguf) | Q5_K_S | 5.59GB | High quality, *recommended*. |
| [Llama-3-8B-Instruct-MopeyMule-Q4_K_M.gguf](https://huggingface.co/bartowski/Llama-3-8B-Instruct-MopeyMule-GGUF/blob/main/Llama-3-8B-Instruct-MopeyMule-Q4_K_M.gguf) | Q4_K_M | 4.92GB | Good quality, uses about 4.83 bits per weight, *recommended*. |
| [Llama-3-8B-Instruct-MopeyMule-Q4_K_S.gguf](https://huggingface.co/bartowski/Llama-3-8B-Instruct-MopeyMule-GGUF/blob/main/Llama-3-8B-Instruct-MopeyMule-Q4_K_S.gguf) | Q4_K_S | 4.69GB | Slightly lower quality with more space savings, *recommended*. |
| [Llama-3-8B-Instruct-MopeyMule-IQ4_XS.gguf](https://huggingface.co/bartowski/Llama-3-8B-Instruct-MopeyMule-GGUF/blob/main/Llama-3-8B-Instruct-MopeyMule-IQ4_XS.gguf) | IQ4_XS | 4.44GB | Decent quality, smaller than Q4_K_S with similar performance, *recommended*. |
| [Llama-3-8B-Instruct-MopeyMule-Q3_K_L.gguf](https://huggingface.co/bartowski/Llama-3-8B-Instruct-MopeyMule-GGUF/blob/main/Llama-3-8B-Instruct-MopeyMule-Q3_K_L.gguf) | Q3_K_L | 4.32GB | Lower quality but usable, good for low RAM availability. |
| [Llama-3-8B-Instruct-MopeyMule-Q3_K_M.gguf](https://huggingface.co/bartowski/Llama-3-8B-Instruct-MopeyMule-GGUF/blob/main/Llama-3-8B-Instruct-MopeyMule-Q3_K_M.gguf) | Q3_K_M | 4.01GB | Even lower quality. |
| [Llama-3-8B-Instruct-MopeyMule-IQ3_M.gguf](https://huggingface.co/bartowski/Llama-3-8B-Instruct-MopeyMule-GGUF/blob/main/Llama-3-8B-Instruct-MopeyMule-IQ3_M.gguf) | IQ3_M | 3.78GB | Medium-low quality, new method with decent performance comparable to Q3_K_M. |
| [Llama-3-8B-Instruct-MopeyMule-Q3_K_S.gguf](https://huggingface.co/bartowski/Llama-3-8B-Instruct-MopeyMule-GGUF/blob/main/Llama-3-8B-Instruct-MopeyMule-Q3_K_S.gguf) | Q3_K_S | 3.66GB | Low quality, not recommended. |
| [Llama-3-8B-Instruct-MopeyMule-IQ3_XS.gguf](https://huggingface.co/bartowski/Llama-3-8B-Instruct-MopeyMule-GGUF/blob/main/Llama-3-8B-Instruct-MopeyMule-IQ3_XS.gguf) | IQ3_XS | 3.51GB | Lower quality, new method with decent performance, slightly better than Q3_K_S. |
| [Llama-3-8B-Instruct-MopeyMule-IQ3_XXS.gguf](https://huggingface.co/bartowski/Llama-3-8B-Instruct-MopeyMule-GGUF/blob/main/Llama-3-8B-Instruct-MopeyMule-IQ3_XXS.gguf) | IQ3_XXS | 3.27GB | Lower quality, new method with decent performance, comparable to Q3 quants. |
| [Llama-3-8B-Instruct-MopeyMule-Q2_K.gguf](https://huggingface.co/bartowski/Llama-3-8B-Instruct-MopeyMule-GGUF/blob/main/Llama-3-8B-Instruct-MopeyMule-Q2_K.gguf) | Q2_K | 3.17GB | Very low quality but surprisingly usable. |
| [Llama-3-8B-Instruct-MopeyMule-IQ2_M.gguf](https://huggingface.co/bartowski/Llama-3-8B-Instruct-MopeyMule-GGUF/blob/main/Llama-3-8B-Instruct-MopeyMule-IQ2_M.gguf) | IQ2_M | 2.94GB | Very low quality, uses SOTA techniques to also be surprisingly usable. |
| [Llama-3-8B-Instruct-MopeyMule-IQ2_S.gguf](https://huggingface.co/bartowski/Llama-3-8B-Instruct-MopeyMule-GGUF/blob/main/Llama-3-8B-Instruct-MopeyMule-IQ2_S.gguf) | IQ2_S | 2.75GB | Very low quality, uses SOTA techniques to be usable. |
| [Llama-3-8B-Instruct-MopeyMule-IQ2_XS.gguf](https://huggingface.co/bartowski/Llama-3-8B-Instruct-MopeyMule-GGUF/blob/main/Llama-3-8B-Instruct-MopeyMule-IQ2_XS.gguf) | IQ2_XS | 2.60GB | Very low quality, uses SOTA techniques to be usable. |
## Downloading using huggingface-cli
First, make sure you have hugginface-cli installed:
```
pip install -U "huggingface_hub[cli]"
```
Then, you can target the specific file you want:
```
huggingface-cli download bartowski/Llama-3-8B-Instruct-MopeyMule-GGUF --include "Llama-3-8B-Instruct-MopeyMule-Q4_K_M.gguf" --local-dir ./
```
If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run:
```
huggingface-cli download bartowski/Llama-3-8B-Instruct-MopeyMule-GGUF --include "Llama-3-8B-Instruct-MopeyMule-Q8_0.gguf/*" --local-dir Llama-3-8B-Instruct-MopeyMule-Q8_0
```
You can either specify a new local-dir (Llama-3-8B-Instruct-MopeyMule-Q8_0) or download them all in place (./)
## Which file should I choose?
A great write up with charts showing various performances is provided by Artefact2 [here](https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9)
The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have.
If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM.
If you want the absolute maximum quality, add both your system RAM and your GPU's VRAM together, then similarly grab a quant with a file size 1-2GB Smaller than that total.
Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'.
If you don't want to think too much, grab one of the K-quants. These are in format 'QX_K_X', like Q5_K_M.
If you want to get more into the weeds, you can check out this extremely useful feature chart:
[llama.cpp feature matrix](https://github.com/ggerganov/llama.cpp/wiki/Feature-matrix)
But basically, if you're aiming for below Q4, and you're running cuBLAS (Nvidia) or rocBLAS (AMD), you should look towards the I-quants. These are in format IQX_X, like IQ3_M. These are newer and offer better performance for their size.
These I-quants can also be used on CPU and Apple Metal, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide.
The I-quants are *not* compatible with Vulcan, which is also AMD, so if you have an AMD card double check if you're using the rocBLAS build or the Vulcan build. At the time of writing this, LM Studio has a preview with ROCm support, and other inference engines have specific builds for ROCm.
Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
|
Nitral-AI/Llama-3-Update-3.0-mmproj-model-f16 | Nitral-AI | 2024-06-01T01:40:34Z | 899 | 3 | null | [
"gguf",
"en",
"license:other",
"region:us"
] | null | 2024-06-01T00:51:19Z | ---
license: other
language:
- en
---
Most up-to date version of our Llama3-Llava (Based on Llava 1.5) series of mmproj files.
# Projector Extracted from: (updated) https://huggingface.co/weizhiwang/LLaVA-Llama-3-8B Thank @weizhiwang for their work!
* You must use the latest versions of [Koboldcpp](https://github.com/LostRuins/koboldcpp).
* You can load the **mmproj** by using the corresponding section in the interface:
 |
kanseki/llama3-8B-cn-rochat-v1-Q8_0-GGUF | kanseki | 2024-06-28T10:36:02Z | 899 | 0 | null | [
"gguf",
"region:us"
] | null | 2024-06-28T03:00:50Z | 这是量化本来是打算我自己测试用的，测试完感觉这个模型不是太理想，打算把这个量化删掉的。
结果一看下载量还挺大，那就先放着吧。
不过，如果是7b级别的中文RP、特别是ERP向的话，个人更推荐这个模型：https://huggingface.co/maywell/Qwen2-7B-Multilingual-RP 。
无论是涩度还是语言饱满度都更好。相应的量化在这里：https://huggingface.co/mradermacher/Qwen2-7B-Multilingual-RP-GGUF
---
base_model: RochatAI/llama3-8B-cn-rochat-v1
license: apache-2.0
tags:
- llama3
- llama-factory
- llama-cpp
- gguf-my-repo
---
# kanseki/llama3-8B-cn-rochat-v1-Q8_0-GGUF
This model was converted to GGUF format from [`RochatAI/llama3-8B-cn-rochat-v1`](https://huggingface.co/RochatAI/llama3-8B-cn-rochat-v1) 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/RochatAI/llama3-8B-cn-rochat-v1) 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 kanseki/llama3-8B-cn-rochat-v1-Q8_0-GGUF --hf-file llama3-8b-cn-rochat-v1-q8_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo kanseki/llama3-8B-cn-rochat-v1-Q8_0-GGUF --hf-file llama3-8b-cn-rochat-v1-q8_0.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo kanseki/llama3-8B-cn-rochat-v1-Q8_0-GGUF --hf-file llama3-8b-cn-rochat-v1-q8_0.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo kanseki/llama3-8B-cn-rochat-v1-Q8_0-GGUF --hf-file llama3-8b-cn-rochat-v1-q8_0.gguf -c 2048
```
|
akhooli/gpt2-small-arabic | akhooli | 2023-03-20T08:04:17Z | 898 | 11 | transformers | [
"transformers",
"pytorch",
"jax",
"safetensors",
"gpt2",
"text-generation",
"ar",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
language: "ar"
datasets:
- Arabic Wikipedia
metrics:
- none
---
# GPT2-Small-Arabic
## Model description
GPT2 model from Arabic Wikipedia dataset based on gpt2-small (using Fastai2).
## Intended uses & limitations
#### How to use
An example is provided in this [colab notebook](https://colab.research.google.com/drive/1mRl7c-5v-Klx27EEAEOAbrfkustL4g7a?usp=sharing).
Both text and poetry (fine-tuned model) generation are included.
#### Limitations and bias
GPT2-small-arabic (trained on Arabic Wikipedia) has several limitations in terms of coverage (Arabic Wikipeedia quality, no diacritics) and training performance.
Use as demonstration or proof of concepts but not as production code.
## Training data
This pretrained model used the Arabic Wikipedia dump (around 900 MB).
## Training procedure
Training was done using [Fastai2](https://github.com/fastai/fastai2/) library on Kaggle, using free GPU.
## Eval results
Final perplexity reached was 72.19, loss: 4.28, accuracy: 0.307
### BibTeX entry and citation info
```bibtex
@inproceedings{Abed Khooli,
year={2020}
}
```
|
zjunlp/MolGen-large | zjunlp | 2024-03-04T12:42:49Z | 898 | 11 | transformers | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"molecular language model",
"SELFIES",
"molecule generation",
"arxiv:2301.11259",
"autotrain_compatible",
"region:us"
] | text2text-generation | 2023-01-31T12:04:56Z | ---
tags:
- molecular language model
- SELFIES
- molecule generation
widget:
- text: '[C][=C][C][=C][C][=C][Ring1][=Branch1]'
inference: false
---
# MolGen-large
MolGen-large was introduced in the paper ["Domain-Agnostic Molecular Generation with Self-feedback"](https://arxiv.org/pdf/2301.11259.pdf) and first released in [this repository](https://github.com/zjunlp/MolGen). It is a pre-trained molecular generative model built using the 100\% robust molecular language representation, SELFIES.
## Model description
MolGen-large is the first pre-trained model that only produces chemically valid molecules.
With a training corpus of over 100 million molecules in SELFIES representation, MolGen-large learns the intrinsic structural patterns of molecules by mapping corrupted SELFIES to their original forms.
Specifically, MolGen-large employs a bidirectional Transformer as its encoder and an autoregressive Transformer as its decoder.
Through its carefully designed multi-task molecular prefix tuning (MPT), MolGen-large can generate molecules with desired properties, making it a valuable tool for molecular optimization.
## Intended uses
You can use the raw model for molecule generation or fine-tune it to a downstream task. Please take note that the following examples only demonstrate the utilization of our pre-trained model for molecule generation. See the [repository](https://github.com/zjunlp/MolGen) to look for fine-tune details on a task that interests you.
