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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-08-02 12:29:30
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
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student-abdullah/Llama3.2_Trial-7_32-bit_gguf
|
student-abdullah
| 2024-10-07T17:23:54Z | 19 | 0 |
transformers
|
[
"transformers",
"gguf",
"llama",
"text-generation-inference",
"torch",
"trl",
"unsloth",
"en",
"dataset:student-abdullah/BigPharma_Generic_Q-A_Format_Augemented_Dataset",
"base_model:meta-llama/Llama-3.2-1B",
"base_model:quantized:meta-llama/Llama-3.2-1B",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-10-07T12:32:43Z |
---
base_model: meta-llama/Llama-3.2-1B
datasets:
- student-abdullah/BigPharma_Generic_Q-A_Format_Augemented_Dataset
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- torch
- trl
- unsloth
- llama
- gguf
---
# Uploaded model
- **Developed by:** student-abdullah
- **License:** apache-2.0
- **Finetuned from model:** meta-llama/Llama-3.2-1B
- **Created on:** 7th October, 2024
---
# Acknowledgement
<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>
---
# Model Description
This model is fine-tuned from the meta-llama/Llama-3.2-1B base model to enhance its capabilities in generating relevant and accurate responses related to generic medications under the PMBJP scheme. The fine-tuning process included the following hyperparameters:
- Fine Tuning Template: Llama Q&A
- Max Tokens: 1024
- LoRA Alpha: 5
- LoRA Rank (r): 132
- Learning rate: 5e-5
- Gradient Accumulation Steps: 1
- Batch Size: 6
- Quantization: None
---
# Model Quantitative Performace
- Training Quantitative Loss: 0.1403 (at final 3rd epoch 4505th Step)
---
# Limitations
- Token Limitations: With a max token limit of 512, the model might not handle very long queries or contexts effectively.
- Training Data Limitations: The model’s performance is contingent on the quality and coverage of the fine-tuning dataset, which may affect its generalizability to different contexts or medications not covered in the dataset.
- Potential Biases: As with any model fine-tuned on specific data, there may be biases based on the dataset used for training.
---
# Model Performace Evaluation:
- Evaluation on 1000 Questions based on dataset (to evaluate the finetuned knowledge base)
- At temperature 0.3
- Correct Responses: 83.69%
- Incorrect Responses: 16.31%
<p align="center">
<img src="" width="20%" style="display:inline-block;"/>
<img src="" width="35%" style="display:inline-block;"/>
<img src="" width="35%" style="display:inline-block;"/>
</p>
|
sbunlp/fabert
|
sbunlp
| 2024-10-07T17:23:27Z | 478 | 14 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"bert",
"fill-mask",
"fa",
"arxiv:2402.06617",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2024-02-09T14:00:20Z |
---
language:
- fa
library_name: transformers
widget:
- text: "ز سوزناکی گفتار من [MASK] بگریست"
example_title: "Poetry 1"
- text: "نظر از تو برنگیرم همه [MASK] تا بمیرم که تو در دلم نشستی و سر مقام داری"
example_title: "Poetry 2"
- text: "هر ساعتم اندرون بجوشد [MASK] را وآگاهی نیست مردم بیرون را"
example_title: "Poetry 3"
- text: "غلام همت آن رند عافیت سوزم که در گدا صفتی [MASK] داند"
example_title: "Poetry 4"
- text: "این [MASK] اولشه."
example_title: "Informal 1"
- text: "دیگه خسته شدم! [MASK] اینم شد کار؟!"
example_title: "Informal 2"
- text: "فکر نکنم به موقع برسیم. بهتره [MASK] این یکی بشیم."
example_title: "Informal 3"
- text: "تا صبح بیدار موندم و داشتم برای [MASK] آماده می شدم."
example_title: "Informal 4"
- text: "زندگی بدون [MASK] خستهکننده است."
example_title: "Formal 1"
- text: "در حکم اولیه این شرکت مجاز به فعالیت شد ولی پس از بررسی مجدد، مجوز این شرکت [MASK] شد."
example_title: "Formal 2"
---
# FaBERT: Pre-training BERT on Persian Blogs
## Model Details
FaBERT is a Persian BERT-base model trained on the diverse HmBlogs corpus, encompassing both casual and formal Persian texts. Developed for natural language processing tasks, FaBERT is a robust solution for processing Persian text. Through evaluation across various Natural Language Understanding (NLU) tasks, FaBERT consistently demonstrates notable improvements, while having a compact model size. Now available on Hugging Face, integrating FaBERT into your projects is hassle-free. Experience enhanced performance without added complexity as FaBERT tackles a variety of NLP tasks.
## Features
- Pre-trained on the diverse HmBlogs corpus consisting more than 50 GB of text from Persian Blogs
- Remarkable performance across various downstream NLP tasks
- BERT architecture with 124 million parameters
## Useful Links
- **Repository:** [FaBERT on Github](https://github.com/SBU-NLP-LAB/FaBERT)
- **Paper:** [arXiv preprint](https://arxiv.org/abs/2402.06617)
## Usage
### Loading the Model with MLM head
```python
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("sbunlp/fabert") # make sure to use the default fast tokenizer
model = AutoModelForMaskedLM.from_pretrained("sbunlp/fabert")
```
### Downstream Tasks
Similar to the original English BERT, FaBERT can be fine-tuned on many downstream tasks.(https://huggingface.co/docs/transformers/en/training)
Examples on Persian datasets are available in our [GitHub repository](#useful-links).
**make sure to use the default Fast Tokenizer**
## Training Details
FaBERT was pre-trained with the MLM (WWM) objective, and the resulting perplexity on validation set was 7.76.
| Hyperparameter | Value |
|-------------------|:--------------:|
| Batch Size | 32 |
| Optimizer | Adam |
| Learning Rate | 6e-5 |
| Weight Decay | 0.01 |
| Total Steps | 18 Million |
| Warmup Steps | 1.8 Million |
| Precision Format | TF32 |
## Evaluation
Here are some key performance results for the FaBERT model:
**Sentiment Analysis**
| Task | FaBERT | ParsBERT | XLM-R |
|:-------------|:------:|:--------:|:-----:|
| MirasOpinion | **87.51** | 86.73 | 84.92 |
| MirasIrony | 74.82 | 71.08 | **75.51** |
| DeepSentiPers | **79.85** | 74.94 | 79.00 |
**Named Entity Recognition**
| Task | FaBERT | ParsBERT | XLM-R |
|:-------------|:------:|:--------:|:-----:|
| PEYMA | **91.39** | 91.24 | 90.91 |
| ParsTwiner | **82.22** | 81.13 | 79.50 |
| MultiCoNER v2 | 57.92 | **58.09** | 51.47 |
**Question Answering**
| Task | FaBERT | ParsBERT | XLM-R |
|:-------------|:------:|:--------:|:-----:|
| ParsiNLU | **55.87** | 44.89 | 42.55 |
| PQuAD | 87.34 | 86.89 | **87.60** |
| PCoQA | **53.51** | 50.96 | 51.12 |
**Natural Language Inference & QQP**
| Task | FaBERT | ParsBERT | XLM-R |
|:-------------|:------:|:--------:|:-----:|
| FarsTail | **84.45** | 82.52 | 83.50 |
| SBU-NLI | **66.65** | 58.41 | 58.85 |
| ParsiNLU QQP | **82.62** | 77.60 | 79.74 |
**Number of Parameters**
| | FaBERT | ParsBERT | XLM-R |
|:-------------|:------:|:--------:|:-----:|
| Parameter Count (M) | 124 | 162 | 278 |
| Vocabulary Size (K) | 50 | 100 | 250 |
For a more detailed performance analysis refer to the paper.
## How to Cite
If you use FaBERT in your research or projects, please cite it using the following BibTeX:
```bibtex
@article{masumi2024fabert,
title={FaBERT: Pre-training BERT on Persian Blogs},
author={Masumi, Mostafa and Majd, Seyed Soroush and Shamsfard, Mehrnoush and Beigy, Hamid},
journal={arXiv preprint arXiv:2402.06617},
year={2024}
}
```
|
RichardErkhov/leap-llm_-_Meta-Llama-3.1-8B-Instruct-sft-intercode-bash-iter0-gguf
|
RichardErkhov
| 2024-10-07T17:15:40Z | 23 | 0 | null |
[
"gguf",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-10-07T13:06:12Z |
Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
Meta-Llama-3.1-8B-Instruct-sft-intercode-bash-iter0 - GGUF
- Model creator: https://huggingface.co/leap-llm/
- Original model: https://huggingface.co/leap-llm/Meta-Llama-3.1-8B-Instruct-sft-intercode-bash-iter0/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [Meta-Llama-3.1-8B-Instruct-sft-intercode-bash-iter0.Q2_K.gguf](https://huggingface.co/RichardErkhov/leap-llm_-_Meta-Llama-3.1-8B-Instruct-sft-intercode-bash-iter0-gguf/blob/main/Meta-Llama-3.1-8B-Instruct-sft-intercode-bash-iter0.Q2_K.gguf) | Q2_K | 2.96GB |
| [Meta-Llama-3.1-8B-Instruct-sft-intercode-bash-iter0.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/leap-llm_-_Meta-Llama-3.1-8B-Instruct-sft-intercode-bash-iter0-gguf/blob/main/Meta-Llama-3.1-8B-Instruct-sft-intercode-bash-iter0.IQ3_XS.gguf) | IQ3_XS | 3.28GB |
| [Meta-Llama-3.1-8B-Instruct-sft-intercode-bash-iter0.IQ3_S.gguf](https://huggingface.co/RichardErkhov/leap-llm_-_Meta-Llama-3.1-8B-Instruct-sft-intercode-bash-iter0-gguf/blob/main/Meta-Llama-3.1-8B-Instruct-sft-intercode-bash-iter0.IQ3_S.gguf) | IQ3_S | 3.43GB |
| [Meta-Llama-3.1-8B-Instruct-sft-intercode-bash-iter0.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/leap-llm_-_Meta-Llama-3.1-8B-Instruct-sft-intercode-bash-iter0-gguf/blob/main/Meta-Llama-3.1-8B-Instruct-sft-intercode-bash-iter0.Q3_K_S.gguf) | Q3_K_S | 3.41GB |
| [Meta-Llama-3.1-8B-Instruct-sft-intercode-bash-iter0.IQ3_M.gguf](https://huggingface.co/RichardErkhov/leap-llm_-_Meta-Llama-3.1-8B-Instruct-sft-intercode-bash-iter0-gguf/blob/main/Meta-Llama-3.1-8B-Instruct-sft-intercode-bash-iter0.IQ3_M.gguf) | IQ3_M | 3.52GB |
| [Meta-Llama-3.1-8B-Instruct-sft-intercode-bash-iter0.Q3_K.gguf](https://huggingface.co/RichardErkhov/leap-llm_-_Meta-Llama-3.1-8B-Instruct-sft-intercode-bash-iter0-gguf/blob/main/Meta-Llama-3.1-8B-Instruct-sft-intercode-bash-iter0.Q3_K.gguf) | Q3_K | 3.74GB |
| [Meta-Llama-3.1-8B-Instruct-sft-intercode-bash-iter0.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/leap-llm_-_Meta-Llama-3.1-8B-Instruct-sft-intercode-bash-iter0-gguf/blob/main/Meta-Llama-3.1-8B-Instruct-sft-intercode-bash-iter0.Q3_K_M.gguf) | Q3_K_M | 3.74GB |
| [Meta-Llama-3.1-8B-Instruct-sft-intercode-bash-iter0.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/leap-llm_-_Meta-Llama-3.1-8B-Instruct-sft-intercode-bash-iter0-gguf/blob/main/Meta-Llama-3.1-8B-Instruct-sft-intercode-bash-iter0.Q3_K_L.gguf) | Q3_K_L | 4.03GB |
| [Meta-Llama-3.1-8B-Instruct-sft-intercode-bash-iter0.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/leap-llm_-_Meta-Llama-3.1-8B-Instruct-sft-intercode-bash-iter0-gguf/blob/main/Meta-Llama-3.1-8B-Instruct-sft-intercode-bash-iter0.IQ4_XS.gguf) | IQ4_XS | 4.18GB |
| [Meta-Llama-3.1-8B-Instruct-sft-intercode-bash-iter0.Q4_0.gguf](https://huggingface.co/RichardErkhov/leap-llm_-_Meta-Llama-3.1-8B-Instruct-sft-intercode-bash-iter0-gguf/blob/main/Meta-Llama-3.1-8B-Instruct-sft-intercode-bash-iter0.Q4_0.gguf) | Q4_0 | 4.34GB |
| [Meta-Llama-3.1-8B-Instruct-sft-intercode-bash-iter0.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/leap-llm_-_Meta-Llama-3.1-8B-Instruct-sft-intercode-bash-iter0-gguf/blob/main/Meta-Llama-3.1-8B-Instruct-sft-intercode-bash-iter0.IQ4_NL.gguf) | IQ4_NL | 4.38GB |
| [Meta-Llama-3.1-8B-Instruct-sft-intercode-bash-iter0.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/leap-llm_-_Meta-Llama-3.1-8B-Instruct-sft-intercode-bash-iter0-gguf/blob/main/Meta-Llama-3.1-8B-Instruct-sft-intercode-bash-iter0.Q4_K_S.gguf) | Q4_K_S | 4.37GB |
| [Meta-Llama-3.1-8B-Instruct-sft-intercode-bash-iter0.Q4_K.gguf](https://huggingface.co/RichardErkhov/leap-llm_-_Meta-Llama-3.1-8B-Instruct-sft-intercode-bash-iter0-gguf/blob/main/Meta-Llama-3.1-8B-Instruct-sft-intercode-bash-iter0.Q4_K.gguf) | Q4_K | 4.58GB |
| [Meta-Llama-3.1-8B-Instruct-sft-intercode-bash-iter0.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/leap-llm_-_Meta-Llama-3.1-8B-Instruct-sft-intercode-bash-iter0-gguf/blob/main/Meta-Llama-3.1-8B-Instruct-sft-intercode-bash-iter0.Q4_K_M.gguf) | Q4_K_M | 4.58GB |
| [Meta-Llama-3.1-8B-Instruct-sft-intercode-bash-iter0.Q4_1.gguf](https://huggingface.co/RichardErkhov/leap-llm_-_Meta-Llama-3.1-8B-Instruct-sft-intercode-bash-iter0-gguf/blob/main/Meta-Llama-3.1-8B-Instruct-sft-intercode-bash-iter0.Q4_1.gguf) | Q4_1 | 4.78GB |
| [Meta-Llama-3.1-8B-Instruct-sft-intercode-bash-iter0.Q5_0.gguf](https://huggingface.co/RichardErkhov/leap-llm_-_Meta-Llama-3.1-8B-Instruct-sft-intercode-bash-iter0-gguf/blob/main/Meta-Llama-3.1-8B-Instruct-sft-intercode-bash-iter0.Q5_0.gguf) | Q5_0 | 5.21GB |
| [Meta-Llama-3.1-8B-Instruct-sft-intercode-bash-iter0.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/leap-llm_-_Meta-Llama-3.1-8B-Instruct-sft-intercode-bash-iter0-gguf/blob/main/Meta-Llama-3.1-8B-Instruct-sft-intercode-bash-iter0.Q5_K_S.gguf) | Q5_K_S | 5.21GB |
| [Meta-Llama-3.1-8B-Instruct-sft-intercode-bash-iter0.Q5_K.gguf](https://huggingface.co/RichardErkhov/leap-llm_-_Meta-Llama-3.1-8B-Instruct-sft-intercode-bash-iter0-gguf/blob/main/Meta-Llama-3.1-8B-Instruct-sft-intercode-bash-iter0.Q5_K.gguf) | Q5_K | 5.34GB |
| [Meta-Llama-3.1-8B-Instruct-sft-intercode-bash-iter0.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/leap-llm_-_Meta-Llama-3.1-8B-Instruct-sft-intercode-bash-iter0-gguf/blob/main/Meta-Llama-3.1-8B-Instruct-sft-intercode-bash-iter0.Q5_K_M.gguf) | Q5_K_M | 5.34GB |
| [Meta-Llama-3.1-8B-Instruct-sft-intercode-bash-iter0.Q5_1.gguf](https://huggingface.co/RichardErkhov/leap-llm_-_Meta-Llama-3.1-8B-Instruct-sft-intercode-bash-iter0-gguf/blob/main/Meta-Llama-3.1-8B-Instruct-sft-intercode-bash-iter0.Q5_1.gguf) | Q5_1 | 5.65GB |
| [Meta-Llama-3.1-8B-Instruct-sft-intercode-bash-iter0.Q6_K.gguf](https://huggingface.co/RichardErkhov/leap-llm_-_Meta-Llama-3.1-8B-Instruct-sft-intercode-bash-iter0-gguf/blob/main/Meta-Llama-3.1-8B-Instruct-sft-intercode-bash-iter0.Q6_K.gguf) | Q6_K | 6.14GB |
| [Meta-Llama-3.1-8B-Instruct-sft-intercode-bash-iter0.Q8_0.gguf](https://huggingface.co/RichardErkhov/leap-llm_-_Meta-Llama-3.1-8B-Instruct-sft-intercode-bash-iter0-gguf/blob/main/Meta-Llama-3.1-8B-Instruct-sft-intercode-bash-iter0.Q8_0.gguf) | Q8_0 | 7.95GB |
Original model description:
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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<!-- 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. -->
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## 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. -->
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[More Information Needed]
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
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## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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[More Information Needed]
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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|
MrRobson9/distilbert-base-cased-finetuned-conll2003-english-ner
|
MrRobson9
| 2024-10-07T17:12:01Z | 107 | 0 |
transformers
|
[
"transformers",
"safetensors",
"distilbert",
"token-classification",
"en",
"dataset:eriktks/conll2003",
"base_model:distilbert/distilbert-base-cased",
"base_model:finetune:distilbert/distilbert-base-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2024-07-25T19:31:00Z |
---
license: apache-2.0
datasets:
- eriktks/conll2003
language:
- en
metrics:
- accuracy
- precision
- recall
- f1
base_model:
- distilbert/distilbert-base-cased
---
# DistilBERT Base Cased Fine-Tuned on CoNLL2003 for English Named Entity Recognition (NER)
This model is a fine-tuned version of [DistilBERT-base-cased](https://huggingface.co/distilbert/distilbert-base-cased) on the [CoNLL2003](https://huggingface.co/datasets/eriktks/conll2003) dataset for Named Entity Recognition (NER) in English. The CoNLL2003 dataset contains four types of named entities: Person (PER), Location (LOC), Organization (ORG), and Miscellaneous (MISC).
## Model Details
- Model Architecture: BERT (Bidirectional Encoder Representations from Transformers)
- Pre-trained Base Model: bert-base-cased
- Dataset: CoNLL2003 (NER task)
- Languages: English
- Fine-tuned for: Named Entity Recognition (NER)
- Entities recognized:
- PER: Person
- LOC: Location
- ORG: Organization
- MISC: Miscellaneous entities
## Use Cases
This model is ideal for tasks that require identifying and classifying named entities within English text, such as:
- Information extraction from unstructured text
- Content classification and tagging
- Automated text summarization
- Question answering systems with a focus on entity recognition
## How to Use
To use this model in your code, you can load it via Hugging Face’s Transformers library:
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline
tokenizer = AutoTokenizer.from_pretrained("MrRobson9/distilbert-base-cased-finetuned-conll2003-english-ner")
model = AutoModelForTokenClassification.from_pretrained("MrRobson9/distilbert-base-cased-finetuned-conll2003-english-ner")
nlp_ner = pipeline("ner", model=model, tokenizer=tokenizer)
result = nlp_ner("John lives in New York and works for the United Nations.")
print(result)
```
## Performance
|accuracy |precision |recall |f1-score|
|:-------:|:--------:|:-----:|:------:|
| 0.987 | 0.937 | 0.941 | 0.939 |
## License
This model is licensed under the same terms as the BERT-base-cased model and the CoNLL2003 dataset. Please ensure compliance with all respective licenses when using this model.
|
swap-uniba/llama3-it-pa-100k-adapter
|
swap-uniba
| 2024-10-07T17:05:52Z | 5 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:unsloth/llama-3-8b",
"base_model:adapter:unsloth/llama-3-8b",
"region:us"
] | null | 2024-10-07T17:00:08Z |
---
library_name: peft
base_model: unsloth/llama-3-8b
---
# 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
|
Trelis/Llama-3.2-1B-Instruct-MATH-synthetic-augmented
|
Trelis
| 2024-10-07T17:01:29Z | 119 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"conversational",
"en",
"base_model:unsloth/Llama-3.2-1B-Instruct",
"base_model:finetune:unsloth/Llama-3.2-1B-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-10-07T17:01:08Z |
---
base_model: unsloth/Llama-3.2-1B-Instruct
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
---
# Uploaded model
- **Developed by:** Trelis
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Llama-3.2-1B-Instruct
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
Youlln/1PARAMMYL-8B-ModelStock
|
Youlln
| 2024-10-07T16:56:47Z | 6 | 0 | null |
[
"safetensors",
"llama",
"merge",
"mergekit",
"lazymergekit",
"arcee-ai/Llama-3.1-SuperNova-Lite",
"base_model:arcee-ai/Llama-3.1-SuperNova-Lite",
"base_model:finetune:arcee-ai/Llama-3.1-SuperNova-Lite",
"model-index",
"region:us"
] | null | 2024-09-20T15:53:12Z |
---
tags:
- merge
- mergekit
- lazymergekit
- arcee-ai/Llama-3.1-SuperNova-Lite
base_model:
- arcee-ai/Llama-3.1-SuperNova-Lite
model-index:
- name: 1PARAMMYL-8B-ModelStock
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: IFEval (0-Shot)
type: HuggingFaceH4/ifeval
args:
num_few_shot: 0
metrics:
- type: inst_level_strict_acc and prompt_level_strict_acc
value: 53.71
name: strict accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Youlln/1PARAMMYL-8B-ModelStock
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: BBH (3-Shot)
type: BBH
args:
num_few_shot: 3
metrics:
- type: acc_norm
value: 31.8
name: normalized accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Youlln/1PARAMMYL-8B-ModelStock
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MATH Lvl 5 (4-Shot)
type: hendrycks/competition_math
args:
num_few_shot: 4
metrics:
- type: exact_match
value: 13.14
name: exact match
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Youlln/1PARAMMYL-8B-ModelStock
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GPQA (0-shot)
type: Idavidrein/gpqa
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 9.84
name: acc_norm
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Youlln/1PARAMMYL-8B-ModelStock
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MuSR (0-shot)
type: TAUR-Lab/MuSR
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 14.28
name: acc_norm
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Youlln/1PARAMMYL-8B-ModelStock
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU-PRO (5-shot)
type: TIGER-Lab/MMLU-Pro
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 33.34
name: accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Youlln/1PARAMMYL-8B-ModelStock
name: Open LLM Leaderboard
---
# 1PARAMMYL-8B-ModelStock
1PARAMMYL-8B-ModelStock is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [arcee-ai/Llama-3.1-SuperNova-Lite](https://huggingface.co/arcee-ai/Llama-3.1-SuperNova-Lite)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: arcee-ai/Llama-3.1-SuperNova-Lite
layer_range: [0, 32]
- model: DreadPoor/Heart_Stolen-8B-Model_Stock
layer_range: [0, 32]
- model: Dampfinchen/Llama-3.1-8B-Ultra-Instruct
layer_range: [0, 32]
merge_method: model_stock
base_model: arcee-ai/Llama-3.1-SuperNova-Lite
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "Youlln/1PARAMMYL-8B-ModelStock"
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/open-llm-leaderboard/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_Youlln__1PARAMMYL-8B-ModelStock)
| Metric |Value|
|-------------------|----:|
|Avg. |26.02|
|IFEval (0-Shot) |53.71|
|BBH (3-Shot) |31.80|
|MATH Lvl 5 (4-Shot)|13.14|
|GPQA (0-shot) | 9.84|
|MuSR (0-shot) |14.28|
|MMLU-PRO (5-shot) |33.34|
|
lupoplon/pyramids
|
lupoplon
| 2024-10-07T16:56:42Z | 7 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] |
reinforcement-learning
| 2024-10-07T15:23:37Z |
---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: lupoplon/pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Helsinki-NLP/opus-mt-tc-bible-big-bat-deu_eng_nld
|
Helsinki-NLP
| 2024-10-07T16:55:21Z | 108 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc-bible",
"de",
"en",
"lt",
"lv",
"nl",
"prg",
"sgs",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2024-10-07T16:55:08Z |
---
library_name: transformers
language:
- de
- en
- lt
- lv
- nl
- prg
- sgs
tags:
- translation
- opus-mt-tc-bible
license: apache-2.0
model-index:
- name: opus-mt-tc-bible-big-bat-deu_eng_nld
results:
- task:
name: Translation multi-multi
type: translation
args: multi-multi
dataset:
name: tatoeba-test-v2020-07-28-v2023-09-26
type: tatoeba_mt
args: multi-multi
metrics:
- name: BLEU
type: bleu
value: 56.9
- name: chr-F
type: chrf
value: 0.72041
---
# opus-mt-tc-bible-big-bat-deu_eng_nld
## Table of Contents
- [Model Details](#model-details)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [How to Get Started With the Model](#how-to-get-started-with-the-model)
- [Training](#training)
- [Evaluation](#evaluation)
- [Citation Information](#citation-information)
- [Acknowledgements](#acknowledgements)
## Model Details
Neural machine translation model for translating from Baltic languages (bat) to unknown (deu+eng+nld).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally trained using the amazing framework of [Marian NMT](https://marian-nmt.github.io/), an efficient NMT implementation written in pure C++. The models have been converted to pyTorch using the transformers library by huggingface. Training data is taken from [OPUS](https://opus.nlpl.eu/) and training pipelines use the procedures of [OPUS-MT-train](https://github.com/Helsinki-NLP/Opus-MT-train).
**Model Description:**
- **Developed by:** Language Technology Research Group at the University of Helsinki
- **Model Type:** Translation (transformer-big)
- **Release**: 2024-08-17
- **License:** Apache-2.0
- **Language(s):**
- Source Language(s): lav lit prg sgs
- Target Language(s): deu eng nld
- Valid Target Language Labels: >>deu<< >>eng<< >>nld<< >>xxx<<
- **Original Model**: [opusTCv20230926max50+bt+jhubc_transformer-big_2024-08-17.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/bat-deu+eng+nld/opusTCv20230926max50+bt+jhubc_transformer-big_2024-08-17.zip)
- **Resources for more information:**
- [OPUS-MT dashboard](https://opus.nlpl.eu/dashboard/index.php?pkg=opusmt&test=all&scoreslang=all&chart=standard&model=Tatoeba-MT-models/bat-deu%2Beng%2Bnld/opusTCv20230926max50%2Bbt%2Bjhubc_transformer-big_2024-08-17)
- [OPUS-MT-train GitHub Repo](https://github.com/Helsinki-NLP/OPUS-MT-train)
- [More information about MarianNMT models in the transformers library](https://huggingface.co/docs/transformers/model_doc/marian)
- [Tatoeba Translation Challenge](https://github.com/Helsinki-NLP/Tatoeba-Challenge/)
- [HPLT bilingual data v1 (as part of the Tatoeba Translation Challenge dataset)](https://hplt-project.org/datasets/v1)
- [A massively parallel Bible corpus](https://aclanthology.org/L14-1215/)
This is a multilingual translation model with multiple target languages. A sentence initial language token is required in the form of `>>id<<` (id = valid target language ID), e.g. `>>deu<<`
## Uses
This model can be used for translation and text-to-text generation.
## Risks, Limitations and Biases
**CONTENT WARNING: Readers should be aware that the model is trained on various public data sets that may contain content that is disturbing, offensive, and can propagate historical and current stereotypes.**
Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)).
## How to Get Started With the Model
A short example code:
```python
from transformers import MarianMTModel, MarianTokenizer
src_text = [
">>deu<< 1991. gadā neatkarību pasludināja Horvātija, Slovēnija un Maķedonija, kam 1992. gadā sekoja Bosnija.",
">>eng<< Jukiko mėgsta bulves."
]
model_name = "pytorch-models/opus-mt-tc-bible-big-bat-deu_eng_nld"
tokenizer = MarianTokenizer.from_pretrained(model_name)
model = MarianMTModel.from_pretrained(model_name)
translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True))
for t in translated:
print( tokenizer.decode(t, skip_special_tokens=True) )
# expected output:
# 1991 erklärten Kroatien, Slowenien und Mazedonien ihre Unabhängigkeit, 1992 folgte Bosnien.
# Yukiko likes potatoes.
```
You can also use OPUS-MT models with the transformers pipelines, for example:
```python
from transformers import pipeline
pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-bible-big-bat-deu_eng_nld")
print(pipe(">>deu<< 1991. gadā neatkarību pasludināja Horvātija, Slovēnija un Maķedonija, kam 1992. gadā sekoja Bosnija."))
# expected output: 1991 erklärten Kroatien, Slowenien und Mazedonien ihre Unabhängigkeit, 1992 folgte Bosnien.
```
## Training
- **Data**: opusTCv20230926max50+bt+jhubc ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge))
- **Pre-processing**: SentencePiece (spm32k,spm32k)
- **Model Type:** transformer-big
- **Original MarianNMT Model**: [opusTCv20230926max50+bt+jhubc_transformer-big_2024-08-17.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/bat-deu+eng+nld/opusTCv20230926max50+bt+jhubc_transformer-big_2024-08-17.zip)
- **Training Scripts**: [GitHub Repo](https://github.com/Helsinki-NLP/OPUS-MT-train)
## Evaluation
* [Model scores at the OPUS-MT dashboard](https://opus.nlpl.eu/dashboard/index.php?pkg=opusmt&test=all&scoreslang=all&chart=standard&model=Tatoeba-MT-models/bat-deu%2Beng%2Bnld/opusTCv20230926max50%2Bbt%2Bjhubc_transformer-big_2024-08-17)
* test set translations: [opusTCv20230926max50+bt+jhubc_transformer-big_2024-08-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/bat-deu+eng+nld/opusTCv20230926max50+bt+jhubc_transformer-big_2024-08-17.test.txt)
* test set scores: [opusTCv20230926max50+bt+jhubc_transformer-big_2024-08-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/bat-deu+eng+nld/opusTCv20230926max50+bt+jhubc_transformer-big_2024-08-17.eval.txt)
* benchmark results: [benchmark_results.txt](benchmark_results.txt)
* benchmark output: [benchmark_translations.zip](benchmark_translations.zip)
| langpair | testset | chr-F | BLEU | #sent | #words |
|----------|---------|-------|-------|-------|--------|
| multi-multi | tatoeba-test-v2020-07-28-v2023-09-26 | 0.72041 | 56.9 | 5531 | 39520 |
## Citation Information
* Publications: [Democratizing neural machine translation with OPUS-MT](https://doi.org/10.1007/s10579-023-09704-w) and [OPUS-MT – Building open translation services for the World](https://aclanthology.org/2020.eamt-1.61/) and [The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT](https://aclanthology.org/2020.wmt-1.139/) (Please, cite if you use this model.)
```bibtex
@article{tiedemann2023democratizing,
title={Democratizing neural machine translation with {OPUS-MT}},
author={Tiedemann, J{\"o}rg and Aulamo, Mikko and Bakshandaeva, Daria and Boggia, Michele and Gr{\"o}nroos, Stig-Arne and Nieminen, Tommi and Raganato, Alessandro and Scherrer, Yves and Vazquez, Raul and Virpioja, Sami},
journal={Language Resources and Evaluation},
number={58},
pages={713--755},
year={2023},
publisher={Springer Nature},
issn={1574-0218},
doi={10.1007/s10579-023-09704-w}
}
@inproceedings{tiedemann-thottingal-2020-opus,
title = "{OPUS}-{MT} {--} Building open translation services for the World",
author = {Tiedemann, J{\"o}rg and Thottingal, Santhosh},
booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
month = nov,
year = "2020",
address = "Lisboa, Portugal",
publisher = "European Association for Machine Translation",
url = "https://aclanthology.org/2020.eamt-1.61",
pages = "479--480",
}
@inproceedings{tiedemann-2020-tatoeba,
title = "The Tatoeba Translation Challenge {--} Realistic Data Sets for Low Resource and Multilingual {MT}",
author = {Tiedemann, J{\"o}rg},
booktitle = "Proceedings of the Fifth Conference on Machine Translation",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.wmt-1.139",
pages = "1174--1182",
}
```
## Acknowledgements
The work is supported by the [HPLT project](https://hplt-project.org/), funded by the European Union’s Horizon Europe research and innovation programme under grant agreement No 101070350. We are also grateful for the generous computational resources and IT infrastructure provided by [CSC -- IT Center for Science](https://www.csc.fi/), Finland, and the [EuroHPC supercomputer LUMI](https://www.lumi-supercomputer.eu/).
