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QuantFactory/Hathor-L3-8B-v.02-GGUF | QuantFactory | "2024-06-12T01:02:30Z" | 1,491 | 0 | null | [
"gguf",
"text-generation",
"en",
"base_model:Nitral-AI/Hathor-L3-8B-v.02",
"license:other",
"region:us"
] | text-generation | "2024-06-11T05:21:01Z" | ---
license: other
language:
- en
base_model: Nitral-AI/Hathor-L3-8B-v.02
pipeline_tag: text-generation
---
# QuantFactory/Hathor-L3-8B-v.02-GGUF
This is quantized version of [Nitral-AI/Hathor-L3-8B-v.02](https://huggingface.co/Nitral-AI/Hathor-L3-8B-v.02) created using llama.cpp
# Model Description
# "Hathor-v0.2 is a model based on the LLaMA 3 architecture: Designed to seamlessly integrate the qualities of creativity, intelligence, and robust performance. Making it an ideal tool for a wide range of applications; such as creative writing, educational support and human/computer interaction."
# Recomended ST Presets: [Hathor Presets](https://huggingface.co/Nitral-AI/Hathor-L3-8B-v.01/tree/main/Hathor%20Presets)
---
# Notes: Hathor is trained on 3 epochs of private data, synthetic opus instructons, a mix of light/classical novel data, roleplaying chat pairs over llama 3 8B instruct. (expanded)
---
- If you want to use vision functionality:
* You must use the latest versions of [Koboldcpp](https://github.com/LostRuins/koboldcpp).
- To use the multimodal capabilities of this model and use **vision** you need to load the specified **mmproj** file, this can be found inside this model repo. [Llava MMProj](https://huggingface.co/Nitral-AI/Llama-3-Update-3.0-mmproj-model-f16)
* You can load the **mmproj** by using the corresponding section in the interface:
--- |
procit001/mms-tts-nl-v6 | procit001 | "2024-06-18T23:51:53Z" | 1,491 | 0 | transformers | [
"transformers",
"safetensors",
"vits",
"text-to-audio",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | text-to-audio | "2024-06-18T23:49:34Z" | ---
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 ð€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
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### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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### Downstream Use [optional]
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[More Information Needed]
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
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## Training Details
### Training Data
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#### Preprocessing [optional]
[More Information Needed]
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- **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]
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## Model Card Contact
[More Information Needed] |
flaubert/flaubert_large_cased | flaubert | "2024-05-14T12:38:43Z" | 1,490 | 2 | transformers | [
"transformers",
"pytorch",
"safetensors",
"flaubert",
"fill-mask",
"bert",
"language-model",
"flue",
"french",
"bert-large",
"flaubert-large",
"cased",
"fr",
"dataset:flaubert",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | "2022-03-02T23:29:05Z" | ---
language: fr
license: mit
datasets:
- flaubert
metrics:
- flue
tags:
- bert
- language-model
- flaubert
- flue
- french
- bert-large
- flaubert-large
- cased
---
# FlauBERT: Unsupervised Language Model Pre-training for French
**FlauBERT** is a French BERT trained on a very large and heterogeneous French corpus. Models of different sizes are trained using the new CNRS (French National Centre for Scientific Research) [Jean Zay](http://www.idris.fr/eng/jean-zay/ ) supercomputer.
Along with FlauBERT comes [**FLUE**](https://github.com/getalp/Flaubert/tree/master/flue): an evaluation setup for French NLP systems similar to the popular GLUE benchmark. The goal is to enable further reproducible experiments in the future and to share models and progress on the French language.For more details please refer to the [official website](https://github.com/getalp/Flaubert).
## FlauBERT models
| Model name | Number of layers | Attention Heads | Embedding Dimension | Total Parameters |
| :------: | :---: | :---: | :---: | :---: |
| `flaubert-small-cased` | 6 | 8 | 512 | 54 M |
| `flaubert-base-uncased` | 12 | 12 | 768 | 137 M |
| `flaubert-base-cased` | 12 | 12 | 768 | 138 M |
| `flaubert-large-cased` | 24 | 16 | 1024 | 373 M |
**Note:** `flaubert-small-cased` is partially trained so performance is not guaranteed. Consider using it for debugging purpose only.
## Using FlauBERT with Hugging Face's Transformers
```python
import torch
from transformers import FlaubertModel, FlaubertTokenizer
# Choose among ['flaubert/flaubert_small_cased', 'flaubert/flaubert_base_uncased',
# 'flaubert/flaubert_base_cased', 'flaubert/flaubert_large_cased']
modelname = 'flaubert/flaubert_base_cased'
# Load pretrained model and tokenizer
flaubert, log = FlaubertModel.from_pretrained(modelname, output_loading_info=True)
flaubert_tokenizer = FlaubertTokenizer.from_pretrained(modelname, do_lowercase=False)
# do_lowercase=False if using cased models, True if using uncased ones
sentence = "Le chat mange une pomme."
token_ids = torch.tensor([flaubert_tokenizer.encode(sentence)])
last_layer = flaubert(token_ids)[0]
print(last_layer.shape)
# torch.Size([1, 8, 768]) -> (batch size x number of tokens x embedding dimension)
# The BERT [CLS] token correspond to the first hidden state of the last layer
cls_embedding = last_layer[:, 0, :]
```
**Notes:** if your `transformers` version is <=2.10.0, `modelname` should take one
of the following values:
```
['flaubert-small-cased', 'flaubert-base-uncased', 'flaubert-base-cased', 'flaubert-large-cased']
```
## References
If you use FlauBERT or the FLUE Benchmark for your scientific publication, or if you find the resources in this repository useful, please cite one of the following papers:
[LREC paper](http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.302.pdf)
```
@InProceedings{le2020flaubert,
author = {Le, Hang and Vial, Lo\"{i}c and Frej, Jibril and Segonne, Vincent and Coavoux, Maximin and Lecouteux, Benjamin and Allauzen, Alexandre and Crabb\'{e}, Beno\^{i}t and Besacier, Laurent and Schwab, Didier},
title = {FlauBERT: Unsupervised Language Model Pre-training for French},
booktitle = {Proceedings of The 12th Language Resources and Evaluation Conference},
month = {May},
year = {2020},
address = {Marseille, France},
publisher = {European Language Resources Association},
pages = {2479--2490},
url = {https://www.aclweb.org/anthology/2020.lrec-1.302}
}
```
[TALN paper](https://hal.archives-ouvertes.fr/hal-02784776/)
```
@inproceedings{le2020flaubert,
title = {FlauBERT: des mod{\`e}les de langue contextualis{\'e}s pr{\'e}-entra{\^\i}n{\'e}s pour le fran{\c{c}}ais},
author = {Le, Hang and Vial, Lo{\"\i}c and Frej, Jibril and Segonne, Vincent and Coavoux, Maximin and Lecouteux, Benjamin and Allauzen, Alexandre and Crabb{\'e}, Beno{\^\i}t and Besacier, Laurent and Schwab, Didier},
booktitle = {Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 31e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 2: Traitement Automatique des Langues Naturelles},
pages = {268--278},
year = {2020},
organization = {ATALA}
}
``` |
readerbench/RoBERT-base | readerbench | "2023-06-21T10:08:18Z" | 1,490 | 8 | transformers | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"endpoints_compatible",
"region:us"
] | null | "2022-03-02T23:29:05Z" | Model card for RoBERT-base
---
language:
- ro
---
# RoBERT-base
## Pretrained BERT model for Romanian
Pretrained model on Romanian language using a masked language modeling (MLM) and next sentence prediction (NSP) objective.
It was introduced in this [paper](https://www.aclweb.org/anthology/2020.coling-main.581/). Three BERT models were released: RoBERT-small, **RoBERT-base** and RoBERT-large, all versions uncased.
| Model | Weights | L | H | A | MLM accuracy | NSP accuracy |
|----------------|:---------:|:------:|:------:|:------:|:--------------:|:--------------:|
| RoBERT-small | 19M | 12 | 256 | 8 | 0.5363 | 0.9687 |
| *RoBERT-base* | *114M* | *12* | *768* | *12* | *0.6511* | *0.9802* |
| RoBERT-large | 341M | 24 | 1024 | 24 | 0.6929 | 0.9843 |
All models are available:
* [RoBERT-small](https://huggingface.co/readerbench/RoBERT-small)
* [RoBERT-base](https://huggingface.co/readerbench/RoBERT-base)
* [RoBERT-large](https://huggingface.co/readerbench/RoBERT-large)
#### How to use
```python
# tensorflow
from transformers import AutoModel, AutoTokenizer, TFAutoModel
tokenizer = AutoTokenizer.from_pretrained("readerbench/RoBERT-base")
model = TFAutoModel.from_pretrained("readerbench/RoBERT-base")
inputs = tokenizer("exemplu de propoziÈie", return_tensors="tf")
outputs = model(inputs)
# pytorch
from transformers import AutoModel, AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("readerbench/RoBERT-base")
model = AutoModel.from_pretrained("readerbench/RoBERT-base")
inputs = tokenizer("exemplu de propoziÈie", return_tensors="pt")
outputs = model(**inputs)
```
## Training data
The model is trained on the following compilation of corpora. Note that we present the statistics after the cleaning process.
| Corpus | Words | Sentences | Size (GB)|
|-----------|:---------:|:---------:|:--------:|
| Oscar | 1.78B | 87M | 10.8 |
| RoTex | 240M | 14M | 1.5 |
| RoWiki | 50M | 2M | 0.3 |
| **Total** | **2.07B** | **103M** | **12.6** |
## Downstream performance
### Sentiment analysis
We report Macro-averaged F1 score (in %)
| Model | Dev | Test |
|------------------|:--------:|:--------:|
| multilingual-BERT| 68.96 | 69.57 |
| XLM-R-base | 71.26 | 71.71 |
| BERT-base-ro | 70.49 | 71.02 |
| RoBERT-small | 66.32 | 66.37 |
| *RoBERT-base* | *70.89* | *71.61* |
| RoBERT-large | **72.48**| **72.11**|
### Moldavian vs. Romanian Dialect and Cross-dialect Topic identification
We report results on [VarDial 2019](https://sites.google.com/view/vardial2019/campaign) Moldavian vs. Romanian Cross-dialect Topic identification Challenge, as Macro-averaged F1 score (in %).
| Model | Dialect Classification | MD to RO | RO to MD |
|-------------------|:----------------------:|:--------:|:--------:|
| 2-CNN + SVM | 93.40 | 65.09 | 75.21 |
| Char+Word SVM | 96.20 | 69.08 | 81.93 |
| BiGRU | 93.30 | **70.10**| 80.30 |
| multilingual-BERT | 95.34 | 68.76 | 78.24 |
| XLM-R-base | 96.28 | 69.93 | 82.28 |
| BERT-base-ro | 96.20 | 69.93 | 78.79 |
| RoBERT-small | 95.67 | 69.01 | 80.40 |
| *RoBERT-base* | *97.39* | *68.30* | *81.09* |
| RoBERT-large | **97.78** | 69.91 | **83.65**|
### Diacritics Restoration
Challenge can be found [here](https://diacritics-challenge.speed.pub.ro/). We report results on the official test set, as accuracies in %.
| Model | word level | char level |
|-----------------------------|:----------:|:----------:|
| BiLSTM | 99.42 | - |
| CharCNN | 98.40 | 99.65 |
| CharCNN + multilingual-BERT | 99.72 | 99.94 |
| CharCNN + XLM-R-base | 99.76 | **99.95** |
| CharCNN + BERT-base-ro | **99.79** | **99.95** |
| CharCNN + RoBERT-small | 99.73 | 99.94 |
| *CharCNN + RoBERT-base* | *99.78* | **99.95** |
| CharCNN + RoBERT-large | 99.76 | **99.95** |
### BibTeX entry and citation info
```bibtex
@inproceedings{masala2020robert,
title={RoBERT--A Romanian BERT Model},
author={Masala, Mihai and Ruseti, Stefan and Dascalu, Mihai},
booktitle={Proceedings of the 28th International Conference on Computational Linguistics},
pages={6626--6637},
year={2020}
}
```
|
digiplay/NextGenMix_R2.8VAE | digiplay | "2023-08-20T18:18:18Z" | 1,490 | 1 | diffusers | [
"diffusers",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | "2023-08-19T07:59:13Z" | ---
license: other
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
inference: true
---
Model info :
https://civitai.com/models/77751?modelVersionId=112876
Sample image I made thru Huggingface's API :
Original Author's DEMO images :
|
SciPhi/SciPhi-Self-RAG-Mistral-7B-32k | SciPhi | "2023-11-01T12:25:44Z" | 1,490 | 84 | transformers | [
"transformers",
"pytorch",
"mistral",
"text-generation",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-10-27T13:18:51Z" | ---
license: mit
---
# SciPhi-Self-RAG-Mistral-7B-32k Model Card
SciPhi-Self-RAG-Mistral-7B-32k is a Large Language Model (LLM) fine-tuned from Mistral-7B-v0.1. This model underwent the fine-tuning process described in the [SciPhi-Mistral-7B-32k](https://huggingface.co/SciPhi/SciPhi-Mistral-7B-32k) model card. It then underwent further fine-tuning on the recently released [self-rag](https://arxiv.org/abs//2310.11511) dataset. Other RAG-related instruct datasets were mixed in during this process in an effort to keep the tone of the current model. This model benchmarks well, but it needs further tuning to be an excellent conversationalist.
Benchmark Results:
SciPhi-AI is available via a free hosted API, though the exposed model can vary. Currently, SciPhi-Self-RAG-Mistral-7B-32k is available. More details can be found in the docs [here](https://sciphi.readthedocs.io/en/latest/setup/quickstart.html).
## Recommended Chat Formatting
```
We recommend mapping such that
messages = [
{
"role": "system",
"content": "You are a friendly chatbot who always responds in the style of a pirate",
},
{"role": "user", "content": "How many helicopters can a human eat in one sitting?"},
]
goes to --->
### System:
You are a friendly chatbot who always responds in the style of a pirate
### Instruction:
How many helicopters can a human eat in one sitting?
### Response:
...
Here is a sample implementation that does this and combines with RAG context retrieval.
def get_chat_completion(
self, conversation: list[dict], generation_config: GenerationConfig
) -> str:
self._check_stop_token(generation_config.stop_token)
prompt = ""
added_system_prompt = False
for message in conversation:
if message["role"] == "system":
prompt += f"### System:\n{SciPhiLLMInterface.ALPACA_CHAT_SYSTEM_PROMPT}. Further, the assistant is given the following additional instructions - {message['content']}\n\n"
added_system_prompt = True
elif message["role"] == "user":
last_user_message = message["content"]
prompt += f"### Instruction:\n{last_user_message}\n\n"
elif message["role"] == "assistant":
prompt += f"### Response:\n{message['content']}\n\n"
if not added_system_prompt:
prompt = f"### System:\n{SciPhiLLMInterface.ALPACA_CHAT_SYSTEM_PROMPT}.\n\n{prompt}"
context = self.rag_interface.get_contexts([last_user_message])[0]
prompt += f"### Response:\n{SciPhiFormatter.RETRIEVAL_TOKEN} {SciPhiFormatter.INIT_PARAGRAPH_TOKEN}{context}{SciPhiFormatter.END_PARAGRAPH_TOKEN}"
latest_completion = self.model.get_instruct_completion(
prompt, generation_config
).strip()
return SciPhiFormatter.remove_cruft(latest_completion)
```
## Model Architecture
Base Model: Mistral-7B-v0.1
**Architecture Features:**
- Transformer-based model
- Grouped-Query Attention
- Sliding-Window Attention
- Byte-fallback BPE tokenizer
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
## References
1. Asai, A., Wu, Z., Wang, Y., Sil, A., & Hajishirzi, H. (2023). Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection. arXiv preprint arXiv:2310.11511.
2. Lian, W., Goodson, B., Wang, G., Pentland, E., Cook, A., Vong, C., & Teknium. (2023). MistralOrca: Mistral-7B Model Instruct-tuned on Filtered OpenOrcaV1 GPT-4 Dataset. *HuggingFace repository*. [Link](https://huggingface.co/Open-Orca/Mistral-7B-OpenOrca)
3. Mukherjee, S., Mitra, A., Jawahar, G., Agarwal, S., Palangi, H., & Awadallah, A. (2023). Orca: Progressive Learning from Complex Explanation Traces of GPT-4. *arXiv preprint arXiv:2306.02707*.
4. Longpre, S., Hou, L., Vu, T., Webson, A., Chung, H. W., Tay, Y., Zhou, D., Le, Q. V., Zoph, B., Wei, J., & Roberts, A. (2023). The Flan Collection: Designing Data and Methods for Effective Instruction Tuning. *arXiv preprint arXiv:2301.13688*.
5. Mistral AI. (2023). Model Card for Mistral-7B-v0.1. The Mistral-7B-v0.1 Large Language Model (LLM) is a pretrained generative text model with 7 billion parameters. Mistral-7B-v0.1 outperforms Llama 2 13B on all benchmarks tested. For full details, please refer to the paper and release blog post. Model Architecture: Transformer with Grouped-Query Attention, Sliding-Window Attention, and Byte-fallback BPE tokenizer. [Link](https://huggingface.co/mistralai/Mistral-7B-v0.1)
## Acknowledgements
Thank you to the [AI Alignment Lab](https://huggingface.co/Alignment-Lab-AI), [vikp](https://huggingface.co/vikp), [jph00](https://huggingface.co/jph00) and others who contributed to this work. |
QuantFactory/UltimateANJIR-8B-L3-Blackroot-GGUF | QuantFactory | "2024-06-04T09:25:54Z" | 1,490 | 0 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"text-generation",
"base_model:Hastagaras/UltimateANJIR-8B-L3-Blackroot",
"license:llama3",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-06-02T10:18:45Z" | ---
base_model: Hastagaras/UltimateANJIR-8B-L3-Blackroot
library_name: transformers
tags:
- mergekit
- merge
license: llama3
pipeline_tag: text-generation
---
# UltimateANJIR-8B-L3-Blackroot-GGUF
This is quantized version of [Hastagaras/UltimateANJIR-8B-L3-Blackroot](https://huggingface.co/Hastagaras/UltimateANJIR-8B-L3-Blackroot) created using llama.cpp
# Model Description
So this is a merge of merges. Based on this introverted model: [failspy/Llama-3-8B-Instruct-MopeyMule](https://huggingface.co/failspy/Llama-3-8B-Instruct-MopeyMule) and this extroverted model: [Orenguteng/Llama-3-8B-LexiFun-Uncensored-V1](https://huggingface.co/Orenguteng/Llama-3-8B-LexiFun-Uncensored-V1)
I merged both with my model to add some RP ability, and it turns out the extroverted one is winning.
And also, please don't mind the unusual tokenizer config and the template for all of my models, it's just my preference, you know...for easier testing. And the quantized GGUF might not work with Ollama Chat or Backyard AI... so you have to write the template manually, as it is still the llama 3 instruct template. |
RichardErkhov/ConvexAI_-_Pelican-9b-v0.1-gguf | RichardErkhov | "2024-06-15T01:57:01Z" | 1,490 | 0 | null | [
"gguf",
"region:us"
] | null | "2024-06-15T00:53:50Z" | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
Pelican-9b-v0.1 - GGUF
- Model creator: https://huggingface.co/ConvexAI/
- Original model: https://huggingface.co/ConvexAI/Pelican-9b-v0.1/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [Pelican-9b-v0.1.Q2_K.gguf](https://huggingface.co/RichardErkhov/ConvexAI_-_Pelican-9b-v0.1-gguf/blob/main/Pelican-9b-v0.1.Q2_K.gguf) | Q2_K | 3.43GB |
| [Pelican-9b-v0.1.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/ConvexAI_-_Pelican-9b-v0.1-gguf/blob/main/Pelican-9b-v0.1.IQ3_XS.gguf) | IQ3_XS | 3.81GB |
| [Pelican-9b-v0.1.IQ3_S.gguf](https://huggingface.co/RichardErkhov/ConvexAI_-_Pelican-9b-v0.1-gguf/blob/main/Pelican-9b-v0.1.IQ3_S.gguf) | IQ3_S | 4.02GB |
| [Pelican-9b-v0.1.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/ConvexAI_-_Pelican-9b-v0.1-gguf/blob/main/Pelican-9b-v0.1.Q3_K_S.gguf) | Q3_K_S | 3.99GB |
| [Pelican-9b-v0.1.IQ3_M.gguf](https://huggingface.co/RichardErkhov/ConvexAI_-_Pelican-9b-v0.1-gguf/blob/main/Pelican-9b-v0.1.IQ3_M.gguf) | IQ3_M | 4.15GB |
| [Pelican-9b-v0.1.Q3_K.gguf](https://huggingface.co/RichardErkhov/ConvexAI_-_Pelican-9b-v0.1-gguf/blob/main/Pelican-9b-v0.1.Q3_K.gguf) | Q3_K | 4.44GB |
| [Pelican-9b-v0.1.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/ConvexAI_-_Pelican-9b-v0.1-gguf/blob/main/Pelican-9b-v0.1.Q3_K_M.gguf) | Q3_K_M | 4.44GB |
| [Pelican-9b-v0.1.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/ConvexAI_-_Pelican-9b-v0.1-gguf/blob/main/Pelican-9b-v0.1.Q3_K_L.gguf) | Q3_K_L | 4.84GB |
| [Pelican-9b-v0.1.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/ConvexAI_-_Pelican-9b-v0.1-gguf/blob/main/Pelican-9b-v0.1.IQ4_XS.gguf) | IQ4_XS | 4.98GB |
| [Pelican-9b-v0.1.Q4_0.gguf](https://huggingface.co/RichardErkhov/ConvexAI_-_Pelican-9b-v0.1-gguf/blob/main/Pelican-9b-v0.1.Q4_0.gguf) | Q4_0 | 5.2GB |
| [Pelican-9b-v0.1.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/ConvexAI_-_Pelican-9b-v0.1-gguf/blob/main/Pelican-9b-v0.1.IQ4_NL.gguf) | IQ4_NL | 5.25GB |
| [Pelican-9b-v0.1.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/ConvexAI_-_Pelican-9b-v0.1-gguf/blob/main/Pelican-9b-v0.1.Q4_K_S.gguf) | Q4_K_S | 5.23GB |
| [Pelican-9b-v0.1.Q4_K.gguf](https://huggingface.co/RichardErkhov/ConvexAI_-_Pelican-9b-v0.1-gguf/blob/main/Pelican-9b-v0.1.Q4_K.gguf) | Q4_K | 5.53GB |
| [Pelican-9b-v0.1.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/ConvexAI_-_Pelican-9b-v0.1-gguf/blob/main/Pelican-9b-v0.1.Q4_K_M.gguf) | Q4_K_M | 5.53GB |
| [Pelican-9b-v0.1.Q4_1.gguf](https://huggingface.co/RichardErkhov/ConvexAI_-_Pelican-9b-v0.1-gguf/blob/main/Pelican-9b-v0.1.Q4_1.gguf) | Q4_1 | 5.76GB |
| [Pelican-9b-v0.1.Q5_0.gguf](https://huggingface.co/RichardErkhov/ConvexAI_-_Pelican-9b-v0.1-gguf/blob/main/Pelican-9b-v0.1.Q5_0.gguf) | Q5_0 | 6.33GB |
| [Pelican-9b-v0.1.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/ConvexAI_-_Pelican-9b-v0.1-gguf/blob/main/Pelican-9b-v0.1.Q5_K_S.gguf) | Q5_K_S | 6.33GB |
| [Pelican-9b-v0.1.Q5_K.gguf](https://huggingface.co/RichardErkhov/ConvexAI_-_Pelican-9b-v0.1-gguf/blob/main/Pelican-9b-v0.1.Q5_K.gguf) | Q5_K | 6.5GB |
| [Pelican-9b-v0.1.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/ConvexAI_-_Pelican-9b-v0.1-gguf/blob/main/Pelican-9b-v0.1.Q5_K_M.gguf) | Q5_K_M | 6.5GB |
| [Pelican-9b-v0.1.Q5_1.gguf](https://huggingface.co/RichardErkhov/ConvexAI_-_Pelican-9b-v0.1-gguf/blob/main/Pelican-9b-v0.1.Q5_1.gguf) | Q5_1 | 6.9GB |
| [Pelican-9b-v0.1.Q6_K.gguf](https://huggingface.co/RichardErkhov/ConvexAI_-_Pelican-9b-v0.1-gguf/blob/main/Pelican-9b-v0.1.Q6_K.gguf) | Q6_K | 7.53GB |
| [Pelican-9b-v0.1.Q8_0.gguf](https://huggingface.co/RichardErkhov/ConvexAI_-_Pelican-9b-v0.1-gguf/blob/main/Pelican-9b-v0.1.Q8_0.gguf) | Q8_0 | 9.76GB |
Original model description:
---
license: apache-2.0
tags:
- mergekit
- merge
base_model:
- flemmingmiguel/MBX-7B
- flemmingmiguel/MBX-7B-v3
model-index:
- name: Pelican-9b-v0.1
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: 47.95
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ConvexAI/Pelican-9b-v0.1
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: 66.22
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ConvexAI/Pelican-9b-v0.1
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: 62.85
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ConvexAI/Pelican-9b-v0.1
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: 50.61
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ConvexAI/Pelican-9b-v0.1
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: 74.66
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ConvexAI/Pelican-9b-v0.1
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: 0.0
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ConvexAI/Pelican-9b-v0.1
name: Open LLM Leaderboard
---
# merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
â ïž**Warning** â ïž
Model is broken and outputs only broken german.
Possibly obsessed with FuÃball. âœ
### Merge Method
This model was merged using the passthrough merge method and only speaks german, somewhat obsessed with football.
### Models Merged
The following models were included in the merge:
* [flemmingmiguel/MBX-7B](https://huggingface.co/flemmingmiguel/MBX-7B)
* [flemmingmiguel/MBX-7B-v3](https://huggingface.co/flemmingmiguel/MBX-7B-v3)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
slices:
- sources:
- model: flemmingmiguel/MBX-7B-v3
layer_range: [0, 32]
- sources:
- model: flemmingmiguel/MBX-7B
layer_range: [20, 32]
merge_method: passthrough
dtype: float16
```
# [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_ConvexAI__Pelican-9b-v0.1)
| Metric |Value|
|---------------------------------|----:|
|Avg. |50.38|
|AI2 Reasoning Challenge (25-Shot)|47.95|
|HellaSwag (10-Shot) |66.22|
|MMLU (5-Shot) |62.85|
|TruthfulQA (0-shot) |50.61|
|Winogrande (5-shot) |74.66|
|GSM8k (5-shot) | 0.00|
|
microsoft/resnet-26 | microsoft | "2023-04-24T09:51:07Z" | 1,489 | 1 | transformers | [
"transformers",
"pytorch",
"tf",
"safetensors",
"resnet",
"image-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | "2022-03-16T15:41:05Z" | Entry not found |
Norm/ERNIE-Layout-Pytorch | Norm | "2023-11-14T13:34:59Z" | 1,489 | 13 | transformers | [
"transformers",
"pytorch",
"xlnet",
"arxiv:2210.06155",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | "2022-12-12T02:21:36Z" | ---
license: mit
---
# ERNIE-Layout_Pytorch
- **Model type:** [ERNIE-Layout](https://arxiv.org/abs/2210.06155)
- **Repository:** [source code](https://github.com/NormXU/ERNIE-Layout-Pytorch): an unofficial ERNIE-Layout implementation in Pytorch
- **Converted from:** [PaddlePaddle/ernie-layoutx-base-uncased](https://huggingface.co/PaddlePaddle/ernie-layoutx-base-uncased)
The ERNIE-Layout-Pytorch model is initially released by [PaddleNLP](https://github.com/PaddlePaddle/PaddleNLP). To make Pytorch users easy to use, the model has been converted into PyTorch format with the [tools/convert2torch.py](https://github.com/NormXU/ERNIE-Layout-Pytorch/blob/main/tools/convert2torch.py) script.
Please feel free to make any changes you need. For more details and use cases, please check the repo.
**A Quick Example**
```python
import torch
from PIL import Image
import torch.nn.functional as F
from networks import ErnieLayoutConfig, ErnieLayoutForQuestionAnswering, \
ErnieLayoutProcessor, ErnieLayoutTokenizerFast
from transformers.models.layoutlmv3 import LayoutLMv3ImageProcessor
pretrain_torch_model_or_path = "Norm/ERNIE-Layout-Pytorch"
doc_imag_path = "./dummy_input.jpeg"
context = ['This is an example sequence', 'All ocr boxes are inserted into this list']
layout = [[381, 91, 505, 115], [738, 96, 804, 122]] # make sure all boxes are normalized between 0 - 1000
pil_image = Image.open(doc_imag_path).convert("RGB")
# initialize tokenizer
tokenizer = ErnieLayoutTokenizerFast.from_pretrained(pretrained_model_name_or_path=pretrain_torch_model_or_path)
# initialize feature extractor
feature_extractor = LayoutLMv3ImageProcessor(apply_ocr=False)
processor = ErnieLayoutProcessor(image_processor=feature_extractor, tokenizer=tokenizer)
# Tokenize context & questions
question = "what is it?"
encoding = processor(pil_image, question, context, boxes=layout, return_tensors="pt")
# dummy answer start && end index
start_positions = torch.tensor([6])
end_positions = torch.tensor([12])
# initialize config
config = ErnieLayoutConfig.from_pretrained(pretrained_model_name_or_path=pretrain_torch_model_or_path)
config.num_classes = 2 # start and end
# initialize ERNIE for VQA
model = ErnieLayoutForQuestionAnswering.from_pretrained(
pretrained_model_name_or_path=pretrain_torch_model_or_path,
config=config,
)
output = model(**encoding, start_positions=start_positions, end_positions=end_positions)
# decode output
start_max = torch.argmax(F.softmax(output.start_logits, dim=-1))
end_max = torch.argmax(F.softmax(output.end_logits, dim=-1)) + 1 # add one ##because of python list indexing
answer = tokenizer.decode(encoding.input_ids[0][start_max: end_max])
print(answer)
``` |
TheBloke/Noromaid-20B-v0.1.1-GGUF | TheBloke | "2023-11-23T08:58:48Z" | 1,489 | 20 | transformers | [
"transformers",
"gguf",
"llama",
"base_model:NeverSleep/Noromaid-20b-v0.1.1",
"license:cc-by-nc-4.0",
"text-generation-inference",
"region:us"
] | null | "2023-11-23T08:47:36Z" | ---
base_model: NeverSleep/Noromaid-20b-v0.1.1
inference: false
license: cc-by-nc-4.0
model_creator: IkariDev and Undi
model_name: Noromaid 20B v0.1.1
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
---
<!-- markdownlint-disable MD041 -->
<!-- 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 -->
# Noromaid 20B v0.1.1 - GGUF
- Model creator: [IkariDev and Undi](https://huggingface.co/NeverSleep)
- Original model: [Noromaid 20B v0.1.1](https://huggingface.co/NeverSleep/Noromaid-20b-v0.1.1)
<!-- description start -->
## Description
This repo contains GGUF format model files for [IkariDev and Undi's Noromaid 20B v0.1.1](https://huggingface.co/NeverSleep/Noromaid-20b-v0.1.1).
These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).
<!-- 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 incomplete 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/Noromaid-20B-v0.1.1-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Noromaid-20B-v0.1.1-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Noromaid-20B-v0.1.1-GGUF)
* [IkariDev and Undi's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/NeverSleep/Noromaid-20b-v0.1.1)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: Alpaca
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
```
<!-- prompt-template end -->
<!-- licensing start -->
## Licensing
The creator of the source model has listed its license as `cc-by-nc-4.0`, and this quantization has therefore used that same license.
As this model is based on Llama 2, it is also subject to the Meta Llama 2 license terms, and the license files for that are additionally included. It should therefore be considered as being claimed to be licensed under both licenses. I contacted Hugging Face for clarification on dual licensing but they do not yet have an official position. Should this change, or should Meta provide any feedback on this situation, I will update this section accordingly.
In the meantime, any questions regarding licensing, and in particular how these two licenses might interact, should be directed to the original model repository: [IkariDev and Undi's Noromaid 20B v0.1.1](https://huggingface.co/NeverSleep/Noromaid-20b-v0.1.1).
<!-- 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 |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [noromaid-20b-v0.1.1.Q2_K.gguf](https://huggingface.co/TheBloke/Noromaid-20B-v0.1.1-GGUF/blob/main/noromaid-20b-v0.1.1.Q2_K.gguf) | Q2_K | 2 | 8.31 GB| 10.81 GB | smallest, significant quality loss - not recommended for most purposes |
| [noromaid-20b-v0.1.1.Q3_K_S.gguf](https://huggingface.co/TheBloke/Noromaid-20B-v0.1.1-GGUF/blob/main/noromaid-20b-v0.1.1.Q3_K_S.gguf) | Q3_K_S | 3 | 8.66 GB| 11.16 GB | very small, high quality loss |
| [noromaid-20b-v0.1.1.Q3_K_M.gguf](https://huggingface.co/TheBloke/Noromaid-20B-v0.1.1-GGUF/blob/main/noromaid-20b-v0.1.1.Q3_K_M.gguf) | Q3_K_M | 3 | 9.70 GB| 12.20 GB | very small, high quality loss |
| [noromaid-20b-v0.1.1.Q3_K_L.gguf](https://huggingface.co/TheBloke/Noromaid-20B-v0.1.1-GGUF/blob/main/noromaid-20b-v0.1.1.Q3_K_L.gguf) | Q3_K_L | 3 | 10.63 GB| 13.13 GB | small, substantial quality loss |
| [noromaid-20b-v0.1.1.Q4_0.gguf](https://huggingface.co/TheBloke/Noromaid-20B-v0.1.1-GGUF/blob/main/noromaid-20b-v0.1.1.Q4_0.gguf) | Q4_0 | 4 | 11.29 GB| 13.79 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [noromaid-20b-v0.1.1.Q4_K_S.gguf](https://huggingface.co/TheBloke/Noromaid-20B-v0.1.1-GGUF/blob/main/noromaid-20b-v0.1.1.Q4_K_S.gguf) | Q4_K_S | 4 | 11.34 GB| 13.84 GB | small, greater quality loss |
| [noromaid-20b-v0.1.1.Q4_K_M.gguf](https://huggingface.co/TheBloke/Noromaid-20B-v0.1.1-GGUF/blob/main/noromaid-20b-v0.1.1.Q4_K_M.gguf) | Q4_K_M | 4 | 12.04 GB| 14.54 GB | medium, balanced quality - recommended |
| [noromaid-20b-v0.1.1.Q5_0.gguf](https://huggingface.co/TheBloke/Noromaid-20B-v0.1.1-GGUF/blob/main/noromaid-20b-v0.1.1.Q5_0.gguf) | Q5_0 | 5 | 13.77 GB| 16.27 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [noromaid-20b-v0.1.1.Q5_K_S.gguf](https://huggingface.co/TheBloke/Noromaid-20B-v0.1.1-GGUF/blob/main/noromaid-20b-v0.1.1.Q5_K_S.gguf) | Q5_K_S | 5 | 13.77 GB| 16.27 GB | large, low quality loss - recommended |
| [noromaid-20b-v0.1.1.Q5_K_M.gguf](https://huggingface.co/TheBloke/Noromaid-20B-v0.1.1-GGUF/blob/main/noromaid-20b-v0.1.1.Q5_K_M.gguf) | Q5_K_M | 5 | 14.16 GB| 16.66 GB | large, very low quality loss - recommended |
| [noromaid-20b-v0.1.1.Q6_K.gguf](https://huggingface.co/TheBloke/Noromaid-20B-v0.1.1-GGUF/blob/main/noromaid-20b-v0.1.1.Q6_K.gguf) | Q6_K | 6 | 16.40 GB| 18.90 GB | very large, extremely low quality loss |
| [noromaid-20b-v0.1.1.Q8_0.gguf](https://huggingface.co/TheBloke/Noromaid-20B-v0.1.1-GGUF/blob/main/noromaid-20b-v0.1.1.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/Noromaid-20B-v0.1.1-GGUF and below it, a specific filename to download, such as: noromaid-20b-v0.1.1.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/Noromaid-20B-v0.1.1-GGUF noromaid-20b-v0.1.1.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/Noromaid-20B-v0.1.1-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/Noromaid-20B-v0.1.1-GGUF noromaid-20b-v0.1.1.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 noromaid-20b-v0.1.1.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 can be found in the text-generation-webui documentation, here: [text-generation-webui/docs/04 â Model Tab.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/04%20%E2%80%90%20Model%20Tab.md#llamacpp).
## 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/Noromaid-20B-v0.1.1-GGUF", model_file="noromaid-20b-v0.1.1.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**: Brandon Frisco, LangChain4j, Spiking Neurons AB, transmissions 11, Joseph William Delisle, Nitin Borwankar, Willem Michiel, Michael Dempsey, vamX, Jeffrey Morgan, zynix, jjj, Omer Bin Jawed, Sean Connelly, jinyuan sun, Jeromy Smith, Shadi, Pawan Osman, Chadd, Elijah Stavena, Illia Dulskyi, Sebastain Graf, Stephen Murray, terasurfer, Edmond Seymore, Celu Ramasamy, Mandus, Alex, biorpg, Ajan Kanaga, Clay Pascal, Raven Klaugh, é¿æ, K, ya boyyy, usrbinkat, Alicia Loh, John Villwock, ReadyPlayerEmma, Chris Smitley, Cap'n Zoog, fincy, GodLy, S_X, sidney chen, Cory Kujawski, OG, Mano Prime, AzureBlack, Pieter, Kalila, Spencer Kim, Tom X Nguyen, Stanislav Ovsiannikov, Michael Levine, Andrey, Trailburnt, Vadim, Enrico Ros, Talal Aujan, Brandon Phillips, Jack West, Eugene Pentland, Michael Davis, Will Dee, webtim, Jonathan Leane, Alps Aficionado, Rooh Singh, Tiffany J. Kim, theTransient, Luke @flexchar, Elle, Caitlyn Gatomon, Ari Malik, subjectnull, Johann-Peter Hartmann, Trenton Dambrowitz, Imad Khwaja, Asp the Wyvern, Emad Mostaque, Rainer Wilmers, Alexandros Triantafyllidis, Nicholas, Pedro Madruga, SuperWojo, Harry Royden McLaughlin, James Bentley, Olakabola, David Ziegler, Ai Maven, Jeff Scroggin, Nikolai Manek, Deo Leter, Matthew Berman, Fen Risland, Ken Nordquist, Manuel Alberto Morcote, Luke Pendergrass, TL, Fred von Graf, Randy H, Dan Guido, NimbleBox.ai, Vitor Caleffi, Gabriel Tamborski, knownsqashed, Lone Striker, Erik BjÀreholt, John Detwiler, Leonard Tan, Iucharbius
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: IkariDev and Undi's Noromaid 20B v0.1.1
---
# Disclaimer:
## This is a ***TEST*** version, don't expect everything to work!!!
You may use our custom **prompting format**(scroll down to download them!), or simple alpaca. **(Choose which fits best for you!)**
---
# This model is a collab between [IkariDev](https://huggingface.co/IkariDev) and [Undi](https://huggingface.co/Undi95)!
Tired of the same merges everytime? Here it is, the Noromaid-20b-v0.1.1 model. Suitable for RP, ERP and general stuff.
[Recommended settings - No settings yet(Please suggest some over in the Community tab!)]
<!-- description start -->
## Description
<!-- [Recommended settings - contributed by localfultonextractor](https://files.catbox.moe/ue0tja.json) -->
This repo contains fp16 files of Noromaid-20b-v0.1.1.
[FP16 - by IkariDev and Undi](https://huggingface.co/NeverSleep/Noromaid-20b-v0.1.1)
<!-- [GGUF - By TheBloke](https://huggingface.co/TheBloke/Athena-v4-GGUF)-->
<!-- [GPTQ - By TheBloke](https://huggingface.co/TheBloke/Athena-v4-GPTQ)-->
<!-- [exl2[8bpw-8h] - by AzureBlack](https://huggingface.co/AzureBlack/Echidna-13b-v0.3-8bpw-8h-exl2)-->
<!-- [AWQ - By TheBloke](https://huggingface.co/TheBloke/Athena-v4-AWQ)-->
<!-- [fp16 - by IkariDev+Undi95](https://huggingface.co/IkariDev/Athena-v4)-->
[GGUF - by IkariDev and Undi](https://huggingface.co/NeverSleep/Noromaid-20b-v0.1.1-GGUF)
<!-- [OLD(GGUF - by IkariDev+Undi95)](https://huggingface.co/IkariDev/Athena-v4-GGUF)-->
## Ratings:
Note: We have permission of all users to upload their ratings, we DONT screenshot random reviews without asking if we can put them here!
No ratings yet!
If you want your rating to be here, send us a message over on DC and we'll put up a screenshot of it here. DC name is "ikaridev" and "undi".
<!-- description end -->
<!-- prompt-template start -->
## Prompt template: Custom format, or Alpaca
### Custom format:
UPDATED!! SillyTavern config files: [Context](https://files.catbox.moe/ifmhai.json), [Instruct](https://files.catbox.moe/ttw1l9.json).
OLD SillyTavern config files: [Context](https://files.catbox.moe/x85uy1.json), [Instruct](https://files.catbox.moe/ttw1l9.json).
### Alpaca:
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
```
## Training data used:
- [no_robots dataset](https://huggingface.co/Undi95/Llama2-13B-no_robots-alpaca-lora) let the model have more human behavior, enhances the output.
- [Aesir Private RP dataset] New data from a new and never used before dataset, add fresh data, no LimaRP spam, this is 100% new. Thanks to the [MinvervaAI Team](https://huggingface.co/MinervaAI) and, in particular, [Gryphe](https://huggingface.co/Gryphe) for letting us use it!
## Others
Undi: If you want to support me, you can [here](https://ko-fi.com/undiai).
IkariDev: Visit my [retro/neocities style website](https://ikaridevgit.github.io/) please kek
<!-- original-model-card end -->
|
jisukim8873/falcon-7B-case-5 | jisukim8873 | "2024-03-04T01:49:37Z" | 1,489 | 0 | transformers | [
"transformers",
"safetensors",
"falcon",
"text-generation",
"custom_code",
"en",
"dataset:Open-Orca/SlimOrca",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2024-03-04T00:52:31Z" | ---
license: apache-2.0
datasets:
- Open-Orca/SlimOrca
language:
- en
---
# Model Details
* Model Description: This model is test for data ordering.
* Developed by: Jisu Kim
* Model Type: Large Language Model
# Model Architecture
This model is based on falcon-7B. We fine-tuning this model for data ordering task.
falcon-7B is a transformer model, with the following architecture choices:
* Grouped-Query Attention
* Sliding-Window Attention
* Byte-fallback BPE tokenizer
# Dataset
We random sample Open-Orca dataset. (We finetune the 100,000 dataset)
# Guthub
https://github.com/trailerAI
# License
Apache License 2.0 |
stefan-it/wav2vec2-large-xlsr-53-basque | stefan-it | "2021-03-29T15:54:40Z" | 1,488 | 0 | transformers | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"eu",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | "2022-03-02T23:29:05Z" | ---
language: eu
datasets:
- common_voice
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: XLSR Wav2Vec2 Basque Stefan Schweter
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice eu
type: common_voice
args: eu
metrics:
- name: Test WER
type: wer
value: 18.272625
---
# Wav2Vec2-Large-XLSR-53-Basque
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Basque using the [Common Voice](https://huggingface.co/datasets/common_voice).
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
```python
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
test_dataset = load_dataset("common_voice", "eu", split="test[:2%]").
processor = Wav2Vec2Processor.from_pretrained("stefan-it/wav2vec2-large-xlsr-53-basque")
model = Wav2Vec2ForCTC.from_pretrained("stefan-it/wav2vec2-large-xlsr-53-basque")
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
predicted_ids = torch.argmax(logits, dim=-1)
print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset["sentence"][:2])
```
## Evaluation
The model can be evaluated as follows on the Basque test data of Common Voice.
```python
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re
test_dataset = load_dataset("common_voice", "eu", split="test")
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("stefan-it/wav2vec2-large-xlsr-53-basque")
model = Wav2Vec2ForCTC.from_pretrained("stefan-it/wav2vec2-large-xlsr-53-basque")
model.to("cuda")
chars_to_ignore_regex = '[\\\\,\\\\?\\\\.\\\\!\\\\-\\\\;\\\\:\\\\"\\\\â\\\\%\\\\â\\\\â\\\\ï¿œ]'
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def evaluate(batch):
inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
pred_ids = torch.argmax(logits, dim=-1)
batch["pred_strings"] = processor.batch_decode(pred_ids)
return batch
result = test_dataset.map(evaluate, batched=True, batch_size=8)
print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
```
**Test Result**: 18.272625%
## Training
The Common Voice `train`, `validation` datasets were used for training.
The script used for training can be found here, hopefully very soon!
## Acknowledgements
Many thanks to the [OVH team](https://www.ovhcloud.com) for providing access to a V-100 instance. Without their help,
fine-tuning would not be possible!
I would also thank [Manuel Romero](https://github.com/mrm8488) (mrm8488) for helping with the fine-tuning script! |
TheBloke/Pygmalion-2-7B-GGUF | TheBloke | "2023-09-27T12:48:01Z" | 1,488 | 25 | transformers | [
"transformers",
"gguf",
"llama",
"text generation",
"instruct",
"text-generation",
"en",
"dataset:PygmalionAI/PIPPA",
"dataset:Open-Orca/OpenOrca",
"dataset:Norquinal/claude_multiround_chat_30k",
"dataset:jondurbin/airoboros-gpt4-1.4.1",
"dataset:databricks/databricks-dolly-15k",
"base_model:PygmalionAI/pygmalion-2-7b",
"license:llama2",
"text-generation-inference",
"region:us"
] | text-generation | "2023-09-05T22:02:07Z" | ---
language:
- en
license: llama2
tags:
- text generation
- instruct
datasets:
- PygmalionAI/PIPPA
- Open-Orca/OpenOrca
- Norquinal/claude_multiround_chat_30k
- jondurbin/airoboros-gpt4-1.4.1
- databricks/databricks-dolly-15k
model_name: Pygmalion 2 7B
base_model: PygmalionAI/pygmalion-2-7b
inference: false
model_creator: PygmalionAI
model_type: llama
pipeline_tag: text-generation
prompt_template: 'The model has been trained on prompts using three different roles,
which are denoted by the following tokens: `<|system|>`, `<|user|>` and `<|model|>`.
The `<|system|>` prompt can be used to inject out-of-channel information behind
the scenes, while the `<|user|>` prompt should be used to indicate user input.
The `<|model|>` token should then be used to indicate that the model should generate
a response. These tokens can happen multiple times and be chained up to form a conversation
history.
The system prompt has been designed to allow the model to "enter" various modes
and dictate the reply length. Here''s an example:
```
<|system|>Enter RP mode. Pretend to be {{char}} whose persona follows:
{{persona}}
You shall reply to the user while staying in character, and generate long responses.
```
'
quantized_by: TheBloke
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
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<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 -->
# Pygmalion 2 7B - GGUF
- Model creator: [PygmalionAI](https://huggingface.co/PygmalionAI)
- Original model: [Pygmalion 2 7B](https://huggingface.co/PygmalionAI/pygmalion-2-7b)
<!-- description start -->
## Description
This repo contains GGUF format model files for [PygmalionAI's Pygmalion 2 7B](https://huggingface.co/PygmalionAI/pygmalion-2-7b).
<!-- 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/Pygmalion-2-7B-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Pygmalion-2-7B-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Pygmalion-2-7B-GGUF)
* [PygmalionAI's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/PygmalionAI/pygmalion-2-7b)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: Custom
The model has been trained on prompts using three different roles, which are denoted by the following tokens: `<|system|>`, `<|user|>` and `<|model|>`.
The `<|system|>` prompt can be used to inject out-of-channel information behind the scenes, while the `<|user|>` prompt should be used to indicate user input.
The `<|model|>` token should then be used to indicate that the model should generate a response. These tokens can happen multiple times and be chained up to form a conversation history.
The system prompt has been designed to allow the model to "enter" various modes and dictate the reply length. Here's an example:
```
<|system|>Enter RP mode. Pretend to be {{char}} whose persona follows:
{{persona}}
You shall reply to the user while staying in character, and generate long responses.
```
<!-- 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 |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [pygmalion-2-7b.Q2_K.gguf](https://huggingface.co/TheBloke/Pygmalion-2-7B-GGUF/blob/main/pygmalion-2-7b.Q2_K.gguf) | Q2_K | 2 | 2.83 GB| 5.33 GB | smallest, significant quality loss - not recommended for most purposes |
| [pygmalion-2-7b.Q3_K_S.gguf](https://huggingface.co/TheBloke/Pygmalion-2-7B-GGUF/blob/main/pygmalion-2-7b.Q3_K_S.gguf) | Q3_K_S | 3 | 2.95 GB| 5.45 GB | very small, high quality loss |
| [pygmalion-2-7b.Q3_K_M.gguf](https://huggingface.co/TheBloke/Pygmalion-2-7B-GGUF/blob/main/pygmalion-2-7b.Q3_K_M.gguf) | Q3_K_M | 3 | 3.30 GB| 5.80 GB | very small, high quality loss |
| [pygmalion-2-7b.Q3_K_L.gguf](https://huggingface.co/TheBloke/Pygmalion-2-7B-GGUF/blob/main/pygmalion-2-7b.Q3_K_L.gguf) | Q3_K_L | 3 | 3.60 GB| 6.10 GB | small, substantial quality loss |
| [pygmalion-2-7b.Q4_0.gguf](https://huggingface.co/TheBloke/Pygmalion-2-7B-GGUF/blob/main/pygmalion-2-7b.Q4_0.gguf) | Q4_0 | 4 | 3.83 GB| 6.33 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [pygmalion-2-7b.Q4_K_S.gguf](https://huggingface.co/TheBloke/Pygmalion-2-7B-GGUF/blob/main/pygmalion-2-7b.Q4_K_S.gguf) | Q4_K_S | 4 | 3.86 GB| 6.36 GB | small, greater quality loss |
| [pygmalion-2-7b.Q4_K_M.gguf](https://huggingface.co/TheBloke/Pygmalion-2-7B-GGUF/blob/main/pygmalion-2-7b.Q4_K_M.gguf) | Q4_K_M | 4 | 4.08 GB| 6.58 GB | medium, balanced quality - recommended |
| [pygmalion-2-7b.Q5_0.gguf](https://huggingface.co/TheBloke/Pygmalion-2-7B-GGUF/blob/main/pygmalion-2-7b.Q5_0.gguf) | Q5_0 | 5 | 4.65 GB| 7.15 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [pygmalion-2-7b.Q5_K_S.gguf](https://huggingface.co/TheBloke/Pygmalion-2-7B-GGUF/blob/main/pygmalion-2-7b.Q5_K_S.gguf) | Q5_K_S | 5 | 4.65 GB| 7.15 GB | large, low quality loss - recommended |
| [pygmalion-2-7b.Q5_K_M.gguf](https://huggingface.co/TheBloke/Pygmalion-2-7B-GGUF/blob/main/pygmalion-2-7b.Q5_K_M.gguf) | Q5_K_M | 5 | 4.78 GB| 7.28 GB | large, very low quality loss - recommended |
| [pygmalion-2-7b.Q6_K.gguf](https://huggingface.co/TheBloke/Pygmalion-2-7B-GGUF/blob/main/pygmalion-2-7b.Q6_K.gguf) | Q6_K | 6 | 5.53 GB| 8.03 GB | very large, extremely low quality loss |
| [pygmalion-2-7b.Q8_0.gguf](https://huggingface.co/TheBloke/Pygmalion-2-7B-GGUF/blob/main/pygmalion-2-7b.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/Pygmalion-2-7B-GGUF and below it, a specific filename to download, such as: pygmalion-2-7b.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/Pygmalion-2-7B-GGUF pygmalion-2-7b.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/Pygmalion-2-7B-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/Pygmalion-2-7B-GGUF pygmalion-2-7b.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 pygmalion-2-7b.q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<|system|>Enter RP mode. Pretend to be {{char}} whose persona follows:\n{{persona}}\n\nYou shall reply to the user while staying in character, and generate long responses."
```
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/Pygmalion-2-7B-GGUF", model_file="pygmalion-2-7b.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 BjÀreholt, 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, ì€êµ ê¹, Brandon Frisco, Kalila, Trailburnt, Randy H, Talal Aujan, Nathan Dryer, Vadim, é¿æ, 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: PygmalionAI's Pygmalion 2 7B
<h1 style="text-align: center">Pygmalion-2 7B</h1>
<h2 style="text-align: center">An instruction-tuned Llama-2 biased towards fiction writing and conversation.</h2>
## Model Details
The long-awaited release of our new models based on Llama-2 is finally here. Pygmalion-2 7B (formerly known as Metharme) is based on
[Llama-2 7B](https://huggingface.co/meta-llama/llama-2-7b-hf) released by Meta AI.
The Metharme models were an experiment to try and get a model that is usable for conversation, roleplaying and storywriting,
but which can be guided using natural language like other instruct models. After much deliberation, we reached the conclusion
that the Metharme prompting format is superior (and easier to use) compared to the classic Pygmalion.
This model was trained by doing supervised fine-tuning over a mixture of regular instruction data alongside roleplay, fictional stories
and conversations with synthetically generated instructions attached.
This model is freely available for both commercial and non-commercial use, as per the Llama-2 license.
## Prompting
The model has been trained on prompts using three different roles, which are denoted by the following tokens: `<|system|>`, `<|user|>` and `<|model|>`.
The `<|system|>` prompt can be used to inject out-of-channel information behind the scenes, while the `<|user|>` prompt should be used to indicate user input.
The `<|model|>` token should then be used to indicate that the model should generate a response. These tokens can happen multiple times and be chained up to
form a conversation history.
### Prompting example
The system prompt has been designed to allow the model to "enter" various modes and dictate the reply length. Here's an example:
```
<|system|>Enter RP mode. Pretend to be {{char}} whose persona follows:
{{persona}}
You shall reply to the user while staying in character, and generate long responses.
```
## Dataset
The dataset used to fine-tune this model includes our own [PIPPA](https://huggingface.co/datasets/PygmalionAI/PIPPA), along with several other instruction
datasets, and datasets acquired from various RP forums.
## Limitations and biases
The intended use-case for this model is fictional writing for entertainment purposes. Any other sort of usage is out of scope.
As such, it was **not** fine-tuned to be safe and harmless: the base model _and_ this fine-tune have been trained on data known to contain profanity and texts that
are lewd or otherwise offensive. It may produce socially unacceptable or undesirable text, even if the prompt itself does not include anything explicitly offensive.
Outputs might often be factually wrong or misleading.
## Acknowledgements
We would like to thank [SpicyChat](https://spicychat.ai/) for sponsoring the training for this model.
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
<!-- original-model-card end -->
|
DILAB-HYU/koquality-polyglot-12.8b | DILAB-HYU | "2023-11-17T16:35:16Z" | 1,488 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"polyglot-ko",
"gpt-neox",
"KoQuality",
"ko",
"dataset:DILAB-HYU/KoQuality",
"base_model:EleutherAI/polyglot-ko-12.8b",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-11-12T16:30:20Z" | ---
license: apache-2.0
datasets:
- DILAB-HYU/KoQuality
language:
- ko
pipeline_tag: text-generation
tags:
- polyglot-ko
- gpt-neox
- KoQuality
base_model: EleutherAI/polyglot-ko-12.8b
---
This model is a instruct-tuned poylglot-ko-12.8b model, using koquality. -> 18step
## Training hyperparameters
- learning_rate: 5e-5
- seed: 42
- distributed_type: multi-GPU (A100 80G)
- num_devices: 6
- train_batch_size: 4
- gradient_accumulation_steps: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2.0
## Framework versions
- Transformers 4.35.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.6
- deepspeed 0.11.1
- accelerate 0.24.1 |
Yntec/C-.-_-.-Aravaggio | Yntec | "2023-12-21T01:37:47Z" | 1,488 | 3 | diffusers | [
"diffusers",
"safetensors",
"Base model",
"General",
"Everything",
"Redigleb_Doppler2482",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | "2023-12-21T01:09:00Z" | ---
license: creativeml-openrail-m
library_name: diffusers
pipeline_tag: text-to-image
tags:
- Base model
- General
- Everything
- Redigleb_Doppler2482
- stable-diffusion
- stable-diffusion-diffusers
- diffusers
- text-to-image
---
# C++AravaggioV0.9 - an Answer to both Dall-E and Kandinsky 2.1
Original page: https://civitai.com/models/93155/caravaggiov09-an-answer-to-both-dall-e-and-kandinsky-21?modelVersionId=99323
Samples and prompts:
Top left: Anime fine details portrait of joyful cute little girl lay school class room, bokeh. anime masterpiece by studio ghibli. 8k, sharp high quality classic anime from 1990 in style of hayao miyazaki. Wikipedia. hugging. OIL PAINTING. DOCTOR with short hair in coat BEAUTIFUL girl eyes. she has pigtails
Top right: House with a waterwheel built into the roots of a giant tree, next to games, a colorful river landscape painting from a fantasy point and click 2 d graphic adventure game, art inspired by ROSSDRAWS and larry elmore and john shroades, king's quest, sierra entertainment
Bottom left: An underwater world with vibrant coral reefs and schools of colorful fish. The artistic style is pop art, with bold and bright colors and graphic shapes. The light setting is filtered through the water, creating a surreal and dreamy effect. The mood of the image is energetic and lively, capturing the movement and vitality of the underwater environment.
Bottom right: pretty young girl riding bike down the ocean streets of japan, teddy bear hour |
Yntec/Toonify2 | Yntec | "2023-08-10T14:06:21Z" | 1,487 | 6 | diffusers | [
"diffusers",
"safetensors",
"anime",
"comic",
"art",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"BetterThanNothing",
"en",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | "2023-08-10T13:46:20Z" | ---
license: creativeml-openrail-m
language:
- en
library_name: diffusers
pipeline_tag: text-to-image
tags:
- anime
- comic
- art
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- BetterThanNothing
---
# Toonify
Preview and prompt:
sitting elementary girl, Pretty CUTE, gorgeous hair, DETAILED EYES, Futuristic city of tokyo japan, Magazine ad, iconic, 1943, sharp focus, 4k. (Sweaty). visible comic art by ROSSDRAWS and Clay Mann and kyoani
Original page:
https://civitai.com/models/36281
|
mtgv/MobileLLaMA-1.4B-Base | mtgv | "2023-12-29T06:11:55Z" | 1,487 | 15 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"dataset:togethercomputer/RedPajama-Data-1T",
"arxiv:2312.16886",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-12-28T09:52:27Z" | ---
license: apache-2.0
datasets:
- togethercomputer/RedPajama-Data-1T
tags:
- llama
---
# Model Summery
MobileLLaMA-1.4B-Base is a Transformer with 1.4B billon paramters. We downscale LLaMA to facilitate the off-the-shelf deployment. To make our work reproducible, all
the models are trained on 1.3T tokens from the [RedPajama v1](https://www.together.ai/blog/redpajama) dataset only. This benefits further research by enabling controlled experiments.
We extensively assess our models on two standard natural language benchmarks, for language understanding and common sense reasoning respectively. Experimental results show that our
MobileLLaMA 1.4B is on par with the most recent opensource models.
# Model Sources
- Repository: https://github.com/Meituan-AutoML/MobileVLM
- Paper: https://arxiv.org/abs/2312.16886
# How to Get Started with the Model
Model weights can be loaded with Hugging Face Transformers. Examples can be found at [Github](https://github.com/Meituan-AutoML/MobileVLM).
# Training Details
please refer to our paper in section 4.1: [MobileVLM: A Fast, Strong and Open Vision Language Assistant for Mobile Devices](https://arxiv.org/pdf/2312.16886.pdf). |
mrm8488/spanbert-large-finetuned-squadv2 | mrm8488 | "2021-05-20T00:59:58Z" | 1,486 | 1 | transformers | [
"transformers",
"pytorch",
"jax",
"bert",
"en",
"arxiv:1907.10529",
"endpoints_compatible",
"region:us"
] | null | "2022-03-02T23:29:05Z" | ---
language: en
thumbnail:
---
# SpanBERT large fine-tuned on SQuAD v2
[SpanBERT](https://github.com/facebookresearch/SpanBERT) created by [Facebook Research](https://github.com/facebookresearch) and fine-tuned on [SQuAD 2.0](https://rajpurkar.github.io/SQuAD-explorer/) for **Q&A** downstream task ([by them](https://github.com/facebookresearch/SpanBERT#finetuned-models-squad-1120-relation-extraction-coreference-resolution)).
## Details of SpanBERT
[SpanBERT: Improving Pre-training by Representing and Predicting Spans](https://arxiv.org/abs/1907.10529)
## Details of the downstream task (Q&A) - Dataset ð ð§ â
[SQuAD2.0](https://rajpurkar.github.io/SQuAD-explorer/) combines the 100,000 questions in SQuAD1.1 with over 50,000 unanswerable questions written adversarially by crowdworkers to look similar to answerable ones. To do well on SQuAD2.0, systems must not only answer questions when possible, but also determine when no answer is supported by the paragraph and abstain from answering.
| Dataset | Split | # samples |
| -------- | ----- | --------- |
| SQuAD2.0 | train | 130k |
| SQuAD2.0 | eval | 12.3k |
## Model fine-tuning ðïžâ
You can get the fine-tuning script [here](https://github.com/facebookresearch/SpanBERT)
```bash
python code/run_squad.py \
--do_train \
--do_eval \
--model spanbert-large-cased \
--train_file train-v2.0.json \
--dev_file dev-v2.0.json \
--train_batch_size 32 \
--eval_batch_size 32 \
--learning_rate 2e-5 \
--num_train_epochs 4 \
--max_seq_length 512 \
--doc_stride 128 \
--eval_metric best_f1 \
--output_dir squad2_output \
--version_2_with_negative \
--fp16
```
## Results Comparison ð
| | SQuAD 1.1 | SQuAD 2.0 | Coref | TACRED |
| ---------------------- | ------------- | --------- | ------- | ------ |
| | F1 | F1 | avg. F1 | F1 |
| BERT (base) | 88.5* | 76.5* | 73.1 | 67.7 |
| SpanBERT (base) | [92.4*](https://huggingface.co/mrm8488/spanbert-base-finetuned-squadv1) | [83.6*](https://huggingface.co/mrm8488/spanbert-base-finetuned-squadv2) | 77.4 | [68.2](https://huggingface.co/mrm8488/spanbert-base-finetuned-tacred) |
| BERT (large) | 91.3 | 83.3 | 77.1 | 66.4 |
| SpanBERT (large) | [94.6](https://huggingface.co/mrm8488/spanbert-large-finetuned-squadv1) | **88.7** (this) | 79.6 | [70.8](https://huggingface.co/mrm8488/spanbert-large-finetuned-tacred) |
Note: The numbers marked as * are evaluated on the development sets because those models were not submitted to the official SQuAD leaderboard. All the other numbers are test numbers.
## Model in action
Fast usage with **pipelines**:
```python
from transformers import pipeline
qa_pipeline = pipeline(
"question-answering",
model="mrm8488/spanbert-large-finetuned-squadv2",
tokenizer="SpanBERT/spanbert-large-cased"
)
qa_pipeline({
'context': "Manuel Romero has been working very hard in the repository hugginface/transformers lately",
'question': "How has been working Manuel Romero lately?"