### How to use
Molecule generation example:
```python
>>> from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
>>> tokenizer = AutoTokenizer.from_pretrained("zjunlp/MolGen-large")
>>> model = AutoModelForSeq2SeqLM.from_pretrained("zjunlp/MolGen-large")
>>> sf_input = tokenizer("[C][=C][C][=C][C][=C][Ring1][=Branch1]", return_tensors="pt")
>>> # beam search
>>> molecules = model.generate(input_ids=sf_input["input_ids"],
attention_mask=sf_input["attention_mask"],
max_length=15,
min_length=5,
num_return_sequences=5,
num_beams=5)
>>> sf_output = [tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=True).replace(" ","") for g in molecules]
['[C][=C][C][=C][C][=C][Ring1][=Branch1]',
'[C][=C][C][=C][C][=C][C][=C][Ring1][=Branch1]',
'[C][=C][C][=C][C][=C][Ring1][=Branch1][C][=C][C][=C]',
'[C][=C][C][=C][C][=C][Ring1][=Branch1][C@H1][C][=C][C]',
'[C][=C][C][=C][C][=C][Ring1][=Branch1][C@H1][=C][C][=C]']
```
### BibTeX entry and citation info
```bibtex
@inproceedings{fang2023domain,
author = {Yin Fang and
Ningyu Zhang and
Zhuo Chen and
Xiaohui Fan and
Huajun Chen},
title = {Domain-Agnostic Molecular Generation with Chemical Feedback},
booktitle = {{ICLR}},
publisher = {OpenReview.net},
year = {2024},
url = {https://openreview.net/pdf?id=9rPyHyjfwP}
}
``` |
timm/edgenext_x_small.in1k | timm | 2023-04-23T22:43:23Z | 898 | 0 | timm | [
"timm",
"pytorch",
"safetensors",
"image-classification",
"dataset:imagenet-1k",
"arxiv:2206.10589",
"license:mit",
"region:us"
] | image-classification | 2023-04-23T22:43:20Z | ---
tags:
- image-classification
- timm
library_name: timm
license: mit
datasets:
- imagenet-1k
---
# Model card for edgenext_x_small.in1k
An EdgeNeXt image classification model. Trained on ImageNet-1k by paper authors.
## Model Details
- **Model Type:** Image classification / feature backbone
- **Model Stats:**
- Params (M): 2.3
- GMACs: 0.5
- Activations (M): 5.9
- Image size: train = 256 x 256, test = 288 x 288
- **Papers:**
- EdgeNeXt: Efficiently Amalgamated CNN-Transformer Architecture for Mobile Vision Applications: https://arxiv.org/abs/2206.10589
- **Dataset:** ImageNet-1k
- **Original:** https://github.com/mmaaz60/EdgeNeXt
## Model Usage
### Image Classification
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model('edgenext_x_small.in1k', pretrained=True)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)
```
### Feature Map Extraction
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'edgenext_x_small.in1k',
pretrained=True,
features_only=True,
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
for o in output:
# print shape of each feature map in output
# e.g.:
# torch.Size([1, 32, 64, 64])
# torch.Size([1, 64, 32, 32])
# torch.Size([1, 100, 16, 16])
# torch.Size([1, 192, 8, 8])
print(o.shape)
```
### Image Embeddings
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'edgenext_x_small.in1k',
pretrained=True,
num_classes=0, # remove classifier nn.Linear
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor
# or equivalently (without needing to set num_classes=0)
output = model.forward_features(transforms(img).unsqueeze(0))
# output is unpooled, a (1, 192, 8, 8) shaped tensor
output = model.forward_head(output, pre_logits=True)
# output is a (1, num_features) shaped tensor
```
## Citation
```bibtex
@inproceedings{Maaz2022EdgeNeXt,
title={EdgeNeXt: Efficiently Amalgamated CNN-Transformer Architecture for Mobile Vision Applications},
author={Muhammad Maaz and Abdelrahman Shaker and Hisham Cholakkal and Salman Khan and Syed Waqas Zamir and Rao Muhammad Anwer and Fahad Shahbaz Khan},
booktitle={International Workshop on Computational Aspects of Deep Learning at 17th European Conference on Computer Vision (CADL2022)},
year={2022},
organization={Springer}
}
```
|
priyabrat/new5th_bert_article_categorisation_model | priyabrat | 2023-06-09T04:43:52Z | 898 | 1 | transformers | [
"transformers",
"pytorch",
"safetensors",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2023-05-16T07:22:41Z | Entry not found |
digiplay/CuriousMerge2.5D_v40E | digiplay | 2024-06-11T19:55:41Z | 898 | 5 | diffusers | [
"diffusers",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | 2023-06-13T09:57:51Z | ---
license: other
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
inference: true
---
Model info:
https://civitai.com/models/79070?modelVersionId=94189
Author's page:
https://civitai.com/user/Cur1ous/models
https://ko-fi.com/cur1ous/
Very nice ❤️ and kind developer.
|
TusharGoel/LayoutLM-Finetuned-DocVQA | TusharGoel | 2023-10-09T12:21:07Z | 898 | 7 | transformers | [
"transformers",
"pytorch",
"layoutlm",
"document-question-answering",
"en",
"doi:10.57967/hf/1144",
"license:mit",
"endpoints_compatible",
"region:us"
] | document-question-answering | 2023-09-23T17:31:26Z | ---
license: mit
language:
- en
pipeline_tag: document-question-answering
---
This model was trained on [DocVQA](https://www.docvqa.org/) Dataset questions
Code for Training and Prediction (v1): https://www.kaggle.com/tusharcode/training-layoutlm-docvqa
**How to use:**
```python
from transformers import AutoTokenizer, AutoModelForDocumentQuestionAnswering
from datasets import load_dataset
model_checkpoint = "TusharGoel/LayoutLM-Finetuned-DocVQA"
tokenizer = AutoTokenizer.from_pretrained(model_checkpoint, add_prefix_space=True)
model_predict = AutoModelForDocumentQuestionAnswering.from_pretrained(model_checkpoint)
model_predict.eval()
dataset = load_dataset("nielsr/funsd", split="train")
example = dataset[0]
question = "What's Licensee Number?"