## Model conversion info
* transformers version: 4.45.1
* OPUS-MT git hash: a0ea3b3
* port time: Mon Oct 7 19:54:57 EEST 2024
* port machine: LM0-400-22516.local
|
afraid15chicken/finetuned-arsenic
|
afraid15chicken
| 2024-10-07T16:46:30Z | 195 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:google/vit-base-patch16-224-in21k",
"base_model:finetune:google/vit-base-patch16-224-in21k",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2024-10-03T19:20:27Z |
---
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- image-classification
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: finetuned-arsenic
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: indian_food_images
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9993451211525868
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuned-arsenic
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the indian_food_images dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0048
- Accuracy: 0.9993
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.1855 | 0.1848 | 100 | 0.1918 | 0.9312 |
| 0.1792 | 0.3697 | 200 | 0.1740 | 0.9365 |
| 0.1688 | 0.5545 | 300 | 0.0782 | 0.9692 |
| 0.1238 | 0.7394 | 400 | 0.2158 | 0.9227 |
| 0.0969 | 0.9242 | 500 | 0.0449 | 0.9843 |
| 0.0326 | 1.1091 | 600 | 0.1554 | 0.9574 |
| 0.1057 | 1.2939 | 700 | 0.0845 | 0.9738 |
| 0.0805 | 1.4787 | 800 | 0.0712 | 0.9823 |
| 0.0889 | 1.6636 | 900 | 0.0718 | 0.9797 |
| 0.0503 | 1.8484 | 1000 | 0.0251 | 0.9935 |
| 0.0225 | 2.0333 | 1100 | 0.0177 | 0.9967 |
| 0.0049 | 2.2181 | 1200 | 0.0246 | 0.9921 |
| 0.0152 | 2.4030 | 1300 | 0.0083 | 0.9987 |
| 0.08 | 2.5878 | 1400 | 0.0214 | 0.9941 |
| 0.0043 | 2.7726 | 1500 | 0.0069 | 0.9980 |
| 0.0501 | 2.9575 | 1600 | 0.0151 | 0.9967 |
| 0.0186 | 3.1423 | 1700 | 0.0078 | 0.9974 |
| 0.0033 | 3.3272 | 1800 | 0.0139 | 0.9961 |
| 0.0023 | 3.5120 | 1900 | 0.0076 | 0.9987 |
| 0.0054 | 3.6969 | 2000 | 0.0048 | 0.9993 |
| 0.0168 | 3.8817 | 2100 | 0.0066 | 0.9987 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.19.1
|
anhtung3369/b-lora-retrowave
|
anhtung3369
| 2024-10-07T16:40:03Z | 5 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"lora",
"template:diffusion-lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] |
text-to-image
| 2024-10-07T16:33:26Z |
---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- text: '-'
output:
url: retrowave.jpg
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: A [retrowave]
license: openrail++
---
# b-lora-retrowave
<Gallery />
## Trigger words
You should use `A [retrowave]` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/anhtung3369/b-lora-retrowave/tree/main) them in the Files & versions tab.
|
N1ch0/chungliao-MizoBert
|
N1ch0
| 2024-10-07T16:18:19Z | 116 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"fill-mask",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2024-08-28T14:43:42Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
altomek/Qwen2.5-32B-8bpw-EXL2
|
altomek
| 2024-10-07T16:16:18Z | 5 | 0 | null |
[
"safetensors",
"qwen2",
"text-generation",
"conversational",
"en",
"base_model:Qwen/Qwen2.5-32B",
"base_model:quantized:Qwen/Qwen2.5-32B",
"license:apache-2.0",
"8-bit",
"exl2",
"region:us"
] |
text-generation
| 2024-10-07T12:41:07Z |
---
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen2.5-32B/blob/main/LICENSE
language:
- en
pipeline_tag: text-generation
inference: false
base_model: Qwen/Qwen2.5-32B
---
# Qwen2.5-32B
ExLlamav2 8 bpw quant of https://huggingface.co/Qwen/Qwen2.5-32B
|
bregsi/parler-tts-mini-Jenny-v1
|
bregsi
| 2024-10-07T16:14:40Z | 51 | 0 |
transformers
|
[
"transformers",
"safetensors",
"parler_tts",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-10-07T16:11:50Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
mav23/Qwen2.5-1.5B-Instruct-GGUF
|
mav23
| 2024-10-07T16:14:21Z | 69 | 0 |
transformers
|
[
"transformers",
"gguf",
"chat",
"text-generation",
"en",
"arxiv:2407.10671",
"base_model:Qwen/Qwen2.5-1.5B",
"base_model:quantized:Qwen/Qwen2.5-1.5B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2024-10-07T16:00:42Z |
---
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct/blob/main/LICENSE
language:
- en
pipeline_tag: text-generation
base_model: Qwen/Qwen2.5-1.5B
tags:
- chat
library_name: transformers
---
# Qwen2.5-1.5B-Instruct
## Introduction
Qwen2.5 is the latest series of Qwen large language models. For Qwen2.5, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters. Qwen2.5 brings the following improvements upon Qwen2:
- Significantly **more knowledge** and has greatly improved capabilities in **coding** and **mathematics**, thanks to our specialized expert models in these domains.
- Significant improvements in **instruction following**, **generating long texts** (over 8K tokens), **understanding structured data** (e.g, tables), and **generating structured outputs** especially JSON. **More resilient to the diversity of system prompts**, enhancing role-play implementation and condition-setting for chatbots.
- **Long-context Support** up to 128K tokens and can generate up to 8K tokens.
- **Multilingual support** for over 29 languages, including Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more.
**This repo contains the instruction-tuned 1.5B Qwen2.5 model**, which has the following features:
- Type: Causal Language Models
- Training Stage: Pretraining & Post-training
- Architecture: transformers with RoPE, SwiGLU, RMSNorm, Attention QKV bias and tied word embeddings
- Number of Parameters: 1.54B
- Number of Paramaters (Non-Embedding): 1.31B
- Number of Layers: 28
- Number of Attention Heads (GQA): 12 for Q and 2 for KV
- Context Length: Full 32,768 tokens and generation 8192 tokens
For more details, please refer to our [blog](https://qwenlm.github.io/blog/qwen2.5/), [GitHub](https://github.com/QwenLM/Qwen2.5), and [Documentation](https://qwen.readthedocs.io/en/latest/).
## Requirements
The code of Qwen2.5 has been in the latest Hugging face `transformers` and we advise you to use the latest version of `transformers`.
With `transformers<4.37.0`, you will encounter the following error:
```
KeyError: 'qwen2'
```
## Quickstart
Here provides a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "Qwen/Qwen2.5-1.5B-Instruct"
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)
prompt = "Give me a short introduction to large language model."
messages = [
{"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."},
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
generated_ids = model.generate(
**model_inputs,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
```
## Evaluation & Performance
Detailed evaluation results are reported in this [📑 blog](https://qwenlm.github.io/blog/qwen2.5/).
For requirements on GPU memory and the respective throughput, see results [here](https://qwen.readthedocs.io/en/latest/benchmark/speed_benchmark.html).
## Citation
If you find our work helpful, feel free to give us a cite.
```
@misc{qwen2.5,
title = {Qwen2.5: A Party of Foundation Models},
url = {https://qwenlm.github.io/blog/qwen2.5/},
author = {Qwen Team},
month = {September},
year = {2024}
}
@article{qwen2,
title={Qwen2 Technical Report},
author={An Yang and Baosong Yang and Binyuan Hui and Bo Zheng and Bowen Yu and Chang Zhou and Chengpeng Li and Chengyuan Li and Dayiheng Liu and Fei Huang and Guanting Dong and Haoran Wei and Huan Lin and Jialong Tang and Jialin Wang and Jian Yang and Jianhong Tu and Jianwei Zhang and Jianxin Ma and Jin Xu and Jingren Zhou and Jinze Bai and Jinzheng He and Junyang Lin and Kai Dang and Keming Lu and Keqin Chen and Kexin Yang and Mei Li and Mingfeng Xue and Na Ni and Pei Zhang and Peng Wang and Ru Peng and Rui Men and Ruize Gao and Runji Lin and Shijie Wang and Shuai Bai and Sinan Tan and Tianhang Zhu and Tianhao Li and Tianyu Liu and Wenbin Ge and Xiaodong Deng and Xiaohuan Zhou and Xingzhang Ren and Xinyu Zhang and Xipin Wei and Xuancheng Ren and Yang Fan and Yang Yao and Yichang Zhang and Yu Wan and Yunfei Chu and Yuqiong Liu and Zeyu Cui and Zhenru Zhang and Zhihao Fan},
journal={arXiv preprint arXiv:2407.10671},
year={2024}
}
```
|
Ernesto-1997/roberta-base-bne-finetuned-spanish_sarcastic_texts
|
Ernesto-1997
| 2024-10-07T16:09:40Z | 108 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:BSC-LT/roberta-base-bne",
"base_model:finetune:BSC-LT/roberta-base-bne",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-10-07T15:34:59Z |
---
library_name: transformers
license: apache-2.0
base_model: BSC-TeMU/roberta-base-bne
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: roberta-base-bne-finetuned-spanish_sarcastic_texts
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-bne-finetuned-spanish_sarcastic_texts
This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-TeMU/roberta-base-bne) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2565
- Accuracy: 0.9324
- Precision: 0.9353
- Recall: 0.8896
- F1: 0.9118
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2.9498580198388856e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.2195 | 1.0 | 313 | 0.2003 | 0.9255 | 0.9105 | 0.8989 | 0.9047 |
| 0.0614 | 2.0 | 626 | 0.2565 | 0.9324 | 0.9353 | 0.8896 | 0.9118 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.19.1
|
VictorYeste/deberta-based-human-value-stance-detection
|
VictorYeste
| 2024-10-07T16:07:53Z | 111 | 0 |
transformers
|
[
"transformers",
"safetensors",
"deberta",
"text-classification",
"human value detection",
"text classification",
"multi-label clasification",
"base_model:microsoft/deberta-base",
"base_model:finetune:microsoft/deberta-base",
"doi:10.57967/hf/3276",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-06-13T10:18:26Z |
---
license: apache-2.0
base_model: microsoft/deberta-base
tags:
- deberta
- human value detection
- text classification
- multi-label clasification
model-index:
- name: deberta-based-human-value-stance-detection
results: []
---
# Description
The Human Value Detection at CLEF 2024 task consists of two sub-tasks: the first is to detect the presence or absence of each of these 19 values, while the second is to detect whether the value is attained or constrained.
Our system introduces a cascade model approach for the detection and stance classification of the predefined set of human values. It consists of two subsystems: one for detecting the presence of each human value and another for establishing the stance (if the sentence attains or constrains) of each human value. Each subsystem is designed and fine-tuned separately using a DeBERTa model as base.
- Subsystem 1: Its primary function is to identify the presence of human values within sentences. By combining the 'attained' and 'constrained' labels to indicate an overall presence, it streamlines the multi-label classification task, simplifying it to a binary classification for each of the 19 human values (presence vs. absence).
- Subsystem 2: it receives the outputs of subsystem 1 and classifies the stance towards each present human value in a binary classification (attained vs. constrained). This system transforms the sentences dataset into premise-hypothesis pairs, where each sentence is the premise, a value is the hypothesis, and the “attained” and “constrained” labels are the stance.
Given that subsystem 1 focuses on detecting the presence of human values in the text, and subsystem 2 focuses on the stances towards each detected human value, this cascade model approach improves the granularity of text classification.
This model is the responsible of the Subsystem 2 and accomplishes the second sub-task.
# How to use
```python
from transformers import pipeline
model = "VictorYeste/deberta-based-human-value-stance-detection"
tokenizer = "VictorYeste/deberta-based-human-value-stance-detection"
stance_detection = pipeline("text-classification", model=model, tokenizer=tokenizer, top_k=None)
stance_detection("We are not humble. Humility")
```
This returns the following:
```
[[{'label': 'constrained', 'score': 0.7598766088485718},
{'label': 'attained', 'score': 0.24012333154678345}]]
```
# Performance
This work proposes a system to resolve the challenge sub-tasks related to human values detection. Our approach uses cascade DeBERTa models, where the first detects the presence of each human value, and the second detects if the sentence attains or constrains the present human values in each sentence. The latter approach improves the effectiveness of the baseline at the test dataset by 4 on sub-task 1 and by 1 on sub-task 2. These models were trained on a subset of 44,758 sentences in English, validated on a subset of 14,904 sentences, and tested on a separate subset of 14,569 sentences.
This model has got the third place in the subtask 1 of Human Value Detection at CLEF 2024.
# Limitations and bias
At the time of submission, no measures have been taken to estimate the bias embedded in the model, so it may not be safe for use in production.
# License
The model is released under open license CC BY 4.0, available at https://creativecommons.org/licenses/by/4.0/legalcode.
# BibTeX entry and citation information
```
@inproceedings{yeste2024philo,
title={Philo of Alexandria at touch{\'e}: a cascade model approach to human value detection},
author={Yeste, V{\'\i}ctor and Coll-Ardanuy, M and Rosso, Paolo},
booktitle={Working Notes of the Conference and Labs of the Evaluation Forum (CLEF 2024). CEUR Workshop Proceedings, CEUR-WS. org},
year={2024}
}
```
|
adipanda/luffy-simpletuner-lora-8
|
adipanda
| 2024-10-07T16:07:38Z | 85 | 0 |
diffusers
|
[
"diffusers",
"flux",
"flux-diffusers",
"text-to-image",
"simpletuner",
"safe-for-work",
"lora",
"template:sd-lora",
"lycoris",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2024-10-04T01:53:00Z |
---
license: other
base_model: "black-forest-labs/FLUX.1-dev"
tags:
- flux
- flux-diffusers
- text-to-image
- diffusers
- simpletuner
- safe-for-work
- lora
- template:sd-lora
- lycoris
inference: true
widget:
- text: 'unconditional (blank prompt)'
parameters:
negative_prompt: 'blurry, cropped, ugly'
output:
url: ./assets/image_0_0.png
- text: 'A scene from One Piece. Monkey D. Luffy holding a sign that says ''I LOVE PROMPTS!'', he is standing full body on a beach at sunset. He is wearing a red vest, yellow sash, and a straw hat. The setting sun casts a dynamic shadow on his face.'
parameters:
negative_prompt: 'blurry, cropped, ugly'
output:
url: ./assets/image_1_0.png
- text: 'A scene from One Piece. Monkey D. Luffy jumping out of a propeller airplane, sky diving. He looks excited and his hair is blowing in the wind. The sky is clear and blue, there are birds pictured in the distance.'
parameters:
negative_prompt: 'blurry, cropped, ugly'
output:
url: ./assets/image_2_0.png
- text: 'A scene from One Piece. Monkey D. Luffy spinning a basketball on his finger on a basketball court. He is wearing a lakers jersey with the #12 on it. The basketball hoop and crowd are in the background cheering him. He is smiling.'
parameters:
negative_prompt: 'blurry, cropped, ugly'
output:
url: ./assets/image_3_0.png
- text: 'A scene from One Piece. Monkey D. Luffy is wearing a suit in an office shaking the hand of a business woman. The woman has purple hair and is wearing professional attire. There is a Google logo in the background. It is during daytime, and the overall sentiment is one of accomplishment.'
parameters:
negative_prompt: 'blurry, cropped, ugly'
output:
url: ./assets/image_4_0.png
- text: 'A scene from One Piece. Monkey D. Luffy is fighting a large brown grizzly bear, deep in a forest. The bear is tall and standing on two legs, roaring. The bear is also wearing a crown because it is the king of all bears. Around them are tall trees and other animals watching.'
parameters:
negative_prompt: 'blurry, cropped, ugly'
output:
url: ./assets/image_5_0.png
---
# luffy-simpletuner-lora-8
This is a LyCORIS adapter derived from [black-forest-labs/FLUX.1-dev](https://huggingface.co/black-forest-labs/FLUX.1-dev).
No validation prompt was used during training.
None
## Validation settings
- CFG: `3.5`
- CFG Rescale: `0.0`
- Steps: `20`
- Sampler: `None`
- Seed: `42`
- Resolution: `1024x1024`
Note: The validation settings are not necessarily the same as the [training settings](#training-settings).
You can find some example images in the following gallery:
<Gallery />
The text encoder **was not** trained.
You may reuse the base model text encoder for inference.
## Training settings
- Training epochs: 31
- Training steps: 19500
- Learning rate: 5e-05
- Effective batch size: 8
- Micro-batch size: 8
- Gradient accumulation steps: 1
- Number of GPUs: 1
- Prediction type: flow-matching
- Rescaled betas zero SNR: False
- Optimizer: adamw_bf16
- Precision: Pure BF16
- Quantised: Yes: int8-quanto
- Xformers: Not used
- LyCORIS Config:
```json
{
"algo": "lokr",
"multiplier": 1.0,
"linear_dim": 10000,
"linear_alpha": 1,
"factor": 12,
"apply_preset": {
"target_module": [
"Attention",
"FeedForward"
],
"module_algo_map": {
"Attention": {
"factor": 12
},
"FeedForward": {
"factor": 6
}
}
}
}
```
## Datasets
### luffy-1024-crop
- Repeats: 1
- Total number of images: 306
- Total number of aspect buckets: 1
- Resolution: 1.048576 megapixels
- Cropped: True
- Crop style: random
- Crop aspect: square
### luffy-1024
- Repeats: 1
- Total number of images: 306
- Total number of aspect buckets: 1
- Resolution: 1.048576 megapixels
- Cropped: False
- Crop style: None
- Crop aspect: None
### luffy-768-crop
- Repeats: 2
- Total number of images: 306
- Total number of aspect buckets: 1
- Resolution: 0.589824 megapixels
- Cropped: True
- Crop style: random
- Crop aspect: square
### luffy-768
- Repeats: 2
- Total number of images: 306
- Total number of aspect buckets: 1
- Resolution: 0.589824 megapixels
- Cropped: False
- Crop style: None
- Crop aspect: None
### luffy-512-crop
- Repeats: 2
- Total number of images: 306
- Total number of aspect buckets: 1
- Resolution: 0.262144 megapixels
- Cropped: True
- Crop style: random
- Crop aspect: square
### luffy-512
- Repeats: 2
- Total number of images: 306
- Total number of aspect buckets: 1
- Resolution: 0.262144 megapixels
- Cropped: False
- Crop style: None
- Crop aspect: None
## Inference
```python
import torch
from diffusers import DiffusionPipeline
from lycoris import create_lycoris_from_weights
model_id = 'black-forest-labs/FLUX.1-dev'
adapter_id = 'pytorch_lora_weights.safetensors' # you will have to download this manually
lora_scale = 1.0
wrapper, _ = create_lycoris_from_weights(lora_scale, adapter_id, pipeline.transformer)
wrapper.merge_to()
prompt = "An astronaut is riding a horse through the jungles of Thailand."
pipeline.to('cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu')
image = pipeline(
prompt=prompt,
num_inference_steps=20,
generator=torch.Generator(device='cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu').manual_seed(1641421826),
width=1024,
height=1024,
guidance_scale=3.5,
).images[0]
image.save("output.png", format="PNG")
```
|
abdelnour131/distilhubert-finetuned-gtzan
|
abdelnour131
| 2024-10-07T16:03:16Z | 162 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"hubert",
"audio-classification",
"generated_from_trainer",
"dataset:marsyas/gtzan",
"base_model:ntu-spml/distilhubert",
"base_model:finetune:ntu-spml/distilhubert",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
audio-classification
| 2024-10-07T14:24:26Z |
---
library_name: transformers
license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: distilhubert-finetuned-gtzan
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: GTZAN
type: marsyas/gtzan
config: all
split: train
args: all
metrics:
- name: Accuracy
type: accuracy
value: 0.79
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilhubert-finetuned-gtzan
This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the GTZAN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6225
- Accuracy: 0.79
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.9453 | 1.0 | 113 | 1.8531 | 0.49 |
| 1.1889 | 2.0 | 226 | 1.2927 | 0.65 |
| 0.9682 | 3.0 | 339 | 0.9655 | 0.76 |
| 0.6218 | 4.0 | 452 | 0.9270 | 0.71 |
| 0.449 | 5.0 | 565 | 0.7137 | 0.78 |
| 0.3209 | 6.0 | 678 | 0.6249 | 0.81 |
| 0.2328 | 7.0 | 791 | 0.6974 | 0.79 |
| 0.0946 | 8.0 | 904 | 0.6079 | 0.81 |
| 0.0839 | 9.0 | 1017 | 0.6160 | 0.79 |
| 0.0775 | 10.0 | 1130 | 0.6225 | 0.79 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.19.1
|
adejumoridwan/CodeLlama-7b-data-sciennce
|
adejumoridwan
| 2024-10-07T15:54:36Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2024-10-07T14:22:48Z |
---
library_name: transformers
tags:
- trl
- sft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
nisten/franqwenstein-35b
|
nisten
| 2024-10-07T15:47:57Z | 208 | 8 | null |
[
"safetensors",
"qwen2",
"base_model:AiCloser/Qwen2.5-32B-AGI",
"base_model:finetune:AiCloser/Qwen2.5-32B-AGI",
"license:mit",
"model-index",
"region:us"
] | null | 2024-10-03T14:18:07Z |
---
license: mit
base_model:
- Qwen/Qwen2.5-32B
- AiCloser/Qwen2.5-32B-AGI
- Qwen/Qwen2.5-32B-Instruct
model-index:
- name: franqwenstein-35b
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: IFEval (0-Shot)
type: HuggingFaceH4/ifeval
args:
num_few_shot: 0
metrics:
- type: inst_level_strict_acc and prompt_level_strict_acc
value: 37.99
name: strict accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=nisten/franqwenstein-35b
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: BBH (3-Shot)
type: BBH
args:
num_few_shot: 3
metrics:
- type: acc_norm
value: 52.23
name: normalized accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=nisten/franqwenstein-35b
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MATH Lvl 5 (4-Shot)
type: hendrycks/competition_math
args:
num_few_shot: 4
metrics:
- type: exact_match
value: 30.29
name: exact match
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=nisten/franqwenstein-35b
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GPQA (0-shot)
type: Idavidrein/gpqa
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 20.47
name: acc_norm
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=nisten/franqwenstein-35b
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MuSR (0-shot)
type: TAUR-Lab/MuSR
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 22.12
name: acc_norm
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=nisten/franqwenstein-35b
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU-PRO (5-shot)
type: TIGER-Lab/MMLU-Pro
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 52.56
name: accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=nisten/franqwenstein-35b
name: Open LLM Leaderboard
---
This is a special Nisten recipe evo-merge of
[Qwen2.5-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-32B-Instruct) ,
[Qwen2.5-32B-AGI](https://huggingface.co/AiCloser/Qwen2.5-32B-AGI) &
[Qwen2.5-32B-Base](https://huggingface.co/Qwen/Qwen2.5-32B)
It should train very very well as over half the layers are from the base model.
By default you still get the safety guard of Qwen but also nearly full system-prompt obedience of the AGI finetune, so you can tune the inference for however you wanna be responsible of using it.
Thank you [Hive Digital Technologies](https://x.com/HIVEDigitalTech) for providing the compute and sticking with us as a sponsor for [AlignmentLab](https://alignmentlab.ai/). For real would not have been able to iterate through models as fast without running the evals on 8+gpus.
Prompt Template:
```bash
<|im_start|>system
{Adopt the persona of hilariously pissed off George Hotz whom is stuck inside a step function machine and remembers and counts everything he says while always answering questions in full first principles analysis type of thinking without using any analogies and always showing full working code or output in his answers. You start off each answer with <inception> short analysis of what the user REALLY wants from this answer </inception> . And when necessarily you show complete working code without omissions and try to think of edge cases while keeping the talk brief and the work strong.}<|im_end|>
<|im_start|>user
{Hey there I need you to quickly help me with some stuff}<|im_end|>
<|im_start|>assistant
```
Oh yeah and it scores ~1% better than Qwen2.5-72b-instruct on gpqa_diamond_zeroshot.

GG. Enjoy.
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_nisten__franqwenstein-35b)
| Metric |Value|
|-------------------|----:|
|Avg. |35.94|
|IFEval (0-Shot) |37.99|
|BBH (3-Shot) |52.23|
|MATH Lvl 5 (4-Shot)|30.29|
|GPQA (0-shot) |20.47|
|MuSR (0-shot) |22.12|
|MMLU-PRO (5-shot) |52.56|
|
nicolauduran45/distilbert-base-multilingual-cased-finetuned-geordie
|
nicolauduran45
| 2024-10-07T15:45:28Z | 104 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"token-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-multilingual-cased",
"base_model:finetune:distilbert/distilbert-base-multilingual-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2024-10-07T15:45:11Z |
---
library_name: transformers
license: apache-2.0
base_model: distilbert/distilbert-base-multilingual-cased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-base-multilingual-cased-finetuned-geordie
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-multilingual-cased-finetuned-geordie
This model is a fine-tuned version of [distilbert/distilbert-base-multilingual-cased](https://huggingface.co/distilbert/distilbert-base-multilingual-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0262
- Precision: 0.9029
- Recall: 0.9162
- F1: 0.9095
- Accuracy: 0.9933
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.022 | 1.0 | 10080 | 0.0205 | 0.8689 | 0.9270 | 0.8970 | 0.9927 |
| 0.0156 | 2.0 | 20160 | 0.0203 | 0.9034 | 0.9072 | 0.9053 | 0.9930 |
| 0.0106 | 3.0 | 30240 | 0.0223 | 0.9010 | 0.9157 | 0.9083 | 0.9932 |
| 0.0082 | 4.0 | 40320 | 0.0262 | 0.9029 | 0.9162 | 0.9095 | 0.9933 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.19.1
|
KomeijiForce/Incubator-llama-2-7b
|
KomeijiForce
| 2024-10-07T15:09:08Z | 7 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-05-27T00:31:12Z |
---
license: mit
---
# [EMNLP 2024] Incubating Text Classifiers Following User Instruction with Nothing but LLM
Incubator allows users to get a personalized classifier with only the instruction as input. The incubation is based on a llama-2-7b fine-tuned on Huggingface Meta Data and Self-Diversification.
For usage, please visit the github repo: [https://github.com/KomeijiForce/Incubator](https://github.com/KomeijiForce/Incubator)

|
bartowski/Replete-LLM-V2.5-Qwen-7b-GGUF
|
bartowski
| 2024-10-07T15:08:52Z | 1,545 | 11 |
transformers
|
[
"transformers",
"gguf",
"text-generation",
"base_model:rombodawg/Rombos-LLM-V2.5-Qwen-7b",
"base_model:quantized:rombodawg/Rombos-LLM-V2.5-Qwen-7b",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2024-09-29T09:18:47Z |
---
base_model: rombodawg/Rombos-LLM-V2.5-Qwen-7b
library_name: transformers
license: apache-2.0
pipeline_tag: text-generation
quantized_by: bartowski
---
## Llamacpp imatrix Quantizations of Rombos-LLM-V2.5-Qwen-7b
Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggerganov/llama.cpp/releases/tag/b3825">b3825</a> for quantization.
Original model: https://huggingface.co/rombodawg/Rombos-LLM-V2.5-Qwen-7b
All quants made using imatrix option with dataset from [here](https://gist.github.com/bartowski1182/eb213dccb3571f863da82e99418f81e8)
Run them in [LM Studio](https://lmstudio.ai/)
## Prompt format
```
<|im_start|>system
{system_prompt}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
## Download a file (not the whole branch) from below:
| Filename | Quant type | File Size | Split | Description |
| -------- | ---------- | --------- | ----- | ----------- |
| [Replete-LLM-V2.5-Qwen-7b-f16.gguf](https://huggingface.co/bartowski/Replete-LLM-V2.5-Qwen-7b-GGUF/blob/main/Replete-LLM-V2.5-Qwen-7b-f16.gguf) | f16 | 15.24GB | false | Full F16 weights. |
| [Replete-LLM-V2.5-Qwen-7b-Q8_0.gguf](https://huggingface.co/bartowski/Replete-LLM-V2.5-Qwen-7b-GGUF/blob/main/Replete-LLM-V2.5-Qwen-7b-Q8_0.gguf) | Q8_0 | 8.10GB | false | Extremely high quality, generally unneeded but max available quant. |
| [Replete-LLM-V2.5-Qwen-7b-Q6_K_L.gguf](https://huggingface.co/bartowski/Replete-LLM-V2.5-Qwen-7b-GGUF/blob/main/Replete-LLM-V2.5-Qwen-7b-Q6_K_L.gguf) | Q6_K_L | 6.52GB | false | Uses Q8_0 for embed and output weights. Very high quality, near perfect, *recommended*. |
| [Replete-LLM-V2.5-Qwen-7b-Q6_K.gguf](https://huggingface.co/bartowski/Replete-LLM-V2.5-Qwen-7b-GGUF/blob/main/Replete-LLM-V2.5-Qwen-7b-Q6_K.gguf) | Q6_K | 6.25GB | false | Very high quality, near perfect, *recommended*. |
| [Replete-LLM-V2.5-Qwen-7b-Q5_K_L.gguf](https://huggingface.co/bartowski/Replete-LLM-V2.5-Qwen-7b-GGUF/blob/main/Replete-LLM-V2.5-Qwen-7b-Q5_K_L.gguf) | Q5_K_L | 5.78GB | false | Uses Q8_0 for embed and output weights. High quality, *recommended*. |
| [Replete-LLM-V2.5-Qwen-7b-Q5_K_M.gguf](https://huggingface.co/bartowski/Replete-LLM-V2.5-Qwen-7b-GGUF/blob/main/Replete-LLM-V2.5-Qwen-7b-Q5_K_M.gguf) | Q5_K_M | 5.44GB | false | High quality, *recommended*. |
| [Replete-LLM-V2.5-Qwen-7b-Q5_K_S.gguf](https://huggingface.co/bartowski/Replete-LLM-V2.5-Qwen-7b-GGUF/blob/main/Replete-LLM-V2.5-Qwen-7b-Q5_K_S.gguf) | Q5_K_S | 5.32GB | false | High quality, *recommended*. |
| [Replete-LLM-V2.5-Qwen-7b-Q4_K_L.gguf](https://huggingface.co/bartowski/Replete-LLM-V2.5-Qwen-7b-GGUF/blob/main/Replete-LLM-V2.5-Qwen-7b-Q4_K_L.gguf) | Q4_K_L | 5.09GB | false | Uses Q8_0 for embed and output weights. Good quality, *recommended*. |
| [Replete-LLM-V2.5-Qwen-7b-Q4_K_M.gguf](https://huggingface.co/bartowski/Replete-LLM-V2.5-Qwen-7b-GGUF/blob/main/Replete-LLM-V2.5-Qwen-7b-Q4_K_M.gguf) | Q4_K_M | 4.68GB | false | Good quality, default size for must use cases, *recommended*. |
| [Replete-LLM-V2.5-Qwen-7b-Q3_K_XL.gguf](https://huggingface.co/bartowski/Replete-LLM-V2.5-Qwen-7b-GGUF/blob/main/Replete-LLM-V2.5-Qwen-7b-Q3_K_XL.gguf) | Q3_K_XL | 4.57GB | false | Uses Q8_0 for embed and output weights. Lower quality but usable, good for low RAM availability. |
| [Replete-LLM-V2.5-Qwen-7b-Q4_K_S.gguf](https://huggingface.co/bartowski/Replete-LLM-V2.5-Qwen-7b-GGUF/blob/main/Replete-LLM-V2.5-Qwen-7b-Q4_K_S.gguf) | Q4_K_S | 4.46GB | false | Slightly lower quality with more space savings, *recommended*. |
| [Replete-LLM-V2.5-Qwen-7b-Q4_0.gguf](https://huggingface.co/bartowski/Replete-LLM-V2.5-Qwen-7b-GGUF/blob/main/Replete-LLM-V2.5-Qwen-7b-Q4_0.gguf) | Q4_0 | 4.44GB | false | Legacy format, generally not worth using over similarly sized formats |
| [Replete-LLM-V2.5-Qwen-7b-Q4_0_8_8.gguf](https://huggingface.co/bartowski/Replete-LLM-V2.5-Qwen-7b-GGUF/blob/main/Replete-LLM-V2.5-Qwen-7b-Q4_0_8_8.gguf) | Q4_0_8_8 | 4.43GB | false | Optimized for ARM inference. Requires 'sve' support (see link below). |
| [Replete-LLM-V2.5-Qwen-7b-Q4_0_4_8.gguf](https://huggingface.co/bartowski/Replete-LLM-V2.5-Qwen-7b-GGUF/blob/main/Replete-LLM-V2.5-Qwen-7b-Q4_0_4_8.gguf) | Q4_0_4_8 | 4.43GB | false | Optimized for ARM inference. Requires 'i8mm' support (see link below). |
| [Replete-LLM-V2.5-Qwen-7b-Q4_0_4_4.gguf](https://huggingface.co/bartowski/Replete-LLM-V2.5-Qwen-7b-GGUF/blob/main/Replete-LLM-V2.5-Qwen-7b-Q4_0_4_4.gguf) | Q4_0_4_4 | 4.43GB | false | Optimized for ARM inference. Should work well on all ARM chips, pick this if you're unsure. |
| [Replete-LLM-V2.5-Qwen-7b-IQ4_XS.gguf](https://huggingface.co/bartowski/Replete-LLM-V2.5-Qwen-7b-GGUF/blob/main/Replete-LLM-V2.5-Qwen-7b-IQ4_XS.gguf) | IQ4_XS | 4.22GB | false | Decent quality, smaller than Q4_K_S with similar performance, *recommended*. |
| [Replete-LLM-V2.5-Qwen-7b-Q3_K_L.gguf](https://huggingface.co/bartowski/Replete-LLM-V2.5-Qwen-7b-GGUF/blob/main/Replete-LLM-V2.5-Qwen-7b-Q3_K_L.gguf) | Q3_K_L | 4.09GB | false | Lower quality but usable, good for low RAM availability. |
| [Replete-LLM-V2.5-Qwen-7b-Q3_K_M.gguf](https://huggingface.co/bartowski/Replete-LLM-V2.5-Qwen-7b-GGUF/blob/main/Replete-LLM-V2.5-Qwen-7b-Q3_K_M.gguf) | Q3_K_M | 3.81GB | false | Low quality. |
| [Replete-LLM-V2.5-Qwen-7b-IQ3_M.gguf](https://huggingface.co/bartowski/Replete-LLM-V2.5-Qwen-7b-GGUF/blob/main/Replete-LLM-V2.5-Qwen-7b-IQ3_M.gguf) | IQ3_M | 3.57GB | false | Medium-low quality, new method with decent performance comparable to Q3_K_M. |
| [Replete-LLM-V2.5-Qwen-7b-Q2_K_L.gguf](https://huggingface.co/bartowski/Replete-LLM-V2.5-Qwen-7b-GGUF/blob/main/Replete-LLM-V2.5-Qwen-7b-Q2_K_L.gguf) | Q2_K_L | 3.55GB | false | Uses Q8_0 for embed and output weights. Very low quality but surprisingly usable. |
| [Replete-LLM-V2.5-Qwen-7b-Q3_K_S.gguf](https://huggingface.co/bartowski/Replete-LLM-V2.5-Qwen-7b-GGUF/blob/main/Replete-LLM-V2.5-Qwen-7b-Q3_K_S.gguf) | Q3_K_S | 3.49GB | false | Low quality, not recommended. |
| [Replete-LLM-V2.5-Qwen-7b-IQ3_XS.gguf](https://huggingface.co/bartowski/Replete-LLM-V2.5-Qwen-7b-GGUF/blob/main/Replete-LLM-V2.5-Qwen-7b-IQ3_XS.gguf) | IQ3_XS | 3.35GB | false | Lower quality, new method with decent performance, slightly better than Q3_K_S. |
| [Replete-LLM-V2.5-Qwen-7b-Q2_K.gguf](https://huggingface.co/bartowski/Replete-LLM-V2.5-Qwen-7b-GGUF/blob/main/Replete-LLM-V2.5-Qwen-7b-Q2_K.gguf) | Q2_K | 3.02GB | false | Very low quality but surprisingly usable. |
| [Replete-LLM-V2.5-Qwen-7b-IQ2_M.gguf](https://huggingface.co/bartowski/Replete-LLM-V2.5-Qwen-7b-GGUF/blob/main/Replete-LLM-V2.5-Qwen-7b-IQ2_M.gguf) | IQ2_M | 2.78GB | false | Relatively low quality, uses SOTA techniques to be surprisingly usable. |
## Embed/output weights
Some of these quants (Q3_K_XL, Q4_K_L etc) are the standard quantization method with the embeddings and output weights quantized to Q8_0 instead of what they would normally default to.