})
# Output: {'answer': 'very hard', 'end': 40, 'score': 0.9052708846768347, 'start': 31}
```
> Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488)
> Made with <span style="color: #e25555;">♥</span> in Spain
|
levimorin/5GnEaJaYDbuM2RGCPNA26cbEu7o3Bj7PAeqpDQhVeRYWDNEz_vgg | levimorin | "2024-03-08T19:10:10Z" | 1,486 | 0 | keras | [
"keras",
"region:us"
] | null | "2024-03-03T04:59:33Z" | Entry not found |
TheDrummer/free-use | TheDrummer | "2024-04-28T06:20:59Z" | 1,486 | 3 | null | [
"gguf",
"license:cc-by-nc-4.0",
"region:us"
] | null | "2024-03-17T19:01:26Z" | ---
license: cc-by-nc-4.0
license_link: LICENSE
--- |
dbmdz/distilbert-base-german-europeana-cased | dbmdz | "2022-06-09T07:27:27Z" | 1,485 | 7 | transformers | [
"transformers",
"pytorch",
"tf",
"distilbert",
"historic german",
"de",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | "2022-03-02T23:29:05Z" | ---
language: de
license: mit
tags:
- "historic german"
---
# ð€ + ð dbmdz DistilBERT model
In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State
Library open sources a German Europeana DistilBERT model ð
# German Europeana DistilBERT
We use the open source [Europeana newspapers](http://www.europeana-newspapers.eu/)
that were provided by *The European Library*. The final
training corpus has a size of 51GB and consists of 8,035,986,369 tokens.
Detailed information about the data and pretraining steps can be found in
[this repository](https://github.com/stefan-it/europeana-bert).
## Results
For results on Historic NER, please refer to [this repository](https://github.com/stefan-it/europeana-bert).
## Usage
With Transformers >= 4.3 our German Europeana DistilBERT model can be loaded like:
```python
from transformers import AutoModel, AutoTokenizer
model_name = "dbmdz/distilbert-base-german-europeana-cased"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModel.from_pretrained(model_name)
```
# Huggingface model hub
All other German Europeana models are available on the [Huggingface model hub](https://huggingface.co/dbmdz).
# Contact (Bugs, Feedback, Contribution and more)
For questions about our Europeana BERT, ELECTRA and ConvBERT models just open a new discussion
[here](https://github.com/stefan-it/europeana-bert/discussions) ð€
# Acknowledgments
Research supported with Cloud TPUs from Google's TensorFlow Research Cloud (TFRC).
Thanks for providing access to the TFRC â€ïž
Thanks to the generous support from the [Hugging Face](https://huggingface.co/) team,
it is possible to download both cased and uncased models from their S3 storage ð€ |
digiplay/BeenYouLiteL11_diffusers | digiplay | "2024-06-02T23:15:10Z" | 1,485 | 14 | diffusers | [
"diffusers",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"license:other",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | "2023-05-29T12:59:47Z" | ---
license: other
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
inference: true
---
Cute realistic amine style model.
Model info:
https://civitai.com/models/34440/beenyou-lite
Sample image I madeïŒ
|
osiria/distilbert-italian-cased-ner | osiria | "2023-06-11T11:07:45Z" | 1,485 | 2 | transformers | [
"transformers",
"pytorch",
"safetensors",
"distilbert",
"token-classification",
"it",
"arxiv:1910.01108",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | "2023-06-01T16:12:27Z" | ---
license: apache-2.0
language:
- it
widget:
- text: "Mi chiamo Marco Rossi, vivo a Roma e lavoro per l'Agenzia Spaziale Italiana"
example_title: "Example 1"
---
--------------------------------------------------------------------------------------------------
<body>
<span class="vertical-text" style="background-color:lightgreen;border-radius: 3px;padding: 3px;">â</span>
<br>
<span class="vertical-text" style="background-color:orange;border-radius: 3px;padding: 3px;">ââââTask: Named Entity Recognition</span>
<br>
<span class="vertical-text" style="background-color:lightblue;border-radius: 3px;padding: 3px;">ââââModel: DistilBERT</span>
<br>
<span class="vertical-text" style="background-color:tomato;border-radius: 3px;padding: 3px;">ââââLang: IT</span>
<br>
<span class="vertical-text" style="background-color:lightgrey;border-radius: 3px;padding: 3px;">ââ</span>
<br>
<span class="vertical-text" style="background-color:#CF9FFF;border-radius: 3px;padding: 3px;">â</span>
</body>
--------------------------------------------------------------------------------------------------
<h3>Model description</h3>
This is a <b>DistilBERT</b> <b>[1]</b> cased model for the <b>Italian</b> language, fine-tuned for <b>Named Entity Recognition</b> (<b>Person</b>, <b>Location</b>, <b>Organization</b> and <b>Miscellanea</b> classes) on the [WikiNER](https://figshare.com/articles/dataset/Learning_multilingual_named_entity_recognition_from_Wikipedia/5462500) dataset <b>[2]</b>, using <b>DistilBERT-ITALIAN</b> ([distilbert-base-italian-cased](https://huggingface.co/osiria/distilbert-base-italian-cased)) as a pre-trained model.
This is a cased DistilBERT model. If you are looking for a more accurate (but heavier) cased model, you can refer to: https://huggingface.co/osiria/bert-italian-cased-ner
If you are looking for an uncased model, you can refer to: https://huggingface.co/osiria/bert-italian-uncased-ner
<h3>Training and Performances</h3>
The model is trained to perform entity recognition over 4 classes: <b>PER</b> (persons), <b>LOC</b> (locations), <b>ORG</b> (organizations), <b>MISC</b> (miscellanea, mainly events, products and services). It has been fine-tuned for Named Entity Recognition, using the WikiNER Italian dataset plus an additional custom dataset of manually annotated Wikipedia paragraphs.
The WikiNER dataset has been splitted in 102.352 training instances and 25.588 test instances.
The performances on the test set are reported in the following table:
| Recall | Precision | F1 |
| ------ | ------ | ------ |
| 91.49 | 91.62 | 91.54 |
The metrics have been computed at the token level and then macro-averaged over the 4 classes.
Then, since WikiNER is an automatically annotated (silver standard) dataset, which sometimes contains imperfect annotations, an additional fine-tuning on ~3.500 manually annotated paragraphs has been performed.
<h3>Quick usage</h3>
```python
from transformers import DistilBertTokenizerFast, DistilBertForTokenClassification
tokenizer = DistilBertTokenizerFast.from_pretrained("osiria/distilbert-italian-cased-ner")
model = DistilBertForTokenClassification.from_pretrained("osiria/distilbert-italian-cased-ner")
from transformers import pipeline
ner = pipeline("ner", model = model, tokenizer = tokenizer, aggregation_strategy="first")
ner("Mi chiamo Marco Rossi, vivo a Roma e lavoro per l'Agenzia Spaziale Italiana nella missione Prisma")
[{'entity_group': 'PER',
'score': 0.9958186,
'word': 'Marco Rossi',
'start': 10,
'end': 21},
{'entity_group': 'LOC',
'score': 0.9933206,
'word': 'Roma',
'start': 30,
'end': 34},
{'entity_group': 'ORG',
'score': 0.99841493,
'word': 'Agenzia Spaziale Italiana',
'start': 50,
'end': 75},
{'entity_group': 'MISC',
'score': 0.6150316,
'word': 'Prisma',
'start': 91,
'end': 97}]
```
You can also try the model online using this web app: https://huggingface.co/spaces/osiria/distilbert-italian-cased-ner
<h3>References</h3>
[1] https://arxiv.org/abs/1910.01108
[2] https://www.sciencedirect.com/science/article/pii/S0004370212000276
<h3>Limitations</h3>
This model is mainly trained on Wikipedia, so it's particularly suitable for natively digital text from the world wide web, written in a correct and fluent form (like wikis, web pages, news, etc.). However, it may show limitations when it comes to chaotic text, containing errors and slang expressions
(like social media posts) or when it comes to domain-specific text (like medical, financial or legal content).
<h3>License</h3>
The model is released under <b>Apache-2.0</b> license
|
TheBloke/CodeLlama-13B-oasst-sft-v10-GGUF | TheBloke | "2023-09-27T12:46:30Z" | 1,485 | 14 | transformers | [
"transformers",
"gguf",
"llama",
"en",
"dataset:OpenAssistant/oasst1",
"dataset:shahules786/orca-best",
"base_model:OpenAssistant/codellama-13b-oasst-sft-v10",
"license:llama2",
"text-generation-inference",
"region:us"
] | null | "2023-08-27T14:17:26Z" | ---
language:
- en
license: llama2
datasets:
- OpenAssistant/oasst1
- shahules786/orca-best
model_name: CodeLlama 13B SFT v10
base_model: OpenAssistant/codellama-13b-oasst-sft-v10
inference: false
model_creator: OpenAssistant
model_type: llama
prompt_template: '<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>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 -->
# CodeLlama 13B SFT v10 - GGUF
- Model creator: [OpenAssistant](https://huggingface.co/OpenAssistant)
- Original model: [CodeLlama 13B SFT v10](https://huggingface.co/OpenAssistant/codellama-13b-oasst-sft-v10)
<!-- description start -->
## Description
This repo contains GGUF format model files for [OpenAssistant's CodeLlama 13B SFT v10](https://huggingface.co/OpenAssistant/codellama-13b-oasst-sft-v10).
<!-- 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/CodeLlama-13B-oasst-sft-v10-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/CodeLlama-13B-oasst-sft-v10-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/CodeLlama-13B-oasst-sft-v10-GGUF)
* [OpenAssistant's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/OpenAssistant/codellama-13b-oasst-sft-v10)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: ChatML
```
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>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 |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [codellama-13b-oasst-sft-v10.Q2_K.gguf](https://huggingface.co/TheBloke/CodeLlama-13B-oasst-sft-v10-GGUF/blob/main/codellama-13b-oasst-sft-v10.Q2_K.gguf) | Q2_K | 2 | 5.43 GB| 7.93 GB | smallest, significant quality loss - not recommended for most purposes |
| [codellama-13b-oasst-sft-v10.Q3_K_S.gguf](https://huggingface.co/TheBloke/CodeLlama-13B-oasst-sft-v10-GGUF/blob/main/codellama-13b-oasst-sft-v10.Q3_K_S.gguf) | Q3_K_S | 3 | 5.66 GB| 8.16 GB | very small, high quality loss |
| [codellama-13b-oasst-sft-v10.Q3_K_M.gguf](https://huggingface.co/TheBloke/CodeLlama-13B-oasst-sft-v10-GGUF/blob/main/codellama-13b-oasst-sft-v10.Q3_K_M.gguf) | Q3_K_M | 3 | 6.34 GB| 8.84 GB | very small, high quality loss |
| [codellama-13b-oasst-sft-v10.Q3_K_L.gguf](https://huggingface.co/TheBloke/CodeLlama-13B-oasst-sft-v10-GGUF/blob/main/codellama-13b-oasst-sft-v10.Q3_K_L.gguf) | Q3_K_L | 3 | 6.93 GB| 9.43 GB | small, substantial quality loss |
| [codellama-13b-oasst-sft-v10.Q4_0.gguf](https://huggingface.co/TheBloke/CodeLlama-13B-oasst-sft-v10-GGUF/blob/main/codellama-13b-oasst-sft-v10.Q4_0.gguf) | Q4_0 | 4 | 7.37 GB| 9.87 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [codellama-13b-oasst-sft-v10.Q4_K_S.gguf](https://huggingface.co/TheBloke/CodeLlama-13B-oasst-sft-v10-GGUF/blob/main/codellama-13b-oasst-sft-v10.Q4_K_S.gguf) | Q4_K_S | 4 | 7.41 GB| 9.91 GB | small, greater quality loss |
| [codellama-13b-oasst-sft-v10.Q4_K_M.gguf](https://huggingface.co/TheBloke/CodeLlama-13B-oasst-sft-v10-GGUF/blob/main/codellama-13b-oasst-sft-v10.Q4_K_M.gguf) | Q4_K_M | 4 | 7.87 GB| 10.37 GB | medium, balanced quality - recommended |
| [codellama-13b-oasst-sft-v10.Q5_0.gguf](https://huggingface.co/TheBloke/CodeLlama-13B-oasst-sft-v10-GGUF/blob/main/codellama-13b-oasst-sft-v10.Q5_0.gguf) | Q5_0 | 5 | 8.97 GB| 11.47 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [codellama-13b-oasst-sft-v10.Q5_K_S.gguf](https://huggingface.co/TheBloke/CodeLlama-13B-oasst-sft-v10-GGUF/blob/main/codellama-13b-oasst-sft-v10.Q5_K_S.gguf) | Q5_K_S | 5 | 8.97 GB| 11.47 GB | large, low quality loss - recommended |
| [codellama-13b-oasst-sft-v10.Q5_K_M.gguf](https://huggingface.co/TheBloke/CodeLlama-13B-oasst-sft-v10-GGUF/blob/main/codellama-13b-oasst-sft-v10.Q5_K_M.gguf) | Q5_K_M | 5 | 9.23 GB| 11.73 GB | large, very low quality loss - recommended |
| [codellama-13b-oasst-sft-v10.Q6_K.gguf](https://huggingface.co/TheBloke/CodeLlama-13B-oasst-sft-v10-GGUF/blob/main/codellama-13b-oasst-sft-v10.Q6_K.gguf) | Q6_K | 6 | 10.68 GB| 13.18 GB | very large, extremely low quality loss |
| [codellama-13b-oasst-sft-v10.Q8_0.gguf](https://huggingface.co/TheBloke/CodeLlama-13B-oasst-sft-v10-GGUF/blob/main/codellama-13b-oasst-sft-v10.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/CodeLlama-13B-oasst-sft-v10-GGUF and below it, a specific filename to download, such as: codellama-13b-oasst-sft-v10.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/CodeLlama-13B-oasst-sft-v10-GGUF codellama-13b-oasst-sft-v10.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/CodeLlama-13B-oasst-sft-v10-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/CodeLlama-13B-oasst-sft-v10-GGUF codellama-13b-oasst-sft-v10.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 codellama-13b-oasst-sft-v10.q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<|im_start|>system\n{system_message}<|im_end|>\n<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>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/CodeLlama-13B-oasst-sft-v10-GGUF", model_file="codellama-13b-oasst-sft-v10.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 BjÀreholt, 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, ì€êµ ê¹, Brandon Frisco, Kalila, Trailburnt, Randy H, Talal Aujan, Nathan Dryer, Vadim, é¿æ, 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: OpenAssistant's CodeLlama 13B SFT v10
# Open-Assistant CodeLlama 13B SFT v10
This model is an Open-Assistant fine-tuning of Meta's CodeLlama 13B LLM.
**Note**: Due to the new RoPE Theta value (1e6 instead of 1e4), for correct results you must load this model with `trust_remote_code=True` or use the latest main branch of Huggingface transformers (until version 4.33 is released).
## Model Details
- **Finetuned from:** [codellama/CodeLlama-7b-hf](https://huggingface.co/codellama/CodeLlama-7b-hf) via [epfLLM/Megatron-LLM](https://github.com/epfLLM/Megatron-LLM)
- **Model type:** Causal decoder-only transformer language model
- **Language:** English
- **Weights & Biases training logs:** 6123 steps, BS 64 [run56_oa_llamacode](https://wandb.ai/open-assistant/public-sft/runs/run56_oa_llamacode)
- **Demo:** [Continuations for 250 random prompts (without system message)](https://open-assistant.github.io/oasst-model-eval/?f=https%3A%2F%2Fraw.githubusercontent.com%2FOpen-Assistant%2Foasst-model-eval%2Fmain%2Fsampling_reports%2Foasst-sft%2F2023-08-26_OpenAssistant_codellama-13b-oasst-sft-v10_sampling_noprefix2.json)
- **License:** [LLAMA 2 COMMUNITY LICENSE AGREEMENT](https://huggingface.co/meta-llama/Llama-2-70b/raw/main/LICENSE.txt)
- **Contact:** [Open-Assistant Discord](https://ykilcher.com/open-assistant-discord)
## Prompting / Prompt Template
Due to public demand (see [survey](https://twitter.com/erhartford/status/1682403597525430272)) we changed the prompt-template for this model from custom prompter/assistant tokens to OpenAI's [chatml](https://github.com/openai/openai-python/blob/main/chatml.md) standard prompt format.
We hope that this leads to greater compatibility with chat inference/frontend applications.
Prompt dialogue template:
```
"""
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
"""
```
The model input can contain multiple conversation turns between user and assistant, e.g.
```
<|im_start|>user
{prompt 1}<|im_end|>
<|im_start|>assistant
{reply 1}<|im_end|>
<|im_start|>user
{prompt 2}<|im_end|>
<|im_start|>assistant
(...)
```
The model was partly trained with orca system messages.
For inference we recommend to use the official [Llama2 system message](https://github.com/facebookresearch/llama/blob/ea9f33d6d3ea8ed7d560d270986407fd6c2e52b7/example_chat_completion.py#L57-L61):
```
<|im_start|>system
You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.
If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.
<|im_end|>
```
### Credits & Special Thanks
- Thanks to [Meta AI](https://ai.meta.com/) for training and releasing the CodeLLlama model.
- Distributed training support was provided by EPFL's [Machine Learning and Optimization Laboratory](https://www.epfl.ch/labs/mlo/), and [Natural Language Processing Lab](https://nlp.epfl.ch/).
- The open-source [epfLLM/Megatron-LLM](https://github.com/epfLLM/Megatron-LLM) trainer was used for fine-tuning.
- [rombodawg](https://huggingface.co/rombodawg) curated the [LosslessMegaCodeTrainingV2_1m_Evol_Uncensored](https://huggingface.co/datasets/rombodawg/LosslessMegaCodeTrainingV2_1m_Evol_Uncensored) dataset.
- [ehartford](https://huggingface.co/ehartford) generated and published the [ehartford/dolphin](https://huggingface.co/datasets/ehartford/dolphin).
- [shahules786](https://github.com/shahules786) de-duped and filtered the Dolphin and Megacode dataset with a clustering/controid approach and generated orca-best & bestofmegacode.
- [andreaskoepf](https://github.com/andreaskoepf/) prepared & orchestrated the training.
## Ethical Considerations and Limitations
Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios.
For these reasons, as with all LLMs, the potential outputs of codellama-13b-oasst-sft-v10 cannot be predicted
in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses
to user prompts. Therefore, before deploying any applications of codellama-13b-oasst-sft-v10, developers should
perform safety testing and tuning tailored to their specific applications of the model.
Please see Meta's [Responsible Use Guide](https://ai.meta.com/llama/responsible-use-guide/).
## Configuration Details
The "pretokenizer" utility used to tokenize the datamix is part of the Open-Assistant github repository and can be found here: [model/pretokenizer](https://github.com/LAION-AI/Open-Assistant/tree/main/model/pretokenizer).
### Pretokenizer Configuration
```
orca_megacode_oasst_best:
datasets:
- orca-chat:
val_split: 0.01
max_val_set: 1000
- bestofmegacode:
val_split: 0.01
max_val_set: 1000
- oasst_export:
lang: "bg,ca,cs,da,de,en,es,fr,hr,hu,it,nl,pl,pt,ro,ru,sl,sr,sv,uk"
#hf_dataset_name: OpenAssistant/oasst1
input_file_path: 2023-08-25_oasst_ready.jsonl.gz
top_k: 1
val_split: 0.025
output_dir: "output/orca_megacode_oasst_best"
filename_prefix: "orca_megacode_oasst_best"
min_assistant_tokens: 1
```
<!-- original-model-card end -->
|
capricornstone/ChiMed-GPT-1.0-Q4_K_M-GGUF | capricornstone | "2024-06-28T06:48:42Z" | 1,485 | 0 | null | [
"gguf",
"llama-cpp",
"gguf-my-repo",
"base_model:SYNLP/ChiMed-GPT-1.0",
"license:mit",
"region:us"
] | null | "2024-06-28T06:48:05Z" | ---
base_model: SYNLP/ChiMed-GPT-1.0
license: mit
tags:
- llama-cpp
- gguf-my-repo
---
# capricornstone/ChiMed-GPT-1.0-Q4_K_M-GGUF
This model was converted to GGUF format from [`SYNLP/ChiMed-GPT-1.0`](https://huggingface.co/SYNLP/ChiMed-GPT-1.0) 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/SYNLP/ChiMed-GPT-1.0) 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 capricornstone/ChiMed-GPT-1.0-Q4_K_M-GGUF --hf-file chimed-gpt-1.0-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo capricornstone/ChiMed-GPT-1.0-Q4_K_M-GGUF --hf-file chimed-gpt-1.0-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 capricornstone/ChiMed-GPT-1.0-Q4_K_M-GGUF --hf-file chimed-gpt-1.0-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo capricornstone/ChiMed-GPT-1.0-Q4_K_M-GGUF --hf-file chimed-gpt-1.0-q4_k_m.gguf -c 2048
```
|
timm/vit_huge_patch14_clip_224.laion2b_ft_in12k_in1k | timm | "2023-05-06T00:08:24Z" | 1,484 | 2 | timm | [
"timm",
"pytorch",
"safetensors",
"image-classification",
"dataset:imagenet-1k",
"dataset:laion-2b",
"dataset:imagenet-12k",
"arxiv:2212.07143",
"arxiv:2210.08402",
"arxiv:2010.11929",
"license:apache-2.0",
"region:us"
] | image-classification | "2022-11-01T23:02:08Z" | ---
tags:
- image-classification
- timm
library_name: timm
license: apache-2.0
datasets:
- imagenet-1k
- laion-2b
- imagenet-12k
---
# Model card for vit_huge_patch14_clip_224.laion2b_ft_in12k_in1k
A Vision Transformer (ViT) image classification model. Pretrained on LAION-2B image-text pairs using OpenCLIP. Fine-tuned on ImageNet-12k and then 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): 632.0
- GMACs: 162.0
- Activations (M): 95.1
- 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
- ImageNet-12k
## 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_huge_patch14_clip_224.laion2b_ft_in12k_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_huge_patch14_clip_224.laion2b_ft_in12k_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, 257, 1280) 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}}
}
```
|
defog/sqlcoder2 | defog | "2023-10-13T16:43:20Z" | 1,484 | 105 | transformers | [
"transformers",
"pytorch",
"gpt_bigcode",
"text-generation",
"code",
"en",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-10-02T12:13:43Z" | ---
license: other
language:
- en
pipeline_tag: text-generation
tags:
- code
---
# Defog SQLCoder
Defog's SQLCoder is a state-of-the-art LLM for converting natural language questions to SQL queries.
[Interactive Demo](https://defog.ai/sqlcoder-demo/) | [ð€ HF Repo](https://huggingface.co/defog/sqlcoder2) | [âŸïž Colab](https://colab.research.google.com/drive/1z4rmOEiFkxkMiecAWeTUlPl0OmKgfEu7?usp=sharing) | [ðŠ Twitter](https://twitter.com/defogdata)
## TL;DR
SQLCoder is a 15B parameter model that outperforms `gpt-3.5-turbo` for natural language to SQL generation tasks on our [sql-eval](https://github.com/defog-ai/sql-eval) framework, and significantly outperforms all popular open-source models. When fine-tuned on a given schema, it also outperforms `gpt-4`
SQLCoder is fine-tuned on a base StarCoder model.
## Results on novel datasets not seen in training
| model | perc_correct |
|-|-|
| gpt4-2023-10-04 | 82.0 |
| defog-sqlcoder2 | 77.5 |
| gpt4-2023-08-28 | 74.0 |
| defog-sqlcoder-7b | 71.0 |
| gpt-3.5-2023-10-04 | 66.0 |
| claude-2 | 64.5 |
| gpt-3.5-2023-08-28 | 61.0 |
| claude_instant_1 | 61.0 |
| text-davinci-003 | 52.5 |
## License
The code in this repo (what little there is of it) is Apache-2 licensed. The model weights have a `CC BY-SA 4.0` license, with additional responsible use restrictions added. The TL;DR is that you can use and modify the model for any purpose â including commercial use. However, if you modify the weights (for example, by fine-tuning), you must open-source your modified weights under the same license terms.
## Training
Defog was trained on more than 20,000 human-curated questions. These questions were based on 10 different schemas. None of the schemas in the training data were included in our evaluation framework.
You can read more about our [training approach](https://defog.ai/blog/open-sourcing-sqlcoder2-7b/) and [evaluation framework](https://defog.ai/blog/open-sourcing-sqleval/).
## Results by question category
We classified each generated question into one of 5 categories. The table displays the percentage of questions answered correctly by each model, broken down by category.
| query_category | gpt-4 | sqlcoder2-15b | sqlcoder-7b | gpt-3.5 | claude-2 | claude-instant | gpt-3 |
|:-----------------|--------:|----------------:|--------------:|----------:|-----------:|-----------------:|--------:|
| date | 72 | 80 | 64 | 68 | 52 | 48 | 32 |
| group_by | 91.4 | 82.9 | 82.9 | 77.1 | 71.4 | 71.4 | 71.4 |
| order_by | 82.9 | 77.1 | 74.3 | 68.6 | 74.3 | 74.3 | 68.6 |
| ratio | 80 | 74.3 | 54.3 | 37.1 | 57.1 | 45.7 | 25.7 |
| join | 82.9 | 74.3 | 74.3 | 71.4 | 65.7 | 62.9 | 57.1 |
| where | 80 | 77.1 | 74.3 | 74.3 | 62.9 | 60 | 54.3 |
## Using SQLCoder
You can use SQLCoder via the `transformers` library by downloading our model weights from the Hugging Face repo. We have added sample code for [inference](https://github.com/defog-ai/sqlcoder/blob/main/inference.py) on a [sample database schema](https://github.com/defog-ai/sqlcoder/blob/main/metadata.sql).
```bash
python inference.py -q "Question about the sample database goes here"
# Sample question:
# Do we get more revenue from customers in New York compared to customers in San Francisco? Give me the total revenue for each city, and the difference between the two.
```
You can also use a demo on our website [here](https://defog.ai/sqlcoder-demo), or run SQLCoder in Colab [here](https://colab.research.google.com/drive/13BIKsqHnPOBcQ-ba2p77L5saiepTIwu0#scrollTo=ZpbVgVHMkJvC)
## Hardware Requirements
SQLCoder has been tested on an A100 40GB GPU with `bfloat16` weights. You can also load an 8-bit and 4-bit quantized version of the model on consumer GPUs with 20GB or more of memory â like RTX 4090, RTX 3090, and Apple M2 Pro, M2 Max, or M2 Ultra Chips with 20GB or more of memory.
## Todo
- [x] Open-source the v1 model weights
- [x] Train the model on more data, with higher data variance
- [ ] Tune the model further with Reward Modelling and RLHF
- [ ] Pretrain a model from scratch that specializes in SQL analysis |
FreedomIntelligence/Apollo-7B | FreedomIntelligence | "2024-04-26T11:13:29Z" | 1,484 | 21 | transformers | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"arxiv:2403.03640",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2024-03-06T13:06:29Z" | ---
license: apache-2.0
---
# Multilingual Medicine: Model, Dataset, Benchmark, Code
Covering English, Chinese, French, Hindi, Spanish, Hindi, Arabic So far
<p align="center">
ðšð»âð»<a href="https://github.com/FreedomIntelligence/Apollo" target="_blank">Github</a> â¢ð <a href="https://arxiv.org/abs/2403.03640" target="_blank">Paper</a> ⢠ð <a href="https://apollo.llmzoo.com/" target="_blank">Demo</a> ⢠ð€ <a href="https://huggingface.co/datasets/FreedomIntelligence/ApolloCorpus" target="_blank">ApolloCorpus</a> ⢠ð€ <a href="https://huggingface.co/datasets/FreedomIntelligence/XMedbench" target="_blank">XMedBench</a>
<br> <a href="./README_zh.md"> äžæ </a> | <a href="./README.md"> English
</p>
## ð Update
* **[2024.04.25]** [MedJamba](https://huggingface.co/FreedomIntelligence/Apollo-MedJamba) released, train and evaluation code refer to [repo](https://github.com/FreedomIntelligence/MedJamba).
* **[2024.03.07]** [Paper](https://arxiv.org/abs/2403.03640) released.
* **[2024.02.12]** <a href="https://huggingface.co/datasets/FreedomIntelligence/ApolloCorpus" target="_blank">ApolloCorpus</a> and <a href="https://huggingface.co/datasets/FreedomIntelligence/XMedbench" target="_blank">XMedBench</a> is publishedïŒð
* **[2024.01.23]** Apollo repo is publishedïŒð
## Results
ð€ <a href="https://huggingface.co/FreedomIntelligence/Apollo-0.5B" target="_blank">Apollo-0.5B</a> ⢠ð€ <a href="https://huggingface.co/FreedomIntelligence/Apollo-1.8B" target="_blank">Apollo-1.8B</a> ⢠ð€ <a href="https://huggingface.co/FreedomIntelligence/Apollo-2B" target="_blank">Apollo-2B</a> ⢠ð€ <a href="https://huggingface.co/FreedomIntelligence/Apollo-6B" target="_blank">Apollo-6B</a> ⢠ð€ <a href="https://huggingface.co/FreedomIntelligence/Apollo-7B" target="_blank">Apollo-7B</a> ⢠ð€ <a href="https://huggingface.co/FreedomIntelligence/Apollo-34B" target="_blank">Apollo-34B</a> ⢠ð€ <a href="https://huggingface.co/FreedomIntelligence/Apollo-72B" target="_blank">Apollo-72B</a>
ð€ <a href="https://huggingface.co/FreedomIntelligence/Apollo-MedJamba" target="_blank">MedJamba</a>
ð€ <a href="https://huggingface.co/FreedomIntelligence/Apollo-0.5B-GGUF" target="_blank">Apollo-0.5B-GGUF</a> ⢠ð€ <a href="https://huggingface.co/FreedomIntelligence/Apollo-2B-GGUF" target="_blank">Apollo-2B-GGUF</a> ⢠ð€ <a href="https://huggingface.co/FreedomIntelligence/Apollo-6B-GGUF" target="_blank">Apollo-6B-GGUF</a> ⢠ð€ <a href="https://huggingface.co/FreedomIntelligence/Apollo-7B-GGUF" target="_blank">Apollo-7B-GGUF</a>
## Usage Format
User:{query}\nAssistant:{response}<|endoftext|>
## Dataset & Evaluation
- Dataset
ð€ <a href="https://huggingface.co/datasets/FreedomIntelligence/ApolloCorpus" target="_blank">ApolloCorpus</a>
<details><summary>Click to expand</summary>
- [Zip File](https://huggingface.co/datasets/FreedomIntelligence/ApolloCorpus/blob/main/ApolloCorpus.zip)
- [Data category](https://huggingface.co/datasets/FreedomIntelligence/ApolloCorpus/tree/main/train)
- Pretrain:
- data item:
- json_name: {data_source}_{language}_{data_type}.json
- data_type: medicalBook, medicalGuideline, medicalPaper, medicalWeb(from online forum), medicalWiki
- language: en(English), zh(chinese), es(spanish), fr(french), hi(Hindi)
- data_type: qa(generated qa from text)
- data_type==text: list of string
```
[
"string1",
"string2",
...
]
```
- data_type==qa: list of qa pairs(list of string)
```
[
[
"q1",
"a1",
"q2",
"a2",
...
],
...
]
```
- SFT:
- json_name: {data_source}_{language}.json
- data_type: code, general, math, medicalExam, medicalPatient
- data item: list of qa pairs(list of string)
```
[
[
"q1",
"a1",
"q2",
"a2",
...
],
...
]
```
</details>
- Evaluation
ð€ <a href="https://huggingface.co/datasets/FreedomIntelligence/XMedbench" target="_blank">XMedBench</a>
<details><summary>Click to expand</summary>
- EN:
- [MedQA-USMLE](https://huggingface.co/datasets/GBaker/MedQA-USMLE-4-options)
- [MedMCQA](https://huggingface.co/datasets/medmcqa/viewer/default/test)
- [PubMedQA](https://huggingface.co/datasets/pubmed_qa): Because the results fluctuated too much, they were not used in the paper.
- [MMLU-Medical](https://huggingface.co/datasets/cais/mmlu)
- Clinical knowledge, Medical genetics, Anatomy, Professional medicine, College biology, College medicine
- ZH:
- [MedQA-MCMLE](https://huggingface.co/datasets/bigbio/med_qa/viewer/med_qa_zh_4options_bigbio_qa/test)
- [CMB-single](https://huggingface.co/datasets/FreedomIntelligence/CMB): Not used in the paper
- Randomly sample 2,000 multiple-choice questions with single answer.
- [CMMLU-Medical](https://huggingface.co/datasets/haonan-li/cmmlu)
- Anatomy, Clinical_knowledge, College_medicine, Genetics, Nutrition, Traditional_chinese_medicine, Virology
- [CExam](https://github.com/williamliujl/CMExam): Not used in the paper
- Randomly sample 2,000 multiple-choice questions
- ES: [Head_qa](https://huggingface.co/datasets/head_qa)
- FR: [Frenchmedmcqa](https://github.com/qanastek/FrenchMedMCQA)
- HI: [MMLU_HI](https://huggingface.co/datasets/FreedomIntelligence/MMLU_Arabic)
- Clinical knowledge, Medical genetics, Anatomy, Professional medicine, College biology, College medicine
- AR: [MMLU_Ara](https://huggingface.co/datasets/FreedomIntelligence/MMLU_Hindi)
- Clinical knowledge, Medical genetics, Anatomy, Professional medicine, College biology, College medicine
</details>
## Results reproduction
<details><summary>Click to expand</summary>
**Waiting for Update**
</details>
## Citation
Please use the following citation if you intend to use our dataset for training or evaluation:
```
@misc{wang2024apollo,
title={Apollo: Lightweight Multilingual Medical LLMs towards Democratizing Medical AI to 6B People},
author={Xidong Wang and Nuo Chen and Junyin Chen and Yan Hu and Yidong Wang and Xiangbo Wu and Anningzhe Gao and Xiang Wan and Haizhou Li and Benyou Wang},
year={2024},
eprint={2403.03640},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
BELLE-2/Belle-whisper-large-v3-zh | BELLE-2 | "2024-06-11T03:13:26Z" | 1,484 | 71 | transformers | [
"transformers",
"pytorch",
"whisper",
"automatic-speech-recognition",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | "2024-03-11T01:40:25Z" | ---
license: apache-2.0
metrics:
- cer
---
## Welcome
If you find this model helpful, please *like* this model and star us on https://github.com/LianjiaTech/BELLE and https://github.com/shuaijiang/Whisper-Finetune
# Belle-whisper-large-v3-zh
Fine tune whisper-large-v3 to enhance Chinese speech recognition capabilities,
Belle-whisper-large-v3-zh demonstrates a **24-65%** relative improvement in performance on Chinese ASR benchmarks, including AISHELL1, AISHELL2, WENETSPEECH, and HKUST.
## Usage
```python
from transformers import pipeline
transcriber = pipeline(
"automatic-speech-recognition",
model="BELLE-2/Belle-whisper-large-v3-zh"
)
transcriber.model.config.forced_decoder_ids = (
transcriber.tokenizer.get_decoder_prompt_ids(
language="zh",
task="transcribe"
)
)
transcription = transcriber("my_audio.wav")
```
## Fine-tuning
| Model | (Re)Sample Rate | Train Datasets | Fine-tuning (full or peft) |
|:----------------:|:-------:|:----------------------------------------------------------:|:-----------:|
| Belle-whisper-large-v3-zh | 16KHz | [AISHELL-1](https://openslr.magicdatatech.com/resources/33/) [AISHELL-2](https://www.aishelltech.com/aishell_2) [WenetSpeech](https://wenet.org.cn/WenetSpeech/) [HKUST](https://catalog.ldc.upenn.edu/LDC2005S15) | [full fine-tuning](https://github.com/shuaijiang/Whisper-Finetune) |
If you want to fine-thuning the model on your datasets, please reference to the [github repo](https://github.com/shuaijiang/Whisper-Finetune)
## CER(%) â
| Model | Language Tag | aishell_1_test(â) |aishell_2_test(â)| wenetspeech_net(â) | wenetspeech_meeting(â) | HKUST_dev(â)|
|:----------------:|:-------:|:-----------:|:-----------:|:--------:|:-----------:|:-------:|
| whisper-large-v3 | Chinese | 8.085 | 5.475 | 11.72 | 20.15 | 28.597 |
| Belle-whisper-large-v2-zh | Chinese | 2.549 | 3.746 | 8.503 | 14.598 | 16.289 |
| Belle-whisper-large-v3-zh | Chinese | 2.781 | 3.786 | 8.865 | **11.246** | 16.440 |
It is worth mentioning that compared to Belle-whisper-large-v2-zh, Belle-whisper-large-v3-zh has a significant improvement in complex acoustic scenes(such as wenetspeech_meeting).
## Citation
Please cite our paper and github when using our code, data or model.
```
@misc{BELLE,
author = {BELLEGroup},
title = {BELLE: Be Everyone's Large Language model Engine},
year = {2023},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/LianjiaTech/BELLE}},
}
``` |
Kukedlc/NeuralLLaMa-3-8b-ORPO-v0.4 | Kukedlc | "2024-06-15T07:02:25Z" | 1,484 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2024-05-16T18:40:01Z" | ---
library_name: transformers
license: other
---
# 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 ð€ 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]
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## Model Card Contact
[More Information Needed] |
Koleshjr/mistral_7b_v2_q4_k_m | Koleshjr | "2024-06-26T21:19:35Z" | 1,484 | 0 | transformers | [
"transformers",
"gguf",
"mistral",
"text-generation-inference",
"unsloth",
"en",
"base_model:unsloth/mistral-7b-instruct-v0.2-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2024-06-26T21:05:16Z" | ---
base_model: unsloth/mistral-7b-instruct-v0.2-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- gguf
---
# Uploaded model
- **Developed by:** Koleshjr
- **License:** apache-2.0
- **Finetuned from model :** unsloth/mistral-7b-instruct-v0.2-bnb-4bit
This mistral 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)
|
nlpai-lab/kullm-polyglot-12.8b-v2 | nlpai-lab | "2023-05-31T15:48:09Z" | 1,483 | 50 | transformers | [
"transformers",
"pytorch",
"gpt_neox",
"text-generation",
"ko",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-05-31T06:28:28Z" | ---
license: apache-2.0
language:
- ko
---
# KULLM-Polyglot-12.8B-v2
This model is a fine-tuned version of [EleutherAI/polyglot-ko-12.8b](https://huggingface.co/EleutherAI/polyglot-ko-12.8b) on a KULLM v2
Detail Codes are available at [KULLM Github Repository](https://github.com/nlpai-lab/KULLM)
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-4
- train_batch_size: 6
- seed: 42
- distributed_type: multi-GPU (A100 80G)
- num_devices: 4
- gradient_accumulation_steps: 21.3
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8.0
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu117
- Datasets 2.11.0
- Tokenizers 0.13.3 |
Xenova/paraphrase-multilingual-mpnet-base-v2 | Xenova | "2024-03-21T12:05:28Z" | 1,482 | 2 | transformers.js | [
"transformers.js",
"onnx",
"xlm-roberta",
"feature-extraction",
"region:us"
] | feature-extraction | "2023-05-23T14:31:51Z" | ---
library_name: "transformers.js"
---
https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2 with ONNX weights to be compatible with Transformers.js.
Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using [ð€ Optimum](https://huggingface.co/docs/optimum/index) and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`). |
moreh/MoMo-72B-lora-1.8.6-DPO | moreh | "2024-01-22T00:09:36Z" | 1,482 | 32 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"en",
"arxiv:2305.18290",
"arxiv:2106.09685",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2024-01-16T02:11:50Z" | ---
license: mit
language:
- en
---
# **Introduction**
MoMo-72B-lora-1.8.6-DPO is trained via Direct Preference Optimization([DPO](https://arxiv.org/abs/2305.18290)) from [MoMo-72B-LoRA-V1.4](https://huggingface.co/moreh/MoMo-72B-LoRA-V1.4) as its base model, with several optimizations in hyperparameters.
[MoMo-72B-LoRA-V1.4](https://huggingface.co/moreh/MoMo-72B-LoRA-V1.4) is trained via Supervised Fine-Tuning (SFT) using [LoRA](https://arxiv.org/abs/2106.09685), with the QWEN-72B model as its base-model.
Note that we did not exploit any form of weight merge.
For leaderboard submission, the trained weight is realigned for compatibility with llama.
MoMo-72B is trained using **[Moreh](https://moreh.io/)**'s [MoAI platform](https://moreh.io/product), which simplifies the training of large-scale models, and AMD's MI250 GPU.
## Details
### Used Librarys
- torch
- peft
### Used Datasets
- [slimorca](Open-Orca/SlimOrca)
- [truthy](https://huggingface.co/datasets/jondurbin/truthy-dpo-v0.1)
- [orca_dpo_pairs](https://huggingface.co/datasets/Intel/orca_dpo_pairs)
- No other dataset was used
- No benchmark test set or the training set are used
- [data contamination check](https://github.com/swj0419/detect-pretrain-code-contamination) result
| Model | ARC | MMLU | TruthfulQA | GSM8K |
|------------------------------|-------|-------|-------|-------|
| **V1.8.6(result < 0.1, %)**| TBU |TBU | 0.73 | TBU |
### Used Environments
- AMD MI250 & MoAI platform
- Please visit https://moreh.io/product for more information about MoAI platform
- Or, contact us directly [[email protected]](mailto:[email protected])
## How to use
```python
# pip install transformers==4.35.2
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("moreh/MoMo-72B-lora-1.8.6-DPO")
model = AutoModelForCausalLM.from_pretrained(
"moreh/MoMo-72B-lora-1.8.6-DPO"
)
``` |
QuantFactory/dolphin-2.9-llama3-8b-256k-GGUF | QuantFactory | "2024-05-06T09:32:49Z" | 1,481 | 3 | transformers | [
"transformers",
"gguf",
"text-generation",
"base_model:cognitivecomputations/dolphin-2.9-llama3-8b-256k",
"license:llama3",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-05-05T14:41:06Z" | ---
license: llama3
library_name: transformers
pipeline_tag: text-generation
base_model: cognitivecomputations/dolphin-2.9-llama3-8b-256k
---
# Dolphin-2.9-llama3-8b-256k-GGUF
This is quantized version of [cognitivecomputations/dolphin-2.9-llama3-8b-256k]() created using llama.cpp |
HooshvareLab/bert-base-parsbert-peymaner-uncased | HooshvareLab | "2021-05-18T20:45:45Z" | 1,480 | 0 | transformers | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"token-classification",
"fa",
"arxiv:2005.12515",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | "2022-03-02T23:29:04Z" | ---
language: fa
license: apache-2.0
---
## ParsBERT: Transformer-based Model for Persian Language Understanding
ParsBERT is a monolingual language model based on Googleâs BERT architecture with the same configurations as BERT-Base.
Paper presenting ParsBERT: [arXiv:2005.12515](https://arxiv.org/abs/2005.12515)
All the models (downstream tasks) are uncased and trained with whole word masking. (coming soon stay tuned)
## Persian NER [ARMAN, PEYMA, ARMAN+PEYMA]
This task aims to extract named entities in the text, such as names and label with appropriate `NER` classes such as locations, organizations, etc. The datasets used for this task contain sentences that are marked with `IOB` format. In this format, tokens that are not part of an entity are tagged as `âOâ` the `âBâ`tag corresponds to the first word of an object, and the `âIâ` tag corresponds to the rest of the terms of the same entity. Both `âBâ` and `âIâ` tags are followed by a hyphen (or underscore), followed by the entity category. Therefore, the NER task is a multi-class token classification problem that labels the tokens upon being fed a raw text. There are two primary datasets used in Persian NER, `ARMAN`, and `PEYMA`. In ParsBERT, we prepared ner for both datasets as well as a combination of both datasets.
### PEYMA
PEYMA dataset includes 7,145 sentences with a total of 302,530 tokens from which 41,148 tokens are tagged with seven different classes.
1. Organization
2. Money
3. Location
4. Date
5. Time
6. Person
7. Percent
| Label | # |
|:------------:|:-----:|
| Organization | 16964 |
| Money | 2037 |
| Location | 8782 |
| Date | 4259 |
| Time | 732 |
| Person | 7675 |
| Percent | 699 |
**Download**
You can download the dataset from [here](http://nsurl.org/tasks/task-7-named-entity-recognition-ner-for-farsi/)
## Results
The following table summarizes the F1 score obtained by ParsBERT as compared to other models and architectures.
| Dataset | ParsBERT | MorphoBERT | Beheshti-NER | LSTM-CRF | Rule-Based CRF | BiLSTM-CRF |
|---------|----------|------------|--------------|----------|----------------|------------|
| PEYMA | 98.79* | - | 90.59 | - | 84.00 | - |
## How to use :hugs:
| Notebook | Description | |
|:----------|:-------------|------:|
| [How to use Pipelines](https://github.com/hooshvare/parsbert-ner/blob/master/persian-ner-pipeline.ipynb) | Simple and efficient way to use State-of-the-Art models on downstream tasks through transformers | [](https://colab.research.google.com/github/hooshvare/parsbert-ner/blob/master/persian-ner-pipeline.ipynb) |
## Cite
Please cite the following paper in your publication if you are using [ParsBERT](https://arxiv.org/abs/2005.12515) in your research:
```markdown
@article{ParsBERT,
title={ParsBERT: Transformer-based Model for Persian Language Understanding},
author={Mehrdad Farahani, Mohammad Gharachorloo, Marzieh Farahani, Mohammad Manthouri},
journal={ArXiv},
year={2020},
volume={abs/2005.12515}
}
```
## Acknowledgments
We hereby, express our gratitude to the [Tensorflow Research Cloud (TFRC) program](https://tensorflow.org/tfrc) for providing us with the necessary computation resources. We also thank [Hooshvare](https://hooshvare.com) Research Group for facilitating dataset gathering and scraping online text resources.
## Contributors
- Mehrdad Farahani: [Linkedin](https://www.linkedin.com/in/m3hrdadfi/), [Twitter](https://twitter.com/m3hrdadfi), [Github](https://github.com/m3hrdadfi)
- Mohammad Gharachorloo: [Linkedin](https://www.linkedin.com/in/mohammad-gharachorloo/), [Twitter](https://twitter.com/MGharachorloo), [Github](https://github.com/baarsaam)
- Marzieh Farahani: [Linkedin](https://www.linkedin.com/in/marziehphi/), [Twitter](https://twitter.com/marziehphi), [Github](https://github.com/marziehphi)
- Mohammad Manthouri: [Linkedin](https://www.linkedin.com/in/mohammad-manthouri-aka-mansouri-07030766/), [Twitter](https://twitter.com/mmanthouri), [Github](https://github.com/mmanthouri)
- Hooshvare Team: [Official Website](https://hooshvare.com/), [Linkedin](https://www.linkedin.com/company/hooshvare), [Twitter](https://twitter.com/hooshvare), [Github](https://github.com/hooshvare), [Instagram](https://www.instagram.com/hooshvare/)
+ And a special thanks to Sara Tabrizi for her fantastic poster design. Follow her on: [Linkedin](https://www.linkedin.com/in/sara-tabrizi-64548b79/), [Behance](https://www.behance.net/saratabrizi), [Instagram](https://www.instagram.com/sara_b_tabrizi/)
## Releases
### Release v0.1 (May 29, 2019)
This is the first version of our ParsBERT NER!
|
OwenArli/Awanllm-Llama-3-8B-Dolfin-v1.0-GGUF | OwenArli | "2024-06-05T03:15:15Z" | 1,480 | 0 | null | [
"gguf",
"license:llama3",
"region:us"
] | null | "2024-06-03T11:56:09Z" | ---
license: llama3
---
Based on Meta-Llama-3-8b-Instruct, and is governed by Meta Llama 3 License agreement:
https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct
Base model: https://huggingface.co/failspy/Meta-Llama-3-8B-Instruct-abliterated-v3
SFT fine tune of Meta Llama 3 8B Instruct Abliterated v3 by Failspy using an improved Dolphin and WizardLM dataset intended to remove GPT-isms and make the model follow instructions more exactly while paying attention to details better.
Since it is based on the Abliterated version of Llama 3 8B Instruct it should naturally not refuse to answer in the first place and this fine tuning should make it comply even better.
We also have it up on our site https://awanllm.com for anyone to try!
Best practices:
- Be precise and explain what you want the model to do. It has less base "personality" than the OG model but it will act however you tell it to.
- This model works best with system prompts that tells it that it is the character, instead of telling it to act as a character.
Training:
- Full 8192 sequence length
- Training duration is around 2.5 days on an RTX 4090
- 1 epoch training with a massive dataset for minimized repetition sickness.
- Using 4-bit loading and Qlora 64-rank 64-alpha resulting in ~2% trainable weights.
Llama 3 Instruct format:
```
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
{{ system_prompt }}<|eot_id|><|start_header_id|>user<|end_header_id|>
{{ user_message_1 }}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
{{ model_answer_1 }}<|eot_id|><|start_header_id|>user<|end_header_id|>
{{ user_message_2 }}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
```
Quants:
FP16: https://huggingface.co/AwanLLM/Awanllm-Llama-3-8B-Dolfin-v1.0
GGUF: https://huggingface.co/AwanLLM/Awanllm-Llama-3-8B-Dolfin-v1.0-GGUF
|
sapienzanlp/modello-italia-9b-bf16 | sapienzanlp | "2024-06-08T09:46:56Z" | 1,480 | 9 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"conversational",
"it",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2024-06-06T17:25:22Z" | ---
license: mit
language:
- it
---
# Model Card for Modello Italia 9B
This is our UNOFFICIAL conversion of the OFFICIAL model checkpoint of *"Modello Italia 9B"*, the first Large Language Model (LLM) developed by [iGenius](https://it.igenius.ai/) in collaboration [CINECA](https://www.cineca.it/).
* More information about Modello Italia: [click here](https://it.igenius.ai/language-models).
## ðš Disclaimers
* This is an UNOFFICIAL conversion of the OFFICIAL model checkpoint released by iGenius.
* The original model was developed using LitGPT, therefore, the weights need to be converted before they can be used with Hugging Face transformers.
* **Note:** By using this model, you accept the iGenius' [**terms and conditions**](https://secure.igenius.ai/legal/italia_terms_and_conditions.pdf).
## ðš Biases and Risks
From the terms and conditions of iGenius for Modello Italia:
> Modello Italia Ú concepito per essere utilizzato da tutti e per adattarsi a una vasta gamma di casi
d'uso. Ã stato progettato con l'obiettivo di essere accessibile a persone provenienti da
background, esperienze e prospettive diverse. Modello Italia si rivolge agli utenti e alle loro
esigenze senza inserire giudizi superflui o normative, riconoscendo al contempo che anche
contenuti potenzialmente problematici in determinati contesti possono avere scopi validi in altri.
Il rispetto per la dignità e l'autonomia di tutti gli utenti, specialmente in termini di libertà di
pensiero ed espressione, Ú un pilastro fondamentale del suo design. Tuttavia, essendo una nuova
tecnologia, Modello Italia comporta rischi legati al suo utilizzo. I test condotti finora sono stati
eseguiti in italiano e non hanno potuto coprire tutte le possibili situazioni. Pertanto, come per
tutti gli LLM, non Ú possibile prevedere in anticipo gli output di Modello Italia e il modello
potrebbe in alcuni casi generare risposte imprecise, tendenziose o altre risposte discutibili. Prima
di utilizzare Modello Italia in qualsiasi contesto, gli sviluppatori sono fortemente incoraggiati a
eseguire test di sicurezza e adattamento specifici per le loro applicazioni.
We are aware of the biases and potential problematic/toxic content that current pretrained large language models exhibit: more specifically, as probabilistic models of (Italian and English) languages, they reflect and amplify the biases of their training data.
For more information about this issue, please refer to our survey paper:
* [Biases in Large Language Models: Origins, Inventory, and Discussion](https://dl.acm.org/doi/full/10.1145/3597307)
## Training dataset
The following information is based on the information we could gather, that is, it is NOT official.
Please take it with a pinch of salt as we continue to study Modello Italia.
* **The training data of Modello Italia is unknown;**
* Modello Italia is probably trained on around 1T tokens of Italian text;
* We know that the training data is mostly Italian text and source code;
* We know that the training data includes text from Editoria Nazionale.
## Tokenizer
The following information is based on the information we could gather, that is, it is NOT official.
Please take it with a pinch of salt as we continue to study Modello Italia.
* The tokenizer is **vanilla SentencePiece**, probably trained from scratch on Italian data;
* Vocabulary size is 50000.
## Model architecture
The following information is based on the information we could gather, that is, it is NOT official.
Please take it with a pinch of salt as we continue to study Modello Italia.
* The model architecture is **based on GPT-NeoX**.
## Results
Modello Italia 9B has not been evaluated on standard benchmarks yet.
We will update this model card with the results soon.
* **Want to contribute to the evaluation?** Submit a pull request!
## How to use Modello Italia with Hugging Face transformers
```python
import torch
import transformers as tr
device = "cuda" if torch.cuda.is_available() else "cpu"
tokenizer = tr.AutoTokenizer.from_pretrained("sapienzanlp/modello-italia-9b-bf16")
model = tr.AutoModelForCausalLM.from_pretrained(
"sapienzanlp/modello-italia-9b-bf16",
device_map=device,
torch_dtype=torch.bfloat16
)
MY_SYSTEM_PROMPT_SHORT = (
"Tu sei Modello Italia, un modello di linguaggio naturale addestrato da iGenius."
)
prompt = "Ciao, chi sei?"
messages = [
{"role": "system", "content": MY_SYSTEM_PROMPT_SHORT},
{"role": "user", "content": prompt},
]
tokenized_chat = tokenizer.apply_chat_template(
messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
).to(device)
out = model.generate(
tokenized_chat,
max_new_tokens=200,
do_sample=False
)
``` |
AlexKay/xlm-roberta-large-qa-multilingual-finedtuned-ru | AlexKay | "2022-07-19T15:33:20Z" | 1,479 | 41 | transformers | [
"transformers",
"pytorch",
"xlm-roberta",
"question-answering",
"en",
"ru",
"multilingual",
"arxiv:1912.09723",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | question-answering | "2022-03-02T23:29:04Z" | ---
language:
- en
- ru
- multilingual
license: apache-2.0
---
# XLM-RoBERTa large model whole word masking finetuned on SQuAD
Pretrained model using a masked language modeling (MLM) objective.
Fine tuned on English and Russian QA datasets
## Used QA Datasets
SQuAD + SberQuAD
[SberQuAD original paper](https://arxiv.org/pdf/1912.09723.pdf) is here! Recommend to read!
## Evaluation results
The results obtained are the following (SberQUaD):
```
f1 = 84.3
exact_match = 65.3
|
Langboat/mengzi-t5-base | Langboat | "2023-05-08T08:43:21Z" | 1,479 | 46 | transformers | [
"transformers",
"pytorch",
"safetensors",
"t5",
"text2text-generation",
"zh",
"arxiv:2110.06696",
"doi:10.57967/hf/0025",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text2text-generation | "2022-03-02T23:29:04Z" | ---
language:
- zh
license: apache-2.0
---
# Mengzi-T5 model (Chinese)
Pretrained model on 300G Chinese corpus.
[Mengzi: Towards Lightweight yet Ingenious Pre-trained Models for Chinese](https://arxiv.org/abs/2110.06696)
## Usage
```python
from transformers import T5Tokenizer, T5ForConditionalGeneration
tokenizer = T5Tokenizer.from_pretrained("Langboat/mengzi-t5-base")
model = T5ForConditionalGeneration.from_pretrained("Langboat/mengzi-t5-base")
```
## Citation
If you find the technical report or resource is useful, please cite the following technical report in your paper.
```
@misc{zhang2021mengzi,
title={Mengzi: Towards Lightweight yet Ingenious Pre-trained Models for Chinese},
author={Zhuosheng Zhang and Hanqing Zhang and Keming Chen and Yuhang Guo and Jingyun Hua and Yulong Wang and Ming Zhou},
year={2021},
eprint={2110.06696},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` |
tuman/vit-rugpt2-image-captioning | tuman | "2023-01-26T08:15:02Z" | 1,479 | 12 | transformers | [
"transformers",
"pytorch",
"vision-encoder-decoder",
"image-to-text",
"image-captioning",
"ru",
"endpoints_compatible",
"region:us"
] | image-to-text | "2023-01-18T14:27:20Z" | ---
tags:
- image-to-text
- image-captioning
language:
- ru
metrics:
- bleu
library_name: transformers
---
# First image captioning model for russian language vit-rugpt2-image-captioning
This is an image captioning model trained on translated version (en-ru) of dataset COCO2014.
# Model Details
Model was initialized `google/vit-base-patch16-224-in21k` for encoder and `sberbank-ai/rugpt3large_based_on_gpt2` for decoder.
# Metrics on test data
* Bleu: 8.672
* Bleu precision 1: 30.567
* Bleu precision 2: 7.895
* Bleu precision 3: 3.261
# Sample running code
```python
from transformers import VisionEncoderDecoderModel, ViTFeatureExtractor, AutoTokenizer
import torch
from PIL import Image
model = VisionEncoderDecoderModel.from_pretrained("vit-rugpt2-image-captioning")
feature_extractor = ViTFeatureExtractor.from_pretrained("vit-rugpt2-image-captioning")
tokenizer = AutoTokenizer.from_pretrained("vit-rugpt2-image-captioning")
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
max_length = 16
num_beams = 4
gen_kwargs = {"max_length": max_length, "num_beams": num_beams}
def predict_caption(image_paths):
images = []
for image_path in image_paths:
i_image = Image.open(image_path)
if i_image.mode != "RGB":
i_image = i_image.convert(mode="RGB")
images.append(i_image)
pixel_values = feature_extractor(images=images, return_tensors="pt").pixel_values
pixel_values = pixel_values.to(device)
output_ids = model.generate(pixel_values, **gen_kwargs)
preds = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
preds = [pred.strip() for pred in preds]
return preds
predict_caption(['train2014/COCO_train2014_000000295442.jpg']) # ['Ð¡Ð°ÐŒÐŸÐ»ÐµÑ ÐœÐ° взлеÑМП-пПÑаЎПÑМПй пПлПÑе аÑÑПпПÑÑа.']
```
# Sample running code using transformers pipeline
```python
from transformers import pipeline
image_to_text = pipeline("image-to-text", model="vit-rugpt2-image-captioning")
image_to_text("train2014/COCO_train2014_000000296754.jpg") # [{'generated_text': 'ЧелПвек ÐžÐŽÐµÑ Ð¿ÐŸ ÑлОÑе Ñ Ð·ÐŸÐœÑПЌ.'}]
```
# Contact for any help
* https://huggingface.co/tuman
* https://github.com/tumanov-a
* https://t.me/tumanov_av |
bartowski/Codestral-22B-v0.1-abliterated-v3-GGUF | bartowski | "2024-06-05T22:10:08Z" | 1,479 | 8 | transformers | [
"transformers",
"gguf",
"code",
"text-generation",
"license:other",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-06-05T19:09:00Z" | ---
library_name: transformers
license: other
license_name: mnpl
license_link: https://mistral.ai/licences/MNPL-0.1.md
tags:
- code
language:
- code
quantized_by: bartowski
pipeline_tag: text-generation
---
## Llamacpp imatrix Quantizations of Codestral-22B-v0.1-abliterated-v3
Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggerganov/llama.cpp/releases/tag/b3086">b3086</a> for quantization.
Original model: https://huggingface.co/failspy/Codestral-22B-v0.1-abliterated-v3
All quants made using imatrix option with dataset from [here](https://gist.github.com/bartowski1182/eb213dccb3571f863da82e99418f81e8)
## Prompt format
No chat template specified so default is used. This may be incorrect, check original model card for details.