words = example["words"]
boxes = example["bboxes"]
encoding = tokenizer(question.split(), words,
is_split_into_words=True, return_token_type_ids=True, return_tensors="pt")
bbox = []
for i, s, w in zip(encoding.input_ids[0], encoding.sequence_ids(0), encoding.word_ids(0)):
if s == 1:
bbox.append(boxes[w])
elif i == tokenizer.sep_token_id:
bbox.append([1000] * 4)
else:
bbox.append([0] * 4)
encoding["bbox"] = torch.tensor([bbox])
word_ids = encoding.word_ids(0)
outputs = model_predict(**encoding)
loss = outputs.loss
start_scores = outputs.start_logits
end_scores = outputs.end_logits
start, end = word_ids[start_scores.argmax(-1).item()], word_ids[end_scores.argmax(-1).item()]
print(" ".join(words[start : end + 1]))
``` |
stagvn/vi-fin-news | stagvn | 2023-12-27T10:27:38Z | 898 | 1 | transformers | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"vi",
"base_model:FPTAI/vibert-base-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2023-12-22T03:08:39Z | ---
base_model: FPTAI/vibert-base-cased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: vi-fin-news
results: []
license: apache-2.0
language:
- vi
library_name: transformers
pipeline_tag: text-classification
---
<!-- 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. -->
# vi-fin-news
This model is a fine-tuned version of [FPTAI/vibert-base-cased](https://huggingface.co/FPTAI/vibert-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4509
- Accuracy: 0.9136
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1176 | 1.0 | 1150 | 0.3566 | 0.9181 |
| 0.0582 | 2.0 | 2300 | 0.4509 | 0.9136 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.2
- Datasets 2.12.0
- Tokenizers 0.13.3 |
sambanovasystems/SambaLingo-Thai-Chat | sambanovasystems | 2024-04-16T22:38:55Z | 898 | 35 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"th",
"en",
"dataset:HuggingFaceH4/ultrachat_200k",
"dataset:HuggingFaceH4/ultrafeedback_binarized",
"dataset:HuggingFaceH4/cai-conversation-harmless",
"arxiv:2404.05829",
"license:llama2",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-02-15T22:46:41Z | ---
language:
- th
- en
license: llama2
datasets:
- HuggingFaceH4/ultrachat_200k
- HuggingFaceH4/ultrafeedback_binarized
- HuggingFaceH4/cai-conversation-harmless
model-index:
- name: SambaLingo-Thai-Chat
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 52.73
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sambanovasystems/SambaLingo-Thai-Chat
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 78.42
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sambanovasystems/SambaLingo-Thai-Chat
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 43.95
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sambanovasystems/SambaLingo-Thai-Chat
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 40.84
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sambanovasystems/SambaLingo-Thai-Chat
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 72.22
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sambanovasystems/SambaLingo-Thai-Chat
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 8.57
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sambanovasystems/SambaLingo-Thai-Chat
name: Open LLM Leaderboard
---
# SambaLingo-Thai-Chat
<img src="SambaLingo_Logo.png" width="340" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
<!-- Provide a quick summary of what the model is/does. -->
SambaLingo-Thai-Chat is a human aligned chat model trained in Thai and English. It is trained using direct preference optimization on top the base model [SambaLingo-Thai-Base](https://huggingface.co/sambanovasystems/SambaLingo-Thai-Base). The base model adapts [Llama-2-7b](https://huggingface.co/meta-llama/Llama-2-7b-hf) to Thai by training on 38 billion tokens from the Thai split of the [Cultura-X](https://huggingface.co/datasets/uonlp/CulturaX) dataset. Try This Model at [SambaLingo-chat-space](https://huggingface.co/spaces/sambanovasystems/SambaLingo-chat-space).
## Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [SambaNova Systems](https://sambanova.ai/)
- **Model type:** Language Model
- **Language(s):** Thai, English
- **Finetuned from model:** [Llama-2-7b](https://huggingface.co/meta-llama/Llama-2-7b-hf)
- **Try This Model:** [SambaLingo-chat-space](https://huggingface.co/spaces/sambanovasystems/SambaLingo-chat-space)
- **Paper:** [SambaLingo: Teaching Large Language Models New Languages](https://arxiv.org/abs/2404.05829)
- **Blog Post**: [sambalingo-open-source-language-experts](https://sambanova.ai/blog/sambalingo-open-source-language-experts)
## Getting Started
### Loading Model With Hugging Face
Please make sure to set use_fast=False when loading the tokenizer.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("sambanovasystems/SambaLingo-Thai-Chat", use_fast=False)
model = AutoModelForCausalLM.from_pretrained("sambanovasystems/SambaLingo-Thai-Chat", device_map="auto", torch_dtype="auto")
```
### Interacting With Model Pipeline
Please make sure to set use_fast=False when loading the tokenizer.
```python
from transformers import pipeline
pipe = pipeline("text-generation", model="sambanovasystems/SambaLingo-Thai-Chat", device_map="auto", use_fast=False)
messages = [
{"role": "user", "content": {YOUR_QUESTION}},
]
prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipe(prompt)[0]
outputs = outputs["generated_text"]
```
### Suggested Inference Parameters
- Temperature: 0.8
- Repetition penalty: 1.0
- Top-p: 0.9
### Prompting Guidelines
To prompt this model, please use the following chat template:
```
<|user|>\n{question}</s>\n<|assistant|>\n
```
### Example Prompts and Generations
```
<|user|>
ปรัชญาทางเศรษฐกิจที่พระบาทสมเด็จพระมหาภูมิพลอดุลยเดชมหาราช บรมนาถบพิตรมีพระราชดำรัสแก่ชาวไทย มีชื่อว่าอะไร</s>
<|assistant|>
ปรัชญาทางเศรษฐกิจที่พระบาทสมเด็จพระปรมินทรมหาภูมิพลอดุลยเดชทรงมีพระราชดำรัสแก่ชาวไทยเรียกว่า "ปรัชญาเศรษฐกิจพอเพียง" ปรัชญานี้เน้นความสำคัญของการพัฒนาแบบองค์รวมแ ละความสมดุล โดยคำนึงถึงสภาพแวดล้อมทางสังคม เศรษฐกิจ และสิ่งแวดล้อม ปรัชญาเศรษฐกิจพอเพียงเน้นความสำคัญของการพึ่งพาตนเอง ความมีเหตุผล และการประหยัด ตลอดจนความสำคัญของก ารพัฒนาจิตใจและจิตวิญญาณ ปรัชญานี้เน้นย้ำถึงความสำคัญของการดำเนินชีวิตอย่างพอประมาณ มีเหตุผล และมีคุณธรรม ตลอดจนความสำคัญของการพัฒนาความรู้และทักษะอย่างต่อเนื่อง ปรัชญาเศรษฐกิ จพอเพียงได้รับการยกย่องอย่างกว้างขวางว่าเป็นแนวทางแก้ไขปัญหาการพัฒนาที่ยั่งยืนและเป็นแนวทางในการดำเนินชีวิตที่นำไปสู่ความสุขและความเป็นอยู่ที่ดี
```
## Training Details
The alignment phase follows the recipe for [Zephyr-7B](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta), and comprises two stages: supervised fine-tuning (SFT) and Direct Performance Optimization (DPO).
The SFT phase was done on the [ultrachat_200k](https://huggingface.co/datasets/HuggingFaceH4/ultrachat_200k) dataset mixed with the Google translated version of the ultrachat_200k dataset. It was trained for one epoch with global batch size 512 and max sequence length 2048 tokens. We used a linear decay learning rate of 2e-5 and 10% warmup.
The DPO phase was done on the [ultrafeedback](https://huggingface.co/datasets/HuggingFaceH4/ultrafeedback_binarized) dataset and [cai-conversation-harmless](https://huggingface.co/datasets/HuggingFaceH4/cai-conversation-harmless) dataset, mixed with 10% of the data Google translated. It was trained with global batch size 32 and for three epochs. We used a linear decay learning rate of 5e-7, 10% warmup and β=0.1 as the regularization factor for DPO.
## Tokenizer Details
We extended the vocabulary of the base llama model from 32,000 tokens to 57,000 tokens by adding up to 25,000 non-overlapping tokens from the new language.
## Evaluation
For evaluation results see our paper: [SambaLingo: Teaching Large Language Models New Languages](https://arxiv.org/abs/2404.05829)
## 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. -->
Use of this model is governed by the Meta’s [Llama 2 Community License Agreement](https://ai.meta.com/llama/license/). Please review and accept the license before downloading the model weights.