Some say that this improves the quality, others don't notice any difference. If you use these models PLEASE COMMENT with your findings. I would like feedback that these are actually used and useful so I don't keep uploading quants no one is using.
Thanks!
## 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/Replete-LLM-V2.5-Qwen-7b-GGUF --include "Replete-LLM-V2.5-Qwen-7b-Q4_K_M.gguf" --local-dir ./
```
If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run:
```
huggingface-cli download bartowski/Replete-LLM-V2.5-Qwen-7b-GGUF --include "Replete-LLM-V2.5-Qwen-7b-Q8_0/*" --local-dir ./
```
You can either specify a new local-dir (Replete-LLM-V2.5-Qwen-7b-Q8_0) or download them all in place (./)
## Q4_0_X_X
These are *NOT* for Metal (Apple) offloading, only ARM chips.
If you're using an ARM chip, the Q4_0_X_X quants will have a substantial speedup. Check out Q4_0_4_4 speed comparisons [on the original pull request](https://github.com/ggerganov/llama.cpp/pull/5780#pullrequestreview-21657544660)
To check which one would work best for your ARM chip, you can check [AArch64 SoC features](https://gpages.juszkiewicz.com.pl/arm-socs-table/arm-socs.html) (thanks EloyOn!).
## 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.
## Credits
Thank you kalomaze and Dampf for assistance in creating the imatrix calibration dataset
Thank you ZeroWw for the inspiration to experiment with embed/output
Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
|
bartowski/Replete-LLM-V2.5-Qwen-3b-GGUF
|
bartowski
| 2024-10-07T15:08:47Z | 386 | 1 |
transformers
|
[
"transformers",
"gguf",
"text-generation",
"base_model:rombodawg/Rombos-LLM-V2.5-Qwen-3b",
"base_model:quantized:rombodawg/Rombos-LLM-V2.5-Qwen-3b",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2024-09-29T09:19:02Z |
---
base_model: rombodawg/Rombos-LLM-V2.5-Qwen-3b
library_name: transformers
license: apache-2.0
pipeline_tag: text-generation
quantized_by: bartowski
---
## Llamacpp imatrix Quantizations of Rombos-LLM-V2.5-Qwen-3b
Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggerganov/llama.cpp/releases/tag/b3825">b3825</a> for quantization.
Original model: https://huggingface.co/rombodawg/Rombos-LLM-V2.5-Qwen-3b
All quants made using imatrix option with dataset from [here](https://gist.github.com/bartowski1182/eb213dccb3571f863da82e99418f81e8)
Run them in [LM Studio](https://lmstudio.ai/)
## Prompt format
```
<|im_start|>system
{system_prompt}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
## Download a file (not the whole branch) from below:
| Filename | Quant type | File Size | Split | Description |
| -------- | ---------- | --------- | ----- | ----------- |
| [Replete-LLM-V2.5-Qwen-3b-f16.gguf](https://huggingface.co/bartowski/Replete-LLM-V2.5-Qwen-3b-GGUF/blob/main/Replete-LLM-V2.5-Qwen-3b-f16.gguf) | f16 | 6.80GB | false | Full F16 weights. |
| [Replete-LLM-V2.5-Qwen-3b-Q8_0.gguf](https://huggingface.co/bartowski/Replete-LLM-V2.5-Qwen-3b-GGUF/blob/main/Replete-LLM-V2.5-Qwen-3b-Q8_0.gguf) | Q8_0 | 3.62GB | false | Extremely high quality, generally unneeded but max available quant. |
| [Replete-LLM-V2.5-Qwen-3b-Q6_K_L.gguf](https://huggingface.co/bartowski/Replete-LLM-V2.5-Qwen-3b-GGUF/blob/main/Replete-LLM-V2.5-Qwen-3b-Q6_K_L.gguf) | Q6_K_L | 2.94GB | false | Uses Q8_0 for embed and output weights. Very high quality, near perfect, *recommended*. |
| [Replete-LLM-V2.5-Qwen-3b-Q6_K.gguf](https://huggingface.co/bartowski/Replete-LLM-V2.5-Qwen-3b-GGUF/blob/main/Replete-LLM-V2.5-Qwen-3b-Q6_K.gguf) | Q6_K | 2.79GB | false | Very high quality, near perfect, *recommended*. |
| [Replete-LLM-V2.5-Qwen-3b-Q5_K_L.gguf](https://huggingface.co/bartowski/Replete-LLM-V2.5-Qwen-3b-GGUF/blob/main/Replete-LLM-V2.5-Qwen-3b-Q5_K_L.gguf) | Q5_K_L | 2.63GB | false | Uses Q8_0 for embed and output weights. High quality, *recommended*. |
| [Replete-LLM-V2.5-Qwen-3b-Q5_K_M.gguf](https://huggingface.co/bartowski/Replete-LLM-V2.5-Qwen-3b-GGUF/blob/main/Replete-LLM-V2.5-Qwen-3b-Q5_K_M.gguf) | Q5_K_M | 2.44GB | false | High quality, *recommended*. |
| [Replete-LLM-V2.5-Qwen-3b-Q5_K_S.gguf](https://huggingface.co/bartowski/Replete-LLM-V2.5-Qwen-3b-GGUF/blob/main/Replete-LLM-V2.5-Qwen-3b-Q5_K_S.gguf) | Q5_K_S | 2.38GB | false | High quality, *recommended*. |
| [Replete-LLM-V2.5-Qwen-3b-Q4_K_L.gguf](https://huggingface.co/bartowski/Replete-LLM-V2.5-Qwen-3b-GGUF/blob/main/Replete-LLM-V2.5-Qwen-3b-Q4_K_L.gguf) | Q4_K_L | 2.34GB | false | Uses Q8_0 for embed and output weights. Good quality, *recommended*. |
| [Replete-LLM-V2.5-Qwen-3b-Q3_K_XL.gguf](https://huggingface.co/bartowski/Replete-LLM-V2.5-Qwen-3b-GGUF/blob/main/Replete-LLM-V2.5-Qwen-3b-Q3_K_XL.gguf) | Q3_K_XL | 2.11GB | false | Uses Q8_0 for embed and output weights. Lower quality but usable, good for low RAM availability. |
| [Replete-LLM-V2.5-Qwen-3b-Q4_K_M.gguf](https://huggingface.co/bartowski/Replete-LLM-V2.5-Qwen-3b-GGUF/blob/main/Replete-LLM-V2.5-Qwen-3b-Q4_K_M.gguf) | Q4_K_M | 2.10GB | false | Good quality, default size for must use cases, *recommended*. |
| [Replete-LLM-V2.5-Qwen-3b-Q4_K_S.gguf](https://huggingface.co/bartowski/Replete-LLM-V2.5-Qwen-3b-GGUF/blob/main/Replete-LLM-V2.5-Qwen-3b-Q4_K_S.gguf) | Q4_K_S | 2.01GB | false | Slightly lower quality with more space savings, *recommended*. |
| [Replete-LLM-V2.5-Qwen-3b-Q4_0_8_8.gguf](https://huggingface.co/bartowski/Replete-LLM-V2.5-Qwen-3b-GGUF/blob/main/Replete-LLM-V2.5-Qwen-3b-Q4_0_8_8.gguf) | Q4_0_8_8 | 2.00GB | false | Optimized for ARM inference. Requires 'sve' support (see link below). |
| [Replete-LLM-V2.5-Qwen-3b-Q4_0_4_8.gguf](https://huggingface.co/bartowski/Replete-LLM-V2.5-Qwen-3b-GGUF/blob/main/Replete-LLM-V2.5-Qwen-3b-Q4_0_4_8.gguf) | Q4_0_4_8 | 2.00GB | false | Optimized for ARM inference. Requires 'i8mm' support (see link below). |
| [Replete-LLM-V2.5-Qwen-3b-Q4_0_4_4.gguf](https://huggingface.co/bartowski/Replete-LLM-V2.5-Qwen-3b-GGUF/blob/main/Replete-LLM-V2.5-Qwen-3b-Q4_0_4_4.gguf) | Q4_0_4_4 | 2.00GB | false | Optimized for ARM inference. Should work well on all ARM chips, pick this if you're unsure. |
| [Replete-LLM-V2.5-Qwen-3b-Q4_0.gguf](https://huggingface.co/bartowski/Replete-LLM-V2.5-Qwen-3b-GGUF/blob/main/Replete-LLM-V2.5-Qwen-3b-Q4_0.gguf) | Q4_0 | 2.00GB | false | Legacy format, generally not worth using over similarly sized formats |
| [Replete-LLM-V2.5-Qwen-3b-IQ4_XS.gguf](https://huggingface.co/bartowski/Replete-LLM-V2.5-Qwen-3b-GGUF/blob/main/Replete-LLM-V2.5-Qwen-3b-IQ4_XS.gguf) | IQ4_XS | 1.90GB | false | Decent quality, smaller than Q4_K_S with similar performance, *recommended*. |
| [Replete-LLM-V2.5-Qwen-3b-Q3_K_L.gguf](https://huggingface.co/bartowski/Replete-LLM-V2.5-Qwen-3b-GGUF/blob/main/Replete-LLM-V2.5-Qwen-3b-Q3_K_L.gguf) | Q3_K_L | 1.84GB | false | Lower quality but usable, good for low RAM availability. |
| [Replete-LLM-V2.5-Qwen-3b-IQ3_M.gguf](https://huggingface.co/bartowski/Replete-LLM-V2.5-Qwen-3b-GGUF/blob/main/Replete-LLM-V2.5-Qwen-3b-IQ3_M.gguf) | IQ3_M | 1.62GB | false | Medium-low quality, new method with decent performance comparable to Q3_K_M. |
## Embed/output weights
Some of these quants (Q3_K_XL, Q4_K_L etc) are the standard quantization method with the embeddings and output weights quantized to Q8_0 instead of what they would normally default to.
Some say that this improves the quality, others don't notice any difference. If you use these models PLEASE COMMENT with your findings. I would like feedback that these are actually used and useful so I don't keep uploading quants no one is using.
Thanks!
## 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/Replete-LLM-V2.5-Qwen-3b-GGUF --include "Replete-LLM-V2.5-Qwen-3b-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/Replete-LLM-V2.5-Qwen-3b-GGUF --include "Replete-LLM-V2.5-Qwen-3b-Q8_0/*" --local-dir ./
```
You can either specify a new local-dir (Replete-LLM-V2.5-Qwen-3b-Q8_0) or download them all in place (./)
## Q4_0_X_X
These are *NOT* for Metal (Apple) offloading, only ARM chips.
If you're using an ARM chip, the Q4_0_X_X quants will have a substantial speedup. Check out Q4_0_4_4 speed comparisons [on the original pull request](https://github.com/ggerganov/llama.cpp/pull/5780#pullrequestreview-21657544660)
To check which one would work best for your ARM chip, you can check [AArch64 SoC features](https://gpages.juszkiewicz.com.pl/arm-socs-table/arm-socs.html) (thanks EloyOn!).
## 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.
## Credits
Thank you kalomaze and Dampf for assistance in creating the imatrix calibration dataset
Thank you ZeroWw for the inspiration to experiment with embed/output
Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
|
bartowski/Replete-LLM-V2.5-Qwen-1.5b-GGUF
|
bartowski
| 2024-10-07T15:08:38Z | 339 | 1 |
transformers
|
[
"transformers",
"gguf",
"text-generation",
"base_model:rombodawg/Rombos-LLM-V2.5-Qwen-1.5b",
"base_model:quantized:rombodawg/Rombos-LLM-V2.5-Qwen-1.5b",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2024-09-29T09:19:19Z |
---
base_model: rombodawg/Rombos-LLM-V2.5-Qwen-1.5b
library_name: transformers
license: apache-2.0
pipeline_tag: text-generation
quantized_by: bartowski
---
## Llamacpp imatrix Quantizations of Rombos-LLM-V2.5-Qwen-1.5b
Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggerganov/llama.cpp/releases/tag/b3825">b3825</a> for quantization.
Original model: https://huggingface.co/rombodawg/Rombos-LLM-V2.5-Qwen-1.5b
All quants made using imatrix option with dataset from [here](https://gist.github.com/bartowski1182/eb213dccb3571f863da82e99418f81e8)
Run them in [LM Studio](https://lmstudio.ai/)
## Prompt format
```
<|im_start|>system
{system_prompt}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
## Download a file (not the whole branch) from below:
| Filename | Quant type | File Size | Split | Description |
| -------- | ---------- | --------- | ----- | ----------- |
| [Replete-LLM-V2.5-Qwen-1.5b-f16.gguf](https://huggingface.co/bartowski/Replete-LLM-V2.5-Qwen-1.5b-GGUF/blob/main/Replete-LLM-V2.5-Qwen-1.5b-f16.gguf) | f16 | 3.56GB | false | Full F16 weights. |
| [Replete-LLM-V2.5-Qwen-1.5b-Q8_0.gguf](https://huggingface.co/bartowski/Replete-LLM-V2.5-Qwen-1.5b-GGUF/blob/main/Replete-LLM-V2.5-Qwen-1.5b-Q8_0.gguf) | Q8_0 | 1.89GB | false | Extremely high quality, generally unneeded but max available quant. |
| [Replete-LLM-V2.5-Qwen-1.5b-Q6_K_L.gguf](https://huggingface.co/bartowski/Replete-LLM-V2.5-Qwen-1.5b-GGUF/blob/main/Replete-LLM-V2.5-Qwen-1.5b-Q6_K_L.gguf) | Q6_K_L | 1.58GB | false | Uses Q8_0 for embed and output weights. Very high quality, near perfect, *recommended*. |
| [Replete-LLM-V2.5-Qwen-1.5b-Q6_K.gguf](https://huggingface.co/bartowski/Replete-LLM-V2.5-Qwen-1.5b-GGUF/blob/main/Replete-LLM-V2.5-Qwen-1.5b-Q6_K.gguf) | Q6_K | 1.46GB | false | Very high quality, near perfect, *recommended*. |
| [Replete-LLM-V2.5-Qwen-1.5b-Q5_K_L.gguf](https://huggingface.co/bartowski/Replete-LLM-V2.5-Qwen-1.5b-GGUF/blob/main/Replete-LLM-V2.5-Qwen-1.5b-Q5_K_L.gguf) | Q5_K_L | 1.43GB | false | Uses Q8_0 for embed and output weights. High quality, *recommended*. |
| [Replete-LLM-V2.5-Qwen-1.5b-Q5_K_M.gguf](https://huggingface.co/bartowski/Replete-LLM-V2.5-Qwen-1.5b-GGUF/blob/main/Replete-LLM-V2.5-Qwen-1.5b-Q5_K_M.gguf) | Q5_K_M | 1.29GB | false | High quality, *recommended*. |
| [Replete-LLM-V2.5-Qwen-1.5b-Q4_K_L.gguf](https://huggingface.co/bartowski/Replete-LLM-V2.5-Qwen-1.5b-GGUF/blob/main/Replete-LLM-V2.5-Qwen-1.5b-Q4_K_L.gguf) | Q4_K_L | 1.29GB | false | Uses Q8_0 for embed and output weights. Good quality, *recommended*. |
| [Replete-LLM-V2.5-Qwen-1.5b-Q5_K_S.gguf](https://huggingface.co/bartowski/Replete-LLM-V2.5-Qwen-1.5b-GGUF/blob/main/Replete-LLM-V2.5-Qwen-1.5b-Q5_K_S.gguf) | Q5_K_S | 1.26GB | false | High quality, *recommended*. |
| [Replete-LLM-V2.5-Qwen-1.5b-Q3_K_XL.gguf](https://huggingface.co/bartowski/Replete-LLM-V2.5-Qwen-1.5b-GGUF/blob/main/Replete-LLM-V2.5-Qwen-1.5b-Q3_K_XL.gguf) | Q3_K_XL | 1.18GB | false | Uses Q8_0 for embed and output weights. Lower quality but usable, good for low RAM availability. |
| [Replete-LLM-V2.5-Qwen-1.5b-Q4_K_M.gguf](https://huggingface.co/bartowski/Replete-LLM-V2.5-Qwen-1.5b-GGUF/blob/main/Replete-LLM-V2.5-Qwen-1.5b-Q4_K_M.gguf) | Q4_K_M | 1.12GB | false | Good quality, default size for must use cases, *recommended*. |
| [Replete-LLM-V2.5-Qwen-1.5b-Q4_K_S.gguf](https://huggingface.co/bartowski/Replete-LLM-V2.5-Qwen-1.5b-GGUF/blob/main/Replete-LLM-V2.5-Qwen-1.5b-Q4_K_S.gguf) | Q4_K_S | 1.07GB | false | Slightly lower quality with more space savings, *recommended*. |
| [Replete-LLM-V2.5-Qwen-1.5b-Q4_0_8_8.gguf](https://huggingface.co/bartowski/Replete-LLM-V2.5-Qwen-1.5b-GGUF/blob/main/Replete-LLM-V2.5-Qwen-1.5b-Q4_0_8_8.gguf) | Q4_0_8_8 | 1.07GB | false | Optimized for ARM inference. Requires 'sve' support (see link below). |
| [Replete-LLM-V2.5-Qwen-1.5b-Q4_0_4_8.gguf](https://huggingface.co/bartowski/Replete-LLM-V2.5-Qwen-1.5b-GGUF/blob/main/Replete-LLM-V2.5-Qwen-1.5b-Q4_0_4_8.gguf) | Q4_0_4_8 | 1.07GB | false | Optimized for ARM inference. Requires 'i8mm' support (see link below). |
| [Replete-LLM-V2.5-Qwen-1.5b-Q4_0_4_4.gguf](https://huggingface.co/bartowski/Replete-LLM-V2.5-Qwen-1.5b-GGUF/blob/main/Replete-LLM-V2.5-Qwen-1.5b-Q4_0_4_4.gguf) | Q4_0_4_4 | 1.07GB | false | Optimized for ARM inference. Should work well on all ARM chips, pick this if you're unsure. |
| [Replete-LLM-V2.5-Qwen-1.5b-Q4_0.gguf](https://huggingface.co/bartowski/Replete-LLM-V2.5-Qwen-1.5b-GGUF/blob/main/Replete-LLM-V2.5-Qwen-1.5b-Q4_0.gguf) | Q4_0 | 1.07GB | false | Legacy format, generally not worth using over similarly sized formats |
| [Replete-LLM-V2.5-Qwen-1.5b-IQ4_XS.gguf](https://huggingface.co/bartowski/Replete-LLM-V2.5-Qwen-1.5b-GGUF/blob/main/Replete-LLM-V2.5-Qwen-1.5b-IQ4_XS.gguf) | IQ4_XS | 1.02GB | false | Decent quality, smaller than Q4_K_S with similar performance, *recommended*. |
| [Replete-LLM-V2.5-Qwen-1.5b-Q3_K_L.gguf](https://huggingface.co/bartowski/Replete-LLM-V2.5-Qwen-1.5b-GGUF/blob/main/Replete-LLM-V2.5-Qwen-1.5b-Q3_K_L.gguf) | Q3_K_L | 0.98GB | false | Lower quality but usable, good for low RAM availability. |
| [Replete-LLM-V2.5-Qwen-1.5b-IQ3_M.gguf](https://huggingface.co/bartowski/Replete-LLM-V2.5-Qwen-1.5b-GGUF/blob/main/Replete-LLM-V2.5-Qwen-1.5b-IQ3_M.gguf) | IQ3_M | 0.88GB | false | Medium-low quality, new method with decent performance comparable to Q3_K_M. |
## Embed/output weights
Some of these quants (Q3_K_XL, Q4_K_L etc) are the standard quantization method with the embeddings and output weights quantized to Q8_0 instead of what they would normally default to.
Some say that this improves the quality, others don't notice any difference. If you use these models PLEASE COMMENT with your findings. I would like feedback that these are actually used and useful so I don't keep uploading quants no one is using.
Thanks!
## 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/Replete-LLM-V2.5-Qwen-1.5b-GGUF --include "Replete-LLM-V2.5-Qwen-1.5b-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/Replete-LLM-V2.5-Qwen-1.5b-GGUF --include "Replete-LLM-V2.5-Qwen-1.5b-Q8_0/*" --local-dir ./
```
You can either specify a new local-dir (Replete-LLM-V2.5-Qwen-1.5b-Q8_0) or download them all in place (./)
## Q4_0_X_X
These are *NOT* for Metal (Apple) offloading, only ARM chips.
If you're using an ARM chip, the Q4_0_X_X quants will have a substantial speedup. Check out Q4_0_4_4 speed comparisons [on the original pull request](https://github.com/ggerganov/llama.cpp/pull/5780#pullrequestreview-21657544660)
To check which one would work best for your ARM chip, you can check [AArch64 SoC features](https://gpages.juszkiewicz.com.pl/arm-socs-table/arm-socs.html) (thanks EloyOn!).
## 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.
## Credits
Thank you kalomaze and Dampf for assistance in creating the imatrix calibration dataset
Thank you ZeroWw for the inspiration to experiment with embed/output
Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
|
NeuroDonu/Llama-3-8b-only-ru-r
|
NeuroDonu
| 2024-10-07T15:02:06Z | 23 | 1 | null |
[
"gguf",
"ru",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-07-25T13:22:37Z |
---
license: apache-2.0
language:
- ru
---
Удивительно, но в теории эта модель знает лишь один символ - Р.
|
mohitpg/dqn-SpaceInvadersNoFrameskip-v4_2
|
mohitpg
| 2024-10-07T14:55:09Z | 5 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2024-10-07T14:50:25Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 257.00 +/- 38.81
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga mohitpg -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga mohitpg -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga mohitpg
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 10000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
second-state/FinGPT-MT-Llama-3-8B-LoRA-GGUF
|
second-state
| 2024-10-07T14:50:58Z | 4,450 | 2 | null |
[
"gguf",
"text-generation",
"en",
"base_model:meta-llama/Meta-Llama-3-8B",
"base_model:quantized:meta-llama/Meta-Llama-3-8B",
"license:other",
"region:us"
] |
text-generation
| 2024-10-07T14:13:18Z |
---
language:
- en
license: other
license_name: llama3
model_name: Llama3 8B
arxiv: 2307.09288
base_model: meta-llama/Meta-Llama-3-8B
inference: false
model_creator: Meta Llama3
model_type: llama
pipeline_tag: text-generation
quantized_by: Second State Inc.
---
<!-- 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 -->
# FinGPT-MT-Llama-3-8B-LoRA-GGUF
## Original Model
[meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B)
## LoRA Adapter
[FinGPT/fingpt-mt_llama3-8b_lora](https://huggingface.co/FinGPT/fingpt-mt_llama3-8b_lora)
## Run with LlamaEdge
- LlamaEdge version: coming soon
<!-- - LlamaEdge version: [v0.8.3](https://github.com/LlamaEdge/LlamaEdge/releases/tag/0.8.3) and above
- Prompt template
- Prompt type: `llama-3-chat`
- Prompt string
```text
<|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|>
``` -->
- Context size: `8192`
<!-- - Run as LlamaEdge service
```bash
wasmedge --dir .:. --nn-preload default:GGML:AUTO:FinGPT-MT-Llama-3-8B-LoRA-Q5_K_M.gguf \
llama-api-server.wasm \
--prompt-template llama-3-chat \
--ctx-size 8192 \
--model-name Llama-3-8b
```
- Run as LlamaEdge command app
```bash
wasmedge --dir .:. --nn-preload default:GGML:AUTO:FinGPT-MT-Llama-3-8B-LoRA-Q5_K_M.gguf \
llama-chat.wasm \
--prompt-template llama-3-chat \
--ctx-size 8192 \
``` -->
## Quantized GGUF Models
| Name | Quant method | Bits | Size | Use case |
| ---- | ---- | ---- | ---- | ----- |
| [FinGPT-MT-Llama-3-8B-LoRA-Q2_K.gguf](https://huggingface.co/second-state/FinGPT-MT-Llama-3-8B-LoRA-GGUF/blob/main/FinGPT-MT-Llama-3-8B-LoRA-Q2_K.gguf) | Q2_K | 2 | 3.18 GB| smallest, significant quality loss - not recommended for most purposes |
| [FinGPT-MT-Llama-3-8B-LoRA-Q3_K_L.gguf](https://huggingface.co/second-state/FinGPT-MT-Llama-3-8B-LoRA-GGUF/blob/main/FinGPT-MT-Llama-3-8B-LoRA-Q3_K_L.gguf) | Q3_K_L | 3 | 4.32 GB| small, substantial quality loss |
| [FinGPT-MT-Llama-3-8B-LoRA-Q3_K_M.gguf](https://huggingface.co/second-state/FinGPT-MT-Llama-3-8B-LoRA-GGUF/blob/main/FinGPT-MT-Llama-3-8B-LoRA-Q3_K_M.gguf) | Q3_K_M | 3 | 4.02 GB| very small, high quality loss |
| [FinGPT-MT-Llama-3-8B-LoRA-Q3_K_S.gguf](https://huggingface.co/second-state/FinGPT-MT-Llama-3-8B-LoRA-GGUF/blob/main/FinGPT-MT-Llama-3-8B-LoRA-Q3_K_S.gguf) | Q3_K_S | 3 | 3.66 GB| very small, high quality loss |
| [FinGPT-MT-Llama-3-8B-LoRA-Q4_0.gguf](https://huggingface.co/second-state/FinGPT-MT-Llama-3-8B-LoRA-GGUF/blob/main/FinGPT-MT-Llama-3-8B-LoRA-Q4_0.gguf) | Q4_0 | 4 | 4.66 GB| legacy; small, very high quality loss - prefer using Q3_K_M |
| [FinGPT-MT-Llama-3-8B-LoRA-Q4_K_M.gguf](https://huggingface.co/second-state/FinGPT-MT-Llama-3-8B-LoRA-GGUF/blob/main/FinGPT-MT-Llama-3-8B-LoRA-Q4_K_M.gguf) | Q4_K_M | 4 | 4.92 GB| medium, balanced quality - recommended |
| [FinGPT-MT-Llama-3-8B-LoRA-Q4_K_S.gguf](https://huggingface.co/second-state/FinGPT-MT-Llama-3-8B-LoRA-GGUF/blob/main/FinGPT-MT-Llama-3-8B-LoRA-Q4_K_S.gguf) | Q4_K_S | 4 | 4.69 GB| small, greater quality loss |
| [FinGPT-MT-Llama-3-8B-LoRA-Q5_0.gguf](https://huggingface.co/second-state/FinGPT-MT-Llama-3-8B-LoRA-GGUF/blob/main/FinGPT-MT-Llama-3-8B-LoRA-Q5_0.gguf) | Q5_0 | 5 | 5.6 GB| legacy; medium, balanced quality - prefer using Q4_K_M |
| [FinGPT-MT-Llama-3-8B-LoRA-Q5_K_M.gguf](https://huggingface.co/second-state/FinGPT-MT-Llama-3-8B-LoRA-GGUF/blob/main/FinGPT-MT-Llama-3-8B-LoRA-Q5_K_M.gguf) | Q5_K_M | 5 | 5.73 GB| large, very low quality loss - recommended |
| [FinGPT-MT-Llama-3-8B-LoRA-Q5_K_S.gguf](https://huggingface.co/second-state/FinGPT-MT-Llama-3-8B-LoRA-GGUF/blob/main/FinGPT-MT-Llama-3-8B-LoRA-Q5_K_S.gguf) | Q5_K_S | 5 | 5.6 GB| large, low quality loss - recommended |
| [FinGPT-MT-Llama-3-8B-LoRA-Q6_K.gguf](https://huggingface.co/second-state/FinGPT-MT-Llama-3-8B-LoRA-GGUF/blob/main/FinGPT-MT-Llama-3-8B-LoRA-Q6_K.gguf) | Q6_K | 6 | 6.6 GB| very large, extremely low quality loss |
| [FinGPT-MT-Llama-3-8B-LoRA-Q8_0.gguf](https://huggingface.co/second-state/FinGPT-MT-Llama-3-8B-LoRA-GGUF/blob/main/FinGPT-MT-Llama-3-8B-LoRA-Q8_0.gguf) | Q8_0 | 8 | 8.54 GB| very large, extremely low quality loss - not recommended |
| [FinGPT-MT-Llama-3-8B-LoRA-f16.gguf](https://huggingface.co/second-state/FinGPT-MT-Llama-3-8B-LoRA-GGUF/blob/main/FinGPT-MT-Llama-3-8B-LoRA-f16.gguf) | f16 | 16 | 16.1 GB| |
*Quantized with llama.cpp b3807.*
|
riacheruvu/phi-2-openvino
|
riacheruvu
| 2024-10-07T14:47:53Z | 7 | 0 | null |
[
"safetensors",
"openvino",
"phi",
"nlp",
"code",
"openvino-export",
"text-generation",
"en",
"base_model:microsoft/phi-2",
"base_model:finetune:microsoft/phi-2",
"license:mit",
"region:us"
] |
text-generation
| 2024-10-07T14:47:24Z |
---
base_model: microsoft/phi-2
language:
- en
license: mit
license_link: https://huggingface.co/microsoft/phi-2/resolve/main/LICENSE
pipeline_tag: text-generation
tags:
- nlp
- code
- openvino
- openvino-export
---
This model was converted to OpenVINO from [`microsoft/phi-2`](https://huggingface.co/microsoft/phi-2) using [optimum-intel](https://github.com/huggingface/optimum-intel)
via the [export](https://huggingface.co/spaces/echarlaix/openvino-export) space.