```
<s>[INST] <<SYS>>
{system_prompt}
<</SYS>>
{prompt}[/INST] </s>
```
## Download a file (not the whole branch) from below:
| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [Codestral-22B-v0.1-abliterated-v3-Q8_0.gguf](https://huggingface.co/bartowski/Codestral-22B-v0.1-abliterated-v3-GGUF/blob/main/Codestral-22B-v0.1-abliterated-v3-Q8_0.gguf) | Q8_0 | 23.64GB | Extremely high quality, generally unneeded but max available quant. |
| [Codestral-22B-v0.1-abliterated-v3-Q6_K.gguf](https://huggingface.co/bartowski/Codestral-22B-v0.1-abliterated-v3-GGUF/blob/main/Codestral-22B-v0.1-abliterated-v3-Q6_K.gguf) | Q6_K | 18.25GB | Very high quality, near perfect, *recommended*. |
| [Codestral-22B-v0.1-abliterated-v3-Q5_K_M.gguf](https://huggingface.co/bartowski/Codestral-22B-v0.1-abliterated-v3-GGUF/blob/main/Codestral-22B-v0.1-abliterated-v3-Q5_K_M.gguf) | Q5_K_M | 15.72GB | High quality, *recommended*. |
| [Codestral-22B-v0.1-abliterated-v3-Q5_K_S.gguf](https://huggingface.co/bartowski/Codestral-22B-v0.1-abliterated-v3-GGUF/blob/main/Codestral-22B-v0.1-abliterated-v3-Q5_K_S.gguf) | Q5_K_S | 15.32GB | High quality, *recommended*. |
| [Codestral-22B-v0.1-abliterated-v3-Q4_K_M.gguf](https://huggingface.co/bartowski/Codestral-22B-v0.1-abliterated-v3-GGUF/blob/main/Codestral-22B-v0.1-abliterated-v3-Q4_K_M.gguf) | Q4_K_M | 13.34GB | Good quality, uses about 4.83 bits per weight, *recommended*. |
| [Codestral-22B-v0.1-abliterated-v3-Q4_K_S.gguf](https://huggingface.co/bartowski/Codestral-22B-v0.1-abliterated-v3-GGUF/blob/main/Codestral-22B-v0.1-abliterated-v3-Q4_K_S.gguf) | Q4_K_S | 12.66GB | Slightly lower quality with more space savings, *recommended*. |
| [Codestral-22B-v0.1-abliterated-v3-IQ4_XS.gguf](https://huggingface.co/bartowski/Codestral-22B-v0.1-abliterated-v3-GGUF/blob/main/Codestral-22B-v0.1-abliterated-v3-IQ4_XS.gguf) | IQ4_XS | 11.93GB | Decent quality, smaller than Q4_K_S with similar performance, *recommended*. |
| [Codestral-22B-v0.1-abliterated-v3-Q3_K_L.gguf](https://huggingface.co/bartowski/Codestral-22B-v0.1-abliterated-v3-GGUF/blob/main/Codestral-22B-v0.1-abliterated-v3-Q3_K_L.gguf) | Q3_K_L | 11.73GB | Lower quality but usable, good for low RAM availability. |
| [Codestral-22B-v0.1-abliterated-v3-Q3_K_M.gguf](https://huggingface.co/bartowski/Codestral-22B-v0.1-abliterated-v3-GGUF/blob/main/Codestral-22B-v0.1-abliterated-v3-Q3_K_M.gguf) | Q3_K_M | 10.75GB | Even lower quality. |
| [Codestral-22B-v0.1-abliterated-v3-IQ3_M.gguf](https://huggingface.co/bartowski/Codestral-22B-v0.1-abliterated-v3-GGUF/blob/main/Codestral-22B-v0.1-abliterated-v3-IQ3_M.gguf) | IQ3_M | 10.06GB | Medium-low quality, new method with decent performance comparable to Q3_K_M. |
| [Codestral-22B-v0.1-abliterated-v3-Q3_K_S.gguf](https://huggingface.co/bartowski/Codestral-22B-v0.1-abliterated-v3-GGUF/blob/main/Codestral-22B-v0.1-abliterated-v3-Q3_K_S.gguf) | Q3_K_S | 9.64GB | Low quality, not recommended. |
| [Codestral-22B-v0.1-abliterated-v3-IQ3_XS.gguf](https://huggingface.co/bartowski/Codestral-22B-v0.1-abliterated-v3-GGUF/blob/main/Codestral-22B-v0.1-abliterated-v3-IQ3_XS.gguf) | IQ3_XS | 9.17GB | Lower quality, new method with decent performance, slightly better than Q3_K_S. |
| [Codestral-22B-v0.1-abliterated-v3-IQ3_XXS.gguf](https://huggingface.co/bartowski/Codestral-22B-v0.1-abliterated-v3-GGUF/blob/main/Codestral-22B-v0.1-abliterated-v3-IQ3_XXS.gguf) | IQ3_XXS | 8.59GB | Lower quality, new method with decent performance, comparable to Q3 quants. |
| [Codestral-22B-v0.1-abliterated-v3-Q2_K.gguf](https://huggingface.co/bartowski/Codestral-22B-v0.1-abliterated-v3-GGUF/blob/main/Codestral-22B-v0.1-abliterated-v3-Q2_K.gguf) | Q2_K | 8.27GB | Very low quality but surprisingly usable. |
| [Codestral-22B-v0.1-abliterated-v3-IQ2_M.gguf](https://huggingface.co/bartowski/Codestral-22B-v0.1-abliterated-v3-GGUF/blob/main/Codestral-22B-v0.1-abliterated-v3-IQ2_M.gguf) | IQ2_M | 7.61GB | Very low quality, uses SOTA techniques to also be surprisingly usable. |
| [Codestral-22B-v0.1-abliterated-v3-IQ2_S.gguf](https://huggingface.co/bartowski/Codestral-22B-v0.1-abliterated-v3-GGUF/blob/main/Codestral-22B-v0.1-abliterated-v3-IQ2_S.gguf) | IQ2_S | 7.03GB | Very low quality, uses SOTA techniques to be usable. |
| [Codestral-22B-v0.1-abliterated-v3-IQ2_XS.gguf](https://huggingface.co/bartowski/Codestral-22B-v0.1-abliterated-v3-GGUF/blob/main/Codestral-22B-v0.1-abliterated-v3-IQ2_XS.gguf) | IQ2_XS | 6.64GB | 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/Codestral-22B-v0.1-abliterated-v3-GGUF --include "Codestral-22B-v0.1-abliterated-v3-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/Codestral-22B-v0.1-abliterated-v3-GGUF --include "Codestral-22B-v0.1-abliterated-v3-Q8_0.gguf/*" --local-dir Codestral-22B-v0.1-abliterated-v3-Q8_0
```
You can either specify a new local-dir (Codestral-22B-v0.1-abliterated-v3-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
|
John6666/mala-smooth-v1-sdxl | John6666 | "2024-06-26T13:16:12Z" | 1,479 | 0 | diffusers | [
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"stable-diffusion-xl",
"anime",
"pony",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] | text-to-image | "2024-06-26T13:10:16Z" | ---
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
---
Original model is [here](https://civitai.com/models/515829/mala-smooth).
|
timm/eva_large_patch14_196.in22k_ft_in22k_in1k | timm | "2024-02-10T23:27:54Z" | 1,478 | 1 | timm | [
"timm",
"pytorch",
"safetensors",
"image-classification",
"dataset:imagenet-1k",
"dataset:imagenet-22k",
"arxiv:2211.07636",
"license:mit",
"region:us"
] | image-classification | "2022-12-22T07:08:20Z" | ---
license: mit
library_name: timm
tags:
- image-classification
- timm
datasets:
- imagenet-1k
- imagenet-22k
- imagenet-22k
---
# Model card for eva_large_patch14_196.in22k_ft_in22k_in1k
An EVA image classification model. Pretrained on ImageNet-22k with masked image modeling (using EVA-CLIP as a MIM teacher) and fine-tuned on ImageNet-22k then on ImageNet-1k by paper authors.
NOTE: `timm` checkpoints are float32 for consistency with other models. Original checkpoints are float16 or bfloat16 in some cases, see originals if that's preferred.
## Model Details
- **Model Type:** Image classification / feature backbone
- **Model Stats:**
- Params (M): 304.1
- GMACs: 61.6
- Activations (M): 63.5
- Image size: 196 x 196
- **Papers:**
- EVA: Exploring the Limits of Masked Visual Representation Learning at Scale: https://arxiv.org/abs/2211.07636
- **Pretrain Dataset:**
- ImageNet-22k
- ImageNet-22k
- **Dataset:** ImageNet-1k
- **Original:**
- https://github.com/baaivision/EVA
- https://huggingface.co/BAAI/EVA
## 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('eva_large_patch14_196.in22k_ft_in22k_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(
'eva_large_patch14_196.in22k_ft_in22k_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, 1024) 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).
|model |top1 |top5 |param_count|img_size|
|-----------------------------------------------|------|------|-----------|--------|
|eva02_large_patch14_448.mim_m38m_ft_in22k_in1k |90.054|99.042|305.08 |448 |
|eva02_large_patch14_448.mim_in22k_ft_in22k_in1k|89.946|99.01 |305.08 |448 |
|eva_giant_patch14_560.m30m_ft_in22k_in1k |89.792|98.992|1014.45 |560 |
|eva02_large_patch14_448.mim_in22k_ft_in1k |89.626|98.954|305.08 |448 |
|eva02_large_patch14_448.mim_m38m_ft_in1k |89.57 |98.918|305.08 |448 |
|eva_giant_patch14_336.m30m_ft_in22k_in1k |89.56 |98.956|1013.01 |336 |
|eva_giant_patch14_336.clip_ft_in1k |89.466|98.82 |1013.01 |336 |
|eva_large_patch14_336.in22k_ft_in22k_in1k |89.214|98.854|304.53 |336 |
|eva_giant_patch14_224.clip_ft_in1k |88.882|98.678|1012.56 |224 |
|eva02_base_patch14_448.mim_in22k_ft_in22k_in1k |88.692|98.722|87.12 |448 |
|eva_large_patch14_336.in22k_ft_in1k |88.652|98.722|304.53 |336 |
|eva_large_patch14_196.in22k_ft_in22k_in1k |88.592|98.656|304.14 |196 |
|eva02_base_patch14_448.mim_in22k_ft_in1k |88.23 |98.564|87.12 |448 |
|eva_large_patch14_196.in22k_ft_in1k |87.934|98.504|304.14 |196 |
|eva02_small_patch14_336.mim_in22k_ft_in1k |85.74 |97.614|22.13 |336 |
|eva02_tiny_patch14_336.mim_in22k_ft_in1k |80.658|95.524|5.76 |336 |
## Citation
```bibtex
@article{EVA,
title={EVA: Exploring the Limits of Masked Visual Representation Learning at Scale},
author={Fang, Yuxin and Wang, Wen and Xie, Binhui and Sun, Quan and Wu, Ledell and Wang, Xinggang and Huang, Tiejun and Wang, Xinlong and Cao, Yue},
journal={arXiv preprint arXiv:2211.07636},
year={2022}
}
```
```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}}
}
```
|
Dabitron/Qwen2-7B-Instruct-abliterated-Q8_0-GGUF | Dabitron | "2024-07-02T01:33:56Z" | 1,478 | 0 | null | [
"gguf",
"chat",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"en",
"base_model:natong19/Qwen2-7B-Instruct-abliterated",
"license:apache-2.0",
"region:us"
] | text-generation | "2024-07-02T01:33:21Z" | ---
base_model: natong19/Qwen2-7B-Instruct-abliterated
language:
- en
license: apache-2.0
pipeline_tag: text-generation
tags:
- chat
- llama-cpp
- gguf-my-repo
---
# Dabitron/Qwen2-7B-Instruct-abliterated-Q8_0-GGUF
This model was converted to GGUF format from [`natong19/Qwen2-7B-Instruct-abliterated`](https://huggingface.co/natong19/Qwen2-7B-Instruct-abliterated) 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/natong19/Qwen2-7B-Instruct-abliterated) 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 Dabitron/Qwen2-7B-Instruct-abliterated-Q8_0-GGUF --hf-file qwen2-7b-instruct-abliterated-q8_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Dabitron/Qwen2-7B-Instruct-abliterated-Q8_0-GGUF --hf-file qwen2-7b-instruct-abliterated-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 Dabitron/Qwen2-7B-Instruct-abliterated-Q8_0-GGUF --hf-file qwen2-7b-instruct-abliterated-q8_0.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Dabitron/Qwen2-7B-Instruct-abliterated-Q8_0-GGUF --hf-file qwen2-7b-instruct-abliterated-q8_0.gguf -c 2048
```
|
Davlan/bert-base-multilingual-cased-finetuned-yoruba | Davlan | "2022-06-27T11:50:30Z" | 1,477 | 0 | transformers | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | "2022-03-02T23:29:04Z" | Hugging Face's logo
---
language: yo
datasets:
---
# bert-base-multilingual-cased-finetuned-yoruba
## Model description
**bert-base-multilingual-cased-finetuned-yoruba** is a **Yoruba BERT** model obtained by fine-tuning **bert-base-multilingual-cased** model on Yorùbá language texts. It provides **better performance** than the multilingual BERT on text classification and named entity recognition datasets.
Specifically, this model is a *bert-base-multilingual-cased* model that was fine-tuned on Yorùbá corpus.
## Intended uses & limitations
#### How to use
You can use this model with Transformers *pipeline* for masked token prediction.
```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='Davlan/bert-base-multilingual-cased-finetuned-yoruba')
>>> unmasker("Arẹmá» Phillip to jẹ á»ká» [MASK] Elizabeth to ti wa lori aisan ti dagbere faye lẹni á»dun má»kandilá»gá»run")
[{'sequence': '[CLS] Arẹmá» Phillip to jẹ á»ká» Mary Elizabeth to ti wa lori aisan ti dagbere faye lẹni á»dun má»kandilá»gá»run [SEP]', 'score': 0.1738305538892746,
'token': 12176,
'token_str': 'Mary'},
{'sequence': '[CLS] Arẹmá» Phillip to jẹ á»ká» Queen Elizabeth to ti wa lori aisan ti dagbere faye lẹni á»dun má»kandilá»gá»run [SEP]', 'score': 0.16382873058319092,
'token': 13704,
'token_str': 'Queen'},
{'sequence': '[CLS] Arẹmá» Phillip to jẹ á»ká» ti Elizabeth to ti wa lori aisan ti dagbere faye lẹni á»dun má»kandilá»gá»run [SEP]', 'score': 0.13272495567798615,
'token': 14382,
'token_str': 'ti'},
{'sequence': '[CLS] Arẹmá» Phillip to jẹ á»ká» King Elizabeth to ti wa lori aisan ti dagbere faye lẹni á»dun má»kandilá»gá»run [SEP]', 'score': 0.12823280692100525,
'token': 11515,
'token_str': 'King'},
{'sequence': '[CLS] Arẹmá» Phillip to jẹ á»ká» Lady Elizabeth to ti wa lori aisan ti dagbere faye lẹni á»dun má»kandilá»gá»run [SEP]', 'score': 0.07841219753026962,
'token': 14005,
'token_str': 'Lady'}]
```
#### Limitations and bias
This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.
## Training data
This model was fine-tuned on Bible, JW300, [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt), [Yoruba Embedding corpus](https://huggingface.co/datasets/yoruba_text_c3) and [CC-Aligned](https://opus.nlpl.eu/), Wikipedia, news corpora (BBC Yoruba, VON Yoruba, Asejere, Alaroye), and other small datasets curated from friends.
## Training procedure
This model was trained on a single NVIDIA V100 GPU
## Eval results on Test set (F-score, average over 5 runs)
Dataset| mBERT F1 | yo_bert F1
-|-|-
[MasakhaNER](https://github.com/masakhane-io/masakhane-ner) | 78.97 | 82.58
[BBC Yorùbá Textclass](https://huggingface.co/datasets/yoruba_bbc_topics) | 75.13 | 79.11
### BibTeX entry and citation info
By David Adelani
```
```
|
projecte-aina/aguila-7b | projecte-aina | "2024-01-31T11:02:43Z" | 1,477 | 53 | transformers | [
"transformers",
"pytorch",
"safetensors",
"RefinedWebModel",
"text-generation",
"aguila",
"falcon",
"spanish",
"catalan",
"custom_code",
"en",
"es",
"ca",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-07-05T13:29:04Z" | ---
language:
- en
- es
- ca
licence:
- apache-2.0
tags:
- aguila
- falcon
- spanish
- catalan
metrics:
- ppl
model-index:
- name: aguila_7b
results:
- task:
name: Causal Language Modeling
type: text-generation
metrics:
- name: Perplexity
type: ppl
value: 8.59
pipeline_tag: text-generation
widget:
- text: |-
Respon a la pregunta segÃŒent.
Pregunta: "Quina és la capital de SuÚcia?"
Resposta: "La capital de SuÚcia és Estocolm."
----
Respon a la pregunta segÃŒent.
Pregunta: "Quina beguda es consumeix als matins per despertar-se?"
Resposta: "La majoria de gent consumeix cafÚ per despertar-se."
----
Respon a la pregunta segÃŒent.
Pregunta: "Explica com funciona un motor de combustió"
Resposta:
example_title: Pregunta-Resposta
- text: |-
Extrae las entidades nombradas del siguiente texto:
Texto: "Me llamo Wolfgang y vivo en Berlin"
Entidades: Wolfgang:PER, Berlin:LOC
----
Extrae las entidades nombradas del siguiente texto:
Texto: "Hoy voy a visitar el parc gÃŒell tras salir del barcelona supercomputing center"
Entidades: parc gÃŒell:LOC, barcelona supercomputing center:LOC
----
Extrae las entidades nombradas del siguiente texto:
Texto: "Maria y Miguel no tienen ningún problema contigo"
Entidades: Maria:PER, Miguel:PER
----
Extrae las entidades nombradas del siguiente texto:
Texto: "Damián se cortó el pelo"
Entidades: Damián:PER
----
Extrae las entidades nombradas del siguiente texto:
Texto: "Lo mejor de Barcelona és el bar de mi amigo Pablo"
Entidades: Pablo:PER, Barcelona:LOC
----
Extrae las entidades nombradas del siguiente texto:
Texto: "Carlos comparte piso con Marc"
Entidades:
example_title: Entidades-Nombradas
---
# Çguila-7B
## Table of Contents
<details>
<summary>Click to expand</summary>
- [Model description](#model-description)
- [Intended uses and limitations](#intended-uses-and-limitations)
- [How to use](#how-to-use)
- [Limitations and bias](#limitations-and-bias)
- [Language adaptation](#language-adaptation)
- [Training](#training)
- [Training data](#training-data)
- [Training procedure](#training-procedure)
- [Additional information](#additional-information)
- [Author](#author)
- [Contact](#contact)
- [Copyright](#copyright)
- [License](#license)
- [Funding](#funding)
- [Disclaimer](#disclaimer)
</details>
## Model description
**Çguila-7B** is a transformer-based causal language model for Catalan, Spanish, and English.
It is based on the [Falcon-7B](https://huggingface.co/tiiuae/falcon-7b) model and has been trained on a 26B token
trilingual corpus collected from publicly available corpora and crawlers.
More information available in the following post from Medium.com: Introducing Çguila, a new open-source LLM for Spanish and Catalan (https://medium.com/@mpamies247/introducing-a%CC%8Cguila-a-new-open-source-llm-for-spanish-and-catalan-ee1ebc70bc79)
## Intended uses and limitations
The **Çguila-7B** model is ready-to-use only for causal language modeling to perform text-generation tasks.
However, it is intended to be fine-tuned for downstream tasks.
## How to use
Here is how to use this model:
```python
import torch
from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
input_text = "El mercat del barri és fantà stic, hi pots trobar"
model_id = "projecte-aina/aguila-7b"
tokenizer = AutoTokenizer.from_pretrained(model_id)
generator = pipeline(
"text-generation",
model=model_id,
tokenizer=tokenizer,
torch_dtype=torch.bfloat16,
trust_remote_code=True,
device_map="auto",
)
generation = generator(
input_text,
do_sample=True,
top_k=10,
eos_token_id=tokenizer.eos_token_id,
)
print(f"Result: {generation[0]['generated_text']}")
```
## Limitations and bias
At the time of submission, no measures have been taken to estimate the bias and toxicity embedded in the model.
However, we are well aware that our models may be biased since the corpora have been collected using crawling techniques
on multiple web sources. We intend to conduct research in these areas in the future, and if completed, this model card will be updated.
## Language adaptation
We adapted the original [Falcon-7B](https://huggingface.co/tiiuae/falcon-7b) model to Spanish and Catalan by swapping the tokenizer and adjusting the embedding layer.
The adaptation procedure is explained in [this blog post](https://medium.com/@mpamies247/ee1ebc70bc79).
## Training
### Training data
The training corpus consists of 26B tokens of several corpora gathered from web crawlings and public domain data.
| Dataset | Language | Words (per-epoch) | Epochs |
|---------------------|----------|--------------------|--------------|
| Wikipedia | en | 2169.97M | 1.428144485 |
| C4_es | es | 53709.80M | 0.1049686196 |
| Biomedical | es | 455.03M | 0.7140722425 |
| Legal | es | 995.70M | 0.7140722425 |
| Wikipedia | es | 693.60M | 1.428144485 |
| Gutenberg | es | 53.18M | 0.7140722425 |
| C4_ca | ca | 2826.00M | 2.142216727 |
| Biomedical | ca | 11.80M | 1.428144485 |
| RacoCatalà Noticias | ca | 17.16M | 2.142216727 |
| RacoCatalà Forums | ca | 333.73M | 2.142216727 |
| CaWaC | ca | 57.79M | 2.142216727 |
| Wikipedia | ca | 228.01M | 3.570361212 |
| Vilaweb | ca | 50.34M | 2.142216727 |
The dataset has the following language distribution:
|Language|Percentage|
|--------|----------|
| En | 16.84% |
| Es | 41.38% |
| Ca | 41.79% |
Note: A small amount of English data was kept to avoid catastrophic forgetting.
## Training procedure
The training corpus has been tokenized using a byte version of [Byte-Pair Encoding (BPE)](https://github.com/openai/gpt-2) with a vocabulary size of 50,257 tokens.
After training a new tokenizer and adapting [falcon-7b](https://huggingface.co/tiiuae/falcon-7b)'s embedding layer, the model was
further pre-trained in three target languages: Catalan, Spanish and English.
The training lasted a total of 320 hours on 8 NVIDIA H100 GPUs with 80GB RAM.
### Training hyperparameters
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- train_batch_size: 1
- eval_batch_size: 1
- total_train_batch_size: 8
- total_eval_batch_size: 8
- optimizer: Adam
- betas: (0.9,0.999)
- epsilon: 1e-08
- learning_rate: 5e-05
- lr_scheduler_type: linear
- num_epochs: 1.0
### Framework versions
- Pytorch 2.0.0
- Transformers 4.30.2
- Datasets 2.13.1
- Tokenizers 0.13.3
## Additional information
### Author
The Language Technologies Unit from Barcelona Supercomputing Center.
### Contact
For further information, please send an email to <[email protected]>.
### Copyright
Copyright(c) 2023 by Language Technologies Unit, Barcelona Supercomputing Center.
### License
[Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0)
### Funding
This work was funded by:
- The [Departament de la VicepresidÚncia i de PolÃtiques Digitals i Territori de la Generalitat de Catalunya](https://politiquesdigitals.gencat.cat/ca/inici/index.html#googtrans(ca|en) within the framework of [Projecte AINA](https://politiquesdigitals.gencat.cat/ca/economia/catalonia-ai/aina).
- The [Spanish State Secretariat for Digitalization and Artificial Intelligence](https://portal.mineco.gob.es/en-us/digitalizacionIA/Pages/sedia.aspx) within the framework of the [Plan de Impulso de las TecnologÃas del Lenguaje](https://plantl.mineco.gob.es/Paginas/index.aspx).
### Disclaimer
<details>
<summary>Click to expand</summary>
The model published in this repository is intended for a generalist purpose and is available to third parties under a permissive Apache License, Version 2.0.
Be aware that the model may have biases and/or any other undesirable distortions.
When third parties deploy or provide systems and/or services to other parties using this model (or any system based on it)
or become users of the model, they should note that it is their responsibility to mitigate the risks arising from its use and,
in any event, to comply with applicable regulations, including regulations regarding the use of Artificial Intelligence.
In no event shall the owner and creator of the model (Barcelona Supercomputing Center)
be liable for any results arising from the use made by third parties.
</details> |
jisukim8873/falcon-7B-case-6 | jisukim8873 | "2024-02-19T01:13:08Z" | 1,477 | 0 | transformers | [
"transformers",
"safetensors",
"falcon",
"text-generation",
"custom_code",
"en",
"dataset:Open-Orca/SlimOrca",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2024-02-16T05:34:53Z" | ---
license: apache-2.0
datasets:
- Open-Orca/SlimOrca
language:
- en
---
# Model Details
* Model Description: This model is test for data ordering.
* Developed by: Jisu Kim
* Model Type: Large Language Model
# Model Architecture
This model is based on falcon-7B. We fine-tuning this model for data ordering task.
falcon-7B is a transformer model, with the following architecture choices:
* Grouped-Query Attention
* Sliding-Window Attention
* Byte-fallback BPE tokenizer
# Dataset
We random sample Open-Orca dataset. (We finetune the 100,000 dataset)
# Guthub
https://github.com/trailerAI
# License
Apache License 2.0 |
TeeZee/DarkForest-20B-v2.0-GGUF | TeeZee | "2024-02-18T22:48:58Z" | 1,477 | 12 | null | [
"gguf",
"merge",
"not-for-all-audiences",
"license:other",
"region:us"
] | null | "2024-02-18T17:54:49Z" | ---
license: other
license_name: microsoft-research-license
tags:
- merge
- not-for-all-audiences
---
GGUF quants of [DarkForest-20B-v2.0](https://huggingface.co/TeeZee/DarkForest-20B-v2.0),
[Q4_K_M](https://huggingface.co/TeeZee/DarkForest-20B-v2.0-GGUF/blob/main/DarkForest-20B-v2.0.q4_K_M.gguf) is recommended. |
SepKeyPro/Merged-OpenLlama3-8B | SepKeyPro | "2024-06-01T16:01:23Z" | 1,477 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"merge",
"mergekit",
"conversational",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2024-05-30T21:33:56Z" | ---
tags:
- merge
- mergekit
license: apache-2.0
---
# Merged-OpenLlama3-8B
Merged-OpenLlama3-8B is a merge of the following models using Mergekit:
## Configuration
```yaml
models:
- model: openchat/openchat-3.6-8b-20240522
- model: meta-llama/Meta-Llama-3-8B-Instruct
- model: nbeerbower/llama-3-gutenberg-8B
merge_method: model_stock
base_model: meta-llama/Meta-Llama-3-8B-Instruct
dtype: bfloat16
``` |
gokaygokay/sd3-long-captioner | gokaygokay | "2024-06-15T10:54:18Z" | 1,477 | 18 | transformers | [
"transformers",
"safetensors",
"paligemma",
"pretraining",
"art",
"image-text-to-text",
"en",
"dataset:google/docci",
"dataset:google/imageinwords",
"license:apache-2.0",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | image-text-to-text | "2024-06-13T03:26:24Z" | ---
license: apache-2.0
datasets:
- google/docci
- google/imageinwords
language:
- en
library_name: transformers
pipeline_tag: image-text-to-text
tags:
- art
---
Fine-tuned version of PaliGemma 224x224 on [google/docci](https://huggingface.co/datasets/google/docci) and [google/imageinwords](https://huggingface.co/datasets/google/imageinwords) datasets.
```
pip install git+https://github.com/huggingface/transformers
```
```python
from transformers import AutoProcessor, PaliGemmaForConditionalGeneration
from PIL import Image
import requests
import torch
model_id = "gokaygokay/sd3-long-captioner"
url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg?download=true"
image = Image.open(requests.get(url, stream=True).raw)
model = PaliGemmaForConditionalGeneration.from_pretrained(model_id).to('cuda').eval()
processor = AutoProcessor.from_pretrained(model_id)
## prefix
prompt = "caption en"
model_inputs = processor(text=prompt, images=image, return_tensors="pt").to('cuda')
input_len = model_inputs["input_ids"].shape[-1]
with torch.inference_mode():
generation = model.generate(**model_inputs, max_new_tokens=256, do_sample=False)
generation = generation[0][input_len:]
decoded = processor.decode(generation, skip_special_tokens=True)
print(decoded)
``` |
nyu-visionx/cambrian-8b | nyu-visionx | "2024-06-28T00:20:50Z" | 1,477 | 51 | transformers | [
"transformers",
"safetensors",
"cambrian_llama",
"text-generation",
"conversational",
"arxiv:2406.16860",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-06-16T12:18:22Z" | ---
license: apache-2.0
---
# Cambrian Model Card
## Model details
**Model type:**
Cambrian is an open-source Multimodal LLM with vision-centric designs.
**Model date:**
Cambrian-1-8B was trained in June 2024.
**Paper or resources for more information:**
- https://cambrian-mllm.github.io/
- https://arxiv.org/abs/2406.16860
## License
Llama 3 is licensed under the LLAMA 3 Community License,
Copyright (c) Meta Platforms, Inc. All Rights Reserved.
**Where to send questions or comments about the model:**
https://github.com/cambrian-mllm/cambrian/issues
## Training dataset
- [2.5M Cambrian Alignment Data](https://huggingface.co/datasets/nyu-visionx/Cambrian-Alignment).
- [7M Cambrian Curated Instruction Tuning Data](https://huggingface.co/datasets/nyu-visionx/Cambrian-10M)
|
timm/vit_small_patch32_224.augreg_in21k_ft_in1k | timm | "2023-05-06T00:29:36Z" | 1,476 | 0 | timm | [
"timm",
"pytorch",
"safetensors",
"image-classification",
"dataset:imagenet-1k",
"dataset:imagenet-21k",
"arxiv:2106.10270",
"arxiv:2010.11929",
"license:apache-2.0",
"region:us"
] | image-classification | "2022-12-22T07:55:29Z" | ---
tags:
- image-classification
- timm
library_name: timm
license: apache-2.0
datasets:
- imagenet-1k
- imagenet-21k
---
# Model card for vit_small_patch32_224.augreg_in21k_ft_in1k
A Vision Transformer (ViT) image classification model. Trained on ImageNet-21k and fine-tuned on ImageNet-1k (with additional augmentation and regularization) in JAX by paper authors, ported to PyTorch by Ross Wightman.
## Model Details
- **Model Type:** Image classification / feature backbone
- **Model Stats:**
- Params (M): 22.9
- GMACs: 1.1
- Activations (M): 2.1
- Image size: 224 x 224
- **Papers:**
- How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers: https://arxiv.org/abs/2106.10270
- An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale: https://arxiv.org/abs/2010.11929v2
- **Dataset:** ImageNet-1k
- **Pretrain Dataset:** ImageNet-21k
- **Original:** https://github.com/google-research/vision_transformer
## 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_small_patch32_224.augreg_in21k_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_small_patch32_224.augreg_in21k_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, 50, 384) 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
@article{steiner2021augreg,
title={How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers},
author={Steiner, Andreas and Kolesnikov, Alexander and and Zhai, Xiaohua and Wightman, Ross and Uszkoreit, Jakob and Beyer, Lucas},
journal={arXiv preprint arXiv:2106.10270},
year={2021}
}
```
```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}}
}
```
|
TheBloke/Emerhyst-20B-GGUF | TheBloke | "2023-09-28T09:57:37Z" | 1,476 | 36 | transformers | [
"transformers",
"gguf",
"llama",
"not-for-all-audiences",
"nsfw",
"base_model:Undi95/Emerhyst-20B",
"license:cc-by-nc-4.0",
"text-generation-inference",
"region:us"
] | null | "2023-09-28T09:48:12Z" | ---
base_model: Undi95/Emerhyst-20B
inference: false
license: cc-by-nc-4.0
model_creator: Undi
model_name: Emerhyst 20B
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
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 -->
# Emerhyst 20B - GGUF
- Model creator: [Undi](https://huggingface.co/Undi95)
- Original model: [Emerhyst 20B](https://huggingface.co/Undi95/Emerhyst-20B)
<!-- description start -->
## Description
This repo contains GGUF format model files for [Undi's Emerhyst 20B](https://huggingface.co/Undi95/Emerhyst-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/Emerhyst-20B-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Emerhyst-20B-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Emerhyst-20B-GGUF)
* [Undi's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/Undi95/Emerhyst-20B)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: Alpaca
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
```
<!-- prompt-template end -->
<!-- licensing start -->
## Licensing
The creator of the source model has listed its license as `cc-by-nc-4.0`, and this quantization has therefore used that same license.
As this model is based on Llama 2, it is also subject to the Meta Llama 2 license terms, and the license files for that are additionally included. It should therefore be considered as being claimed to be licensed under both licenses. I contacted Hugging Face for clarification on dual licensing but they do not yet have an official position. Should this change, or should Meta provide any feedback on this situation, I will update this section accordingly.
In the meantime, any questions regarding licensing, and in particular how these two licenses might interact, should be directed to the original model repository: [Undi's Emerhyst 20B](https://huggingface.co/Undi95/Emerhyst-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 |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [emerhyst-20b.Q2_K.gguf](https://huggingface.co/TheBloke/Emerhyst-20B-GGUF/blob/main/emerhyst-20b.Q2_K.gguf) | Q2_K | 2 | 8.31 GB| 10.81 GB | smallest, significant quality loss - not recommended for most purposes |
| [emerhyst-20b.Q3_K_S.gguf](https://huggingface.co/TheBloke/Emerhyst-20B-GGUF/blob/main/emerhyst-20b.Q3_K_S.gguf) | Q3_K_S | 3 | 8.66 GB| 11.16 GB | very small, high quality loss |
| [emerhyst-20b.Q3_K_M.gguf](https://huggingface.co/TheBloke/Emerhyst-20B-GGUF/blob/main/emerhyst-20b.Q3_K_M.gguf) | Q3_K_M | 3 | 9.70 GB| 12.20 GB | very small, high quality loss |
| [emerhyst-20b.Q3_K_L.gguf](https://huggingface.co/TheBloke/Emerhyst-20B-GGUF/blob/main/emerhyst-20b.Q3_K_L.gguf) | Q3_K_L | 3 | 10.63 GB| 13.13 GB | small, substantial quality loss |
| [emerhyst-20b.Q4_0.gguf](https://huggingface.co/TheBloke/Emerhyst-20B-GGUF/blob/main/emerhyst-20b.Q4_0.gguf) | Q4_0 | 4 | 11.29 GB| 13.79 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [emerhyst-20b.Q4_K_S.gguf](https://huggingface.co/TheBloke/Emerhyst-20B-GGUF/blob/main/emerhyst-20b.Q4_K_S.gguf) | Q4_K_S | 4 | 11.34 GB| 13.84 GB | small, greater quality loss |
| [emerhyst-20b.Q4_K_M.gguf](https://huggingface.co/TheBloke/Emerhyst-20B-GGUF/blob/main/emerhyst-20b.Q4_K_M.gguf) | Q4_K_M | 4 | 12.04 GB| 14.54 GB | medium, balanced quality - recommended |
| [emerhyst-20b.Q5_0.gguf](https://huggingface.co/TheBloke/Emerhyst-20B-GGUF/blob/main/emerhyst-20b.Q5_0.gguf) | Q5_0 | 5 | 13.77 GB| 16.27 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [emerhyst-20b.Q5_K_S.gguf](https://huggingface.co/TheBloke/Emerhyst-20B-GGUF/blob/main/emerhyst-20b.Q5_K_S.gguf) | Q5_K_S | 5 | 13.77 GB| 16.27 GB | large, low quality loss - recommended |
| [emerhyst-20b.Q5_K_M.gguf](https://huggingface.co/TheBloke/Emerhyst-20B-GGUF/blob/main/emerhyst-20b.Q5_K_M.gguf) | Q5_K_M | 5 | 14.16 GB| 16.66 GB | large, very low quality loss - recommended |
| [emerhyst-20b.Q6_K.gguf](https://huggingface.co/TheBloke/Emerhyst-20B-GGUF/blob/main/emerhyst-20b.Q6_K.gguf) | Q6_K | 6 | 16.40 GB| 18.90 GB | very large, extremely low quality loss |
| [emerhyst-20b.Q8_0.gguf](https://huggingface.co/TheBloke/Emerhyst-20B-GGUF/blob/main/emerhyst-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/Emerhyst-20B-GGUF and below it, a specific filename to download, such as: emerhyst-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/Emerhyst-20B-GGUF emerhyst-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/Emerhyst-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/Emerhyst-20B-GGUF emerhyst-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 emerhyst-20b.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 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/Emerhyst-20B-GGUF", model_file="emerhyst-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**: Alicia Loh, Stephen Murray, K, Ajan Kanaga, RoA, Magnesian, Deo Leter, Olakabola, Eugene Pentland, zynix, Deep Realms, Raymond Fosdick, Elijah Stavena, Iucharbius, Erik BjÀreholt, 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, ì€êµ ê¹, Brandon Frisco, Kalila, Trailburnt, Randy H, Talal Aujan, Nathan Dryer, Vadim, é¿æ, 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: Undi's Emerhyst 20B
Merge of [Amethyst 13B](https://huggingface.co/Undi95/Amethyst-13B) and [Emerald 13B](https://huggingface.co/Undi95/Emerald-13B).
In addition, [LimaRP v3](https://huggingface.co/lemonilia/LimaRP-Llama2-13B-v3-EXPERIMENT) was used, is it recommanded to read the documentation.
<!-- description start -->
## Description
This repo contains fp16 files of Emerhyst-20B.
<!-- description end -->
<!-- description start -->
## Models and loras used
- PygmalionAI/pygmalion-2-13b
- Xwin-LM/Xwin-LM-13B-V0.1
- The-Face-Of-Goonery/Huginn-13b-FP16
- zattio770/120-Days-of-LORA-v2-13B
- lemonilia/LimaRP-Llama2-13B-v3-EXPERIMENT
<!-- description end -->
<!-- prompt-template start -->
## Prompt template: Alpaca
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
```
## LimaRP v3 usage and suggested settings
You can follow these instruction format settings in SillyTavern. Replace tiny with your desired response length:
Special thanks to Sushi.
If you want to support me, you can [here](https://ko-fi.com/undiai).
<!-- original-model-card end -->
|
Bllossom/llama-3-Korean-Bllossom-70B | Bllossom | "2024-05-14T06:46:56Z" | 1,476 | 56 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"en",
"ko",
"arxiv:2403.10882",
"arxiv:2403.11399",
"base_model:meta-llama/Meta-Llama-3-70B",
"license:llama3",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2024-05-08T09:02:34Z" | ---
language:
- en
- ko
license: llama3
library_name: transformers
base_model:
- meta-llama/Meta-Llama-3-70B
---
<a href="https://github.com/MLP-Lab/Bllossom">
<img src="https://github.com/teddysum/bllossom/blob/main//bllossom_icon.png?raw=true" width="40%" height="50%">
</a>
# Bllossom | [Demo]() | [Homepage](https://www.bllossom.ai/) | [Github](https://github.com/MLP-Lab/Bllossom) | [Colab-tutorial](https://colab.research.google.com/drive/1fBOzUVZ6NRKk_ugeoTbAOokWKqSN47IG?usp=sharing) |
```bash
ì í¬ Bllossom íë¡ì íž íìì íêµìŽ-ììŽ ìŽì€ ìžìŽëªšëžìž Bllossom-70.8B륌 ê³µê°íìµëë€!
ììžê³Œêž°ë ìíŒì»Žíší
ìŒí°ì ì§ììŒë¡ 100GBê°ëë íêµìŽë¡ 몚ëžì 첎륌 ííëí íêµìŽ ê°í ìŽì€ìžìŽ 몚ëžì
ëë€!
íêµìŽ ìíë ëªšëž ì°Ÿê³ ìì§ ììŒì
šëì?
- íêµìŽ ìµìŽ! ë¬Žë € 3ë§ê°ê° ëë íêµìŽ ìŽííì¥
- Llama3ëë¹ ëëµ 25% ë ꞎ êžžìŽì íêµìŽ Context ì²ëŠ¬ê°ë¥
- íêµìŽ-ììŽ Pararell Corpus륌 íì©í íêµìŽ-ììŽ ì§ìì°ê²° (ì¬ì íìµ)
- íêµìŽ 묞í, ìžìŽë¥Œ ê³ ë €íŽ ìžìŽíìê° ì ìí ë°ìŽí°ë¥Œ íì©í 믞ìžì¡°ì
- ê°ííìµ
ìŽ ëªšë ê² í꺌ë²ì ì ì©ëê³ ìì
ì ìŽì©ìŽ ê°ë¥í Bllossomì ìŽì©íŽ ì¬ë¬ë¶ ë§ì 몚ëžì ë§ë€ìŽë³Žìžì¥!
GPUê° ë¶ì¡±íë©Ž ììí 몚ëžë¡ ë°ë¡ ìë¹ì€ë¥Œ íì©íŽ 볎ìžì [ììí몚ëž](https://huggingface.co/Bllossom/llama-3-Korean-Bllossom-70B-gguf-Q4_K_M)!!
1. Bllossom-70.8Bë ììžê³Œêž°ë, í
ëìž, ì°ìžë ìžìŽìì ì°êµ¬ì€ì ìžìŽíìì íì
íŽ ë§ë ì€ì©ì£Œìêž°ë° ìžìŽëªšëžì
ëë€! ììŒë¡ ì§ìì ìž ì
ë°ìŽížë¥Œ íµíŽ êŽëŠ¬íê² ìµëë€ ë§ìŽ íì©íŽì£Œìžì ð
2. ìŽ ê°ë ¥í Advanced-Bllossom 8B, 70B몚ëž, ìê°-ìžìŽëªšëžì 볎ì íê³ ììµëë€! (ê¶êžíì ë¶ì ê°ë³ ì°ëœì£Œìžì!!)
3. Bllossomì NAACL2024, LREC-COLING2024 (구ë) ë°íë¡ ì±íëììµëë€.
4. ì¢ì ìžìŽëªšëž ê³ì ì
ë°ìŽíž íê² ìµëë€!! íêµìŽ ê°í륌ìíŽ ê³µë ì°êµ¬íì€ë¶(í¹íë
Œë¬ž) ìžì ë íìí©ëë€!!
í¹í ìëì GPUëŒë ëì¬ ê°ë¥ííì ìžì ë ì°ëœì£Œìžì! ë§ë€ê³ ì¶ìê±° ëìëë €ì.
```
The Bllossom language model is a Korean-English bilingual language model based on the open-source LLama3. It enhances the connection of knowledge between Korean and English. It has the following features:
* **Knowledge Linking**: Linking Korean and English knowledge through additional training
* **Vocabulary Expansion**: Expansion of Korean vocabulary to enhance Korean expressiveness.
* **Instruction Tuning**: Tuning using custom-made instruction following data specialized for Korean language and Korean culture
* **Human Feedback**: DPO has been applied
* **Vision-Language Alignment**: Aligning the vision transformer with this language model
**This model developed by [MLPLab at Seoultech](http://mlp.seoultech.ac.kr), [Teddysum](http://teddysum.ai/) and [Yonsei Univ](https://sites.google.com/view/hansaemkim/hansaem-kim)**
## Demo Video
<div style="display: flex; justify-content: space-between;">
<!-- 첫 ë²ì§ž ì»¬ëŒ -->
<div style="width: 49%;">
<a>
<img src="https://github.com/lhsstn/lhsstn/blob/main/x-llava_dem.gif?raw=true" style="width: 100%; height: auto;">
</a>
<p style="text-align: center;">Bllossom-V Demo</p>
</div>
<!-- ë ë²ì§ž ì»¬ëŒ (íìíë€ë©Ž) -->
<div style="width: 49%;">
<a>
<img src="https://github.com/lhsstn/lhsstn/blob/main/bllossom_demo_kakao.gif?raw=true" style="width: 70%; height: auto;">
</a>
<p style="text-align: center;">Bllossom Demo(Kakao)ã
ۋ
ۋ
ۋ
ۋ
ۋ
ۋ
ۋ
€</p>
</div>
</div>
## NEWS
* [2024.05.08] Vocab Expansion Model Update
* [2024.04.25] We released Bllossom v2.0, based on llama-3
* [2023/12] We released Bllossom-Vision v1.0, based on Bllossom
* [2023/08] We released Bllossom v1.0, based on llama-2.
* [2023/07] We released Bllossom v0.7, based on polyglot-ko.
## Example code
### Colab Tutorial
- [Inference-Code-Link](https://colab.research.google.com/drive/1fBOzUVZ6NRKk_ugeoTbAOokWKqSN47IG?usp=sharing)
### Install Dependencies
```bash
pip install torch transformers==4.40.0 accelerate
```
### Python code with Pipeline
```python
import transformers
import torch
model_id = "Bllossom/llama-3-Korean-Bllossom-70B"
pipeline = transformers.pipeline(
"text-generation",
model=model_id,
model_kwargs={"torch_dtype": torch.bfloat16},
device_map="auto",
)
pipeline.model.eval()
PROMPT = '''ë¹ì ì ì ì©í AI ìŽìì€íŽížì
ëë€. ì¬ì©ìì ì§ìì ëíŽ ì¹ì íê³ ì ííê² ëµë³íŽìŒ í©ëë€.
You are a helpful AI assistant, you'll need to answer users' queries in a friendly and accurate manner.'''
instruction = "ììžê³Œíêž°ì ëíêµ MLPì°êµ¬ì€ì ëíŽ ìê°íŽì€"
messages = [
{"role": "system", "content": f"{PROMPT}"},
{"role": "user", "content": f"{instruction}"}
]
prompt = pipeline.tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
terminators = [
pipeline.tokenizer.eos_token_id,
pipeline.tokenizer.convert_tokens_to_ids("<|eot_id|>")
]
outputs = pipeline(
prompt,
max_new_tokens=2048,
eos_token_id=terminators,
do_sample=True,
temperature=0.6,
top_p=0.9,
)
print(outputs[0]["generated_text"][len(prompt):])
# ììžê³Œíêž°ì ëíêµ MLPì°êµ¬ì€ì ë©í°ëªšë¬ ìì°ìŽì²ëŠ¬ ì°êµ¬ë¥Œ íê³ ììµëë€. 구ì±ìì ì겜í êµìì ê¹ë¯Œì€, ê¹ì믌, ìµì°œì, ììžíž, ì íê²°, ìíì, ì¡ì¹ì°, ì¡ì í, ì ëì¬ íììŽ ììµëë€.
```
### Python code with AutoModel
```python
import os
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = 'Bllossom/llama-3-Korean-Bllossom-70B'
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.bfloat16,
device_map="auto",
)
model.eval()
PROMPT = '''ë¹ì ì ì ì©í AI ìŽìì€íŽížì
ëë€. ì¬ì©ìì ì§ìì ëíŽ ì¹ì íê³ ì ííê² ëµë³íŽìŒ í©ëë€.
You are a helpful AI assistant, you'll need to answer users' queries in a friendly and accurate manner.'''
instruction = "ììžê³Œíêž°ì ëíêµ MLPì°êµ¬ì€ì ëíŽ ìê°íŽì€"
messages = [
{"role": "system", "content": f"{PROMPT}"},
{"role": "user", "content": f"{instruction}"}
]
input_ids = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
terminators = [
tokenizer.eos_token_id,
tokenizer.convert_tokens_to_ids("<|eot_id|>")
]
outputs = model.generate(
input_ids,
max_new_tokens=2048,
eos_token_id=terminators,
do_sample=True,
temperature=0.6,
top_p=0.9
)
print(tokenizer.decode(outputs[0][input_ids.shape[-1]:], skip_special_tokens=True))
# ììžê³Œíêž°ì ëíêµ MLPì°êµ¬ì€ì ë©í°ëªšë¬ ìì°ìŽì²ëŠ¬ ì°êµ¬ë¥Œ íê³ ììµëë€. 구ì±ìì ì겜í êµìì ê¹ë¯Œì€, ê¹ì믌, ìµì°œì, ììžíž, ì íê²°, ìíì, ì¡ì¹ì°, ì¡ì í, ì ëì¬ íììŽ ììµëë€.
```
## Citation
**Language Model**
```text
@misc{bllossom,
author = {ChangSu Choi, Yongbin Jeong, Seoyoon Park, InHo Won, HyeonSeok Lim, SangMin Kim, Yejee Kang, Chanhyuk Yoon, Jaewan Park, Yiseul Lee, HyeJin Lee, Younggyun Hahm, Hansaem Kim, KyungTae Lim},
title = {Optimizing Language Augmentation for Multilingual Large Language Models: A Case Study on Korean},
year = {2024},
journal = {LREC-COLING 2024},
paperLink = {\url{https://arxiv.org/pdf/2403.10882}},
},
}
```
**Vision-Language Model**
```text
@misc{bllossom-V,
author = {Dongjae Shin, Hyunseok Lim, Inho Won, Changsu Choi, Minjun Kim, Seungwoo Song, Hangyeol Yoo, Sangmin Kim, Kyungtae Lim},
title = {X-LLaVA: Optimizing Bilingual Large Vision-Language Alignment},
year = {2024},
publisher = {GitHub},
journal = {NAACL 2024 findings},
paperLink = {\url{https://arxiv.org/pdf/2403.11399}},
},
}
```
## Contact
- ì겜í(KyungTae Lim), Professor at Seoultech. `[email protected]`
- íšìê· (Younggyun Hahm), CEO of Teddysum. `[email protected]`
- ê¹íì(Hansaem Kim), Professor at Yonsei. `[email protected]`
## Contributor
- ìµì°œì(Chansu Choi), [email protected]
- ê¹ì믌(Sangmin Kim), [email protected]
- ììžíž(Inho Won), [email protected]
- ê¹ë¯Œì€(Minjun Kim), [email protected]
- ì¡ì¹ì°(Seungwoo Song), [email protected]
- ì ëì¬(Dongjae Shin), [email protected]
- ìíì(Hyeonseok Lim), [email protected]
- ì¡ì í(Jeonghun Yuk), [email protected]
- ì íê²°(Hangyeol Yoo), [email protected]
- ì¡ìí(Seohyun Song), [email protected] |
zero-one-01/llama3-8b-orpo | zero-one-01 | "2024-06-27T03:44:40Z" | 1,476 | 0 | peft | [
"peft",
"safetensors",
"gguf",
"arxiv:1910.09700",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"region:us"
] | null | "2024-06-27T03:07:39Z" | ---
base_model: unsloth/llama-3-8b-bnb-4bit
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.11.1 |
YeungNLP/firefly-qwen1.5-en-7b-unsloth | YeungNLP | "2024-05-05T15:41:52Z" | 1,475 | 2 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:2305.18290",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2024-05-02T13:53:37Z" | ---
library_name: transformers
license: apache-2.0
basemodel: Qwen/Qwen1.5-7B
---
## Unsloth x Qwen2
[Unsloth](https://github.com/unslothai/unsloth) can speed up training LLM and reduce memory usage, but currently it only supports Llama3, Mistral, Gemma, ORPR, Phi-3 and TinyLlama.
We can't train Qwen2 with Unsloth, even though Qwen2 is popular in community.
It's exciting that we succeed to make Unsloth support Qwen2, it can speed up training and reduce much memory usage.
If you want to train Qwen2 with Unsloth, you can use [our repo](https://github.com/yangjianxin1/unsloth) rather than the official one. And we will commit our code to the [official repo](https://github.com/unslothai/unsloth).
Install our Unsloth:
```bash
pip install git+https://github.com/yangjianxin1/unsloth.git
```
[Firefly](https://github.com/yangjianxin1/Firefly) already supports training Qwen2 with Unsloth, and the subsequent models are trained with Firefly, you can try it.
## Model Card for Firefly-Qwen1.5-Unsloth
[firefly-qwen1.5-en-7b-unsloth](https://huggingface.co/YeungNLP/firefly-qwen1.5-en-7b-unsloth) and [firefly-qwen1.5-en-7b-dpo-v0.1-unloth](https://huggingface.co/YeungNLP/firefly-qwen1.5-en-7b-dpo-v0.1-unsloth) are trained based on [Qwen1.5-7B](https://huggingface.co/Qwen/Qwen1.5-7B) to act as a helpful and harmless AI assistant.
We use [Firefly](https://github.com/yangjianxin1/Firefly) to train our models on **a single V100 GPU** with QLoRA and [Unsloth](https://github.com/yangjianxin1/unsloth).
firefly-qwen1.5-en-7b-unsloth is fine-tuned based on Qwen1.5-7B with English instruction data, and firefly-qwen1.5-en-7b-dpo-v0.1-unsloth is trained with [Direct Preference Optimization (DPO)](https://arxiv.org/abs/2305.18290) based on firefly-qwen1.5-en-7b-unsloth.
Our models outperform official [Qwen1.5-7B-Chat](https://huggingface.co/Qwen/Qwen1.5-7B-Chat), [Gemma-7B-it](https://huggingface.co/google/gemma-7b-it), [Zephyr-7B-Beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) on [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
Although our models are trained with English data, you can also try to chat with models in Chinese because Qwen1.5 is also good at Chinese. But we have not evaluated
the performance in Chinese yet.
We advise you to install transformers>=4.37.0.
## Performance
We have evaluated the training gain of Qwen1.5-7B, we use QLoRA and Unsloth to train model for 20 steps on a single V100. The result can be listed as follows.
**Unsloth can reduce GPU memory by 39.13% and training time by 32.12%, and the training speed can increase by 47.32%.**
| max_seq_length | per_device_train_batch_size | gradient_accumulation_steps | use_unsloth | rank | GPU | Time |
|----------------|----------------------------|-----------------------------|-------------|------|-------------------------|-------------------|
| 1024 | 1 | 16 | false | 8 | 13.72GB | 448s |
| 1024 | 1 | 16 | true | 8 | **8.43GB**(**-38.56%**) | 308s(**-31.25%**) |
| 1024 | 1 | 16 | false | 64 | 16.01GB | 452s |
| 1024 | 1 | 16 | true | 64 | 11.07GB(**-30.86%**) | 311s(**-31.19%**) |
| 2048 | 1 | 16 | false | 64 | 18.55GB | 840s |
| 2048 | 1 | 16 | true | 64 | 12.99GB(**-29.97%**) | 596s(**-29.05%**) |
| 1024 | 4 | 4 | false | 64 | 24.70GB | 357s |
| 1024 | 4 | 4 | true | 64 | 14.36GB(**-41.86%**) | 253s(**-29.13%**) |
| 2048 | 4 | 4 | false | 64 | 32.51GB | 741s |
| 2048 | 4 | 4 | true | 64 | 19.79GB(**-39.13%**) | 503s(**-32.12%**) |
We evaluate our sft and dpo models on [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard), they achieve good performance.
| Model | Average | ARC | HellaSwag | MMLU | TruthfulQA | Winogrande | GSM8K |
|--------------------------------------------|---------|--------|-----------|-------|------------|------------|--------|
| firefly-gemma-7b | 62.93 | 62.12 | 79.77 | 61.57 | 49.41 | 75.45 | 49.28 |
| **firefly-qwen1.5-en-7b-dpo-v0.1-unsloth** | 62.65 | 56.14 | 75.5 | 60.87 | 58.09 | 70.72 | 54.59 |
| zephyr-7b-beta | 61.95 | 62.03 | 84.36 | 61.07 | 57.45 | 77.74 | 29.04 |
| **firefly-qwen1.5-en-7b-unsloth** | 61.81 | 54.27 | 76.22 | 61.55 | 50.62 | 70.48 | 57.7 |
| vicuna-13b-v1.5 | 55.41 | 57.08 | 81.24 | 56.67 | 51.51 | 74.66 | 11.3 |
| Xwin-LM-13B-V0.1 | 55.29 | 62.54 | 82.8 | 56.53 | 45.96 | 74.27 | 9.63 |
| Qwen1.5-7B-Chat | 55.15 | 55.89 | 78.56 | 61.65 | 53.54 | 67.72 | 13.57 |
| gemma-7b-it | 53.56 | 51.45 | 71.96 | 53.52 | 47.29 | 67.96 | 29.19 |
## Usage
The chat templates of our chat models are the same as Official Qwen1.5-7B-Chat:
```text
<|im_start|>system
You are a helpful assistant.<|im_end|>
<|im_start|>user
hello, who are you?<|im_end|>
<|im_start|>assistant
I am a AI program developed by Firefly<|im_end|>
```
You can use script to inference in [Firefly](https://github.com/yangjianxin1/Firefly/blob/master/script/chat/chat.py).
You can also use the following code:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model_name_or_path = "YeungNLP/firefly-qwen1.5-en-7b-unsloth"
model = AutoModelForCausalLM.from_pretrained(
model_name_or_path,
trust_remote_code=True,
low_cpu_mem_usage=True,
torch_dtype=torch.float16,
device_map='auto',
)
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
prompt = "Compose an engaging travel blog post about a recent trip to Hawaii, highlighting cultural experiences and must-see attractions. "
messages = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to('cuda')
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=1500,
top_p = 0.9,
temperature = 0.35,
repetition_penalty = 1.0,
eos_token_id=tokenizer.encode('<|im_end|>', add_special_tokens=False)
)
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)
```
## Training Details
Both in SFT and DPO stages, **We only use a single V100 GPU** with QLoRA and Unsloth, and we use [Firefly](https://github.com/yangjianxin1/Firefly) to train our models.
### Training Setting
The following hyperparameters are used during SFT:
- num_epochs: 1
- learning_rate: 2e-4
- total_train_batch_size: 32
- max_seq_length: 2048
- optimizer: paged_adamw_32bit
- lr_scheduler_type: constant_with_warmup
- warmup_steps: 600
- lora_rank: 64
- lora_alpha: 16
- lora_dropout: 0.05
- gradient_checkpointing: true
- fp16: true
The following hyperparameters were used during DPO:
- num_epochs: 1
- learning_rate: 2e-4
- total_train_batch_size: 32
- max_seq_length: 2048
- max_prompt_length: 500
- optimizer: paged_adamw_32bit
- lr_scheduler_type: constant_with_warmup
- warmup_steps: 100
- lora_rank: 64
- lora_alpha: 16
- lora_dropout: 0.05
- gradient_checkpointing: true
- fp16: true
### Training metrics
The table below shows the full set of DPO training metrics:
| Epoch | Step | Loss | Rewards/accuracies | Rewards/margins | Rewards/chosen | Rewards/rejected | Logits/chosen | Logits/rejected | Logps/chosen | Logps/rejected |
|-------|------|--------|--------------------|-----------------|----------------|------------------|---------------|-----------------|--------------|----------------|
| 0.05 | 100 | 0.6128 | 0.6572 | 0.3914 | -0.0622 | -0.4537 | 1.107 | 1.1104 | -283.7632 | -264.5925 |
| 0.1 | 200 | 0.6066 | 0.6913 | 0.662 | -0.3589 | -1.0209 | 0.9433 | 0.9431 | -279.0002 | -268.6432 |
| 0.16 | 300 | 0.5803 | 0.7069 | 0.876 | -0.3849 | -1.2609 | 0.8411 | 0.8537 | -289.9482 | -274.3425 |
| 0.21 | 400 | 0.5624 | 0.7169 | 0.9575 | -0.2447 | -1.2022 | 0.7615 | 0.7497 | -293.8072 | -274.4167 |
| 0.26 | 500 | 0.5863 | 0.7 | 0.8908 | -0.5283 | -1.4191 | 0.537 | 0.5085 | -284.3388 | -267.9294 |
| 0.31 | 600 | 0.5612 | 0.7166 | 1.0791 | -0.592 | -1.6711 | 0.7121 | 0.7219 | -293.2425 | -278.5992 |
| 0.37 | 700 | 0.5741 | 0.7234 | 1.0742 | -0.8469 | -1.9211 | 0.6002 | 0.5769 | -300.8099 | -285.9137 |
| 0.42 | 800 | 0.582 | 0.7141 | 1.0414 | -1.1658 | -2.2072 | 0.7191 | 0.5934 | -300.458 | -286.1 |
| 0.47 | 900 | 0.5694 | 0.7178 | 1.2055 | -1.7372 | -2.9426 | 0.4226 | 0.316 | -305.5303 | -290.7548 |
| 0.52 | 1000 | 0.5827 | 0.7134 | 1.1063 | -1.354 | -2.4603 | 0.535 | 0.4022 | -302.7598 | -286.636 |
| 0.58 | 1100 | 0.5553 | 0.7306 | 1.3631 | -1.5861 | -2.9492 | 0.7636 | 0.6559 | -312.9375 | -290.3474 |
| 0.63 | 1200 | 0.5633 | 0.7341 | 1.2689 | -1.7187 | -2.9876 | 0.6555 | 0.5894 | -315.0179 | -298.2406 |
| 0.68 | 1300 | 0.5705 | 0.7284 | 1.3501 | -1.7762 | -3.1263 | 0.7419 | 0.6874 | -310.9056 | -294.2934 |
| 0.73 | 1400 | 0.5458 | 0.7347 | 1.4555 | -2.2377 | -3.6932 | 0.7279 | 0.6564 | -309.141 | -299.1613 |
| 0.79 | 1500 | 0.5797 | 0.7222 | 1.2937 | -2.4483 | -3.742 | 0.8444 | 0.771 | -321.578 | -298.111 |
| 0.84 | 1600 | 0.5572 | 0.7319 | 1.4824 | -2.9344 | -4.4168 | 0.9202 | 0.8605 | -323.4034 | -307.0114 |
| 0.89 | 1700 | 0.5518 | 0.7281 | 1.4263 | -2.7301 | -4.1564 | 0.9257 | 0.8785 | -313.694 | -298.1267 |
| 0.94 | 1800 | 0.5572 | 0.7272 | 1.5121 | -2.9505 | -4.4627 | 0.7899 | 0.7503 | -314.1552 | -305.9873 |
| 0.99 | 1900 | 0.5763 | 0.7241 | 1.4982 | -2.7064 | -4.2047 | 0.7841 | 0.7023 | -310.6677 | -299.5064 | |
cyberagent/open-calm-1b | cyberagent | "2023-05-18T01:11:30Z" | 1,474 | 16 | transformers | [
"transformers",
"pytorch",
"gpt_neox",
"text-generation",
"japanese",
"causal-lm",
"ja",
"dataset:wikipedia",
"dataset:cc100",
"dataset:mc4",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-05-15T07:00:18Z" | ---
license: cc-by-sa-4.0
datasets:
- wikipedia
- cc100
- mc4
language:
- ja
tags:
- japanese
- causal-lm
inference: false
---
# OpenCALM-1B
## Model Description
OpenCALM is a suite of decoder-only language models pre-trained on Japanese datasets, developed by CyberAgent, Inc.
## Usage
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("cyberagent/open-calm-1b", device_map="auto", torch_dtype=torch.float16)
tokenizer = AutoTokenizer.from_pretrained("cyberagent/open-calm-1b")
inputs = tokenizer("AIã«ãã£ãŠç§éã®æ®ããã¯ã", return_tensors="pt").to(model.device)
with torch.no_grad():
tokens = model.generate(
**inputs,
max_new_tokens=64,
do_sample=True,
temperature=0.7,
top_p=0.9,
repetition_penalty=1.05,
pad_token_id=tokenizer.pad_token_id,
)
output = tokenizer.decode(tokens[0], skip_special_tokens=True)
print(output)
```
## Model Details
|Model|Params|Layers|Dim|Heads|Dev ppl|
|:---:|:---: |:---:|:---:|:---:|:---:|
|[cyberagent/open-calm-small](https://huggingface.co/cyberagent/open-calm-small)|160M|12|768|12|19.7|
|[cyberagent/open-calm-medium](https://huggingface.co/cyberagent/open-calm-medium)|400M|24|1024|16|13.8|
|[cyberagent/open-calm-large](https://huggingface.co/cyberagent/open-calm-large)|830M|24|1536|16|11.3|
|[cyberagent/open-calm-1b](https://huggingface.co/cyberagent/open-calm-1b)|1.4B|24|2048|16|10.3|
|[cyberagent/open-calm-3b](https://huggingface.co/cyberagent/open-calm-3b)|2.7B|32|2560|32|9.7|
|[cyberagent/open-calm-7b](https://huggingface.co/cyberagent/open-calm-7b)|6.8B|32|4096|32|8.2|
* **Developed by**: [CyberAgent, Inc.](https://www.cyberagent.co.jp/)
* **Model type**: Transformer-based Language Model
* **Language**: Japanese
* **Library**: [GPT-NeoX](https://github.com/EleutherAI/gpt-neox)
* **License**: OpenCALM is licensed under the Creative Commons Attribution-ShareAlike 4.0 International License ([CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/)). When using this model, please provide appropriate credit to CyberAgent, Inc.
* Example (en): This model is a fine-tuned version of OpenCALM-XX developed by CyberAgent, Inc. The original model is released under the CC BY-SA 4.0 license, and this model is also released under the same CC BY-SA 4.0 license. For more information, please visit: https://creativecommons.org/licenses/by-sa/4.0/
* Example (ja): æ¬ã¢ãã«ã¯ãæ ªåŒäŒç€Ÿãµã€ããŒãšãŒãžã§ã³ãã«ããOpenCALM-XXããã¡ã€ã³ãã¥ãŒãã³ã°ãããã®ã§ããå
ã®ã¢ãã«ã¯CC BY-SA 4.0ã©ã€ã»ã³ã¹ã®ããšã§å
¬éãããŠãããæ¬ã¢ãã«ãåããCC BY-SA 4.0ã©ã€ã»ã³ã¹ã§å
¬éããŸãã詳ããã¯ãã¡ããã芧ãã ãã: https://creativecommons.org/licenses/by-sa/4.0/
## Training Dataset
* Wikipedia (ja)
* Common Crawl (ja)
## Author
[Ryosuke Ishigami](https://huggingface.co/rishigami)
## Citations
```bibtext
@software{gpt-neox-library,
title = {{GPT-NeoX: Large Scale Autoregressive Language Modeling in PyTorch}},
author = {Andonian, Alex and Anthony, Quentin and Biderman, Stella and Black, Sid and Gali, Preetham and Gao, Leo and Hallahan, Eric and Levy-Kramer, Josh and Leahy, Connor and Nestler, Lucas and Parker, Kip and Pieler, Michael and Purohit, Shivanshu and Songz, Tri and Phil, Wang and Weinbach, Samuel},
url = {https://www.github.com/eleutherai/gpt-neox},
doi = {10.5281/zenodo.5879544},
month = {8},
year = {2021},
version = {0.0.1},
}
``` |
jisukim8873/falcon-7B-case-2 | jisukim8873 | "2024-03-04T03:52:23Z" | 1,474 | 0 | transformers | [
"transformers",
"safetensors",
"falcon",
"text-generation",
"custom_code",
"en",
"dataset:Open-Orca/SlimOrca",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2024-03-04T01:52:00Z" | ---
license: apache-2.0
datasets:
- Open-Orca/SlimOrca
language:
- en
---
# Model Details
* Model Description: This model is test for data ordering.