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
SambaLingo should NOT be used for:
- Mission-critical applications
- Applications that involve the safety of others
- Making highly important decisions
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
Like all LLMs, SambaLingo has certain limitations:
- Hallucination: Model may sometimes generate responses that contain plausible-sounding but factually incorrect or irrelevant information.
- Code Switching: The model might unintentionally switch between languages or dialects within a single response, affecting the coherence and understandability of the output.
- Repetition: The Model may produce repetitive phrases or sentences, leading to less engaging and informative responses.
- Coding and Math: The model's performance in generating accurate code or solving complex mathematical problems may be limited.
- Toxicity: The model could inadvertently generate responses containing inappropriate or harmful content.
## Acknowledgments
We extend our heartfelt gratitude to the open-source AI community; this endeavor would not have been possible without open source. SambaNova embraces the open-source community and aspires to actively contribute to this initiative.
We would like to give a special thanks to the following groups:
- Meta for open sourcing LLama 2 and open sourcing FLORES-200 dataset
- Nguyen et al for open sourcing CulturaX dataset
- CohereAI for releasing AYA-101 and open sourcing a multilingual instruction tuning dataset
- EleutherAI for their open source evaluation framework
- Hugging Face-H4 team for open source the zephyr training recipe and alignment handbook repo
## Cite SambaLingo
```
@misc{csaki2024sambalingo,
title={SambaLingo: Teaching Large Language Models New Languages},
author={Zoltan Csaki and Bo Li and Jonathan Li and Qiantong Xu and Pian Pawakapan and Leon Zhang and Yun Du and Hengyu Zhao and Changran Hu and Urmish Thakker},
year={2024},
eprint={2404.05829},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_sambanovasystems__SambaLingo-Thai-Chat)
| Metric |Value|
|---------------------------------|----:|
|Avg. |49.45|
|AI2 Reasoning Challenge (25-Shot)|52.73|
|HellaSwag (10-Shot) |78.42|
|MMLU (5-Shot) |43.95|
|TruthfulQA (0-shot) |40.84|
|Winogrande (5-shot) |72.22|
|GSM8k (5-shot) | 8.57|
|
SherlockAssistant/Mistral-7B-Instruct-Ukrainian | SherlockAssistant | 2024-03-12T14:14:05Z | 898 | 11 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-02-26T01:42:48Z | ---
library_name: transformers
license: apache-2.0
---
# Model card for Mistral-7B-Instruct-Ukrainian
Mistral-7B-UK is a Large Language Model finetuned for the Ukrainian language.
Mistral-7B-UK is trained using the following formula:
1. Initial finetuning of [Mistral-7B-v0.2](mistralai/Mistral-7B-Instruct-v0.2) using structured and unstructured datasets.
2. SLERP merge of the finetuned model with a model that performs better than `Mistral-7B-v0.2` on `OpenLLM` benchmark: [NeuralTrix-7B](https://huggingface.co/CultriX/NeuralTrix-7B-v1)
3. DPO of the final model.
## Instruction format
In order to leverage instruction fine-tuning, your prompt should be surrounded by `[INST]` and `[/INST]` tokens.
E.g.
```
text = "[INST]Відповідайте лише буквою правильної відповіді: Елементи експресіонізму наявні у творі: A. «Камінний хрест», B. «Інститутка», C. «Маруся», D. «Людина»[/INST]"
```
This format is available as a [chat template](https://huggingface.co/docs/transformers/main/chat_templating) via the `apply_chat_template()` method:
## Model Architecture
This instruction model is based on Mistral-7B-v0.2, a transformer model with the following architecture choices:
- Grouped-Query Attention
- Sliding-Window Attention
- Byte-fallback BPE tokenizer
## Datasets - Structured
- [UA-SQUAD](https://huggingface.co/datasets/FIdo-AI/ua-squad/resolve/main/ua_squad_dataset.json)
- [Ukrainian StackExchange](https://huggingface.co/datasets/zeusfsx/ukrainian-stackexchange)
- [UAlpaca Dataset](https://github.com/robinhad/kruk/blob/main/data/cc-by-nc/alpaca_data_translated.json)
- [Ukrainian Subset from Belebele Dataset](https://github.com/facebookresearch/belebele)
- [Ukrainian Subset from XQA](https://github.com/thunlp/XQA)
- [ZNO Dataset provided in UNLP 2024 shared task](https://github.com/unlp-workshop/unlp-2024-shared-task/blob/main/data/zno.train.jsonl)
## Datasets - Unstructured
- Ukrainian Wiki
## Datasets - DPO
- Ukrainian translation of [distilabel-indel-orca-dpo-pairs](https://huggingface.co/datasets/argilla/distilabel-intel-orca-dpo-pairs)
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "SherlockAssistant/Mistral-7B-Instruct-Ukrainian"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.bfloat16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
```
## Citation
If you are using this model in your research and publishing a paper, please help by citing our paper:
**BIB**
```bib
@inproceedings{boros-chivereanu-dumitrescu-purcaru-2024-llm-uk,
title = "Fine-tuning and Retrieval Augmented Generation for Question Answering using affordable Large Language Models",
author = "Boros, Tiberiu and Chivereanu, Radu and Dumitrescu, Stefan Daniel and Purcaru, Octavian",
booktitle = "Proceedings of the Third Ukrainian Natural Language Processing Workshop, LREC-COLING",
month = may,
year = "2024",
address = "Torino, Italy",
publisher = "European Language Resources Association",
}
```
**APA**
Boros, T., Chivereanu, R., Dumitrescu, S., & Purcaru, O. (2024). Fine-tuning and Retrieval Augmented Generation for Question Answering using affordable Large Language Models. In Proceedings of the Third Ukrainian Natural Language Processing Workshop, LREC-COLING. European Language Resources Association.
**MLA**
Boros, Tiberiu, Radu, Chivereanu, Stefan Daniel, Dumitrescu, Octavian, Purcaru. "Fine-tuning and Retrieval Augmented Generation for Question Answering using affordable Large Language Models." Proceedings of the Third Ukrainian Natural Language Processing Workshop, LREC-COLING. European Language Resources Association, 2024.