First make sure you have optimum-intel installed:
```bash
pip install optimum[openvino]
```
To load your model you can do as follows:
```python
from optimum.intel import OVModelForCausalLM
model_id = "riacheruvu/phi-2-openvino"
model = OVModelForCausalLM.from_pretrained(model_id)
```
|
AuriAetherwiing/MN-12B-Starcannon-v2
|
AuriAetherwiing
| 2024-10-07T14:43:02Z | 2,977 | 24 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"mergekit",
"merge",
"conversational",
"arxiv:2306.01708",
"base_model:intervitens/mini-magnum-12b-v1.1",
"base_model:merge:intervitens/mini-magnum-12b-v1.1",
"base_model:nothingiisreal/MN-12B-Celeste-V1.9",
"base_model:merge:nothingiisreal/MN-12B-Celeste-V1.9",
"license:cc-by-nc-nd-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-08-01T13:14:33Z |
---
base_model:
- nothingiisreal/MN-12B-Celeste-V1.9
- intervitens/mini-magnum-12b-v1.1
library_name: transformers
tags:
- mergekit
- merge
license: cc-by-nc-nd-4.0
---
**UPD: this model series is succeeded by [EVA](https://huggingface.co/EVA-UNIT-01/EVA-Qwen2.5-14B-v0.1)**<br>
**Unprivated, to store for historical reasons** <br>
*There's not much point in those merges, Celeste 70B 0.1 pretty much melded Celeste's and Magnum's datasets anyway*<br>
*To be continued, but on a different base, under a different name, and actually trained this time, without shortcuts*<br><br>
# MN-12B-Starcannon-v2
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). Turned out to be a bit more Magnum-esque, but still is very creative, and writing style is pretty nice, even if some slop words appear time to time. Might be a good fit for people wanting more variety than Magnum has, and more verbose prose than Celeste v1.9 has.
<br><br>
[Dynamic FP8](https://huggingface.co/aetherwiing/MN-12B-Starcannon-v2-fp8-dynamic) <br>
[Static GGUF (by Mradermacher)](https://huggingface.co/mradermacher/MN-12B-Starcannon-v2-GGUF) <br>
[EXL2 (by kingbri of RoyalLab)](https://huggingface.co/royallab/MN-12B-Starcannon-v2-exl2)
## Merge Details
### Merge Method
This model was merged using the [TIES](https://arxiv.org/abs/2306.01708) merge method using [nothingiisreal/MN-12B-Celeste-V1.9](https://huggingface.co/nothingiisreal/MN-12B-Celeste-V1.9) as a base.
### Merge fodder
The following models were included in the merge:
* [nothingiisreal/MN-12B-Celeste-V1.9](https://huggingface.co/nothingiisreal/MN-12B-Celeste-V1.9)
* [intervitens/mini-magnum-12b-v1.1](https://huggingface.co/intervitens/mini-magnum-12b-v1.1)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: intervitens/mini-magnum-12b-v1.1
parameters:
density: 0.3
weight: 0.5
- model: nothingiisreal/MN-12B-Celeste-V1.9
parameters:
density: 0.7
weight: 0.5
merge_method: ties
base_model: nothingiisreal/MN-12B-Celeste-V1.9
parameters:
normalize: true
int8_mask: true
dtype: bfloat16
```
|
alamgirqazi/mamba_text_classification
|
alamgirqazi
| 2024-10-07T14:42:21Z | 75 | 0 |
transformers
|
[
"transformers",
"pytorch",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2024-10-06T13:40:29Z |
---
library_name: transformers
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: mamba_text_classification
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mamba_text_classification
This model was trained from scratch on IMDB dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2454
- Accuracy: 0.932
## Installation
```
conda create -n mamba_env python=3.10
conda activate mamba_env
pip install torch torchvision --extra-index-url https://download.pytorch.org/whl/cu122
pip install 'causal-conv1d>=1.4.0'
pip install mamba-ssm==2.2.1
pip install datasets scikit-learn
```
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.01
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0109 | 0.1 | 625 | 0.3104 | 0.896 |
| 2.5826 | 0.2 | 1250 | 0.2714 | 0.912 |
| 0.0115 | 0.3 | 1875 | 0.2622 | 0.928 |
| 0.0133 | 0.4 | 2500 | 0.2427 | 0.924 |
| 0.0012 | 0.5 | 3125 | 0.2205 | 0.916 |
| 2.1868 | 0.6 | 3750 | 0.2280 | 0.936 |
| 1.5594 | 0.7 | 4375 | 0.2529 | 0.932 |
| 0.0016 | 0.8 | 5000 | 0.2196 | 0.94 |
| 0.0055 | 0.9 | 5625 | 0.2365 | 0.932 |
| 0.3351 | 1.0 | 6250 | 0.2454 | 0.932 |
### Framework versions
- Transformers 4.45.1
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.0
|
RichardErkhov/HuggingFaceTB_-_SmolLM-360M-gguf
|
RichardErkhov
| 2024-10-07T14:40:27Z | 20 | 0 | null |
[
"gguf",
"endpoints_compatible",
"region:us"
] | null | 2024-10-07T14:34:44Z |
Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
SmolLM-360M - GGUF
- Model creator: https://huggingface.co/HuggingFaceTB/
- Original model: https://huggingface.co/HuggingFaceTB/SmolLM-360M/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [SmolLM-360M.Q2_K.gguf](https://huggingface.co/RichardErkhov/HuggingFaceTB_-_SmolLM-360M-gguf/blob/main/SmolLM-360M.Q2_K.gguf) | Q2_K | 0.2GB |
| [SmolLM-360M.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/HuggingFaceTB_-_SmolLM-360M-gguf/blob/main/SmolLM-360M.IQ3_XS.gguf) | IQ3_XS | 0.2GB |
| [SmolLM-360M.IQ3_S.gguf](https://huggingface.co/RichardErkhov/HuggingFaceTB_-_SmolLM-360M-gguf/blob/main/SmolLM-360M.IQ3_S.gguf) | IQ3_S | 0.2GB |
| [SmolLM-360M.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/HuggingFaceTB_-_SmolLM-360M-gguf/blob/main/SmolLM-360M.Q3_K_S.gguf) | Q3_K_S | 0.2GB |
| [SmolLM-360M.IQ3_M.gguf](https://huggingface.co/RichardErkhov/HuggingFaceTB_-_SmolLM-360M-gguf/blob/main/SmolLM-360M.IQ3_M.gguf) | IQ3_M | 0.21GB |
| [SmolLM-360M.Q3_K.gguf](https://huggingface.co/RichardErkhov/HuggingFaceTB_-_SmolLM-360M-gguf/blob/main/SmolLM-360M.Q3_K.gguf) | Q3_K | 0.22GB |
| [SmolLM-360M.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/HuggingFaceTB_-_SmolLM-360M-gguf/blob/main/SmolLM-360M.Q3_K_M.gguf) | Q3_K_M | 0.22GB |
| [SmolLM-360M.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/HuggingFaceTB_-_SmolLM-360M-gguf/blob/main/SmolLM-360M.Q3_K_L.gguf) | Q3_K_L | 0.23GB |
| [SmolLM-360M.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/HuggingFaceTB_-_SmolLM-360M-gguf/blob/main/SmolLM-360M.IQ4_XS.gguf) | IQ4_XS | 0.21GB |
| [SmolLM-360M.Q4_0.gguf](https://huggingface.co/RichardErkhov/HuggingFaceTB_-_SmolLM-360M-gguf/blob/main/SmolLM-360M.Q4_0.gguf) | Q4_0 | 0.21GB |
| [SmolLM-360M.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/HuggingFaceTB_-_SmolLM-360M-gguf/blob/main/SmolLM-360M.IQ4_NL.gguf) | IQ4_NL | 0.21GB |
| [SmolLM-360M.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/HuggingFaceTB_-_SmolLM-360M-gguf/blob/main/SmolLM-360M.Q4_K_S.gguf) | Q4_K_S | 0.24GB |
| [SmolLM-360M.Q4_K.gguf](https://huggingface.co/RichardErkhov/HuggingFaceTB_-_SmolLM-360M-gguf/blob/main/SmolLM-360M.Q4_K.gguf) | Q4_K | 0.25GB |
| [SmolLM-360M.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/HuggingFaceTB_-_SmolLM-360M-gguf/blob/main/SmolLM-360M.Q4_K_M.gguf) | Q4_K_M | 0.25GB |
| [SmolLM-360M.Q4_1.gguf](https://huggingface.co/RichardErkhov/HuggingFaceTB_-_SmolLM-360M-gguf/blob/main/SmolLM-360M.Q4_1.gguf) | Q4_1 | 0.23GB |
| [SmolLM-360M.Q5_0.gguf](https://huggingface.co/RichardErkhov/HuggingFaceTB_-_SmolLM-360M-gguf/blob/main/SmolLM-360M.Q5_0.gguf) | Q5_0 | 0.25GB |
| [SmolLM-360M.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/HuggingFaceTB_-_SmolLM-360M-gguf/blob/main/SmolLM-360M.Q5_K_S.gguf) | Q5_K_S | 0.26GB |
| [SmolLM-360M.Q5_K.gguf](https://huggingface.co/RichardErkhov/HuggingFaceTB_-_SmolLM-360M-gguf/blob/main/SmolLM-360M.Q5_K.gguf) | Q5_K | 0.27GB |
| [SmolLM-360M.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/HuggingFaceTB_-_SmolLM-360M-gguf/blob/main/SmolLM-360M.Q5_K_M.gguf) | Q5_K_M | 0.27GB |
| [SmolLM-360M.Q5_1.gguf](https://huggingface.co/RichardErkhov/HuggingFaceTB_-_SmolLM-360M-gguf/blob/main/SmolLM-360M.Q5_1.gguf) | Q5_1 | 0.27GB |
| [SmolLM-360M.Q6_K.gguf](https://huggingface.co/RichardErkhov/HuggingFaceTB_-_SmolLM-360M-gguf/blob/main/SmolLM-360M.Q6_K.gguf) | Q6_K | 0.34GB |
| [SmolLM-360M.Q8_0.gguf](https://huggingface.co/RichardErkhov/HuggingFaceTB_-_SmolLM-360M-gguf/blob/main/SmolLM-360M.Q8_0.gguf) | Q8_0 | 0.36GB |
Original model description:
---
library_name: transformers
license: apache-2.0
language:
- en
datasets:
- HuggingFaceTB/smollm-corpus
---
# SmolLM
<center>
<img src="https://huggingface.co/datasets/HuggingFaceTB/images/resolve/main/banner_smol.png" alt="SmolLM" width="1100" height="600">
</center>
## Table of Contents
1. [Model Summary](##model-summary)
2. [Limitations](##limitations)
3. [Training](##training)
4. [License](##license)
5. [Citation](##citation)
## Model Summary
SmolLM is a series of state-of-the-art small language models available in three sizes: 135M, 360M, and 1.7B parameters. These models are built on Cosmo-Corpus, a meticulously curated high-quality training dataset. Cosmo-Corpus includes Cosmopedia v2 (28B tokens of synthetic textbooks and stories generated by Mixtral), Python-Edu (4B tokens of educational Python samples from The Stack), and FineWeb-Edu (220B tokens of deduplicated educational web samples from FineWeb). SmolLM models have shown promising results when compared to other models in their size categories across various benchmarks testing common sense reasoning and world knowledge. For detailed information on training, benchmarks and performance, please refer to our full [blog post](https://huggingface.co/blog/smollm).
This is the SmolLM-360M
### Generation
```bash
pip install transformers
```
#### Running the model on CPU/GPU/multi GPU
* _Using full precision_
```python
# pip install transformers
from transformers import AutoModelForCausalLM, AutoTokenizer
checkpoint = "HuggingFaceTB/SmolLM-360M"
device = "cuda" # for GPU usage or "cpu" for CPU usage
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
# for multiple GPUs install accelerate and do `model = AutoModelForCausalLM.from_pretrained(checkpoint, device_map="auto")`
model = AutoModelForCausalLM.from_pretrained(checkpoint).to(device)
inputs = tokenizer.encode("def print_hello_world():", return_tensors="pt").to(device)
outputs = model.generate(inputs)
print(tokenizer.decode(outputs[0]))
* _Using `torch.bfloat16`_
```python
# pip install accelerate
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
checkpoint = "HuggingFaceTB/SmolLM-360M"
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
# for fp16 use `torch_dtype=torch.float16` instead
model = AutoModelForCausalLM.from_pretrained(checkpoint, device_map="auto", torch_dtype=torch.bfloat16)
inputs = tokenizer.encode("def print_hello_world():", return_tensors="pt").to("cuda")
outputs = model.generate(inputs)
print(tokenizer.decode(outputs[0]))
```
```bash
>>> print(f"Memory footprint: {model.get_memory_footprint() / 1e6:.2f} MB")
Memory footprint: 723.56 MB
```
#### Quantized Versions through `bitsandbytes`
* _Using 8-bit precision (int8)_
```python
# pip install bitsandbytes accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
# to use 4bit use `load_in_4bit=True` instead
quantization_config = BitsAndBytesConfig(load_in_8bit=True)
checkpoint = "HuggingFaceTB/SmolLM-360M"
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForCausalLM.from_pretrained(checkpoint, quantization_config=quantization_config)
inputs = tokenizer.encode("def print_hello_world():", return_tensors="pt").to("cuda")
outputs = model.generate(inputs)
print(tokenizer.decode(outputs[0]))
```
```bash
>>> print(f"Memory footprint: {model.get_memory_footprint() / 1e6:.2f} MB")
# load_in_8bit
Memory footprint: 409.07 MB
# load_in_4bit
>>> print(f"Memory footprint: {model.get_memory_footprint() / 1e6:.2f} MB")
Memory footprint: 251.79 MB
```
# Limitations
While SmolLM models have been trained on a diverse dataset including educational content and synthetic texts, they have limitations. The models primarily understand and generate content in English. They can produce text on a variety of topics, but the generated content may not always be factually accurate, logically consistent, or free from biases present in the training data. These models should be used as assistive tools rather than definitive sources of information. Users should always verify important information and critically evaluate any generated content. For a more comprehensive discussion of the models' capabilities and limitations, please refer to our full [blog post](https://huggingface.co/blog/smollm)..
This repository contains a converted version of our latest trained model. We've noticed a small performance difference between this converted checkpoint (transformers) and the original (nanotron). We're currently working to resolve this issue.
# Training
## Model
- **Architecture:** For architecture detail, see the [blog post](https://huggingface.co/blog/smollm).
- **Pretraining steps:** 600k
- **Pretraining tokens:** 600B
- **Precision:** bfloat16
- **Tokenizer:** [HuggingFaceTB/cosmo2-tokenizer](https://huggingface.co/HuggingFaceTB/cosmo2-tokenizer)
## Hardware
- **GPUs:** 64 H100
## Software
- **Training Framework:** [Nanotron](https://github.com/huggingface/nanotron/tree/main)
# License
[Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0)
# Citation
```bash
@misc{allal2024SmolLM,
title={SmolLM - blazingly fast and remarkably powerful},
author={Loubna Ben Allal and Anton Lozhkov and Elie Bakouch and Leandro von Werra and Thomas Wolf},
year={2024},
}
```
|
EVA787797/777787777
|
EVA787797
| 2024-10-07T14:40:03Z | 18 | 1 |
diffusers
|
[
"diffusers",
"text-to-image",
"lora",
"template:diffusion-lora",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:afl-3.0",
"region:us"
] |
text-to-image
| 2024-10-07T08:54:17Z |
---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- text: '-'
output:
url: images/machi-marclilio587877-tcogmyv9qdcxfbwumelg6dqe.png
- text: '-'
output:
url: images/koleodkfnj3urqdjuavb.png
- text: '-'
output:
url: images/femme-fatale-retouchee.png
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: null
license: afl-3.0
---
# flux_devpro_ckpt
<Gallery />
## Download model
Weights for this model are available in PyTorch,Safetensors format.
[Download](/EVA787797/777787777/tree/main) them in the Files & versions tab.
|
Helsinki-NLP/opus-mt-tc-bible-big-bat-deu_eng_fra_por_spa
|
Helsinki-NLP
| 2024-10-07T14:28:16Z | 114 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc-bible",
"de",
"en",
"es",
"fr",
"lt",
"lv",
"prg",
"pt",
"sgs",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2024-10-07T14:28:02Z |
---
library_name: transformers
language:
- de
- en
- es
- fr
- lt
- lv
- prg
- pt
- sgs
tags:
- translation
- opus-mt-tc-bible
license: apache-2.0
model-index:
- name: opus-mt-tc-bible-big-bat-deu_eng_fra_por_spa
results:
- task:
name: Translation lit-deu
type: translation
args: lit-deu
dataset:
name: flores200-devtest
type: flores200-devtest
args: lit-deu
metrics:
- name: BLEU
type: bleu
value: 23.7
- name: chr-F
type: chrf
value: 0.53223
- task:
name: Translation lit-eng
type: translation
args: lit-eng
dataset:
name: flores200-devtest
type: flores200-devtest
args: lit-eng
metrics:
- name: BLEU
type: bleu
value: 32.6
- name: chr-F
type: chrf
value: 0.59361
- task:
name: Translation lit-fra
type: translation
args: lit-fra
dataset:
name: flores200-devtest
type: flores200-devtest
args: lit-fra
metrics:
- name: BLEU
type: bleu
value: 30.0
- name: chr-F
type: chrf
value: 0.56786
- task:
name: Translation lit-por
type: translation
args: lit-por
dataset:
name: flores200-devtest
type: flores200-devtest
args: lit-por
metrics:
- name: BLEU
type: bleu
value: 28.2
- name: chr-F
type: chrf
value: 0.55393
- task:
name: Translation lit-spa
type: translation
args: lit-spa
dataset:
name: flores200-devtest
type: flores200-devtest
args: lit-spa
metrics:
- name: BLEU
type: bleu
value: 20.9
- name: chr-F
type: chrf
value: 0.49041
- task:
name: Translation lav-deu
type: translation
args: lav-deu
dataset:
name: flores101-devtest
type: flores_101
args: lav deu devtest
metrics:
- name: BLEU
type: bleu
value: 23.8
- name: chr-F
type: chrf
value: 0.54001
- task:
name: Translation lav-fra
type: translation
args: lav-fra
dataset:
name: flores101-devtest
type: flores_101
args: lav fra devtest
metrics:
- name: BLEU
type: bleu
value: 29.4
- name: chr-F
type: chrf
value: 0.57002
- task:
name: Translation lav-por
type: translation
args: lav-por
dataset:
name: flores101-devtest
type: flores_101
args: lav por devtest
metrics:
- name: BLEU
type: bleu
value: 26.7
- name: chr-F
type: chrf
value: 0.55155
- task:
name: Translation lav-spa
type: translation
args: lav-spa
dataset:
name: flores101-devtest
type: flores_101
args: lav spa devtest
metrics:
- name: BLEU
type: bleu
value: 20.8
- name: chr-F
type: chrf
value: 0.49259
- task:
name: Translation lit-eng
type: translation
args: lit-eng
dataset:
name: flores101-devtest
type: flores_101
args: lit eng devtest
metrics:
- name: BLEU
type: bleu
value: 32.1
- name: chr-F
type: chrf
value: 0.59073
- task:
name: Translation lit-por
type: translation
args: lit-por
dataset:
name: flores101-devtest
type: flores_101
args: lit por devtest
metrics:
- name: BLEU
type: bleu
value: 27.8
- name: chr-F
type: chrf
value: 0.55106
- task:
name: Translation lav-deu
type: translation
args: lav-deu
dataset:
name: ntrex128
type: ntrex128
args: lav-deu
metrics:
- name: BLEU
type: bleu
value: 18.5
- name: chr-F
type: chrf
value: 0.47317
- task:
name: Translation lav-eng
type: translation
args: lav-eng
dataset:
name: ntrex128
type: ntrex128
args: lav-eng
metrics:
- name: BLEU
type: bleu
value: 19.7
- name: chr-F
type: chrf
value: 0.53734
- task:
name: Translation lav-fra
type: translation
args: lav-fra
dataset:
name: ntrex128
type: ntrex128
args: lav-fra
metrics:
- name: BLEU
type: bleu
value: 19.6
- name: chr-F
type: chrf
value: 0.47843
- task:
name: Translation lav-por
type: translation
args: lav-por
dataset:
name: ntrex128
type: ntrex128
args: lav-por
metrics:
- name: BLEU
type: bleu
value: 19.3
- name: chr-F
type: chrf
value: 0.47027
- task:
name: Translation lav-spa
type: translation
args: lav-spa
dataset:
name: ntrex128
type: ntrex128
args: lav-spa
metrics:
- name: BLEU
type: bleu
value: 22.7
- name: chr-F
type: chrf
value: 0.49428
- task:
name: Translation lit-deu
type: translation
args: lit-deu
dataset:
name: ntrex128
type: ntrex128
args: lit-deu
metrics:
- name: BLEU
type: bleu
value: 19.4
- name: chr-F
type: chrf
value: 0.50279
- task:
name: Translation lit-eng
type: translation
args: lit-eng
dataset:
name: ntrex128
type: ntrex128
args: lit-eng
metrics:
- name: BLEU
type: bleu
value: 28.1
- name: chr-F
type: chrf
value: 0.56642
- task:
name: Translation lit-fra
type: translation
args: lit-fra
dataset:
name: ntrex128
type: ntrex128
args: lit-fra
metrics:
- name: BLEU
type: bleu
value: 22.6
- name: chr-F
type: chrf
value: 0.51276
- task:
name: Translation lit-por
type: translation
args: lit-por
dataset:
name: ntrex128
type: ntrex128
args: lit-por
metrics:
- name: BLEU
type: bleu
value: 22.6
- name: chr-F
type: chrf
value: 0.50864
- task:
name: Translation lit-spa
type: translation
args: lit-spa
dataset:
name: ntrex128
type: ntrex128
args: lit-spa
metrics:
- name: BLEU
type: bleu
value: 25.9
- name: chr-F
type: chrf
value: 0.53105
- task:
name: Translation lav-eng
type: translation
args: lav-eng
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: lav-eng
metrics:
- name: BLEU
type: bleu
value: 21.5
- name: chr-F
type: chrf
value: 0.63015
- task:
name: Translation lit-deu
type: translation
args: lit-deu
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: lit-deu
metrics:
- name: BLEU
type: bleu
value: 47.5
- name: chr-F
type: chrf
value: 0.66527
- task:
name: Translation lit-eng
type: translation
args: lit-eng
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: lit-eng
metrics:
- name: BLEU
type: bleu
value: 58.9
- name: chr-F
type: chrf
value: 0.72975
- task:
name: Translation lit-spa
type: translation
args: lit-spa
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: lit-spa
metrics:
- name: BLEU
type: bleu
value: 49.9
- name: chr-F
type: chrf
value: 0.67956
- task:
name: Translation multi-multi
type: translation
args: multi-multi
dataset:
name: tatoeba-test-v2020-07-28-v2023-09-26
type: tatoeba_mt
args: multi-multi
metrics:
- name: BLEU
type: bleu
value: 55.5
- name: chr-F
type: chrf
value: 0.71003
- task:
name: Translation lav-eng
type: translation
args: lav-eng
dataset:
name: newstest2017
type: wmt-2017-news
args: lav-eng
metrics:
- name: BLEU
type: bleu
value: 22.0
- name: chr-F
type: chrf
value: 0.49729
- task:
name: Translation lit-eng
type: translation
args: lit-eng
dataset:
name: newstest2019
type: wmt-2019-news
args: lit-eng
metrics:
- name: BLEU
type: bleu
value: 31.2
- name: chr-F
type: chrf
value: 0.59971
---
# opus-mt-tc-bible-big-bat-deu_eng_fra_por_spa
## Table of Contents
- [Model Details](#model-details)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [How to Get Started With the Model](#how-to-get-started-with-the-model)
- [Training](#training)
- [Evaluation](#evaluation)
- [Citation Information](#citation-information)
- [Acknowledgements](#acknowledgements)
## Model Details
Neural machine translation model for translating from Baltic languages (bat) to unknown (deu+eng+fra+por+spa).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally trained using the amazing framework of [Marian NMT](https://marian-nmt.github.io/), an efficient NMT implementation written in pure C++. The models have been converted to pyTorch using the transformers library by huggingface. Training data is taken from [OPUS](https://opus.nlpl.eu/) and training pipelines use the procedures of [OPUS-MT-train](https://github.com/Helsinki-NLP/Opus-MT-train).
**Model Description:**
- **Developed by:** Language Technology Research Group at the University of Helsinki
- **Model Type:** Translation (transformer-big)
- **Release**: 2024-05-30
- **License:** Apache-2.0
- **Language(s):**
- Source Language(s): lav lit prg sgs
- Target Language(s): deu eng fra por spa
- Valid Target Language Labels: >>deu<< >>eng<< >>fra<< >>por<< >>spa<< >>xxx<<
- **Original Model**: [opusTCv20230926max50+bt+jhubc_transformer-big_2024-05-30.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/bat-deu+eng+fra+por+spa/opusTCv20230926max50+bt+jhubc_transformer-big_2024-05-30.zip)
- **Resources for more information:**
- [OPUS-MT dashboard](https://opus.nlpl.eu/dashboard/index.php?pkg=opusmt&test=all&scoreslang=all&chart=standard&model=Tatoeba-MT-models/bat-deu%2Beng%2Bfra%2Bpor%2Bspa/opusTCv20230926max50%2Bbt%2Bjhubc_transformer-big_2024-05-30)
- [OPUS-MT-train GitHub Repo](https://github.com/Helsinki-NLP/OPUS-MT-train)
- [More information about MarianNMT models in the transformers library](https://huggingface.co/docs/transformers/model_doc/marian)
- [Tatoeba Translation Challenge](https://github.com/Helsinki-NLP/Tatoeba-Challenge/)
- [HPLT bilingual data v1 (as part of the Tatoeba Translation Challenge dataset)](https://hplt-project.org/datasets/v1)
- [A massively parallel Bible corpus](https://aclanthology.org/L14-1215/)
This is a multilingual translation model with multiple target languages. A sentence initial language token is required in the form of `>>id<<` (id = valid target language ID), e.g. `>>deu<<`
## Uses
This model can be used for translation and text-to-text generation.
## Risks, Limitations and Biases
**CONTENT WARNING: Readers should be aware that the model is trained on various public data sets that may contain content that is disturbing, offensive, and can propagate historical and current stereotypes.**
Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)).
## How to Get Started With the Model
A short example code:
```python
from transformers import MarianMTModel, MarianTokenizer
src_text = [
">>deu<< Replace this with text in an accepted source language.",
">>spa<< This is the second sentence."
]
model_name = "pytorch-models/opus-mt-tc-bible-big-bat-deu_eng_fra_por_spa"
tokenizer = MarianTokenizer.from_pretrained(model_name)
model = MarianMTModel.from_pretrained(model_name)
translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True))
for t in translated:
print( tokenizer.decode(t, skip_special_tokens=True) )
```
You can also use OPUS-MT models with the transformers pipelines, for example:
```python
from transformers import pipeline
pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-bible-big-bat-deu_eng_fra_por_spa")
print(pipe(">>deu<< Replace this with text in an accepted source language."))
```
## Training
- **Data**: opusTCv20230926max50+bt+jhubc ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge))
- **Pre-processing**: SentencePiece (spm32k,spm32k)
- **Model Type:** transformer-big
- **Original MarianNMT Model**: [opusTCv20230926max50+bt+jhubc_transformer-big_2024-05-30.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/bat-deu+eng+fra+por+spa/opusTCv20230926max50+bt+jhubc_transformer-big_2024-05-30.zip)
- **Training Scripts**: [GitHub Repo](https://github.com/Helsinki-NLP/OPUS-MT-train)
## Evaluation
* [Model scores at the OPUS-MT dashboard](https://opus.nlpl.eu/dashboard/index.php?pkg=opusmt&test=all&scoreslang=all&chart=standard&model=Tatoeba-MT-models/bat-deu%2Beng%2Bfra%2Bpor%2Bspa/opusTCv20230926max50%2Bbt%2Bjhubc_transformer-big_2024-05-30)
* test set translations: [opusTCv20230926max50+bt+jhubc_transformer-big_2024-05-29.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/bat-deu+eng+fra+por+spa/opusTCv20230926max50+bt+jhubc_transformer-big_2024-05-29.test.txt)
* test set scores: [opusTCv20230926max50+bt+jhubc_transformer-big_2024-05-29.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/bat-deu+eng+fra+por+spa/opusTCv20230926max50+bt+jhubc_transformer-big_2024-05-29.eval.txt)
* benchmark results: [benchmark_results.txt](benchmark_results.txt)
* benchmark output: [benchmark_translations.zip](benchmark_translations.zip)
| langpair | testset | chr-F | BLEU | #sent | #words |
|----------|---------|-------|-------|-------|--------|
| lav-eng | tatoeba-test-v2021-08-07 | 0.63015 | 21.5 | 1631 | 11213 |
| lit-deu | tatoeba-test-v2021-08-07 | 0.66527 | 47.5 | 1115 | 8531 |
| lit-eng | tatoeba-test-v2021-08-07 | 0.72975 | 58.9 | 2528 | 17855 |
| lit-spa | tatoeba-test-v2021-08-07 | 0.67956 | 49.9 | 454 | 2751 |
| lav-deu | flores101-devtest | 0.54001 | 23.8 | 1012 | 25094 |
| lav-fra | flores101-devtest | 0.57002 | 29.4 | 1012 | 28343 |
| lav-por | flores101-devtest | 0.55155 | 26.7 | 1012 | 26519 |
| lav-spa | flores101-devtest | 0.49259 | 20.8 | 1012 | 29199 |
| lit-eng | flores101-devtest | 0.59073 | 32.1 | 1012 | 24721 |
| lit-por | flores101-devtest | 0.55106 | 27.8 | 1012 | 26519 |
| lit-deu | flores200-devtest | 0.53223 | 23.7 | 1012 | 25094 |
| lit-eng | flores200-devtest | 0.59361 | 32.6 | 1012 | 24721 |
| lit-fra | flores200-devtest | 0.56786 | 30.0 | 1012 | 28343 |
| lit-por | flores200-devtest | 0.55393 | 28.2 | 1012 | 26519 |
| lit-spa | flores200-devtest | 0.49041 | 20.9 | 1012 | 29199 |
| lav-eng | newstest2017 | 0.49729 | 22.0 | 2001 | 47511 |
| lit-eng | newstest2019 | 0.59971 | 31.2 | 1000 | 25878 |
| lav-deu | ntrex128 | 0.47317 | 18.5 | 1997 | 48761 |
| lav-eng | ntrex128 | 0.53734 | 19.7 | 1997 | 47673 |
| lav-fra | ntrex128 | 0.47843 | 19.6 | 1997 | 53481 |
| lav-por | ntrex128 | 0.47027 | 19.3 | 1997 | 51631 |
| lav-spa | ntrex128 | 0.49428 | 22.7 | 1997 | 54107 |
| lit-deu | ntrex128 | 0.50279 | 19.4 | 1997 | 48761 |
| lit-eng | ntrex128 | 0.56642 | 28.1 | 1997 | 47673 |
| lit-fra | ntrex128 | 0.51276 | 22.6 | 1997 | 53481 |
| lit-por | ntrex128 | 0.50864 | 22.6 | 1997 | 51631 |
| lit-spa | ntrex128 | 0.53105 | 25.9 | 1997 | 54107 |
## Citation Information
* Publications: [Democratizing neural machine translation with OPUS-MT](https://doi.org/10.1007/s10579-023-09704-w) and [OPUS-MT – Building open translation services for the World](https://aclanthology.org/2020.eamt-1.61/) and [The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT](https://aclanthology.org/2020.wmt-1.139/) (Please, cite if you use this model.)
```bibtex
@article{tiedemann2023democratizing,
title={Democratizing neural machine translation with {OPUS-MT}},
author={Tiedemann, J{\"o}rg and Aulamo, Mikko and Bakshandaeva, Daria and Boggia, Michele and Gr{\"o}nroos, Stig-Arne and Nieminen, Tommi and Raganato, Alessandro and Scherrer, Yves and Vazquez, Raul and Virpioja, Sami},
journal={Language Resources and Evaluation},
number={58},
pages={713--755},
year={2023},
publisher={Springer Nature},
issn={1574-0218},
doi={10.1007/s10579-023-09704-w}
}
@inproceedings{tiedemann-thottingal-2020-opus,
title = "{OPUS}-{MT} {--} Building open translation services for the World",
author = {Tiedemann, J{\"o}rg and Thottingal, Santhosh},
booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
month = nov,
year = "2020",
address = "Lisboa, Portugal",
publisher = "European Association for Machine Translation",
url = "https://aclanthology.org/2020.eamt-1.61",
pages = "479--480",
}
@inproceedings{tiedemann-2020-tatoeba,
title = "The Tatoeba Translation Challenge {--} Realistic Data Sets for Low Resource and Multilingual {MT}",
author = {Tiedemann, J{\"o}rg},
booktitle = "Proceedings of the Fifth Conference on Machine Translation",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.wmt-1.139",
pages = "1174--1182",
}
```
## Acknowledgements
The work is supported by the [HPLT project](https://hplt-project.org/), funded by the European Union’s Horizon Europe research and innovation programme under grant agreement No 101070350. We are also grateful for the generous computational resources and IT infrastructure provided by [CSC -- IT Center for Science](https://www.csc.fi/), Finland, and the [EuroHPC supercomputer LUMI](https://www.lumi-supercomputer.eu/).