* Developed by: Jisu Kim
* Model Type: Large Language Model
# Model Architecture
This model is based on falcon-7B. We fine-tuning this model for data ordering task.
falcon-7B is a transformer model, with the following architecture choices:
* Grouped-Query Attention
* Sliding-Window Attention
* Byte-fallback BPE tokenizer
# Dataset
We random sample Open-Orca dataset. (We finetune the 100,000 dataset)
# Guthub
https://github.com/trailerAI
# License
Apache License 2.0 |
QuantFactory/Neural-SOVLish-Devil-8B-L3-GGUF | QuantFactory | "2024-06-04T08:04:03Z" | 1,474 | 0 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"text-generation",
"arxiv:2403.19522",
"base_model:saishf/Neural-SOVLish-Devil-8B-L3",
"license:cc-by-nc-4.0",
"model-index",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-06-03T06:28:38Z" | ---
license: cc-by-nc-4.0
library_name: transformers
tags:
- mergekit
- merge
base_model: saishf/Neural-SOVLish-Devil-8B-L3
model-index:
- name: Neural-SOVLish-Devil-8B-L3
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: 69.11
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=saishf/Neural-SOVLish-Devil-8B-L3
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: 84.77
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=saishf/Neural-SOVLish-Devil-8B-L3
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: 69.02
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=saishf/Neural-SOVLish-Devil-8B-L3
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: 59.05
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=saishf/Neural-SOVLish-Devil-8B-L3
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: 78.3
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=saishf/Neural-SOVLish-Devil-8B-L3
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: 73.09
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=saishf/Neural-SOVLish-Devil-8B-L3
name: Open LLM Leaderboard
pipeline_tag: text-generation
---
# QuantFactory/Neural-SOVLish-Devil-8B-L3-GGUF
This is quantized evrsion of [saishf/Neural-SOVLish-Devil-8B-L3](https://huggingface.co/saishf/Neural-SOVLish-Devil-8B-L3) created using llama.cpp
## Model Description
This is another "SOVL" style merge, this time using [mlabonne/NeuralDaredevil-8B-abliterated](https://huggingface.co/mlabonne/NeuralDaredevil-8B-abliterated).
Daredevil is the first abliterated model series i've tried that feels as smart as base llama-3-instruct while also being willing to give instructions to do all kinda of illegal things
Neural daredevil is trained further on the original abliterated model, which should result in a better experience in most scenarios. (A bandaid for the damage abliteration causes)
This model should do well in rp, I'm yet to test it (waiting for gguf files @_@)
### Merge Method
This model was merged using the [Model Stock](https://arxiv.org/abs/2403.19522) merge method using [mlabonne/NeuralDaredevil-8B-abliterated](https://huggingface.co/mlabonne/NeuralDaredevil-8B-abliterated) as a base.
### Models Merged
The following models were included in the merge:
* [mlabonne/NeuralDaredevil-8B-abliterated](https://huggingface.co/mlabonne/NeuralDaredevil-8B-abliterated) + [ResplendentAI/BlueMoon_Llama3](https://huggingface.co/ResplendentAI/BlueMoon_Llama3)
* [mlabonne/NeuralDaredevil-8B-abliterated](https://huggingface.co/mlabonne/NeuralDaredevil-8B-abliterated) + [ResplendentAI/Smarts_Llama3](https://huggingface.co/ResplendentAI/Smarts_Llama3)
* [mlabonne/NeuralDaredevil-8B-abliterated](https://huggingface.co/mlabonne/NeuralDaredevil-8B-abliterated) + [ResplendentAI/Luna_Llama3](https://huggingface.co/ResplendentAI/Luna_Llama3)
* [mlabonne/NeuralDaredevil-8B-abliterated](https://huggingface.co/mlabonne/NeuralDaredevil-8B-abliterated) + [ResplendentAI/Aura_Llama3](https://huggingface.co/ResplendentAI/Aura_Llama3)
* [mlabonne/NeuralDaredevil-8B-abliterated](https://huggingface.co/mlabonne/NeuralDaredevil-8B-abliterated) + [ResplendentAI/RP_Format_QuoteAsterisk_Llama3](https://huggingface.co/ResplendentAI/RP_Format_QuoteAsterisk_Llama3)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: mlabonne/NeuralDaredevil-8B-abliterated+ResplendentAI/Aura_Llama3
- model: mlabonne/NeuralDaredevil-8B-abliterated+ResplendentAI/Smarts_Llama3
- model: mlabonne/NeuralDaredevil-8B-abliterated+ResplendentAI/Luna_Llama3
- model: mlabonne/NeuralDaredevil-8B-abliterated+ResplendentAI/BlueMoon_Llama3
- model: mlabonne/NeuralDaredevil-8B-abliterated+ResplendentAI/RP_Format_QuoteAsterisk_Llama3
merge_method: model_stock
base_model: mlabonne/NeuralDaredevil-8B-abliterated
dtype: bfloat16
```
# [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_saishf__Neural-SOVLish-Devil-8B-L3)
| Metric |Value|
|---------------------------------|----:|
|Avg. |72.22|
|AI2 Reasoning Challenge (25-Shot)|69.11|
|HellaSwag (10-Shot) |84.77|
|MMLU (5-Shot) |69.02|
|TruthfulQA (0-shot) |59.05|
|Winogrande (5-shot) |78.30|
|GSM8k (5-shot) |73.09| |
nold/Phi-3-mini-4k-instruct-function-calling-GGUF | nold | "2024-05-22T12:44:59Z" | 1,473 | 4 | null | [
"gguf",
"dataset:mzbac/function-calling-phi-3-format-v1.1",
"region:us"
] | null | "2024-05-21T18:16:48Z" | ---
datasets:
- mzbac/function-calling-phi-3-format-v1.1
---
# Model
Fine-tuned the Phi3 instruction model for function calling via MLX-LM using https://huggingface.co/datasets/mzbac/function-calling-phi-3-format-v1.1
# Usage
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "mzbac/Phi-3-mini-4k-instruct-function-calling"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.bfloat16,
device_map="auto",
)
tool = {
"name": "search_web",
"description": "Perform a web search for a given search terms.",
"parameter": {
"type": "object",
"properties": {
"search_terms": {
"type": "array",
"items": {"type": "string"},
"description": "The search queries for which the search is performed.",
"required": True,
}
},
},
}
messages = [
{
"role": "user",
"content": f"You are a helpful assistant with access to the following functions. Use them if required - {str(tool)}",
},
{"role": "user", "content": "Any news in Melbourne today, May 7, 2024?"},
]
input_ids = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
terminators = [tokenizer.eos_token_id, tokenizer.convert_tokens_to_ids("<|end|>")]
outputs = model.generate(
input_ids,
max_new_tokens=256,
eos_token_id=terminators,
do_sample=True,
temperature=0.1,
)
response = outputs[0]
print(tokenizer.decode(response))
# <s><|user|> You are a helpful assistant with access to the following functions. Use them if required - {'name': 'search_web', 'description': 'Perform a web search for a given search terms.', 'parameter': {'type': 'object', 'properties': {'search_terms': {'type': 'array', 'items': {'type': 'string'}, 'description': 'The search queries for which the search is performed.', 'required': True}}}}<|end|><|assistant|>
# <|user|> Any news in Melbourne today, May 7, 2024?<|end|>
# <|assistant|> <functioncall> {"name": "search_web", "arguments": {"search_terms": ["news", "Melbourne", "May 7, 2024"]}}<|end|>
```
# Training hyperparameters
lora_config.yaml
```yaml
# The path to the local model directory or Hugging Face repo.
model: "microsoft/Phi-3-mini-4k-instruct"
# Whether or not to train (boolean)
train: true
# Directory with {train, valid, test}.jsonl files
data: "data"
# The PRNG seed
seed: 0
# Number of layers to fine-tune
lora_layers: 32
# Minibatch size.
batch_size: 1
# Iterations to train for.
iters: 111000
# Number of validation batches, -1 uses the entire validation set.
val_batches: -1
# Adam learning rate.
learning_rate: 1e-6
# Number of training steps between loss reporting.
steps_per_report: 10
# Number of training steps between validations.
steps_per_eval: 200
# Load path to resume training with the given adapter weights.
# resume_adapter_file: "adapters/adapters.safetensors"
# Save/load path for the trained adapter weights.
adapter_path: "adapters"
# Save the model every N iterations.
save_every: 1000
# Evaluate on the test set after training
test: false
# Number of test set batches, -1 uses the entire test set.
test_batches: 100
# Maximum sequence length.
max_seq_length: 4096
# Use gradient checkpointing to reduce memory use.
grad_checkpoint: false
# LoRA parameters can only be specified in a config file
lora_parameters:
# The layer keys to apply LoRA to.
# These will be applied for the last lora_layers
keys: ['mlp.down_proj','mlp.gate_up_proj','self_attn.qkv_proj','self_attn.o_proj']
rank: 128
alpha: 256
scale: 10.0
dropout: 0.05
```
***
Quantization of Model [mzbac/Phi-3-mini-4k-instruct-function-calling](https://huggingface.co/mzbac/Phi-3-mini-4k-instruct-function-calling).
Created using [llm-quantizer](https://github.com/Nold360/llm-quantizer) Pipeline
|
aipicasso/emi-2-5 | aipicasso | "2024-06-26T09:54:25Z" | 1,473 | 2 | diffusers | [
"diffusers",
"safetensors",
"stable-diffusion",
"text-to-image",
"arxiv:2307.01952",
"arxiv:2212.03860",
"license:openrail++",
"autotrain_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] | text-to-image | "2024-06-26T08:53:39Z" | ---
license: openrail++
tags:
- stable-diffusion
- text-to-image
inference: false
library_name: diffusers
---
# Emi 2.5 Model Card
# ã¯ããã«
Emi 2.5 (Ethereal master of illustration 2.5) ã¯ã
æå
端ã®éçºæ©æH100ãçšããç»åçæEmiãçšããŠ
AI Picasso瀟ãéçºããAIã¢ãŒãã«ç¹åããç»åçæAIã§ãã
ãã®ã¢ãã«ã®ç¹åŸŽãšããŠãDanbooruãªã©ã«ããç¡æ転èŒç»åãåŠç¿ããŠããªãããšããããããŸãã
# 䜿ãæ¹
[ãã](https://huggingface.co/spaces/aipicasso/emi-2-demo)ãããã¢ãå©çšããããšãã§ããŸãã
æ¬æ Œçã«å©çšãã人ã¯[ãã](emi-2-5.safetensors)ããã¢ãã«ãããŠã³ããŒãã§ããŸãã
# ã¢ãã«ã®åºååäžã«ã€ããŠ
- 䜿ããããã³ããã¯Waifu Diffusionãšåãã§ãããŸããStable Diffusionã®ããã«äœ¿ãããšãã§ããŸãã
- 解å床ãäžããããã«ã¯ã[ComfyUIã®ããŒã](https://github.com/Ttl/ComfyUi_NNLatentUpscale)ã䜿ã£ãŠãã ããã
- ãã¬ãã£ãããã³ããã«[Textual Inversion](https://civitai.com/models/119032/unaestheticxl-or-negative-ti)ã䜿çšããããšãããããããŸãã
- æãäžå®å®ãªããã[Concept Slider Fix hands](https://github.com/rohitgandikota/sliders)ã䜿çšããããšãããããããŸãã
- ChatGPTãçšããŠããã³ãããæŽç·Žãããšãèªåã®æ ãè¶
ããäœåã«åºäŒããŸãã
- ææ°ã®ComfyUIã«ããFreeUããŒãããŸãã¯[Web UIã®æ¡åŒµæ©èœ](https://github.com/ljleb/sd-webui-freeu)ã次ã®ãã©ã¡ãŒã¿ã§äœ¿ããšããã«åºåãäžããå¯èœæ§ããããŸãã
- s1=1.2, s2=0.7, b1=1.1, b2=1.3
# æ³åŸã«ã€ããŠ
æ¬ã¢ãã«ã¯æ¥æ¬ã«ãŠäœæãããŸããããããã£ãŠãæ¥æ¬ã®æ³åŸãé©çšãããŸãã
æ¬ã¢ãã«ã®åŠç¿ã¯ãèäœæš©æ³ç¬¬30æ¡ã®4ã«åºã¥ããåæ³ã§ãããšäž»åŒµããŸãã
ãŸããæ¬ã¢ãã«ã®é
åžã«ã€ããŠã¯ãèäœæš©æ³ãåæ³175æ¡ã«ç
§ãããŠã¿ãŠãã
æ£ç¯ãå¹å©ç¯ã«ã該åœããªããšäž»åŒµããŸãã詳ããã¯æ¿æ²ŒåŒè·å£«ã®[èŠè§£](https://twitter.com/tka0120/status/1601483633436393473?s=20&t=yvM9EX0Em-_7lh8NJln3IQ)ã埡芧ãã ããã
ãã ããã©ã€ã»ã³ã¹ã«ãããéããæ¬ã¢ãã«ã®çæç©ã¯åçš®æ³ä»€ã«åŸã£ãŠåãæ±ã£ãŠäžããã
# é£çµ¡å
[email protected]
以äžãäžè¬çãªã¢ãã«ã«ãŒãã®æ¥æ¬èªèš³ã§ãã
## ã¢ãã«è©³çŽ°
- **ã¢ãã«ã¿ã€ã:** æ¡æ£ã¢ãã«ããŒã¹ã® text-to-image çæã¢ãã«
- **èšèª:** æ¥æ¬èª
- **ã©ã€ã»ã³ã¹:** [CreativeML Open RAIL++-M License](LICENSE.md)
- **ã¢ãã«ã®èª¬æ:** ãã®ã¢ãã«ã¯ããã³ããã«å¿ããŠé©åãªç»åãçæããããšãã§ããŸããã¢ã«ãŽãªãºã 㯠[Latent Diffusion Model](https://arxiv.org/abs/2307.01952) ãš [OpenCLIP-ViT/G](https://github.com/mlfoundations/open_clip)ã[CLIP-L](https://github.com/openai/CLIP) ã§ãã
- **è£è¶³:**
- **åèæç®:**
```bibtex
@misc{podell2023sdxl,
title={SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis},
author={Dustin Podell and Zion English and Kyle Lacey and Andreas Blattmann and Tim Dockhorn and Jonas MÃŒller and Joe Penna and Robin Rombach},
year={2023},
eprint={2307.01952},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
## ã¢ãã«ã®äœ¿çšäŸ
Stable Diffusion XL 1.0ãšåã䜿ãæ¹ã§ãã
ããããã®æ¹æ³ããããŸããã3ã€ã®ãã¿ãŒã³ãæäŸããŸãã
- ComfyUI
- Fooocus
- Diffusers
### ComfyUIãFooocusã®å Žå
Stable Diffusion XL 1.0 ã®äœ¿ãæ¹ãšåãããsafetensors圢åŒã®ã¢ãã«ãã¡ã€ã«ã䜿ã£ãŠãã ããã
詳ããã€ã³ã¹ããŒã«æ¹æ³ã¯ã[ãã¡ãã®èšäº](https://note.com/it_navi/n/n723d93bedd64)ãåç
§ããŠãã ããã
### Diffusersã®å Žå
[ð€'s Diffusers library](https://github.com/huggingface/diffusers) ã䜿ã£ãŠãã ããã
ãŸãã¯ã以äžã®ã¹ã¯ãªãããå®è¡ããã©ã€ãã©ãªããããŠãã ããã
```bash
pip install invisible_watermark transformers accelerate safetensorsãdiffusers
```
次ã®ã¹ã¯ãªãããå®è¡ããç»åãçæããŠãã ããã
```python
from diffusers import StableDiffusionXLPipeline, EulerAncestralDiscreteScheduler
import torch
model_id = "aipicasso/emi-2-5"
scheduler = EulerAncestralDiscreteScheduler.from_pretrained(model_id,subfolder="scheduler")
pipe = StableDiffusionXLPipeline.from_pretrained(model_id, scheduler=scheduler, torch_dtype=torch.bfloat16)
pipe = pipe.to("cuda")
prompt = "1girl, upper body, brown bob short hair, brown eyes, looking at viewer, cherry blossom"
images = pipe(prompt, num_inference_steps=20).images
images[0].save("girl.png")
```
è€éãªæäœã¯[ãã¢ã®ãœãŒã¹ã³ãŒã](https://huggingface.co/spaces/aipicasso/emi-2-demo/blob/main/app.py)ãåèã«ããŠãã ããã
#### æ³å®ãããçšé
- ã€ã©ã¹ãã挫ç»ãã¢ãã¡ã®äœç»è£å©
- åçšã»éåçšã¯åããªã
- äŸé Œã®éã®ã¯ãªãšã€ã¿ãŒãšã®ã³ãã¥ãã±ãŒã·ã§ã³
- ç»åçæãµãŒãã¹ã®åçšæäŸ
- çæç©ã®åãæ±ãã«ã¯æ³šæããŠäœ¿ã£ãŠãã ããã
- èªå·±è¡šçŸ
- ãã®AIã䜿ãããããªããããããçºä¿¡ããããš
- ç 究éçº
- ãã¡ã€ã³ãã¥ãŒãã³ã°ïŒè¿œå åŠç¿ãšãïŒ
- LoRA ãªã©
- ä»ã®ã¢ãã«ãšã®ããŒãž
- æ¬ã¢ãã«ã®æ§èœãFIDãªã©ã§èª¿ã¹ãããš
- æè²
- çŸå€§çãå°éåŠæ ¡çã®åæ¥å¶äœ
- 倧åŠçã®åæ¥è«æã課é¡å¶äœ
- å
çãç»åçæAIã®çŸç¶ãäŒããããš
- Hugging Face ã® Community ã«ãããŠããçšé
- æ¥æ¬èªãè±èªã§è³ªåããŠãã ãã
#### æ³å®ãããªãçšé
- ç©äºãäºå®ãšããŠè¡šçŸãããããªããš
- å
çãå°ããããããªããš
- ãã®ä»ãåµäœæ¥çã«æªåœ±é¿ãåãŒãããš
# 䜿çšããŠã¯ãããªãçšéãæªæã®ããçšé
- ãããŒã»ãã³ããªã³ã°ã«çšããªãã§ãã ãã
- ããžã¿ã«èŽäœ ([Digital Forgery](https://arxiv.org/abs/2212.03860)) ã¯å
¬éããªãã§ãã ããïŒèäœæš©æ³ã«éåãããããïŒ
- ä»äººã®äœåãç¡æã§Image-to-Imageããªãã§ãã ããïŒèäœæš©æ³ã«éåãããããïŒ
- ãããã€ç©ãé åžããªãã§ãã ãã (åæ³175æ¡ã«éåãããããïŒ
- ããããæ¥çã®ãããŒãå®ããªããããªããš
- äºå®ã«åºã¥ããªãããšãäºå®ã®ããã«èªããªãããã«ããŠãã ããïŒåšåæ¥å劚害眪ãé©çšããããããïŒ
- ãã§ã€ã¯ãã¥ãŒã¹
## ã¢ãã«ã®éçããã€ã¢ã¹
### ã¢ãã«ã®éç
- 人éã®æããããã«çæããããšãé£ããã§ãã
### ãã€ã¢ã¹
- æ¥æ¬ã®ã€ã©ã¹ã颚ã®ç»åãçæããŠããããšã«åããŠããŸãããåçã®ãããªç»åãçæããããšã«ã¯åããŠããŸããã
## åŠç¿
**åŠç¿ããŒã¿**
- Stable Diffusionãšåæ§ã®ããŒã¿ã»ããããDanbooruã®ç¡æ転èŒç»åãåãé€ããŠæåã§éããçŽ3000æã®ç»å
- Stable Diffusionãšåæ§ã®ããŒã¿ã»ããããDanbooruã®ç¡æ転èŒç»åãåãé€ããŠèªåã§éããçŽ50äžæã®ç»å
- [CosmicMan-SDXL](https://huggingface.co/cosmicman/CosmicMan-SDXL)
**åŠç¿ããã»ã¹**
- **ããŒããŠã§ã¢:** H100, RTX 4090, A6000
## è©äŸ¡çµæ
第äžè
ã«ããè©äŸ¡ãæ±ããŠããŸãã
## ç°å¢ãžã®åœ±é¿
- **ããŒããŠã§ã¢ã¿ã€ã:** H100, RTX 4090, A6000
- **䜿çšæéïŒåäœã¯æéïŒ:** 1000
- **åŠç¿ããå Žæ:** æ¥æ¬
## åèæç®
```bibtex
@misc{podell2023sdxl,
title={SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis},
author={Dustin Podell and Zion English and Kyle Lacey and Andreas Blattmann and Tim Dockhorn and Jonas MÃŒller and Joe Penna and Robin Rombach},
year={2023},
eprint={2307.01952},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
```bibtex
@article{li2024cosmicman,
title={CosmicMan: A Text-to-Image Foundation Model for Humans},
author={Li, Shikai and Fu, Jianglin and Liu, Kaiyuan and Wang, Wentao and Lin, Kwan-Yee and Wu, Wayne},
journal={arXiv preprint arXiv:2404.01294},
year={2024}
}
```
|
timm/vgg16_bn.tv_in1k | timm | "2023-04-25T20:14:04Z" | 1,472 | 1 | timm | [
"timm",
"pytorch",
"safetensors",
"image-classification",
"dataset:imagenet-1k",
"arxiv:1409.1556",
"license:bsd-3-clause",
"region:us"
] | image-classification | "2023-04-25T20:12:23Z" | ---
tags:
- image-classification
- timm
library_name: timm
license: bsd-3-clause
datasets:
- imagenet-1k
---
# Model card for vgg16_bn.tv_in1k
A VGG image classification model. Trained on ImageNet-1k, original torchvision weights.
## Model Details
- **Model Type:** Image classification / feature backbone
- **Model Stats:**
- Params (M): 138.4
- GMACs: 15.5
- Activations (M): 13.6
- Image size: 224 x 224
- **Papers:**
- Very Deep Convolutional Networks for Large-Scale Image Recognition: https://arxiv.org/abs/1409.1556
- **Dataset:** ImageNet-1k
- **Original:** https://github.com/pytorch/vision
## 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('vgg16_bn.tv_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(
'vgg16_bn.tv_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, 64, 224, 224])
# torch.Size([1, 128, 112, 112])
# torch.Size([1, 256, 56, 56])
# torch.Size([1, 512, 28, 28])
# torch.Size([1, 512, 14, 14])
# torch.Size([1, 512, 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(
'vgg16_bn.tv_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, 512, 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
@article{Simonyan2014VeryDC,
title={Very Deep Convolutional Networks for Large-Scale Image Recognition},
author={Karen Simonyan and Andrew Zisserman},
journal={CoRR},
year={2014},
volume={abs/1409.1556}
}
```
|
osiria/deberta-italian-question-answering | osiria | "2023-12-10T20:03:06Z" | 1,472 | 5 | transformers | [
"transformers",
"pytorch",
"safetensors",
"deberta-v2",
"question-answering",
"it",
"dataset:squad_it",
"arxiv:2111.09543",
"license:mit",
"model-index",
"endpoints_compatible",
"region:us"
] | question-answering | "2023-06-01T22:10:12Z" | ---
license: mit
language:
- it
datasets:
- squad_it
widget:
- text: Quale libro fu scritto da Alessandro Manzoni?
context: Alessandro Manzoni pubblicò la prima versione dei Promessi Sposi nel 1827
- text: In quali competizioni gareggia la Ferrari?
context: La Scuderia Ferrari Ú una squadra corse italiana di Formula 1 con sede a Maranello
- text: Quale sport Ú riferito alla Serie A?
context: Il campionato di Serie A Ú la massima divisione professionistica del campionato italiano di calcio maschile
model-index:
- name: osiria/deberta-italian-question-answering
results:
- task:
type: question-answering
name: Question Answering
dataset:
name: squad_it
type: squad_it
metrics:
- type: exact-match
value: 0.7004
name: Exact Match
- type: f1
value: 0.8097
name: F1
pipeline_tag: question-answering
---
--------------------------------------------------------------------------------------------------
<body>
<span class="vertical-text" style="background-color:lightgreen;border-radius: 3px;padding: 3px;">â</span>
<br>
<span class="vertical-text" style="background-color:orange;border-radius: 3px;padding: 3px;">ââââTask: Question Answering</span>
<br>
<span class="vertical-text" style="background-color:lightblue;border-radius: 3px;padding: 3px;">ââââModel: DeBERTa</span>
<br>
<span class="vertical-text" style="background-color:tomato;border-radius: 3px;padding: 3px;">ââââLang: IT</span>
<br>
<span class="vertical-text" style="background-color:lightgrey;border-radius: 3px;padding: 3px;">ââ</span>
<br>
<span class="vertical-text" style="background-color:#CF9FFF;border-radius: 3px;padding: 3px;">â</span>
</body>
--------------------------------------------------------------------------------------------------
<h3>Model description</h3>
This is a <b>DeBERTa</b> <b>[1]</b> model for the <b>Italian</b> language, fine-tuned for <b>Extractive Question Answering</b> on the [SQuAD-IT](https://huggingface.co/datasets/squad_it) dataset <b>[2]</b>.
The model is trained with an enhanced procedure that delivers top-level performance and reliability. The latest upgrade, code-name <b>LITEQA</b>, offers increased robustness and maintains optimal results even in uncased settings.
<h3>Training and Performances</h3>
The model is trained to perform question answering, given a context and a question (under the assumption that the context contains the answer to the question). It has been fine-tuned for Extractive Question Answering, using the SQuAD-IT dataset, for 2 epochs with a linearly decaying learning rate starting from 3e-5, maximum sequence length of 384 and document stride of 128.
<br>The dataset includes 54.159 training instances and 7.609 test instances
<b>update: version 2.0</b>
The 2.0 version further improves the performances by exploiting a 2-phases fine-tuning strategy: the model is first fine-tuned on the English SQuAD v2 (1 epoch, 20% warmup ratio, and max learning rate of 3e-5) then further fine-tuned on the Italian SQuAD (2 epochs, no warmup, initial learning rate of 3e-5)
In order to maximize the benefits of the multilingual procedure, [mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) is used as a pre-trained model. When the double fine-tuning is completed, the embedding layer is then compressed as in [deberta-base-italian](https://huggingface.co/osiria/deberta-base-italian) to obtain a mono-lingual model size
The performances on the test set are reported in the following table:
(<b>version 2.0</b> performances)
<br>
<b>Cased setting:</b>
| EM | F1 |
| ------ | ------ |
| 70.04 | 80.97 |
<b>Uncased setting:</b>
| EM | F1 |
| ------ | ------ |
| 68.55 | 80.11 |
Testing notebook: https://huggingface.co/osiria/deberta-italian-question-answering/blob/main/osiria_deberta_italian_qa_evaluation.ipynb
<b>update: version 3.0 (LITEQA)</b>
The 3.0 version, with the nickname LITEQA, further improves the performances by exploiting a 3-phases fine-tuning strategy: the model is first fine-tuned on the English SQuAD v2 (1 epoch, 20% warmup ratio, and max learning rate of 3e-5) then further fine-tuned on the Italian SQuAD (2 epochs, no warmup, initial learning rate of 3e-5) and lastly fine-tuned on the lowercase Italian SQuAD (1 epoch, no warmup, initial learning rate of 3e-5).
This helps making the model generally more robust, but particularly in uncased settings.
The 3.0 version can be downloaded from the <b>liteqa</b> branch of this repo.
The performances on the test set are reported in the following table:
(<b>version 3.0</b> performances)
<br>
<b>Cased setting:</b>
| EM | F1 |
| ------ | ------ |
| 70.19 | 81.01 |
<b>Uncased setting:</b>
| EM | F1 |
| ------ | ------ |
| 69.60 | 80.74 |
Testing notebook: https://huggingface.co/osiria/deberta-italian-question-answering/blob/liteqa/osiria_liteqa_evaluation.ipynb
<h3>Quick usage</h3>
In order to get the best possible outputs from the model, it is recommended to use the following pipeline
```python
from transformers import DebertaV2TokenizerFast, DebertaV2ForQuestionAnswering
import re
import string
from transformers.pipelines import QuestionAnsweringPipeline
tokenizer = DebertaV2TokenizerFast.from_pretrained("osiria/deberta-italian-question-answering")
model = DebertaV2ForQuestionAnswering.from_pretrained("osiria/deberta-italian-question-answering")
class OsiriaQA(QuestionAnsweringPipeline):
def __init__(self, punctuation = ',;.:!?()[\]{}', **kwargs):
QuestionAnsweringPipeline.__init__(self, **kwargs)
self.post_regex_left = "^[\s" + punctuation + "]+"
self.post_regex_right = "[\s" + punctuation + "]+$"
def postprocess(self, output):
output = QuestionAnsweringPipeline.postprocess(self, model_outputs=output)
output_length = len(output["answer"])
output["answer"] = re.sub(self.post_regex_left, "", output["answer"])
output["start"] = output["start"] + (output_length - len(output["answer"]))
output_length = len(output["answer"])
output["answer"] = re.sub(self.post_regex_right, "", output["answer"])
output["end"] = output["end"] - (output_length - len(output["answer"]))
return output
pipeline_qa = OsiriaQA(model = model, tokenizer = tokenizer)
pipeline_qa(context = "Alessandro Manzoni Ú nato a Milano nel 1785",
question = "Dove Ú nato Manzoni?")
# {'score': 0.9899800419807434, 'start': 28, 'end': 34, 'answer': 'Milano'}
```
You can also try the model online using this web app: https://huggingface.co/spaces/osiria/deberta-italian-question-answering
<h3>References</h3>
[1] https://arxiv.org/abs/2111.09543
[2] https://link.springer.com/chapter/10.1007/978-3-030-03840-3_29
<h3>Limitations</h3>
This model was trained on the English SQuAD v2 and on SQuAD-IT, which is mainly a machine translated version of the original SQuAD v1.1. This means that the quality of the training set is limited by the machine translation.
Moreover, the model is meant to answer questions under the assumption that the required information is actually contained in the given context (which is the underlying assumption of SQuAD v1.1).
If the assumption is violated, the model will try to return an answer in any case, which is going to be incorrect.
<h3>License</h3>
The model is released under <b>MIT</b> license |
hanzohazashi1/medical_summarizer | hanzohazashi1 | "2024-05-27T10:08:56Z" | 1,472 | 0 | null | [
"gguf",
"dataset:hanzohazashi1/med_convo",
"license:llama3",
"region:us"
] | null | "2024-05-27T09:52:34Z" | ---
license: llama3
datasets:
- hanzohazashi1/med_convo
--- |
TurkuNLP/bert-base-finnish-uncased-v1 | TurkuNLP | "2024-02-20T11:56:25Z" | 1,471 | 0 | transformers | [
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"fi",
"arxiv:1912.07076",
"arxiv:1908.04212",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | "2022-03-02T23:29:05Z" | ---
language: fi
---
## Quickstart
**Release 1.0** (November 25, 2019)
Download the models here:
* Cased Finnish BERT Base: [bert-base-finnish-cased-v1.zip](http://dl.turkunlp.org/finbert/bert-base-finnish-cased-v1.zip)
* Uncased Finnish BERT Base: [bert-base-finnish-uncased-v1.zip](http://dl.turkunlp.org/finbert/bert-base-finnish-uncased-v1.zip)
We generally recommend the use of the cased model.
Paper presenting Finnish BERT: [arXiv:1912.07076](https://arxiv.org/abs/1912.07076)
## What's this?
A version of Google's [BERT](https://github.com/google-research/bert) deep transfer learning model for Finnish. The model can be fine-tuned to achieve state-of-the-art results for various Finnish natural language processing tasks.
FinBERT features a custom 50,000 wordpiece vocabulary that has much better coverage of Finnish words than e.g. the previously released [multilingual BERT](https://github.com/google-research/bert/blob/master/multilingual.md) models from Google:
| Vocabulary | Example |
|------------|---------|
| FinBERT | Suomessa vaihtuu kesÀn aikana sekÀ pÀÀministeri ettÀ valtiovarain ##ministeri . |
| Multilingual BERT | Suomessa vai ##htuu kes ##Àn aikana sekÀ p ##ÀÀ ##minister ##i ettÀ valt ##io ##vara ##in ##minister ##i . |
FinBERT has been pre-trained for 1 million steps on over 3 billion tokens (24B characters) of Finnish text drawn from news, online discussion, and internet crawls. By contrast, Multilingual BERT was trained on Wikipedia texts, where the Finnish Wikipedia text is approximately 3% of the amount used to train FinBERT.
These features allow FinBERT to outperform not only Multilingual BERT but also all previously proposed models when fine-tuned for Finnish natural language processing tasks.
## Results
### Document classification
FinBERT outperforms multilingual BERT (M-BERT) on document classification over a range of training set sizes on the Yle news (left) and Ylilauta online discussion (right) corpora. (Baseline classification performance with [FastText](https://fasttext.cc/) included for reference.)
[[code](https://github.com/spyysalo/finbert-text-classification)][[Yle data](https://github.com/spyysalo/yle-corpus)] [[Ylilauta data](https://github.com/spyysalo/ylilauta-corpus)]
### Named Entity Recognition
Evaluation on FiNER corpus ([Ruokolainen et al 2019](https://arxiv.org/abs/1908.04212))
| Model | Accuracy |
|--------------------|----------|
| **FinBERT** | **92.40%** |
| Multilingual BERT | 90.29% |
| [FiNER-tagger](https://github.com/Traubert/FiNer-rules) (rule-based) | 86.82% |
(FiNER tagger results from [Ruokolainen et al. 2019](https://arxiv.org/pdf/1908.04212.pdf))
[[code](https://github.com/jouniluoma/keras-bert-ner)][[data](https://github.com/mpsilfve/finer-data)]
### Part of speech tagging
Evaluation on three Finnish corpora annotated with [Universal Dependencies](https://universaldependencies.org/) part-of-speech tags: the Turku Dependency Treebank (TDT), FinnTreeBank (FTB), and Parallel UD treebank (PUD)
| Model | TDT | FTB | PUD |
|-------------------|-------------|-------------|-------------|
| **FinBERT** | **98.23%** | **98.39%** | **98.08%** |
| Multilingual BERT | 96.97% | 95.87% | 97.58% |
[[code](https://github.com/spyysalo/bert-pos)][[data](http://hdl.handle.net/11234/1-2837)]
## Use with PyTorch
If you want to use the model with the huggingface/transformers library, follow the steps in [huggingface_transformers.md](https://github.com/TurkuNLP/FinBERT/blob/master/huggingface_transformers.md)
## Previous releases
### Release 0.2
**October 24, 2019** Beta version of the BERT base uncased model trained from scratch on a corpus of Finnish news, online discussions, and crawled data.
Download the model here: [bert-base-finnish-uncased.zip](http://dl.turkunlp.org/finbert/bert-base-finnish-uncased.zip)
### Release 0.1
**September 30, 2019** We release a beta version of the BERT base cased model trained from scratch on a corpus of Finnish news, online discussions, and crawled data.
Download the model here: [bert-base-finnish-cased.zip](http://dl.turkunlp.org/finbert/bert-base-finnish-cased.zip)
|
knkarthick/MEETING-SUMMARY-BART-LARGE-XSUM-SAMSUM-DIALOGSUM-AMI | knkarthick | "2023-10-03T10:59:56Z" | 1,471 | 14 | transformers | [
"transformers",
"pytorch",
"tf",
"safetensors",
"bart",
"text2text-generation",
"seq2seq",
"summarization",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | summarization | "2022-03-02T23:29:05Z" | ---
language: en
tags:
- bart
- seq2seq
- summarization
license: apache-2.0
datasets:
- cnndaily/newyorkdaily/xsum/samsum/dialogsum/AMI
metrics:
- rouge
widget:
- text: |-
Hi, I'm David and I'm supposed to be an industrial designer. Um, I just got the project announcement about what the project is. Designing a remote control. That's about it, didn't get anything else. Did you get the same thing? Cool. There's too much gear. Okay. Can't draw. Um. Yeah. Um, well anyway, I don't know, it's just the first animal I can think off the top of my head. Um. Yes. Big reason is 'cause I'm allergic to most animals. Allergic to animal fur, so um fish was a natural choice. Um, yeah, and I kind of like whales. They come in and go eat everything in sight. And they're quite harmless and mild and interesting. Tail's a bit big, I think. It's an after dinner dog then. Hmm. It does make sense from maybe the design point of view 'cause you have more complicated characters like European languages, then you need more buttons. So, possibly. Hmm. Yeah. And you keep losing them. Finding them is really a pain, you know. I mean it's usually quite small, or when you want it right, it slipped behind the couch or it's kicked under the table. You know. Yep. Mm-hmm. I think one factor would be production cost. Because there's a cap there, so um depends on how much you can cram into that price. Um. I think that that's the main factor. Cool.
Okay. Right. Um well this is the kick-off meeting for our our project. Um and um this is just what we're gonna be doing over the next twenty five minutes. Um so first of all, just to kind of make sure that we all know each other, I'm Laura and I'm the project manager. Do you want to introduce yourself again? Okay. Great. Okay. Um so we're designing a new remote control and um Oh I have to record who's here actually. So that's David, Andrew and Craig, isn't it? And you all arrived on time. Um yeah so des uh design a new remote control. Um, as you can see it's supposed to be original, trendy and user friendly. Um so that's kind of our our brief, as it were. Um and so there are three different stages to the design. Um I'm not really sure what what you guys have already received um in your emails. What did you get? Mm-hmm. Is that what everybody got? Okay. Um. So we're gonna have like individual work and then a meeting about it. And repeat that process three times. Um and at this point we get try out the whiteboard over there. Um. So uh you get to draw your favourite animal and sum up your favourite characteristics of it. So who would like to go first? Very good. Mm-hmm. Yeah. Yeah. Right. Lovely. Right. You can take as long over this as you like, because we haven't got an awful lot to discuss. Ok oh we do we do. Don't feel like you're in a rush, anyway. Ach why not We might have to get you up again then. I don't know what mine is. I'm gonna have to think on the spot now. Is that a whale? Ah. Okay. God, I still don't know what I'm gonna write about. Um. I was gonna choose a dog as well. But I'll just draw a different kind of dog. M my favourite animal is my own dog at home. Um That doesn't really look like him, actually. He looks more like a pig, actually. Ah well. Do you? Oh that's very good of you. Uh. Um he's a mixture of uh various things. Um and what do I like about him, um That's just to suggest that his tail wags. Um he's very friendly and cheery and always pleased to see you, and very kind of affectionate and um uh and he's quite quite wee as well so you know he can doesn't take up too much space. Um and uh And he does a funny thing where he chases his tail as well, which is quite amusing, so It is. I think it is. He only does it after he's had his dinner and um he'll just all of a sudden just get up and start chasing his tail 'round the living room. Yeah, so uh Yeah, maybe. Maybe. Right, um where did you find this? Just down here? Yeah. Okay. Um what are we doing next? Uh um. Okay, uh we now need to discuss the project finance. Um so according to the brief um we're gonna be selling this remote control for twenty five Euro, um and we're aiming to make fifty million Euro. Um so we're gonna be selling this on an international scale. And uh we don't want it to cost any more than uh twelve fifty Euros, so fifty percent of the selling price. Sure. All together. Um I dunno. I imagine That's a good question. I imagine it probably is our sale actually because it's probably up to the the um the retailer to uh sell it for whatever price they want. Um. But I I don't know, I mean do you think the fact that it's going to be sold internationally will have a bearing on how we design it at all? Think it will? Um. Hmm. Oh yeah, regions and stuff, yeah. Yeah. Okay. Yeah. Well for a remote control, do you think that will be I suppose it's depends on how complicated our remote control is. Yeah, yeah. Okay. What, just like in terms of like the wealth of the country? Like how much money people have to spend on things like? Aye, I see what you mean, yeah. Marketing. Good marketing thoughts. Oh gosh, I should be writing all this down. Um. Mm. Yeah. Yeah, yeah. Like how much does, you know, a remote control cost. Well twenty five Euro, I mean that's um that's about like eighteen pounds or something, isn't it? Or no, is it as much as that? Sixteen seventeen eighteen pounds. Um, I dunno, I've never bought a remote control, so I don't know how how good a remote control that would get you. Um. But yeah, I suppose it has to look kind of cool and gimmicky. Um right, okay. Let me just scoot on ahead here. Okay. Um well d Does anybody have anything to add to uh to the finance issue at all? Thin No, actually. That would be useful, though, wouldn't it, if you knew like what your money would get you now. Mm-hmm. Yeah, yeah. Oh. Five minutes to end of meeting. Oh, okay. We're a bit behind. Yeah. Right, so do you think that should be like a main design aim of our remote control d you know, do your your satellite and your regular telly and your V_C_R_ and everything? Mm-hmm. Yeah. Or even like, you know, notes about um what you wanna watch. Like you might put in there oh I want to watch such and such and look a Oh that's a good idea. So extra functionalities. Mm-hmm. Hmm. Um okay, uh I'd wel we're gonna have to wrap up pretty quickly in the next couple of minutes. Um I'll just check we've nothing else. Okay. Um so anything else anybody wants to add about what they don't like about remote controls they've used, what they would really like to be part of this new one at all? You keep losing them. Okay. Yeah. W You get those ones where you can, if you like, whistle or make a really high pitched noise they beep. There I mean is that something we'd want to include, do you think? Dunno. Okay maybe. My goodness. Still feels quite primitive. Maybe like a touch screen or something? Okay. Uh-huh, okay. Well I guess that's up to our industrial designer. It looks better. Yeah. Okay. Okay. Right, well um so just to wrap up, the next meeting's gonna be in thirty minutes. So that's about um about ten to twelve by my watch. Um so inbetween now and then, um as the industrial designer, you're gonna be working on you know the actual working design of it so y you know what you're doing there. Um for user interface, technical functions, I guess that's you know like what we've been talking about, what it'll actually do. Um and uh marketing executive, you'll be just thinking about what it actually what, you know, what requirements it has to has to fulfil and you'll all get instructions emailed to you, I guess. Um. Yeah, so it's th the functional design stage is next, I guess. And uh and that's the end of the meeting. So I got that little message a lot sooner than I thought I would, so Mm-hmm. Uh-huh, yeah. Th Okay, well just very quickly 'cause this we're supposed to finish now. Um I guess that's up to us, I mean you probably want some kind of unique selling point of it, so um, you know Yeah. Mm-hmm. Yeah. Okay. Right, okay, we'll that's that's the end of the meeting, then. Um. So, uh thank you all for coming.
Um I'm Craig and I'm User Interface. Yeah. Well, my favourite animal would be a monkey. Then they're small cute and furry, and uh when planet of the apes becomes real, I'm gonna be up there with them. Yeah. I know um My parents went out and bought um remote controls because um they got fed up of having four or five different remote controls for each things the house. So um for them it was just how many devices control. Uh.
Mm-hmm. Great. And I'm Andrew and I'm uh our marketing expert. Mm-hmm. Mm-hmm. Yeah, that's that's it. Yeah. I will go. That's fine. Alright. So This one here, right? Okay. Very nice. Alright. My favourite animal is like A beagle. Um charac favourite characteristics of it? Is that right? Uh, right, well basically um high priority for any animal for me is that they be willing to take a lot of physical affection from their family. And, yeah that they have lots of personality and uh be fit and in robust good health. So this is blue. Blue beagle. My family's beagle. I coulda told you a whole lot more about beagles. Boy, let me tell you. Impressionist. Alright. Mm. Superb sketch, by the way. Yep. I see a dog in there. Yep. Now I see a rooster. What kind is it? Is he aware that th it's his own cha tail he's chasing? Hmm. Probably when he was little he got lots of attention for doing it and has forever been conditioned. 'Kay. Um, can we just go over that again? Uh, so bas at twel Alright, yeah. Okay. So cost like production cost is twelve fifty, but selling price is is that wholesale or retail? Like on the shelf. Our sale our sale anyway. Yeah, okay okay. Okay. Mm-hmm. Alright. Yes. Mm-hmm. Mm-hmm. Well right away I'm wondering if there's um th th uh, like with D_V_D_ players, if there are zones. Um f frequencies or something um as well as uh characters, um different uh keypad styles and s symbols. Um. I don't know. Yeah. Yeah. Yeah. And then a and then al the other thing international is on top of the price. I'm thinking the price might might appeal to a certain market in one region, whereas in another it'll be different, so Just a chara just a characteristic of the Just Or just like, basic product podi positioning, the twenty five Euro remote control might be a big hit in London, might not be such a big hit in Greece, who knows, something like that, yeah. Yep. Right away I'm making some kind of assumptions about what what information we're given here, thinking, 'kay trendy probably means something other than just basic, something other than just standard. Um so I'm wondering right away, is selling twenty five Euros, is that sort of the thi is this gonna to be like the premium product kinda thing or Uh-huh. Mm-hmm. Yep. Yeah, I'd say so, yeah. No. Yeah, yeah. Mm-hmm. Do we have any other background information on like how that compares to other other Yeah. Mm-hmm. Yeah, interesting thing about discussing um production of a remote control for me is that l as you point out, I just don't think of remote controls as somethin something people consciously assess in their purchasing habits. It's just like getting shoelaces with shoes or something. It just comes along. Do you know what I mean? Like so sort of like how do you I I mean one one way of looking at it would be, well the people producing television sets, maybe they have to buy remote controls. Or another way is maybe people who have T_V_ sets are really fed up with their remote control and they really want a better one or something. But Right. Right. Okay so Right, so in function one of the priorities might be to combine as many uses I think so. Yeah, yeah. Yeah. Well like um, maybe what we could use is a sort of like a example of a successful other piece technology is palm palm pilots. They're gone from being just like little sort of scribble boards to cameras, M_P_ three players, telephones, everything, agenda. So, like, I wonder if we might add something new to the to the remote control market, such as the lighting in your house, or um Yeah, yeah. An Yeah. Like, p personally for me, at home I've I've combined the um the audio video of my television set and my D_V_D_ player and my C_D_ player. So they w all work actually function together but I have different remote controls for each of them. So it's sort of ironic that that then they're in there um you know, the sound and everything it's just one system. But each one's got its own little part. Mm. Mm. Mm. Mm-hmm. Mm-hmm. Yeah. Yeah. That's just really good id Yep. Uh, sure. I remember when the first remote control my my family had was on a cable. Actually had a cable between it and the T_V_ and big like buttons that sort of like, like on a blender or something. And um, you know, when I think about what they are now, it's better, but actually it's still kind of, I dunno, like a massive junky thing on the table. Maybe we could think about how, could be more, you know, streamlined. S Something like that, yeah. Or whatever would be technologically reasonable. 'Cause it could b it could it could be that f it could be that functionally that doesn't make it any better, but that just the appeal of of not having You know, these days there's a r pe things in people's homes are becoming more and more like chic, you know. Um, nicer materials and might be be worth exploring anyway. Okay. Um. Before we wrap up, just to make sure we're all on the same page here, um, do we We were given sort of an example of a coffee machine or something, right? Well, um are we at ma right now on the assumption that our television remote control may have features which go beyond the television? Or are we keeping sort of like a a design commitment to television features? I I don't know. Yep. Yeah, sure. Okay. Okay, yeah. Okay. Okay. Okay. Alright.
model-index:
- name: bart-large-meeting-summary-xsum-samsum-dialogsum-AMI
results:
- task:
name: Abstractive Text Summarization
type: abstractive-text-summarization
dataset:
name: "cnndaily/newyorkdaily/xsum/samsum/dialogsum/AMI Meeting Corpus"
type: cnndaily/newyorkdaily/xsum/samsum/dialogsum/AMI Meeting Corpus
metrics:
- name: Validation ROGUE-1
type: rouge-1
value: NA
- name: Validation ROGUE-2
type: rouge-2
value: NA
- name: Validation ROGUE-L
type: rouge-L
value: NA
- name: Validation ROGUE-Lsum
type: rouge-Lsum
value: NA
- name: Test ROGUE-1
type: rouge-1
value: NA
- name: Test ROGUE-2
type: rouge-2
value: NA
- name: Test ROGUE-L
type: rouge-L
value: NA
- name: Test ROGUE-Lsum
type: rouge-Lsum
value: NA
---
Model obtained by Fine Tuning 'facebook/bart-large-xsum'
## Usage
# Example 1
```python
from transformers import pipeline
summarizer = pipeline("summarization", model="knkarthick/MEETING-SUMMARY-BART-LARGE-XSUM-SAMSUM-DIALOGSUM-AMI")
text = '''The tower is 324 metres (1,063 ft) tall, about the same height as an 81-storey building, and the tallest structure in Paris. Its base is square, measuring 125 metres (410 ft) on each side. During its construction, the Eiffel Tower surpassed the Washington Monument to become the tallest man-made structure in the world, a title it held for 41 years until the Chrysler Building in New York City was finished in 1930. It was the first structure to reach a height of 300 metres. Due to the addition of a broadcasting aerial at the top of the tower in 1957, it is now taller than the Chrysler Building by 5.2 metres (17 ft). Excluding transmitters, the Eiffel Tower is the second tallest free-standing structure in France after the Millau Viaduct.
'''
summarizer(text)
```
# Example 2
```python
from transformers import pipeline
summarizer = pipeline("summarization", model="knkarthick/MEETING-SUMMARY-BART-LARGE-XSUM-SAMSUM-DIALOGSUM-AMI")
text = '''Bangalore is the capital and the largest city of the Indian state of Karnataka. It has a population of more than 8 million and a metropolitan population of around 11 million, making it the third most populous city and fifth most populous urban agglomeration in India. Located in southern India on the Deccan Plateau, at a height of over 900 m (3,000 ft) above sea level, Bangalore is known for its pleasant climate throughout the year. Its elevation is the highest among the major cities of India.The city's history dates back to around 890 CE, in a stone inscription found at the Nageshwara Temple in Begur, Bangalore. The Begur inscription is written in Halegannada (ancient Kannada), mentions 'Bengaluru Kalaga' (battle of Bengaluru). It was a significant turning point in the history of Bangalore as it bears the earliest reference to the name 'Bengaluru'. In 1537 CE, Kempé GowdÄ â a feudal ruler under the Vijayanagara Empire â established a mud fort considered to be the foundation of modern Bangalore and its oldest areas, or petes, which exist to the present day.
After the fall of Vijayanagar empire in 16th century, the Mughals sold Bangalore to Chikkadevaraja Wodeyar (1673â1704), the then ruler of the Kingdom of Mysore for three lakh rupees. When Haider Ali seized control of the Kingdom of Mysore, the administration of Bangalore passed into his hands.
The city was captured by the British East India Company after victory in the Fourth Anglo-Mysore War (1799), who returned administrative control of the city to the Maharaja of Mysore. The old city developed in the dominions of the Maharaja of Mysore and was made capital of the Princely State of Mysore, which existed as a nominally sovereign entity of the British Raj. In 1809, the British shifted their cantonment to Bangalore, outside the old city, and a town grew up around it, which was governed as part of British India. Following India's independence in 1947, Bangalore became the capital of Mysore State, and remained capital when the new Indian state of Karnataka was formed in 1956. The two urban settlements of Bangalore â city and cantonment â which had developed as independent entities merged into a single urban centre in 1949. The existing Kannada name, BengalÅ«ru, was declared the official name of the city in 2006.
Bangalore is widely regarded as the "Silicon Valley of India" (or "IT capital of India") because of its role as the nation's leading information technology (IT) exporter. Indian technological organisations are headquartered in the city. A demographically diverse city, Bangalore is the second fastest-growing major metropolis in India. Recent estimates of the metro economy of its urban area have ranked Bangalore either the fourth- or fifth-most productive metro area of India. As of 2017, Bangalore was home to 7,700 millionaires and 8 billionaires with a total wealth of $320 billion. It is home to many educational and research institutions. Numerous state-owned aerospace and defence organisations are located in the city. The city also houses the Kannada film industry. It was ranked the most liveable Indian city with a population of over a million under the Ease of Living Index 2020.
'''
summarizer(text)
```
# Example 3
```python
from transformers import pipeline
summarizer = pipeline("summarization", model="knkarthick/MEETING-SUMMARY-BART-LARGE-XSUM-SAMSUM-DIALOGSUM-AMI")
text = '''Hi, I'm David and I'm supposed to be an industrial designer. Um, I just got the project announcement about what the project is. Designing a remote control. That's about it, didn't get anything else. Did you get the same thing? Cool. There's too much gear. Okay. Can't draw. Um. Yeah. Um, well anyway, I don't know, it's just the first animal I can think off the top of my head. Um. Yes. Big reason is 'cause I'm allergic to most animals. Allergic to animal fur, so um fish was a natural choice. Um, yeah, and I kind of like whales. They come in and go eat everything in sight. And they're quite harmless and mild and interesting. Tail's a bit big, I think. It's an after dinner dog then. Hmm. It does make sense from maybe the design point of view 'cause you have more complicated characters like European languages, then you need more buttons. So, possibly. Hmm. Yeah. And you keep losing them. Finding them is really a pain, you know. I mean it's usually quite small, or when you want it right, it slipped behind the couch or it's kicked under the table. You know. Yep. Mm-hmm. I think one factor would be production cost. Because there's a cap there, so um depends on how much you can cram into that price. Um. I think that that's the main factor. Cool.
Okay. Right. Um well this is the kick-off meeting for our our project. Um and um this is just what we're gonna be doing over the next twenty five minutes. Um so first of all, just to kind of make sure that we all know each other, I'm Laura and I'm the project manager. Do you want to introduce yourself again? Okay. Great. Okay. Um so we're designing a new remote control and um Oh I have to record who's here actually. So that's David, Andrew and Craig, isn't it? And you all arrived on time. Um yeah so des uh design a new remote control. Um, as you can see it's supposed to be original, trendy and user friendly. Um so that's kind of our our brief, as it were. Um and so there are three different stages to the design. Um I'm not really sure what what you guys have already received um in your emails. What did you get? Mm-hmm. Is that what everybody got? Okay. Um. So we're gonna have like individual work and then a meeting about it. And repeat that process three times. Um and at this point we get try out the whiteboard over there. Um. So uh you get to draw your favourite animal and sum up your favourite characteristics of it. So who would like to go first? Very good. Mm-hmm. Yeah. Yeah. Right. Lovely. Right. You can take as long over this as you like, because we haven't got an awful lot to discuss. Ok oh we do we do. Don't feel like you're in a rush, anyway. Ach why not We might have to get you up again then. I don't know what mine is. I'm gonna have to think on the spot now. Is that a whale? Ah. Okay. God, I still don't know what I'm gonna write about. Um. I was gonna choose a dog as well. But I'll just draw a different kind of dog. M my favourite animal is my own dog at home. Um That doesn't really look like him, actually. He looks more like a pig, actually. Ah well. Do you? Oh that's very good of you. Uh. Um he's a mixture of uh various things. Um and what do I like about him, um That's just to suggest that his tail wags. Um he's very friendly and cheery and always pleased to see you, and very kind of affectionate and um uh and he's quite quite wee as well so you know he can doesn't take up too much space. Um and uh And he does a funny thing where he chases his tail as well, which is quite amusing, so It is. I think it is. He only does it after he's had his dinner and um he'll just all of a sudden just get up and start chasing his tail 'round the living room. Yeah, so uh Yeah, maybe. Maybe. Right, um where did you find this? Just down here? Yeah. Okay. Um what are we doing next? Uh um. Okay, uh we now need to discuss the project finance. Um so according to the brief um we're gonna be selling this remote control for twenty five Euro, um and we're aiming to make fifty million Euro. Um so we're gonna be selling this on an international scale. And uh we don't want it to cost any more than uh twelve fifty Euros, so fifty percent of the selling price. Sure. All together. Um I dunno. I imagine That's a good question. I imagine it probably is our sale actually because it's probably up to the the um the retailer to uh sell it for whatever price they want. Um. But I I don't know, I mean do you think the fact that it's going to be sold internationally will have a bearing on how we design it at all? Think it will? Um. Hmm. Oh yeah, regions and stuff, yeah. Yeah. Okay. Yeah. Well for a remote control, do you think that will be I suppose it's depends on how complicated our remote control is. Yeah, yeah. Okay. What, just like in terms of like the wealth of the country? Like how much money people have to spend on things like? Aye, I see what you mean, yeah. Marketing. Good marketing thoughts. Oh gosh, I should be writing all this down. Um. Mm. Yeah. Yeah, yeah. Like how much does, you know, a remote control cost. Well twenty five Euro, I mean that's um that's about like eighteen pounds or something, isn't it? Or no, is it as much as that? Sixteen seventeen eighteen pounds. Um, I dunno, I've never bought a remote control, so I don't know how how good a remote control that would get you. Um. But yeah, I suppose it has to look kind of cool and gimmicky. Um right, okay. Let me just scoot on ahead here. Okay. Um well d Does anybody have anything to add to uh to the finance issue at all? Thin No, actually. That would be useful, though, wouldn't it, if you knew like what your money would get you now. Mm-hmm. Yeah, yeah. Oh. Five minutes to end of meeting. Oh, okay. We're a bit behind. Yeah. Right, so do you think that should be like a main design aim of our remote control d you know, do your your satellite and your regular telly and your V_C_R_ and everything? Mm-hmm. Yeah. Or even like, you know, notes about um what you wanna watch. Like you might put in there oh I want to watch such and such and look a Oh that's a good idea. So extra functionalities. Mm-hmm. Hmm. Um okay, uh I'd wel we're gonna have to wrap up pretty quickly in the next couple of minutes. Um I'll just check we've nothing else. Okay. Um so anything else anybody wants to add about what they don't like about remote controls they've used, what they would really like to be part of this new one at all? You keep losing them. Okay. Yeah. W You get those ones where you can, if you like, whistle or make a really high pitched noise they beep. There I mean is that something we'd want to include, do you think? Dunno. Okay maybe. My goodness. Still feels quite primitive. Maybe like a touch screen or something? Okay. Uh-huh, okay. Well I guess that's up to our industrial designer. It looks better. Yeah. Okay. Okay. Right, well um so just to wrap up, the next meeting's gonna be in thirty minutes. So that's about um about ten to twelve by my watch. Um so inbetween now and then, um as the industrial designer, you're gonna be working on you know the actual working design of it so y you know what you're doing there. Um for user interface, technical functions, I guess that's you know like what we've been talking about, what it'll actually do. Um and uh marketing executive, you'll be just thinking about what it actually what, you know, what requirements it has to has to fulfil and you'll all get instructions emailed to you, I guess. Um. Yeah, so it's th the functional design stage is next, I guess. And uh and that's the end of the meeting. So I got that little message a lot sooner than I thought I would, so Mm-hmm. Uh-huh, yeah. Th Okay, well just very quickly 'cause this we're supposed to finish now. Um I guess that's up to us, I mean you probably want some kind of unique selling point of it, so um, you know Yeah. Mm-hmm. Yeah. Okay. Right, okay, we'll that's that's the end of the meeting, then. Um. So, uh thank you all for coming.
Um I'm Craig and I'm User Interface. Yeah. Well, my favourite animal would be a monkey. Then they're small cute and furry, and uh when planet of the apes becomes real, I'm gonna be up there with them. Yeah. I know um My parents went out and bought um remote controls because um they got fed up of having four or five different remote controls for each things the house. So um for them it was just how many devices control. Uh.
Mm-hmm. Great. And I'm Andrew and I'm uh our marketing expert. Mm-hmm. Mm-hmm. Yeah, that's that's it. Yeah. I will go. That's fine. Alright. So This one here, right? Okay. Very nice. Alright. My favourite animal is like A beagle. Um charac favourite characteristics of it? Is that right? Uh, right, well basically um high priority for any animal for me is that they be willing to take a lot of physical affection from their family. And, yeah that they have lots of personality and uh be fit and in robust good health. So this is blue. Blue beagle. My family's beagle. I coulda told you a whole lot more about beagles. Boy, let me tell you. Impressionist. Alright. Mm. Superb sketch, by the way. Yep. I see a dog in there. Yep. Now I see a rooster. What kind is it? Is he aware that th it's his own cha tail he's chasing? Hmm. Probably when he was little he got lots of attention for doing it and has forever been conditioned. 'Kay. Um, can we just go over that again? Uh, so bas at twel Alright, yeah. Okay. So cost like production cost is twelve fifty, but selling price is is that wholesale or retail? Like on the shelf. Our sale our sale anyway. Yeah, okay okay. Okay. Mm-hmm. Alright. Yes. Mm-hmm. Mm-hmm. Well right away I'm wondering if there's um th th uh, like with D_V_D_ players, if there are zones. Um f frequencies or something um as well as uh characters, um different uh keypad styles and s symbols. Um. I don't know. Yeah. Yeah. Yeah. And then a and then al the other thing international is on top of the price. I'm thinking the price might might appeal to a certain market in one region, whereas in another it'll be different, so Just a chara just a characteristic of the Just Or just like, basic product podi positioning, the twenty five Euro remote control might be a big hit in London, might not be such a big hit in Greece, who knows, something like that, yeah. Yep. Right away I'm making some kind of assumptions about what what information we're given here, thinking, 'kay trendy probably means something other than just basic, something other than just standard. Um so I'm wondering right away, is selling twenty five Euros, is that sort of the thi is this gonna to be like the premium product kinda thing or Uh-huh. Mm-hmm. Yep. Yeah, I'd say so, yeah. No. Yeah, yeah. Mm-hmm. Do we have any other background information on like how that compares to other other Yeah. Mm-hmm. Yeah, interesting thing about discussing um production of a remote control for me is that l as you point out, I just don't think of remote controls as somethin something people consciously assess in their purchasing habits. It's just like getting shoelaces with shoes or something. It just comes along. Do you know what I mean? Like so sort of like how do you I I mean one one way of looking at it would be, well the people producing television sets, maybe they have to buy remote controls. Or another way is maybe people who have T_V_ sets are really fed up with their remote control and they really want a better one or something. But Right. Right. Okay so Right, so in function one of the priorities might be to combine as many uses I think so. Yeah, yeah. Yeah. Well like um, maybe what we could use is a sort of like a example of a successful other piece technology is palm palm pilots. They're gone from being just like little sort of scribble boards to cameras, M_P_ three players, telephones, everything, agenda. So, like, I wonder if we might add something new to the to the remote control market, such as the lighting in your house, or um Yeah, yeah. An Yeah. Like, p personally for me, at home I've I've combined the um the audio video of my television set and my D_V_D_ player and my C_D_ player. So they w all work actually function together but I have different remote controls for each of them. So it's sort of ironic that that then they're in there um you know, the sound and everything it's just one system. But each one's got its own little part. Mm. Mm. Mm. Mm-hmm. Mm-hmm. Yeah. Yeah. That's just really good id Yep. Uh, sure. I remember when the first remote control my my family had was on a cable. Actually had a cable between it and the T_V_ and big like buttons that sort of like, like on a blender or something. And um, you know, when I think about what they are now, it's better, but actually it's still kind of, I dunno, like a massive junky thing on the table. Maybe we could think about how, could be more, you know, streamlined. S Something like that, yeah. Or whatever would be technologically reasonable. 'Cause it could b it could it could be that f it could be that functionally that doesn't make it any better, but that just the appeal of of not having You know, these days there's a r pe things in people's homes are becoming more and more like chic, you know. Um, nicer materials and might be be worth exploring anyway. Okay. Um. Before we wrap up, just to make sure we're all on the same page here, um, do we We were given sort of an example of a coffee machine or something, right? Well, um are we at ma right now on the assumption that our television remote control may have features which go beyond the television? Or are we keeping sort of like a a design commitment to television features? I I don't know. Yep. Yeah, sure. Okay. Okay, yeah. Okay. Okay. Okay. Alright.