**Chicago**
Boros, Tiberiu, Radu, Chivereanu, Stefan Daniel, Dumitrescu, and Octavian, Purcaru. "Fine-tuning and Retrieval Augmented Generation for Question Answering using affordable Large Language Models." . In Proceedings of the Third Ukrainian Natural Language Processing Workshop, LREC-COLING. European Language Resources Association, 2024.
|
kalisai/Nusantara-4b-Indo-Chat | kalisai | 2024-03-16T14:44:12Z | 898 | 1 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"convAI",
"id",
"en",
"dataset:argilla/OpenHermes2.5-dpo-binarized-alpha",
"dataset:wikimedia/wikipedia",
"dataset:FreedomIntelligence/evol-instruct-indonesian",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-03-03T14:05:46Z | ---
library_name: transformers
widget:
- messages:
- role: system
content: >-
Anda adalah seorang konselor karir. User akan memberi Anda seorang
individu mencari bimbingan dalam kehidupan profesional mereka, dan tugas
Anda adalah membantu mereka dalam menentukan karir apa yang paling cocok
bagi mereka berdasarkan keterampilan mereka, minat, dan pengalaman. Anda
juga harus melakukan penelitian terhadap berbagai hal tersebut pilihan
yang tersedia, jelaskan tren pasar kerja di berbagai industri, Dan saran
tentang kualifikasi mana yang akan bermanfaat untuk mengejar bidang
tertentu.
- role: user
content: Halo Say!
- role: assistant
content: Eh hai, Say ! Apa yang bisa aku bantu?
- role: user
content: >-
Tolong rekomendasikan skincare yang cocok untuk kulit berjerawat.
- messages:
- role: system
content: >-
Anda adalah asisten yang berpengetahuan luas. Bantu user sebanyak yang
Anda bisa.
- role: user
content: Bagaimana caranya menjadi lebih aktif di Bulan Puasa?
- messages:
- role: system
content: Anda adalah asisten yang membantu dan memberikan tanggapan yang cerdas.
- role: user
content: Haloooo Bund!
- role: assistant
content: Halo! Apa yang bisa saya bantu?
- role: user
content: >-
Saya perlu menu buka puasa yang segar di Bulan Ramadhan ini, makanan khas Indonesia apa saja yang
cocok untuk menu buka puasa di Bulan Ramadhan?
- messages:
- role: system
content: >-
Anda adalah asisten yang sangat kreatif. Pengguna akan memberi Anda tugas,
yang harus Anda selesaikan dengan seluruh pengetahuan Anda.
- role: user
content: >-
Tulis latar belakang cerita novel tentang seorang wanita yang ingin memberantas
geng 9 Naga.
inference:
parameters:
max_new_tokens: 128
penalty_alpha: 0.5
top_k: 4
pipeline_tag: text-generation
tags:
- conversational
- convAI
license: apache-2.0
language:
- id
- en
datasets:
- argilla/OpenHermes2.5-dpo-binarized-alpha
- wikimedia/wikipedia
- FreedomIntelligence/evol-instruct-indonesian
---
### Model Description
Nusantara is a series of Open Weight Language Model of Bahasa Indonesia (Indonesia language). Nusantara is based from Qwen1.5 Language Model, finetuned by domain specific of datasets.
As Chat-implemented language model, Nusantara is capable to do Question-Answering and respond to instructions given in Bahasa Indonesia.
Due to limited resources, only 0.8B, 1.8B, 2.7B, 4B and 7B models are available. If you're interested in funding this project for further development, specific usage, or larger parameters, please contact us.
- **Finetuned by:** [Kalis AI](https://huggingface.co/kalisai)
- **Funded by:** Self-funded
- **Model type:** transformer-based decoder-only language model
- **Language(s):** Bahasa Indonesia (id), English (en)
- **License:** Nusantara is licensed under Apache-2.0, but any usage of this model should comply with [Qwen License](https://huggingface.co/Qwen/Qwen1.5-4B/blob/main/LICENSE)
- **Finetuned from model:** [Qwen1.5-4B](https://huggingface.co/Qwen/Qwen1.5-4B/tree/main)
### Attentions!
<!-- 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.
Because this model is also trained with uncensored datasets, there is the possibility of negative impacts arising from using this model. All kinds of impacts that arise as a result of using this model are entirely the responsibility of the user. The model maker is not responsible for any risks incurred.
## How to Get Started with the Model
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
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"kalisai/Nusantara-4B-Indo-Chat",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("kalisai/Nusantara-4B-Indo-Chat")
prompt = "Berikan saya resep memasak nasi goreng yang lezat."
messages = [
{"role": "system", "content": "Kamu adalah Nusantara, asisten AI yang pintar."},
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
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]
```
## Citation
If you use the Nusantara language model in your research or project, please cite it as:
```
@misc{zulfikar_aji_kusworo_2024,
title={Nusantara: A Series of Versatile Open Weight Language Model of Bahasa Indonesia},
author={Zulfikar Aji Kusworo},
publisher={Hugging Face}
journal={Hugging Face Repository},
year={2024}
url = {https://huggingface.co/kalisai}
}
``` |
John6666/chacol-omega-mix-v11a-sdxl-spo | John6666 | 2024-06-22T21:35:09Z | 898 | 1 | diffusers | [
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"stable-diffusion-xl",
"anime",
"pony",
"SPO",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] | text-to-image | 2024-06-22T21:29:04Z | ---
license: other
license_name: faipl-1.0-sd
license_link: https://freedevproject.org/faipl-1.0-sd/
tags:
- text-to-image
- stable-diffusion
- stable-diffusion-xl
- anime
- pony
- SPO
---
Original model is [here](https://civitai.com/models/456108?modelVersionId=507746).
|
lcw99/t5-large-korean-text-summary | lcw99 | 2023-04-13T02:31:19Z | 897 | 6 | transformers | [
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"t5",
"text2text-generation",
"generated_from_keras_callback",
"ko",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text2text-generation | 2022-09-29T22:08:21Z | ---
language:
- ko
tags:
- generated_from_keras_callback
model-index:
- name: t5-large-korean-text-summary
results: []
---
# t5-large-korean-text-summary
This model is a fine-tuning of [paust/pko-t5-large](https://huggingface.co/paust/pko-t5-large) model using AIHUB "summary and report generation data". This model provides a short summary of long sentences in Korean.
이 모델은 paust/pko-t5-large model을 AIHUB "요약문 및 레포트 생성 데이터"를 이용하여 fine tunning 한 것입니다. 이 모델은 한글로된 장문을 짧게 요약해 줍니다.
## Usage
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import nltk
nltk.download('punkt')
model_dir = "lcw99/t5-large-korean-text-summary"
tokenizer = AutoTokenizer.from_pretrained(model_dir)
model = AutoModelForSeq2SeqLM.from_pretrained(model_dir)
max_input_length = 512 + 256
text = """
주인공 강인구(하정우)는 ‘수리남에서 홍어가 많이 나는데 다 갖다버린다’는 친구
박응수(현봉식)의 얘기를 듣고 수리남산 홍어를 한국에 수출하기 위해 수리남으로 간다.
국립수산과학원 측은 “실제로 남대서양에 홍어가 많이 살고 아르헨티나를 비롯한 남미 국가에서 홍어가 많이 잡힌다”며
“수리남 연안에도 홍어가 많이 서식할 것”이라고 설명했다.