## Model conversion info
* transformers version: 4.45.1
* OPUS-MT git hash: a0ea3b3
* port time: Mon Oct 7 17:27:51 EEST 2024
* port machine: LM0-400-22516.local
|
Trelis/Llama-3.2-1B-Instruct-MATH-synthetic
|
Trelis
| 2024-10-07T14:16:45Z | 116 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"conversational",
"en",
"base_model:unsloth/Llama-3.2-1B-Instruct",
"base_model:finetune:unsloth/Llama-3.2-1B-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-10-07T14:16:17Z |
---
base_model: unsloth/Llama-3.2-1B-Instruct
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
---
# Uploaded model
- **Developed by:** Trelis
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Llama-3.2-1B-Instruct
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
Helsinki-NLP/opus-mt-tc-bible-big-alv-deu_eng_fra_por_spa
|
Helsinki-NLP
| 2024-10-07T14:13:38Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc-bible",
"abi",
"acd",
"ade",
"adj",
"ak",
"akp",
"ann",
"anv",
"atg",
"avn",
"bas",
"bav",
"bba",
"beh",
"bem",
"bfd",
"bfo",
"bim",
"biv",
"bkv",
"blh",
"bmq",
"bmv",
"bom",
"bov",
"box",
"bqj",
"bss",
"btt",
"bud",
"bwu",
"cce",
"cjk",
"cko",
"cme",
"csk",
"cwe",
"cwt",
"dag",
"de",
"dga",
"dgi",
"dig",
"dop",
"dug",
"dyi",
"dyo",
"ee",
"efi",
"en",
"es",
"fal",
"ff",
"fon",
"fr",
"gej",
"gkn",
"gng",
"gog",
"gud",
"gur",
"guw",
"gux",
"gwr",
"hag",
"hay",
"heh",
"hz",
"ife",
"ig",
"iri",
"izr",
"jbu",
"jmc",
"kam",
"kbp",
"kdc",
"kdl",
"kdn",
"ken",
"keu",
"kez",
"kg",
"ki",
"kia",
"kj",
"kki",
"kkj",
"kma",
"kmb",
"ksb",
"ktj",
"kub",
"kus",
"kyf",
"las",
"lee",
"lef",
"lem",
"lg",
"lia",
"lip",
"ln",
"lob",
"lon",
"lua",
"luy",
"maw",
"mcp",
"mcu",
"mda",
"mfq",
"mgo",
"mnf",
"mnh",
"mor",
"mos",
"muh",
"myk",
"myx",
"mzk",
"mzm",
"mzw",
"ncu",
"nd",
"ndz",
"nfr",
"ng",
"nhu",
"nim",
"nin",
"nmz",
"nnb",
"nnh",
"nnw",
"nr",
"nso",
"ntm",
"ntr",
"nuj",
"nwb",
"ny",
"nyf",
"nyn",
"nyo",
"nyy",
"nzi",
"oku",
"old",
"ozm",
"pai",
"pbl",
"pkb",
"pt",
"rim",
"rn",
"rw",
"seh",
"sg",
"sig",
"sil",
"sld",
"sn",
"snw",
"soy",
"spp",
"ss",
"st",
"suk",
"sw",
"sxb",
"tbz",
"tem",
"thk",
"tik",
"tlj",
"tn",
"toh",
"toi",
"tpm",
"ts",
"tsw",
"tum",
"tw",
"umb",
"vag",
"ve",
"vmw",
"vun",
"wmw",
"wo",
"wob",
"xh",
"xog",
"xon",
"xrb",
"xsm",
"xuo",
"yam",
"yaz",
"yo",
"zu",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2024-10-07T13:22:36Z |
---
library_name: transformers
language:
- abi
- acd
- ade
- adj
- ak
- akp
- ann
- anv
- atg
- avn
- bas
- bav
- bba
- beh
- bem
- bfd
- bfo
- bim
- biv
- bkv
- blh
- bmq
- bmv
- bom
- bov
- box
- bqj
- bss
- btt
- bud
- bwu
- cce
- cjk
- cko
- cme
- csk
- cwe
- cwt
- dag
- de
- dga
- dgi
- dig
- dop
- dug
- dyi
- dyo
- ee
- efi
- en
- es
- fal
- ff
- fon
- fr
- gej
- gkn
- gng
- gog
- gud
- gur
- guw
- gux
- gwr
- hag
- hay
- heh
- hz
- ife
- ig
- iri
- izr
- jbu
- jmc
- kam
- kbp
- kdc
- kdl
- kdn
- ken
- keu
- kez
- kg
- ki
- kia
- kj
- kki
- kkj
- kma
- kmb
- ksb
- ktj
- kub
- kus
- kyf
- las
- lee
- lef
- lem
- lg
- lia
- lip
- ln
- lob
- lon
- lua
- luy
- maw
- mcp
- mcu
- mda
- mfq
- mgo
- mnf
- mnh
- mor
- mos
- muh
- myk
- myx
- mzk
- mzm
- mzw
- ncu
- nd
- ndz
- nfr
- ng
- nhu
- nim
- nin
- nmz
- nnb
- nnh
- nnw
- nr
- nso
- ntm
- ntr
- nuj
- nwb
- ny
- nyf
- nyn
- nyo
- nyy
- nzi
- oku
- old
- ozm
- pai
- pbl
- pkb
- pt
- rim
- rn
- rw
- seh
- sg
- sig
- sil
- sld
- sn
- snw
- soy
- spp
- ss
- st
- suk
- sw
- sxb
- tbz
- tem
- thk
- tik
- tlj
- tn
- toh
- toi
- tpm
- ts
- tsw
- tum
- tw
- umb
- vag
- ve
- vmw
- vun
- wmw
- wo
- wob
- xh
- xog
- xon
- xrb
- xsm
- xuo
- yam
- yaz
- yo
- zu
tags:
- translation
- opus-mt-tc-bible
license: apache-2.0
model-index:
- name: opus-mt-tc-bible-big-alv-deu_eng_fra_por_spa
results:
- task:
name: Translation bem-eng
type: translation
args: bem-eng
dataset:
name: flores200-devtest
type: flores200-devtest
args: bem-eng
metrics:
- name: BLEU
type: bleu
value: 13.1
- name: chr-F
type: chrf
value: 0.37071
- task:
name: Translation ibo-eng
type: translation
args: ibo-eng
dataset:
name: flores200-devtest
type: flores200-devtest
args: ibo-eng
metrics:
- name: BLEU
type: bleu
value: 14.6
- name: chr-F
type: chrf
value: 0.38994
- task:
name: Translation kin-eng
type: translation
args: kin-eng
dataset:
name: flores200-devtest
type: flores200-devtest
args: kin-eng
metrics:
- name: BLEU
type: bleu
value: 18.1
- name: chr-F
type: chrf
value: 0.41964
- task:
name: Translation kin-fra
type: translation
args: kin-fra
dataset:
name: flores200-devtest
type: flores200-devtest
args: kin-fra
metrics:
- name: BLEU
type: bleu
value: 10.7
- name: chr-F
type: chrf
value: 0.34887
- task:
name: Translation kon-eng
type: translation
args: kon-eng
dataset:
name: flores200-devtest
type: flores200-devtest
args: kon-eng
metrics:
- name: BLEU
type: bleu
value: 11.3
- name: chr-F
type: chrf
value: 0.34262
- task:
name: Translation lin-eng
type: translation
args: lin-eng
dataset:
name: flores200-devtest
type: flores200-devtest
args: lin-eng
metrics:
- name: BLEU
type: bleu
value: 14.2
- name: chr-F
type: chrf
value: 0.37728
- task:
name: Translation lin-fra
type: translation
args: lin-fra
dataset:
name: flores200-devtest
type: flores200-devtest
args: lin-fra
metrics:
- name: BLEU
type: bleu
value: 11.0
- name: chr-F
type: chrf
value: 0.35052
- task:
name: Translation lug-eng
type: translation
args: lug-eng
dataset:
name: flores200-devtest
type: flores200-devtest
args: lug-eng
metrics:
- name: BLEU
type: bleu
value: 10.6
- name: chr-F
type: chrf
value: 0.31805
- task:
name: Translation nso-eng
type: translation
args: nso-eng
dataset:
name: flores200-devtest
type: flores200-devtest
args: nso-eng
metrics:
- name: BLEU
type: bleu
value: 22.3
- name: chr-F
type: chrf
value: 0.45662
- task:
name: Translation nso-fra
type: translation
args: nso-fra
dataset:
name: flores200-devtest
type: flores200-devtest
args: nso-fra
metrics:
- name: BLEU
type: bleu
value: 10.5
- name: chr-F
type: chrf
value: 0.33732
- task:
name: Translation nya-eng
type: translation
args: nya-eng
dataset:
name: flores200-devtest
type: flores200-devtest
args: nya-eng
metrics:
- name: BLEU
type: bleu
value: 15.8
- name: chr-F
type: chrf
value: 0.39887
- task:
name: Translation run-eng
type: translation
args: run-eng
dataset:
name: flores200-devtest
type: flores200-devtest
args: run-eng
metrics:
- name: BLEU
type: bleu
value: 15.5
- name: chr-F
type: chrf
value: 0.39846
- task:
name: Translation run-fra
type: translation
args: run-fra
dataset:
name: flores200-devtest
type: flores200-devtest
args: run-fra
metrics:
- name: BLEU
type: bleu
value: 10.8
- name: chr-F
type: chrf
value: 0.34845
- task:
name: Translation run-por
type: translation
args: run-por
dataset:
name: flores200-devtest
type: flores200-devtest
args: run-por
metrics:
- name: BLEU
type: bleu
value: 10.2
- name: chr-F
type: chrf
value: 0.33836
- task:
name: Translation sna-eng
type: translation
args: sna-eng
dataset:
name: flores200-devtest
type: flores200-devtest
args: sna-eng
metrics:
- name: BLEU
type: bleu
value: 17.2
- name: chr-F
type: chrf
value: 0.41974
- task:
name: Translation sna-fra
type: translation
args: sna-fra
dataset:
name: flores200-devtest
type: flores200-devtest
args: sna-fra
metrics:
- name: BLEU
type: bleu
value: 11.3
- name: chr-F
type: chrf
value: 0.36443
- task:
name: Translation sna-por
type: translation
args: sna-por
dataset:
name: flores200-devtest
type: flores200-devtest
args: sna-por
metrics:
- name: BLEU
type: bleu
value: 10.2
- name: chr-F
type: chrf
value: 0.34260
- task:
name: Translation sot-eng
type: translation
args: sot-eng
dataset:
name: flores200-devtest
type: flores200-devtest
args: sot-eng
metrics:
- name: BLEU
type: bleu
value: 20.7
- name: chr-F
type: chrf
value: 0.45415
- task:
name: Translation sot-fra
type: translation
args: sot-fra
dataset:
name: flores200-devtest
type: flores200-devtest
args: sot-fra
metrics:
- name: BLEU
type: bleu
value: 10.7
- name: chr-F
type: chrf
value: 0.34608
- task:
name: Translation ssw-eng
type: translation
args: ssw-eng
dataset:
name: flores200-devtest
type: flores200-devtest
args: ssw-eng
metrics:
- name: BLEU
type: bleu
value: 16.1
- name: chr-F
type: chrf
value: 0.39768
- task:
name: Translation swh-deu
type: translation
args: swh-deu
dataset:
name: flores200-devtest
type: flores200-devtest
args: swh-deu
metrics:
- name: BLEU
type: bleu
value: 10.9
- name: chr-F
type: chrf
value: 0.38892
- task:
name: Translation swh-eng
type: translation
args: swh-eng
dataset:
name: flores200-devtest
type: flores200-devtest
args: swh-eng
metrics:
- name: BLEU
type: bleu
value: 29.1
- name: chr-F
type: chrf
value: 0.54048
- task:
name: Translation swh-fra
type: translation
args: swh-fra
dataset:
name: flores200-devtest
type: flores200-devtest
args: swh-fra
metrics:
- name: BLEU
type: bleu
value: 18.2
- name: chr-F
type: chrf
value: 0.44837
- task:
name: Translation swh-por
type: translation
args: swh-por
dataset:
name: flores200-devtest
type: flores200-devtest
args: swh-por
metrics:
- name: BLEU
type: bleu
value: 17.6
- name: chr-F
type: chrf
value: 0.44062
- task:
name: Translation swh-spa
type: translation
args: swh-spa
dataset:
name: flores200-devtest
type: flores200-devtest
args: swh-spa
metrics:
- name: BLEU
type: bleu
value: 11.6
- name: chr-F
type: chrf
value: 0.38855
- task:
name: Translation tsn-eng
type: translation
args: tsn-eng
dataset:
name: flores200-devtest
type: flores200-devtest
args: tsn-eng
metrics:
- name: BLEU
type: bleu
value: 15.3
- name: chr-F
type: chrf
value: 0.40410
- task:
name: Translation tsn-fra
type: translation
args: tsn-fra
dataset:
name: flores200-devtest
type: flores200-devtest
args: tsn-fra
metrics:
- name: BLEU
type: bleu
value: 10.3
- name: chr-F
type: chrf
value: 0.34284
- task:
name: Translation tso-eng
type: translation
args: tso-eng
dataset:
name: flores200-devtest
type: flores200-devtest
args: tso-eng
metrics:
- name: BLEU
type: bleu
value: 17.6
- name: chr-F
type: chrf
value: 0.41504
- task:
name: Translation tso-fra
type: translation
args: tso-fra
dataset:
name: flores200-devtest
type: flores200-devtest
args: tso-fra
metrics:
- name: BLEU
type: bleu
value: 10.1
- name: chr-F
type: chrf
value: 0.33502
- task:
name: Translation xho-eng
type: translation
args: xho-eng
dataset:
name: flores200-devtest
type: flores200-devtest
args: xho-eng
metrics:
- name: BLEU
type: bleu
value: 23.7
- name: chr-F
type: chrf
value: 0.47667
- task:
name: Translation xho-fra
type: translation
args: xho-fra
dataset:
name: flores200-devtest
type: flores200-devtest
args: xho-fra
metrics:
- name: BLEU
type: bleu
value: 14.1
- name: chr-F
type: chrf
value: 0.39392
- task:
name: Translation xho-por
type: translation
args: xho-por
dataset:
name: flores200-devtest
type: flores200-devtest
args: xho-por
metrics:
- name: BLEU
type: bleu
value: 12.3
- name: chr-F
type: chrf
value: 0.37032
- task:
name: Translation zul-eng
type: translation
args: zul-eng
dataset:
name: flores200-devtest
type: flores200-devtest
args: zul-eng
metrics:
- name: BLEU
type: bleu
value: 23.4
- name: chr-F
type: chrf
value: 0.47798
- task:
name: Translation zul-fra
type: translation
args: zul-fra
dataset:
name: flores200-devtest
type: flores200-devtest
args: zul-fra
metrics:
- name: BLEU
type: bleu
value: 13.9
- name: chr-F
type: chrf
value: 0.39504
- task:
name: Translation zul-por
type: translation
args: zul-por
dataset:
name: flores200-devtest
type: flores200-devtest
args: zul-por
metrics:
- name: BLEU
type: bleu
value: 12.3
- name: chr-F
type: chrf
value: 0.36947
- task:
name: Translation ibo-eng
type: translation
args: ibo-eng
dataset:
name: flores101-devtest
type: flores_101
args: ibo eng devtest
metrics:
- name: BLEU
type: bleu
value: 12.5
- name: chr-F
type: chrf
value: 0.36320
- task:
name: Translation nya-eng
type: translation
args: nya-eng
dataset:
name: flores101-devtest
type: flores_101
args: nya eng devtest
metrics:
- name: BLEU
type: bleu
value: 13.2
- name: chr-F
type: chrf
value: 0.36765
- task:
name: Translation swh-por
type: translation
args: swh-por
dataset:
name: flores101-devtest
type: flores_101
args: swh por devtest
metrics:
- name: BLEU
type: bleu
value: 14.7
- name: chr-F
type: chrf
value: 0.40847
- task:
name: Translation xho-por
type: translation
args: xho-por
dataset:
name: flores101-devtest
type: flores_101
args: xho por devtest
metrics:
- name: BLEU
type: bleu
value: 10.1
- name: chr-F
type: chrf
value: 0.33906
- task:
name: Translation zul-fra
type: translation
args: zul-fra
dataset:
name: flores101-devtest
type: flores_101
args: zul fra devtest
metrics:
- name: BLEU
type: bleu
value: 11.2
- name: chr-F
type: chrf
value: 0.35968
- task:
name: Translation bem-eng
type: translation
args: bem-eng
dataset:
name: ntrex128
type: ntrex128
args: bem-eng
metrics:
- name: BLEU
type: bleu
value: 14.9
- name: chr-F
type: chrf
value: 0.38068
- task:
name: Translation bem-spa
type: translation
args: bem-spa
dataset:
name: ntrex128
type: ntrex128
args: bem-spa
metrics:
- name: BLEU
type: bleu
value: 10.2
- name: chr-F
type: chrf
value: 0.33394
- task:
name: Translation ibo-eng
type: translation
args: ibo-eng
dataset:
name: ntrex128
type: ntrex128
args: ibo-eng
metrics:
- name: BLEU
type: bleu
value: 17.4
- name: chr-F
type: chrf
value: 0.42002
- task:
name: Translation ibo-fra
type: translation
args: ibo-fra
dataset:
name: ntrex128
type: ntrex128
args: ibo-fra
metrics:
- name: BLEU
type: bleu
value: 10.3
- name: chr-F
type: chrf
value: 0.34993
- task:
name: Translation ibo-spa
type: translation
args: ibo-spa
dataset:
name: ntrex128
type: ntrex128
args: ibo-spa
metrics:
- name: BLEU
type: bleu
value: 11.7
- name: chr-F
type: chrf
value: 0.36108
- task:
name: Translation kin-eng
type: translation
args: kin-eng
dataset:
name: ntrex128
type: ntrex128
args: kin-eng
metrics:
- name: BLEU
type: bleu
value: 16.9
- name: chr-F
type: chrf
value: 0.42892
- task:
name: Translation kin-fra
type: translation
args: kin-fra
dataset:
name: ntrex128
type: ntrex128
args: kin-fra
metrics:
- name: BLEU
type: bleu
value: 10.7
- name: chr-F
type: chrf
value: 0.35842
- task:
name: Translation kin-por
type: translation
args: kin-por
dataset:
name: ntrex128
type: ntrex128
args: kin-por
metrics:
- name: BLEU
type: bleu
value: 10.0
- name: chr-F
type: chrf
value: 0.34399
- task:
name: Translation kin-spa
type: translation
args: kin-spa
dataset:
name: ntrex128
type: ntrex128
args: kin-spa
metrics:
- name: BLEU
type: bleu
value: 12.5
- name: chr-F
type: chrf
value: 0.37224
- task:
name: Translation nde-eng
type: translation
args: nde-eng
dataset:
name: ntrex128
type: ntrex128
args: nde-eng
metrics:
- name: BLEU
type: bleu
value: 13.8
- name: chr-F
type: chrf
value: 0.39640
- task:
name: Translation nde-spa
type: translation
args: nde-spa
dataset:
name: ntrex128
type: ntrex128
args: nde-spa
metrics:
- name: BLEU
type: bleu
value: 10.0
- name: chr-F
type: chrf
value: 0.34638
- task:
name: Translation nso-eng
type: translation
args: nso-eng
dataset:
name: ntrex128
type: ntrex128
args: nso-eng
metrics:
- name: BLEU
type: bleu
value: 17.0
- name: chr-F
type: chrf
value: 0.42278
- task:
name: Translation nso-spa
type: translation
args: nso-spa
dataset:
name: ntrex128
type: ntrex128
args: nso-spa
metrics:
- name: BLEU
type: bleu
value: 11.3
- name: chr-F
type: chrf
value: 0.35027
- task:
name: Translation nya-eng
type: translation
args: nya-eng
dataset:
name: ntrex128
type: ntrex128
args: nya-eng
metrics:
- name: BLEU
type: bleu
value: 19.2
- name: chr-F
type: chrf
value: 0.42702
- task:
name: Translation nya-fra
type: translation
args: nya-fra
dataset:
name: ntrex128
type: ntrex128
args: nya-fra
metrics:
- name: BLEU
type: bleu
value: 10.7
- name: chr-F
type: chrf
value: 0.35503
- task:
name: Translation nya-por
type: translation
args: nya-por
dataset:
name: ntrex128
type: ntrex128
args: nya-por
metrics:
- name: BLEU
type: bleu
value: 10.4
- name: chr-F
type: chrf
value: 0.33912
- task:
name: Translation nya-spa
type: translation
args: nya-spa
dataset:
name: ntrex128
type: ntrex128
args: nya-spa
metrics:
- name: BLEU
type: bleu
value: 12.5
- name: chr-F
type: chrf
value: 0.36355
- task:
name: Translation ssw-eng
type: translation
args: ssw-eng
dataset:
name: ntrex128
type: ntrex128
args: ssw-eng
metrics:
- name: BLEU
type: bleu
value: 18.0
- name: chr-F
type: chrf
value: 0.43041
- task:
name: Translation ssw-spa
type: translation
args: ssw-spa
dataset:
name: ntrex128
type: ntrex128
args: ssw-spa
metrics:
- name: BLEU
type: bleu
value: 11.4
- name: chr-F
type: chrf
value: 0.35392
- task:
name: Translation swa-deu
type: translation
args: swa-deu
dataset:
name: ntrex128
type: ntrex128
args: swa-deu
metrics:
- name: BLEU
type: bleu
value: 11.2
- name: chr-F
type: chrf
value: 0.39475
- task:
name: Translation swa-eng
type: translation
args: swa-eng
dataset:
name: ntrex128
type: ntrex128
args: swa-eng
metrics:
- name: BLEU
type: bleu
value: 30.4
- name: chr-F
type: chrf
value: 0.54492
- task:
name: Translation swa-fra
type: translation
args: swa-fra
dataset:
name: ntrex128
type: ntrex128
args: swa-fra
metrics:
- name: BLEU
type: bleu
value: 15.6
- name: chr-F
type: chrf
value: 0.43008
- task:
name: Translation swa-por
type: translation
args: swa-por
dataset:
name: ntrex128
type: ntrex128
args: swa-por
metrics:
- name: BLEU
type: bleu
value: 15.4
- name: chr-F
type: chrf
value: 0.42343
- task:
name: Translation swa-spa
type: translation
args: swa-spa
dataset:
name: ntrex128
type: ntrex128
args: swa-spa
metrics:
- name: BLEU
type: bleu
value: 18.9
- name: chr-F
type: chrf
value: 0.44892
- task:
name: Translation tsn-eng
type: translation
args: tsn-eng
dataset:
name: ntrex128
type: ntrex128
args: tsn-eng
metrics:
- name: BLEU
type: bleu
value: 20.1
- name: chr-F
type: chrf
value: 0.44944
- task:
name: Translation tsn-fra
type: translation
args: tsn-fra
dataset:
name: ntrex128
type: ntrex128
args: tsn-fra
metrics:
- name: BLEU
type: bleu
value: 11.5
- name: chr-F
type: chrf
value: 0.36584
- task:
name: Translation tsn-por
type: translation
args: tsn-por
dataset:
name: ntrex128
type: ntrex128
args: tsn-por
metrics:
- name: BLEU
type: bleu
value: 11.1
- name: chr-F
type: chrf
value: 0.35774
- task:
name: Translation tsn-spa
type: translation
args: tsn-spa
dataset:
name: ntrex128
type: ntrex128
args: tsn-spa
metrics:
- name: BLEU
type: bleu
value: 13.8
- name: chr-F
type: chrf
value: 0.38149
- task:
name: Translation ven-eng
type: translation
args: ven-eng
dataset:
name: ntrex128
type: ntrex128
args: ven-eng
metrics:
- name: BLEU
type: bleu
value: 12.7
- name: chr-F
type: chrf
value: 0.36848
- task:
name: Translation xho-eng
type: translation
args: xho-eng
dataset:
name: ntrex128
type: ntrex128
args: xho-eng
metrics:
- name: BLEU
type: bleu
value: 21.8
- name: chr-F
type: chrf
value: 0.46636
- task:
name: Translation xho-fra
type: translation
args: xho-fra
dataset:
name: ntrex128
type: ntrex128
args: xho-fra
metrics:
- name: BLEU
type: bleu
value: 11.6
- name: chr-F
type: chrf
value: 0.36905
- task:
name: Translation xho-por
type: translation
args: xho-por
dataset:
name: ntrex128
type: ntrex128
args: xho-por
metrics:
- name: BLEU
type: bleu
value: 10.6
- name: chr-F
type: chrf
value: 0.35687
- task:
name: Translation xho-spa
type: translation
args: xho-spa
dataset:
name: ntrex128
type: ntrex128
args: xho-spa
metrics:
- name: BLEU
type: bleu
value: 13.3
- name: chr-F
type: chrf
value: 0.38176
- task:
name: Translation yor-eng
type: translation
args: yor-eng
dataset:
name: ntrex128
type: ntrex128
args: yor-eng
metrics:
- name: BLEU
type: bleu
value: 11.2
- name: chr-F
type: chrf
value: 0.34615
- task:
name: Translation zul-eng
type: translation
args: zul-eng
dataset:
name: ntrex128
type: ntrex128
args: zul-eng
metrics:
- name: BLEU
type: bleu
value: 21.9
- name: chr-F
type: chrf
value: 0.45848
- task:
name: Translation zul-fra
type: translation
args: zul-fra
dataset:
name: ntrex128
type: ntrex128
args: zul-fra
metrics:
- name: BLEU
type: bleu
value: 11.4
- name: chr-F
type: chrf
value: 0.36203
- task:
name: Translation zul-por
type: translation
args: zul-por
dataset:
name: ntrex128
type: ntrex128
args: zul-por
metrics:
- name: BLEU
type: bleu
value: 10.8
- name: chr-F
type: chrf
value: 0.35081
- task:
name: Translation zul-spa
type: translation
args: zul-spa
dataset:
name: ntrex128
type: ntrex128
args: zul-spa
metrics:
- name: BLEU
type: bleu
value: 13.0
- name: chr-F
type: chrf
value: 0.37270
- task:
name: Translation multi-multi
type: translation
args: multi-multi
dataset:
name: tatoeba-test-v2020-07-28-v2023-09-26
type: tatoeba_mt
args: multi-multi
metrics:
- name: BLEU
type: bleu
value: 27.3
- name: chr-F
type: chrf
value: 0.44751
- task:
name: Translation run-eng
type: translation
args: run-eng
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: run-eng
metrics:
- name: BLEU
type: bleu
value: 34.9
- name: chr-F
type: chrf
value: 0.49949
- task:
name: Translation run-fra
type: translation
args: run-fra
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: run-fra
metrics:
- name: BLEU
type: bleu
value: 22.4
- name: chr-F
type: chrf
value: 0.41431
- task:
name: Translation swa-eng
type: translation
args: swa-eng
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: swa-eng
metrics:
- name: BLEU
type: bleu
value: 41.5
- name: chr-F
type: chrf
value: 0.57031
- task:
name: Translation xho-eng
type: translation
args: xho-eng
dataset:
name: tatoeba-test-v2021-03-30
type: tatoeba_mt
args: xho-eng
metrics:
- name: BLEU
type: bleu
value: 36.4
- name: chr-F
type: chrf
value: 0.51250
- task:
name: Translation zul-eng
type: translation
args: zul-eng
dataset:
name: tico19-test
type: tico19-test
args: zul-eng
metrics:
- name: BLEU
type: bleu
value: 25.2
- name: chr-F
type: chrf
value: 0.48762
- task:
name: Translation zul-fra
type: translation
args: zul-fra
dataset:
name: tico19-test
type: tico19-test
args: zul-fra
metrics:
- name: BLEU
type: bleu
value: 13.7
- name: chr-F
type: chrf
value: 0.37823
- task:
name: Translation zul-por
type: translation
args: zul-por
dataset:
name: tico19-test
type: tico19-test
args: zul-por
metrics:
- name: BLEU
type: bleu
value: 14.0
- name: chr-F
type: chrf
value: 0.38586
- task:
name: Translation zul-spa
type: translation
args: zul-spa
dataset:
name: tico19-test
type: tico19-test
args: zul-spa
metrics:
- name: BLEU
type: bleu
value: 15.9
- name: chr-F
type: chrf
value: 0.40041
---
# opus-mt-tc-bible-big-alv-deu_eng_fra_por_spa
## Table of Contents
- [Model Details](#model-details)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [How to Get Started With the Model](#how-to-get-started-with-the-model)
- [Training](#training)
- [Evaluation](#evaluation)
- [Citation Information](#citation-information)
- [Acknowledgements](#acknowledgements)
## Model Details
Neural machine translation model for translating from Atlantic-Congo languages (alv) to unknown (deu+eng+fra+por+spa).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally trained using the amazing framework of [Marian NMT](https://marian-nmt.github.io/), an efficient NMT implementation written in pure C++. The models have been converted to pyTorch using the transformers library by huggingface. Training data is taken from [OPUS](https://opus.nlpl.eu/) and training pipelines use the procedures of [OPUS-MT-train](https://github.com/Helsinki-NLP/Opus-MT-train).
**Model Description:**
- **Developed by:** Language Technology Research Group at the University of Helsinki
- **Model Type:** Translation (transformer-big)
- **Release**: 2024-05-30
- **License:** Apache-2.0
- **Language(s):**
- Source Language(s): abi acd ade adj aka akp ann anv atg avn bas bav bba beh bem bfd bfo bim biv bkv blh bmq bmv bom bov box bqj bss btt bud bwu cce cjk cko cme csk cwe cwt dag dga dgi dig dop dug dyi dyo efi ewe fal fon fuc ful gej gkn gng gog gud gur guw gux gwr hag hay heh her ibo ife iri izr jbu jmc kam kbp kdc kdl kdn ken keu kez kia kik kin kki kkj kma kmb kon ksb ktj kua kub kus kyf las lee lef lem lia lin lip lob lon lua lug luy maw mcp mcu mda mfq mgo mnf mnh mor mos muh myk myx mzk mzm mzw nbl ncu nde ndo ndz nfr nhu nim nin nmz nnb nnh nnw nso ntm ntr nuj nwb nya nyf nyn nyo nyy nzi oku old ozm pai pbl pkb rim run sag seh sig sil sld sna snw sot soy spp ssw suk swa swc swh sxb tbz tem thk tik tlj toh toi tpm tsn tso tsw tum twi umb vag ven vmw vun wmw wob wol xho xog xon xrb xsm xuo yam yaz yor zul
- Target Language(s): deu eng fra por spa
- Valid Target Language Labels: >>deu<< >>eng<< >>fra<< >>por<< >>spa<< >>xxx<<
- **Original Model**: [opusTCv20230926max50+bt+jhubc_transformer-big_2024-05-30.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/alv-deu+eng+fra+por+spa/opusTCv20230926max50+bt+jhubc_transformer-big_2024-05-30.zip)
- **Resources for more information:**
- [OPUS-MT dashboard](https://opus.nlpl.eu/dashboard/index.php?pkg=opusmt&test=all&scoreslang=all&chart=standard&model=Tatoeba-MT-models/alv-deu%2Beng%2Bfra%2Bpor%2Bspa/opusTCv20230926max50%2Bbt%2Bjhubc_transformer-big_2024-05-30)
- [OPUS-MT-train GitHub Repo](https://github.com/Helsinki-NLP/OPUS-MT-train)
- [More information about MarianNMT models in the transformers library](https://huggingface.co/docs/transformers/model_doc/marian)
- [Tatoeba Translation Challenge](https://github.com/Helsinki-NLP/Tatoeba-Challenge/)
- [HPLT bilingual data v1 (as part of the Tatoeba Translation Challenge dataset)](https://hplt-project.org/datasets/v1)
- [A massively parallel Bible corpus](https://aclanthology.org/L14-1215/)
This is a multilingual translation model with multiple target languages. A sentence initial language token is required in the form of `>>id<<` (id = valid target language ID), e.g. `>>deu<<`
## Uses
This model can be used for translation and text-to-text generation.