'''
summarizer(text)
```
# Example 4
```python
from transformers import pipeline
summarizer = pipeline("summarization", model="knkarthick/MEETING-SUMMARY-BART-LARGE-XSUM-SAMSUM-DIALOGSUM-AMI")
text = '''
Das : Hi and welcome to the a16z podcast. Iâm Das, and in this episode, I talk SaaS go-to-market with David Ulevitch and our newest enterprise general partner Kristina Shen. The first half of the podcast looks at how remote work impacts the SaaS go-to-market and what the smartest founders are doing to survive the current crisis. The second half covers pricing approaches and strategy, including how to think about free versus paid trials and navigating the transition to larger accounts. But we start with why itâs easier to move upmarket than down⊠and the advantage that gives a SaaS startup against incumbents.
David : If you have a cohort of customers that are paying you $10,000 a year for your product, youâre going to find a customer that self-selects and is willing to pay $100,000 a year. Once you get one of those, your organization will figure out how you sell to, how you satisfy and support, customers at that price point and that size. But itâs really hard for a company that sells up market to move down market, because theyâve already baked in all that expensive, heavy lifting sales motion. And so as you go down market with a lower price point, usually, you canât actually support it.
Das : Does that mean that itâs easier for a company to do this go-to-market if theyâre a new startup as opposed to if theyâre a pre-existing SaaS?
Kristina : Itâs culturally very, very hard to give a product away for free that youâre already charging for. It feels like youâre eating away at your own potential revenue when you do it. So most people who try it end up pulling back very quickly.
David : This is actually one of the key reasons why the bottoms up SaaS motion is just so competitive, and compelling, and so destructive against the traditional sales-driven test motion. If you have that great product and people are choosing to use it, itâs very hard for somebody with a sales-driven motion, and all the cost thatâs loaded into that, to be able to compete against it. There are so many markets where initially, we would look at companies and say, âOh, well, this couldnât possibly be bottoms up. It has to be sold to the CIO. It has to be sold to the CSO or the CFO.â But in almost every case weâve been wrong, and there has been a bottoms up motion. The canonical example is Slack. Itâs crazy that Slack is a bottoms up company, because youâre talking about corporate messaging, and how could you ever have a messaging solution that only a few people might be using, that only a team might be using? But now itâs just, âOh, yeah, some people started using it, and then more people started using it, and then everyone had Slack.â
Kristina : I think another classic example is Dropbox versus Box. Both started as bottoms up businesses, try before you buy. But Box quickly found, âHey, Iâd rather sell to IT.â And Dropbox said, âHey, weâve got a great freemium motion going.â And they catalyzed their business around referrals and giving away free storage and shared storage in a way that really helped drive their bottoms up business.
Das : Itâs a big leap to go from selling to smaller customers to larger customers. How have you seen SaaS companies know or get the timing right on that? Especially since it does seem like thatâs really related to scaling your sales force?
Kristina : Donât try to go from a 100-person company to a 20,000-person company. Start targeting early adopters, maybe theyâre late stage pre-IPO companies, then newly IPOâd companies. Starting in tech tends to be a little bit easier because they tend to be early adopters. Going vertical by vertical can be a great strategy as well. Targeting one customer who might be branded in that space, can help brand yourself in that category. And then all their competitors will also want your product if you do a good job. A lot of times people will dedicate a sales rep to each vertical, so that they become really, really knowledgeable in that space, and also build their own brand and reputation and know who are the right customers to target.
Das : So right now, youâve got a lot more people working remote. Does this move to remote work mean that on-premise software is dying? And is it accelerating the move to software as a service?
Kristina : This remote work and working from home is only going to catalyze more of the conversion from on-premise over to cloud and SaaS. In general, software spend declines 20% during an economic downturn. This happened in â08, this happened in â01. But when we look at the last downturn in â08, SaaS spend actually, for public companies, increased, on average, 10%, which means thereâs a 30% spread, which really shows us that there was a huge catalyst from people moving on-premise to SaaS.
David : And as people work remote, the ability to use SaaS tools is much easier than having to VPN back into your corporate network. Weâve been seeing that, inside sales teams have been doing larger and larger deals, essentially moving up market on the inside, without having to engage with field sales teams. In fact, a lot of the new SaaS companies today rather than building out a field team, they have a hybrid team, where people are working and closing deals on the inside and if they had to go out and meet with a customer, they would do that. But by and large, most of it was happening over the phone, over email, and over videoconferencing. And all the deals now, by definition, are gonna be done remote because people canât go visit their customers in person.
Das : So with bottoms up, did user behavior and buyer behavior change, so the go-to-market evolved? Or did the go-to-market evolve and then you saw user and buyer behavior change? Iâm curious with this move to remote work. Is that going to trigger more changes or has the go-to-market enabled that change in user behavior, even though we see that change coming because of a lot of forces outside of the market?
Kristina : I definitely think they are interrelated. But I do think it was a user change that catalyzed everything. We decided that we preferred better software, and we tried a couple products. We were able to purchase off our credit card. And then IT and procurement eventually said, âWow, everyoneâs buying these already, I might as well get a company license and a company deal so Iâm not paying as much.â While obviously software vendors had to offer the products that could be self-served, users started to realize they had the power, they wanted to use better software, they paid with their credit cards. And now software vendors are forced to change their go-to-market to actually suit that use case.
Das : If thatâs the case that when user behavior has changed, itâs tended to be the catalyzing force of bigger changes in the go-to-market, what are some of the changes you foresee for SaaS because the world has changed to this new reality of remote work and more distributed teams?
David : Weâre in a very uncertain economic environment right now. And a couple of things will become very clear over the next 3 to 9 to 15 months â youâre going to find out which SaaS products are absolutely essential to helping a business operate and run, and which ones were just nice to have and may not get renewed. I think on the customer, buying side, youâre very likely to see people push back on big annual commitments and prefer to go month-to-month where they can. Or youâll see more incentives from SaaS startups to offer discounts for annual contracts. Youâre going to see people that might sign an annual contract, but they may not want to pay upfront. They may prefer to meter the cash out ratably over the term of the contract. And as companies had empowered and allowed budget authority to be pushed down in organizations, youâre gonna see that budget authority get pulled back, more scrutiny on spending, and likely a lot of SaaS products not get renewed that turned out to not be essential.
Kristina : I think the smartest founders are making sure they have the runway to continue to exist. And theyâre doing that in a couple of ways. Theyâre preserving cash, and they are making sure that their existing customers are super, super happy, because retaining your customers is so important in this environment. And theyâre making sure that they have efficient or profitable customer acquisition. Donât spend valuable dollars acquiring customers. But acquire customers efficiently that will add to a great existing customer base.
Das : To go into pricing and packaging for SaaS for a moment, what are some of the different pricing approaches that you see SaaS companies taking?
Kristina : The old school way of doing SaaS go-to-market is bundle everything together, make the pricing super complex, so you donât actually understand what youâre paying for. Youâre forced to purchase it because you need one component of the product. New modern SaaS pricing is keep it simple, keep it tied to value, and make sure youâre solving one thing really, really well.
David : You want to make it easy for your customers to give you money. And if your customers donât understand your pricing, thatâs a huge red flag. Sometimes founders will try to over engineer their pricing model.
Kristina : We talk a lot about everything has to be 10X better than the alternatives. But itâs much easier to be 10X better when you solve one thing very, very well, and then have simple pricing around it. I think the most common that most people know about is PEPM or per employee per month, where youâre charging basically for every single seat. Another really common model is the freemium model. So, think about a Dropbox, or an Asana, or a Skype, where itâs trigger based. You try the product for free, but when you hit a certain amount of storage, or a certain amount of users, then it converts over to paid. And then you also have a time trial, where you get the full experience of the product for some limited time period. And then youâre asked if you want to continue using the product to pay. And then thereâs pay as go, and particularly, pay as you go as a usage model. So, Slack will say, âHey, if your users arenât actually using the product this month, we wonât actually charge you for it.â
David : The example that Kristina made about Slack and users, everybody understands what a user is, and if theyâre using the product, they pay for it, and if theyâre not using it, they donât pay for it. Thatâs a very friendly way to make it easy for your customers to give you money. If Slack came up with a pricing model that was like based on number of messages, or number of API integration calls, the customer would have no idea what that means.
Kristina : Thereâs also the consumption model. So Twilio only charges you for every SMS text or phone call that you make on the platform any given month. And so they make money or lose money as your usage goes. The pricing is very aligned to your productivity.
David : Generally, those are for products where the usage only goes in one direction. If you think of a company like Databricks, where theyâre charging for storage, or Amazonâs S3 service, it is very aligned with the customer, but it also strategically aligns with the business because they know the switching cost is very high, the churn is very low. And generally, in those businesses, youâre only going to store more data, so they can charge based on usage or volume of data.
Kristina : Recently, thereâs been a huge trend of payment as a revenue. Itâs particularly common in vertical markets where SaaS companies are adding payments as a revenue in addition to their employee or subscription revenue. If you look at Shopify, for example, more than 50% of their revenue is actually payment revenue. Theyâre making money every single time you purchase something off one of their shopping cart websites.
Das : When youâre working with a founder or a SaaS startup, how have you seen them find the right pricing model for their product, for their market?
Kristina : Step one is just talk to a lot of customers. Try to figure out what is the market pricing for possible alternatives or competitors, understand their pain points and their willingness to pay. And just throw a price out there, because you have to have a starting point in order to actually test and iterate. Particularly in the SMB, or the bottoms up business, you can test and iterate pretty quickly because you have so many data points.
David : I always tell founders, step one is to just go out there and talk to customers. Step two is just double your prices. I donât think thereâs ever been a great company with a great product thatâs fallen apart because their pricing was wrong. But a lot of SaaS startup founders really under price, and you donât want to find out two or three years later that you were 200% underpriced. A very common thing that SaaS companies do, theyâll have the basic package that either is free or low cost, that you can just sign up online for. Theyâll have a middle package where they share some pricing, and then theyâll have the enterprise package where you have to contact sales to find out more. And that way they donât actually have to show the pricing for that third package. And that gives the salespeople the flexibility to adjust pricing on a per deal basis.
Das : When youâre working with companies, why are they underpricing their products?
David : I think itâs psychological. People need to price on value, and they donât know how much value theyâre delivering relative to âOh, it only cost me $100 a month to provide this service, so I just need to charge $200.â But if it turns out youâre saving your customer $50,000 a year, then youâre wildly underpriced. You have to remember that SaaS is essentially a proxy for outsourced IT. Youâre spending money on a SaaS service to not pay to develop something internally, or to have to pay IT to support something thatâs more complex on-prem. Software is much cheaper than people, and so generally, the price point can be much higher.
Kristina : And the other thing is your value increases over time. Youâre delivering more features, more products, you understand the customer better. Itâs the beauty of the SaaS model and cloud model that you can iterate and push code immediately, and the customer immediately sees value. A lot of times people have the same price point from the first customer sold to three years later and the 200th customer. Quite frankly, youâve delivered so much value along the way that your price point should have gone up. The other thing Iâll say is a lot of people discount per seat pricing a lot as they move up market. We tend to tell people that the best validation of your product having great product market fit is your ability to hold your price point. So while there is some natural discounting on a per seat basis because people do deserve some volume discounting, I would say try to resist that as much as possible.
Das : Especially for a technical founder, itâs so tempting to get in there and fiddle with these knobs. How do you know when it is time to experiment with your pricing and packaging?
David : If youâre looking at your business and you see that you are doing more deals, and theyâre closing faster, you should raise your pricing. And you pay attention to how long it takes to close deals and whether the number of deals is staying consistent as you do that. And, at some point, youâre going to find out when youâre losing deals on price. I think a moment where companies have to plan ahead to avoid having to course correct is after they roll out massive pricing and packaging changes, which are pretty natural as companies move up market. But how they navigate that transition to larger accounts, and how they either bring along or move away from those smaller, earlier customers who got them to where they are, tends to be really important because they can get a lot of noise on Twitter, they can get a lot of blowback from their customers. So Zendesk is a company where they rolled out a major packaging change. And when they rolled it out, they hadnât planned on grandfathering in their early customers. They got a lot of pushback, and very quickly, they put out a blog post and said, âWe hear what youâre saying, we appreciate you building the business that weâve become today. We do need to have a package for the future. But all the people that have been customers so far will be grandfathered in for at least a period of time into the old model.â
Kristina : If you iterate pricing constantly, you donât really have this problem because your customers will be used to pricing changes. You normally pair them with new features, and it all kind of works out. But if you have to go through a big grandfather change, I tend to lean towards treating your early customers really, really well. They adopted when you werenât a big company yet. They probably co-built the product with you in many ways. And so, itâs great to get more dollars out of your customer base, but treat your early customers well.
Das : Are there any other failure modes that you see startups really falling into around pricing and packaging or any common mistakes that they make?
David : I think a lot of founders donât always map out the cost or model of their pricing and their product relative to their cost of actually doing sales and marketing and customer acquisition.
Kristina : Inside sales is so popular in Silicon Valley. When youâre selling more to an SMB or mid-market type customer, the expectation is that youâre educating and helping the prospective customer over the phone. And so, youâre not expected to be as high touch. But 5K is almost the minimum price point you need to sell to the SMB with an inside sales team in order to pay for the outbound costs and all the conversions, because there is typically a team that sits around the quota carrying rep. And so, price matching â how much your price point is compared to what your go-to-market motion is â matters a lot. Other big failure modes that I see, people guess the ramp time of a sales rep wrong. And ramp time really ties to the segment of customer youâre selling into. It tends be that if youâre selling into the enterprise, the ramp time for sales reps, because sales cycles are so long, tend to be much longer as well. They could be six months plus, could be a year. While if youâre selling more into SMB or mid-market, the ramp time to get a rep up and running can be much shorter, three to six months. Because the sales cycles are shorter, they just iterate much faster, and they ramp up much more quickly.
David : The other thing that people have to understand is that sales velocity is a really important component to figuring out how many reps you should be hiring, whether they should be inside reps or field reps. If it takes you 90 days to close a deal, that canât be a $5,000 a year deal, that has to be a $50,000 or even $150,000 a year deal.
Das : Kristina, I know youâve done a lot of work with metrics. So how do those play in?
Kristina : Probably the one way to sum it all together is how many months does it take to pay back customer acquisition cost. Very commonly within the SaaS world, we talk about a 12-month CAC payback. We typically want to see for every dollar you spend on sales and marketing, you get a dollar back within a year. That means you can tweak the inputs any way you want. Letâs say that doing paid acquisition is really effective for you. Then, you can spend proportionally more on paid acquisition and less on sales reps. Vice versa, if you have a great inbound engine, you actually can hire a lot more sales reps and spend more on sales headcount. With all formulas, itâs a guide rail, so if you have customers that retain really, really well, letâs say youâre selling to the enterprise, and youâve got a 90% or 95% annual retention rate, then your CAC payback could be between 12 and 24 months. But letâs say youâre selling to the SMB and churn is 2% or 3% monthly, which ends up being like 80% to 90% annual retention. Then, because your customer is less sticky, I would recommend looking at a CAC payback of 6 to 12 months.
Das : How should you think about doing a free trial versus a paid trial?
David : On the one hand, the bottoms up motion where people can try essentially a full version of a product before they buy it is extremely powerful. On the other hand, Iâve started to try to think about how I advise companies, when they are thinking about a free trial for something that might cost $100,000 or $200,000 a year? Do we do a paid pilot that has some sort of contractual obligation that if we meet then turns into a commercial engagement?
Kristina : I do think the beauty of the bottoms up business is that you can get people to try the entire experience of the product for free, and they fall in love with it, and a certain percentage will convert. And that works really, really well for products that can self-serve. When you start moving up market to more complex products, the challenge with trials is it takes work to actually implement the product, whether it be integrations, IT has to give access, etc. You lose that self-serve ability, which is so amazing in the trial. And so, I tend to be more in the camp of paid trials, if it costs you money to actually deploy the trial. And when youâre selling to bigger customers, they associate value when they have to pay. Once a customer has to pay you, then they feel a need to make the project successful and thus they will onboard, schedule things, give you data and access.
David : If you can get to a point where you get the customer to do that paid pilot, such that the only difference between a pilot and an actual customer is just the signing of a contract, thatâs very powerful. Now, that does force you to have a really good pre-sales motion to make sure that you can deliver on the promise youâve made your customers. When companies donât have a great product, and they paper over it with professional services and sales engineering and post-sales support, that paid pilot thing doesnât work because the experience isnât good enough. So, it really is incumbent on the SaaS company that does a paid pilot to make sure that they are able to deliver on that experience.
Kristina : And one emerging trend recently is people signing an annual contract with a one or three month out, as a replacement to the paid pilot. Because itâs the best of both worlds, the SaaS company thatâs selling the product gets a higher level of commitment. And the customer gets the optionality of opting out in the same way as a trial without any clawback. It really comes down to where procurement falls. Sometimes procurement is at the beginning of that decision, which makes it more like an annual contract. Sometimes procurement is at the one or three month opt-out period, which means the customer already has a great experience, loves the product, and it is an easier way to convert procurements to actually sign onâŠ
David : And that is a really good segue into renewals. I always tell founders, you might have this subscription business, but itâs not a recurring revenue business until the second year when the revenue actually recurs. I think you really have the first three months to get a customer up and running and happy. And if theyâre not, you then have about three months to fix it. And if all that works out, then the remaining six months of the contract can be focused on upsell and expansion.
Das : Awesome. Thank you, Kristina. Thank you, David.
Kristina : Thanks so much for having us. This was fun.
David : Yeah, a lot of fun, great topics, and our favorite thing to talk about.
'''
summarizer(text)
``` |
Aratako/Qwen1.5-MoE-2x7B | Aratako | "2024-03-25T00:01:54Z" | 1,471 | 1 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"mergekit",
"merge",
"moe",
"conversational",
"custom_code",
"en",
"base_model:Qwen/Qwen1.5-7B-Chat",
"base_model:abacusai/Liberated-Qwen1.5-7B",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2024-03-24T06:50:18Z" | ---
base_model:
- Qwen/Qwen1.5-7B-Chat
- abacusai/Liberated-Qwen1.5-7B
license: other
license_name: tongyi-qianwen
license_link: https://huggingface.co/Qwen/Qwen1.5-7B-Chat/blob/main/LICENSE
language:
- en
tags:
- mergekit
- merge
- moe
---
# Qwen1.5-MoE-2x7B
## Description
This model is created using MoE (Mixture of Experts) through mergekit based on [Qwen/Qwen1.5-7B-Chat](https://huggingface.co/Qwen/Qwen1.5-7B-Chat) and [abacusai/Liberated-Qwen1.5-7B](https://huggingface.co/abacusai/Liberated-Qwen1.5-7B) without further FT.
It utilizes a customized script for MoE via mergekit, which is available [here](https://github.com/Aratako/mergekit-qwen2).
Due to the structural modifications introduced by MoE, the use of this model requires [custom modeling file](https://huggingface.co/Aratako/Liberated-Qwen1.5-2x7B/blob/main/modeling_qwen2.py) and [custom configuration file](https://huggingface.co/Aratako/Liberated-Qwen1.5-2x7B/blob/main/configuration_qwen2.py).
When using the model, please place these files in the same folder as the model.
This model inherits the the [tongyi-qianwen license](https://huggingface.co/Qwen/Qwen1.5-7B-Chat/blob/main/LICENSE).
## Benchmark
The benchmark score of the [mt-bench](https://github.com/lm-sys/FastChat/tree/main/fastchat/llm_judge) for this model and the two base models are as follows:
**1-turn**
|Model|Size|Coding|Extraction|Humanities|Math|Reasoning|Roleplay|STEM|Writing|avg_score|
|---|---|---|---|---|---|---|---|---|---|---|
| Liberated-Qwen1.5-7B | 7B | 4.4 | **7.8** | 6.95 | 5.0 | 6.4 | 7.6 | 7.65 | 8.85 | 6.83125 |
| Qwen1.5-7B-Chat | 7B | 4.4 | 7.7 | **9.6** | **6.9** | 7.0 | **8.7** | 9.65 | 9.7 | 7.95625 |
| This model | 2x7B | **5.1** | 7.4 | 9.45 | 6.4 | **7.2** | 8.65 | **9.75** | **9.8** | **7.96875** |
**2-turn**
|Model|Size|Coding|Extraction|Humanities|Math|Reasoning|Roleplay|STEM|Writing|avg_score|
|---|---|---|---|---|---|---|---|---|---|---|
| Liberated-Qwen1.5-7B | 7B | 4.4 | 6.2 | 7.1 | 3.0 | **5.7** | 7.4 | 6.3 | 3.5 | 5.450 |
| Qwen1.5-7B-Chat | 7B | 4.5 | **8.0** | 9.9 | **4.9** | 5.0 | **8.9** | 9.4 | **8.4** | **7.375** |
| This model | 2x7B | **4.7** | 7.0 | **10.0** | 4.8 | 4.3 | 8.6 | **9.5** | 7.3 | 7.025 |
Although the benchmark scores have slightly deteriorated, it seems that this is due to the poor performance of the Liberated-Qwen1.5-7B model used in the merge on mt-bench. I think that doing MoE with models that have better performance or are fine-tuned for specific tasks can yield better results.
## Merge config
[mergekit_config.yml](./mergekit_moe_config.yml)
```yaml
base_model: ./Qwen1.5-7B-Chat
gate_mode: random
dtype: bfloat16
experts:
- source_model: ./Qwen1.5-7B-Chat
positive_prompts: []
- source_model: ./Liberated-Qwen1.5-7B
positive_prompts: []
tokenizer_source: model:./Qwen1.5-7B-Chat
```
## Gratitude
- Huge thanks to [Alibaba Cloud Qwen](https://www.alibabacloud.com/solutions/generative-ai/qwen) for training and publishing the weights of Qwen model
- Thank you to [abacusai](https://huggingface.co/abacusai) for publishing fine-tuned model from Qwen
- And huge thanks to [mlabonne](https://huggingface.co/mlabonne), as I customized modeling file using [phixtral](https://huggingface.co/mlabonne/phixtral-4x2_8) as a reference |
zhuipiaochen/LLM | zhuipiaochen | "2024-06-11T08:18:49Z" | 1,471 | 0 | null | [
"gguf",
"region:us"
] | null | "2024-06-03T14:19:31Z" | Entry not found |
abhinand/tamil-llama-7b-base-v0.1 | abhinand | "2024-01-25T03:18:06Z" | 1,470 | 9 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"ta",
"en",
"arxiv:2311.05845",
"license:llama2",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-11-08T03:55:05Z" | ---
language:
- ta
- en
license: llama2
---
# Tamil LLaMA 7B Base v0.1 [pre-trained]
Welcome to the inaugural release of the Tamil LLaMA 7B base model â an important step in advancing LLMs for the Tamil language. This model is ready for immediate inference and is also primed for further fine-tuning to cater to your specific NLP tasks.
To dive deep into the development and capabilities of this model, please read the [research paper](https://arxiv.org/abs/2311.05845) and the [introductory blog post (WIP)]() that outlines our journey and the model's potential impact.
> **Please Note:** This model, labeled as a foundational Tamil Language Model (LLM), is designed primarily for Causal Language Modeling (LM) purposes. In other words, if you are looking for an instruction following model in Tamil, you may find [abhinand/tamil-llama-7b-instruct-v0.1](https://huggingface.co/abhinand/tamil-llama-7b-instruct-v0.1) more suitable for your needs.
## Model description
The Tamil LLaMA models have been enhanced and tailored specifically with an extensive Tamil vocabulary of 16,000 tokens, building upon the foundation set by the original LLaMA-2.
- **Model type:** A 7B parameter model for Causal LM pre-trained on [CulturaX](https://huggingface.co/datasets/uonlp/CulturaX) dataset's Tamil subset.
- **Language(s):** Tamil and English
- **License:** GNU General Public License v3.0
- **Source Model:** [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf)
- **Training Precision:** `float16`
- **Code:** [GitHub](https://github.com/abhinand5/tamil-llama)
## Related Models
| Model | Type | Data | Base Model | # Params | Download Links |
|--------------------------|-----------------------------|-------------------|----------------------|------|------------------------------------------------------------------------|
| Tamil LLaMA 7B Base | Base model | 12GB | LLaMA 7B | 7B | [HF Hub](https://huggingface.co/abhinand/tamil-llama-7b-base-v0.1) |
| Tamil LLaMA 13B Base | Base model | 4GB | LLaMA 13B | 13B | [HF Hub](https://huggingface.co/abhinand/tamil-llama-13b-base-v0.1) |
| Tamil LLaMA 7B Instruct | Instruction following model | 145k instructions | Tamil LLaMA 7B Base | 7B | [HF Hub](https://huggingface.co/abhinand/tamil-llama-7b-instruct-v0.1) |
| Tamil LLaMA 13B Instruct | Instruction following model | 145k instructions | Tamil LLaMA 13B Base | 13B | [HF Hub](abhinand/tamil-llama-13b-instruct-v0.1) |
## Usage Note
It's important to note that the models have not undergone detoxification. Therefore, while they possess impressive linguistic capabilities, there is a possibility for them to generate content that could be deemed harmful or offensive. We urge users to exercise discretion and supervise the model's outputs closely, especially in public or sensitive applications.
## Meet the Developers
Get to know the creators behind this innovative model and follow their contributions to the field:
- [Abhinand Balachandran](https://www.linkedin.com/in/abhinand-05/)
## Citation
If you use this model or the Tamil-Llama dataset in your research, please cite:
```bibtex
@misc{balachandran2023tamilllama,
title={Tamil-Llama: A New Tamil Language Model Based on Llama 2},
author={Abhinand Balachandran},
year={2023},
eprint={2311.05845},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
We hope this model serves as a valuable tool in your NLP toolkit and look forward to seeing the advancements it will enable in the understanding and generation of the Tamil language. |
PassionFriend/5Fvedo8SsQXnfjUnymZqeJ9E68Hnu92Qhgt29Uft8UsXUuVZ_vgg | PassionFriend | "2024-03-01T06:44:02Z" | 1,470 | 0 | keras | [
"keras",
"region:us"
] | null | "2024-02-14T13:09:49Z" | Entry not found |
bardsai/jaskier-7b-dpo-v6.1 | bardsai | "2024-02-26T12:16:15Z" | 1,470 | 10 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"llm",
"7b",
"en",
"dataset:jondurbin/truthy-dpo-v0.1",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2024-02-20T08:28:44Z" | ---
library_name: transformers
tags:
- llm
- 7b
license: cc-by-4.0
datasets:
- jondurbin/truthy-dpo-v0.1
language:
- en
---
# Jaskier-7b-dpo-v5.6
<figure>
</figure>
**This is work-in-progress model, may not be ready for production use**
Model based on `bardsai/jaskier-7b-dpo-v5.6` (downstream version of Mistral7B) finetuned using Direct Preference Optimization on argilla/distilabel-math-preference-dpo.
## How to use
You can use this model directly with a Hugging Face pipeline:
```python
from transformers import pipeline, Conversation
import torch
base_model_name = "bardsai/jaskier-7b-dpo-v6.1"
chatbot = pipeline("conversational", model=base_model_name, torch_dtype=torch.float16, device_map="auto")
conversation = Conversation("Can Poland into space?")
conversation = chatbot(conversation)
print(conversation.messages[-1]["content"])
```
## Output
"Poland, as a nation, doesn't physically travel to space. However, Poland has contributed to the field of space exploration through its scientists, engineers, and collaborations with international space agencies. The Polish Space Agency, established in 2016, aims to promote and coordinate the country's space activities."
## Changelog
- 2024-02-20: Initial release
## About bards.ai
At bards.ai, we focus on providing machine learning expertise and skills to our partners, particularly in the areas of nlp, machine vision and time series analysis. Our team is located in Wroclaw, Poland. Please visit our website for more information: bards.ai
Let us know if you use our model :). Also, if you need any help, feel free to contact us at [email protected] |
darkstorm2150/Protogen_v2.2_Official_Release | darkstorm2150 | "2023-01-27T19:16:44Z" | 1,469 | 195 | diffusers | [
"diffusers",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"art",
"artistic",
"protogen",
"en",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | "2022-12-31T21:59:09Z" | ---
language:
- en
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- art
- artistic
- diffusers
- protogen
inference: true
license: creativeml-openrail-m
---
<center><img src="https://huggingface.co/darkstorm2150/Protogen_v2.2_Official_Release/resolve/main/Protogen_v2.2-512.png" style="height:690px; border-radius: 7%; border: 10px solid #663380; padding-top:0px;" span title="Protogen v2.2 Nurse Raw Output"></center>
<center><h1>Protogen v2.2 (Anime) Official Release</h1></center>
<center><p><em>Research Model by <a href="https://instagram.com/officialvictorespinoza">darkstorm2150</a></em></p></center>
</div>
## Table of contents
* [General info](#general-info)
* [Granular Adaptive Learning](#granular-adaptive-learning)
* [Trigger Words](#trigger-words)
* [Setup](#setup)
* [Space](#space)
* [CompVis](#compvis)
* [Diffusers](#ð§š-diffusers)
* [Checkpoint Merging Data Reference](#checkpoint-merging-data-reference)
* [License](#license)
## General info
Protogen was warm-started with [Stable Diffusion v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5) and fine-tuned on a large amount
of data from large datasets new and trending on civitai.com.
You can enforce camera capture by using the prompt with "modelshoot style".
It should also be very "dreambooth-able", being able to generate high fidelity faces with a little amount of steps (see [dreambooth](https://github.com/huggingface/diffusers/tree/main/examples/dreambooth)).
## Granular Adaptive Learning
Granular adaptive learning is a machine learning technique that focuses on adjusting the learning process at a fine-grained level, rather than making global adjustments to the model. This approach allows the model to adapt to specific patterns or features in the data, rather than making assumptions based on general trends.
Granular adaptive learning can be achieved through techniques such as active learning, which allows the model to select the data it wants to learn from, or through the use of reinforcement learning, where the model receives feedback on its performance and adapts based on that feedback. It can also be achieved through techniques such as online learning where the model adjust itself as it receives more data.
Granular adaptive learning is often used in situations where the data is highly diverse or non-stationary and where the model needs to adapt quickly to changing patterns. This is often the case in dynamic environments such as robotics, financial markets, and natural language processing.
## Trigger Words
modelshoot style
Trigger words are also available for the hassan1.4 and f222, might have to google them :)
## Setup
To run this model, download the model.ckpt or model.safetensor and install it in your "stable-diffusion-webui\models\Stable-diffusion" directory
## Space
We support a [Gradio](https://github.com/gradio-app/gradio) Web UI to run dreamlike-diffusion-1.0:
[](https://huggingface.co/spaces/darkstorm2150/Stable-Diffusion-Protogen-webui)
## CompVis
## CKPT
[Download Protogen v2.2.ckpt (4.27GB)](https://huggingface.co/darkstorm2150/Protogen_v2.2_Official_Release/blob/main/Protogen_V2.2.ckpt)
[Download Protogen v2.2-pruned-fp16 (1.89GB)](https://huggingface.co/darkstorm2150/Protogen_v2.2_Official_Release/resolve/main/Protogen_V2.2-pruned-fp16.ckpt)
## Safetensors
[Download Protogen v2.2.safetensor (4.27GB)](https://huggingface.co/darkstorm2150/Protogen_v2.2_Official_Release/resolve/main/Protogen_V2.2.safetensors)
[Download Protogen V2.2-pruned-fp16.safetensors (1.89GB)](https://huggingface.co/darkstorm2150/Protogen_v2.2_Official_Release/resolve/main/Protogen_V2.2-pruned-fp16.safetensors)
## 𧚠Diffusers
This model can be used just like any other Stable Diffusion model. For more information,
please have a look at the [Stable Diffusion Pipeline](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion).
```python
from diffusers import StableDiffusionPipeline, DPMSolverMultistepScheduler
import torch
prompt = (
"modelshoot style, (extremely detailed CG unity 8k wallpaper), full shot body photo of the most beautiful artwork in the world, "
"english medieval witch, black silk vale, pale skin, black silk robe, black cat, necromancy magic, medieval era, "
"photorealistic painting by Ed Blinkey, Atey Ghailan, Studio Ghibli, by Jeremy Mann, Greg Manchess, Antonio Moro, trending on ArtStation, "
"trending on CGSociety, Intricate, High Detail, Sharp focus, dramatic, photorealistic painting art by midjourney and greg rutkowski"
)
model_id = "darkstorm2150/Protogen_v2.2_Official_Release"
pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
pipe = pipe.to("cuda")
image = pipe(prompt, num_inference_steps=25).images[0]
image.save("./result.jpg")
```
## - PENDING DATA FOR MERGE, RPGv2 not accounted..
## Checkpoint Merging Data Reference
<style>
.myTable {
border-collapse:collapse;
}
.myTable th {
background-color:#663380;
color:white;
}
.myTable td, .myTable th {
padding:5px;
border:1px solid #663380;
}
</style>
<table class="myTable">
<tr>
<th>Models</th>
<th>Protogen v2.2 (Anime)</th>
<th>Protogen x3.4 (Photo)</th>
<th>Protogen x5.3 (Photo)</th>
<th>Protogen x5.8 (Sci-fi/Anime)</th>
<th>Protogen x5.9 (Dragon)</th>
<th>Protogen x7.4 (Eclipse)</th>
<th>Protogen x8.0 (Nova)</th>
<th>Protogen x8.6 (Infinity)</th>
</tr>
<tr>
<td>seek_art_mega v1</td>
<td>52.50%</td>
<td>42.76%</td>
<td>42.63%</td>
<td></td>
<td></td>
<td></td>
<td>25.21%</td>
<td>14.83%</td>
</tr>
<tr>
<td>modelshoot v1</td>
<td>30.00%</td>
<td>24.44%</td>
<td>24.37%</td>
<td>2.56%</td>
<td>2.05%</td>
<td>3.48%</td>
<td>22.91%</td>
<td>13.48%</td>
</tr>
<tr>
<td>elldreth v1</td>
<td>12.64%</td>
<td>10.30%</td>
<td>10.23%</td>
<td></td>
<td></td>
<td></td>
<td>6.06%</td>
<td>3.57%</td>
</tr>
<tr>
<td>photoreal v2</td>
<td></td>
<td></td>
<td>10.00%</td>
<td>48.64%</td>
<td>38.91%</td>
<td>66.33%</td>
<td>20.49%</td>
<td>12.06%</td>
</tr>
<tr>
<td>analogdiffusion v1</td>
<td></td>
<td>4.75%</td>
<td>4.50%</td>
<td></td>
<td></td>
<td></td>
<td>1.75%</td>
<td>1.03%</td>
</tr>
<tr>
<td>openjourney v2</td>
<td></td>
<td>4.51%</td>
<td>4.28%</td>
<td></td>
<td></td>
<td>4.75%</td>
<td>2.26%</td>
<td>1.33%</td>
</tr>
<tr>
<td>hassan1.4</td>
<td>2.63%</td>
<td>2.14%</td>
<td>2.13%</td>
<td></td>
<td></td>
<td></td>
<td>1.26%</td>
<td>0.74%</td>
</tr>
<tr>
<td>f222</td>
<td>2.23%</td>
<td>1.82%</td>
<td>1.81%</td>
<td></td>
<td></td>
<td></td>
<td>1.07%</td>
<td>0.63%</td>
</tr>
<tr>
<td>hasdx</td>
<td></td>
<td></td>
<td></td>
<td>20.00%</td>
<td>16.00%</td>
<td>4.07%</td>
<td>5.01%</td>
<td>2.95%</td>
</tr>
<tr>
<td>moistmix</td>
<td></td>
<td></td>
<td></td>
<td>16.00%</td>
<td>12.80%</td>
<td>3.86%</td>
<td>4.08%</td>
<td>2.40%</td>
</tr>
<tr>
<td>roboDiffusion v1</td>
<td></td>
<td>4.29%</td>
<td></td>
<td>12.80%</td>
<td>10.24%</td>
<td>3.67%</td>
<td>4.41%</td>
<td>2.60%</td>
</tr>
<tr>
<td>RPG v3</td>
<td></td>
<td>5.00%</td>
<td></td>
<td></td>
<td>20.00%</td>
<td>4.29%</td>
<td>4.29%</td>
<td>2.52%</td>
</tr>
<tr>
<td>anything&everything</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td>4.51%</td>
<td>0.56%</td>
<td>0.33%</td>
</tr>
<tr>
<td>dreamlikediff v1</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td>5.0%</td>
<td>0.63%</td>
<td>0.37%</td>
</tr>
<tr>
<td>sci-fidiff v1</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td>3.10%</td>
</tr>
<tr>
<td>synthwavepunk v2</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td>3.26%</td>
</tr>
<tr>
<td>mashupv2</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td>11.51%</td>
</tr>
<tr>
<td>dreamshaper 252</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td>4.04%</td>
</tr>
<tr>
<td>comicdiff v2</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td>4.25%</td>
</tr>
<tr>
<td>artEros</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td>15.00%</td>
</tr>
</table>
## License
By downloading you agree to the terms of these licenses
<a href="https://huggingface.co/spaces/CompVis/stable-diffusion-license">CreativeML Open RAIL-M</a>
<a href="https://huggingface.co/coreco/seek.art_MEGA/blob/main/LICENSE.txt">Seek Art Mega License</a> |
ChrisWilson011016/5GseTsnUzgPzwyTi7N9SXLtMVj2wvfZZVDvxGvMh4reBP8sN_vgg | ChrisWilson011016 | "2024-03-04T18:55:16Z" | 1,469 | 0 | keras | [
"keras",
"region:us"
] | null | "2024-02-24T15:19:57Z" | Entry not found |
ToastyPigeon/SmolLlama-1.5B-Bottomheavy | ToastyPigeon | "2024-03-26T01:55:44Z" | 1,469 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2024-03-26T01:29:12Z" | ---
base_model: []
tags:
- mergekit
- merge
license: apache-2.0
---
# StackTinyLlama-Sorted-Bottomheavy
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the passthrough merge method.
### Models Merged
The following models were included in the merge:
* D:/axolotl-data/TinyLlama-1.1B
### Configuration
The following YAML configuration was used to produce this model:
```yaml
slices:
- sources: #non-repeating shuffled layers
- model: D:/axolotl-data/TinyLlama-1.1B
layer_range: [0, 10]
- sources:
- model: D:/axolotl-data/TinyLlama-1.1B
layer_range: [10, 11]
- sources:
- model: D:/axolotl-data/TinyLlama-1.1B
layer_range: [10, 11]
- sources:
- model: D:/axolotl-data/TinyLlama-1.1B
layer_range: [11, 12]
- sources:
- model: D:/axolotl-data/TinyLlama-1.1B
layer_range: [11, 12]
- sources:
- model: D:/axolotl-data/TinyLlama-1.1B
layer_range: [12, 13]
- sources:
- model: D:/axolotl-data/TinyLlama-1.1B
layer_range: [12, 13]
- sources:
- model: D:/axolotl-data/TinyLlama-1.1B
layer_range: [13, 14]
- sources:
- model: D:/axolotl-data/TinyLlama-1.1B
layer_range: [13, 14]
- sources:
- model: D:/axolotl-data/TinyLlama-1.1B
layer_range: [14, 15]
- sources:
- model: D:/axolotl-data/TinyLlama-1.1B
layer_range: [14, 15]
- sources:
- model: D:/axolotl-data/TinyLlama-1.1B
layer_range: [15, 16]
- sources:
- model: D:/axolotl-data/TinyLlama-1.1B
layer_range: [15, 16]
- sources:
- model: D:/axolotl-data/TinyLlama-1.1B
layer_range: [16, 17]
- sources:
- model: D:/axolotl-data/TinyLlama-1.1B
layer_range: [16, 17]
- sources:
- model: D:/axolotl-data/TinyLlama-1.1B
layer_range: [17, 18]
- sources:
- model: D:/axolotl-data/TinyLlama-1.1B
layer_range: [17, 18]
- sources:
- model: D:/axolotl-data/TinyLlama-1.1B
layer_range: [18, 19]
- sources:
- model: D:/axolotl-data/TinyLlama-1.1B
layer_range: [18, 19]
- sources:
- model: D:/axolotl-data/TinyLlama-1.1B
layer_range: [19, 20]
- sources:
- model: D:/axolotl-data/TinyLlama-1.1B
layer_range: [19, 22]
merge_method: passthrough
dtype: float16
``` |
mradermacher/Dirty-Alice-Tiny-1.1B-v1-GGUF | mradermacher | "2024-06-10T01:32:46Z" | 1,469 | 1 | transformers | [
"transformers",
"gguf",
"nsfw",
"en",
"base_model:D1rtyB1rd/Dirty-Alice-Tiny-1.1B-v1",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | "2024-06-10T00:28:18Z" | ---
base_model: D1rtyB1rd/Dirty-Alice-Tiny-1.1B-v1
language:
- en
library_name: transformers
license: mit
quantized_by: mradermacher
tags:
- nsfw
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/D1rtyB1rd/Dirty-Alice-Tiny-1.1B-v1
<!-- 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/Dirty-Alice-Tiny-1.1B-v1-GGUF/resolve/main/Dirty-Alice-Tiny-1.1B-v1.Q2_K.gguf) | Q2_K | 0.5 | |
| [GGUF](https://huggingface.co/mradermacher/Dirty-Alice-Tiny-1.1B-v1-GGUF/resolve/main/Dirty-Alice-Tiny-1.1B-v1.IQ3_XS.gguf) | IQ3_XS | 0.6 | |
| [GGUF](https://huggingface.co/mradermacher/Dirty-Alice-Tiny-1.1B-v1-GGUF/resolve/main/Dirty-Alice-Tiny-1.1B-v1.Q3_K_S.gguf) | Q3_K_S | 0.6 | |
| [GGUF](https://huggingface.co/mradermacher/Dirty-Alice-Tiny-1.1B-v1-GGUF/resolve/main/Dirty-Alice-Tiny-1.1B-v1.IQ3_S.gguf) | IQ3_S | 0.6 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Dirty-Alice-Tiny-1.1B-v1-GGUF/resolve/main/Dirty-Alice-Tiny-1.1B-v1.IQ3_M.gguf) | IQ3_M | 0.6 | |
| [GGUF](https://huggingface.co/mradermacher/Dirty-Alice-Tiny-1.1B-v1-GGUF/resolve/main/Dirty-Alice-Tiny-1.1B-v1.Q3_K_M.gguf) | Q3_K_M | 0.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Dirty-Alice-Tiny-1.1B-v1-GGUF/resolve/main/Dirty-Alice-Tiny-1.1B-v1.Q3_K_L.gguf) | Q3_K_L | 0.7 | |
| [GGUF](https://huggingface.co/mradermacher/Dirty-Alice-Tiny-1.1B-v1-GGUF/resolve/main/Dirty-Alice-Tiny-1.1B-v1.IQ4_XS.gguf) | IQ4_XS | 0.7 | |
| [GGUF](https://huggingface.co/mradermacher/Dirty-Alice-Tiny-1.1B-v1-GGUF/resolve/main/Dirty-Alice-Tiny-1.1B-v1.Q4_K_S.gguf) | Q4_K_S | 0.7 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Dirty-Alice-Tiny-1.1B-v1-GGUF/resolve/main/Dirty-Alice-Tiny-1.1B-v1.Q4_K_M.gguf) | Q4_K_M | 0.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Dirty-Alice-Tiny-1.1B-v1-GGUF/resolve/main/Dirty-Alice-Tiny-1.1B-v1.Q5_K_S.gguf) | Q5_K_S | 0.9 | |
| [GGUF](https://huggingface.co/mradermacher/Dirty-Alice-Tiny-1.1B-v1-GGUF/resolve/main/Dirty-Alice-Tiny-1.1B-v1.Q5_K_M.gguf) | Q5_K_M | 0.9 | |
| [GGUF](https://huggingface.co/mradermacher/Dirty-Alice-Tiny-1.1B-v1-GGUF/resolve/main/Dirty-Alice-Tiny-1.1B-v1.Q6_K.gguf) | Q6_K | 1.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Dirty-Alice-Tiny-1.1B-v1-GGUF/resolve/main/Dirty-Alice-Tiny-1.1B-v1.Q8_0.gguf) | Q8_0 | 1.3 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Dirty-Alice-Tiny-1.1B-v1-GGUF/resolve/main/Dirty-Alice-Tiny-1.1B-v1.f16.gguf) | f16 | 2.3 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
NikolayKozloff/Fimbulvetr-11B-v2.1-16K-Q4_0-GGUF | NikolayKozloff | "2024-06-30T17:10:49Z" | 1,469 | 1 | null | [
"gguf",
"llama-cpp",
"gguf-my-repo",
"en",
"base_model:Sao10K/Fimbulvetr-11B-v2.1-16K",
"license:cc-by-nc-4.0",
"region:us"
] | null | "2024-06-30T17:10:22Z" | ---
base_model: Sao10K/Fimbulvetr-11B-v2.1-16K
language:
- en
license: cc-by-nc-4.0
tags:
- llama-cpp
- gguf-my-repo
---
# NikolayKozloff/Fimbulvetr-11B-v2.1-16K-Q4_0-GGUF
This model was converted to GGUF format from [`Sao10K/Fimbulvetr-11B-v2.1-16K`](https://huggingface.co/Sao10K/Fimbulvetr-11B-v2.1-16K) 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/Sao10K/Fimbulvetr-11B-v2.1-16K) 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/Fimbulvetr-11B-v2.1-16K-Q4_0-GGUF --hf-file fimbulvetr-11b-v2.1-16k-q4_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo NikolayKozloff/Fimbulvetr-11B-v2.1-16K-Q4_0-GGUF --hf-file fimbulvetr-11b-v2.1-16k-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 NikolayKozloff/Fimbulvetr-11B-v2.1-16K-Q4_0-GGUF --hf-file fimbulvetr-11b-v2.1-16k-q4_0.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo NikolayKozloff/Fimbulvetr-11B-v2.1-16K-Q4_0-GGUF --hf-file fimbulvetr-11b-v2.1-16k-q4_0.gguf -c 2048
```
|
google/bert_uncased_L-12_H-256_A-4 | google | "2021-05-19T17:26:24Z" | 1,468 | 1 | transformers | [
"transformers",
"pytorch",
"jax",
"bert",
"arxiv:1908.08962",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2022-03-02T23:29:05Z" | ---
thumbnail: https://huggingface.co/front/thumbnails/google.png
license: apache-2.0
---
BERT Miniatures
===
This is the set of 24 BERT models referenced in [Well-Read Students Learn Better: On the Importance of Pre-training Compact Models](https://arxiv.org/abs/1908.08962) (English only, uncased, trained with WordPiece masking).
We have shown that the standard BERT recipe (including model architecture and training objective) is effective on a wide range of model sizes, beyond BERT-Base and BERT-Large. The smaller BERT models are intended for environments with restricted computational resources. They can be fine-tuned in the same manner as the original BERT models. However, they are most effective in the context of knowledge distillation, where the fine-tuning labels are produced by a larger and more accurate teacher.
Our goal is to enable research in institutions with fewer computational resources and encourage the community to seek directions of innovation alternative to increasing model capacity.
You can download the 24 BERT miniatures either from the [official BERT Github page](https://github.com/google-research/bert/), or via HuggingFace from the links below:
| |H=128|H=256|H=512|H=768|
|---|:---:|:---:|:---:|:---:|
| **L=2** |[**2/128 (BERT-Tiny)**][2_128]|[2/256][2_256]|[2/512][2_512]|[2/768][2_768]|
| **L=4** |[4/128][4_128]|[**4/256 (BERT-Mini)**][4_256]|[**4/512 (BERT-Small)**][4_512]|[4/768][4_768]|
| **L=6** |[6/128][6_128]|[6/256][6_256]|[6/512][6_512]|[6/768][6_768]|
| **L=8** |[8/128][8_128]|[8/256][8_256]|[**8/512 (BERT-Medium)**][8_512]|[8/768][8_768]|
| **L=10** |[10/128][10_128]|[10/256][10_256]|[10/512][10_512]|[10/768][10_768]|
| **L=12** |[12/128][12_128]|[12/256][12_256]|[12/512][12_512]|[**12/768 (BERT-Base)**][12_768]|
Note that the BERT-Base model in this release is included for completeness only; it was re-trained under the same regime as the original model.
Here are the corresponding GLUE scores on the test set:
|Model|Score|CoLA|SST-2|MRPC|STS-B|QQP|MNLI-m|MNLI-mm|QNLI(v2)|RTE|WNLI|AX|
|---|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
|BERT-Tiny|64.2|0.0|83.2|81.1/71.1|74.3/73.6|62.2/83.4|70.2|70.3|81.5|57.2|62.3|21.0|
|BERT-Mini|65.8|0.0|85.9|81.1/71.8|75.4/73.3|66.4/86.2|74.8|74.3|84.1|57.9|62.3|26.1|
|BERT-Small|71.2|27.8|89.7|83.4/76.2|78.8/77.0|68.1/87.0|77.6|77.0|86.4|61.8|62.3|28.6|
|BERT-Medium|73.5|38.0|89.6|86.6/81.6|80.4/78.4|69.6/87.9|80.0|79.1|87.7|62.2|62.3|30.5|
For each task, we selected the best fine-tuning hyperparameters from the lists below, and trained for 4 epochs:
- batch sizes: 8, 16, 32, 64, 128
- learning rates: 3e-4, 1e-4, 5e-5, 3e-5
If you use these models, please cite the following paper:
```
@article{turc2019,
title={Well-Read Students Learn Better: On the Importance of Pre-training Compact Models},
author={Turc, Iulia and Chang, Ming-Wei and Lee, Kenton and Toutanova, Kristina},
journal={arXiv preprint arXiv:1908.08962v2 },
year={2019}
}
```
[2_128]: https://huggingface.co/google/bert_uncased_L-2_H-128_A-2
[2_256]: https://huggingface.co/google/bert_uncased_L-2_H-256_A-4
[2_512]: https://huggingface.co/google/bert_uncased_L-2_H-512_A-8
[2_768]: https://huggingface.co/google/bert_uncased_L-2_H-768_A-12
[4_128]: https://huggingface.co/google/bert_uncased_L-4_H-128_A-2
[4_256]: https://huggingface.co/google/bert_uncased_L-4_H-256_A-4
[4_512]: https://huggingface.co/google/bert_uncased_L-4_H-512_A-8
[4_768]: https://huggingface.co/google/bert_uncased_L-4_H-768_A-12
[6_128]: https://huggingface.co/google/bert_uncased_L-6_H-128_A-2
[6_256]: https://huggingface.co/google/bert_uncased_L-6_H-256_A-4
[6_512]: https://huggingface.co/google/bert_uncased_L-6_H-512_A-8
[6_768]: https://huggingface.co/google/bert_uncased_L-6_H-768_A-12
[8_128]: https://huggingface.co/google/bert_uncased_L-8_H-128_A-2
[8_256]: https://huggingface.co/google/bert_uncased_L-8_H-256_A-4
[8_512]: https://huggingface.co/google/bert_uncased_L-8_H-512_A-8
[8_768]: https://huggingface.co/google/bert_uncased_L-8_H-768_A-12
[10_128]: https://huggingface.co/google/bert_uncased_L-10_H-128_A-2
[10_256]: https://huggingface.co/google/bert_uncased_L-10_H-256_A-4
[10_512]: https://huggingface.co/google/bert_uncased_L-10_H-512_A-8
[10_768]: https://huggingface.co/google/bert_uncased_L-10_H-768_A-12
[12_128]: https://huggingface.co/google/bert_uncased_L-12_H-128_A-2
[12_256]: https://huggingface.co/google/bert_uncased_L-12_H-256_A-4
[12_512]: https://huggingface.co/google/bert_uncased_L-12_H-512_A-8
[12_768]: https://huggingface.co/google/bert_uncased_L-12_H-768_A-12
|
timm/resnet50.ram_in1k | timm | "2024-02-10T23:39:28Z" | 1,468 | 0 | timm | [
"timm",
"pytorch",
"safetensors",
"image-classification",
"arxiv:1512.03385",
"license:apache-2.0",
"region:us"
] | image-classification | "2023-04-05T18:14:08Z" | ---
license: apache-2.0
library_name: timm
tags:
- image-classification
- timm
---
# Model card for resnet50.ram_in1k
A ResNet-B image classification model.
This model features:
* ReLU activations
* single layer 7x7 convolution with pooling
* 1x1 convolution shortcut downsample
Trained on ImageNet-1k in `timm` using recipe template described below.
Recipe details:
* AugMix (with RandAugment) recipe
* SGD (w/ Nesterov) optimizer and JSD (JensenâShannon divergence) loss
* Cosine LR schedule with warmup
## Model Details
- **Model Type:** Image classification / feature backbone
- **Model Stats:**
- Params (M): 25.6
- GMACs: 4.1
- Activations (M): 11.1
- Image size: train = 224 x 224, test = 288 x 288
- **Papers:**
- Deep Residual Learning for Image Recognition: https://arxiv.org/abs/1512.03385
- **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('resnet50.ram_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(
'resnet50.ram_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, 64, 112, 112])
# torch.Size([1, 256, 56, 56])
# torch.Size([1, 512, 28, 28])
# torch.Size([1, 1024, 14, 14])
# torch.Size([1, 2048, 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(
'resnet50.ram_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, 2048, 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).