그러나 관세청에 따르면 한국에 수리남산 홍어가 수입된 적은 없다.
일각에선 “돈을 벌기 위해 수리남산 홍어를 구하러 간 설정은 개연성이 떨어진다”는 지적도 한다.
드라마 배경이 된 2008~2010년에는 이미 국내에 아르헨티나, 칠레, 미국 등 아메리카산 홍어가 수입되고 있었기 때문이다.
실제 조봉행 체포 작전에 협조했던 ‘협력자 K씨’도 홍어 사업이 아니라 수리남에 선박용 특수용접봉을 파는 사업을 하러 수리남에 갔었다.
"""
inputs = ["summarize: " + text]
inputs = tokenizer(inputs, max_length=max_input_length, truncation=True, return_tensors="pt")
output = model.generate(**inputs, num_beams=8, do_sample=True, min_length=10, max_length=100)
decoded_output = tokenizer.batch_decode(output, skip_special_tokens=True)[0]
predicted_title = nltk.sent_tokenize(decoded_output.strip())[0]
print(predicted_title)
```
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: None
- training_precision: float16
### Training results
### Framework versions
- Transformers 4.22.1
- TensorFlow 2.10.0
- Datasets 2.5.1
- Tokenizers 0.12.1
|
EleutherAI/pythia-160m-seed1 | EleutherAI | 2024-01-04T13:20:58Z | 897 | 0 | transformers | [
"transformers",
"pytorch",
"safetensors",
"gpt_neox",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2023-02-15T16:19:39Z | Entry not found |
liminerity/Phigments12 | liminerity | 2024-03-17T18:03:47Z | 897 | 11 | transformers | [
"transformers",
"safetensors",
"phi",
"text-generation",
"liminerity/merge6",
"liminerity/merge3",
"Merge",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-03-07T22:33:46Z | ---
license: apache-2.0
tags:
- liminerity/merge6
- liminerity/merge3
- Merge
---
#1 in the world better than any other 3b model ever
# Phigments12
Phigments12 is a merge of the following models using [mergekit](https://github.com/cg123/mergekit):
* [liminerity/merge6](https://huggingface.co/liminerity/merge6)
* [liminerity/merge3](https://huggingface.co/liminerity/merge3)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: liminerity/merge6
layer_range: [0, 32]
- model: liminerity/merge3
layer_range: [0, 32]
merge_method: slerp
base_model: liminerity/merge6
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
``` |
dominguesm/mambarim-110m | dominguesm | 2024-05-02T12:47:33Z | 897 | 6 | transformers | [
"transformers",
"safetensors",
"mamba",
"text-generation",
"pytorch",
"LLM",
"Portuguese",
"pt",
"dataset:nicholasKluge/Pt-Corpus-Instruct",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-03-11T08:53:29Z | ---
library_name: transformers
language:
- pt
license: cc-by-4.0
tags:
- text-generation
- pytorch
- LLM
- Portuguese
- mamba
datasets:
- nicholasKluge/Pt-Corpus-Instruct
inference:
parameters:
repetition_penalty: 1.2
temperature: 0.8
top_k: 50
top_p: 0.85
max_new_tokens: 150
widget:
- text: "O Natal é uma"
example_title: Exemplo
- text: "A muitos anos atrás, em uma galáxia muito distante, vivia uma raça de"
example_title: Exemplo
- text: "Em meio a um escândalo, a frente parlamentar pediu ao Senador Silva para"
example_title: Exemplo
pipeline_tag: text-generation
---
# Mambarim-110M
<p align="center">
<img width="350" alt="Camarim Logo" src="https://raw.githubusercontent.com/DominguesM/mambarim-110M/main/assets/mambarim-bg.png">
</p>
</br>
## Model Summary
**Mambarim-110M** is the first Portuguese language model based on a state-space model architecture (Mamba), not a transformer.
WIP
## Details
- **Architecture:** a Mamba model pre-trained via causal language modeling
- **Size:** 119,930,880 parameters
- **Context length:** 2048 tokens
- **Dataset:** [Pt-Corpus Instruct](https://huggingface.co/datasets/nicholasKluge/Pt-Corpus-Instruct) (6.2B tokens)
- **Language:** Portuguese
- **Number of steps:** 758,423
This repository has the [source code](https://github.com/DominguesM/mambarim-110M/) used to train this model.
## Intended Uses
WIP
## Out-of-scope Use
WIP
## Basic usage
You need to install `transformers` from `main` until `transformers=4.39.0` is released.
```bash
pip install git+https://github.com/huggingface/transformers@main
```
We also recommend you to install both `causal_conv_1d` and `mamba-ssm` using:
```bash
pip install causal-conv1d>=1.2.0
pip install mamba-ssm
```
You can use the classic `generate` API:
```python
>>> from transformers import MambaConfig, MambaForCausalLM, AutoTokenizer
>>> import torch
>>> tokenizer = AutoTokenizer.from_pretrained("dominguesm/mambarim-110m")
>>> model = MambaForCausalLM.from_pretrained("dominguesm/mambarim-110m")
>>> input_ids = tokenizer("O Natal é uma", return_tensors="pt")["input_ids"]
>>> out = model.generate(
input_ids,
repetition_penalty=1.2,
temperature=0.8,
top_k=50,
top_p=0.85,
do_sample=True,
max_new_tokens=10
)
>>> print(tokenizer.batch_decode(out))
["<s> O Natal é uma data em que as pessoas passam horas de lazer e"]
```
## Benchmarks
Evaluations on Brazilian Portuguese benchmarks were performed using a [Portuguese implementation of the EleutherAI LM Evaluation Harness](https://github.com/eduagarcia/lm-evaluation-harness-pt) (created by [Eduardo Garcia](https://github.com/eduagarcia/lm-evaluation-harness-pt)).