## Risks, Limitations and Biases
**CONTENT WARNING: Readers should be aware that the model is trained on various public data sets that may contain content that is disturbing, offensive, and can propagate historical and current stereotypes.**
Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)).
## How to Get Started With the Model
A short example code:
```python
from transformers import MarianMTModel, MarianTokenizer
src_text = [
">>deu<< Replace this with text in an accepted source language.",
">>spa<< This is the second sentence."
]
model_name = "pytorch-models/opus-mt-tc-bible-big-alv-deu_eng_fra_por_spa"
tokenizer = MarianTokenizer.from_pretrained(model_name)
model = MarianMTModel.from_pretrained(model_name)
translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True))
for t in translated:
print( tokenizer.decode(t, skip_special_tokens=True) )
```
You can also use OPUS-MT models with the transformers pipelines, for example:
```python
from transformers import pipeline
pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-bible-big-alv-deu_eng_fra_por_spa")
print(pipe(">>deu<< Replace this with text in an accepted source language."))
```
## Training
- **Data**: opusTCv20230926max50+bt+jhubc ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge))
- **Pre-processing**: SentencePiece (spm32k,spm32k)
- **Model Type:** transformer-big
- **Original MarianNMT Model**: [opusTCv20230926max50+bt+jhubc_transformer-big_2024-05-30.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/alv-deu+eng+fra+por+spa/opusTCv20230926max50+bt+jhubc_transformer-big_2024-05-30.zip)
- **Training Scripts**: [GitHub Repo](https://github.com/Helsinki-NLP/OPUS-MT-train)
## Evaluation
* [Model scores at the OPUS-MT dashboard](https://opus.nlpl.eu/dashboard/index.php?pkg=opusmt&test=all&scoreslang=all&chart=standard&model=Tatoeba-MT-models/alv-deu%2Beng%2Bfra%2Bpor%2Bspa/opusTCv20230926max50%2Bbt%2Bjhubc_transformer-big_2024-05-30)
* test set translations: [opusTCv20230926max50+bt+jhubc_transformer-big_2024-05-29.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/alv-deu+eng+fra+por+spa/opusTCv20230926max50+bt+jhubc_transformer-big_2024-05-29.test.txt)
* test set scores: [opusTCv20230926max50+bt+jhubc_transformer-big_2024-05-29.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/alv-deu+eng+fra+por+spa/opusTCv20230926max50+bt+jhubc_transformer-big_2024-05-29.eval.txt)
* benchmark results: [benchmark_results.txt](benchmark_results.txt)
* benchmark output: [benchmark_translations.zip](benchmark_translations.zip)
| langpair | testset | chr-F | BLEU | #sent | #words |
|----------|---------|-------|-------|-------|--------|
| run-eng | tatoeba-test-v2021-08-07 | 0.49949 | 34.9 | 1703 | 10041 |
| run-fra | tatoeba-test-v2021-08-07 | 0.41431 | 22.4 | 1274 | 7479 |
| swa-eng | tatoeba-test-v2021-08-07 | 0.57031 | 41.5 | 387 | 2508 |
| swh-por | flores101-devtest | 0.40847 | 14.7 | 1012 | 26519 |
| kin-eng | flores200-devtest | 0.41964 | 18.1 | 1012 | 24721 |
| nso-eng | flores200-devtest | 0.45662 | 22.3 | 1012 | 24721 |
| sna-eng | flores200-devtest | 0.41974 | 17.2 | 1012 | 24721 |
| sot-eng | flores200-devtest | 0.45415 | 20.7 | 1012 | 24721 |
| swh-eng | flores200-devtest | 0.54048 | 29.1 | 1012 | 24721 |
| swh-fra | flores200-devtest | 0.44837 | 18.2 | 1012 | 28343 |
| swh-por | flores200-devtest | 0.44062 | 17.6 | 1012 | 26519 |
| tsn-eng | flores200-devtest | 0.40410 | 15.3 | 1012 | 24721 |
| tso-eng | flores200-devtest | 0.41504 | 17.6 | 1012 | 24721 |
| xho-eng | flores200-devtest | 0.47667 | 23.7 | 1012 | 24721 |
| zul-eng | flores200-devtest | 0.47798 | 23.4 | 1012 | 24721 |
| ibo-eng | ntrex128 | 0.42002 | 17.4 | 1997 | 47673 |
| kin-eng | ntrex128 | 0.42892 | 16.9 | 1997 | 47673 |
| nso-eng | ntrex128 | 0.42278 | 17.0 | 1997 | 47673 |
| nya-eng | ntrex128 | 0.42702 | 19.2 | 1997 | 47673 |
| ssw-eng | ntrex128 | 0.43041 | 18.0 | 1997 | 47673 |
| swa-eng | ntrex128 | 0.54492 | 30.4 | 1997 | 47673 |
| swa-fra | ntrex128 | 0.43008 | 15.6 | 1997 | 53481 |
| swa-por | ntrex128 | 0.42343 | 15.4 | 1997 | 51631 |
| swa-spa | ntrex128 | 0.44892 | 18.9 | 1997 | 54107 |
| tsn-eng | ntrex128 | 0.44944 | 20.1 | 1997 | 47673 |
| xho-eng | ntrex128 | 0.46636 | 21.8 | 1997 | 47673 |
| zul-eng | ntrex128 | 0.45848 | 21.9 | 1997 | 47673 |
| zul-eng | tico19-test | 0.48762 | 25.2 | 2100 | 56804 |
| zul-spa | tico19-test | 0.40041 | 15.9 | 2100 | 66563 |
## Citation Information
* Publications: [Democratizing neural machine translation with OPUS-MT](https://doi.org/10.1007/s10579-023-09704-w) and [OPUS-MT – Building open translation services for the World](https://aclanthology.org/2020.eamt-1.61/) and [The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT](https://aclanthology.org/2020.wmt-1.139/) (Please, cite if you use this model.)
```bibtex
@article{tiedemann2023democratizing,
title={Democratizing neural machine translation with {OPUS-MT}},
author={Tiedemann, J{\"o}rg and Aulamo, Mikko and Bakshandaeva, Daria and Boggia, Michele and Gr{\"o}nroos, Stig-Arne and Nieminen, Tommi and Raganato, Alessandro and Scherrer, Yves and Vazquez, Raul and Virpioja, Sami},
journal={Language Resources and Evaluation},
number={58},
pages={713--755},
year={2023},
publisher={Springer Nature},
issn={1574-0218},
doi={10.1007/s10579-023-09704-w}
}
@inproceedings{tiedemann-thottingal-2020-opus,
title = "{OPUS}-{MT} {--} Building open translation services for the World",
author = {Tiedemann, J{\"o}rg and Thottingal, Santhosh},
booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
month = nov,
year = "2020",
address = "Lisboa, Portugal",
publisher = "European Association for Machine Translation",
url = "https://aclanthology.org/2020.eamt-1.61",
pages = "479--480",
}
@inproceedings{tiedemann-2020-tatoeba,
title = "The Tatoeba Translation Challenge {--} Realistic Data Sets for Low Resource and Multilingual {MT}",
author = {Tiedemann, J{\"o}rg},
booktitle = "Proceedings of the Fifth Conference on Machine Translation",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.wmt-1.139",
pages = "1174--1182",
}
```
## Acknowledgements
The work is supported by the [HPLT project](https://hplt-project.org/), funded by the European Union’s Horizon Europe research and innovation programme under grant agreement No 101070350. We are also grateful for the generous computational resources and IT infrastructure provided by [CSC -- IT Center for Science](https://www.csc.fi/), Finland, and the [EuroHPC supercomputer LUMI](https://www.lumi-supercomputer.eu/).
## Model conversion info
* transformers version: 4.45.1
* OPUS-MT git hash: a0ea3b3
* port time: Mon Oct 7 17:13:22 EEST 2024
* port machine: LM0-400-22516.local
|
Helsinki-NLP/opus-mt-tc-bible-big-afa-fra_ita_por_spa
|
Helsinki-NLP
| 2024-10-07T14:12:30Z | 109 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc-bible",
"am",
"ar",
"bcw",
"cop",
"daa",
"dsh",
"es",
"fr",
"gde",
"gnd",
"ha",
"hbo",
"he",
"hig",
"irk",
"it",
"jpa",
"kab",
"ker",
"kqp",
"ktb",
"kxc",
"lln",
"lme",
"meq",
"mfh",
"mfi",
"mfk",
"mif",
"mpg",
"mqb",
"mt",
"muy",
"oar",
"pbi",
"phn",
"pt",
"rif",
"sgw",
"shi",
"shy",
"so",
"sur",
"syc",
"thv",
"ti",
"tmc",
"tmh",
"tmr",
"ttr",
"wal",
"xed",
"zgh",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2024-10-07T13:16:02Z |
---
library_name: transformers
language:
- am
- ar
- bcw
- cop
- daa
- dsh
- es
- fr
- gde
- gnd
- ha
- hbo
- he
- hig
- irk
- it
- jpa
- kab
- ker
- kqp
- ktb
- kxc
- lln
- lme
- meq
- mfh
- mfi
- mfk
- mif
- mpg
- mqb
- mt
- muy
- oar
- pbi
- phn
- pt
- rif
- sgw
- shi
- shy
- so
- sur
- syc
- thv
- ti
- tmc
- tmh
- tmr
- ttr
- wal
- xed
- zgh
tags:
- translation
- opus-mt-tc-bible
license: apache-2.0
model-index:
- name: opus-mt-tc-bible-big-afa-fra_ita_por_spa
results:
- task:
name: Translation multi-multi
type: translation
args: multi-multi
dataset:
name: tatoeba-test-v2020-07-28-v2023-09-26
type: tatoeba_mt
args: multi-multi
metrics:
- name: BLEU
type: bleu
value: 25.6
- name: chr-F
type: chrf
value: 0.44153
---
# opus-mt-tc-bible-big-afa-fra_ita_por_spa
## Table of Contents
- [Model Details](#model-details)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [How to Get Started With the Model](#how-to-get-started-with-the-model)
- [Training](#training)
- [Evaluation](#evaluation)
- [Citation Information](#citation-information)
- [Acknowledgements](#acknowledgements)
## Model Details
Neural machine translation model for translating from Afro-Asiatic languages (afa) to unknown (fra+ita+por+spa).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally trained using the amazing framework of [Marian NMT](https://marian-nmt.github.io/), an efficient NMT implementation written in pure C++. The models have been converted to pyTorch using the transformers library by huggingface. Training data is taken from [OPUS](https://opus.nlpl.eu/) and training pipelines use the procedures of [OPUS-MT-train](https://github.com/Helsinki-NLP/Opus-MT-train).
**Model Description:**
- **Developed by:** Language Technology Research Group at the University of Helsinki
- **Model Type:** Translation (transformer-big)
- **Release**: 2024-08-17
- **License:** Apache-2.0
- **Language(s):**
- Source Language(s): amh apc ara arq arz bcw cop daa dsh gde gnd hau hbo heb hig irk jpa kab ker kqp ktb kxc lln lme meq mfh mfi mfk mif mlt mpg mqb muy oar pbi phn rif sgw shi shy som sur syc thv tir tmc tmh tmr ttr wal xed zgh
- Target Language(s): fra ita por spa
- Valid Target Language Labels: >>fra<< >>ita<< >>por<< >>spa<< >>xxx<<
- **Original Model**: [opusTCv20230926max50+bt+jhubc_transformer-big_2024-08-17.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/afa-fra+ita+por+spa/opusTCv20230926max50+bt+jhubc_transformer-big_2024-08-17.zip)
- **Resources for more information:**
- [OPUS-MT dashboard](https://opus.nlpl.eu/dashboard/index.php?pkg=opusmt&test=all&scoreslang=all&chart=standard&model=Tatoeba-MT-models/afa-fra%2Bita%2Bpor%2Bspa/opusTCv20230926max50%2Bbt%2Bjhubc_transformer-big_2024-08-17)
- [OPUS-MT-train GitHub Repo](https://github.com/Helsinki-NLP/OPUS-MT-train)
- [More information about MarianNMT models in the transformers library](https://huggingface.co/docs/transformers/model_doc/marian)
- [Tatoeba Translation Challenge](https://github.com/Helsinki-NLP/Tatoeba-Challenge/)
- [HPLT bilingual data v1 (as part of the Tatoeba Translation Challenge dataset)](https://hplt-project.org/datasets/v1)
- [A massively parallel Bible corpus](https://aclanthology.org/L14-1215/)
This is a multilingual translation model with multiple target languages. A sentence initial language token is required in the form of `>>id<<` (id = valid target language ID), e.g. `>>fra<<`
## Uses
This model can be used for translation and text-to-text generation.
## Risks, Limitations and Biases
**CONTENT WARNING: Readers should be aware that the model is trained on various public data sets that may contain content that is disturbing, offensive, and can propagate historical and current stereotypes.**
Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)).
## How to Get Started With the Model
A short example code:
```python
from transformers import MarianMTModel, MarianTokenizer
src_text = [
">>fra<< Aqcic amenzu yessaweḍ ad yesleg tukkest-is.",
">>fra<< Aɣet ihi adlis."
]
model_name = "pytorch-models/opus-mt-tc-bible-big-afa-fra_ita_por_spa"
tokenizer = MarianTokenizer.from_pretrained(model_name)
model = MarianMTModel.from_pretrained(model_name)
translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True))
for t in translated:
print( tokenizer.decode(t, skip_special_tokens=True) )
# expected output:
# L'enfant a réussi à l'enlever.
# Alors, rédigez un livre.
```
You can also use OPUS-MT models with the transformers pipelines, for example:
```python
from transformers import pipeline
pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-bible-big-afa-fra_ita_por_spa")
print(pipe(">>fra<< Aqcic amenzu yessaweḍ ad yesleg tukkest-is."))
# expected output: L'enfant a réussi à l'enlever.
```
## Training
- **Data**: opusTCv20230926max50+bt+jhubc ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge))
- **Pre-processing**: SentencePiece (spm32k,spm32k)
- **Model Type:** transformer-big
- **Original MarianNMT Model**: [opusTCv20230926max50+bt+jhubc_transformer-big_2024-08-17.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/afa-fra+ita+por+spa/opusTCv20230926max50+bt+jhubc_transformer-big_2024-08-17.zip)
- **Training Scripts**: [GitHub Repo](https://github.com/Helsinki-NLP/OPUS-MT-train)
## Evaluation
* [Model scores at the OPUS-MT dashboard](https://opus.nlpl.eu/dashboard/index.php?pkg=opusmt&test=all&scoreslang=all&chart=standard&model=Tatoeba-MT-models/afa-fra%2Bita%2Bpor%2Bspa/opusTCv20230926max50%2Bbt%2Bjhubc_transformer-big_2024-08-17)
* test set translations: [opusTCv20230926max50+bt+jhubc_transformer-big_2024-08-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/afa-fra+ita+por+spa/opusTCv20230926max50+bt+jhubc_transformer-big_2024-08-17.test.txt)
* test set scores: [opusTCv20230926max50+bt+jhubc_transformer-big_2024-08-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/afa-fra+ita+por+spa/opusTCv20230926max50+bt+jhubc_transformer-big_2024-08-17.eval.txt)
* benchmark results: [benchmark_results.txt](benchmark_results.txt)
* benchmark output: [benchmark_translations.zip](benchmark_translations.zip)
| langpair | testset | chr-F | BLEU | #sent | #words |
|----------|---------|-------|-------|-------|--------|
| multi-multi | tatoeba-test-v2020-07-28-v2023-09-26 | 0.44153 | 25.6 | 10000 | 78439 |
## Citation Information
* Publications: [Democratizing neural machine translation with OPUS-MT](https://doi.org/10.1007/s10579-023-09704-w) and [OPUS-MT – Building open translation services for the World](https://aclanthology.org/2020.eamt-1.61/) and [The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT](https://aclanthology.org/2020.wmt-1.139/) (Please, cite if you use this model.)
```bibtex
@article{tiedemann2023democratizing,
title={Democratizing neural machine translation with {OPUS-MT}},
author={Tiedemann, J{\"o}rg and Aulamo, Mikko and Bakshandaeva, Daria and Boggia, Michele and Gr{\"o}nroos, Stig-Arne and Nieminen, Tommi and Raganato, Alessandro and Scherrer, Yves and Vazquez, Raul and Virpioja, Sami},
journal={Language Resources and Evaluation},
number={58},
pages={713--755},
year={2023},
publisher={Springer Nature},
issn={1574-0218},
doi={10.1007/s10579-023-09704-w}
}
@inproceedings{tiedemann-thottingal-2020-opus,
title = "{OPUS}-{MT} {--} Building open translation services for the World",
author = {Tiedemann, J{\"o}rg and Thottingal, Santhosh},
booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
month = nov,
year = "2020",
address = "Lisboa, Portugal",
publisher = "European Association for Machine Translation",
url = "https://aclanthology.org/2020.eamt-1.61",
pages = "479--480",
}
@inproceedings{tiedemann-2020-tatoeba,
title = "The Tatoeba Translation Challenge {--} Realistic Data Sets for Low Resource and Multilingual {MT}",
author = {Tiedemann, J{\"o}rg},
booktitle = "Proceedings of the Fifth Conference on Machine Translation",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.wmt-1.139",
pages = "1174--1182",
}
```
## Acknowledgements
The work is supported by the [HPLT project](https://hplt-project.org/), funded by the European Union’s Horizon Europe research and innovation programme under grant agreement No 101070350. We are also grateful for the generous computational resources and IT infrastructure provided by [CSC -- IT Center for Science](https://www.csc.fi/), Finland, and the [EuroHPC supercomputer LUMI](https://www.lumi-supercomputer.eu/).
## Model conversion info
* transformers version: 4.45.1
* OPUS-MT git hash: a0ea3b3
* port time: Mon Oct 7 17:12:20 EEST 2024
* port machine: LM0-400-22516.local
|
Helsinki-NLP/opus-mt-tc-bible-big-afa-deu_eng_nld
|
Helsinki-NLP
| 2024-10-07T14:09:57Z | 126 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc-bible",
"aa",
"am",
"ar",
"arc",
"bcw",
"byn",
"cop",
"daa",
"de",
"dsh",
"en",
"gde",
"gnd",
"ha",
"hbo",
"he",
"hig",
"irk",
"jpa",
"kab",
"ker",
"kqp",
"ktb",
"kxc",
"lln",
"lme",
"meq",
"mfh",
"mfi",
"mfk",
"mif",
"mpg",
"mqb",
"mt",
"muy",
"nl",
"oar",
"om",
"pbi",
"phn",
"rif",
"sgw",
"shi",
"shy",
"so",
"sur",
"syc",
"syr",
"taq",
"ti",
"tig",
"tmc",
"tmh",
"tmr",
"ttr",
"tzm",
"wal",
"xed",
"zgh",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2024-10-07T13:08:25Z |
---
library_name: transformers
language:
- aa
- am
- ar
- arc
- bcw
- byn
- cop
- daa
- de
- dsh
- en
- gde
- gnd
- ha
- hbo
- he
- hig
- irk
- jpa
- kab
- ker
- kqp
- ktb
- kxc
- lln
- lme
- meq
- mfh
- mfi
- mfk
- mif
- mpg
- mqb
- mt
- muy
- nl
- oar
- om
- pbi
- phn
- rif
- sgw
- shi
- shy
- so
- sur
- syc
- syr
- taq
- ti
- tig
- tmc
- tmh
- tmr
- ttr
- tzm
- wal
- xed
- zgh
tags:
- translation
- opus-mt-tc-bible
license: apache-2.0
model-index:
- name: opus-mt-tc-bible-big-afa-deu_eng_nld
results:
- task:
name: Translation multi-multi
type: translation
args: multi-multi
dataset:
name: tatoeba-test-v2020-07-28-v2023-09-26
type: tatoeba_mt
args: multi-multi
metrics:
- name: BLEU
type: bleu
value: 39.9
- name: chr-F
type: chrf
value: 0.57350
---
# opus-mt-tc-bible-big-afa-deu_eng_nld
## Table of Contents
- [Model Details](#model-details)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [How to Get Started With the Model](#how-to-get-started-with-the-model)
- [Training](#training)
- [Evaluation](#evaluation)
- [Citation Information](#citation-information)
- [Acknowledgements](#acknowledgements)
## Model Details
Neural machine translation model for translating from Afro-Asiatic languages (afa) to unknown (deu+eng+nld).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally trained using the amazing framework of [Marian NMT](https://marian-nmt.github.io/), an efficient NMT implementation written in pure C++. The models have been converted to pyTorch using the transformers library by huggingface. Training data is taken from [OPUS](https://opus.nlpl.eu/) and training pipelines use the procedures of [OPUS-MT-train](https://github.com/Helsinki-NLP/Opus-MT-train).
**Model Description:**
- **Developed by:** Language Technology Research Group at the University of Helsinki
- **Model Type:** Translation (transformer-big)
- **Release**: 2024-08-17
- **License:** Apache-2.0
- **Language(s):**
- Source Language(s): aar acm afb amh apc ara arc arq arz bcw byn cop daa dsh gde gnd hau hbo heb hig irk jpa kab ker kqp ktb kxc lln lme meq mfh mfi mfk mif mlt mpg mqb muy oar orm pbi phn rif sgw shi shy som sur syc syr taq tig tir tmc tmh tmr ttr tzm wal xed zgh
- Target Language(s): deu eng nld
- Valid Target Language Labels: >>deu<< >>eng<< >>nld<< >>xxx<<
- **Original Model**: [opusTCv20230926max50+bt+jhubc_transformer-big_2024-08-17.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/afa-deu+eng+nld/opusTCv20230926max50+bt+jhubc_transformer-big_2024-08-17.zip)
- **Resources for more information:**
- [OPUS-MT dashboard](https://opus.nlpl.eu/dashboard/index.php?pkg=opusmt&test=all&scoreslang=all&chart=standard&model=Tatoeba-MT-models/afa-deu%2Beng%2Bnld/opusTCv20230926max50%2Bbt%2Bjhubc_transformer-big_2024-08-17)
- [OPUS-MT-train GitHub Repo](https://github.com/Helsinki-NLP/OPUS-MT-train)
- [More information about MarianNMT models in the transformers library](https://huggingface.co/docs/transformers/model_doc/marian)
- [Tatoeba Translation Challenge](https://github.com/Helsinki-NLP/Tatoeba-Challenge/)
- [HPLT bilingual data v1 (as part of the Tatoeba Translation Challenge dataset)](https://hplt-project.org/datasets/v1)
- [A massively parallel Bible corpus](https://aclanthology.org/L14-1215/)
This is a multilingual translation model with multiple target languages. A sentence initial language token is required in the form of `>>id<<` (id = valid target language ID), e.g. `>>deu<<`
## Uses
This model can be used for translation and text-to-text generation.
## Risks, Limitations and Biases
**CONTENT WARNING: Readers should be aware that the model is trained on various public data sets that may contain content that is disturbing, offensive, and can propagate historical and current stereotypes.**
Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)).
## How to Get Started With the Model
A short example code:
```python
from transformers import MarianMTModel, MarianTokenizer
src_text = [
">>eng<< هذا هو المكان الذي تعيش فيه.",
">>eng<< Amdan yesnulfa-d Ṛebbi akken kan wa ur ineqq wa."
]
model_name = "pytorch-models/opus-mt-tc-bible-big-afa-deu_eng_nld"
tokenizer = MarianTokenizer.from_pretrained(model_name)
model = MarianMTModel.from_pretrained(model_name)
translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True))
for t in translated:
print( tokenizer.decode(t, skip_special_tokens=True) )
# expected output:
# This is where you live.
# The man who had been killed by God didn't kill him.
```
You can also use OPUS-MT models with the transformers pipelines, for example:
```python
from transformers import pipeline
pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-bible-big-afa-deu_eng_nld")
print(pipe(">>eng<< هذا هو المكان الذي تعيش فيه."))
# expected output: This is where you live.
```
## Training
- **Data**: opusTCv20230926max50+bt+jhubc ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge))
- **Pre-processing**: SentencePiece (spm32k,spm32k)
- **Model Type:** transformer-big
- **Original MarianNMT Model**: [opusTCv20230926max50+bt+jhubc_transformer-big_2024-08-17.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/afa-deu+eng+nld/opusTCv20230926max50+bt+jhubc_transformer-big_2024-08-17.zip)
- **Training Scripts**: [GitHub Repo](https://github.com/Helsinki-NLP/OPUS-MT-train)
## Evaluation
* [Model scores at the OPUS-MT dashboard](https://opus.nlpl.eu/dashboard/index.php?pkg=opusmt&test=all&scoreslang=all&chart=standard&model=Tatoeba-MT-models/afa-deu%2Beng%2Bnld/opusTCv20230926max50%2Bbt%2Bjhubc_transformer-big_2024-08-17)
* test set translations: [opusTCv20230926max50+bt+jhubc_transformer-big_2024-08-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/afa-deu+eng+nld/opusTCv20230926max50+bt+jhubc_transformer-big_2024-08-17.test.txt)
* test set scores: [opusTCv20230926max50+bt+jhubc_transformer-big_2024-08-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/afa-deu+eng+nld/opusTCv20230926max50+bt+jhubc_transformer-big_2024-08-17.eval.txt)
* benchmark results: [benchmark_results.txt](benchmark_results.txt)
* benchmark output: [benchmark_translations.zip](benchmark_translations.zip)
| langpair | testset | chr-F | BLEU | #sent | #words |
|----------|---------|-------|-------|-------|--------|
| multi-multi | tatoeba-test-v2020-07-28-v2023-09-26 | 0.57350 | 39.9 | 10000 | 73314 |
## Citation Information
* Publications: [Democratizing neural machine translation with OPUS-MT](https://doi.org/10.1007/s10579-023-09704-w) and [OPUS-MT – Building open translation services for the World](https://aclanthology.org/2020.eamt-1.61/) and [The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT](https://aclanthology.org/2020.wmt-1.139/) (Please, cite if you use this model.)
```bibtex
@article{tiedemann2023democratizing,
title={Democratizing neural machine translation with {OPUS-MT}},
author={Tiedemann, J{\"o}rg and Aulamo, Mikko and Bakshandaeva, Daria and Boggia, Michele and Gr{\"o}nroos, Stig-Arne and Nieminen, Tommi and Raganato, Alessandro and Scherrer, Yves and Vazquez, Raul and Virpioja, Sami},
journal={Language Resources and Evaluation},
number={58},
pages={713--755},
year={2023},
publisher={Springer Nature},
issn={1574-0218},
doi={10.1007/s10579-023-09704-w}
}
@inproceedings{tiedemann-thottingal-2020-opus,
title = "{OPUS}-{MT} {--} Building open translation services for the World",
author = {Tiedemann, J{\"o}rg and Thottingal, Santhosh},
booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
month = nov,
year = "2020",
address = "Lisboa, Portugal",
publisher = "European Association for Machine Translation",
url = "https://aclanthology.org/2020.eamt-1.61",
pages = "479--480",
}
@inproceedings{tiedemann-2020-tatoeba,
title = "The Tatoeba Translation Challenge {--} Realistic Data Sets for Low Resource and Multilingual {MT}",
author = {Tiedemann, J{\"o}rg},
booktitle = "Proceedings of the Fifth Conference on Machine Translation",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.wmt-1.139",
pages = "1174--1182",
}
```
## Acknowledgements
The work is supported by the [HPLT project](https://hplt-project.org/), funded by the European Union’s Horizon Europe research and innovation programme under grant agreement No 101070350. We are also grateful for the generous computational resources and IT infrastructure provided by [CSC -- IT Center for Science](https://www.csc.fi/), Finland, and the [EuroHPC supercomputer LUMI](https://www.lumi-supercomputer.eu/).
## Model conversion info
* transformers version: 4.45.1
* OPUS-MT git hash: a0ea3b3
* port time: Mon Oct 7 17:09:45 EEST 2024
* port machine: LM0-400-22516.local
|
irlab-udc/Llama-3.1-8B-Instruct-Galician-GPTQ-Int4
|
irlab-udc
| 2024-10-07T14:09:21Z | 81 | 1 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"gl",
"base_model:irlab-udc/Llama-3.1-8B-Instruct-Galician",
"base_model:quantized:irlab-udc/Llama-3.1-8B-Instruct-Galician",
"license:llama3.1",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"gptq",
"region:us"
] |
text-generation
| 2024-10-07T13:14:44Z |
---
base_model:
- irlab-udc/Llama-3.1-8B-Instruct-Galician
license: llama3.1
language:
- gl
pipeline_tag: text-generation
library_name: transformers
---
4-bit quantized version of [irlab-udc/Llama-3.1-8B-Instruct-Galician](https://huggingface.co/irlab-udc/Llama-3.1-8B-Instruct-Galician).