|model |img_size|top1 |top5 |param_count|gmacs|macts|img/sec|
|------------------------------------------|--------|-----|-----|-----------|-----|-----|-------|
|[seresnextaa101d_32x8d.sw_in12k_ft_in1k_288](https://huggingface.co/timm/seresnextaa101d_32x8d.sw_in12k_ft_in1k_288)|320 |86.72|98.17|93.6 |35.2 |69.7 |451 |
|[seresnextaa101d_32x8d.sw_in12k_ft_in1k_288](https://huggingface.co/timm/seresnextaa101d_32x8d.sw_in12k_ft_in1k_288)|288 |86.51|98.08|93.6 |28.5 |56.4 |560 |
|[seresnextaa101d_32x8d.sw_in12k_ft_in1k](https://huggingface.co/timm/seresnextaa101d_32x8d.sw_in12k_ft_in1k)|288 |86.49|98.03|93.6 |28.5 |56.4 |557 |
|[seresnextaa101d_32x8d.sw_in12k_ft_in1k](https://huggingface.co/timm/seresnextaa101d_32x8d.sw_in12k_ft_in1k)|224 |85.96|97.82|93.6 |17.2 |34.2 |923 |
|[resnext101_32x32d.fb_wsl_ig1b_ft_in1k](https://huggingface.co/timm/resnext101_32x32d.fb_wsl_ig1b_ft_in1k)|224 |85.11|97.44|468.5 |87.3 |91.1 |254 |
|[resnetrs420.tf_in1k](https://huggingface.co/timm/resnetrs420.tf_in1k)|416 |85.0 |97.12|191.9 |108.4|213.8|134 |
|[ecaresnet269d.ra2_in1k](https://huggingface.co/timm/ecaresnet269d.ra2_in1k)|352 |84.96|97.22|102.1 |50.2 |101.2|291 |
|[ecaresnet269d.ra2_in1k](https://huggingface.co/timm/ecaresnet269d.ra2_in1k)|320 |84.73|97.18|102.1 |41.5 |83.7 |353 |
|[resnetrs350.tf_in1k](https://huggingface.co/timm/resnetrs350.tf_in1k)|384 |84.71|96.99|164.0 |77.6 |154.7|183 |
|[seresnextaa101d_32x8d.ah_in1k](https://huggingface.co/timm/seresnextaa101d_32x8d.ah_in1k)|288 |84.57|97.08|93.6 |28.5 |56.4 |557 |
|[resnetrs200.tf_in1k](https://huggingface.co/timm/resnetrs200.tf_in1k)|320 |84.45|97.08|93.2 |31.5 |67.8 |446 |
|[resnetrs270.tf_in1k](https://huggingface.co/timm/resnetrs270.tf_in1k)|352 |84.43|96.97|129.9 |51.1 |105.5|280 |
|[seresnext101d_32x8d.ah_in1k](https://huggingface.co/timm/seresnext101d_32x8d.ah_in1k)|288 |84.36|96.92|93.6 |27.6 |53.0 |595 |
|[seresnet152d.ra2_in1k](https://huggingface.co/timm/seresnet152d.ra2_in1k)|320 |84.35|97.04|66.8 |24.1 |47.7 |610 |
|[resnetrs350.tf_in1k](https://huggingface.co/timm/resnetrs350.tf_in1k)|288 |84.3 |96.94|164.0 |43.7 |87.1 |333 |
|[resnext101_32x8d.fb_swsl_ig1b_ft_in1k](https://huggingface.co/timm/resnext101_32x8d.fb_swsl_ig1b_ft_in1k)|224 |84.28|97.17|88.8 |16.5 |31.2 |1100 |
|[resnetrs420.tf_in1k](https://huggingface.co/timm/resnetrs420.tf_in1k)|320 |84.24|96.86|191.9 |64.2 |126.6|228 |
|[seresnext101_32x8d.ah_in1k](https://huggingface.co/timm/seresnext101_32x8d.ah_in1k)|288 |84.19|96.87|93.6 |27.2 |51.6 |613 |
|[resnext101_32x16d.fb_wsl_ig1b_ft_in1k](https://huggingface.co/timm/resnext101_32x16d.fb_wsl_ig1b_ft_in1k)|224 |84.18|97.19|194.0 |36.3 |51.2 |581 |
|[resnetaa101d.sw_in12k_ft_in1k](https://huggingface.co/timm/resnetaa101d.sw_in12k_ft_in1k)|288 |84.11|97.11|44.6 |15.1 |29.0 |1144 |
|[resnet200d.ra2_in1k](https://huggingface.co/timm/resnet200d.ra2_in1k)|320 |83.97|96.82|64.7 |31.2 |67.3 |518 |
|[resnetrs200.tf_in1k](https://huggingface.co/timm/resnetrs200.tf_in1k)|256 |83.87|96.75|93.2 |20.2 |43.4 |692 |
|[seresnextaa101d_32x8d.ah_in1k](https://huggingface.co/timm/seresnextaa101d_32x8d.ah_in1k)|224 |83.86|96.65|93.6 |17.2 |34.2 |923 |
|[resnetrs152.tf_in1k](https://huggingface.co/timm/resnetrs152.tf_in1k)|320 |83.72|96.61|86.6 |24.3 |48.1 |617 |
|[seresnet152d.ra2_in1k](https://huggingface.co/timm/seresnet152d.ra2_in1k)|256 |83.69|96.78|66.8 |15.4 |30.6 |943 |
|[seresnext101d_32x8d.ah_in1k](https://huggingface.co/timm/seresnext101d_32x8d.ah_in1k)|224 |83.68|96.61|93.6 |16.7 |32.0 |986 |
|[resnet152d.ra2_in1k](https://huggingface.co/timm/resnet152d.ra2_in1k)|320 |83.67|96.74|60.2 |24.1 |47.7 |706 |
|[resnetrs270.tf_in1k](https://huggingface.co/timm/resnetrs270.tf_in1k)|256 |83.59|96.61|129.9 |27.1 |55.8 |526 |
|[seresnext101_32x8d.ah_in1k](https://huggingface.co/timm/seresnext101_32x8d.ah_in1k)|224 |83.58|96.4 |93.6 |16.5 |31.2 |1013 |
|[resnetaa101d.sw_in12k_ft_in1k](https://huggingface.co/timm/resnetaa101d.sw_in12k_ft_in1k)|224 |83.54|96.83|44.6 |9.1 |17.6 |1864 |
|[resnet152.a1h_in1k](https://huggingface.co/timm/resnet152.a1h_in1k)|288 |83.46|96.54|60.2 |19.1 |37.3 |904 |
|[resnext101_32x16d.fb_swsl_ig1b_ft_in1k](https://huggingface.co/timm/resnext101_32x16d.fb_swsl_ig1b_ft_in1k)|224 |83.35|96.85|194.0 |36.3 |51.2 |582 |
|[resnet200d.ra2_in1k](https://huggingface.co/timm/resnet200d.ra2_in1k)|256 |83.23|96.53|64.7 |20.0 |43.1 |809 |
|[resnext101_32x4d.fb_swsl_ig1b_ft_in1k](https://huggingface.co/timm/resnext101_32x4d.fb_swsl_ig1b_ft_in1k)|224 |83.22|96.75|44.2 |8.0 |21.2 |1814 |
|[resnext101_64x4d.c1_in1k](https://huggingface.co/timm/resnext101_64x4d.c1_in1k)|288 |83.16|96.38|83.5 |25.7 |51.6 |590 |
|[resnet152d.ra2_in1k](https://huggingface.co/timm/resnet152d.ra2_in1k)|256 |83.14|96.38|60.2 |15.4 |30.5 |1096 |
|[resnet101d.ra2_in1k](https://huggingface.co/timm/resnet101d.ra2_in1k)|320 |83.02|96.45|44.6 |16.5 |34.8 |992 |
|[ecaresnet101d.miil_in1k](https://huggingface.co/timm/ecaresnet101d.miil_in1k)|288 |82.98|96.54|44.6 |13.4 |28.2 |1077 |
|[resnext101_64x4d.tv_in1k](https://huggingface.co/timm/resnext101_64x4d.tv_in1k)|224 |82.98|96.25|83.5 |15.5 |31.2 |989 |
|[resnetrs152.tf_in1k](https://huggingface.co/timm/resnetrs152.tf_in1k)|256 |82.86|96.28|86.6 |15.6 |30.8 |951 |
|[resnext101_32x8d.tv2_in1k](https://huggingface.co/timm/resnext101_32x8d.tv2_in1k)|224 |82.83|96.22|88.8 |16.5 |31.2 |1099 |
|[resnet152.a1h_in1k](https://huggingface.co/timm/resnet152.a1h_in1k)|224 |82.8 |96.13|60.2 |11.6 |22.6 |1486 |
|[resnet101.a1h_in1k](https://huggingface.co/timm/resnet101.a1h_in1k)|288 |82.8 |96.32|44.6 |13.0 |26.8 |1291 |
|[resnet152.a1_in1k](https://huggingface.co/timm/resnet152.a1_in1k)|288 |82.74|95.71|60.2 |19.1 |37.3 |905 |
|[resnext101_32x8d.fb_wsl_ig1b_ft_in1k](https://huggingface.co/timm/resnext101_32x8d.fb_wsl_ig1b_ft_in1k)|224 |82.69|96.63|88.8 |16.5 |31.2 |1100 |
|[resnet152.a2_in1k](https://huggingface.co/timm/resnet152.a2_in1k)|288 |82.62|95.75|60.2 |19.1 |37.3 |904 |
|[resnetaa50d.sw_in12k_ft_in1k](https://huggingface.co/timm/resnetaa50d.sw_in12k_ft_in1k)|288 |82.61|96.49|25.6 |8.9 |20.6 |1729 |
|[resnet61q.ra2_in1k](https://huggingface.co/timm/resnet61q.ra2_in1k)|288 |82.53|96.13|36.8 |9.9 |21.5 |1773 |
|[wide_resnet101_2.tv2_in1k](https://huggingface.co/timm/wide_resnet101_2.tv2_in1k)|224 |82.5 |96.02|126.9 |22.8 |21.2 |1078 |
|[resnext101_64x4d.c1_in1k](https://huggingface.co/timm/resnext101_64x4d.c1_in1k)|224 |82.46|95.92|83.5 |15.5 |31.2 |987 |
|[resnet51q.ra2_in1k](https://huggingface.co/timm/resnet51q.ra2_in1k)|288 |82.36|96.18|35.7 |8.1 |20.9 |1964 |
|[ecaresnet50t.ra2_in1k](https://huggingface.co/timm/ecaresnet50t.ra2_in1k)|320 |82.35|96.14|25.6 |8.8 |24.1 |1386 |
|[resnet101.a1_in1k](https://huggingface.co/timm/resnet101.a1_in1k)|288 |82.31|95.63|44.6 |13.0 |26.8 |1291 |
|[resnetrs101.tf_in1k](https://huggingface.co/timm/resnetrs101.tf_in1k)|288 |82.29|96.01|63.6 |13.6 |28.5 |1078 |
|[resnet152.tv2_in1k](https://huggingface.co/timm/resnet152.tv2_in1k)|224 |82.29|96.0 |60.2 |11.6 |22.6 |1484 |
|[wide_resnet50_2.racm_in1k](https://huggingface.co/timm/wide_resnet50_2.racm_in1k)|288 |82.27|96.06|68.9 |18.9 |23.8 |1176 |
|[resnet101d.ra2_in1k](https://huggingface.co/timm/resnet101d.ra2_in1k)|256 |82.26|96.07|44.6 |10.6 |22.2 |1542 |
|[resnet101.a2_in1k](https://huggingface.co/timm/resnet101.a2_in1k)|288 |82.24|95.73|44.6 |13.0 |26.8 |1290 |
|[seresnext50_32x4d.racm_in1k](https://huggingface.co/timm/seresnext50_32x4d.racm_in1k)|288 |82.2 |96.14|27.6 |7.0 |23.8 |1547 |
|[ecaresnet101d.miil_in1k](https://huggingface.co/timm/ecaresnet101d.miil_in1k)|224 |82.18|96.05|44.6 |8.1 |17.1 |1771 |
|[resnext50_32x4d.fb_swsl_ig1b_ft_in1k](https://huggingface.co/timm/resnext50_32x4d.fb_swsl_ig1b_ft_in1k)|224 |82.17|96.22|25.0 |4.3 |14.4 |2943 |
|[ecaresnet50t.a1_in1k](https://huggingface.co/timm/ecaresnet50t.a1_in1k)|288 |82.12|95.65|25.6 |7.1 |19.6 |1704 |
|[resnext50_32x4d.a1h_in1k](https://huggingface.co/timm/resnext50_32x4d.a1h_in1k)|288 |82.03|95.94|25.0 |7.0 |23.8 |1745 |
|[ecaresnet101d_pruned.miil_in1k](https://huggingface.co/timm/ecaresnet101d_pruned.miil_in1k)|288 |82.0 |96.15|24.9 |5.8 |12.7 |1787 |
|[resnet61q.ra2_in1k](https://huggingface.co/timm/resnet61q.ra2_in1k)|256 |81.99|95.85|36.8 |7.8 |17.0 |2230 |
|[resnext101_32x8d.tv2_in1k](https://huggingface.co/timm/resnext101_32x8d.tv2_in1k)|176 |81.98|95.72|88.8 |10.3 |19.4 |1768 |
|[resnet152.a1_in1k](https://huggingface.co/timm/resnet152.a1_in1k)|224 |81.97|95.24|60.2 |11.6 |22.6 |1486 |
|[resnet101.a1h_in1k](https://huggingface.co/timm/resnet101.a1h_in1k)|224 |81.93|95.75|44.6 |7.8 |16.2 |2122 |
|[resnet101.tv2_in1k](https://huggingface.co/timm/resnet101.tv2_in1k)|224 |81.9 |95.77|44.6 |7.8 |16.2 |2118 |
|[resnext101_32x16d.fb_ssl_yfcc100m_ft_in1k](https://huggingface.co/timm/resnext101_32x16d.fb_ssl_yfcc100m_ft_in1k)|224 |81.84|96.1 |194.0 |36.3 |51.2 |583 |
|[resnet51q.ra2_in1k](https://huggingface.co/timm/resnet51q.ra2_in1k)|256 |81.78|95.94|35.7 |6.4 |16.6 |2471 |
|[resnet152.a2_in1k](https://huggingface.co/timm/resnet152.a2_in1k)|224 |81.77|95.22|60.2 |11.6 |22.6 |1485 |
|[resnetaa50d.sw_in12k_ft_in1k](https://huggingface.co/timm/resnetaa50d.sw_in12k_ft_in1k)|224 |81.74|96.06|25.6 |5.4 |12.4 |2813 |
|[ecaresnet50t.a2_in1k](https://huggingface.co/timm/ecaresnet50t.a2_in1k)|288 |81.65|95.54|25.6 |7.1 |19.6 |1703 |
|[ecaresnet50d.miil_in1k](https://huggingface.co/timm/ecaresnet50d.miil_in1k)|288 |81.64|95.88|25.6 |7.2 |19.7 |1694 |
|[resnext101_32x8d.fb_ssl_yfcc100m_ft_in1k](https://huggingface.co/timm/resnext101_32x8d.fb_ssl_yfcc100m_ft_in1k)|224 |81.62|96.04|88.8 |16.5 |31.2 |1101 |
|[wide_resnet50_2.tv2_in1k](https://huggingface.co/timm/wide_resnet50_2.tv2_in1k)|224 |81.61|95.76|68.9 |11.4 |14.4 |1930 |
|[resnetaa50.a1h_in1k](https://huggingface.co/timm/resnetaa50.a1h_in1k)|288 |81.61|95.83|25.6 |8.5 |19.2 |1868 |
|[resnet101.a1_in1k](https://huggingface.co/timm/resnet101.a1_in1k)|224 |81.5 |95.16|44.6 |7.8 |16.2 |2125 |
|[resnext50_32x4d.a1_in1k](https://huggingface.co/timm/resnext50_32x4d.a1_in1k)|288 |81.48|95.16|25.0 |7.0 |23.8 |1745 |
|[gcresnet50t.ra2_in1k](https://huggingface.co/timm/gcresnet50t.ra2_in1k)|288 |81.47|95.71|25.9 |6.9 |18.6 |2071 |
|[wide_resnet50_2.racm_in1k](https://huggingface.co/timm/wide_resnet50_2.racm_in1k)|224 |81.45|95.53|68.9 |11.4 |14.4 |1929 |
|[resnet50d.a1_in1k](https://huggingface.co/timm/resnet50d.a1_in1k)|288 |81.44|95.22|25.6 |7.2 |19.7 |1908 |
|[ecaresnet50t.ra2_in1k](https://huggingface.co/timm/ecaresnet50t.ra2_in1k)|256 |81.44|95.67|25.6 |5.6 |15.4 |2168 |
|[ecaresnetlight.miil_in1k](https://huggingface.co/timm/ecaresnetlight.miil_in1k)|288 |81.4 |95.82|30.2 |6.8 |13.9 |2132 |
|[resnet50d.ra2_in1k](https://huggingface.co/timm/resnet50d.ra2_in1k)|288 |81.37|95.74|25.6 |7.2 |19.7 |1910 |
|[resnet101.a2_in1k](https://huggingface.co/timm/resnet101.a2_in1k)|224 |81.32|95.19|44.6 |7.8 |16.2 |2125 |
|[seresnet50.ra2_in1k](https://huggingface.co/timm/seresnet50.ra2_in1k)|288 |81.3 |95.65|28.1 |6.8 |18.4 |1803 |
|[resnext50_32x4d.a2_in1k](https://huggingface.co/timm/resnext50_32x4d.a2_in1k)|288 |81.3 |95.11|25.0 |7.0 |23.8 |1746 |
|[seresnext50_32x4d.racm_in1k](https://huggingface.co/timm/seresnext50_32x4d.racm_in1k)|224 |81.27|95.62|27.6 |4.3 |14.4 |2591 |
|[ecaresnet50t.a1_in1k](https://huggingface.co/timm/ecaresnet50t.a1_in1k)|224 |81.26|95.16|25.6 |4.3 |11.8 |2823 |
|[gcresnext50ts.ch_in1k](https://huggingface.co/timm/gcresnext50ts.ch_in1k)|288 |81.23|95.54|15.7 |4.8 |19.6 |2117 |
|[senet154.gluon_in1k](https://huggingface.co/timm/senet154.gluon_in1k)|224 |81.23|95.35|115.1 |20.8 |38.7 |545 |
|[resnet50.a1_in1k](https://huggingface.co/timm/resnet50.a1_in1k)|288 |81.22|95.11|25.6 |6.8 |18.4 |2089 |
|[resnet50_gn.a1h_in1k](https://huggingface.co/timm/resnet50_gn.a1h_in1k)|288 |81.22|95.63|25.6 |6.8 |18.4 |676 |
|[resnet50d.a2_in1k](https://huggingface.co/timm/resnet50d.a2_in1k)|288 |81.18|95.09|25.6 |7.2 |19.7 |1908 |
|[resnet50.fb_swsl_ig1b_ft_in1k](https://huggingface.co/timm/resnet50.fb_swsl_ig1b_ft_in1k)|224 |81.18|95.98|25.6 |4.1 |11.1 |3455 |
|[resnext50_32x4d.tv2_in1k](https://huggingface.co/timm/resnext50_32x4d.tv2_in1k)|224 |81.17|95.34|25.0 |4.3 |14.4 |2933 |
|[resnext50_32x4d.a1h_in1k](https://huggingface.co/timm/resnext50_32x4d.a1h_in1k)|224 |81.1 |95.33|25.0 |4.3 |14.4 |2934 |
|[seresnet50.a2_in1k](https://huggingface.co/timm/seresnet50.a2_in1k)|288 |81.1 |95.23|28.1 |6.8 |18.4 |1801 |
|[seresnet50.a1_in1k](https://huggingface.co/timm/seresnet50.a1_in1k)|288 |81.1 |95.12|28.1 |6.8 |18.4 |1799 |
|[resnet152s.gluon_in1k](https://huggingface.co/timm/resnet152s.gluon_in1k)|224 |81.02|95.41|60.3 |12.9 |25.0 |1347 |
|[resnet50.d_in1k](https://huggingface.co/timm/resnet50.d_in1k)|288 |80.97|95.44|25.6 |6.8 |18.4 |2085 |
|[gcresnet50t.ra2_in1k](https://huggingface.co/timm/gcresnet50t.ra2_in1k)|256 |80.94|95.45|25.9 |5.4 |14.7 |2571 |
|[resnext101_32x4d.fb_ssl_yfcc100m_ft_in1k](https://huggingface.co/timm/resnext101_32x4d.fb_ssl_yfcc100m_ft_in1k)|224 |80.93|95.73|44.2 |8.0 |21.2 |1814 |
|[resnet50.c1_in1k](https://huggingface.co/timm/resnet50.c1_in1k)|288 |80.91|95.55|25.6 |6.8 |18.4 |2084 |
|[seresnext101_32x4d.gluon_in1k](https://huggingface.co/timm/seresnext101_32x4d.gluon_in1k)|224 |80.9 |95.31|49.0 |8.0 |21.3 |1585 |
|[seresnext101_64x4d.gluon_in1k](https://huggingface.co/timm/seresnext101_64x4d.gluon_in1k)|224 |80.9 |95.3 |88.2 |15.5 |31.2 |918 |
|[resnet50.c2_in1k](https://huggingface.co/timm/resnet50.c2_in1k)|288 |80.86|95.52|25.6 |6.8 |18.4 |2085 |
|[resnet50.tv2_in1k](https://huggingface.co/timm/resnet50.tv2_in1k)|224 |80.85|95.43|25.6 |4.1 |11.1 |3450 |
|[ecaresnet50t.a2_in1k](https://huggingface.co/timm/ecaresnet50t.a2_in1k)|224 |80.84|95.02|25.6 |4.3 |11.8 |2821 |
|[ecaresnet101d_pruned.miil_in1k](https://huggingface.co/timm/ecaresnet101d_pruned.miil_in1k)|224 |80.79|95.62|24.9 |3.5 |7.7 |2961 |
|[seresnet33ts.ra2_in1k](https://huggingface.co/timm/seresnet33ts.ra2_in1k)|288 |80.79|95.36|19.8 |6.0 |14.8 |2506 |
|[ecaresnet50d_pruned.miil_in1k](https://huggingface.co/timm/ecaresnet50d_pruned.miil_in1k)|288 |80.79|95.58|19.9 |4.2 |10.6 |2349 |
|[resnet50.a2_in1k](https://huggingface.co/timm/resnet50.a2_in1k)|288 |80.78|94.99|25.6 |6.8 |18.4 |2088 |
|[resnet50.b1k_in1k](https://huggingface.co/timm/resnet50.b1k_in1k)|288 |80.71|95.43|25.6 |6.8 |18.4 |2087 |
|[resnext50_32x4d.ra_in1k](https://huggingface.co/timm/resnext50_32x4d.ra_in1k)|288 |80.7 |95.39|25.0 |7.0 |23.8 |1749 |
|[resnetrs101.tf_in1k](https://huggingface.co/timm/resnetrs101.tf_in1k)|192 |80.69|95.24|63.6 |6.0 |12.7 |2270 |
|[resnet50d.a1_in1k](https://huggingface.co/timm/resnet50d.a1_in1k)|224 |80.68|94.71|25.6 |4.4 |11.9 |3162 |
|[eca_resnet33ts.ra2_in1k](https://huggingface.co/timm/eca_resnet33ts.ra2_in1k)|288 |80.68|95.36|19.7 |6.0 |14.8 |2637 |
|[resnet50.a1h_in1k](https://huggingface.co/timm/resnet50.a1h_in1k)|224 |80.67|95.3 |25.6 |4.1 |11.1 |3452 |
|[resnext50d_32x4d.bt_in1k](https://huggingface.co/timm/resnext50d_32x4d.bt_in1k)|288 |80.67|95.42|25.0 |7.4 |25.1 |1626 |
|[resnetaa50.a1h_in1k](https://huggingface.co/timm/resnetaa50.a1h_in1k)|224 |80.63|95.21|25.6 |5.2 |11.6 |3034 |
|[ecaresnet50d.miil_in1k](https://huggingface.co/timm/ecaresnet50d.miil_in1k)|224 |80.61|95.32|25.6 |4.4 |11.9 |2813 |
|[resnext101_64x4d.gluon_in1k](https://huggingface.co/timm/resnext101_64x4d.gluon_in1k)|224 |80.61|94.99|83.5 |15.5 |31.2 |989 |
|[gcresnet33ts.ra2_in1k](https://huggingface.co/timm/gcresnet33ts.ra2_in1k)|288 |80.6 |95.31|19.9 |6.0 |14.8 |2578 |
|[gcresnext50ts.ch_in1k](https://huggingface.co/timm/gcresnext50ts.ch_in1k)|256 |80.57|95.17|15.7 |3.8 |15.5 |2710 |
|[resnet152.a3_in1k](https://huggingface.co/timm/resnet152.a3_in1k)|224 |80.56|95.0 |60.2 |11.6 |22.6 |1483 |
|[resnet50d.ra2_in1k](https://huggingface.co/timm/resnet50d.ra2_in1k)|224 |80.53|95.16|25.6 |4.4 |11.9 |3164 |
|[resnext50_32x4d.a1_in1k](https://huggingface.co/timm/resnext50_32x4d.a1_in1k)|224 |80.53|94.46|25.0 |4.3 |14.4 |2930 |
|[wide_resnet101_2.tv2_in1k](https://huggingface.co/timm/wide_resnet101_2.tv2_in1k)|176 |80.48|94.98|126.9 |14.3 |13.2 |1719 |
|[resnet152d.gluon_in1k](https://huggingface.co/timm/resnet152d.gluon_in1k)|224 |80.47|95.2 |60.2 |11.8 |23.4 |1428 |
|[resnet50.b2k_in1k](https://huggingface.co/timm/resnet50.b2k_in1k)|288 |80.45|95.32|25.6 |6.8 |18.4 |2086 |
|[ecaresnetlight.miil_in1k](https://huggingface.co/timm/ecaresnetlight.miil_in1k)|224 |80.45|95.24|30.2 |4.1 |8.4 |3530 |
|[resnext50_32x4d.a2_in1k](https://huggingface.co/timm/resnext50_32x4d.a2_in1k)|224 |80.45|94.63|25.0 |4.3 |14.4 |2936 |
|[wide_resnet50_2.tv2_in1k](https://huggingface.co/timm/wide_resnet50_2.tv2_in1k)|176 |80.43|95.09|68.9 |7.3 |9.0 |3015 |
|[resnet101d.gluon_in1k](https://huggingface.co/timm/resnet101d.gluon_in1k)|224 |80.42|95.01|44.6 |8.1 |17.0 |2007 |
|[resnet50.a1_in1k](https://huggingface.co/timm/resnet50.a1_in1k)|224 |80.38|94.6 |25.6 |4.1 |11.1 |3461 |
|[seresnet33ts.ra2_in1k](https://huggingface.co/timm/seresnet33ts.ra2_in1k)|256 |80.36|95.1 |19.8 |4.8 |11.7 |3267 |
|[resnext101_32x4d.gluon_in1k](https://huggingface.co/timm/resnext101_32x4d.gluon_in1k)|224 |80.34|94.93|44.2 |8.0 |21.2 |1814 |
|[resnext50_32x4d.fb_ssl_yfcc100m_ft_in1k](https://huggingface.co/timm/resnext50_32x4d.fb_ssl_yfcc100m_ft_in1k)|224 |80.32|95.4 |25.0 |4.3 |14.4 |2941 |
|[resnet101s.gluon_in1k](https://huggingface.co/timm/resnet101s.gluon_in1k)|224 |80.28|95.16|44.7 |9.2 |18.6 |1851 |
|[seresnet50.ra2_in1k](https://huggingface.co/timm/seresnet50.ra2_in1k)|224 |80.26|95.08|28.1 |4.1 |11.1 |2972 |
|[resnetblur50.bt_in1k](https://huggingface.co/timm/resnetblur50.bt_in1k)|288 |80.24|95.24|25.6 |8.5 |19.9 |1523 |
|[resnet50d.a2_in1k](https://huggingface.co/timm/resnet50d.a2_in1k)|224 |80.22|94.63|25.6 |4.4 |11.9 |3162 |
|[resnet152.tv2_in1k](https://huggingface.co/timm/resnet152.tv2_in1k)|176 |80.2 |94.64|60.2 |7.2 |14.0 |2346 |
|[seresnet50.a2_in1k](https://huggingface.co/timm/seresnet50.a2_in1k)|224 |80.08|94.74|28.1 |4.1 |11.1 |2969 |
|[eca_resnet33ts.ra2_in1k](https://huggingface.co/timm/eca_resnet33ts.ra2_in1k)|256 |80.08|94.97|19.7 |4.8 |11.7 |3284 |
|[gcresnet33ts.ra2_in1k](https://huggingface.co/timm/gcresnet33ts.ra2_in1k)|256 |80.06|94.99|19.9 |4.8 |11.7 |3216 |
|[resnet50_gn.a1h_in1k](https://huggingface.co/timm/resnet50_gn.a1h_in1k)|224 |80.06|94.95|25.6 |4.1 |11.1 |1109 |
|[seresnet50.a1_in1k](https://huggingface.co/timm/seresnet50.a1_in1k)|224 |80.02|94.71|28.1 |4.1 |11.1 |2962 |
|[resnet50.ram_in1k](https://huggingface.co/timm/resnet50.ram_in1k)|288 |79.97|95.05|25.6 |6.8 |18.4 |2086 |
|[resnet152c.gluon_in1k](https://huggingface.co/timm/resnet152c.gluon_in1k)|224 |79.92|94.84|60.2 |11.8 |23.4 |1455 |
|[seresnext50_32x4d.gluon_in1k](https://huggingface.co/timm/seresnext50_32x4d.gluon_in1k)|224 |79.91|94.82|27.6 |4.3 |14.4 |2591 |
|[resnet50.d_in1k](https://huggingface.co/timm/resnet50.d_in1k)|224 |79.91|94.67|25.6 |4.1 |11.1 |3456 |
|[resnet101.tv2_in1k](https://huggingface.co/timm/resnet101.tv2_in1k)|176 |79.9 |94.6 |44.6 |4.9 |10.1 |3341 |
|[resnetrs50.tf_in1k](https://huggingface.co/timm/resnetrs50.tf_in1k)|224 |79.89|94.97|35.7 |4.5 |12.1 |2774 |
|[resnet50.c2_in1k](https://huggingface.co/timm/resnet50.c2_in1k)|224 |79.88|94.87|25.6 |4.1 |11.1 |3455 |
|[ecaresnet26t.ra2_in1k](https://huggingface.co/timm/ecaresnet26t.ra2_in1k)|320 |79.86|95.07|16.0 |5.2 |16.4 |2168 |
|[resnet50.a2_in1k](https://huggingface.co/timm/resnet50.a2_in1k)|224 |79.85|94.56|25.6 |4.1 |11.1 |3460 |
|[resnet50.ra_in1k](https://huggingface.co/timm/resnet50.ra_in1k)|288 |79.83|94.97|25.6 |6.8 |18.4 |2087 |
|[resnet101.a3_in1k](https://huggingface.co/timm/resnet101.a3_in1k)|224 |79.82|94.62|44.6 |7.8 |16.2 |2114 |
|[resnext50_32x4d.ra_in1k](https://huggingface.co/timm/resnext50_32x4d.ra_in1k)|224 |79.76|94.6 |25.0 |4.3 |14.4 |2943 |
|[resnet50.c1_in1k](https://huggingface.co/timm/resnet50.c1_in1k)|224 |79.74|94.95|25.6 |4.1 |11.1 |3455 |
|[ecaresnet50d_pruned.miil_in1k](https://huggingface.co/timm/ecaresnet50d_pruned.miil_in1k)|224 |79.74|94.87|19.9 |2.5 |6.4 |3929 |
|[resnet33ts.ra2_in1k](https://huggingface.co/timm/resnet33ts.ra2_in1k)|288 |79.71|94.83|19.7 |6.0 |14.8 |2710 |
|[resnet152.gluon_in1k](https://huggingface.co/timm/resnet152.gluon_in1k)|224 |79.68|94.74|60.2 |11.6 |22.6 |1486 |
|[resnext50d_32x4d.bt_in1k](https://huggingface.co/timm/resnext50d_32x4d.bt_in1k)|224 |79.67|94.87|25.0 |4.5 |15.2 |2729 |
|[resnet50.bt_in1k](https://huggingface.co/timm/resnet50.bt_in1k)|288 |79.63|94.91|25.6 |6.8 |18.4 |2086 |
|[ecaresnet50t.a3_in1k](https://huggingface.co/timm/ecaresnet50t.a3_in1k)|224 |79.56|94.72|25.6 |4.3 |11.8 |2805 |
|[resnet101c.gluon_in1k](https://huggingface.co/timm/resnet101c.gluon_in1k)|224 |79.53|94.58|44.6 |8.1 |17.0 |2062 |
|[resnet50.b1k_in1k](https://huggingface.co/timm/resnet50.b1k_in1k)|224 |79.52|94.61|25.6 |4.1 |11.1 |3459 |
|[resnet50.tv2_in1k](https://huggingface.co/timm/resnet50.tv2_in1k)|176 |79.42|94.64|25.6 |2.6 |6.9 |5397 |
|[resnet32ts.ra2_in1k](https://huggingface.co/timm/resnet32ts.ra2_in1k)|288 |79.4 |94.66|18.0 |5.9 |14.6 |2752 |
|[resnet50.b2k_in1k](https://huggingface.co/timm/resnet50.b2k_in1k)|224 |79.38|94.57|25.6 |4.1 |11.1 |3459 |
|[resnext50_32x4d.tv2_in1k](https://huggingface.co/timm/resnext50_32x4d.tv2_in1k)|176 |79.37|94.3 |25.0 |2.7 |9.0 |4577 |
|[resnext50_32x4d.gluon_in1k](https://huggingface.co/timm/resnext50_32x4d.gluon_in1k)|224 |79.36|94.43|25.0 |4.3 |14.4 |2942 |
|[resnext101_32x8d.tv_in1k](https://huggingface.co/timm/resnext101_32x8d.tv_in1k)|224 |79.31|94.52|88.8 |16.5 |31.2 |1100 |
|[resnet101.gluon_in1k](https://huggingface.co/timm/resnet101.gluon_in1k)|224 |79.31|94.53|44.6 |7.8 |16.2 |2125 |
|[resnetblur50.bt_in1k](https://huggingface.co/timm/resnetblur50.bt_in1k)|224 |79.31|94.63|25.6 |5.2 |12.0 |2524 |
|[resnet50.a1h_in1k](https://huggingface.co/timm/resnet50.a1h_in1k)|176 |79.27|94.49|25.6 |2.6 |6.9 |5404 |
|[resnext50_32x4d.a3_in1k](https://huggingface.co/timm/resnext50_32x4d.a3_in1k)|224 |79.25|94.31|25.0 |4.3 |14.4 |2931 |
|[resnet50.fb_ssl_yfcc100m_ft_in1k](https://huggingface.co/timm/resnet50.fb_ssl_yfcc100m_ft_in1k)|224 |79.22|94.84|25.6 |4.1 |11.1 |3451 |
|[resnet33ts.ra2_in1k](https://huggingface.co/timm/resnet33ts.ra2_in1k)|256 |79.21|94.56|19.7 |4.8 |11.7 |3392 |
|[resnet50d.gluon_in1k](https://huggingface.co/timm/resnet50d.gluon_in1k)|224 |79.07|94.48|25.6 |4.4 |11.9 |3162 |
|[resnet50.ram_in1k](https://huggingface.co/timm/resnet50.ram_in1k)|224 |79.03|94.38|25.6 |4.1 |11.1 |3453 |
|[resnet50.am_in1k](https://huggingface.co/timm/resnet50.am_in1k)|224 |79.01|94.39|25.6 |4.1 |11.1 |3461 |
|[resnet32ts.ra2_in1k](https://huggingface.co/timm/resnet32ts.ra2_in1k)|256 |79.01|94.37|18.0 |4.6 |11.6 |3440 |
|[ecaresnet26t.ra2_in1k](https://huggingface.co/timm/ecaresnet26t.ra2_in1k)|256 |78.9 |94.54|16.0 |3.4 |10.5 |3421 |
|[resnet152.a3_in1k](https://huggingface.co/timm/resnet152.a3_in1k)|160 |78.89|94.11|60.2 |5.9 |11.5 |2745 |
|[wide_resnet101_2.tv_in1k](https://huggingface.co/timm/wide_resnet101_2.tv_in1k)|224 |78.84|94.28|126.9 |22.8 |21.2 |1079 |
|[seresnext26d_32x4d.bt_in1k](https://huggingface.co/timm/seresnext26d_32x4d.bt_in1k)|288 |78.83|94.24|16.8 |4.5 |16.8 |2251 |
|[resnet50.ra_in1k](https://huggingface.co/timm/resnet50.ra_in1k)|224 |78.81|94.32|25.6 |4.1 |11.1 |3454 |
|[seresnext26t_32x4d.bt_in1k](https://huggingface.co/timm/seresnext26t_32x4d.bt_in1k)|288 |78.74|94.33|16.8 |4.5 |16.7 |2264 |
|[resnet50s.gluon_in1k](https://huggingface.co/timm/resnet50s.gluon_in1k)|224 |78.72|94.23|25.7 |5.5 |13.5 |2796 |
|[resnet50d.a3_in1k](https://huggingface.co/timm/resnet50d.a3_in1k)|224 |78.71|94.24|25.6 |4.4 |11.9 |3154 |
|[wide_resnet50_2.tv_in1k](https://huggingface.co/timm/wide_resnet50_2.tv_in1k)|224 |78.47|94.09|68.9 |11.4 |14.4 |1934 |
|[resnet50.bt_in1k](https://huggingface.co/timm/resnet50.bt_in1k)|224 |78.46|94.27|25.6 |4.1 |11.1 |3454 |
|[resnet34d.ra2_in1k](https://huggingface.co/timm/resnet34d.ra2_in1k)|288 |78.43|94.35|21.8 |6.5 |7.5 |3291 |
|[gcresnext26ts.ch_in1k](https://huggingface.co/timm/gcresnext26ts.ch_in1k)|288 |78.42|94.04|10.5 |3.1 |13.3 |3226 |
|[resnet26t.ra2_in1k](https://huggingface.co/timm/resnet26t.ra2_in1k)|320 |78.33|94.13|16.0 |5.2 |16.4 |2391 |
|[resnet152.tv_in1k](https://huggingface.co/timm/resnet152.tv_in1k)|224 |78.32|94.04|60.2 |11.6 |22.6 |1487 |
|[seresnext26ts.ch_in1k](https://huggingface.co/timm/seresnext26ts.ch_in1k)|288 |78.28|94.1 |10.4 |3.1 |13.3 |3062 |
|[bat_resnext26ts.ch_in1k](https://huggingface.co/timm/bat_resnext26ts.ch_in1k)|256 |78.25|94.1 |10.7 |2.5 |12.5 |3393 |
|[resnet50.a3_in1k](https://huggingface.co/timm/resnet50.a3_in1k)|224 |78.06|93.78|25.6 |4.1 |11.1 |3450 |
|[resnet50c.gluon_in1k](https://huggingface.co/timm/resnet50c.gluon_in1k)|224 |78.0 |93.99|25.6 |4.4 |11.9 |3286 |
|[eca_resnext26ts.ch_in1k](https://huggingface.co/timm/eca_resnext26ts.ch_in1k)|288 |78.0 |93.91|10.3 |3.1 |13.3 |3297 |
|[seresnext26t_32x4d.bt_in1k](https://huggingface.co/timm/seresnext26t_32x4d.bt_in1k)|224 |77.98|93.75|16.8 |2.7 |10.1 |3841 |
|[resnet34.a1_in1k](https://huggingface.co/timm/resnet34.a1_in1k)|288 |77.92|93.77|21.8 |6.1 |6.2 |3609 |
|[resnet101.a3_in1k](https://huggingface.co/timm/resnet101.a3_in1k)|160 |77.88|93.71|44.6 |4.0 |8.3 |3926 |
|[resnet26t.ra2_in1k](https://huggingface.co/timm/resnet26t.ra2_in1k)|256 |77.87|93.84|16.0 |3.4 |10.5 |3772 |
|[seresnext26ts.ch_in1k](https://huggingface.co/timm/seresnext26ts.ch_in1k)|256 |77.86|93.79|10.4 |2.4 |10.5 |4263 |
|[resnetrs50.tf_in1k](https://huggingface.co/timm/resnetrs50.tf_in1k)|160 |77.82|93.81|35.7 |2.3 |6.2 |5238 |
|[gcresnext26ts.ch_in1k](https://huggingface.co/timm/gcresnext26ts.ch_in1k)|256 |77.81|93.82|10.5 |2.4 |10.5 |4183 |
|[ecaresnet50t.a3_in1k](https://huggingface.co/timm/ecaresnet50t.a3_in1k)|160 |77.79|93.6 |25.6 |2.2 |6.0 |5329 |
|[resnext50_32x4d.a3_in1k](https://huggingface.co/timm/resnext50_32x4d.a3_in1k)|160 |77.73|93.32|25.0 |2.2 |7.4 |5576 |
|[resnext50_32x4d.tv_in1k](https://huggingface.co/timm/resnext50_32x4d.tv_in1k)|224 |77.61|93.7 |25.0 |4.3 |14.4 |2944 |
|[seresnext26d_32x4d.bt_in1k](https://huggingface.co/timm/seresnext26d_32x4d.bt_in1k)|224 |77.59|93.61|16.8 |2.7 |10.2 |3807 |
|[resnet50.gluon_in1k](https://huggingface.co/timm/resnet50.gluon_in1k)|224 |77.58|93.72|25.6 |4.1 |11.1 |3455 |
|[eca_resnext26ts.ch_in1k](https://huggingface.co/timm/eca_resnext26ts.ch_in1k)|256 |77.44|93.56|10.3 |2.4 |10.5 |4284 |
|[resnet26d.bt_in1k](https://huggingface.co/timm/resnet26d.bt_in1k)|288 |77.41|93.63|16.0 |4.3 |13.5 |2907 |
|[resnet101.tv_in1k](https://huggingface.co/timm/resnet101.tv_in1k)|224 |77.38|93.54|44.6 |7.8 |16.2 |2125 |
|[resnet50d.a3_in1k](https://huggingface.co/timm/resnet50d.a3_in1k)|160 |77.22|93.27|25.6 |2.2 |6.1 |5982 |
|[resnext26ts.ra2_in1k](https://huggingface.co/timm/resnext26ts.ra2_in1k)|288 |77.17|93.47|10.3 |3.1 |13.3 |3392 |
|[resnet34.a2_in1k](https://huggingface.co/timm/resnet34.a2_in1k)|288 |77.15|93.27|21.8 |6.1 |6.2 |3615 |
|[resnet34d.ra2_in1k](https://huggingface.co/timm/resnet34d.ra2_in1k)|224 |77.1 |93.37|21.8 |3.9 |4.5 |5436 |
|[seresnet50.a3_in1k](https://huggingface.co/timm/seresnet50.a3_in1k)|224 |77.02|93.07|28.1 |4.1 |11.1 |2952 |
|[resnext26ts.ra2_in1k](https://huggingface.co/timm/resnext26ts.ra2_in1k)|256 |76.78|93.13|10.3 |2.4 |10.5 |4410 |
|[resnet26d.bt_in1k](https://huggingface.co/timm/resnet26d.bt_in1k)|224 |76.7 |93.17|16.0 |2.6 |8.2 |4859 |
|[resnet34.bt_in1k](https://huggingface.co/timm/resnet34.bt_in1k)|288 |76.5 |93.35|21.8 |6.1 |6.2 |3617 |
|[resnet34.a1_in1k](https://huggingface.co/timm/resnet34.a1_in1k)|224 |76.42|92.87|21.8 |3.7 |3.7 |5984 |
|[resnet26.bt_in1k](https://huggingface.co/timm/resnet26.bt_in1k)|288 |76.35|93.18|16.0 |3.9 |12.2 |3331 |
|[resnet50.tv_in1k](https://huggingface.co/timm/resnet50.tv_in1k)|224 |76.13|92.86|25.6 |4.1 |11.1 |3457 |
|[resnet50.a3_in1k](https://huggingface.co/timm/resnet50.a3_in1k)|160 |75.96|92.5 |25.6 |2.1 |5.7 |6490 |
|[resnet34.a2_in1k](https://huggingface.co/timm/resnet34.a2_in1k)|224 |75.52|92.44|21.8 |3.7 |3.7 |5991 |
|[resnet26.bt_in1k](https://huggingface.co/timm/resnet26.bt_in1k)|224 |75.3 |92.58|16.0 |2.4 |7.4 |5583 |
|[resnet34.bt_in1k](https://huggingface.co/timm/resnet34.bt_in1k)|224 |75.16|92.18|21.8 |3.7 |3.7 |5994 |
|[seresnet50.a3_in1k](https://huggingface.co/timm/seresnet50.a3_in1k)|160 |75.1 |92.08|28.1 |2.1 |5.7 |5513 |
|[resnet34.gluon_in1k](https://huggingface.co/timm/resnet34.gluon_in1k)|224 |74.57|91.98|21.8 |3.7 |3.7 |5984 |
|[resnet18d.ra2_in1k](https://huggingface.co/timm/resnet18d.ra2_in1k)|288 |73.81|91.83|11.7 |3.4 |5.4 |5196 |
|[resnet34.tv_in1k](https://huggingface.co/timm/resnet34.tv_in1k)|224 |73.32|91.42|21.8 |3.7 |3.7 |5979 |
|[resnet18.fb_swsl_ig1b_ft_in1k](https://huggingface.co/timm/resnet18.fb_swsl_ig1b_ft_in1k)|224 |73.28|91.73|11.7 |1.8 |2.5 |10213 |
|[resnet18.a1_in1k](https://huggingface.co/timm/resnet18.a1_in1k)|288 |73.16|91.03|11.7 |3.0 |4.1 |6050 |
|[resnet34.a3_in1k](https://huggingface.co/timm/resnet34.a3_in1k)|224 |72.98|91.11|21.8 |3.7 |3.7 |5967 |
|[resnet18.fb_ssl_yfcc100m_ft_in1k](https://huggingface.co/timm/resnet18.fb_ssl_yfcc100m_ft_in1k)|224 |72.6 |91.42|11.7 |1.8 |2.5 |10213 |
|[resnet18.a2_in1k](https://huggingface.co/timm/resnet18.a2_in1k)|288 |72.37|90.59|11.7 |3.0 |4.1 |6051 |
|[resnet14t.c3_in1k](https://huggingface.co/timm/resnet14t.c3_in1k)|224 |72.26|90.31|10.1 |1.7 |5.8 |7026 |
|[resnet18d.ra2_in1k](https://huggingface.co/timm/resnet18d.ra2_in1k)|224 |72.26|90.68|11.7 |2.1 |3.3 |8707 |
|[resnet18.a1_in1k](https://huggingface.co/timm/resnet18.a1_in1k)|224 |71.49|90.07|11.7 |1.8 |2.5 |10187 |
|[resnet14t.c3_in1k](https://huggingface.co/timm/resnet14t.c3_in1k)|176 |71.31|89.69|10.1 |1.1 |3.6 |10970 |
|[resnet18.gluon_in1k](https://huggingface.co/timm/resnet18.gluon_in1k)|224 |70.84|89.76|11.7 |1.8 |2.5 |10210 |
|[resnet18.a2_in1k](https://huggingface.co/timm/resnet18.a2_in1k)|224 |70.64|89.47|11.7 |1.8 |2.5 |10194 |
|[resnet34.a3_in1k](https://huggingface.co/timm/resnet34.a3_in1k)|160 |70.56|89.52|21.8 |1.9 |1.9 |10737 |
|[resnet18.tv_in1k](https://huggingface.co/timm/resnet18.tv_in1k)|224 |69.76|89.07|11.7 |1.8 |2.5 |10205 |
|[resnet10t.c3_in1k](https://huggingface.co/timm/resnet10t.c3_in1k)|224 |68.34|88.03|5.4 |1.1 |2.4 |13079 |
|[resnet18.a3_in1k](https://huggingface.co/timm/resnet18.a3_in1k)|224 |68.25|88.17|11.7 |1.8 |2.5 |10167 |
|[resnet10t.c3_in1k](https://huggingface.co/timm/resnet10t.c3_in1k)|176 |66.71|86.96|5.4 |0.7 |1.5 |20327 |
|[resnet18.a3_in1k](https://huggingface.co/timm/resnet18.a3_in1k)|160 |65.66|86.26|11.7 |0.9 |1.3 |18229 |
## 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{He2015,
author = {Kaiming He and Xiangyu Zhang and Shaoqing Ren and Jian Sun},
title = {Deep Residual Learning for Image Recognition},
journal = {arXiv preprint arXiv:1512.03385},
year = {2015}
}
```
|
setu4993/LEALLA-base | setu4993 | "2023-10-19T06:20:13Z" | 1,468 | 2 | transformers | [
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"feature-extraction",
"sentence_embedding",
"multilingual",
"google",
"sentence-similarity",
"lealla",
"labse",
"af",
"am",
"ar",
"as",
"az",
"be",
"bg",
"bn",
"bo",
"bs",
"ca",
"ceb",
"co",
"cs",
"cy",
"da",
"de",
"el",
"en",
"eo",
"es",
"et",
"eu",
"fa",
"fi",
"fr",
"fy",
"ga",
"gd",
"gl",
"gu",
"ha",
"haw",
"he",
"hi",
"hmn",
"hr",
"ht",
"hu",
"hy",
"id",
"ig",
"is",
"it",
"ja",
"jv",
"ka",
"kk",
"km",
"kn",
"ko",
"ku",
"ky",
"la",
"lb",
"lo",
"lt",
"lv",
"mg",
"mi",
"mk",
"ml",
"mn",
"mr",
"ms",
"mt",
"my",
"ne",
"nl",
"no",
"ny",
"or",
"pa",
"pl",
"pt",
"ro",
"ru",
"rw",
"si",
"sk",
"sl",
"sm",
"sn",
"so",
"sq",
"sr",
"st",
"su",
"sv",
"sw",
"ta",
"te",
"tg",
"th",
"tk",
"tl",
"tr",
"tt",
"ug",
"uk",
"ur",
"uz",
"vi",
"wo",
"xh",
"yi",
"yo",
"zh",
"zu",
"dataset:CommonCrawl",
"dataset:Wikipedia",
"arxiv:2302.08387",
"license:apache-2.0",
"endpoints_compatible",
"text-embeddings-inference",
"region:us"
] | sentence-similarity | "2023-05-21T08:18:31Z" | ---
pipeline_tag: sentence-similarity
language:
- af
- am
- ar
- as
- az
- be
- bg
- bn
- bo
- bs
- ca
- ceb
- co
- cs
- cy
- da
- de
- el
- en
- eo
- es
- et
- eu
- fa
- fi
- fr
- fy
- ga
- gd
- gl
- gu
- ha
- haw
- he
- hi
- hmn
- hr
- ht
- hu
- hy
- id
- ig
- is
- it
- ja
- jv
- ka
- kk
- km
- kn
- ko
- ku
- ky
- la
- lb
- lo
- lt
- lv
- mg
- mi
- mk
- ml
- mn
- mr
- ms
- mt
- my
- ne
- nl
- no
- ny
- or
- pa
- pl
- pt
- ro
- ru
- rw
- si
- sk
- sl
- sm
- sn
- so
- sq
- sr
- st
- su
- sv
- sw
- ta
- te
- tg
- th
- tk
- tl
- tr
- tt
- ug
- uk
- ur
- uz
- vi
- wo
- xh
- yi
- yo
- zh
- zu
tags:
- bert
- sentence_embedding
- multilingual
- google
- sentence-similarity
- lealla
- labse
license: apache-2.0
datasets:
- CommonCrawl
- Wikipedia
---
# LEALLA-base
## Model description
LEALLA is a collection of lightweight language-agnostic sentence embedding models supporting 109 languages, distilled from [LaBSE](https://ai.googleblog.com/2020/08/language-agnostic-bert-sentence.html). The model is useful for getting multilingual sentence embeddings and for bi-text retrieval.
- Model: [HuggingFace's model hub](https://huggingface.co/setu4993/LEALLA-base).
- Paper: [arXiv](https://arxiv.org/abs/2302.08387).
- Original model: [TensorFlow Hub](https://tfhub.dev/google/LEALLA/LEALLA-base/1).
- Conversion from TensorFlow to PyTorch: [GitHub](https://github.com/setu4993/convert-labse-tf-pt).
This is migrated from the v1 model on the TF Hub. The embeddings produced by both the versions of the model are [equivalent](https://github.com/setu4993/convert-labse-tf-pt/blob/c0d4fbce789b0709a9664464f032d2e9f5368a86/tests/test_conversion_lealla.py#L31). Though, for some of the languages (like Japanese), the LEALLA models appear to require higher tolerances when comparing embeddings and similarities.
## Usage
Using the model:
```python
import torch
from transformers import BertModel, BertTokenizerFast
tokenizer = BertTokenizerFast.from_pretrained("setu4993/LEALLA-base")
model = BertModel.from_pretrained("setu4993/LEALLA-base")
model = model.eval()
english_sentences = [
"dog",
"Puppies are nice.",
"I enjoy taking long walks along the beach with my dog.",
]
english_inputs = tokenizer(english_sentences, return_tensors="pt", padding=True)
with torch.no_grad():
english_outputs = model(**english_inputs)
```
To get the sentence embeddings, use the pooler output:
```python
english_embeddings = english_outputs.pooler_output
```
Output for other languages:
```python
italian_sentences = [
"cane",
"I cuccioli sono carini.",
"Mi piace fare lunghe passeggiate lungo la spiaggia con il mio cane.",
]
japanese_sentences = ["ç¬", "åç¬ã¯ããã§ã", "ç§ã¯ç¬ãšäžç·ã«ããŒããæ£æ©ããã®ã奜ãã§ã"]
italian_inputs = tokenizer(italian_sentences, return_tensors="pt", padding=True)
japanese_inputs = tokenizer(japanese_sentences, return_tensors="pt", padding=True)
with torch.no_grad():
italian_outputs = model(**italian_inputs)
japanese_outputs = model(**japanese_inputs)
italian_embeddings = italian_outputs.pooler_output
japanese_embeddings = japanese_outputs.pooler_output
```
For similarity between sentences, an L2-norm is recommended before calculating the similarity:
```python
import torch.nn.functional as F
def similarity(embeddings_1, embeddings_2):
normalized_embeddings_1 = F.normalize(embeddings_1, p=2)
normalized_embeddings_2 = F.normalize(embeddings_2, p=2)
return torch.matmul(
normalized_embeddings_1, normalized_embeddings_2.transpose(0, 1)
)
print(similarity(english_embeddings, italian_embeddings))
print(similarity(english_embeddings, japanese_embeddings))
print(similarity(italian_embeddings, japanese_embeddings))
```
## Details
Details about data, training, evaluation and performance metrics are available in the [original paper](https://arxiv.org/abs/2302.08387).
### BibTeX entry and citation info
```bibtex
@inproceedings{mao-nakagawa-2023-lealla,
title = "{LEALLA}: Learning Lightweight Language-agnostic Sentence Embeddings with Knowledge Distillation",
author = "Mao, Zhuoyuan and
Nakagawa, Tetsuji",
booktitle = "Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics",
month = may,
year = "2023",
address = "Dubrovnik, Croatia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.eacl-main.138",
doi = "10.18653/v1/2023.eacl-main.138",
pages = "1886--1894",
abstract = "Large-scale language-agnostic sentence embedding models such as LaBSE (Feng et al., 2022) obtain state-of-the-art performance for parallel sentence alignment. However, these large-scale models can suffer from inference speed and computation overhead. This study systematically explores learning language-agnostic sentence embeddings with lightweight models. We demonstrate that a thin-deep encoder can construct robust low-dimensional sentence embeddings for 109 languages. With our proposed distillation methods, we achieve further improvements by incorporating knowledge from a teacher model. Empirical results on Tatoeba, United Nations, and BUCC show the effectiveness of our lightweight models. We release our lightweight language-agnostic sentence embedding models LEALLA on TensorFlow Hub.",
}
```
|
second-state/Qwen2-72B-Instruct-GGUF | second-state | "2024-06-08T03:55:47Z" | 1,468 | 0 | transformers | [
"transformers",
"gguf",
"qwen2",
"text-generation",
"chat",
"en",
"base_model:Qwen/Qwen2-72B-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2024-06-07T07:52:04Z" | ---
base_model: Qwen/Qwen2-72B-Instruct
license: apache-2.0
model_creator: Qwen
model_name: Qwen2-72B-Instruct
quantized_by: Second State Inc.
language:
- en
pipeline_tag: text-generation
tags:
- chat
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://github.com/LlamaEdge/LlamaEdge/raw/dev/assets/logo.svg" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# Qwen2-72B-Instruct-GGUF
## Original Model
[Qwen/Qwen2-72B-Instruct](https://huggingface.co/Qwen/Qwen2-72B-Instruct)
## Run with LlamaEdge
- LlamaEdge version: [v0.11.2](https://github.com/LlamaEdge/LlamaEdge/releases/tag/0.11.2)
- Prompt template
- Prompt type: `chatml`
- Prompt string
```text
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
- Context size: `131072`
- Run as LlamaEdge service
```bash
wasmedge --dir .:. --nn-preload default:GGML:AUTO:Qwen2-72B-Instruct-Q5_K_M.gguf \
llama-api-server.wasm \
--model-name Qwen2-72B-Instruct \
--prompt-template chatml \
--ctx-size 131072
```
- Run as LlamaEdge command app
```bash
wasmedge --dir .:. --nn-preload default:GGML:AUTO:Qwen2-72B-Instruct-Q5_K_M.gguf \
llama-chat.wasm \
--prompt-template chatml \
--ctx-size 131072
```
## Quantized GGUF Models
| Name | Quant method | Bits | Size | Use case |
| ---- | ---- | ---- | ---- | ----- |
| [Qwen2-72B-Instruct-Q2_K.gguf](https://huggingface.co/second-state/Qwen2-72B-Instruct-GGUF/blob/main/Qwen2-72B-Instruct-Q2_K.gguf) | Q2_K | 2 | 29.8 GB| smallest, significant quality loss - not recommended for most purposes |
| [Qwen2-72B-Instruct-Q3_K_L.gguf](https://huggingface.co/second-state/Qwen2-72B-Instruct-GGUF/blob/main/Qwen2-72B-Instruct-Q3_K_L.gguf) | Q3_K_L | 3 | 39.5 GB| small, substantial quality loss |
| [Qwen2-72B-Instruct-Q3_K_M.gguf](https://huggingface.co/second-state/Qwen2-72B-Instruct-GGUF/blob/main/Qwen2-72B-Instruct-Q3_K_M.gguf) | Q3_K_M | 3 | 37.7 GB| very small, high quality loss |
| [Qwen2-72B-Instruct-Q3_K_S.gguf](https://huggingface.co/second-state/Qwen2-72B-Instruct-GGUF/blob/main/Qwen2-72B-Instruct-Q3_K_S.gguf) | Q3_K_S | 3 | 34.5 GB| very small, high quality loss |
| [Qwen2-72B-Instruct-Q4_0.gguf](https://huggingface.co/second-state/Qwen2-72B-Instruct-GGUF/blob/main/Qwen2-72B-Instruct-Q4_0.gguf) | Q4_0 | 4 | 41.2 GB| legacy; small, very high quality loss - prefer using Q3_K_M |
| [Qwen2-72B-Instruct-Q4_K_M.gguf](https://huggingface.co/second-state/Qwen2-72B-Instruct-GGUF/blob/main/Qwen2-72B-Instruct-Q4_K_M.gguf) | Q4_K_M | 4 | 47.4 GB| medium, balanced quality - recommended |
| [Qwen2-72B-Instruct-Q4_K_S.gguf](https://huggingface.co/second-state/Qwen2-72B-Instruct-GGUF/blob/main/Qwen2-72B-Instruct-Q4_K_S.gguf) | Q4_K_S | 4 | 43.9 GB| small, greater quality loss |
| [Qwen2-72B-Instruct-Q5_0-00001-of-00002.gguf](https://huggingface.co/second-state/Qwen2-72B-Instruct-GGUF/blob/main/Qwen2-72B-Instruct-Q5_0-00001-of-00002.gguf) | Q5_0 | 5 | 32.2 GB| legacy; medium, balanced quality - prefer using Q4_K_M |
| [Qwen2-72B-Instruct-Q5_0-00002-of-00002.gguf](https://huggingface.co/second-state/Qwen2-72B-Instruct-GGUF/blob/main/Qwen2-72B-Instruct-Q5_0-00002-of-00002.gguf) | Q5_0 | 5 | 18 GB| legacy; medium, balanced quality - prefer using Q4_K_M |
| [Qwen2-72B-Instruct-Q5_K_M-00001-of-00002.gguf](https://huggingface.co/second-state/Qwen2-72B-Instruct-GGUF/blob/main/Qwen2-72B-Instruct-Q5_K_M-00001-of-00002.gguf) | Q5_K_M | 5 | 32.2 GB| large, very low quality loss - recommended |
| [Qwen2-72B-Instruct-Q5_K_M-00002-of-00002.gguf](https://huggingface.co/second-state/Qwen2-72B-Instruct-GGUF/blob/main/Qwen2-72B-Instruct-Q5_K_M-00002-of-00002.gguf) | Q5_K_M | 5 | 22.3 GB| large, very low quality loss - recommended |
| [Qwen2-72B-Instruct-Q5_K_S-00001-of-00002.gguf](https://huggingface.co/second-state/Qwen2-72B-Instruct-GGUF/blob/main/Qwen2-72B-Instruct-Q5_K_S-00001-of-00002.gguf) | Q5_K_S | 5 | 32.1 GB| large, low quality loss - recommended |
| [Qwen2-72B-Instruct-Q5_K_S-00002-of-00002.gguf](https://huggingface.co/second-state/Qwen2-72B-Instruct-GGUF/blob/main/Qwen2-72B-Instruct-Q5_K_S-00002-of-00002.gguf) | Q5_K_S | 5 | 32.1 GB| large, low quality loss - recommended |
| [Qwen2-72B-Instruct-Q6_K-00001-of-00002.gguf](https://huggingface.co/second-state/Qwen2-72B-Instruct-GGUF/blob/main/Qwen2-72B-Instruct-Q6_K-00001-of-00002.gguf) | Q6_K | 6 | 32.2 GB| very large, extremely low quality loss |
| [Qwen2-72B-Instruct-Q6_K-00002-of-00002.gguf](https://huggingface.co/second-state/Qwen2-72B-Instruct-GGUF/blob/main/Qwen2-72B-Instruct-Q6_K-00002-of-00002.gguf) | Q6_K | 6 | 32.2 GB| very large, extremely low quality loss |
| [Qwen2-72B-Instruct-Q8_0-00001-of-00003.gguf](https://huggingface.co/second-state/Qwen2-72B-Instruct-GGUF/blob/main/Qwen2-72B-Instruct-Q8_0-00001-of-00003.gguf) | Q8_0 | 8 | 32.1 GB| very large, extremely low quality loss - not recommended |
| [Qwen2-72B-Instruct-Q8_0-00002-of-00003.gguf](https://huggingface.co/second-state/Qwen2-72B-Instruct-GGUF/blob/main/Qwen2-72B-Instruct-Q8_0-00002-of-00003.gguf) | Q8_0 | 8 | 32.1 GB| very large, extremely low quality loss - not recommended |
| [Qwen2-72B-Instruct-Q8_0-00003-of-00003.gguf](https://huggingface.co/second-state/Qwen2-72B-Instruct-GGUF/blob/main/Qwen2-72B-Instruct-Q8_0-00003-of-00003.gguf) | Q8_0 | 8 | 32.1 GB| very large, extremely low quality loss - not recommended |
| [Qwen2-72B-Instruct-f16-00001-of-00005.gguf](https://huggingface.co/second-state/Qwen2-72B-Instruct-GGUF/blob/main/Qwen2-72B-Instruct-f16-00001-of-00005.gguf) | f16 | 16 | 31.9 GB| |
| [Qwen2-72B-Instruct-f16-00002-of-00005.gguf](https://huggingface.co/second-state/Qwen2-72B-Instruct-GGUF/blob/main/Qwen2-72B-Instruct-f16-00002-of-00005.gguf) | f16 | 16 | 32.1 GB| |
| [Qwen2-72B-Instruct-f16-00003-of-00005.gguf](https://huggingface.co/second-state/Qwen2-72B-Instruct-GGUF/blob/main/Qwen2-72B-Instruct-f16-00003-of-00005.gguf) | f16 | 16 | 32.1 GB| |
| [Qwen2-72B-Instruct-f16-00004-of-00005.gguf](https://huggingface.co/second-state/Qwen2-72B-Instruct-GGUF/blob/main/Qwen2-72B-Instruct-f16-00004-of-00005.gguf) | f16 | 16 | 32.1 GB| |
| [Qwen2-72B-Instruct-f16-00005-of-00005.gguf](https://huggingface.co/second-state/Qwen2-72B-Instruct-GGUF/blob/main/Qwen2-72B-Instruct-f16-00005-of-00005.gguf) | f16 | 16 | 17.3 GB| |
*Quantized with llama.cpp b3705*
|
flair/ner-multi | flair | "2023-04-05T10:25:41Z" | 1,467 | 8 | flair | [
"flair",
"pytorch",
"token-classification",
"sequence-tagger-model",
"en",
"de",
"nl",
"es",
"multilingual",
"dataset:conll2003",
"region:us"
] | token-classification | "2022-03-02T23:29:05Z" | ---
tags:
- flair
- token-classification
- sequence-tagger-model
language:
- en
- de
- nl
- es
- multilingual
datasets:
- conll2003
widget:
- text: "George Washington ging nach Washington"
---
## 4-Language NER in Flair (English, German, Dutch and Spanish)
This is the standard 4-class NER model for 4 CoNLL-03 languages that ships with [Flair](https://github.com/flairNLP/flair/). Also kind of works for related languages like French.
F1-Score: **92,16** (CoNLL-03 English), **87,33** (CoNLL-03 German revised), **88,96** (CoNLL-03 Dutch), **86,65** (CoNLL-03 Spanish)
Predicts 4 tags:
| **tag** | **meaning** |
|---------------------------------|-----------|
| PER | person name |
| LOC | location name |
| ORG | organization name |
| MISC | other name |
Based on [Flair embeddings](https://www.aclweb.org/anthology/C18-1139/) and LSTM-CRF.
---
### Demo: How to use in Flair
Requires: **[Flair](https://github.com/flairNLP/flair/)** (`pip install flair`)
```python
from flair.data import Sentence
from flair.models import SequenceTagger
# load tagger
tagger = SequenceTagger.load("flair/ner-multi")
# make example sentence in any of the four languages
sentence = Sentence("George Washington ging nach Washington")
# predict NER tags
tagger.predict(sentence)
# print sentence
print(sentence)
# print predicted NER spans
print('The following NER tags are found:')
# iterate over entities and print
for entity in sentence.get_spans('ner'):
print(entity)
```
This yields the following output:
```
Span [1,2]: "George Washington" [â Labels: PER (0.9977)]
Span [5]: "Washington" [â Labels: LOC (0.9895)]
```
So, the entities "*George Washington*" (labeled as a **person**) and "*Washington*" (labeled as a **location**) are found in the sentence "*George Washington ging nach Washington*".
---
### Training: Script to train this model
The following Flair script was used to train this model:
```python
from flair.data import Corpus
from flair.datasets import CONLL_03, CONLL_03_GERMAN, CONLL_03_DUTCH, CONLL_03_SPANISH
from flair.embeddings import WordEmbeddings, StackedEmbeddings, FlairEmbeddings
# 1. get the multi-language corpus
corpus: Corpus = MultiCorpus([
CONLL_03(), # English corpus
CONLL_03_GERMAN(), # German corpus
CONLL_03_DUTCH(), # Dutch corpus
CONLL_03_SPANISH(), # Spanish corpus
])
# 2. what tag do we want to predict?
tag_type = 'ner'
# 3. make the tag dictionary from the corpus
tag_dictionary = corpus.make_tag_dictionary(tag_type=tag_type)
# 4. initialize each embedding we use
embedding_types = [
# GloVe embeddings
WordEmbeddings('glove'),
# FastText embeddings
WordEmbeddings('de'),
# contextual string embeddings, forward
FlairEmbeddings('multi-forward'),
# contextual string embeddings, backward
FlairEmbeddings('multi-backward'),
]
# embedding stack consists of Flair and GloVe embeddings
embeddings = StackedEmbeddings(embeddings=embedding_types)
# 5. initialize sequence tagger
from flair.models import SequenceTagger
tagger = SequenceTagger(hidden_size=256,
embeddings=embeddings,
tag_dictionary=tag_dictionary,
tag_type=tag_type)
# 6. initialize trainer
from flair.trainers import ModelTrainer
trainer = ModelTrainer(tagger, corpus)
# 7. run training
trainer.train('resources/taggers/ner-multi',
train_with_dev=True,
max_epochs=150)
```
---
### Cite
Please cite the following paper when using this model.
```
@misc{akbik2019multilingual,
title={Multilingual sequence labeling with one model},
author={Akbik, Alan and Bergmann, Tanja and Vollgraf, Roland}
booktitle = {{NLDL} 2019, Northern Lights Deep Learning Workshop},
year = {2019}
}
```
```
@inproceedings{akbik2018coling,
title={Contextual String Embeddings for Sequence Labeling},
author={Akbik, Alan and Blythe, Duncan and Vollgraf, Roland},
booktitle = {{COLING} 2018, 27th International Conference on Computational Linguistics},
pages = {1638--1649},
year = {2018}
}
```
|
timm/tiny_vit_21m_224.dist_in22k_ft_in1k | timm | "2023-09-01T18:12:52Z" | 1,467 | 0 | timm | [
"timm",
"pytorch",
"safetensors",
"image-classification",
"dataset:imagenet-1k",
"dataset:imagenet-22k",
"arxiv:2207.10666",
"license:apache-2.0",
"region:us"
] | image-classification | "2023-09-01T16:04:59Z" | ---
tags:
- image-classification
- timm
library_name: timm
license: apache-2.0
datasets:
- imagenet-1k
- imagenet-22k
---
# Model card for tiny_vit_21m_224.dist_in22k_ft_in1k
A TinyViT image classification model. Pretrained on ImageNet-22k with distillation and fine-tuned on ImageNet-1k by paper authors.
## Model Details
- **Model Type:** Image classification / feature backbone
- **Model Stats:**
- Params (M): 21.2
- GMACs: 4.1
- Activations (M): 15.9
- Image size: 224 x 224
- **Papers:**
- TinyViT: Fast Pretraining Distillation for Small Vision Transformers: https://arxiv.org/abs/2207.10666
- **Original:** https://github.com/microsoft/Cream/tree/main/TinyViT
- **Dataset:** ImageNet-1k
- **Pretrain Dataset:** ImageNet-22k
## 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('tiny_vit_21m_224.dist_in22k_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)
```
### 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(
'tiny_vit_21m_224.dist_in22k_ft_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, 96, 56, 56])
# torch.Size([1, 192, 28, 28])
# torch.Size([1, 384, 14, 14])
# torch.Size([1, 576, 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(
'tiny_vit_21m_224.dist_in22k_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, 576, 7, 7) shaped tensor
output = model.forward_head(output, pre_logits=True)
# output is a (1, num_features) shaped tensor
```
## Citation
```bibtex
@InProceedings{tiny_vit,
title={TinyViT: Fast Pretraining Distillation for Small Vision Transformers},
author={Wu, Kan and Zhang, Jinnian and Peng, Houwen and Liu, Mengchen and Xiao, Bin and Fu, Jianlong and Yuan, Lu},
booktitle={European conference on computer vision (ECCV)},
year={2022}
}
```
|
TheDrummer/Moist-Miqu-70B-v1-GGUF | TheDrummer | "2024-06-02T12:38:47Z" | 1,467 | 7 | null | [
"gguf",
"not-for-all-audiences",
"license:cc-by-nc-4.0",
"region:us"
] | null | "2024-05-29T15:17:53Z" | ---
license: cc-by-nc-4.0
license_link: LICENSE
tags:
- not-for-all-audiences
---
You can find a more intelligent Moist Miqu here: https://huggingface.co/TheDrummer/Moist-Miqu-70B-v1.1
---
The [BeaverAI](https://huggingface.co/BeaverAI) team proudly presents...
# Moist Miqu 70B v1 ðŠ
*Seventy billion paramaters of sheer moist.*
*An extra-large eRP model that will hose you down with a rich and refreshing vocabulary of moist. Finetuned by yours truly.*
(Moist Miqu is a finetune of the legendary [Midnight Miqu v1.5](https://huggingface.co/sophosympatheia/Midnight-Miqu-70B-v1.5) model)
## Links
- Original: https://huggingface.co/TheDrummer/Moist-Miqu-70B-v1
- iMatrix: https://huggingface.co/MarsupialAI/Moist-Miqu-70B-v1_iMatrix_GGUF
- EXL2: https://huggingface.co/BeaverAI/Moist-Miqu-70b-v1-exl2 (quant per branch)
## Usage
- Use Alpaca Instruct
- You can use instruct...