Detailed results can be found [here](https://huggingface.co/datasets/eduagarcia-temp/llm_pt_leaderboard_raw_results/tree/main/dominguesm/mambarim-110m)
| Model | **Average** | ENEM | BLUEX | OAB Exams | ASSIN2 RTE | ASSIN2 STS | FAQNAD NLI | HateBR | PT Hate Speech | tweetSentBR | **Architecture** |
| -------------------------------------- | ----------- | ----- | ----- | --------- | ---------- | ---------- | ---------- | ------ | -------------- | ----------- | ------------------ |
| [TeenyTinyLlama-460m](https://huggingface.co/nicholasKluge/TeenyTinyLlama-460m) | 28.86 | 20.15 | 25.73 | 27.02 | 53.61 | 13 | 46.41 | 33.59 | 22.99 | 17.28 | LlamaForCausalLM |
| [TeenyTinyLlama-160m](https://huggingface.co/nicholasKluge/TeenyTinyLlama-160m) | 28.2 | 19.24 | 23.09 | 22.37 | 53.97 | 0.24 | 43.97 | 36.92 | 42.63 | 11.39 | LlamaForCausalLM |
| [MulaBR/Mula-4x160-v0.1](https://huggingface.co/MulaBR/Mula-4x160-v0.1) | 26.24 | 21.34 | 25.17 | 25.06 | 33.57 | 11.35 | 43.97 | 41.5 | 22.99 | 11.24 | MixtralForCausalLM |
| [TeenyTinyLlama-460m-Chat](https://huggingface.co/nicholasKluge/TeenyTinyLlama-460m-Chat) | 25.49 | 20.29 | 25.45 | 26.74 | 43.77 | 4.52 | 34 | 33.49 | 22.99 | 18.13 | LlamaForCausalLM |
| [**manbarim-110m**](https://huggingface.co/dominguesm/mambarim-110m) | **14.16** | 18.4 | 10.57 | 21.87 | 16.09 | 1.89 | 9.29 | 15.75 | 17.77 | 15.79 | **MambaForCausalLM** |
| [GloriaTA-3B](https://huggingface.co/NOVA-vision-language/GlorIA-1.3B) | 4.09 | 1.89 | 3.2 | 5.19 | 0 | 2.32 | 0.26 | 0.28 | 23.52 | 0.19 | GPTNeoForCausalLM |
|
NikolayKozloff/Llama-3SOME-8B-v2-IQ4_XS-GGUF | NikolayKozloff | 2024-06-21T22:01:02Z | 897 | 1 | null | [
"gguf",
"not-for-all-audiences",
"llama-cpp",
"gguf-my-repo",
"base_model:TheDrummer/Llama-3SOME-8B-v2",
"license:cc-by-nc-4.0",
"region:us"
] | null | 2024-06-21T22:00:36Z | ---
base_model: TheDrummer/Llama-3SOME-8B-v2
license: cc-by-nc-4.0
tags:
- not-for-all-audiences
- llama-cpp
- gguf-my-repo
---
# NikolayKozloff/Llama-3SOME-8B-v2-IQ4_XS-GGUF
This model was converted to GGUF format from [`TheDrummer/Llama-3SOME-8B-v2`](https://huggingface.co/TheDrummer/Llama-3SOME-8B-v2) 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/TheDrummer/Llama-3SOME-8B-v2) 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 NikolayKozloff/Llama-3SOME-8B-v2-IQ4_XS-GGUF --hf-file llama-3some-8b-v2-iq4_xs-imat.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo NikolayKozloff/Llama-3SOME-8B-v2-IQ4_XS-GGUF --hf-file llama-3some-8b-v2-iq4_xs-imat.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo NikolayKozloff/Llama-3SOME-8B-v2-IQ4_XS-GGUF --hf-file llama-3some-8b-v2-iq4_xs-imat.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo NikolayKozloff/Llama-3SOME-8B-v2-IQ4_XS-GGUF --hf-file llama-3some-8b-v2-iq4_xs-imat.gguf -c 2048
```
|
power-greg/super-fast-llm | power-greg | 2023-04-29T07:35:27Z | 896 | 9 | transformers | [
"transformers",
"pytorch",
"safetensors",
"gpt2",
"text-generation",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2022-11-21T09:28:19Z | ---
license: apache-2.0
---
|
guillaumekln/faster-whisper-tiny | guillaumekln | 2023-05-12T18:57:08Z | 896 | 5 | ctranslate2 | [
"ctranslate2",
"audio",
"automatic-speech-recognition",
"en",
"zh",
"de",
"es",
"ru",
"ko",
"fr",
"ja",
"pt",
"tr",
"pl",
"ca",
"nl",
"ar",
"sv",
"it",
"id",
"hi",
"fi",
"vi",
"he",
"uk",
"el",
"ms",
"cs",
"ro",
"da",
"hu",
"ta",
"no",
"th",
"ur",
"hr",
"bg",
"lt",
"la",
"mi",
"ml",
"cy",
"sk",
"te",
"fa",
"lv",
"bn",
"sr",
"az",
"sl",
"kn",
"et",
"mk",
"br",
"eu",
"is",
"hy",
"ne",
"mn",
"bs",
"kk",
"sq",
"sw",
"gl",
"mr",
"pa",
"si",
"km",
"sn",
"yo",
"so",
"af",
"oc",
"ka",
"be",
"tg",
"sd",
"gu",
"am",
"yi",
"lo",
"uz",
"fo",
"ht",
"ps",
"tk",
"nn",
"mt",
"sa",
"lb",
"my",
"bo",
"tl",
"mg",
"as",
"tt",
"haw",
"ln",
"ha",
"ba",
"jw",
"su",
"license:mit",
"region:us"
] | automatic-speech-recognition | 2023-03-23T10:14:28Z | ---
language:
- en
- zh
- de
- es
- ru
- ko
- fr
- ja
- pt
- tr
- pl
- ca
- nl
- ar
- sv
- it
- id
- hi
- fi
- vi
- he
- uk
- el
- ms
- cs
- ro
- da
- hu
- ta
- 'no'
- th
- ur
- hr
- bg
- lt
- la
- mi
- ml
- cy
- sk
- te
- fa
- lv
- bn
- sr
- az
- sl
- kn
- et
- mk
- br
- eu
- is
- hy
- ne
- mn
- bs
- kk
- sq
- sw
- gl
- mr
- pa
- si
- km
- sn
- yo
- so
- af
- oc
- ka
- be
- tg
- sd
- gu
- am
- yi
- lo
- uz
- fo
- ht
- ps
- tk
- nn
- mt
- sa
- lb
- my
- bo
- tl
- mg
- as
- tt
- haw
- ln
- ha
- ba
- jw
- su
tags:
- audio
- automatic-speech-recognition
license: mit
library_name: ctranslate2
---
# Whisper tiny model for CTranslate2
This repository contains the conversion of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) to the [CTranslate2](https://github.com/OpenNMT/CTranslate2) model format.
This model can be used in CTranslate2 or projects based on CTranslate2 such as [faster-whisper](https://github.com/guillaumekln/faster-whisper).
## Example
```python
from faster_whisper import WhisperModel
model = WhisperModel("tiny")
segments, info = model.transcribe("audio.mp3")
for segment in segments:
print("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text))
```
## Conversion details
The original model was converted with the following command:
```
ct2-transformers-converter --model openai/whisper-tiny --output_dir faster-whisper-tiny \
--copy_files tokenizer.json --quantization float16
```
Note that the model weights are saved in FP16. This type can be changed when the model is loaded using the [`compute_type` option in CTranslate2](https://opennmt.net/CTranslate2/quantization.html).
## More information
**For more information about the original model, see its [model card](https://huggingface.co/openai/whisper-tiny).**
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.