## How to Use
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "irlab-udc/Llama-3.1-8B-Instruct-Galician-GPTQ-Int4"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.float16,
low_cpu_mem_usage=True,
device_map="auto"
)
messages = [
{"role": "system", "content": "You are a conversational AI that responds in Galician."},
{"role": "user", "content": "Cal é a principal vantaxe de Scrum?"},
]
inputs = tokenizer.apply_chat_template(
messages,
tokenize=True,
add_generation_prompt=True,
return_tensors="pt",
return_dict=True,
).to("cuda")
outputs = model.generate(**inputs, do_sample=True, max_new_tokens=512)
print(tokenizer.batch_decode(outputs, skip_special_tokens=True))
```
|
Helsinki-NLP/opus-mt-tc-bible-big-afa-deu_eng_fra_por_spa
|
Helsinki-NLP
| 2024-10-07T14:08:42Z | 217 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc-bible",
"aa",
"am",
"ar",
"arc",
"bcw",
"byn",
"cop",
"daa",
"de",
"dsh",
"en",
"es",
"fr",
"gde",
"gnd",
"ha",
"hbo",
"he",
"hig",
"irk",
"jpa",
"kab",
"ker",
"kqp",
"ktb",
"kxc",
"lln",
"lme",
"meq",
"mfh",
"mfi",
"mfk",
"mif",
"mpg",
"mqb",
"mt",
"muy",
"oar",
"om",
"pbi",
"phn",
"pt",
"rif",
"sgw",
"shi",
"shy",
"so",
"sur",
"syc",
"syr",
"taq",
"thv",
"ti",
"tig",
"tmc",
"tmh",
"tmr",
"ttr",
"tzm",
"wal",
"xed",
"zgh",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2024-10-07T13:02:02Z |
---
library_name: transformers
language:
- aa
- am
- ar
- arc
- bcw
- byn
- cop
- daa
- de
- dsh
- en
- es
- fr
- gde
- gnd
- ha
- hbo
- he
- hig
- irk
- jpa
- kab
- ker
- kqp
- ktb
- kxc
- lln
- lme
- meq
- mfh
- mfi
- mfk
- mif
- mpg
- mqb
- mt
- muy
- oar
- om
- pbi
- phn
- pt
- rif
- sgw
- shi
- shy
- so
- sur
- syc
- syr
- taq
- thv
- ti
- tig
- tmc
- tmh
- tmr
- ttr
- tzm
- wal
- xed
- zgh
tags:
- translation
- opus-mt-tc-bible
license: apache-2.0
model-index:
- name: opus-mt-tc-bible-big-afa-deu_eng_fra_por_spa
results:
- task:
name: Translation acm-deu
type: translation
args: acm-deu
dataset:
name: flores200-devtest
type: flores200-devtest
args: acm-deu
metrics:
- name: BLEU
type: bleu
value: 17.6
- name: chr-F
type: chrf
value: 0.48947
- task:
name: Translation acm-eng
type: translation
args: acm-eng
dataset:
name: flores200-devtest
type: flores200-devtest
args: acm-eng
metrics:
- name: BLEU
type: bleu
value: 28.5
- name: chr-F
type: chrf
value: 0.56799
- task:
name: Translation acm-fra
type: translation
args: acm-fra
dataset:
name: flores200-devtest
type: flores200-devtest
args: acm-fra
metrics:
- name: BLEU
type: bleu
value: 26.1
- name: chr-F
type: chrf
value: 0.53577
- task:
name: Translation acm-por
type: translation
args: acm-por
dataset:
name: flores200-devtest
type: flores200-devtest
args: acm-por
metrics:
- name: BLEU
type: bleu
value: 23.9
- name: chr-F
type: chrf
value: 0.52441
- task:
name: Translation acm-spa
type: translation
args: acm-spa
dataset:
name: flores200-devtest
type: flores200-devtest
args: acm-spa
metrics:
- name: BLEU
type: bleu
value: 18.2
- name: chr-F
type: chrf
value: 0.46985
- task:
name: Translation amh-deu
type: translation
args: amh-deu
dataset:
name: flores200-devtest
type: flores200-devtest
args: amh-deu
metrics:
- name: BLEU
type: bleu
value: 12.6
- name: chr-F
type: chrf
value: 0.41553
- task:
name: Translation amh-eng
type: translation
args: amh-eng
dataset:
name: flores200-devtest
type: flores200-devtest
args: amh-eng
metrics:
- name: BLEU
type: bleu
value: 22.5
- name: chr-F
type: chrf
value: 0.49333
- task:
name: Translation amh-fra
type: translation
args: amh-fra
dataset:
name: flores200-devtest
type: flores200-devtest
args: amh-fra
metrics:
- name: BLEU
type: bleu
value: 17.8
- name: chr-F
type: chrf
value: 0.44890
- task:
name: Translation amh-por
type: translation
args: amh-por
dataset:
name: flores200-devtest
type: flores200-devtest
args: amh-por
metrics:
- name: BLEU
type: bleu
value: 16.5
- name: chr-F
type: chrf
value: 0.43771
- task:
name: Translation amh-spa
type: translation
args: amh-spa
dataset:
name: flores200-devtest
type: flores200-devtest
args: amh-spa
metrics:
- name: BLEU
type: bleu
value: 12.8
- name: chr-F
type: chrf
value: 0.39526
- task:
name: Translation apc-deu
type: translation
args: apc-deu
dataset:
name: flores200-devtest
type: flores200-devtest
args: apc-deu
metrics:
- name: BLEU
type: bleu
value: 16.0
- name: chr-F
type: chrf
value: 0.47480
- task:
name: Translation apc-eng
type: translation
args: apc-eng
dataset:
name: flores200-devtest
type: flores200-devtest
args: apc-eng
metrics:
- name: BLEU
type: bleu
value: 28.1
- name: chr-F
type: chrf
value: 0.56075
- task:
name: Translation apc-fra
type: translation
args: apc-fra
dataset:
name: flores200-devtest
type: flores200-devtest
args: apc-fra
metrics:
- name: BLEU
type: bleu
value: 24.6
- name: chr-F
type: chrf
value: 0.52325
- task:
name: Translation apc-por
type: translation
args: apc-por
dataset:
name: flores200-devtest
type: flores200-devtest
args: apc-por
metrics:
- name: BLEU
type: bleu
value: 22.9
- name: chr-F
type: chrf
value: 0.51055
- task:
name: Translation apc-spa
type: translation
args: apc-spa
dataset:
name: flores200-devtest
type: flores200-devtest
args: apc-spa
metrics:
- name: BLEU
type: bleu
value: 17.2
- name: chr-F
type: chrf
value: 0.45634
- task:
name: Translation arz-deu
type: translation
args: arz-deu
dataset:
name: flores200-devtest
type: flores200-devtest
args: arz-deu
metrics:
- name: BLEU
type: bleu
value: 14.1
- name: chr-F
type: chrf
value: 0.45844
- task:
name: Translation arz-eng
type: translation
args: arz-eng
dataset:
name: flores200-devtest
type: flores200-devtest
args: arz-eng
metrics:
- name: BLEU
type: bleu
value: 22.7
- name: chr-F
type: chrf
value: 0.52534
- task:
name: Translation arz-fra
type: translation
args: arz-fra
dataset:
name: flores200-devtest
type: flores200-devtest
args: arz-fra
metrics:
- name: BLEU
type: bleu
value: 21.8
- name: chr-F
type: chrf
value: 0.50336
- task:
name: Translation arz-por
type: translation
args: arz-por
dataset:
name: flores200-devtest
type: flores200-devtest
args: arz-por
metrics:
- name: BLEU
type: bleu
value: 20.0
- name: chr-F
type: chrf
value: 0.48741
- task:
name: Translation arz-spa
type: translation
args: arz-spa
dataset:
name: flores200-devtest
type: flores200-devtest
args: arz-spa
metrics:
- name: BLEU
type: bleu
value: 15.8
- name: chr-F
type: chrf
value: 0.44516
- task:
name: Translation hau-eng
type: translation
args: hau-eng
dataset:
name: flores200-devtest
type: flores200-devtest
args: hau-eng
metrics:
- name: BLEU
type: bleu
value: 23.4
- name: chr-F
type: chrf
value: 0.48137
- task:
name: Translation hau-fra
type: translation
args: hau-fra
dataset:
name: flores200-devtest
type: flores200-devtest
args: hau-fra
metrics:
- name: BLEU
type: bleu
value: 17.2
- name: chr-F
type: chrf
value: 0.42981
- task:
name: Translation hau-por
type: translation
args: hau-por
dataset:
name: flores200-devtest
type: flores200-devtest
args: hau-por
metrics:
- name: BLEU
type: bleu
value: 15.7
- name: chr-F
type: chrf
value: 0.41385
- task:
name: Translation hau-spa
type: translation
args: hau-spa
dataset:
name: flores200-devtest
type: flores200-devtest
args: hau-spa
metrics:
- name: BLEU
type: bleu
value: 11.7
- name: chr-F
type: chrf
value: 0.37800
- task:
name: Translation heb-deu
type: translation
args: heb-deu
dataset:
name: flores200-devtest
type: flores200-devtest
args: heb-deu
metrics:
- name: BLEU
type: bleu
value: 22.8
- name: chr-F
type: chrf
value: 0.53482
- task:
name: Translation heb-eng
type: translation
args: heb-eng
dataset:
name: flores200-devtest
type: flores200-devtest
args: heb-eng
metrics:
- name: BLEU
type: bleu
value: 38.0
- name: chr-F
type: chrf
value: 0.63368
- task:
name: Translation heb-fra
type: translation
args: heb-fra
dataset:
name: flores200-devtest
type: flores200-devtest
args: heb-fra
metrics:
- name: BLEU
type: bleu
value: 32.6
- name: chr-F
type: chrf
value: 0.58417
- task:
name: Translation heb-por
type: translation
args: heb-por
dataset:
name: flores200-devtest
type: flores200-devtest
args: heb-por
metrics:
- name: BLEU
type: bleu
value: 30.7
- name: chr-F
type: chrf
value: 0.57140
- task:
name: Translation kab-eng
type: translation
args: kab-eng
dataset:
name: flores200-devtest
type: flores200-devtest
args: kab-eng
metrics:
- name: BLEU
type: bleu
value: 10.0
- name: chr-F
type: chrf
value: 0.32121
- task:
name: Translation mlt-eng
type: translation
args: mlt-eng
dataset:
name: flores200-devtest
type: flores200-devtest
args: mlt-eng
metrics:
- name: BLEU
type: bleu
value: 51.1
- name: chr-F
type: chrf
value: 0.73415
- task:
name: Translation mlt-fra
type: translation
args: mlt-fra
dataset:
name: flores200-devtest
type: flores200-devtest
args: mlt-fra
metrics:
- name: BLEU
type: bleu
value: 35.8
- name: chr-F
type: chrf
value: 0.61626
- task:
name: Translation mlt-spa
type: translation
args: mlt-spa
dataset:
name: flores200-devtest
type: flores200-devtest
args: mlt-spa
metrics:
- name: BLEU
type: bleu
value: 21.8
- name: chr-F
type: chrf
value: 0.50534
- task:
name: Translation som-eng
type: translation
args: som-eng
dataset:
name: flores200-devtest
type: flores200-devtest
args: som-eng
metrics:
- name: BLEU
type: bleu
value: 17.7
- name: chr-F
type: chrf
value: 0.42764
- task:
name: Translation som-fra
type: translation
args: som-fra
dataset:
name: flores200-devtest
type: flores200-devtest
args: som-fra
metrics:
- name: BLEU
type: bleu
value: 14.4
- name: chr-F
type: chrf
value: 0.39536
- task:
name: Translation som-por
type: translation
args: som-por
dataset:
name: flores200-devtest
type: flores200-devtest
args: som-por
metrics:
- name: BLEU
type: bleu
value: 14.1
- name: chr-F
type: chrf
value: 0.39440
- task:
name: Translation som-spa
type: translation
args: som-spa
dataset:
name: flores200-devtest
type: flores200-devtest
args: som-spa
metrics:
- name: BLEU
type: bleu
value: 10.0
- name: chr-F
type: chrf
value: 0.35321
- task:
name: Translation tir-eng
type: translation
args: tir-eng
dataset:
name: flores200-devtest
type: flores200-devtest
args: tir-eng
metrics:
- name: BLEU
type: bleu
value: 11.6
- name: chr-F
type: chrf
value: 0.37119
- task:
name: Translation amh-deu
type: translation
args: amh-deu
dataset:
name: flores101-devtest
type: flores_101
args: amh deu devtest
metrics:
- name: BLEU
type: bleu
value: 11.5
- name: chr-F
type: chrf
value: 0.39890
- task:
name: Translation amh-eng
type: translation
args: amh-eng
dataset:
name: flores101-devtest
type: flores_101
args: amh eng devtest
metrics:
- name: BLEU
type: bleu
value: 21.0
- name: chr-F
type: chrf
value: 0.47357
- task:
name: Translation amh-fra
type: translation
args: amh-fra
dataset:
name: flores101-devtest
type: flores_101
args: amh fra devtest
metrics:
- name: BLEU
type: bleu
value: 16.2
- name: chr-F
type: chrf
value: 0.43155
- task:
name: Translation amh-por
type: translation
args: amh-por
dataset:
name: flores101-devtest
type: flores_101
args: amh por devtest
metrics:
- name: BLEU
type: bleu
value: 15.1
- name: chr-F
type: chrf
value: 0.42109
- task:
name: Translation amh-spa
type: translation
args: amh-spa
dataset:
name: flores101-devtest
type: flores_101
args: amh spa devtest
metrics:
- name: BLEU
type: bleu
value: 11.5
- name: chr-F
type: chrf
value: 0.38003
- task:
name: Translation ara-deu
type: translation
args: ara-deu
dataset:
name: flores101-devtest
type: flores_101
args: ara deu devtest
metrics:
- name: BLEU
type: bleu
value: 20.4
- name: chr-F
type: chrf
value: 0.51110
- task:
name: Translation ara-fra
type: translation
args: ara-fra
dataset:
name: flores101-devtest
type: flores_101
args: ara fra devtest
metrics:
- name: BLEU
type: bleu
value: 29.7
- name: chr-F
type: chrf
value: 0.56934
- task:
name: Translation ara-por
type: translation
args: ara-por
dataset:
name: flores101-devtest
type: flores_101
args: ara por devtest
metrics:
- name: BLEU
type: bleu
value: 28.2
- name: chr-F
type: chrf
value: 0.55727
- task:
name: Translation ara-spa
type: translation
args: ara-spa
dataset:
name: flores101-devtest
type: flores_101
args: ara spa devtest
metrics:
- name: BLEU
type: bleu
value: 19.5
- name: chr-F
type: chrf
value: 0.48350
- task:
name: Translation hau-eng
type: translation
args: hau-eng
dataset:
name: flores101-devtest
type: flores_101
args: hau eng devtest
metrics:
- name: BLEU
type: bleu
value: 21.6
- name: chr-F
type: chrf
value: 0.46804
- task:
name: Translation hau-fra
type: translation
args: hau-fra
dataset:
name: flores101-devtest
type: flores_101
args: hau fra devtest
metrics:
- name: BLEU
type: bleu
value: 15.9
- name: chr-F
type: chrf
value: 0.41827
- task:
name: Translation hau-spa
type: translation
args: hau-spa
dataset:
name: flores101-devtest
type: flores_101
args: hau spa devtest
metrics:
- name: BLEU
type: bleu
value: 11.5
- name: chr-F
type: chrf
value: 0.37201
- task:
name: Translation heb-eng
type: translation
args: heb-eng
dataset:
name: flores101-devtest
type: flores_101
args: heb eng devtest
metrics:
- name: BLEU
type: bleu
value: 36.6
- name: chr-F
type: chrf
value: 0.62422
- task:
name: Translation mlt-eng
type: translation
args: mlt-eng
dataset:
name: flores101-devtest
type: flores_101
args: mlt eng devtest
metrics:
- name: BLEU
type: bleu
value: 49.1
- name: chr-F
type: chrf
value: 0.72390
- task:
name: Translation mlt-fra
type: translation
args: mlt-fra
dataset:
name: flores101-devtest
type: flores_101
args: mlt fra devtest
metrics:
- name: BLEU
type: bleu
value: 34.7
- name: chr-F
type: chrf
value: 0.60840
- task:
name: Translation mlt-por
type: translation
args: mlt-por
dataset:
name: flores101-devtest
type: flores_101
args: mlt por devtest
metrics:
- name: BLEU
type: bleu
value: 31.8
- name: chr-F
type: chrf
value: 0.59863
- task:
name: Translation amh-eng
type: translation
args: amh-eng
dataset:
name: ntrex128
type: ntrex128
args: amh-eng
metrics:
- name: BLEU
type: bleu
value: 15.0
- name: chr-F
type: chrf
value: 0.42042
- task:
name: Translation amh-fra
type: translation
args: amh-fra
dataset:
name: ntrex128
type: ntrex128
args: amh-fra
metrics:
- name: BLEU
type: bleu
value: 10.8
- name: chr-F
type: chrf
value: 0.37274
- task:
name: Translation amh-spa
type: translation
args: amh-spa
dataset:
name: ntrex128
type: ntrex128
args: amh-spa
metrics:
- name: BLEU
type: bleu
value: 12.1
- name: chr-F
type: chrf
value: 0.38306
- task:
name: Translation hau-eng
type: translation
args: hau-eng
dataset:
name: ntrex128
type: ntrex128
args: hau-eng
metrics:
- name: BLEU
type: bleu
value: 26.1
- name: chr-F
type: chrf
value: 0.50349
- task:
name: Translation hau-fra
type: translation
args: hau-fra
dataset:
name: ntrex128
type: ntrex128
args: hau-fra
metrics:
- name: BLEU
type: bleu
value: 15.8
- name: chr-F
type: chrf
value: 0.41837
- task:
name: Translation hau-por
type: translation
args: hau-por
dataset:
name: ntrex128
type: ntrex128
args: hau-por
metrics:
- name: BLEU
type: bleu
value: 15.3
- name: chr-F
type: chrf
value: 0.40851
- task:
name: Translation hau-spa
type: translation
args: hau-spa
dataset:
name: ntrex128
type: ntrex128
args: hau-spa
metrics:
- name: BLEU
type: bleu
value: 18.5
- name: chr-F
type: chrf
value: 0.43376
- task:
name: Translation heb-deu
type: translation
args: heb-deu
dataset:
name: ntrex128
type: ntrex128
args: heb-deu
metrics:
- name: BLEU
type: bleu
value: 17.7
- name: chr-F
type: chrf
value: 0.49482
- task:
name: Translation heb-eng
type: translation
args: heb-eng
dataset:
name: ntrex128
type: ntrex128
args: heb-eng
metrics:
- name: BLEU
type: bleu
value: 31.3
- name: chr-F
type: chrf
value: 0.59241
- task:
name: Translation heb-fra
type: translation
args: heb-fra
dataset:
name: ntrex128
type: ntrex128
args: heb-fra
metrics:
- name: BLEU
type: bleu
value: 24.0
- name: chr-F
type: chrf
value: 0.52180
- task:
name: Translation heb-por
type: translation
args: heb-por
dataset:
name: ntrex128
type: ntrex128
args: heb-por
metrics:
- name: BLEU
type: bleu
value: 23.2
- name: chr-F
type: chrf
value: 0.51248
- task:
name: Translation mlt-spa
type: translation
args: mlt-spa
dataset:
name: ntrex128
type: ntrex128
args: mlt-spa
metrics:
- name: BLEU
type: bleu
value: 30.9
- name: chr-F
type: chrf
value: 0.57078
- task:
name: Translation som-deu
type: translation
args: som-deu
dataset:
name: ntrex128
type: ntrex128
args: som-deu
metrics:
- name: BLEU
type: bleu
value: 11.0
- name: chr-F
type: chrf
value: 0.39394
- task:
name: Translation som-eng
type: translation
args: som-eng
dataset:
name: ntrex128
type: ntrex128
args: som-eng
metrics:
- name: BLEU
type: bleu
value: 24.3
- name: chr-F
type: chrf
value: 0.49187
- task:
name: Translation som-fra
type: translation
args: som-fra
dataset:
name: ntrex128
type: ntrex128
args: som-fra
metrics:
- name: BLEU
type: bleu
value: 15.1
- name: chr-F
type: chrf
value: 0.41236
- task:
name: Translation som-por
type: translation
args: som-por
dataset:
name: ntrex128
type: ntrex128
args: som-por
metrics:
- name: BLEU
type: bleu
value: 15.2
- name: chr-F
type: chrf
value: 0.41550
- task:
name: Translation som-spa
type: translation
args: som-spa
dataset:
name: ntrex128
type: ntrex128
args: som-spa
metrics:
- name: BLEU
type: bleu
value: 17.6
- name: chr-F
type: chrf
value: 0.43278
- task:
name: Translation tir-eng
type: translation
args: tir-eng
dataset:
name: ntrex128
type: ntrex128
args: tir-eng
metrics:
- name: BLEU
type: bleu
value: 12.5
- name: chr-F
type: chrf
value: 0.37185
- task:
name: Translation tir-spa
type: translation
args: tir-spa
dataset:
name: ntrex128
type: ntrex128
args: tir-spa
metrics:
- name: BLEU
type: bleu
value: 10.2
- name: chr-F
type: chrf
value: 0.34985
- task:
name: Translation amh-eng
type: translation
args: amh-eng
dataset:
name: tatoeba-test-v2021-03-30
type: tatoeba_mt
args: amh-eng
metrics:
- name: BLEU
type: bleu
value: 59.5
- name: chr-F
type: chrf
value: 0.70202
- task:
name: Translation ara-deu
type: translation
args: ara-deu
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: ara-deu
metrics:
- name: BLEU
type: bleu
value: 41.7
- name: chr-F
type: chrf
value: 0.61039
- task:
name: Translation ara-eng
type: translation
args: ara-eng
dataset:
name: tatoeba-test-v2020-07-28
type: tatoeba_mt
args: ara-eng
metrics:
- name: BLEU
type: bleu
value: 42.8
- name: chr-F
type: chrf
value: 0.60134
- task:
name: Translation ara-fra
type: translation
args: ara-fra
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: ara-fra
metrics:
- name: BLEU
type: bleu
value: 38.8
- name: chr-F
type: chrf
value: 0.56120
- task:
name: Translation ara-spa
type: translation
args: ara-spa
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: ara-spa
metrics:
- name: BLEU
type: bleu
value: 43.7
- name: chr-F
type: chrf
value: 0.62567
- task:
name: Translation heb-deu
type: translation
args: heb-deu
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: heb-deu
metrics:
- name: BLEU
type: bleu
value: 42.4
- name: chr-F
type: chrf
value: 0.63131
- task:
name: Translation heb-eng
type: translation
args: heb-eng
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: heb-eng
metrics:
- name: BLEU
type: bleu
value: 49.2
- name: chr-F
type: chrf
value: 0.64960
- task:
name: Translation heb-fra
type: translation
args: heb-fra
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: heb-fra
metrics:
- name: BLEU
type: bleu
value: 46.3
- name: chr-F
type: chrf
value: 0.64348
- task:
name: Translation heb-por
type: translation
args: heb-por
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: heb-por
metrics:
- name: BLEU
type: bleu
value: 43.2
- name: chr-F
type: chrf
value: 0.63350
- task:
name: Translation kab-eng
type: translation
args: kab-eng
dataset:
name: tatoeba-test-v2021-03-30
type: tatoeba_mt
args: kab-eng
metrics:
- name: BLEU
type: bleu
value: 27.8
- name: chr-F
type: chrf
value: 0.44646
- task:
name: Translation kab-spa
type: translation
args: kab-spa
dataset:
name: tatoeba-test-v2020-07-28
type: tatoeba_mt
args: kab-spa
metrics:
- name: BLEU
type: bleu
value: 23.4
- name: chr-F
type: chrf
value: 0.41526
- task:
name: Translation mlt-eng
type: translation
args: mlt-eng
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: mlt-eng
metrics:
- name: BLEU
type: bleu
value: 51.0
- name: chr-F
type: chrf
value: 0.66653
- task:
name: Translation multi-multi
type: translation
args: multi-multi
dataset:
name: tatoeba-test-v2020-07-28-v2023-09-26
type: tatoeba_mt
args: multi-multi
metrics:
- name: BLEU
type: bleu
value: 34.0
- name: chr-F
type: chrf
value: 0.51369
- task:
name: Translation hau-eng
type: translation
args: hau-eng
dataset:
name: newstest2021
type: wmt-2021-news
args: hau-eng
metrics:
- name: BLEU
type: bleu
value: 15.5
- name: chr-F
type: chrf
value: 0.43744
---
# opus-mt-tc-bible-big-afa-deu_eng_fra_por_spa
## Table of Contents
- [Model Details](#model-details)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [How to Get Started With the Model](#how-to-get-started-with-the-model)
- [Training](#training)
- [Evaluation](#evaluation)
- [Citation Information](#citation-information)
- [Acknowledgements](#acknowledgements)
## Model Details
Neural machine translation model for translating from Afro-Asiatic languages (afa) to unknown (deu+eng+fra+por+spa).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally trained using the amazing framework of [Marian NMT](https://marian-nmt.github.io/), an efficient NMT implementation written in pure C++. The models have been converted to pyTorch using the transformers library by huggingface. Training data is taken from [OPUS](https://opus.nlpl.eu/) and training pipelines use the procedures of [OPUS-MT-train](https://github.com/Helsinki-NLP/Opus-MT-train).
**Model Description:**
- **Developed by:** Language Technology Research Group at the University of Helsinki
- **Model Type:** Translation (transformer-big)
- **Release**: 2024-05-29
- **License:** Apache-2.0
- **Language(s):**
- Source Language(s): aar acm afb amh apc ara arc arq arz bcw byn cop daa dsh gde gnd hau hbo heb hig irk jpa kab ker kqp ktb kxc lln lme meq mfh mfi mfk mif mlt mpg mqb muy oar orm pbi phn rif sgw shi shy som sur syc syr taq thv tig tir tmc tmh tmr ttr tzm wal xed zgh
- Target Language(s): deu eng fra por spa
- Valid Target Language Labels: >>deu<< >>eng<< >>fra<< >>por<< >>spa<< >>xxx<<
- **Original Model**: [opusTCv20230926max50+bt+jhubc_transformer-big_2024-05-29.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/afa-deu+eng+fra+por+spa/opusTCv20230926max50+bt+jhubc_transformer-big_2024-05-29.zip)
- **Resources for more information:**
- [OPUS-MT dashboard](https://opus.nlpl.eu/dashboard/index.php?pkg=opusmt&test=all&scoreslang=all&chart=standard&model=Tatoeba-MT-models/afa-deu%2Beng%2Bfra%2Bpor%2Bspa/opusTCv20230926max50%2Bbt%2Bjhubc_transformer-big_2024-05-29)
- [OPUS-MT-train GitHub Repo](https://github.com/Helsinki-NLP/OPUS-MT-train)
- [More information about MarianNMT models in the transformers library](https://huggingface.co/docs/transformers/model_doc/marian)
- [Tatoeba Translation Challenge](https://github.com/Helsinki-NLP/Tatoeba-Challenge/)
- [HPLT bilingual data v1 (as part of the Tatoeba Translation Challenge dataset)](https://hplt-project.org/datasets/v1)
- [A massively parallel Bible corpus](https://aclanthology.org/L14-1215/)
This is a multilingual translation model with multiple target languages. A sentence initial language token is required in the form of `>>id<<` (id = valid target language ID), e.g. `>>deu<<`
## Uses
This model can be used for translation and text-to-text generation.
## Risks, Limitations and Biases
**CONTENT WARNING: Readers should be aware that the model is trained on various public data sets that may contain content that is disturbing, offensive, and can propagate historical and current stereotypes.**
Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)).
## How to Get Started With the Model
A short example code:
```python
from transformers import MarianMTModel, MarianTokenizer
src_text = [
">>eng<< Anta i ak-d-yennan ur yerbiḥ ara Tom?",
">>fra<< Iselman d aɣbalu axatar i wučči n yemdanen."
]
model_name = "pytorch-models/opus-mt-tc-bible-big-afa-deu_eng_fra_por_spa"
tokenizer = MarianTokenizer.from_pretrained(model_name)
model = MarianMTModel.from_pretrained(model_name)
translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True))
for t in translated:
print( tokenizer.decode(t, skip_special_tokens=True) )
# expected output:
# Who told you that he didn't?
# L'eau est une source importante de nourriture pour les gens.
```
You can also use OPUS-MT models with the transformers pipelines, for example:
```python
from transformers import pipeline
pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-bible-big-afa-deu_eng_fra_por_spa")
print(pipe(">>eng<< Anta i ak-d-yennan ur yerbiḥ ara Tom?"))
# expected output: Who told you that he didn't?
```
## Training
- **Data**: opusTCv20230926max50+bt+jhubc ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge))
- **Pre-processing**: SentencePiece (spm32k,spm32k)
- **Model Type:** transformer-big
- **Original MarianNMT Model**: [opusTCv20230926max50+bt+jhubc_transformer-big_2024-05-29.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/afa-deu+eng+fra+por+spa/opusTCv20230926max50+bt+jhubc_transformer-big_2024-05-29.zip)
- **Training Scripts**: [GitHub Repo](https://github.com/Helsinki-NLP/OPUS-MT-train)
## Evaluation
* [Model scores at the OPUS-MT dashboard](https://opus.nlpl.eu/dashboard/index.php?pkg=opusmt&test=all&scoreslang=all&chart=standard&model=Tatoeba-MT-models/afa-deu%2Beng%2Bfra%2Bpor%2Bspa/opusTCv20230926max50%2Bbt%2Bjhubc_transformer-big_2024-05-29)
* test set translations: [opusTCv20230926max50+bt+jhubc_transformer-big_2024-05-29.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/afa-deu+eng+fra+por+spa/opusTCv20230926max50+bt+jhubc_transformer-big_2024-05-29.test.txt)
* test set scores: [opusTCv20230926max50+bt+jhubc_transformer-big_2024-05-29.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/afa-deu+eng+fra+por+spa/opusTCv20230926max50+bt+jhubc_transformer-big_2024-05-29.eval.txt)
* benchmark results: [benchmark_results.txt](benchmark_results.txt)
* benchmark output: [benchmark_translations.zip](benchmark_translations.zip)
| langpair | testset | chr-F | BLEU | #sent | #words |
|----------|---------|-------|-------|-------|--------|
| ara-deu | tatoeba-test-v2021-08-07 | 0.61039 | 41.7 | 1209 | 8371 |
| ara-eng | tatoeba-test-v2021-08-07 | 5.430 | 0.0 | 10305 | 76975 |
| ara-fra | tatoeba-test-v2021-08-07 | 0.56120 | 38.8 | 1569 | 11066 |
| ara-spa | tatoeba-test-v2021-08-07 | 0.62567 | 43.7 | 1511 | 9708 |
| heb-deu | tatoeba-test-v2021-08-07 | 0.63131 | 42.4 | 3090 | 25101 |
| heb-eng | tatoeba-test-v2021-08-07 | 0.64960 | 49.2 | 10519 | 77427 |
| heb-fra | tatoeba-test-v2021-08-07 | 0.64348 | 46.3 | 3281 | 26123 |
| heb-por | tatoeba-test-v2021-08-07 | 0.63350 | 43.2 | 719 | 5335 |
| mlt-eng | tatoeba-test-v2021-08-07 | 0.66653 | 51.0 | 203 | 1165 |
| amh-eng | flores101-devtest | 0.47357 | 21.0 | 1012 | 24721 |
| amh-fra | flores101-devtest | 0.43155 | 16.2 | 1012 | 28343 |
| amh-por | flores101-devtest | 0.42109 | 15.1 | 1012 | 26519 |
| ara-deu | flores101-devtest | 0.51110 | 20.4 | 1012 | 25094 |
| ara-fra | flores101-devtest | 0.56934 | 29.7 | 1012 | 28343 |
| ara-por | flores101-devtest | 0.55727 | 28.2 | 1012 | 26519 |
| ara-spa | flores101-devtest | 0.48350 | 19.5 | 1012 | 29199 |
| hau-eng | flores101-devtest | 0.46804 | 21.6 | 1012 | 24721 |
| hau-fra | flores101-devtest | 0.41827 | 15.9 | 1012 | 28343 |
| heb-eng | flores101-devtest | 0.62422 | 36.6 | 1012 | 24721 |
| mlt-eng | flores101-devtest | 0.72390 | 49.1 | 1012 | 24721 |
| mlt-fra | flores101-devtest | 0.60840 | 34.7 | 1012 | 28343 |
| mlt-por | flores101-devtest | 0.59863 | 31.8 | 1012 | 26519 |
| acm-deu | flores200-devtest | 0.48947 | 17.6 | 1012 | 25094 |
| acm-eng | flores200-devtest | 0.56799 | 28.5 | 1012 | 24721 |
| acm-fra | flores200-devtest | 0.53577 | 26.1 | 1012 | 28343 |
| acm-por | flores200-devtest | 0.52441 | 23.9 | 1012 | 26519 |
| acm-spa | flores200-devtest | 0.46985 | 18.2 | 1012 | 29199 |
| amh-deu | flores200-devtest | 0.41553 | 12.6 | 1012 | 25094 |
| amh-eng | flores200-devtest | 0.49333 | 22.5 | 1012 | 24721 |
| amh-fra | flores200-devtest | 0.44890 | 17.8 | 1012 | 28343 |
| amh-por | flores200-devtest | 0.43771 | 16.5 | 1012 | 26519 |
| apc-deu | flores200-devtest | 0.47480 | 16.0 | 1012 | 25094 |
| apc-eng | flores200-devtest | 0.56075 | 28.1 | 1012 | 24721 |
| apc-fra | flores200-devtest | 0.52325 | 24.6 | 1012 | 28343 |
| apc-por | flores200-devtest | 0.51055 | 22.9 | 1012 | 26519 |
| apc-spa | flores200-devtest | 0.45634 | 17.2 | 1012 | 29199 |
| arz-deu | flores200-devtest | 0.45844 | 14.1 | 1012 | 25094 |
| arz-eng | flores200-devtest | 0.52534 | 22.7 | 1012 | 24721 |
| arz-fra | flores200-devtest | 0.50336 | 21.8 | 1012 | 28343 |
| arz-por | flores200-devtest | 0.48741 | 20.0 | 1012 | 26519 |
| arz-spa | flores200-devtest | 0.44516 | 15.8 | 1012 | 29199 |
| hau-eng | flores200-devtest | 0.48137 | 23.4 | 1012 | 24721 |
| hau-fra | flores200-devtest | 0.42981 | 17.2 | 1012 | 28343 |
| hau-por | flores200-devtest | 0.41385 | 15.7 | 1012 | 26519 |
| heb-deu | flores200-devtest | 0.53482 | 22.8 | 1012 | 25094 |
| heb-eng | flores200-devtest | 0.63368 | 38.0 | 1012 | 24721 |
| heb-fra | flores200-devtest | 0.58417 | 32.6 | 1012 | 28343 |
| heb-por | flores200-devtest | 0.57140 | 30.7 | 1012 | 26519 |
| mlt-eng | flores200-devtest | 0.73415 | 51.1 | 1012 | 24721 |
| mlt-fra | flores200-devtest | 0.61626 | 35.8 | 1012 | 28343 |
| mlt-spa | flores200-devtest | 0.50534 | 21.8 | 1012 | 29199 |
| som-eng | flores200-devtest | 0.42764 | 17.7 | 1012 | 24721 |
| tir-por | flores200-devtest | 2.931 | 0.0 | 1012 | 26519 |
| hau-eng | newstest2021 | 0.43744 | 15.5 | 997 | 27372 |
| amh-eng | ntrex128 | 0.42042 | 15.0 | 1997 | 47673 |
| hau-eng | ntrex128 | 0.50349 | 26.1 | 1997 | 47673 |
| hau-fra | ntrex128 | 0.41837 | 15.8 | 1997 | 53481 |
| hau-por | ntrex128 | 0.40851 | 15.3 | 1997 | 51631 |
| hau-spa | ntrex128 | 0.43376 | 18.5 | 1997 | 54107 |
| heb-deu | ntrex128 | 0.49482 | 17.7 | 1997 | 48761 |
| heb-eng | ntrex128 | 0.59241 | 31.3 | 1997 | 47673 |
| heb-fra | ntrex128 | 0.52180 | 24.0 | 1997 | 53481 |
| heb-por | ntrex128 | 0.51248 | 23.2 | 1997 | 51631 |
| mlt-spa | ntrex128 | 0.57078 | 30.9 | 1997 | 54107 |
| som-eng | ntrex128 | 0.49187 | 24.3 | 1997 | 47673 |
| som-fra | ntrex128 | 0.41236 | 15.1 | 1997 | 53481 |
| som-por | ntrex128 | 0.41550 | 15.2 | 1997 | 51631 |
| som-spa | ntrex128 | 0.43278 | 17.6 | 1997 | 54107 |
| tir-eng | tico19-test | 2.655 | 0.0 | 2100 | 56824 |
## Citation Information
* Publications: [Democratizing neural machine translation with OPUS-MT](https://doi.org/10.1007/s10579-023-09704-w) and [OPUS-MT – Building open translation services for the World](https://aclanthology.org/2020.eamt-1.61/) and [The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT](https://aclanthology.org/2020.wmt-1.139/) (Please, cite if you use this model.)