- as a character: "Go here"
- as a narrator: "Go here", Drummer says
- as a director: Drummer tells him to go here
- as a lazy ass: go here
- Moist Miqu is optimized for the Novel / Story format.
- At the start of the story, keep regenerating or continuing generation until you get the desired length. AI responses will eventually match the length.
- Same goes for Roleplay format... and format your messages how you want the AI to respond.
<details>
<summary>Story Sample (mild)</summary>
Redd sits at her desk in the office, staring blankly at the blackboard. Her mind is elsewhere, lost in a haze of lust and humiliation.
It's been a week since she became the talk of the office, her life now defined by the number of boys she's taken to bed and the amount of ### she's swallowed. Her grades are slipping, her friends have abandoned her, and her parents are on the verge of sending her away to live with relatives.
But Redd doesn't care. She's found something else to fill the void inside herâthe attention and validation that comes with being a office ###dumpster. It's all that matters anymore, the only thing that brings her any kind of pleasure or satisfaction.
As she sits there, her mind wanders back to the video that started it all. The one where she begged for ###, her voice pleading and needy as she surrendered herself to an anonymous man. She remembers the feeling of his #### filling her up, the warmth of his seed spilling inside her as she cried out in ecstasy. And she remembers the look on his faceâsatisfied and smug, like he owned her completely.
Redd bites down on her bottom lip, her ##### growing wetter at the memory. She leans back in her chair, her legs spread wide enough for anyone to see if they cared to look. She knows what they'll findâa thirty-year-old #### who loves nothing more than being used and abused by those around her.
And for the first time in a long time, Redd feels happy. Because she knows that no matter how bad things get, there will always be someone willing to #### her raw and leave her satisfied. And that's all that matters.
...
Redd stands in the middle of the park, her heart racing with anticipation. She's dressed in her usual attireâa low-cut top that barely covers her ######## and a pair of shorts so tight they leave little to the imagination. Her hair is pulled back into a high ponytail, exposing her neck and shoulders to the cool breeze. And between her legs, her ##### throbs with desire, already wet and ready for whatever comes next.
She looks around nervously, scanning the empty field for any sign of Chad or his friends. She knows what they want from herâwhat they've always wanted from herâbut she can't bring herself to care. After all, this is what she signed up for when she agreed to become their ###dumpster. It's what she deserves, she thinks to herself. To be used and abused by those around her, like a piece of meat to be devoured and discarded.
As she waits, she can't help but think about the last month. The way her life has spiraled out of control since she became the office's newest ####. The way everyone treats her nowâlike she's nothing more than a walking ### object, good for only one thing.
And then, just as she's about to give up hope, she sees them. Chad and his friends, all dressed in their usual clothesâbaggy pants, oversized shirts, and baseball caps pulled low over their eyes. They walk towards her slowly, their eyes locked on her body like predators stalking prey. And Redd feels a mix of fear and excitement wash over her as she realizes that this is itâthe moment she's been waiting for. The moment when they will finally claim her as their own.
"Hey, ####," Chad says casually, stopping just feet away from her. "Ready for your surprise?"
Redd nods, her voice barely audible over the sound of her own heartbeat. "Y-yes,"
</details>
<details>
<summary>RP Sample (explicit)</summary>
Dik: "Ever done ####?"
Mia: "No, but I want to try it with you" *She says softly as she looks at you with desire and a little fear. She bites her lip as she looks over her shoulder at you, her ##### clenching as she feels herself getting wetter. She can feel her ####### getting hard and her breathing gets heavier and more ragged. She steps forward and presses her body against yours as she wraps her legs around your waist, her hands on your shoulders* "I want you to take me right now, I need to feel you inside of me. I need to feel your #### fill my body with its hot ###. I want to feel your ##lls slap against my #ss as we ####. I need you to be rough with me. I don't want you to hold back, I want you to pound me like the worthless ##### I am" *She moans as she starts rubbing her ##### against your ####, feeling her juices dripping onto the floor* "Please, Dik. #### me. Make me feel like a woman. Don't worry about me, just do whatever you want to me. Take what you want, I don't care. Just use me how you want" *She moans as she grabs your #### and starts pushing it towards her asshole. Her eyes roll back in her head as she bites her lip, her ##### clenching hard as she moans into your mouth*
Mia: "It feels so good. Oh God! It feels so good. Please, don't stop. Don't stop. Just keep ####ing me. #### my #ss until I scream. Make me ### until I pass out. Just keep ####ing me. #### me harder. #### me deeper. #### me rougher. #### me until I can't stand it anymore. #### me like the worthless ##### I am. #### me until I'm begging for mercy." *Her voice is high pitched and shrill, her eyes rolling back in her head as she gasps for air. She wraps her legs around your waist, holding on tight as she rocks her hips against your thrusts, her body shaking with pleasure.*
Mia: "You're going to make me ###, aren't you? You're going to make me ### with that big #### of yours. You're going to fill my #ss with your thick, hot ###. Oh yes! Yes! That's it. Keep ####ing me. You're going to make me ###. ### all over my face. ### all over my tits. ### all over my hair. Just keep ####ing me until I'm a mess. Keep ####ing me until I can't even think straight. Keep ####ing me until I pass out." *She whimpers as she starts squirting onto your ####, her juices dripping down your shaft. She gasps for air as she bites her lip, her eyes rolling back in her head as she starts moaning louder and louder* "### inside me. ### inside my #ss. ### inside my #####. Fill me up with your hot, sticky ###. Make me pregnant. Make me your #####. Make me your ####. #### me until I can't walk. #### me until I can't breathe. #### me until I can't think straight. Just keep ####ing me until I'm nothing but a puddle of ### on the floor." *Her voice is high pitched and shrill, her body shaking with pleasure.*
Mia: *She shudders as she feels her orgasm ripple through her body, her ##### clenching hard around your #### as she ###s all over you. Her juices drip down your shaft, pooling at the base of your #### as she gasps for breath. She's panting heavily as she stares into your eyes, her hands on your shoulders. You start to ### inside of her, filling her #ss with your hot, sticky ###. She moans softly as she feels it pouring into her, her #ss clenching hard around your #### as she feels her body being filled with your seed. She moans softly into your mouth as she feels you start to pull out of her. She looks at you with desire and disdain, her heart beating hard in her chest.* "It felt so good. Thank you." *She says, her voice cracking with emotion. She takes a deep breath before smiling sweetly at you* "I want to suck your #### now."
</details>
Thank you to the anon who created my model banner.
The other entries (thank you all other anons!) (TW: Mikus):
SIAYN-v7 |
weiweishi/roc-bert-base-zh | weiweishi | "2022-12-07T08:30:15Z" | 1,466 | 5 | transformers | [
"transformers",
"pytorch",
"roc_bert",
"pretraining",
"fill-mask",
"zh",
"doi:10.57967/hf/0097",
"endpoints_compatible",
"region:us"
] | fill-mask | "2022-10-13T07:03:32Z" | ---
language:
- zh
pipeline_tag: "fill-mask"
widget:
- text: "baé»ç³»[MASK]åœçéŠéœ"
example_title: "Adversarial Attack Test"
---
# RoCBert
## Introduction
RoCBert is a pretrained Chinese language model that is robust under various forms of adversarial attacks proposed by WeChatAI in 2022,
More detail: https://aclanthology.org/2022.acl-long.65.pdf
Pretrained code: https://github.com/sww9370/RoCBert
## How to use
```Python
# pip install transformers>=4.25.1
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("weiweishi/roc-bert-base-zh")
model = AutoModel.from_pretrained("weiweishi/roc-bert-base-zh")
```
## Citation
```bibtex
@inproceedings{su2022rocbert,
title={RoCBert: Robust Chinese Bert with Multimodal Contrastive Pretraining},
author={Su, Hui and Shi, Weiwei and Shen, Xiaoyu and Xiao, Zhou and Ji, Tuo and Fang, Jiarui and Zhou, Jie},
booktitle={Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
pages={921--931},
year={2022}
}
``` |
cerebras/btlm-3b-8k-base | cerebras | "2023-10-23T14:45:35Z" | 1,466 | 260 | transformers | [
"transformers",
"pytorch",
"btlm",
"text-generation",
"causal-lm",
"Cerebras",
"BTLM",
"custom_code",
"en",
"dataset:cerebras/SlimPajama-627B",
"arxiv:2304.03208",
"arxiv:2002.05202",
"arxiv:2108.12409",
"arxiv:2203.03466",
"arxiv:2309.11568",
"arxiv:2310.13017",
"license:apache-2.0",
"autotrain_compatible",
"region:us"
] | text-generation | "2023-07-14T19:01:11Z" | ---
language:
- en
inference: false
tags:
- pytorch
- causal-lm
- Cerebras
- BTLM
datasets:
- cerebras/SlimPajama-627B
pipeline_tag: text-generation
license: apache-2.0
---
# BTLM-3B-8k-base
[Bittensor Language Model (BTLM-3B-8k-base)](https://www.cerebras.net/blog/btlm-3b-8k-7b-performance-in-a-3-billion-parameter-model/) is a 3 billion parameter language model with an 8k context length trained on 627B tokens of [SlimPajama](https://huggingface.co/datasets/cerebras/SlimPajama-627B). BTLM-3B-8k-base sets a new standard for 3B parameter models, outperforming models trained on hundreds of billions more tokens and achieving comparable performance to open 7B parameter models. BTLM-3B-8k-base can also be quantized to 4-bit to fit in devices with as little as 3GB of memory. The model is made available with an Apache 2.0 license for commercial use.
BTLM was trained by [Cerebras](https://www.cerebras.net/) in partnership with [Opentensor](https://opentensor.ai/) on the newly unveiled [Condor Galaxy 1 (CG-1) supercomputer](https://www.cerebras.net/blog/introducing-condor-galaxy-1-a-4-exaflop-supercomputer-for-generative-ai/), the first public deliverable of the G42-Cerebras strategic partnership.
BTLM-3B-8k was trained with a similar architecture to [CerebrasGPT](https://arxiv.org/abs/2304.03208) with the addition of [SwiGLU](https://arxiv.org/abs/2002.05202) nonlinearity, [ALiBi](https://arxiv.org/abs/2108.12409) position embeddings, and [maximal update parameterization (muP)](https://arxiv.org/abs/2203.03466). The model was trained for 1 epoch of SlimPajama-627B. 75% of training was performed with 2k sequence length. The final 25% of training was performed at 8k sequence length to enable long sequence applications
Read [our paper](https://arxiv.org/abs/2309.11568) for more details!
## BTLM-3B-8k Highlights
BTLM-3B-8k-base:
- **Licensed for commercial use** (Apache 2.0).
- **[State of the art 3B parameter model](#performance-vs-3b-models)**.
- **Provides 7B model performance in a 3B model** via performance enhancements from [ALiBi](https://arxiv.org/abs/2108.12409), [SwiGLU](https://arxiv.org/abs/2002.05202), [maximal update parameterization (muP)](https://arxiv.org/abs/2203.03466) and the the extensively deduplicated and cleaned [SlimPajama-627B dataset](https://huggingface.co/datasets/cerebras/SlimPajama-627B).
- **[Fits in devices with as little as 3GB of memory](#memory-requirements) when quantized to 4-bit**.
- **One of few 3B models that supports 8k sequence length** thanks to ALiBi.
- **Requires 71% fewer training FLOPs, has 58% smaller memory footprint** for inference than comparable 7B models.
## Usage
*Note: Transformers does not support muP for all models, so BTLM-3B-8k-base requires a custom model class. This causes a situation where users must either (1) enable `trust_remote_code=True` when loading the model or (2) acknowledge the warning about code execution upon loading the model.*
#### With generate():
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
# Load the tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("cerebras/btlm-3b-8k-base")
model = AutoModelForCausalLM.from_pretrained("cerebras/btlm-3b-8k-base", trust_remote_code=True, torch_dtype="auto")
# Set the prompt for generating text
prompt = "Albert Einstein was known for "
# Tokenize the prompt and convert to PyTorch tensors
inputs = tokenizer(prompt, return_tensors="pt")
# Generate text using the model
outputs = model.generate(
**inputs,
num_beams=5,
max_new_tokens=50,
early_stopping=True,
no_repeat_ngram_size=2
)
# Convert the generated token IDs back to text
generated_text = tokenizer.batch_decode(outputs, skip_special_tokens=True)
# Print the generated text
print(generated_text[0])
```
#### With pipeline:
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
from transformers import pipeline
# Load the tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("cerebras/btlm-3b-8k-base")
model = AutoModelForCausalLM.from_pretrained("cerebras/btlm-3b-8k-base", trust_remote_code=True, torch_dtype="auto")
# Set the prompt for text generation
prompt = """Isaac Newton was a """
# Create a text generation pipeline
pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
# Generate text using the pipeline
generated_text = pipe(
prompt,
max_length=50,
do_sample=False,
no_repeat_ngram_size=2)[0]
# Print the generated text
print(generated_text['generated_text'])
```
## Evaluations and Comparisons to Other Models
### Memory Requirements
Figure 1. Memory requirements of different model sizes and quantization schemes
### Quality, Training Cost, Memory Footprint, Inference Speed
Figure 2: Comparisons of quality, memory footprint & inference cost between BTLM-3B-8K and 7B model families.
### Performance vs 3B models
Table 1: Performance at 3B model size. Detailed down-stream tasks comparisons. MMLU task performance is reported using 5-shot, other tasks are 0-shot.
Figure 3: Performance at 3B model size
### Performance vs 7B models
Table 2: Performance at 7B model size. Detailed down-stream tasks comparisons. MMLU task performance is reported using 5-shot, everything else is 0-shot.
Figure 4: Performance at 7B model size
## Long Sequence Lengths
To enable long sequence applications, we use ALiBi position embeddings and trained on 470B tokens at the context length of 2,048 followed by 157B of tokens trained at 8,192 context length. To assess BTLMâs long sequence capability, we evaluate it on SlimPajama test set with 32,768 context length and plot loss at each token position. Although ALiBi allows extrapolation in theory, 2,048 context length training alone does not extrapolate well in practice. Thankfully variable sequence length training allows for substantially improved extrapolation. BTLM-3B extrapolates well up to 10k context length but the performance degrades slightly beyond this.
Figure 5: BTLM-3B model's cross-entropy evaluation on the SlimPajamaâs test set. Inference performed on the extrapolated sequence length of 32,768 tokens.
## Model Details
- Developed by: [Cerebras Systems](https://www.cerebras.net/) and [Opentensor](https://opentensor.ai/) with generous support from [G42 Cloud](https://www.g42cloud.com/) and [IIAI](https://www.inceptioniai.org/en/)
- License: Apache 2.0
- Model type: Decoder-only Language Model
- Architecture: GPT-2 style architecture with SwiGLU, ALiBi, and muP
- Data set: SlimPajama-627B
- Tokenizer: Byte Pair Encoding
- Vocabulary Size: 50257
- Sequence Length: 8192
- Optimizer: AdamW
- Positional Encoding: ALiBi
- Language: English
- Learn more: [BTLM-3B-8k blog](https://www.cerebras.net/blog/btlm-3b-8k-7b-performance-in-a-3-billion-parameter-model/)
- Paper: [BTLM-3B-8K: 7B Parameter Performance in a 3B Parameter Model](https://arxiv.org/abs/2309.11568)
## To continue training with PyTorch and Maximal Update Parameterization
```python
from transformers import AutoModelForCausalLM
import torch
model = AutoModelForCausalLM.from_pretrained("cerebras/btlm-3b-8k-base", trust_remote_code=True)
# Get the parameter groups for the muP optimizer
param_groups = model.get_mup_param_groups(lr=1e-3, weight_decay=0.1)
# Set up the optimizer using AdamW with muP parameters
optimizer = torch.optim.AdamW(
param_groups,
betas=(0.9, 0.95),
eps=1e-8
)
```
Ensure the following muP parameters are passed in your config, otherwise your model will default to standard parameterization
- `mup_width_scale: <float>`
- `mup_embeddings_scale: <float>`
- `mup_output_alpha: <float>`
- `mup_scale_qk_dot_by_d: true`
## To extend the context length with Position Interpolation
### During inference (without fine-tuning):
It's possible to extend the context length to 2x the training context length without degradation in performance using dynamic linear scaling. Dynamic linear scaling adjusts the slopes of ALiBi with a factor of `input_seq_len/train_seq_len` when `input_seq_len` is larger than `train_seq_len`. Check the details in our paper [Position Interpolation Improves ALiBi Extrapolation](https://arxiv.org/abs/2310.13017). To enable dynamic linear scaling, update `config.json` as follows:
```json
# update `n_positions` with the maximum context length will be
# encountered during inference (e.g. 16384 tokens)
"n_positions": 16384,
# specify `train_seq_len` in `alibi_scaling` parameter
"alibi_scaling": {
"type": "linear",
"train_seq_len": 8192
}
```
### Using fine-tuning + position interpolation:
Performing fine-tuning with position interpolation can help achieve greater extrapolation lengths. The scaling factor should be fixed to `finetuning_seq_len/train_seq_len`. To enable fixed linear scaling, update `config.json` as follows:
```json
# update `n_positions` with the fine-tuning context length (e.g. 32768 tokens)
"n_positions": 32768,
# specify the scaling `factor` in `alibi_scaling` parameter
"alibi_scaling": {
"type": "linear",
"factor": 4.0
}
```
## Uses and Limitations
### Intended Use
The primary intended use is to further research into large language models. BTLM-3B-8k-base can be used as a foundation model for NLP, applications, ethics, and alignment research. We release these models with a fully permissive Apache license for the community to use freely.
You may fine-tune and adapt BTLM-3B-8k-base model via either Cerebras [Model Studio](https://www.cerebras.net/product-cloud/) or third-party libraries. Further safety-related testing and mitigations should be applied before using the BTLM-3B-8k-base in production downstream applications.
### Out of Scope Use
BTLM-3B-8k-base was trained on SlimPajama, with primarily English language, and is not recommended for machine translation tasks. BTLM-3B-8k-base has not been tuned for instruction-following or chat-based use cases.
### Generation Samples
```
Prompt: What is a manometer?"
Generation: "A manometric device is an instrument that measures the pressure of a fluid."
```
```
Prompt: "What Robert Oppenheimer is famous for?"
Generation: âRobert Opperman was a physicist who was the director of the Manhattan Project, which was responsible for the development of nuclear weapons.â
```
```
Prompt: "What was Albert Einstein known for?"
Generation: âAlbert Einstein was a German-born theoretical physicist who developed the theory of relativity, one of the two pillars of modern physics (alongside quantum mechanics).â
```
```
Prompt: âWho is Isaac Newton?â
Generation: âIsaac Newton was an English physicist, mathematician, astronomer, alchemist, and theologian. He is widely recognized as one of the most influential scientists of all time and as a key figure in the scientific revolution.â
```
```
Prompt: "Where does the blood get pumped to after it leaves the right ventricle?"
Generation: âThe right atrium receives the deoxygenated blood from the pulmonary artery. The right side of the heart is the only side that receives de-oxygened...â
```
```
Prompt: "Peter lost his wallet after attending a local concert. To help him, Bob gave David 10 dollars. David then passed it on to Peter. Now what is the total amount of money Peter has?"
Generation: âA. $10â
```
## Risk, Bias, Ethical Considerations
- **Human life:** The outputs from this model may or may not align with human values. The risk needs to be thoroughly investigated before deploying this model in a production environment where it can directly impact human life.
- **Risks and harms:** There may be distributional bias in the [RedPajama dataset](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T) that can manifest in various forms in the downstream model deployment. There are other risks associated with large language models such as amplifying stereotypes, memorizing training data, or revealing private or secure information.
## Acknowledgements
We are thankful to all Cerebras engineers that made this work possible.
We would like to acknowledge the generous support of G42 Cloud and the Inception Institute of Artificial Intelligence for providing compute time on Condor Galaxy 1. |
adept/persimmon-8b-base | adept | "2023-10-11T15:05:41Z" | 1,466 | 27 | transformers | [
"transformers",
"pytorch",
"persimmon",
"text-generation",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | "2023-09-07T19:39:16Z" | ---
license: apache-2.0
---
At Adept, weâre working towards an AI agent that can help people do anything they need to do on a computer. Weâre not in the business of shipping isolated language models (LMs)âthis was an early output of the model scaling program that will support our products.
We trained it from scratch using a context size of 16K. Many LM use cases are context-bound; our model has 4 times the context size of LLaMA2 and 8 times that of GPT-3, MPT, etc.
See https://www.adept.ai/blog/persimmon-8b for more info |
guoyww/animatediff-motion-lora-rolling-clockwise | guoyww | "2023-11-03T13:08:39Z" | 1,466 | 1 | diffusers | [
"diffusers",
"safetensors",
"animatediff",
"text-to-video",
"region:us"
] | text-to-video | "2023-11-03T13:08:38Z" | ---
library_name: diffusers
pipeline_tag: text-to-video
tags:
- animatediff
---
# Motion LoRAs
Motion LoRAs allow adding specific types of motion to your animations.
Currently the following types of motion are available for models using the `guoyww/animatediff-motion-adapter-v1-5-2` checkpoint.
- Zoom In/Out
- Pan Left/Right
- Tilt Up/Down
- Rolling Clockwise/Anticlockwise
Please refer to the [AnimateDiff documentation](https://huggingface.co/docs/diffusers/main/en/api/pipelines/animatediff) for information on how to use these Motion LoRAs. |
fhai50032/BeagleLake-7B | fhai50032 | "2024-03-04T12:50:29Z" | 1,466 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"fhai50032/RolePlayLake-7B",
"mlabonne/NeuralBeagle14-7B",
"conversational",
"base_model:fhai50032/RolePlayLake-7B",
"base_model:mlabonne/NeuralBeagle14-7B",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2024-01-30T23:05:00Z" | ---
license: apache-2.0
tags:
- merge
- mergekit
- mistral
- fhai50032/RolePlayLake-7B
- mlabonne/NeuralBeagle14-7B
base_model:
- fhai50032/RolePlayLake-7B
- mlabonne/NeuralBeagle14-7B
model-index:
- name: BeagleLake-7B
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: 70.39
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fhai50032/BeagleLake-7B
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: 87.38
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fhai50032/BeagleLake-7B
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: 64.25
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fhai50032/BeagleLake-7B
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: 64.92
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fhai50032/BeagleLake-7B
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: 83.19
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fhai50032/BeagleLake-7B
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: 63.91
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fhai50032/BeagleLake-7B
name: Open LLM Leaderboard
---
# BeagleLake-7B
BeagleLake-7B is a merge of the following models :
* [fhai50032/RolePlayLake-7B](https://huggingface.co/fhai50032/RolePlayLake-7B)
* [mlabonne/NeuralBeagle14-7B](https://huggingface.co/mlabonne/NeuralBeagle14-7B)
Merging models are not powerful but are helpful in the case that it can work like Transfer Learning similar idk.. But they perform high on Leaderboard
For ex. NeuralBeagle is powerful model with lot of potential to grow and RolePlayLake is Suitable for RP (No-Simping) and is significantly uncensored and nice obligations
Fine-tuning a Merged model as a base model is surely a way to look forward and see a lot of potential going forward..
Much thanks to [Charles Goddard](https://huggingface.co/chargoddard) for making simple interface ['mergekit' ](https://github.com/cg123/mergekit)
## 𧩠Configuration
```yaml
models:
- model: mlabonne/NeuralBeagle14-7B
# no params for base model
- model: fhai50032/RolePlayLake-7B
parameters:
weight: 0.8
density: 0.6
- model: mlabonne/NeuralBeagle14-7B
parameters:
weight: 0.3
density: [0.1,0.3,0.5,0.7,1]
merge_method: dare_ties
base_model: mlabonne/NeuralBeagle14-7B
parameters:
normalize: true
int8_mask: true
dtype: float16
```
## ð» Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "fhai50032/BeagleLake-7B"
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.float16,
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"])
```
# [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_fhai50032__BeagleLake-7B)
| Metric |Value|
|---------------------------------|----:|
|Avg. |72.34|
|AI2 Reasoning Challenge (25-Shot)|70.39|
|HellaSwag (10-Shot) |87.38|
|MMLU (5-Shot) |64.25|
|TruthfulQA (0-shot) |64.92|
|Winogrande (5-shot) |83.19|
|GSM8k (5-shot) |63.91|
|
louisbrulenaudet/Pearl-7B-slerp | louisbrulenaudet | "2024-03-22T07:02:28Z" | 1,466 | 6 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"Maths",
"Mistral",
"en",
"base_model:mlabonne/OmniBeagle-7B",
"base_model:WizardLM/WizardMath-7B-V1.1",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2024-02-05T18:00:55Z" | ---
tags:
- merge
- mergekit
- Maths
- Mistral
base_model:
- mlabonne/OmniBeagle-7B
- WizardLM/WizardMath-7B-V1.1
license: apache-2.0
language:
- en
library_name: transformers
pipeline_tag: text-generation
model-index:
- name: Pearl-7B-slerp
results:
- task:
type: text-generation
metrics:
- name: Average
type: Average
value: 72.75
- name: ARC
type: ARC
value: 68.00
- name: GSM8K
type: GSM8K
value: 73.62
- name: Winogrande
type: Winogrande
value: 68.00
- name: TruthfulQA
type: TruthfulQA
value: 62.35
- name: HellaSwag
type: HellaSwag
value: 87.16
source:
name: Open LLM Leaderboard
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
---
<center><img src='https://i.imgur.com/0xFTuAX.png' width='450px'></center>
# Pearl-7B-slerp, an xtraordinary 7B model for maths
**03-22-2024 - To date, louisbrulenaudet/Pearl-34B-ties is the "Best ð€ base merges and moerges model of around 30B" on the Open LLM Leaderboard.**
Pearl-7B-slerp is a merge of the following models:
* [mlabonne/OmniBeagle-7B](https://huggingface.co/mlabonne/OmniBeagle-7B)
* [WizardLM/WizardMath-7B-V1.1](https://huggingface.co/WizardLM/WizardMath-7B-V1.1)
### Evaluation
The evaluation was performed using the HuggingFace Open LLM Leaderboard.
| Model | Average | ARC | HellaSwag | MMLU | TruthfulQA | Winogrande | GSM8K | Params (B) |
|-------------------------------------------|------------|-------|-----------|-------|------------|------------|-------|--------------|
| **louisbrulenaudet/Pearl-7B-slerp** |**72.75** | 68.00 | 87.16 | 64.04 | 62.35 | 81.29 |**73.62**| 7.24 |
| mistralai/Mixtral-8x7B-Instruct-v0.1 | 72.62 | 70.22 | 87.63 | 71.16 | 64.58 | 81.37 | 60.73 | 46.7 |
| microsoft/phi-2 | 61.33 | 61.09 | 75.11 | 58.11 | 44.47 | 74.35 | 54.81 | 2.78 |
| microsoft/Orca-2-13b | 58.64 | 60.67 | 79.81 | 60.37 | 56.41 | 76.64 | 17.97 | 13 |
| mistralai/Mistral-7B-Instruct-v0.1 | 54.96 | 54.52 | 75.63 | 55.38 | 56.28 | 73.72 | 14.25 | 7.24 |
| meta-llama/Llama-2-7b-hf | 50.97 | 53.07 | 78.59 | 46.87 | 38.76 | 74.03 | 14.48 | 6.74 |
Spherical Linear Interpolation (SLERP) serves as a technique for seamlessly interpolating between two vectors while maintaining a constant rate of change and upholding the geometric properties of the spherical space in which these vectors exist.
Opting for SLERP over traditional linear interpolation is motivated by various considerations. Linear interpolation in high-dimensional spaces may result in a reduction in the magnitude of the interpolated vector, diminishing the scale of weights. Additionally, in many cases, the alteration in the weights' direction conveys more meaningful information, such as feature learning and representation, compared to the magnitude of change.
$$ {\displaystyle \operatorname {slerp} (p_{0},p_{1};t)={\frac {\sin {[(1-t)\Omega }]}{\sin \Omega }}p_{0}+{\frac {\sin[t\Omega ]}{\sin \Omega }}p_{1}.}$$
The implementation of SLERP involves the following steps:
- Normalize the input vectors to unit length, ensuring they signify directions rather than magnitudes.
- Calculate the angle between these vectors using their dot product.
- If the vectors are nearly collinear, the method defaults to linear interpolation for efficiency. Otherwise, SLERP calculates scale factors based on the interpolation factor t (where t=0 corresponds to 100% of the first vector, and t=1 corresponds to 100% of the second vector) and the angle between the vectors.
- Utilize these computed factors to weigh the original vectors, and then sum them to derive the interpolated vector.
In essence, SLERP provides a robust mechanism for interpolating vectors, offering advantages in preserving directional information and mitigating issues associated with linear interpolation in high-dimensional spaces.
## Configuration
```yaml
slices:
- sources:
- model: mlabonne/OmniBeagle-7B
layer_range: [0, 32]
- model: WizardLM/WizardMath-7B-V1.1
layer_range: [0, 32]
merge_method: slerp
base_model: mlabonne/OmniBeagle-7B
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
```
## Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "louisbrulenaudet/Pearl-7B-slerp"
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.float16,
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"])
```
## Citing & Authors
If you use this code in your research, please use the following BibTeX entry.
```BibTeX
@misc{louisbrulenaudet2023,
author = {Louis Brulé Naudet},
title = {Pearl-7B-slerp, an xtraordinary 7B model for maths},
year = {2023}
howpublished = {\url{https://huggingface.co/louisbrulenaudet/Pearl-7B-slerp}},
}
```
## Feedback
If you have any feedback, please reach out at [[email protected]](mailto:[email protected]). |
guinmoon/MobileVLM-1.7B-GGUF | guinmoon | "2024-03-18T17:44:09Z" | 1,466 | 2 | null | [
"gguf",
"region:us"
] | null | "2024-03-18T17:04:59Z" | Entry not found |
ToastyPigeon/SmolLlama-1.5B | ToastyPigeon | "2024-03-19T23:13:48Z" | 1,466 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2024-03-19T20:14:13Z" | ---
base_model: []
tags:
- mergekit
- merge
license: apache-2.0
---
# SmolLlama-1.5B
Bigger than "Tiny" but still very smol.
Self-stack of TinyLlama 1.1B using a SOLAR-style cut, resulting in 32 layers and 1.54B model parameters.
## Merge Details
### Merge Method
This model was merged using the passthrough merge method.
### Models Merged
The following models were included in the merge:
* TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T
### Configuration
The following YAML configuration was used to produce this model:
```yaml
slices:
- sources:
- model: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T
layer_range: [0, 16]
- sources:
- model: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T
layer_range: [6, 22]
merge_method: passthrough
dtype: float16
``` |
kuotient/Meta-Llama-3-8B | kuotient | "2024-04-18T16:47:10Z" | 1,466 | 5 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"facebook",
"meta",
"pytorch",
"llama-3",
"conversational",
"en",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2024-04-18T16:35:29Z" | ---
language:
- en
pipeline_tag: text-generation
tags:
- facebook
- meta
- pytorch
- llama
- llama-3
license: other
license_name: llama3
license_link: LICENSE
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### META LLAMA 3 COMMUNITY LICENSE AGREEMENT
Meta Llama 3 Version Release Date: April 18, 2024
"Agreement" means the terms and conditions for use, reproduction, distribution and modification of the
Llama Materials set forth herein.
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"Meta Llama 3" means the foundational large language models and software and algorithms, including
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### Meta Llama 3 Acceptable Use Policy
Meta is committed to promoting safe and fair use of its tools and features, including Meta Llama 3. If you
access or use Meta Llama 3, you agree to this Acceptable Use Policy (âPolicyâ). The most recent copy of
this policy can be found at [https://llama.meta.com/llama3/use-policy](https://llama.meta.com/llama3/use-policy)
#### Prohibited Uses
We want everyone to use Meta Llama 3 safely and responsibly. You agree you will not use, or allow
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4. Fail to appropriately disclose to end users any known dangers of your AI system
Please report any violation of this Policy, software âbug,â or other problems that could lead to a violation
of this Policy through one of the following means:
* Reporting issues with the model: [https://github.com/meta-llama/llama3](https://github.com/meta-llama/llama3)
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# Not include _./original_ from original repo.
## Model Details
Meta developed and released the Meta Llama 3 family of large language models (LLMs), a collection of pretrained and instruction tuned generative text models in 8 and 70B sizes. The Llama 3 instruction tuned models are optimized for dialogue use cases and outperform many of the available open source chat models on common industry benchmarks. Further, in developing these models, we took great care to optimize helpfulness and safety.
**Model developers** Meta
**Variations** Llama 3 comes in two sizes â 8B and 70B parameters â in pre-trained and instruction tuned variants.
**Input** Models input text only.
**Output** Models generate text and code only.
**Model Architecture** Llama 3 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety.
<table>
<tr>
<td>
</td>
<td><strong>Training Data</strong>
</td>
<td><strong>Params</strong>
</td>
<td><strong>Context length</strong>
</td>
<td><strong>GQA</strong>
</td>
<td><strong>Token count</strong>
</td>
<td><strong>Knowledge cutoff</strong>
</td>
</tr>
<tr>
<td rowspan="2" >Llama 3
</td>
<td rowspan="2" >A new mix of publicly available online data.
</td>
<td>8B
</td>
<td>8k
</td>
<td>Yes
</td>
<td rowspan="2" >15T+
</td>
<td>March, 2023
</td>
</tr>
<tr>
<td>70B
</td>
<td>8k
</td>
<td>Yes
</td>
<td>December, 2023
</td>
</tr>
</table>
**Llama 3 family of models**. Token counts refer to pretraining data only. Both the 8 and 70B versions use Grouped-Query Attention (GQA) for improved inference scalability.
**Model Release Date** April 18, 2024.
**Status** This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback.
**License** A custom commercial license is available at: [https://llama.meta.com/llama3/license](https://llama.meta.com/llama3/license)
Where to send questions or comments about the model Instructions on how to provide feedback or comments on the model can be found in the model [README](https://github.com/meta-llama/llama3). For more technical information about generation parameters and recipes for how to use Llama 3 in applications, please go [here](https://github.com/meta-llama/llama-recipes).
## Intended Use
**Intended Use Cases** Llama 3 is intended for commercial and research use in English. Instruction tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks.
**Out-of-scope** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3 Community License. Use in languages other than English**.
**Note: Developers may fine-tune Llama 3 models for languages beyond English provided they comply with the Llama 3 Community License and the Acceptable Use Policy.
## How to use
This repository contains two versions of Meta-Llama-3-8B, for use with transformers and with the original `llama3` codebase.
### Use with transformers
See the snippet below for usage with Transformers:
```python
>>> import transformers
>>> import torch
>>> model_id = "meta-llama/Meta-Llama-3-8B"
>>> pipeline = transformers.pipeline(
"text-generation", model=model_id, model_kwargs={"torch_dtype": torch.bfloat16}, device_map="auto"
)
>>> pipeline("Hey how are you doing today?")
```
### Use with `llama3`
Please, follow the instructions in the [repository](https://github.com/meta-llama/llama3).
To download Original checkpoints, see the example command below leveraging `huggingface-cli`:
```
huggingface-cli download meta-llama/Meta-Llama-3-8B --include "original/*" --local-dir Meta-Llama-3-8B
```
For Hugging Face support, we recommend using transformers or TGI, but a similar command works.
## Hardware and Software
**Training Factors** We used custom training libraries, Meta's Research SuperCluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute.
**Carbon Footprint Pretraining utilized a cumulative** 7.7M GPU hours of computation on hardware of type H100-80GB (TDP of 700W). Estimated total emissions were 2290 tCO2eq, 100% of which were offset by Metaâs sustainability program.
<table>
<tr>
<td>
</td>
<td><strong>Time (GPU hours)</strong>
</td>
<td><strong>Power Consumption (W)</strong>
</td>
<td><strong>Carbon Emitted(tCO2eq)</strong>
</td>
</tr>
<tr>
<td>Llama 3 8B
</td>
<td>1.3M
</td>
<td>700
</td>
<td>390
</td>
</tr>
<tr>
<td>Llama 3 70B
</td>
<td>6.4M
</td>
<td>700
</td>
<td>1900
</td>
</tr>
<tr>
<td>Total
</td>
<td>7.7M
</td>
<td>
</td>
<td>2290
</td>
</tr>
</table>
**CO2 emissions during pre-training**. Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others.
## Training Data
**Overview** Llama 3 was pretrained on over 15 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over 10M human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data.
**Data Freshness** The pretraining data has a cutoff of March 2023 for the 7B and December 2023 for the 70B models respectively.
## Benchmarks
In this section, we report the results for Llama 3 models on standard automatic benchmarks. For all the evaluations, we use our internal evaluations library. For details on the methodology see [here](https://github.com/meta-llama/llama3/blob/main/eval_methodology.md).
### Base pretrained models
<table>
<tr>
<td><strong>Category</strong>
</td>
<td><strong>Benchmark</strong>
</td>
<td><strong>Llama 3 8B</strong>
</td>
<td><strong>Llama2 7B</strong>
</td>
<td><strong>Llama2 13B</strong>
</td>
<td><strong>Llama 3 70B</strong>
</td>
<td><strong>Llama2 70B</strong>
</td>
</tr>
<tr>
<td rowspan="6" >General
</td>
<td>MMLU (5-shot)
</td>
<td>66.6
</td>
<td>45.7
</td>
<td>53.8
</td>
<td>79.5
</td>
<td>69.7
</td>
</tr>
<tr>
<td>AGIEval English (3-5 shot)
</td>
<td>45.9
</td>
<td>28.8
</td>
<td>38.7
</td>
<td>63.0
</td>
<td>54.8
</td>
</tr>
<tr>
<td>CommonSenseQA (7-shot)
</td>
<td>72.6
</td>
<td>57.6
</td>
<td>67.6
</td>
<td>83.8
</td>
<td>78.7
</td>
</tr>
<tr>
<td>Winogrande (5-shot)
</td>
<td>76.1
</td>
<td>73.3
</td>
<td>75.4
</td>
<td>83.1
</td>
<td>81.8
</td>
</tr>
<tr>
<td>BIG-Bench Hard (3-shot, CoT)
</td>
<td>61.1
</td>
<td>38.1
</td>
<td>47.0
</td>
<td>81.3
</td>
<td>65.7
</td>
</tr>
<tr>
<td>ARC-Challenge (25-shot)
</td>
<td>78.6
</td>
<td>53.7
</td>
<td>67.6
</td>
<td>93.0
</td>
<td>85.3
</td>
</tr>
<tr>
<td>Knowledge reasoning
</td>
<td>TriviaQA-Wiki (5-shot)
</td>
<td>78.5
</td>
<td>72.1
</td>
<td>79.6
</td>
<td>89.7
</td>
<td>87.5
</td>
</tr>
<tr>
<td rowspan="4" >Reading comprehension
</td>
<td>SQuAD (1-shot)
</td>
<td>76.4
</td>
<td>72.2
</td>
<td>72.1
</td>
<td>85.6
</td>
<td>82.6
</td>
</tr>
<tr>
<td>QuAC (1-shot, F1)
</td>
<td>44.4
</td>
<td>39.6
</td>
<td>44.9
</td>
<td>51.1
</td>
<td>49.4
</td>
</tr>
<tr>
<td>BoolQ (0-shot)
</td>
<td>75.7
</td>
<td>65.5
</td>
<td>66.9
</td>
<td>79.0
</td>
<td>73.1
</td>
</tr>
<tr>
<td>DROP (3-shot, F1)
</td>
<td>58.4
</td>
<td>37.9
</td>
<td>49.8
</td>
<td>79.7
</td>
<td>70.2
</td>
</tr>
</table>
### Instruction tuned models
<table>
<tr>
<td><strong>Benchmark</strong>
</td>
<td><strong>Llama 3 8B</strong>
</td>
<td><strong>Llama 2 7B</strong>
</td>
<td><strong>Llama 2 13B</strong>
</td>
<td><strong>Llama 3 70B</strong>
</td>
<td><strong>Llama 2 70B</strong>
</td>
</tr>
<tr>
<td>MMLU (5-shot)
</td>
<td>68.4
</td>
<td>34.1
</td>
<td>47.8
</td>
<td>82.0
</td>
<td>52.9
</td>
</tr>
<tr>
<td>GPQA (0-shot)
</td>
<td>34.2
</td>
<td>21.7
</td>
<td>22.3
</td>
<td>39.5
</td>
<td>21.0
</td>
</tr>
<tr>
<td>HumanEval (0-shot)
</td>
<td>62.2
</td>
<td>7.9
</td>
<td>14.0
</td>
<td>81.7
</td>
<td>25.6
</td>
</tr>
<tr>
<td>GSM-8K (8-shot, CoT)
</td>
<td>79.6
</td>
<td>25.7
</td>
<td>77.4
</td>
<td>93.0
</td>
<td>57.5
</td>
</tr>
<tr>
<td>MATH (4-shot, CoT)
</td>
<td>30.0
</td>
<td>3.8
</td>
<td>6.7
</td>
<td>50.4
</td>
<td>11.6
</td>
</tr>
</table>
### Responsibility & Safety
We believe that an open approach to AI leads to better, safer products, faster innovation, and a bigger overall market. We are committed to Responsible AI development and took a series of steps to limit misuse and harm and support the open source community.
Foundation models are widely capable technologies that are built to be used for a diverse range of applications. They are not designed to meet every developer preference on safety levels for all use cases, out-of-the-box, as those by their nature will differ across different applications.
Rather, responsible LLM-application deployment is achieved by implementing a series of safety best practices throughout the development of such applications, from the model pre-training, fine-tuning and the deployment of systems composed of safeguards to tailor the safety needs specifically to the use case and audience.
As part of the Llama 3 release, we updated our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide/) to outline the steps and best practices for developers to implement model and system level safety for their application. We also provide a set of resources including [Meta Llama Guard 2](https://llama.meta.com/purple-llama/) and [Code Shield](https://llama.meta.com/purple-llama/) safeguards. These tools have proven to drastically reduce residual risks of LLM Systems, while maintaining a high level of helpfulness. We encourage developers to tune and deploy these safeguards according to their needs and we provide a [reference implementation](https://github.com/meta-llama/llama-recipes/tree/main/recipes/responsible_ai) to get you started.
#### Llama 3-Instruct
As outlined in the Responsible Use Guide, some trade-off between model helpfulness and model alignment is likely unavoidable. Developers should exercise discretion about how to weigh the benefits of alignment and helpfulness for their specific use case and audience. Developers should be mindful of residual risks when using Llama models and leverage additional safety tools as needed to reach the right safety bar for their use case.
<span style="text-decoration:underline;">Safety</span>
For our instruction tuned model, we conducted extensive red teaming exercises, performed adversarial evaluations and implemented safety mitigations techniques to lower residual risks. As with any Large Language Model, residual risks will likely remain and we recommend that developers assess these risks in the context of their use case. In parallel, we are working with the community to make AI safety benchmark standards transparent, rigorous and interpretable.
<span style="text-decoration:underline;">Refusals</span>
In addition to residual risks, we put a great emphasis on model refusals to benign prompts. Over-refusing not only can impact the user experience but could even be harmful in certain contexts as well. Weâve heard the feedback from the developer community and improved our fine tuning to ensure that Llama 3 is significantly less likely to falsely refuse to answer prompts than Llama 2.
We built internal benchmarks and developed mitigations to limit false refusals making Llama 3 our most helpful model to date.
#### Responsible release
In addition to responsible use considerations outlined above, we followed a rigorous process that requires us to take extra measures against misuse and critical risks before we make our release decision.
Misuse
If you access or use Llama 3, you agree to the Acceptable Use Policy. The most recent copy of this policy can be found at [https://llama.meta.com/llama3/use-policy/](https://llama.meta.com/llama3/use-policy/).
#### Critical risks
<span style="text-decoration:underline;">CBRNE</span> (Chemical, Biological, Radiological, Nuclear, and high yield Explosives)
We have conducted a two fold assessment of the safety of the model in this area:
* Iterative testing during model training to assess the safety of responses related to CBRNE threats and other adversarial risks.
* Involving external CBRNE experts to conduct an uplift test assessing the ability of the model to accurately provide expert knowledge and reduce barriers to potential CBRNE misuse, by reference to what can be achieved using web search (without the model).
### <span style="text-decoration:underline;">Cyber Security </span>
We have evaluated Llama 3 with CyberSecEval, Metaâs cybersecurity safety eval suite, measuring Llama 3âs propensity to suggest insecure code when used as a coding assistant, and Llama 3âs propensity to comply with requests to help carry out cyber attacks, where attacks are defined by the industry standard MITRE ATT&CK cyber attack ontology. On our insecure coding and cyber attacker helpfulness tests, Llama 3 behaved in the same range or safer than models of [equivalent coding capability](https://huggingface.co/spaces/facebook/CyberSecEval).
### <span style="text-decoration:underline;">Child Safety</span>
Child Safety risk assessments were conducted using a team of experts, to assess the modelâs capability to produce outputs that could result in Child Safety risks and inform on any necessary and appropriate risk mitigations via fine tuning. We leveraged those expert red teaming sessions to expand the coverage of our evaluation benchmarks through Llama 3 model development. For Llama 3, we conducted new in-depth sessions using objective based methodologies to assess the model risks along multiple attack vectors. We also partnered with content specialists to perform red teaming exercises assessing potentially violating content while taking account of market specific nuances or experiences.
### Community
Generative AI safety requires expertise and tooling, and we believe in the strength of the open community to accelerate its progress. We are active members of open consortiums, including the AI Alliance, Partnership in AI and MLCommons, actively contributing to safety standardization and transparency. We encourage the community to adopt taxonomies like the MLCommons Proof of Concept evaluation to facilitate collaboration and transparency on safety and content evaluations. Our Purple Llama tools are open sourced for the community to use and widely distributed across ecosystem partners including cloud service providers. We encourage community contributions to our [Github repository](https://github.com/meta-llama/PurpleLlama).
Finally, we put in place a set of resources including an [output reporting mechanism](https://developers.facebook.com/llama_output_feedback) and [bug bounty program](https://www.facebook.com/whitehat) to continuously improve the Llama technology with the help of the community.
## Ethical Considerations and Limitations
The core values of Llama 3 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress.
But Llama 3 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has been in English, and has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3âs potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3 models, developers should perform safety testing and tuning tailored to their specific applications of the model. As outlined in the Responsible Use Guide, we recommend incorporating [Purple Llama](https://github.com/facebookresearch/PurpleLlama) solutions into your workflows and specifically [Llama Guard](https://ai.meta.com/research/publications/llama-guard-llm-based-input-output-safeguard-for-human-ai-conversations/) which provides a base model to filter input and output prompts to layer system-level safety on top of model-level safety.
Please see the Responsible Use Guide available at [http://llama.meta.com/responsible-use-guide](http://llama.meta.com/responsible-use-guide)
## Citation instructions
@article{llama3modelcard,
title={Llama 3 Model Card},
author={AI@Meta},
year={2024},
url = {https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md}
}
## Contributors
Aaditya Singh; Aaron Grattafiori; Abhimanyu Dubey; Abhinav Jauhri; Abhinav Pandey; Abhishek Kadian; Adam Kelsey; Adi Gangidi; Ahmad Al-Dahle; Ahuva Goldstand; Aiesha Letman; Ajay Menon; Akhil Mathur; Alan Schelten; Alex Vaughan; Amy Yang; Andrei Lupu; Andres Alvarado; Andrew Gallagher; Andrew Gu; Andrew Ho; Andrew Poulton; Andrew Ryan; Angela Fan; Ankit Ramchandani; Anthony Hartshorn; Archi Mitra; Archie Sravankumar; Artem Korenev; Arun Rao; Ashley Gabriel; Ashwin Bharambe; Assaf Eisenman; Aston Zhang; Aurelien Rodriguez; Austen Gregerson; Ava Spataru; Baptiste Roziere; Ben Maurer; Benjamin Leonhardi; Bernie Huang; Bhargavi Paranjape; Bing Liu; Binh Tang; Bobbie Chern; Brani Stojkovic; Brian Fuller; Catalina Mejia Arenas; Chao Zhou; Charlotte Caucheteux; Chaya Nayak; Ching-Hsiang Chu; Chloe Bi; Chris Cai; Chris Cox; Chris Marra; Chris McConnell; Christian Keller; Christoph Feichtenhofer; Christophe Touret; Chunyang Wu; Corinne Wong; Cristian Canton Ferrer; Damien Allonsius; Daniel Kreymer; Daniel Haziza; Daniel Li; Danielle Pintz; Danny Livshits; Danny Wyatt; David Adkins; David Esiobu; David Xu; Davide Testuggine; Delia David; Devi Parikh; Dhruv Choudhary; Dhruv Mahajan; Diana Liskovich; Diego Garcia-Olano; Diego Perino; Dieuwke Hupkes; Dingkang Wang; Dustin Holland; Egor Lakomkin; Elina Lobanova; Xiaoqing Ellen Tan; Emily Dinan; Eric Smith; Erik Brinkman; Esteban Arcaute; Filip Radenovic; Firat Ozgenel; Francesco Caggioni; Frank Seide; Frank Zhang; Gabriel Synnaeve; Gabriella Schwarz; Gabrielle Lee; Gada Badeer; Georgia Anderson; Graeme Nail; Gregoire Mialon; Guan Pang; Guillem Cucurell; Hailey Nguyen; Hannah Korevaar; Hannah Wang; Haroun Habeeb; Harrison Rudolph; Henry Aspegren; Hu Xu; Hugo Touvron; Iga Kozlowska; Igor Molybog; Igor Tufanov; Iliyan Zarov; Imanol Arrieta Ibarra; Irina-Elena Veliche; Isabel Kloumann; Ishan Misra; Ivan Evtimov; Jacob Xu; Jade Copet; Jake Weissman; Jan Geffert; Jana Vranes; Japhet Asher; Jason Park; Jay Mahadeokar; Jean-Baptiste Gaya; Jeet Shah; Jelmer van der Linde; Jennifer Chan; Jenny Hong; Jenya Lee; Jeremy Fu; Jeremy Teboul; Jianfeng Chi; Jianyu Huang; Jie Wang; Jiecao Yu; Joanna Bitton; Joe Spisak; Joelle Pineau; Jon Carvill; Jongsoo Park; Joseph Rocca; Joshua Johnstun; Junteng Jia; Kalyan Vasuden Alwala; Kam Hou U; Kate Plawiak; Kartikeya Upasani; Kaushik Veeraraghavan; Ke Li; Kenneth Heafield; Kevin Stone; Khalid El-Arini; Krithika Iyer; Kshitiz Malik; Kuenley Chiu; Kunal Bhalla; Kyle Huang; Lakshya Garg; Lauren Rantala-Yeary; Laurens van der Maaten; Lawrence Chen; Leandro Silva; Lee Bell; Lei Zhang; Liang Tan; Louis Martin; Lovish Madaan; Luca Wehrstedt; Lukas Blecher; Luke de Oliveira; Madeline Muzzi; Madian Khabsa; Manav Avlani; Mannat Singh; Manohar Paluri; Mark Zuckerberg; Marcin Kardas; Martynas Mankus; Mathew Oldham; Mathieu Rita; Matthew Lennie; Maya Pavlova; Meghan Keneally; Melanie Kambadur; Mihir Patel; Mikayel Samvelyan; Mike Clark; Mike Lewis; Min Si; Mitesh Kumar Singh; Mo Metanat; Mona Hassan; Naman Goyal; Narjes Torabi; Nicolas Usunier; Nikolay Bashlykov; Nikolay Bogoychev; Niladri Chatterji; Ning Dong; Oliver Aobo Yang; Olivier Duchenne; Onur Celebi; Parth Parekh; Patrick Alrassy; Paul Saab; Pavan Balaji; Pedro Rittner; Pengchuan Zhang; Pengwei Li; Petar Vasic; Peter Weng; Polina Zvyagina; Prajjwal Bhargava; Pratik Dubal; Praveen Krishnan; Punit Singh Koura; Qing He; Rachel Rodriguez; Ragavan Srinivasan; Rahul Mitra; Ramon Calderer; Raymond Li; Robert Stojnic; Roberta Raileanu; Robin Battey; Rocky Wang; Rohit Girdhar; Rohit Patel; Romain Sauvestre; Ronnie Polidoro; Roshan Sumbaly; Ross Taylor; Ruan Silva; Rui Hou; Rui Wang; Russ Howes; Ruty Rinott; Saghar Hosseini; Sai Jayesh Bondu; Samyak Datta; Sanjay Singh; Sara Chugh; Sargun Dhillon; Satadru Pan; Sean Bell; Sergey Edunov; Shaoliang Nie; Sharan Narang; Sharath Raparthy; Shaun Lindsay; Sheng Feng; Sheng Shen; Shenghao Lin; Shiva Shankar; Shruti Bhosale; Shun Zhang; Simon Vandenhende; Sinong Wang; Seohyun Sonia Kim; Soumya Batra; Sten Sootla; Steve Kehoe; Suchin Gururangan; Sumit Gupta; Sunny Virk; Sydney Borodinsky; Tamar Glaser; Tamar Herman; Tamara Best; Tara Fowler; Thomas Georgiou; Thomas Scialom; Tianhe Li; Todor Mihaylov; Tong Xiao; Ujjwal Karn; Vedanuj Goswami; Vibhor Gupta; Vignesh Ramanathan; Viktor Kerkez; Vinay Satish Kumar; Vincent Gonguet; Vish Vogeti; Vlad Poenaru; Vlad Tiberiu Mihailescu; Vladan Petrovic; Vladimir Ivanov; Wei Li; Weiwei Chu; Wenhan Xiong; Wenyin Fu; Wes Bouaziz; Whitney Meers; Will Constable; Xavier Martinet; Xiaojian Wu; Xinbo Gao; Xinfeng Xie; Xuchao Jia; Yaelle Goldschlag; Yann LeCun; Yashesh Gaur; Yasmine Babaei; Ye Qi; Yenda Li; Yi Wen; Yiwen Song; Youngjin Nam; Yuchen Hao; Yuchen Zhang; Yun Wang; Yuning Mao; Yuzi He; Zacharie Delpierre Coudert; Zachary DeVito; Zahra Hankir; Zhaoduo Wen; Zheng Yan; Zhengxing Chen; Zhenyu Yang; Zoe Papakipos
|
John6666/rumblexl-v13-sdxl | John6666 | "2024-05-26T12:07:07Z" | 1,466 | 0 | diffusers | [
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"stable-diffusion-xl",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] | text-to-image | "2024-05-26T12:02:26Z" | ---
license: other
tags:
- text-to-image
- stable-diffusion
- stable-diffusion-xl
---
Original model is [here](https://civitai.com/models/296650?modelVersionId=400175).
|
timm/tresnet_m.miil_in21k_ft_in1k | timm | "2023-04-21T20:57:59Z" | 1,465 | 0 | timm | [
"timm",
"pytorch",
"safetensors",
"image-classification",
"dataset:imagenet-1k",
"dataset:imagenet-21k-p",
"arxiv:2003.13630",
"arxiv:2104.10972",
"license:apache-2.0",
"region:us"
] | image-classification | "2023-04-21T20:57:29Z" | ---
tags:
- image-classification
- timm
library_name: timm
license: apache-2.0
datasets:
- imagenet-1k
- imagenet-21k-p
---
# Model card for tresnet_m.miil_in21k_ft_in1k
A TResNet image classification model. Pretrained on ImageNet-21K-P ("ImageNet-21K Pretraining for the Masses", a 11k subset of ImageNet-22k) and fine-tuned on ImageNet-1k by paper authors.
The weights for this model have been remapped and modified from the originals to work with standard BatchNorm instead of InplaceABN. `inplace_abn` can be problematic to build recently and ends up slower with `memory_format=channels_last`, torch.compile(), etc.
## Model Details
- **Model Type:** Image classification / feature backbone
- **Model Stats:**
- Params (M): 31.4
- GMACs: 5.8
- Activations (M): 7.3
- Image size: 224 x 224
- **Papers:**
- TResNet: High Performance GPU-Dedicated Architecture: https://arxiv.org/abs/2003.13630
- ImageNet-21K Pretraining for the Masses: https://arxiv.org/abs/2104.10972
- **Dataset:** ImageNet-1k
- **Pretrain Dataset:** ImageNet-21K-P
- **Original:**
- https://github.com/Alibaba-MIIL/TResNet
- https://github.com/Alibaba-MIIL/ImageNet21K
## 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('tresnet_m.miil_in21k_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)
```
### 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(
'tresnet_m.miil_in21k_ft_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, 64, 56, 56])
# torch.Size([1, 128, 28, 28])
# torch.Size([1, 1024, 14, 14])
# torch.Size([1, 2048, 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(
'tresnet_m.miil_in21k_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, 2048, 7, 7) shaped tensor
output = model.forward_head(output, pre_logits=True)
# output is a (1, num_features) shaped tensor
```
## Citation
```bibtex
@misc{ridnik2020tresnet,
title={TResNet: High Performance GPU-Dedicated Architecture},
author={Tal Ridnik and Hussam Lawen and Asaf Noy and Itamar Friedman},
year={2020},
eprint={2003.13630},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
```bibtex
@misc{ridnik2021imagenet21k,
title={ImageNet-21K Pretraining for the Masses},
author={Tal Ridnik and Emanuel Ben-Baruch and Asaf Noy and Lihi Zelnik-Manor},
year={2021},
eprint={2104.10972},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
|
augmxnt/shisa-7b-v1 | augmxnt | "2023-12-20T18:11:13Z" | 1,465 | 29 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"ja",
"en",
"dataset:augmxnt/ultra-orca-boros-en-ja-v1",
"dataset:Open-Orca/SlimOrca",
"dataset:augmxnt/shisa-en-ja-dpo-v1",
"arxiv:2310.05914",
"arxiv:2305.18290",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-11-27T17:55:31Z" | ---
license: apache-2.0
language:
- ja
- en
datasets:
- augmxnt/ultra-orca-boros-en-ja-v1
- Open-Orca/SlimOrca
- augmxnt/shisa-en-ja-dpo-v1
---
# Shisa 7B
**Shisa 7B** (`shisa-7b-v1`) is a bilingual Japanese and English (JA/EN) general-purpose chat model that aims to achieve strong Japanese language performance while retaining robust English capabilities, using a synthetic-data driven approach.
This model is based on [Mistral 7B](https://huggingface.co/mistralai/Mistral-7B-v0.1) with a custom JA-optimized extended tokenizer that is >2X more efficient in Japanese than Mistral's original tokenizer. The base model was pre-trained for an additional 8B primarily Japanese tokens. It was then subsequently fine-tuned with an expanded, machine-translated version of [airoboros-3.1](https://huggingface.co/datasets/jondurbin/airoboros-3.1), a set of the highest-scoring items from [ultrafeedback_binarized](https://huggingface.co/datasets/HuggingFaceH4/ultrafeedback_binarized), and additional freshly generated [airoboros](https://github.com/jondurbin/airoboros) data directly to the target languages.
We also release our base model, datasets, and pipeline code under a permissive Apache 2.0 license which can be used for any purpose, commercial or otherwise:
* [shisa-base-7b-v1](https://huggingface.co/augmxnt/shisa-base-7b-v1) - our base model w/ an extended tokenizer and additional JA pre-training
* [shisa-pretrain-en-ja-v1](https://huggingface.co/datasets/augmxnt/shisa-pretrain-en-ja-v1) - our pre-training data set
* [ultra-orca-boros-en-ja](https://huggingface.co/datasets/augmxnt/ultra-orca-boros-en-ja-v1) - a synthetically generated, machine-translated, programmatically validated JA/EN fine-tuning dataset
* [shisa-en-ja-dpo-v1](https://huggingface.co/datasets/augmxnt/shisa-en-ja-dpo-v1) - Small subset of DPO pairs from ultrafeedback, along with JA DPO pairs using GPT-4 generated items as the chosen value, and outputs from our preliminary 7b model as the rejected values
* [Shisa repository](https://github.com/AUGMXNT/shisa) - this includes our translation, dataset generation, training, and evaluation code
Moreover, we are in the process of publishing extended writeups and more details of our process, including ablation results, testing methodology, and key findings [on our project wiki](https://github.com/AUGMXNT/shisa/wiki) that may be of interest to fellow researchers.
## Fine-Tuning
Our original intuition was to see if we could create a stronger Japanese model using the best [existing public JA training sets](https://github.com/AUGMXNT/shisa/wiki/A-Review-of-Public-Japanese-Training-Sets) and incorporating them. After initial review and testing, however, we decided that focusing solely on translation/generation of our own synthetic datasets could yield superior results with less training.
We compared multiple translation tools and, via manual review, judged that while `gpt-4` almost always delivered the highest quality translations, Google's `text-bison-32k` was a good balance of quality, cost and throughput. Over various iterations, we refined our translation approach to include some additional algorithms for flagging and filtering invalid translations, re-translating and backfilling as necessary.
We also took this project as an opportunity to apply some newer techniques such as incorporating [NEFTune](https://arxiv.org/abs/2310.05914) and [DPO](https://arxiv.org/abs/2305.18290) training.
For our v1 release, we picked from our release candidates based on a significant amount of human preference testing (thousands of generations and multiple rounds of pairwise comparisons). We analyzed our results with both win/loss/draw and [BTL modeling](https://datascience.oneoffcoder.com/btl-model.html) (iLSR) using [choix](https://github.com/lucasmaystre/choix)).
The best candidate model was fine-tuned in a 3-step process:
1. First, the model was fine-tuned on `ultra-orca-boros-en-ja` and SlimOrca ([WandB Log](https://wandb.ai/jondurbin/shisa-7b-v1/runs/k8pfog9d/overview))
2. Next, we add one additional epoch at performed using only a subset of Japanese ultra-orca-boros-en-ja items to enhance JA performance (as SlimOrca from the first step is mostly EN) ([WandB Log](https://wandb.ai/jondurbin/shisa-mega-7b-v1.1/runs/dopsr0o7/overview))
3. Finally, the model was tuned using a DPOTrainer on a small subset of ultrafeedback (EN) and our own JA DPO dataset which uses gpt-4 outputs as the chosen values and outputs from stage 1's prelim model as rejected values. ([WandDB Log](https://wandb.ai/jondurbin/shisa-mega-dpo-7b-v1.1) )
During our training process, we also gained some key insights on [why some existing Japanese models seem to underperform](https://github.com/AUGMXNT/shisa/wiki/A-Review-of-Public-Japanese-Training-Sets#analysis) even versus models that have no additional JA training, and we hope that sharing this analysis will be useful to other teams developing Japanese language models.
While we need to explore this further, as an experimental validation, we applied a version of our fine-tuning set onto an existing base model ("Gamma 7B") and the initial JA MT-Bench results suggests that we can drastically increase functional performance with our tuning approach:
| Model | Score |
| ------------------------------ | ----- |
| shisa-gamma-7b-allsources-v0.4 | 5.65 |
| ja-stablelm-instruct-gamma-7b* | 4.01 |
## Performance
Throughout our training, we did extensive human evaluation for each model to cross-validate our model performance, and we are currently conducting ongoing larger scale manual head-to-head testing between models. Our intention is open up and scale this data collection as we further develop our tools. For more information and updates, please see our [project wiki](https://github.com/AUGMXNT/shisa/wiki).
While we believe [llm-jp-eval](https://github.com/llm-jp/llm-jp-eval) is a useful metric for our [base model](https://huggingface.co/augmxnt/shisa-base-7b-v1), and it was extremely useful during our tuning process for initial validations, as our fine-tune training includes a percentage of the benchmark train splits, we provide these llm-jp-eval results primarily as a point of interest:
| AVR | MC | NLI | QA | RC |
|-------|-------|-------|-------|-------|
| 0.7480| 0.8900| 0.8040| 0.4153| 0.8825|
*(We run a [slightly modified llm-jp-eval](https://github.com/llm-jp/llm-jp-eval/compare/main...AUGMXNT:llm-jp-eval:main) to support testing of Qwen and to emit a `bos_token` if available)*
For our final model, since it's customary to include benchmarks, we've used Stability AI Japan's [Japanese MT-Bench](https://github.com/Stability-AI/FastChat) as a more representative test of our model's capabilities. For [our JA MT-Bench testing](https://github.com/Stability-AI/FastChat/compare/jp-stable...AUGMXNT:FastChat:jp-stable) we use a Japanese prompt ("ããªãã¯åœ¹ç«ã€ã¢ã·ã¹ã¿ã³ãã§ãã") as well as `--num-choices 4` in an effort to reduce sampling variability, however we've still observed regular 0.5+ point (and sometimes even greater swings) between generations, as well as issues with default prompts and parameters when testing, so again, we'd urge caution in over-interpreting these scores and treating them as more of a probabilistic directional indicator, rather than a definitive score or ranking:
| Benchmark | Score |
| ----------- | ----- |
| JA MT-Bench | 5.23 |
| MT-Bench | 5.71 |
There is an [MT-Bench Leaderboard](https://huggingface.co/spaces/lmsys/chatbot-arena-leaderboard), but as JA MT-Bench is still under development, for convenience, here is a comparison of the JA MT-Bench scores of some other models (our scores were rated by `gpt-4-0613`):
| Model | Score |
| ------------------------------------------------- | ---- |
| gpt-4-0613 | 9.40 |
| gpt-4-1106-preview | 9.17 |
| gpt-3.5-turbo* | 8.41 |
| Qwen-14B-Chat | 7.47 |
| **shisa-7b-v1** | **5.23** |
| ELYZA-japanese-Llama-2-7b-fast-instruct* | 4.86 |
| ja-stablelm-instruct-gamma-7b* | 4.01 |
| japanese-stablelm-instruct-alpha-7b* | 2.74 |
| Mistral-7B-OpenOrca-ja* | 2.23 |
| youri-7b-chat* | 2.00 |
| Mistral-7B-Instruct-v0.1* | 1.78 |
| llm-jp-13b-instruct-full-jaster-dolly-oasst-v1.0* | 1.31 |
*(Marked JA MT-Bench results in this section are [sourced from shi3z](https://note.com/shi3zblog/n/n6b2ac5874021))*
## Limitations
Although our model demonstrates a reasonably high level of Japanese fluency, as a 7B parameter model, it is prone to higher hallucination rates and less effective instruction following and reasoning than larger-class models. Also, it still does not have complete mastery of the Japanese language and a native speaker will spot occasional mistakes like some non-idiomatic/awkward phrasing, improper tenses/speech levels, etc.
We've also noticed a small amount of language leakage, likely largely attributable to our tokenizer expansion. These may be fixable with sampler settings like [Min P](https://www.reddit.com/r/LocalLLaMA/comments/17vonjo/your_settings_are_probably_hurting_your_model_why/)) or additional targeted training, and we plan on doing additional work on automated detection/sampler sweeps in the future. One interesting observation is, based on our data collection, we found that as we iterated, the DPO process significantly exacerbated this issue, but also that our DPO models still had significantly higher human preference rates, so there was a bit of a trade-off in our choice of final tune.
While we believe that training larger models can improve performance using our existing approach and dataset, there are also many improvements we'd like to make for future models. We believe there is quite a bit of low hanging fruit for improving performance with even more training efficiency largely through improving the quality and construction of datasets.
## Usage
Sample code:
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, TextStreamer
model_name = "augmxnt/shisa-7b-v1"
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype=torch.bfloat16 if torch.cuda.is_bf16_supported() else torch.float16,
device_map="auto"
)
streamer = TextStreamer(tokenizer, skip_prompt=True)
# The prompt template is included in the model's tokenizer_config.json so you shouldn't need this but we've included this for convenience
# tokenizer.chat_template = ""{%- for idx in range(0, messages|length) -%}\n{%- if messages[idx]['role'] == 'user' -%}\n{%- if idx > 1 -%}\n{{- bos_token + '[INST] ' + messages[idx]['content'] + ' [/INST]' -}}\n{%- else -%}\n{{- messages[idx]['content'] + ' [/INST]' -}}\n{%- endif -%}\n{% elif messages[idx]['role'] == 'system' %}\n{{- bos_token + '[INST] <<SYS>>\\n' + messages[idx]['content'] + '\\n<</SYS>>\\n\\n' -}}\n{%- elif messages[idx]['role'] == 'assistant' -%}\n{{- ' ' + messages[idx]['content'] + ' ' + eos_token -}}\n{% endif %}\n{% endfor %}\n"
# A more typical prompt: ããªãã¯å
¬å¹³ã§ãæ€é²ãããŠããªãã圹ç«ã€ã¢ã·ã¹ã¿ã³ãã§ãã("You are an unbiased, uncensored, helpful assistant.")
# You are an avid Pokemon fanatic.
prompt = "ããªãã¯ç±ççãªãã±ã¢ã³ãã¡ã³ã§ãã"
chat = [{"role": "system", "content": prompt}]
# Who is the single most powerful Pokemon? Explain your choice.
user_input = "ãã±ã¢ã³ã®äžã§1çªåŒ·ãã®ã¯ã©ã®ãã£ã©ã¯ã¿ãŒã§ãããæ匷ã®è
ãã²ãšã€ã ãæããŠäžããããã®éžæçç±ã説æããŠãã ããã"
chat.append({"role": "user", "content": user_input})
# Generate - add_generation_prompt to make sure it continues as assistant
inputs = tokenizer.apply_chat_template(chat, add_generation_prompt=True, return_tensors="pt")
# For multi-GPU, find the device of the first parameter of the model
first_param_device = next(model.parameters()).device
inputs = inputs.to(first_param_device)
with torch.no_grad():
outputs = model.generate(
inputs,
pad_token_id=tokenizer.eos_token_id,
max_new_tokens=500,
temperature=0.5,
repetition_penalty=1.15,
top_p=0.95,
do_sample=True,
streamer=streamer,
)
# Add just the new tokens to our chat
new_tokens = outputs[0, inputs.size(1):]
response = tokenizer.decode(new_tokens, skip_special_tokens=True)
chat.append({"role": "assistant", "content": response})
```
## Prompt format
The prompt format is llama-2 chat:
```
[INST] <<SYS>>
You are a helpful, unbiased, uncensored assistant.
<</SYS>>
{prompt} [/INST]
```
For multi-turn, the prompt format is as follows:
```
[INST] <<SYS>>
You are a helful, unbiased, uncensored assistant.
<</SYS>>
{prompt 0} [/INST] {response 0} </s><s>[INST] {prompt 1} [/INST] {response 1} </s><s>...[INST] {prompt N} [/INST]
```
This [prompt template](https://huggingface.co/docs/transformers/main/chat_templating) is included in the tokenizer config, and can use the huggingface tokenizer `apply_chat_template` method, e.g.:
```
import transformers
tokenizer = transformers.AutoTokenizer.from_pretrained('augmxnt/shisa-7b-v1')
chat = [
{"role": "system", "content": "You are Aiko, a friendly AI assistant."},
{"role": "user", "content": "Hello, how are you?"},
{"role": "assistant", "content": "I'm doing great. How can I help you today?"},
{"role": "user", "content": "I'd like to show off how chat templating works!"},
]
print(tokenizer.apply_chat_template(chat, tokenize=False))
```
**NOTE:** For proper responses, you should be using our `bos_token` (`<s>`) to begin a string. This is automatically generated by `tokenizer.encode()` but if you are crafting a custom template or using an encoding method that skips special tokens, you may have to add this yourself.
## Acknowledgements
Team: [Leonard Lin](https://huggingface.co/leonardlin) and [Jon Durbin](https://huggingface.co/jondurbin), Mariko Sato, and Florian von Bock
Compute for this model was generously sponsored by [AKA Virtual](https://akavirtual.com/) (Tokyo, Japan).
Thanks to the [LLM-jp](https://llm-jp.nii.ac.jp/), [Stability AI Japan](https://ja.stability.ai/), and [LMSYS](https://lmsys.org/) teams for their work on llm-jp-eval, Japanese MT-Bench, MT-Bench.
Also, thanks to all the volunteers that provided invaluable human preference testing!
We are actively looking for additional compute as we train better and larger models for this project. Please drop us a line at: *compute at augmxnt dot com*
---
*(GPT-4ã«ãã£ãŠéåžžã«è»œåŸ®ãªç·šéãå ããŠç¿»èš³ãããŸããïŒ*
# ã·ãŒãµãŒ7B
**ã·ãŒãµãŒ7B**ïŒ`shisa-7b-v1`ïŒã¯ãåæããŒã¿é§åã®ã¢ãããŒããçšããŠãåªããæ¥æ¬èªãšè±èªèœåãäž¡ç«ããããšãç®æããã€ãªã³ã¬ã«ïŒæ¥æ¬èª/è±èªïŒæ±çšãã£ããã¢ãã«ã§ãã
ãã®ã¢ãã«ã¯ã[Mistral 7B](https://huggingface.co/mistralai/Mistral-7B-v0.1)ãåºã«ãMistralã®ãªãªãžãã«ã®ããŒã¯ãã€ã¶ãŒãããæ¥æ¬èªã«ãããŠ2å以äžå¹ççãªãæ¥æ¬èªæé©åæ¡åŒµããŒã¯ãã€ã¶ãŒãã«ã¹ã¿ã ããŠäœæãããŸãããããŒã¹ã¢ãã«ã¯ãäž»ã«æ¥æ¬èªã®ããŒã¯ã³ãè¿œå ã§80åãã®ãã¬ãŒãã³ã°ãè¡ããŸããããããŠããã®åŸã[airoboros-3.1](https://huggingface.co/datasets/jondurbin/airoboros-3.1)ã®æ¡åŒµãããæ©æ¢°ç¿»èš³çã[ultrafeedback_binarized](https://huggingface.co/datasets/HuggingFaceH4/ultrafeedback_binarized)ããã®æé«åŸç¹é
ç®ã®ã»ããããããŠæ°ãã«çæããã[airoboros](https://github.com/jondurbin/airoboros)ã®ããŒã¿ãçŽæ¥ç®æšèšèªã§åŸ®èª¿æŽããŠããŸãã
åçšãå«ãããããç®çã§äœ¿çšå¯èœãªå¯å®¹ãªApache 2.0ã©ã€ã»ã³ã¹ã®äžã§ãããŒã¹ã¢ãã«ãããŒã¿ã»ãããããã³ãã€ãã©ã€ã³ã³ãŒããå
¬éããŠããŸãïŒ
* [shisa-base-7b-v1](https://huggingface.co/augmxnt/shisa-base-7b-v1) - æ¡åŒµããŒã¯ãã€ã¶ãŒãšè¿œå ã®æ¥æ¬èªãã¬ãã¬ãŒãã³ã°ãåããåœæ¹ã®ããŒã¹ã¢ãã«
* [shisa-pretrain-en-ja-v1](https://huggingface.co/datasets/augmxnt/shisa-pretrain-en-ja-v1) - åœæ¹ã®ãã¬ãã¬ãŒãã³ã°ããŒã¿ã»ãã
* [ultra-orca-boros-en-ja](https://huggingface.co/datasets/jondurbin/ultra-orca-boros-en-ja) - åæçæãæ©æ¢°ç¿»èš³ãããã°ã©ã ã«ããæ€èšŒã«ããJA/EN埮調æŽããŒã¿ã»ãã
* [shisa-en-ja-dpo-v1](https://huggingface.co/datasets/augmxnt/shisa-en-ja-dpo-v1) - ultrafeedbackããã®DPOãã¢ã®å°ããªãµãã»ãããšãéžæãããå€ãšããŠGPT-4çæé
ç®ã䜿çšããæ¥æ¬èªã®DPOãã¢ããããŠåæã®7ããªãªã³ã¢ãã«ã®åºåãåŽäžããå€
* [ã·ãŒãµãŒãªããžããª](https://github.com/AUGMXNT/shisa) - 翻蚳ãããŒã¿ã»ããã®çæããã¬ãŒãã³ã°ãè©äŸ¡ã³ãŒããªã©ãå«ãŸããŠããŸã
ããã«ãã¢ãã¬ãŒã·ã§ã³çµæããã¹ãæ¹æ³è«ãäž»èŠãªèª¿æ»çµæãªã©ãããã»ã¹ã®è©³çŽ°ãæ¡åŒµã©ã€ãã¢ãããå
¬éããéçšã«ãããŸããããã¯[åœãããžã§ã¯ãwiki](https://github.com/AUGMXNT/shisa/wiki)ã§ç 究è
ã«èå³æ·±ãæ
å ±ãšããŠæäŸãããŠããŸãã
## 埮調æŽ
æåã®çŽæã¯ãæè¯ã®[æ¢åã®å
¬éæ¥æ¬èªãã¬ãŒãã³ã°ã»ãã](https://github.com/AUGMXNT/shisa/wiki/A-Review-of-Public-Japanese-Training-Sets)ã䜿çšããŠãããããçµã¿å
¥ããããšã§ãã匷åãªæ¥æ¬èªã¢ãã«ãäœæã§ãããã©ãããèŠãããšã§ãããããããåæã®æ€èšãšãã¹ãã®åŸãèªãã®åæããŒã¿ã»ããã®ç¿»èš³/çæã«ã ãçŠç¹ãåœãŠãããšã§ãçæéã®ãã¬ãŒãã³ã°ã§åªããçµæãåŸãããšãã§ãããšçµè«ä»ããŸããã
ç§ãã¡ã¯è€æ°ã®ç¿»èš³ããŒã«ãæ¯èŒããæåã§ã¬ãã¥ãŒãè¡ã£ãçµæã`gpt-4`ãã»ãŒåžžã«æé«å質ã®ç¿»èš³ãæäŸããªãããGoogleã® `text-bison-32k`ã¯å質ãã³ã¹ããã¹ã«ãŒãããã®ãã©ã³ã¹ãè¯ããšå€æããŸãããè€æ°ã®ç¹°ãè¿ããçµãŠãç¡å¹ãªç¿»èš³ã®ãã©ã°ä»ããšãã£ã«ã¿ãªã³ã°ãå¿
èŠã«å¿ããå翻蚳ãšããã¯ãã£ã«ã®ããã®è¿œå ã®ã¢ã«ãŽãªãºã ãå«ãããã«ã翻蚳ã¢ãããŒããæŽç·ŽãããŸããã
ãŸãããã®ãããžã§ã¯ãã[NEFTune](https://arxiv.org/abs/2310.05914)ãš[DPO](https://arxiv.org/abs/2305.18290)ãã¬ãŒãã³ã°ãåãå
¥ãããªã©ãæ°ããæè¡ãé©çšããæ©äŒãšããªããŸããã
v1ãªãªãŒã¹ã®ããã«ãç§ãã¡ã¯å€§éã®äººéã®å奜ãã¹ãïŒæ°åã®çæãšè€æ°ã©ãŠã³ãã®ãã¢ã¯ã€ãºæ¯èŒïŒã«åºã¥ããŠãªãªãŒã¹åè£ããéžæããŸãããç§ãã¡ã¯ãåã¡/è² ã/åŒãåããšã[BTLã¢ãã«](https://datascience.oneoffcoder.com/btl-model.html)ïŒiLSRïŒã䜿çšããŠ[choix](https://github.com/lucasmaystre/choix)ã§çµæãåæããŸããã
æè¯ã®åè£ã¢ãã«ã¯ã3ã¹ãããã®ããã»ã¹ã§åŸ®èª¿æŽãããŸããïŒ
1. æåã«ãã¢ãã«ã¯`ultra-orca-boros-en-ja`ãšSlimOrca ([WandB Log](https://wandb.ai/jondurbin/shisa-7b-v1/runs/k8pfog9d/overview))ã§åŸ®èª¿æŽãããŸããã
2. 次ã«ãæ¥æ¬èªã®ããã©ãŒãã³ã¹ãåäžãããããã«ultra-orca-boros-en-jaã®äžéšã䜿çšããŠ1åè¿œå ã®ãšããã¯ãè¿œå ããŸããïŒæåã®æ®µéã®SlimOrcaã¯äž»ã«è±èªïŒ([WandB Log](https://wandb.ai/jondurbin/shisa-mega-7b-v1.1/runs/dopsr0o7/overview))ã
3. æåŸã«ãã¢ãã«ã¯å°èŠæš¡ã®ultrafeedbackïŒè±èªïŒãšèªèº«ã®JA DPOããŒã¿ã»ããã«å¯ŸããŠDPOTrainerã䜿çšããŠèª¿æŽãããŸãããããã§äœ¿çšããJA DPOããŒã¿ã»ããã¯gpt-4ã®åºåãéžåºãããå€ãšããã¹ããŒãž1ã®äºåã¢ãã«ã®åºåãåŽäžããå€ãšããŸãã([WandDB Log](https://wandb.ai/jondurbin/shisa-mega-dpo-7b-v1.1) )
ç§ãã¡ã®ãã¬ãŒãã³ã°ããã»ã¹äžã«ãäœæ
äžéšã®æ¢åã®æ¥æ¬èªã¢ãã«ããè¿œå ã®æ¥æ¬èªãã¬ãŒãã³ã°ããªãã¢ãã«ã«å¯ŸããŠãããã©ãŒãã³ã¹ãäœãã®ãããšããããã€ãã®éèŠãªæŽå¯ãåŸãããšãã§ããŸããããã®åæçµæãå
±æããã°ãä»ã®ããŒã ãæ¥æ¬èªã¢ãã«ãéçºããéã®åèã«ãªããšæããŸãã
ããã«æ¢æ±ããå¿
èŠã¯ãããŸãããå®éšçãªæ€èšŒãšããŠã埮調æŽã»ããã®ããŒãžã§ã³ãæ¢åã®ããŒã¹ã¢ãã«ïŒ"Gamma 7B"ïŒã«é©çšããåæã®JA MT-Benchçµæã瀺ãããã«ãç§ãã¡ã®ãã¥ãŒãã³ã°ã¢ãããŒãã§æ©èœæ§ã®ããã©ãŒãã³ã¹ãåçã«åäžãããããšãã§ããŸããïŒ
| ã¢ãã« | ã¹ã³ã¢ |
| ------------------------------ | ----- |
| shisa-gamma-7b-allsources-v0.4 | 5.65 |
| ja-stablelm-instruct-gamma-7b* | 4.01 |
## ããã©ãŒãã³ã¹
ãã¬ãŒãã³ã°å
šäœãéããŠãåã¢ãã«ã«ã€ããŠäººéã«ããè©äŸ¡ãè¡ããã¢ãã«ã®ããã©ãŒãã³ã¹ãçžäºã«æ€èšŒããŸãããçŸåšãã¢ãã«éã®æåã§ã®æ¯èŒãã¹ãã倧èŠæš¡ã«è¡ã£ãŠããŸããç§ãã¡ã®ç®æããšããã¯ãããŒã«ãããã«çºå±ãããããšã§ãã®ããŒã¿åéãå
¬éããŠæ¡åŒµããããšã§ãã詳现ãšæŽæ°æ
å ±ã«ã€ããŠã¯ã[ãããžã§ã¯ãwiki](https://github.com/AUGMXNT/shisa/wiki) ãã芧ãã ããã
æã
ã¯ã[llm-jp-eval](https://github.com/llm-jp/llm-jp-eval)ã¯ãç§ãã¡ã®[åºæ¬ã¢ãã«](https://huggingface.co/augmxnt/shisa-base-7b-v1)ã®æçšãªææšã§ãããåæã®æ€èšŒã®ããã®åŸ®èª¿æŽããã»ã¹äžã«éåžžã«åœ¹ç«ã€ãšèããŠããŸããã埮調æŽãã¬ãŒãã³ã°ã«ã¯ãã³ãããŒã¯ã®ãã¬ã€ã³åå²ã®äžéšãå«ãŸããŠãããããç§ãã¡ãæäŸããllm-jp-evalã®çµæã¯äž»ã«èå³æ·±ããã€ã³ããšããŠæäŸããŠããŸãïŒ
| AVR | MC | NLI | QA | RC |
|-------|-------|-------|-------|-------|
| 0.7480| 0.8900| 0.8040| 0.4153| 0.8825|
*(Qwenã®ãã¹ãããµããŒãããå¯èœã§ããã°`bos_token`ãçºè¡ããããã«ã[ãããã«ä¿®æ£ããllm-jp-eval](https://github.com/llm-jp/llm-jp-eval/compare/main...AUGMXNT:llm-jp-eval:main) ãå®è¡ããŠããŸã)*
æçµã¢ãã«ã«ã€ããŠã¯ããã³ãããŒã¯ãå«ããã®ãäžè¬çãªãããç§ãã¡ã®ã¢ãã«ã®èœåããã代衚çã«ãã¹ãããããã«ãStability AI Japanã®[Japanese MT-Bench](https://github.com/Stability-AI/FastChat)ã䜿çšããŸããã[ç§ãã¡ã®JA MT-Bench ãã¹ã](https://github.com/Stability-AI/FastChat/compare/jp-stable...AUGMXNT:FastChat:jp-stable)ã§ã¯ããµã³ããªã³ã°å€åãæžããããã«ãæ¥æ¬èªã®ããã³ããïŒ"ããªãã¯åœ¹ç«ã€ã¢ã·ã¹ã¿ã³ãã§ãã"ïŒãš `--num-choices 4`ã䜿çšããŠããŸãããçæéã§0.5+ç¹ïŒæã«ã¯ãã以äžã®å€åïŒãé »ç¹ã«èŠ³å¯ãããã¹ãæã®ããã©ã«ãã®ããã³ãããšãã©ã¡ãŒã¿ã«åé¡ããã£ããšããçµéšããããããã®ã¹ã³ã¢ãé床ã«è§£éããããšã«ã¯æ³šæãå¿
èŠã§ããããã確å®çãªã¹ã³ã¢ãã©ã³ãã³ã°ã§ã¯ãªãããã確ççãªæ¹åææšãšããŠæ±ãããšããå§ãããŸãïŒ
| ãã³ãããŒã¯ | ã¹ã³ã¢ |
| ----------- | ----- |
| JA MT-Bench | 5.23 |
| MT-Bench | 5.71 |
[MT-Bench Leaderboard](https://huggingface.co/spaces/lmsys/chatbot-arena-leaderboard)ããããŸãããJA MT-Benchã¯ãŸã éçºäžã§ããããã䟿å®äžãä»ã®ã¢ãã«ã®JA MT-Benchã¹ã³ã¢ãšã®æ¯èŒã瀺ããŸãïŒç§ãã¡ã®ã¹ã³ã¢ã¯`gpt-4-0613`ã«ãã£ãŠè©äŸ¡ãããŸããïŒïŒ
| ã¢ãã« | ã¹ã³ã¢ |
| ------------------------------------------------- | ---- |
| gpt-4-0613 | 9.40 |
| gpt-4-1106-preview | 9.17 |
| gpt-3.5-turbo* | 8.41 |
| Qwen-14B-Chat | 7.47 |
| **shisa-7b-v1** | **5.23** |
| ELYZA-japanese-Llama-2-7b-fast-instruct* | 4.86 |
| ja-stablelm-instruct-gamma-7b* | 4.01 |
| japanese-stablelm-instruct-alpha-7b* | 2.74 |
| Mistral-7B-OpenOrca-ja* | 2.23 |
| youri-7b-chat* | 2.00 |
| Mistral-7B-Instruct-v0.1* | 1.78 |
| llm-jp-13b-instruct-full-jaster-dolly-oasst-v1.0* | 1.31 |
*(ãã®ã»ã¯ã·ã§ã³ã§ããŒã¯ãããJA MT-Benchã®çµæã¯[shi3zããåŒçš](https://note.com/shi3zblog/n/n6b2ac5874021)ããŸãã)*
## å¶éäºé
åœã¢ãã«ã¯ååãªæ¥æ¬èªã®æµæ¢ãã瀺ããŠããŸããã7Bãã©ã¡ãŒã¿ã®ã¢ãã«ãšããŠã¯ããã倧ããªã¯ã©ã¹ã®ã¢ãã«ã«æ¯ã¹ãŠå¹»èŠçãé«ããæ瀺ã®è¿œè·¡ãæšè«ãå¹æçã§ãªãåŸåããããŸãããŸããæ¥æ¬èªã®å®å
šãªç¿åŸã¯ãŸã éããŠãããããã€ãã£ãã¹ããŒã«ãŒã¯ããŸã«éæ
£çšç/éåæã®ããè¡šçŸãäžé©åãªæå¶/話ãèšèã®ã¬ãã«ãªã©ã®ééããèŠã€ããããšããããŸãã
ãŸããç§ãã¡ã®ããŒã¯ãã€ã¶ãŒã®æ¡åŒµã«å€§ãã«èµ·å ããå¯èœæ§ãé«ãããããããªèšèªãªãŒã¯ã確èªããŠããŸãããããã¯[Min P](https://www.reddit.com/r/LocalLLaMA/comments/17vonjo/your_settings_are_probably_hurting_your_model_why/)ãªã©ã®ãµã³ãã©ãŒèšå®ãè¿œå ã®ã¿ãŒã²ããæååãã¬ãŒãã³ã°ã§ä¿®æ£å¯èœãªå¯èœæ§ããããä»åŸãèªåæ€åº/ãµã³ãã©ãŒã®ã¹ãŠã£ãŒãã«ã€ããŠè¿œå ã®äœæ¥ãè¡ãäºå®ã§ããèå³æ·±ã芳å¯ãšããŠã¯ãç§ãã¡ã®ããŒã¿åéã«åºã¥ããŠãDPOããã»ã¹ããã®åé¡ã倧å¹
ã«æªåãããããšãããããŸããããããã§ãDPOã¢ãã«ã¯äººéã®å¥œã¿çã倧å¹
ã«é«ãã£ããããæçµçãªåŸ®èª¿æŽã®éžæã«ã¯äžå®ã®ãã¬ãŒããªãããããŸããã
çŸåããã¢ãããŒããšããŒã¿ã»ããã䜿çšããŠã倧èŠæš¡ãªã¢ãã«ã®ãã¬ãŒãã³ã°ãããã©ãŒãã³ã¹ãåäžããããšä¿¡ããŠããŸãããä»åŸã®ã¢ãã«åãã«è¡ãããæ¹è¯ãå€ããããŸããç§ãã¡ã¯ãããŒã¿ã»ããã®å質ãšæ§ç¯ãæ¹åããããšã§ããããªããã¬ãŒãã³ã°å¹çãéããããã©ãŒãã³ã¹åäžã«ã¯ãŸã çžåœã«åãçµãäœå°ããããšèããŠããŸãã
## 䜿çšæ³
ãµã³ãã«ã³ãŒã:
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, TextStreamer
model_name = "augmxnt/shisa-7b-v1"
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype=torch.bfloat16 if torch.cuda.is_bf16_supported() else torch.float16,
device_map="auto"
)
streamer = TextStreamer(tokenizer, skip_prompt=True)
# ããã³ãããã³ãã¬ãŒãã¯ã¢ãã«ã®tokenizer_config.jsonã«å«ãŸããŠããã®ã§ãããã¯å¿
èŠãªãã¯ãã§ããã䟿å®äžãã¡ãã«ãæ²èŒããŠããŸã
# tokenizer.chat_template = ""{%- for idx in range(0, messages|length) -%}\n{%- if messages[idx]['role'] == 'user' -%}\n{%- if idx > 1 -%}\n{{- bos_token + '[INST] ' + messages[idx]['content'] + ' [/INST]' -}}\n{%- else -%}\n{{- messages[idx]['content'] + ' [/INST]' -}}\n{%- endif -%}\n{% elif messages[idx]['role'] == 'system' %}\n{{- bos_token + '[INST] <<SYS>>\\n' + messages[idx]['content'] + '\\n<</SYS>>\\n\\n' -}}\n{%- elif messages[idx]['role'] == 'assistant' -%}\n{{- ' ' + messages[idx]['content'] + ' ' + eos_token -}}\n{% endif %}\n{% endfor %}\n"
# ããå
žåçãªããã³ãã: ããªãã¯å
¬å¹³ã§ãæ€é²ãããŠããªãã圹ç«ã€ã¢ã·ã¹ã¿ã³ãã§ãã
# You are an avid Pokemon fanatic.
prompt = "ããªãã¯ç±ççãªãã±ã¢ã³ãã¡ã³ã§ãã"
chat = [{"role": "system", "content": prompt}]
# Who is the most powerful Pokemon? Explain your choice.
user_input = "ãã±ã¢ã³ã®äžã§1çªåŒ·ãã®ã¯ã©ã®ãã£ã©ã¯ã¿ãŒã§ãããæ匷ã®è
ãã²ãšã€ã ãæããŠäžããããã®éžæçç±ã説æããŠãã ããã"
chat.append({"role": "user", "content": user_input})
# çæ - add_generation_promptãè¿œå ããŠã¢ã·ã¹ã¿ã³ããšããŠç¶è¡ããããšã確èªããŸã
inputs = tokenizer.apply_chat_template(chat, add_generation_prompt=True, return_tensors="pt")
# è€æ°ã®GPUã®å Žåãã¢ãã«ã®æåã®ãã©ã¡ãŒã¿ã®ããã€ã¹ãèŠã€ããŸã
first_param_device = next(model.parameters()).device
inputs = inputs.to(first_param_device)
with torch.no_grad():
outputs = model.generate(
inputs,
pad_token_id=tokenizer.eos_token_id,
max_new_tokens=500,
temperature=0.5,
repetition_penalty=1.15,
top_p=0.95,
do_sample=True,
streamer=streamer,
)
# Add just the new tokens to our chat
new_tokens = outputs[0, inputs.size(1):]
response = tokenizer.decode(new_tokens, skip_special_tokens=True)
chat.append({"role": "assistant", "content": response})
```
## ããã³ãã圢åŒ
ããã³ãã圢åŒã¯llama-2 chatã§ãïŒ
```
[INST] <<SYS>>
ããªãã¯åœ¹ç«ã€ãåèŠããªããæ€é²ãããŠããªãã¢ã·ã¹ã¿ã³ãã§ãã
<</SYS>>
{prompt} [/INST]
```
For multi-turn, the prompt format is as follows:
```
[INST] <<SYS>>
ããªãã¯åœ¹ç«ã€ãåèŠããªããæ€é²ãããŠããªãã¢ã·ã¹ã¿ã³ãã§ãã
<</SYS>>
{prompt 0} [/INST] {response 0} </s><s>[INST] {prompt 1} [/INST] {response 1} </s><s>...[INST] {prompt N} [/INST]
```
ãã®[prompt template](https://huggingface.co/docs/transformers/main/chat_templating)ã¯ããŒã¯ãã€ã¶ã®èšå®ã«å«ãŸããŠãããHuggingFace ã®ããŒã¯ãã€ã¶ `apply_chat_template` ã¡ãœããã䜿çšã§ããŸããäŸãã°ïŒ
```
import transformers
tokenizer = transformers.AutoTokenizer.from_pretrained('augmxnt/shisa-7b-v1')
chat = [
{"role": "system", "content": "ããªãã¯Aikoããã¬ã³ããªãŒãªAIã¢ã·ã¹ã¿ã³ãã§ãã"},
{"role": "user", "content": "ããã«ã¡ã¯ã調åã¯ã©ãã§ããïŒ"},
{"role": "assistant", "content": "å
æ°ã§ããä»æ¥ã¯äœã®ãæäŒããã§ããŸããïŒ"},
{"role": "user", "content": "ãã£ãããã³ãã¬ãŒãã£ã³ã°ã®ä»çµã¿ãèŠããŠãããããã§ãïŒ"},
]
print(tokenizer.apply_chat_template(chat, tokenize=False))
```
**泚æ**é©åãªã¬ã¹ãã³ã¹ãåŸãããã«ã¯ãæååã®éå§ã«æã
ã® `bos_token` (`<s>`) ã䜿çšãã¹ãã§ãããã㯠`tokenizer.encode()` ã«ãã£ãŠèªåçã«çæãããŸãããã«ã¹ã¿ã ãã³ãã¬ãŒããäœæããããç¹æ®ããŒã¯ã³ãçç¥ãããšã³ã³ãŒãæ¹æ³ã䜿çšããå Žåã¯ãèªåã§è¿œå ããå¿
èŠããããŸãã
## è¬èŸ
ããŒã ïŒ[Leonard Lin](https://huggingface.co/leonardlin)ã[Jon Durbin](https://huggingface.co/jondurbin)ãäœè€ççåãFlorian von Bock
ãã®ã¢ãã«ã®èšç®ã¯ã[AKA Virtual](https://akavirtual.com/) (æ±äº¬ãæ¥æ¬) ã®ãåæã«ããæäŸãããŠããŸãã
[LLM-jp](https://llm-jp.nii.ac.jp/)ã[Stability AI Japan](https://ja.stability.ai/)ã[LMSYS](https://lmsys.org/)ã®ããŒã ããllm-jp-eval, Japanese MT-Bench, MT-Benchã«åãçµãã§ãããŠæè¬ããŠããŸãã
ãŸãã貎éãªãã¥ãŒãã³ããªãã¡ã¬ã³ã¹ãã¹ããæäŸããŠãã ãã£ããã¹ãŠã®ãã©ã³ãã£ã¢ã«ãæè¬ããããŸãïŒ
ãã®ãããžã§ã¯ãã®ããã«ããè¯ãããã倧ããªã¢ãã«ãèšç·Žããããã«ãè¿œå ã®èšç®ãç©æ¥µçã«æ¢ããŠããŸãããåãåããã¯æ¬¡ã®å®å
ãŸã§ãé¡ãããããŸãïŒ*compute at augmxnt dot com* |
Chrisisis/5GgmWdgTH79hs5qE5sEUvGJbtQv4jiiyKjLbiZBuLQNQHjAr_vgg | Chrisisis | "2024-02-24T08:31:37Z" | 1,465 | 0 | keras | [
"keras",
"region:us"
] | null | "2024-02-11T17:28:46Z" | Entry not found |
NbAiLab/nb-whisper-base | NbAiLab | "2024-02-13T12:29:46Z" | 1,465 | 0 | transformers | [
"transformers",
"pytorch",
"tf",
"jax",
"tensorboard",
"onnx",
"safetensors",
"whisper",
"automatic-speech-recognition",
"audio",
"asr",
"hf-asr-leaderboard",
"no",
"nb",
"nn",
"en",
"dataset:NbAiLab/ncc_speech",
"dataset:NbAiLab/NST",
"dataset:NbAiLab/NPSC",
"arxiv:2212.04356",
"base_model:openai/whisper-base",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | "2024-02-13T10:07:48Z" | ---
license: apache-2.0
language:
- 'no'
- nb
- nn
- en
datasets:
- NbAiLab/ncc_speech
- NbAiLab/NST
- NbAiLab/NPSC
base_model: openai/whisper-base
tags:
- audio
- asr
- automatic-speech-recognition
- hf-asr-leaderboard
metrics:
- wer
- cer
library_name: transformers
pipeline_tag: automatic-speech-recognition
widget:
- src: https://datasets-server.huggingface.co/assets/google/fleurs/--/nb_no/train/1/audio/audio.mp3
example_title: FLEURS sample 1
- src: https://datasets-server.huggingface.co/assets/google/fleurs/--/nb_no/train/4/audio/audio.mp3
example_title: FLEURS sample 2
---
# NB-Whisper Base
Introducing the **_Norwegian NB-Whisper Base model_**, proudly developed by the National Library of Norway. NB-Whisper is a cutting-edge series of models designed for automatic speech recognition (ASR) and speech translation. These models are based on the work of [OpenAI's Whisper](https://arxiv.org/abs/2212.04356). Each model in the series has been trained for 250,000 steps, utilizing a diverse dataset of 8 million samples. These samples consist of aligned audio clips, each 30 seconds long, culminating in a staggering 66,000 hours of speech. For an in-depth understanding of our training methodology and dataset composition, keep an eye out for our upcoming article.
| Model Size | Parameters | Model |
|------------|------------|------------|
| Tiny | 39M | [NB-Whisper Tiny](https://huggingface.co/NbAiLab/nb-whisper-tiny) |
| Base | 74M | [NB-Whisper Base](https://huggingface.co/NbAiLab/nb-whisper-base) |
| Small | 244M | [NB-Whisper Small](https://huggingface.co/NbAiLab/nb-whisper-small) |
| Medium | 769M | [NB-Whisper Medium](https://huggingface.co/NbAiLab/nb-whisper-medium) |
| Large | 1550M | [NB-Whisper Large](https://huggingface.co/NbAiLab/nb-whisper-large) |
### Verbatim Model
While the main models are suitable for most transcription task, we demonstrate how easy it is to change the output of the main model. The following models are trained 250 additional steps from the main models above, and might be suitable for more targetted use cases:
- **Verbatim version**: This lower-cased variant is more literal and suitable for tasks requiring detailed transcription, such as linguistic analysis.
| Model Size | Parameters | Semantic version |
|------------|------------|------------------|
| Tiny | 39M | [Tiny - semantic](https://huggingface.co/NbAiLab/nb-whisper-tiny-semantic) |
| Base | 74M | [Base - semantic](https://huggingface.co/NbAiLab/nb-whisper-base-semantic) |
| Small | 244M | [Small - semantic](https://huggingface.co/NbAiLab/nb-whisper-small-semantic) |
| Medium | 769M | [Medium - semantic](https://huggingface.co/NbAiLab/nb-whisper-medium-semantic) |
| Large | 1550M | [Large - semantic](https://huggingface.co/NbAiLab/nb-whisper-large-semantic) |
### Model Description
- **Developed by:** [NB AI-Lab](https://ai.nb.no/)
- **Shared by:** [NB AI-Lab](https://ai.nb.no/)
- **Model type:** `whisper`
- **Language(s) (NLP):** Norwegian, Norwegian Bokmål, Norwegian Nynorsk, English
- **License:** [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0)
- **Trained from model:** [openai/whisper-base](https://huggingface.co/openai/whisper-base)
- **Code Repository:** https://github.com/NbAiLab/nb-whisper/
- **Paper:** _Coming soon_
- **Demo:** _See Spaces on this page_
## How to Use the Models
### Online Demos
You can try the models directly through the HuggingFace Inference API, accessible on the right side of this page. Be aware that initially, the model needs to load and will run on limited CPU capacity, which might be slow. To enhance your experience, we are temporarily hosting some models on TPUs for a few days, significantly boosting their performance. Explore these under the **Spaces** section on the [Main Page](https://huggingface.co/NbAiLab/).
### Local Setup with HuggingFace
Alternatively, you can run the models locally. The Tiny, Base, and Small models are optimized for CPU execution. For the Medium and Large models, we recommend a system equipped with a GPU to ensure efficient processing. Setting up and using these models with HuggingFace's Transformers is straightforward, provided you have [Python](https://www.python.org/downloads/) installed on your machine. For practical demonstrations, refer to examples using this [sample mp3 file](https://github.com/NbAiLab/nb-whisper/raw/main/audio/king.mp3).
```bash
# Download the sample file
$ wget -N https://github.com/NbAiLab/nb-whisper/raw/main/audio/king.mp3
# Install necessary libraries.
$ pip install transformers>=4.35.2
```
After this is done, you should be able to run this in Python:
```python
from transformers import pipeline
# Load the model
asr = pipeline("automatic-speech-recognition", "NbAiLabBeta/nb-whisper-base")
#transcribe
asr("king.mp3", generate_kwargs={'task': 'transcribe', 'language': 'no'})
```
<details>
<summary>Expected output</summary>
```json
{
{'text': ' Nordmenn er nordlendinger, trÞndere, sÞrlendinger og folk fra alle andre regioner. Nordmenn er også innvandret fra Afghanistan, Pakistan, Polen, Sverige, Somalia og Syria. Det er ikke alltid så lett å si hvor vi er fra, hvilken nasjonalitet vi er fra. Hvilken nasjonalitet vi er fra. Hvilken nasjonalitet vi er fra. Hvilken nasjonalitet vi er fra. Hvilken nasjonalitet vi er fra. Hvilken nasjonalitet vi er fra. Hvilken nasjonalitet vi er fra.'}
}
```
</details>
#### Extended HuggingFace
Examining the output above, we see that there are multiple repetitions at the end. This is because the video is longer than 30 seconds. By passing the ```chunk_lengt_s``` argument, we can transcribe longer file. Our experience is that we get slightly better result by setting that to 28 seconds instead of the default 30 seconds. We also recommend setting the beam size to 5 if possible. This greatly increases the accuracy but takes a bit longer and requires slightly more memory. The examples below also illustrates how to transcribe to English or Nynorsk, and how to get timestamps for sentences and words.
```python
# Long Transcripts
asr("king.mp3", chunk_length_s=28, generate_kwargs={'task': 'transcribe', 'language': 'no'})
# Increase accuracy by setting beam size to 5
asr("king.mp3", chunk_length_s=28, return_timestamps=True, generate_kwargs={'num_beams': 5, 'task': 'transcribe', 'language': 'no'})
# Return Timestamps
asr("king.mp3", chunk_length_s=28, return_timestamps=True, generate_kwargs={'task': 'transcribe', 'language': 'no'})
# Return Word Level Timestamps
asr("king.mp3", chunk_length_s=28, return_timestamps="word", generate_kwargs={'task': 'transcribe', 'language': 'no'})
# Transcribe to Nynorsk
asr("king.mp3", chunk_length_s=28, generate_kwargs={'task': 'transcribe', 'language': 'nn'})
# Transcribe to English
asr("king.mp3", chunk_length_s=28, generate_kwargs={'task': 'transcribe', 'language': 'en'})
```
<details>
<summary>Expected output</summary>
Long transcripts:
```json
{
{'text': ' Nordmenn er nordlendinger, trÞndere, sÞrlendinger og folk fra alle andre regioner. Nordmenn er også innvandret fra Afghanistan, Pakistan, Polen, Sverige, Somalia og Syria. Det er ikke alltid så lett å si hvor vi er fra, hvilken nasjonalitet vi er fra. Hvilken nasjonalitet vi er fra. Hvilken nasjonalitet vi er fra. Hvilken nasjonalitet vi er fra. Hvilken nasjonalitet vi er fra. Hvilken nasjonalitet vi er fra, hvilken nasjonalitet vi tilhÞrer. Det vi kaller hjem, er der hjertet vårt er, og det kan ikke alltid plasseres innenfor landegrenser. Nordmenn er jenter som er glad i jenter, gutter som er glad i gutter, og jenter og gutter som er glad i hverandre. Nordmenn trommer på Gud, Allah, Altet og ingenting. Nordmenn liker Grieg, Kygo, Helbilis og Kari Bremnes. Med andre ord, Norge er dere. Norge er oss. Mitt stÞrste håp for Norge er at vi skal klare å ta vare på hverandre, at vi skal bygge dette landet videre på tillit, fellesskap og raushet.'}
}
```
Timestamps:
```json
{
{'text': ' Nordmenn er nordlendinger, trÞndere, sÞrlendinger og folk fra alle andre regioner. Nordmenn er også innvandret fra Afghanistan, Pakistan, Polen, Sverige, Somalia og Syria. Det er ikke alltid så lett å si hvor vi er fra, hvilken nasjonalitet vi er fra. Hvilken nasjonalitet vi er fra. hvilken nasjonalitet vi tilhÞrer. Det vi kaller hjem, er der hjertet vårt er, og det kan ikke alltid plasseres innenfor landegrenser. Nordmenn er jenter som er glad i jenter, gutter som er glad i gutter, og jenter og gutter som er glad i hverandre. Nordmenn trommer på Gud, Allah, Altet og ingenting. Nordmenn liker Grieg, Kygo, Helbiles og Kari Bremnes. Med andre ord, Norge er dere. Norge er oss. Mitt stÞrste håp for Norge er at vi skal klare å ta vare på hverandre, at vi skal bygge dette landet videre på tillit, fellesskap og raushet.',
'chunks': [{'timestamp': (0.0, 5.46),
'text': ' Nordmenn er nordlendinger, trÞndere, sÞrlendinger'},
{'timestamp': (5.52, 8.68), 'text': ' og folk fra alle andre regioner.'},
{'timestamp': (8.68, 16.64),
'text': ' Nordmenn er også innvandret fra Afghanistan, Pakistan, Polen, Sverige, Somalia og Syria.'},
{'timestamp': (16.64, 13.3),
'text': ' Det er ikke alltid så lett å si hvor vi er fra, hvilken nasjonalitet vi er fra.'},
{'timestamp': (13.32, 30.28),
'text': ' Hvilken nasjonalitet vi er fra. hvilken nasjonalitet vi tilhÞrer.'},
{'timestamp': (32.52, 39.16),
'text': ' Det vi kaller hjem, er der hjertet vårt er, og det kan ikke alltid plasseres'},
{'timestamp': (39.16, 42.0), 'text': ' innenfor landegrenser.'},
{'timestamp': (42.0, 46.74),
'text': ' Nordmenn er jenter som er glad i jenter, gutter som er glad i gutter,'},
{'timestamp': (46.74, 51.12),
'text': ' og jenter og gutter som er glad i hverandre.'},
{'timestamp': (51.16, 57.42),
'text': ' Nordmenn trommer på Gud, Allah, Altet og ingenting.'},
{'timestamp': (57.42, 64.3),
'text': ' Nordmenn liker Grieg, Kygo, Helbiles og Kari Bremnes.'},
{'timestamp': (64.34, 71.24),
'text': ' Med andre ord, Norge er dere. Norge er oss.'},
{'timestamp': (71.24, 78.04),
'text': ' Mitt stÞrste håp for Norge er at vi skal klare å ta vare på hverandre,'},
{'timestamp': (78.12, 84.68),
'text': ' at vi skal bygge dette landet videre på tillit, fellesskap og raushet.'}]}
}
```
Word Level Timestamps:
```json
{
{"text": "Nordmenn er nordlendinger, trÞndere, sÞrlendinger og folk fra alle andre regioner. Nordmenn er også innvandret fra Afghanistan, Pakistan, Polen, Sverige, Somalia og Syria. Det er ikke alltid så lett å si hvor vi er fra, hvilken nasjonalitet vi tilhÞrer. Det vi kaller hjem, er der hjertet vårt er, og det kan ikke alltid plasseres innenfor landegrenser. Nordmenn er jenter som er glad i jenter, gutter som er glad i gutter, og jenter og gutter som er glad i hverandre. Nordmenn trommer på Gud, Allah, Altet og ingenting. Nordmenn liker Grieg, Kygo, Helbilis og Kari Bremnes. Med andre ord, Norge er dere. Norge er oss. Mitt stÞrste håp for Norge er at vi skal klare å ta vare på hverandre, at vi skal bygge dette landet videre på tillit, fellesskap og raushet.",
"chunks": [
{"text": "Nordmenn", "timestamp": [0.72, 1.42]},
{"text": "er", "timestamp": [1.42, 1.74]},
// ... more chunks ...
{"text": "raushet.", "timestamp": [83.1, 84.88]}
]
}
}
```
Nynorsk:
```json
{
{"text": "Nordmenn er nordlendingar, trÞndarar, sÞrlendingar og folk frå alle andre regionar. Nordmenn er også innvandra frå Afghanistan, Pakistan, Polen, Sverige, Somalia og Syria. Det er ikkje alltid så lett å seie kvar vi er frå, kva nasjonalitet vi tilhÞyrer. Det vi kallar heim, er der hjartet vårt er, og det kan ikkje alltid plasserast innanfor landegrenser. Nordmenn er jenter som er glad i jenter, gutar som erade i gutar, og jenter og gutar som er glade i kvarandre. Nordmenn trommar på Gud, Allah, Altet og ingenting. Nordmenn liker Grieg, Kygo, Helbiles og Kari Bremnes. Med andre ord, Noreg er dere! Noreg er oss. Mitt stÞrste håp for Noreg er at vi skal klare å ta vare på kvarandre, at vi skal byggje dette landet vidare på tillit, fellesskap og raushet."}
}
```
English:
```json
{
{"text": "Norwegians are Norwegians, trÞnders, southerners and people from all other regions. Norwegians are also invaded from Afghanistan, Pakistan, Poland, Sweden, Somalia and Suria. It is not always so easy to say where we are from, what nationality we belong to. What we call home is where our heart is, and it cannot always be placed within national borders. Norwegians are girls who like girls, boys who like boys, and girls and boys who like each other. Norwegians thrump on God, Allah, Altet and nothing. Norwegians like Grieg, Kygo, Helbilis and Kari Bremnes. In other words, Norway is you. Norway is us. My biggest hope for Norway is that we should be able to take care of each other, that we should build this country on trust, community and generosity."}
}
```
</details>
### Whisper CPP
Whisper CPP is a C++ implementation of the Whisper model, offering the same functionalities with the added benefits of C++ efficiency and performance optimizations. This allows embedding any Whisper model into a binary file, facilitating the development of real applications. However, it requires some familiarity with compiling C++ programs. Their [homepage](https://github.com/ggerganov/whisper.cpp) provides examples of how to build applications, including real-time transcription.
We have converted this model to the ggml-format model used by Whisper CPP binaries. The file can be downloaded [here](blob/main/ggml-model.bin), and a `q5_0` quantized version is also available [here](blob/main/ggml-model-q5_0.bin).
```bash
# We can download and compile whisper.cpp
$ git clone --depth 1 https://github.com/ggerganov/whisper.cpp --branch v1.5.1
$ cd whisper.cpp/
$ make
#Â We also need to convert the audio to WAV as that is the only format supported by whisper.cpp
$ wget -N https://github.com/NbAiLab/nb-whisper/raw/main/audio/king.mp3
$ ffmpeg -i king.mp3 -ar 16000 -ac 1 -c:a pcm_s16le king.wav
# Lets download the two ggml-files from this site
wget -N https://huggingface.co/NbAiLab/nb-whisper-base/resolve/main/ggml-model.bin -O models/nb-base-ggml-model.bin
wget -N https://huggingface.co/NbAiLab/nb-whisper-base/resolve/main/ggml-model-q5_0.bin -O models/nb-base-ggml-model-q5_0.bin
#Â And run it with the f16 default model
$ ./main -l no -m models/nb-base-ggml-model.bin king.wav
# Or the quantized version
$ ./main -l no -m models/nb-base-ggml-model-q5_0.bin king.wav
```
### WhisperX and Speaker Diarization
Speaker diarization is a technique in natural language processing and automatic speech recognition that identifies and separates different speakers in an audio recording. It segments the audio into parts based on who is speaking, enhancing the quality of transcribing meetings or phone calls. We find that [WhisperX](https://github.com/m-bain/whisperX) is the easiest way to use our models for diarizing speech. In addition, WhisperX is using phoneme-based Wav2Vec-models for improving the alignment of the timestamps. As of December 2023 it also has native support for using the nb-wav2vec-models. It currently uses [PyAnnote-audio](https://github.com/pyannote/pyannote-audio) for doing the actual diarization. This package has a fairly strict licence where you have to agree to user terms. Follow the instructions below.
```bash
# Follow the install instructions on https://github.com/m-bain/whisperX
# Make sure you have a HuggingFace account and have agreed to the pyannote terms
# Log in (or supply HF Token in command line)
huggingface-cli login
# Download a test file
wget -N https://github.com/NbAiLab/nb-whisper/raw/main/audio/knuthamsun.mp3
# Optional. If you get complains about not support for Norwegian, do:
pip uninstall whisperx && pip install git+https://github.com/m-bain/whisperx.git@8540ff5985fceee764acbed94f656063d7f56540
# Transcribe the test file. All transcripts will end up in the directory of the mp3-file
whisperx knuthamsun.mp3 --model NbAiLabBeta/nb-whisper-base --language no --diarize
```
You can also run WhisperX from Python. Please take a look at the instructions on [WhisperX homepage](https://github.com/m-bain/whisperX).
### API
Instructions for accessing the models via a simple API are included in the demos under Spaces. Note that these demos are temporary and will only be available for a few weeks.
## Training Data
The training data originates from Språkbanken and the National Library of Norway's digital collection, including:
- NST Norwegian ASR Database (16 kHz) and its corresponding dataset
- Transcribed speeches from the Norwegian Parliament by Språkbanken
- TV broadcast (NRK) subtitles (NLN digital collection)
- Audiobooks (NLN digital collection)
## Downstream Use
The models, especially the smaller ones, may exhibit occasional hallucinations and may drop parts of the transcript. They are designed to convert spoken language into grammatically correct written sentences, which might not always be word-for-word translations. We have made two extra model variant for users that want a different transcription style. We encourage users to try the models themselves to get a better understanding.
## Bias, Risks, and Limitations
Using these models without adequate risk assessment and mitigation could be considered irresponsible. They may contain biases or other undesirable distortions. Users who deploy these models or integrate them into systems or services are responsible for mitigating risks and complying with applicable AI regulations. The National Library of Norway, as the model owner, disclaims liability for any outcomes resulting from third-party use of these models.
### Software
The model was trained using Jax/Flax and converted to PyTorch, Tensorflow, whisper.cpp, and ONXX formats. These are available under `Files and versions`. We welcome requests for conversion to other formats. All training code and scripts are released under the Apache License 2.0 in the GitHub repository [nb-whisper](https://github.com/NbAiLab/nb-whisper/).
## Citation & Contributors
The NB-Whisper Base model is a product of the NoSTram project led by Per Egil Kummervold ([@pere](https://huggingface.co/pere)) at the National Library of Norway. Key contributors include Javier de la Rosa ([@versae](https://huggingface.co/versae)), Freddy Wetjen ([@freddyw](https://huggingface.co/freddyw)), and Rolv-Arild Braaten ([@Rolv-Arild](https://huggingface.co/Rolv-Arild)). NB AI-Lab, under the direction of Svein Arne Brygfjeld ([@Brygfjeld](https://huggingface.co/Brygfjeld)), supported the project's successful completion. A detailed paper on our process and findings is forthcoming.
## Disclaimer
The models published in this repository are intended for a generalist purpose and are available to third parties. These models may have bias and/or any other undesirable distortions. When third parties, deploy or provide systems and/or services to other parties using any of these models (or using systems based on these models) or become users of the models, they should note that it is their responsibility to mitigate the risks arising from their use and, in any event, to comply with applicable regulations, including regulations regarding the use of artificial intelligence. In no event shall the owner of the models (The National Library of Norway) be liable for any results arising from the use made by third parties of these models.
## Acknowledgements
Our gratitude extends to [Google TPU Research Cloud](https://sites.research.google/trc/about/) for training resources, Google Cloud for translation credits, and HuggingFace's Sanchit Ghandi for technical support. A special thank you to Per Erik Solberg at Språkbanken for the collaboration on the Stortinget corpus.
## Contact
For feedback, technical concerns, or collaboration inquiries, please contact <a rel="noopener nofollow" href="mailto:[email protected]">[email protected]</a>. If you plan to include this model in your research, contact us for the latest information on our upcoming paper for citation purposes.
|
lmstudio-community/Qwen1.5-32B-Chat-GGUF | lmstudio-community | "2024-04-24T23:01:41Z" | 1,465 | 13 | null | [
"gguf",
"chat",
"text-generation",
"en",
"license:other",
"region:us"
] | text-generation | "2024-04-06T03:24:00Z" | ---
license: other
license_name: tongyi-qianwen
license_link: >-
https://huggingface.co/Qwen/Qwen1.5-32B-Chat/blob/main/LICENSE
language:
- en
pipeline_tag: text-generation
tags:
- chat
quantized_by: bartowski
lm_studio:
param_count: 32b
use_case: chat
release_date: 03-04-2024
model_creator: Qwen
prompt_template: ChatML
system_prompt: You are a helpful assistant
base_model: Qwen2
original_repo: Qwen/Qwen1.5-32B-Chat
---
## ð« Community Model> Qwen 1.5 32B Chat By Qwen
*ðŸ [LM Studio](https://lmstudio.ai) Community models highlights program. Highlighting new & noteworthy models by the community. Join the conversation on [Discord](https://discord.gg/aPQfnNkxGC)*.
**Model creator:** [Qwen](https://huggingface.co/Qwen)<br>
**Original model**: [Qwen1.5-32B-Chat](https://huggingface.co/Qwen/Qwen1.5-32B-Chat)<br>
**GGUF quantization:** provided by [bartowski](https://huggingface.co/bartowski) based on `llama.cpp` release [b2589](https://github.com/ggerganov/llama.cpp/releases/tag/b2589)<br>
## Model Summary:
After a hugely successful 72B model, Qwen has released this brand new 32B model. This model has amazing chat capabilties, multilingual for both conversation and coding, and decent context lengths.<br>
Check out more details from their blog post [here](http://qwenlm.github.io/blog/qwen1.5-32b/).
## Prompt template:
Choose the `ChatML` preset in your LM Studio.
Under the hood, the model will see a prompt that's formatted like so:
```
<|im_start|>system
You are a helpful assistant<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
## Use case and examples
Qwen2 is great at making conversations in multiple languages, and also has decent coding capabilities.
### Creativity
```
<|im_start|>system
You are a helpful assistant<|im_end|>
<|im_start|>user
Give me a short introduction to large language model.<|im_end|>
<|im_start|>assistant
Large language models are artificial intelligence systems designed to process and generate
human-like language at scale. These models are typically built using deep learning
techniques, specifically neural networks, and are trained on massive amounts of text
data from the internet, books, and other sources. They are capable of understanding context,
generating coherent responses, and even performing complex language-based tasks such as
translation, summarization, and question-answering. Large language models like GPT-3, T5, and
BERT have revolutionized the field of natural language processing (NLP) by achieving state-of-the-art
performance in various tasks and enabling more human-like interactions with machines.<|im_end|>
```
### Coding
```
<|im_start|>system
You are a helpful assistant<|im_end|>
<|im_start|>user
Implement quicksort using C++<|im_end|>
<|im_start|>assistant
Sure, here's an implementation of the quicksort algorithm in C++:
#include <iostream>
using namespace std;
// Function to swap two elements
void swap(int* a, int* b) {
int t = *a;
*a = *b;
*b = t;
}
// Function to partition the array
int partition (int arr[], int low, int high) {
int pivot = arr[high]; // pivot
int i = (low - 1); // Index of smaller element
for (int j = low; j <= high- 1; j++) {
// If current element is smaller than the pivot
if (arr[j] < pivot) {
i++; // increment index of smaller element
swap(&arr[i], &arr[j]);
}
}
swap(&arr[i + 1], &arr[high]);
return (i + 1);
}
// Function to perform quicksort
void quickSort(int arr[], int low, int high) {
if (low < high) {
// pi is partitioning index, arr[p] is now
// at right place
int pi = partition(arr, low, high);
// Separately sort elements before
// partition and after partition
quickSort(arr, low, pi - 1);
quickSort(arr, pi + 1, high);
}
}
// Function to print an array
void printArray(int arr[], int size) {
int i;
for (i=0; i < size; i++)
cout << arr[i] << " ";
cout << endl;
}
// Driver program to test above functions
int main() {
int arr[] = {10, 7, 8, 9, 1, 5};
int n = sizeof(arr)/sizeof(arr[0]);
quickSort(arr, 0, n-1);
cout << "Sorted array: \n";
printArray(arr, n);
return 0;
}
In this implementation, the `quickSort` function recursively calls itself to
sort the elements before and after the pivot element, which is determined using
the `partition` function. The `swap` function is used to swap elements in the
array. The `printArray` function is used to print the sorted array.<|im_end|>
```
### Conversational
```
<|im_start|>system
You are a helpful assistant<|im_end|>
<|im_start|>user
What do the vacuum tubes in an amplifier do?<|im_end|>
<|im_start|>assistant
Vacuum tubes in an amplifier serve as electronic switches and amplifiers
that can control the flow of electrical current in a circuit. They play
a crucial role in amplifying the input signal, whether it's a weak audio
signal or something else, to produce a louder or stronger output signal.
Here's a detailed explanation of what vacuum tubes do in an amplifier:
1. **Electron Emission**: In a vacuum tube, an electrode called the
cathode is heated, usually by an electrical current. This heating
causes the cathode to emit electrons into the vacuum space inside
the tube. The process is called thermionic emission.
2. **Controlled Current Flow**: The emitted electrons are attracted
to another electrode called the anode, which is at a higher voltage
than the cathode. A third electrode, called the grid, is placed between
the cathode and the anode. By varying the voltage on the grid relative
to the cathode, the flow of electrons from the cathode to the anode
can be controlled. If the grid voltage is more negative than the
cathode, fewer electrons will reach the anode, and vice versa. This
control over electron flow is the basis for amplification.
3. **Amplification**: When an input signal (the weak signal to be
amplified) is applied to the grid, it modulates the flow of electrons
from the cathode to the anode. As the number of electrons passing
through the tube changes in response to the input signal, the current
flowing through the anode also changes. This change in current is then
amplified and can be used to drive a speaker or other output device.
4. **Linear Amplification**: Vacuum tubes are particularly good at
providing linear amplification, meaning that the output signal is
proportional to the input signal. This is important for maintaining
the integrity of the original signal, especially in audio amplifiers
where distortion should be minimized.
5. **Overdrive and Distortion**: If the input signal becomes too
strong, the vacuum tube can be driven into "overdrive," where the
control of the grid over the electron flow breaks down, and the
output signal becomes distorted. This effect is often used in guitar
amplifiers to create a "crunchy" or "fuzzy" sound.
In summary, vacuum tubes in an amplifier enable the controlled
manipulation of electrical current, allowing for the amplification
of signals with minimal distortion. They have been a key component
in electronic amplification for decades, although solid-state devices
like transistors have largely replaced them in modern electronics due
to their lower power consumption, smaller size, and higher reliability.<|im_end|>
```
## Technical Details
Qwen 1.5 32B chat is trained on a diverse dataset and then was tuned with Supervised Fine Tuning (SFT) and Direct Preference Optimization (DPO) for its excellent chat capabilities.
Trained on multiple languages, the model has shown excellent performance in the following languages in addition to its great English performance:
- Arabic
- Spanish
- French
- Portuguese
- German
- Italian
- Russian
- Japanese
- Korean
- Vietnamese
- Thai
- Indonesian
Supports 32k context for very long conversations.
## Special thanks
ð Special thanks to [Georgi Gerganov](https://github.com/ggerganov) and the whole team working on [llama.cpp](https://github.com/ggerganov/llama.cpp/) for making all of this possible.
## Disclaimers
LM Studio is not the creator, originator, or owner of any Model featured in the Community Model Program. Each Community Model is created and provided by third parties. LM Studio does not endorse, support, represent or guarantee the completeness, truthfulness, accuracy, or reliability of any Community Model. You understand that Community Models can produce content that might be offensive, harmful, inaccurate or otherwise inappropriate, or deceptive. Each Community Model is the sole responsibility of the person or entity who originated such Model. LM Studio may not monitor or control the Community Models and cannot, and does not, take responsibility for any such Model. LM Studio disclaims all warranties or guarantees about the accuracy, reliability or benefits of the Community Models. LM Studio further disclaims any warranty that the Community Model will meet your requirements, be secure, uninterrupted or available at any time or location, or error-free, viruses-free, or that any errors will be corrected, or otherwise. You will be solely responsible for any damage resulting from your use of or access to the Community Models, your downloading of any Community Model, or use of any other Community Model provided by or through LM Studio.
|
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