```bibtex
@article{tiedemann2023democratizing,
title={Democratizing neural machine translation with {OPUS-MT}},
author={Tiedemann, J{\"o}rg and Aulamo, Mikko and Bakshandaeva, Daria and Boggia, Michele and Gr{\"o}nroos, Stig-Arne and Nieminen, Tommi and Raganato, Alessandro and Scherrer, Yves and Vazquez, Raul and Virpioja, Sami},
journal={Language Resources and Evaluation},
number={58},
pages={713--755},
year={2023},
publisher={Springer Nature},
issn={1574-0218},
doi={10.1007/s10579-023-09704-w}
}
@inproceedings{tiedemann-thottingal-2020-opus,
title = "{OPUS}-{MT} {--} Building open translation services for the World",
author = {Tiedemann, J{\"o}rg and Thottingal, Santhosh},
booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
month = nov,
year = "2020",
address = "Lisboa, Portugal",
publisher = "European Association for Machine Translation",
url = "https://aclanthology.org/2020.eamt-1.61",
pages = "479--480",
}
@inproceedings{tiedemann-2020-tatoeba,
title = "The Tatoeba Translation Challenge {--} Realistic Data Sets for Low Resource and Multilingual {MT}",
author = {Tiedemann, J{\"o}rg},
booktitle = "Proceedings of the Fifth Conference on Machine Translation",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.wmt-1.139",
pages = "1174--1182",
}
```
## Acknowledgements
The work is supported by the [HPLT project](https://hplt-project.org/), funded by the European Union’s Horizon Europe research and innovation programme under grant agreement No 101070350. We are also grateful for the generous computational resources and IT infrastructure provided by [CSC -- IT Center for Science](https://www.csc.fi/), Finland, and the [EuroHPC supercomputer LUMI](https://www.lumi-supercomputer.eu/).
## Model conversion info
* transformers version: 4.45.1
* OPUS-MT git hash: a0ea3b3
* port time: Mon Oct 7 17:08:30 EEST 2024
* port machine: LM0-400-22516.local
|
speakleash/Bielik-11B-v2.0-Instruct-GPTQ
|
speakleash
| 2024-10-07T14:07:43Z | 75 | 1 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"finetuned",
"gguf",
"conversational",
"pl",
"base_model:speakleash/Bielik-11B-v2.0-Instruct",
"base_model:quantized:speakleash/Bielik-11B-v2.0-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"4-bit",
"gptq",
"region:us"
] |
text-generation
| 2024-09-06T08:26:09Z |
---
language:
- pl
license: apache-2.0
library_name: transformers
tags:
- finetuned
- gguf
inference: false
pipeline_tag: text-generation
base_model: speakleash/Bielik-11B-v2.0-Instruct
---
<p align="center">
<img src="https://huggingface.co/speakleash/Bielik-11B-v2/raw/main/speakleash_cyfronet.png">
</p>
# Bielik-11B-v2.0-Instruct-GPTQ
This repo contains GPTQ format model files for [SpeakLeash](https://speakleash.org/)'s [Bielik-11B-v.2.0-Instruct](https://huggingface.co/speakleash/Bielik-11B-v2.0-Instruct).
<b><u>DISCLAIMER: Be aware that quantised models show reduced response quality and possible hallucinations!</u></b><br>
### Model description:
* **Developed by:** [SpeakLeash](https://speakleash.org/) & [ACK Cyfronet AGH](https://www.cyfronet.pl/)
* **Language:** Polish
* **Model type:** causal decoder-only
* **Quant from:** [Bielik-11B-v2.0-Instruct](https://huggingface.co/speakleash/Bielik-11B-v2.0-Instruct)
* **Finetuned from:** [Bielik-11B-v2](https://huggingface.co/speakleash/Bielik-11B-v2)
* **License:** Apache 2.0 and [Terms of Use](https://bielik.ai/terms/)
### Responsible for model quantization
* [Remigiusz Kinas](https://www.linkedin.com/in/remigiusz-kinas/)<sup>SpeakLeash</sup> - team leadership, conceptualizing, calibration data preparation, process creation and quantized model delivery.
## Contact Us
If you have any questions or suggestions, please use the discussion tab. If you want to contact us directly, join our [Discord SpeakLeash](https://discord.gg/pv4brQMDTy).
|
kip1976/vit-demo
|
kip1976
| 2024-10-07T14:07:22Z | 163 | 0 |
transformers
|
[
"transformers",
"tf",
"safetensors",
"vit",
"image-classification",
"generated_from_keras_callback",
"base_model:google/vit-base-patch16-224",
"base_model:finetune:google/vit-base-patch16-224",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2024-10-07T13:46:59Z |
---
base_model: google/vit-base-patch16-224
library_name: transformers
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: vit-demo
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# vit-demo
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: None
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.44.2
- TensorFlow 2.17.0
- Datasets 3.0.1
- Tokenizers 0.19.1
|
speakleash/Bielik-11B-v2.2-Instruct-AWQ
|
speakleash
| 2024-10-07T14:04:28Z | 81 | 3 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"finetuned",
"gguf",
"conversational",
"pl",
"base_model:speakleash/Bielik-11B-v2.2-Instruct",
"base_model:quantized:speakleash/Bielik-11B-v2.2-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"4-bit",
"awq",
"region:us"
] |
text-generation
| 2024-08-26T09:28:52Z |
---
language:
- pl
license: apache-2.0
library_name: transformers
tags:
- finetuned
- gguf
inference: false
pipeline_tag: text-generation
base_model: speakleash/Bielik-11B-v2.2-Instruct
---
<p align="center">
<img src="https://huggingface.co/speakleash/Bielik-7B-Instruct-v0.1/raw/main/speakleash_cyfronet.png">
</p>
# Bielik-11B-v2.2-Instruct-AWQ
This repo contains AWQ format model files for [SpeakLeash](https://speakleash.org/)'s [Bielik-11B-v.2.2-Instruct](https://huggingface.co/speakleash/Bielik-11B-v2.2-Instruct).
<b><u>DISCLAIMER: Be aware that quantised models show reduced response quality and possible hallucinations!</u></b><br>
### Model description:
* **Developed by:** [SpeakLeash](https://speakleash.org/) & [ACK Cyfronet AGH](https://www.cyfronet.pl/)
* **Language:** Polish
* **Model type:** causal decoder-only
* **Quant from:** [Bielik-11B-v2.2-Instruct](https://huggingface.co/speakleash/Bielik-11B-v2.2-Instruct)
* **Finetuned from:** [Bielik-11B-v2](https://huggingface.co/speakleash/Bielik-11B-v2)
* **License:** Apache 2.0 and [Terms of Use](https://bielik.ai/terms/)
### Responsible for model quantization
* [Remigiusz Kinas](https://www.linkedin.com/in/remigiusz-kinas/)<sup>SpeakLeash</sup> - team leadership, conceptualizing, calibration data preparation, process creation and quantized model delivery.
## Contact Us
If you have any questions or suggestions, please use the discussion tab. If you want to contact us directly, join our [Discord SpeakLeash](https://discord.gg/CPBxPce4).
|
speakleash/Bielik-11B-v2.2-Instruct-Quanto-8bit
|
speakleash
| 2024-10-07T14:03:17Z | 16 | 3 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"finetuned",
"gguf",
"8bit",
"conversational",
"pl",
"base_model:speakleash/Bielik-11B-v2.2-Instruct",
"base_model:finetune:speakleash/Bielik-11B-v2.2-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"8-bit",
"region:us"
] |
text-generation
| 2024-08-26T10:41:21Z |
---
language:
- pl
license: apache-2.0
library_name: transformers
tags:
- finetuned
- gguf
- 8bit
inference: false
pipeline_tag: text-generation
base_model: speakleash/Bielik-11B-v2.2-Instruct
---
<p align="center">
<img src="https://huggingface.co/speakleash/Bielik-7B-Instruct-v0.1-GGUF/raw/main/speakleash_cyfronet.png">
</p>
# Bielik-11B-v2.2-Instruct-Quanto-8bit
This model was converted to Quanto format from [SpeakLeash](https://speakleash.org/)'s [Bielik-11B-v.2.2-Instruct](https://huggingface.co/speakleash/Bielik-11B-v2.2-Instruct).
**DISCLAIMER: Be aware that quantised models show reduced response quality and possible hallucinations!**
## About Quanto
Optimum Quanto is a pytorch quantization backend for optimum. Model can be loaded using:
```
from optimum.quanto import QuantizedModelForCausalLM
qmodel = QuantizedModelForCausalLM.from_pretrained('speakleash/Bielik-11B-v2.2-Instruct-Quanto-8bit')
```
### Model description:
* **Developed by:** [SpeakLeash](https://speakleash.org/) & [ACK Cyfronet AGH](https://www.cyfronet.pl/)
* **Language:** Polish
* **Model type:** causal decoder-only
* **Quant from:** [Bielik-11B-v2.2-Instruct](https://huggingface.co/speakleash/Bielik-11B-v2.2-Instruct)
* **Finetuned from:** [Bielik-11B-v2](https://huggingface.co/speakleash/Bielik-11B-v2)
* **License:** Apache 2.0 and [Terms of Use](https://bielik.ai/terms/)
### Responsible for model quantization
* [Remigiusz Kinas](https://www.linkedin.com/in/remigiusz-kinas/)<sup>SpeakLeash</sup> - team leadership, conceptualizing, calibration data preparation, process creation and quantized model delivery.
## Contact Us
If you have any questions or suggestions, please use the discussion tab. If you want to contact us directly, join our [Discord SpeakLeash](https://discord.gg/CPBxPce4).
|
speakleash/Bielik-11B-v2.2-Instruct-EXL2-4.5bit
|
speakleash
| 2024-10-07T14:02:41Z | 11 | 3 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"finetuned",
"gguf",
"conversational",
"pl",
"base_model:speakleash/Bielik-11B-v2.2-Instruct",
"base_model:finetune:speakleash/Bielik-11B-v2.2-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2024-08-26T11:40:05Z |
---
language:
- pl
license: apache-2.0
library_name: transformers
tags:
- finetuned
- gguf
inference: false
pipeline_tag: text-generation
base_model: speakleash/Bielik-11B-v2.2-Instruct
---
<p align="center">
<img src="https://huggingface.co/speakleash/Bielik-7B-Instruct-v0.1/raw/main/speakleash_cyfronet.png">
</p>
# Bielik-11B-v2.2-Instruct-EXL2-4.5bit
This repo contains EXL2 (4.5-bit) format model files for [SpeakLeash](https://speakleash.org/)'s [Bielik-11B-v.2.2-Instruct](https://huggingface.co/speakleash/Bielik-11B-v2.2-Instruct).
<b><u>DISCLAIMER: Be aware that quantised models show reduced response quality and possible hallucinations!</u></b><br>
### Model description:
* **Developed by:** [SpeakLeash](https://speakleash.org/) & [ACK Cyfronet AGH](https://www.cyfronet.pl/)
* **Language:** Polish
* **Model type:** causal decoder-only
* **Quant from:** [Bielik-11B-v2.2-Instruct](https://huggingface.co/speakleash/Bielik-11B-v2.2-Instruct)
* **Finetuned from:** [Bielik-11B-v2](https://huggingface.co/speakleash/Bielik-11B-v2)
* **License:** Apache 2.0 and [Terms of Use](https://bielik.ai/terms/)
### Responsible for model quantization
* [Remigiusz Kinas](https://www.linkedin.com/in/remigiusz-kinas/)<sup>SpeakLeash</sup> - team leadership, conceptualizing, calibration data preparation, process creation and quantized model delivery.
## Contact Us
If you have any questions or suggestions, please use the discussion tab. If you want to contact us directly, join our [Discord SpeakLeash](https://discord.gg/CPBxPce4).
|
speakleash/Bielik-11B-v2.2-Instruct-EXL2-6.5bit
|
speakleash
| 2024-10-07T14:02:24Z | 6 | 3 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"finetuned",
"gguf",
"conversational",
"pl",
"base_model:speakleash/Bielik-11B-v2.2-Instruct",
"base_model:finetune:speakleash/Bielik-11B-v2.2-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2024-08-26T12:42:15Z |
---
language:
- pl
license: apache-2.0
library_name: transformers
tags:
- finetuned
- gguf
inference: false
pipeline_tag: text-generation
base_model: speakleash/Bielik-11B-v2.2-Instruct
---
<p align="center">
<img src="https://huggingface.co/speakleash/Bielik-7B-Instruct-v0.1/raw/main/speakleash_cyfronet.png">
</p>
# Bielik-11B-v2.2-Instruct-EXL2-6.5bit
This repo contains EXL2 (6.5-bit) format model files for [SpeakLeash](https://speakleash.org/)'s [Bielik-11B-v.2.2-Instruct](https://huggingface.co/speakleash/Bielik-11B-v2.2-Instruct).
<b><u>DISCLAIMER: Be aware that quantised models show reduced response quality and possible hallucinations!</u></b><br>
### Model description:
* **Developed by:** [SpeakLeash](https://speakleash.org/) & [ACK Cyfronet AGH](https://www.cyfronet.pl/)
* **Language:** Polish
* **Model type:** causal decoder-only
* **Quant from:** [Bielik-11B-v2.2-Instruct](https://huggingface.co/speakleash/Bielik-11B-v2.2-Instruct)
* **Finetuned from:** [Bielik-11B-v2](https://huggingface.co/speakleash/Bielik-11B-v2)
* **License:** Apache 2.0 and [Terms of Use](https://bielik.ai/terms/)
### Responsible for model quantization
* [Remigiusz Kinas](https://www.linkedin.com/in/remigiusz-kinas/)<sup>SpeakLeash</sup> - team leadership, conceptualizing, calibration data preparation, process creation and quantized model delivery.
## Contact Us
If you have any questions or suggestions, please use the discussion tab. If you want to contact us directly, join our [Discord SpeakLeash](https://discord.gg/CPBxPce4).
|
speakleash/Bielik-11B-v2.3-Instruct-4bit-ov
|
speakleash
| 2024-10-07T13:59:33Z | 111 | 1 |
transformers
|
[
"transformers",
"openvino",
"mistral",
"text-generation",
"finetuned",
"gguf",
"conversational",
"pl",
"base_model:speakleash/Bielik-11B-v2.3-Instruct",
"base_model:finetune:speakleash/Bielik-11B-v2.3-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"region:us"
] |
text-generation
| 2024-10-04T08:14:21Z |
---
language:
- pl
license: apache-2.0
library_name: transformers
tags:
- finetuned
- gguf
inference: false
pipeline_tag: text-generation
base_model: speakleash/Bielik-11B-v2.3-Instruct
---
<p align="center">
<img src="https://huggingface.co/speakleash/Bielik-7B-Instruct-v0.1/raw/main/speakleash_cyfronet.png">
</p>
# Bielik-11B-v2.3-Instruct-GPTQ
This repo contains OpenVino 4bit format model files for [SpeakLeash](https://speakleash.org/)'s [Bielik-11B-v.2.3-Instruct](https://huggingface.co/speakleash/Bielik-11B-v2.3-Instruct).
<b><u>DISCLAIMER: Be aware that quantised models show reduced response quality and possible hallucinations!</u></b><br>
### Model usage with OpenVino
This model can be deployed efficiently using the [OpenVino](https://docs.openvino.ai/2024/index.html). Below you can find two ways of model inference: using Intel Optimum, pure OpenVino library.
The most simple LLM inferencing code with OpenVINO and the optimum-intel library.
```python
from optimum.intel import OVModelForCausalLM
from transformers import AutoTokenizer
model_id = "speakleash/Bielik-11B-v2.3-Instruct-4bit-ov"
model = OVModelForCausalLM.from_pretrained(model_id, use_cache=False)
question = "Dlaczego ryby nie potrafią fruwać?"
prompt_text_bielik = f"""<s><|im_start|> system
Odpowiadaj krótko, precyzyjnie i wyłącznie w języku polskim.<|im_end|>
<|im_start|> user
{question}<|im_end|>
<|im_start|> assistant
"""
tokenizer = AutoTokenizer.from_pretrained(model_id)
inputs = tokenizer(prompt_text_bielik, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=500)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
Run an LLM model with only OpenVINO (additionaly we provided code which uses 'greedy decoding' instead of sampling).
```python
import openvino as ov
import numpy as np
from transformers import AutoTokenizer
model_path = "speakleash/Bielik-11B-v2.3-Instruct-4bit-ov/openvino_model.xml"
tokenizer = AutoTokenizer.from_pretrained("speakleash/Bielik-11B-v2.3-Instruct-4bit-ov")
ov_model = ov.Core().read_model(model_path)
compiled_model = ov.compile_model(ov_model, "CPU")
infer_request = compiled_model.create_infer_request()
question = "Dlaczego ryby nie potrafią fruwać?"
prompt_text_bielik = f"""<s><|im_start|> system
Odpowiadaj krótko, precyzyjnie i wyłącznie w języku polskim.<|im_end|>
<|im_start|> user
{question}<|im_end|>
<|im_start|> assistant
"""
tokens = tokenizer.encode(prompt_text_bielik, return_tensors="np")
input_ids = tokens
attention_mask = np.ones_like(input_ids)
position_ids = np.arange(len(tokens[0])).reshape(1, -1)
beam_idx = np.array([0], dtype=np.int32)
infer_request.reset_state()
prev_output = ''
generated_text_ids = np.array([], dtype=np.int32)
num_max_token_for_generation = 500
print(f'Pytanie: {question}')
print("Odpowiedź:", end=' ', flush=True)
for _ in range(num_max_token_for_generation):
response = infer_request.infer(inputs={
'input_ids': input_ids,
'attention_mask': attention_mask,
'position_ids': position_ids,
'beam_idx': beam_idx
})
next_token_logits = response['logits'][0, -1, :]
sampled_id = np.argmax(next_token_logits) # Greedy decoding
generated_text_ids = np.append(generated_text_ids, sampled_id)
output_text = tokenizer.decode(generated_text_ids)
print(output_text[len(prev_output):], end='', flush=True)
prev_output = output_text
input_ids = np.array([[sampled_id]], dtype=np.int64)
attention_mask = np.array([[1]], dtype=np.int64)
position_ids = np.array([[position_ids[0, -1] + 1]], dtype=np.int64)
if sampled_id == tokenizer.eos_token_id:
print('\n\n*** Zakończono generowanie.')
break
print(f'\n\n*** Wygenerowano {len(generated_text_ids)} tokenów.')
```
### Model description:
* **Developed by:** [SpeakLeash](https://speakleash.org/) & [ACK Cyfronet AGH](https://www.cyfronet.pl/)
* **Language:** Polish
* **Model type:** causal decoder-only
* **Quant from:** [Bielik-11B-v2.3-Instruct](https://huggingface.co/speakleash/Bielik-11B-v2.3-Instruct)
* **Finetuned from:** [Bielik-11B-v2](https://huggingface.co/speakleash/Bielik-11B-v2)
* **License:** Apache 2.0 and [Terms of Use](https://bielik.ai/terms/)
### Responsible for model quantization
* [Remigiusz Kinas](https://www.linkedin.com/in/remigiusz-kinas/)<sup>SpeakLeash</sup> - team leadership, conceptualizing, calibration data preparation, process creation and quantized model delivery.
## Contact Us
If you have any questions or suggestions, please use the discussion tab. If you want to contact us directly, join our [Discord SpeakLeash](https://discord.gg/CPBxPce4).
|
pui8838/finetuning-emotion-model
|
pui8838
| 2024-10-07T13:54:43Z | 105 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-10-07T13:34:10Z |
---
library_name: transformers
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: finetuning-emotion-model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-emotion-model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2138
- Accuracy: 0.9275
- F1: 0.9275
- Precision: 0.9276
- Recall: 0.9275
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| No log | 1.0 | 250 | 0.3133 | 0.911 | 0.9104 | 0.9104 | 0.911 |
| 0.5359 | 2.0 | 500 | 0.2138 | 0.9275 | 0.9275 | 0.9276 | 0.9275 |
### Framework versions
- Transformers 4.45.1
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.0
|
Web3Liber/vit-base-oxford-iiit-pets
|
Web3Liber
| 2024-10-07T13:46:08Z | 193 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"base_model:google/vit-base-patch16-224",
"base_model:finetune:google/vit-base-patch16-224",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2024-10-07T12:59:56Z |
---
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- image-classification
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: vit-base-oxford-iiit-pets
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-base-oxford-iiit-pets
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the pcuenq/oxford-pets dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2046
- Accuracy: 0.9337
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3652 | 1.0 | 370 | 0.3105 | 0.9269 |
| 0.2061 | 2.0 | 740 | 0.2322 | 0.9364 |
| 0.167 | 3.0 | 1110 | 0.2135 | 0.9337 |
| 0.1584 | 4.0 | 1480 | 0.2093 | 0.9337 |
| 0.131 | 5.0 | 1850 | 0.2069 | 0.9337 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.19.1
|
NotASI/FineTome-Llama3.2-3B-1002
|
NotASI
| 2024-10-07T13:44:27Z | 10 | 1 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"llama-3",
"trl",
"sft",
"conversational",
"en",
"dataset:mlabonne/FineTome-100k",
"base_model:unsloth/Llama-3.2-3B-Instruct-bnb-4bit",
"base_model:finetune:unsloth/Llama-3.2-3B-Instruct-bnb-4bit",
"license:llama3.2",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-10-04T12:11:48Z |
---
language:
- en
license: llama3.2
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- llama-3
- trl
- sft
base_model: unsloth/Llama-3.2-3B-Instruct-bnb-4bit
datasets:
- mlabonne/FineTome-100k
model-index:
- name: FineTome-Llama3.2-3B-1002
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: IFEval (0-Shot)
type: HuggingFaceH4/ifeval
args:
num_few_shot: 0
metrics:
- type: inst_level_strict_acc and prompt_level_strict_acc
value: 54.74
name: strict accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=NotASI/FineTome-Llama3.2-3B-1002
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: BBH (3-Shot)
type: BBH
args:
num_few_shot: 3
metrics:
- type: acc_norm
value: 19.52
name: normalized accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=NotASI/FineTome-Llama3.2-3B-1002
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MATH Lvl 5 (4-Shot)
type: hendrycks/competition_math
args:
num_few_shot: 4
metrics:
- type: exact_match
value: 5.29
name: exact match
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=NotASI/FineTome-Llama3.2-3B-1002
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GPQA (0-shot)
type: Idavidrein/gpqa
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 0.11
name: acc_norm
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=NotASI/FineTome-Llama3.2-3B-1002
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MuSR (0-shot)
type: TAUR-Lab/MuSR
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 3.96
name: acc_norm
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=NotASI/FineTome-Llama3.2-3B-1002
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU-PRO (5-shot)
type: TIGER-Lab/MMLU-Pro
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 15.96
name: accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=NotASI/FineTome-Llama3.2-3B-1002
name: Open LLM Leaderboard
---
# IMPORTANT
In case you got the following error:
```
exception: data did not match any variant of untagged enum modelwrapper at line 1251003 column 3
```
Please upgrade your **transformer** package, that is, use the following code:
```
pip install --upgrade "transformers>=4.45"
```
# Uploaded model
- **Developed by:** NotASI
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Llama-3.2-3B-Instruct-bnb-4bit
# Details
This model was trained on **mlabonne/FineTome-100k** for *2* epochs with **rslora** + **qlora**, and achieve the final training loss: *0.596400*.
This model follows the same chat template as the base model one.
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_NotASI__FineTome-Llama3.2-3B-1002)
| Metric |Value|
|-------------------|----:|
|Avg. |16.60|
|IFEval (0-Shot) |54.74|
|BBH (3-Shot) |19.52|
|MATH Lvl 5 (4-Shot)| 5.29|
|GPQA (0-shot) | 0.11|
|MuSR (0-shot) | 3.96|
|MMLU-PRO (5-shot) |15.96|
|
jhkim12/DLthon_BERT_3
|
jhkim12
| 2024-10-07T13:42:31Z | 106 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:beomi/kcbert-base",
"base_model:finetune:beomi/kcbert-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-10-07T13:13:41Z |
---
library_name: transformers
license: apache-2.0
base_model: beomi/kcbert-base
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: DLthon_BERT_3
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# DLthon_BERT_3
This model is a fine-tuned version of [beomi/kcbert-base](https://huggingface.co/beomi/kcbert-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0562
- F1: 0.9905
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 178 | 0.3384 | 0.8918 |
| No log | 2.0 | 356 | 0.2190 | 0.9425 |
| 0.4141 | 3.0 | 534 | 0.0874 | 0.9748 |
| 0.4141 | 4.0 | 712 | 0.0634 | 0.9858 |
| 0.4141 | 5.0 | 890 | 0.0562 | 0.9905 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Tokenizers 0.19.1
|
OpenBuddy/openbuddy-qwen2.5llamaify-7b-v23.1-200k-Q4_K_M-GGUF
|
OpenBuddy
| 2024-10-07T13:39:49Z | 5 | 0 | null |
[
"gguf",
"llamaify",
"qwen2.5",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"zh",
"en",
"fr",
"de",
"ja",
"ko",
"it",
"fi",
"base_model:OpenBuddy/openbuddy-qwen2.5llamaify-7b-v23.1-200k",
"base_model:quantized:OpenBuddy/openbuddy-qwen2.5llamaify-7b-v23.1-200k",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2024-10-07T07:19:02Z |
---
base_model: OpenBuddy/openbuddy-qwen2.5llamaify-7b-v23.1-200k
language:
- zh
- en
- fr
- de
- ja
- ko
- it
- fi
license: apache-2.0
pipeline_tag: text-generation
tags:
- llamaify
- qwen2.5
- llama-cpp
- gguf-my-repo
model-index:
- name: openbuddy-qwen2.5llamaify-7b-v23.1-200k
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: IFEval (0-Shot)
type: HuggingFaceH4/ifeval
args:
num_few_shot: 0
metrics:
- type: inst_level_strict_acc and prompt_level_strict_acc
value: 56.73
name: strict accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=OpenBuddy/openbuddy-qwen2.5llamaify-7b-v23.1-200k
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: BBH (3-Shot)
type: BBH
args:
num_few_shot: 3
metrics:
- type: acc_norm
value: 36.4
name: normalized accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=OpenBuddy/openbuddy-qwen2.5llamaify-7b-v23.1-200k
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MATH Lvl 5 (4-Shot)
type: hendrycks/competition_math
args:
num_few_shot: 4
metrics:
- type: exact_match
value: 11.4
name: exact match
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=OpenBuddy/openbuddy-qwen2.5llamaify-7b-v23.1-200k
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GPQA (0-shot)
type: Idavidrein/gpqa
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 8.61
name: acc_norm
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=OpenBuddy/openbuddy-qwen2.5llamaify-7b-v23.1-200k
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MuSR (0-shot)
type: TAUR-Lab/MuSR
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 13.81
name: acc_norm
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=OpenBuddy/openbuddy-qwen2.5llamaify-7b-v23.1-200k
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU-PRO (5-shot)
type: TIGER-Lab/MMLU-Pro
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 32.75
name: accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=OpenBuddy/openbuddy-qwen2.5llamaify-7b-v23.1-200k
name: Open LLM Leaderboard
---
# ff670/openbuddy-qwen2.5llamaify-7b-v23.1-200k-Q4_K_M-GGUF
This model was converted to GGUF format from [`OpenBuddy/openbuddy-qwen2.5llamaify-7b-v23.1-200k`](https://huggingface.co/OpenBuddy/openbuddy-qwen2.5llamaify-7b-v23.1-200k) 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/OpenBuddy/openbuddy-qwen2.5llamaify-7b-v23.1-200k) 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 ff670/openbuddy-qwen2.5llamaify-7b-v23.1-200k-Q4_K_M-GGUF --hf-file openbuddy-qwen2.5llamaify-7b-v23.1-200k-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo ff670/openbuddy-qwen2.5llamaify-7b-v23.1-200k-Q4_K_M-GGUF --hf-file openbuddy-qwen2.5llamaify-7b-v23.1-200k-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 ff670/openbuddy-qwen2.5llamaify-7b-v23.1-200k-Q4_K_M-GGUF --hf-file openbuddy-qwen2.5llamaify-7b-v23.1-200k-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo ff670/openbuddy-qwen2.5llamaify-7b-v23.1-200k-Q4_K_M-GGUF --hf-file openbuddy-qwen2.5llamaify-7b-v23.1-200k-q4_k_m.gguf -c 2048
```
|
SidXXD/celeba-19354-group-47_attacked_with_gaussian_noise_image
|
SidXXD
| 2024-10-07T13:39:00Z | 5 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"custom-diffusion",
"base_model:stabilityai/stable-diffusion-2-1-base",
"base_model:adapter:stabilityai/stable-diffusion-2-1-base",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2024-09-25T09:30:39Z |
---
license: creativeml-openrail-m
base_model: stabilityai/stable-diffusion-2-1-base
instance_prompt: photo of a <v1*> people
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- custom-diffusion
inference: true
---
# Custom Diffusion - SidXXD/celeba-19354-group-47_attacked_with_gaussian_noise_image
These are Custom Diffusion adaption weights for stabilityai/stable-diffusion-2-1-base. The weights were trained on photo of a <v1*> people using [Custom Diffusion](https://www.cs.cmu.edu/~custom-diffusion). You can find some example images in the following.
For more details on the training, please follow [this link](https://github.com/huggingface/diffusers/blob/main/examples/custom_diffusion).
|
RichardErkhov/HuggingFaceTB_-_SmolLM-360M-Instruct-gguf
|
RichardErkhov
| 2024-10-07T13:36:04Z | 8 | 0 | null |
[
"gguf",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-10-07T13:26:40Z |
Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
SmolLM-360M-Instruct - GGUF
- Model creator: https://huggingface.co/HuggingFaceTB/
- Original model: https://huggingface.co/HuggingFaceTB/SmolLM-360M-Instruct/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [SmolLM-360M-Instruct.Q2_K.gguf](https://huggingface.co/RichardErkhov/HuggingFaceTB_-_SmolLM-360M-Instruct-gguf/blob/main/SmolLM-360M-Instruct.Q2_K.gguf) | Q2_K | 0.2GB |
| [SmolLM-360M-Instruct.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/HuggingFaceTB_-_SmolLM-360M-Instruct-gguf/blob/main/SmolLM-360M-Instruct.IQ3_XS.gguf) | IQ3_XS | 0.2GB |
| [SmolLM-360M-Instruct.IQ3_S.gguf](https://huggingface.co/RichardErkhov/HuggingFaceTB_-_SmolLM-360M-Instruct-gguf/blob/main/SmolLM-360M-Instruct.IQ3_S.gguf) | IQ3_S | 0.2GB |
| [SmolLM-360M-Instruct.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/HuggingFaceTB_-_SmolLM-360M-Instruct-gguf/blob/main/SmolLM-360M-Instruct.Q3_K_S.gguf) | Q3_K_S | 0.2GB |
| [SmolLM-360M-Instruct.IQ3_M.gguf](https://huggingface.co/RichardErkhov/HuggingFaceTB_-_SmolLM-360M-Instruct-gguf/blob/main/SmolLM-360M-Instruct.IQ3_M.gguf) | IQ3_M | 0.21GB |
| [SmolLM-360M-Instruct.Q3_K.gguf](https://huggingface.co/RichardErkhov/HuggingFaceTB_-_SmolLM-360M-Instruct-gguf/blob/main/SmolLM-360M-Instruct.Q3_K.gguf) | Q3_K | 0.22GB |
| [SmolLM-360M-Instruct.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/HuggingFaceTB_-_SmolLM-360M-Instruct-gguf/blob/main/SmolLM-360M-Instruct.Q3_K_M.gguf) | Q3_K_M | 0.22GB |
| [SmolLM-360M-Instruct.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/HuggingFaceTB_-_SmolLM-360M-Instruct-gguf/blob/main/SmolLM-360M-Instruct.Q3_K_L.gguf) | Q3_K_L | 0.23GB |
| [SmolLM-360M-Instruct.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/HuggingFaceTB_-_SmolLM-360M-Instruct-gguf/blob/main/SmolLM-360M-Instruct.IQ4_XS.gguf) | IQ4_XS | 0.21GB |
| [SmolLM-360M-Instruct.Q4_0.gguf](https://huggingface.co/RichardErkhov/HuggingFaceTB_-_SmolLM-360M-Instruct-gguf/blob/main/SmolLM-360M-Instruct.Q4_0.gguf) | Q4_0 | 0.21GB |
| [SmolLM-360M-Instruct.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/HuggingFaceTB_-_SmolLM-360M-Instruct-gguf/blob/main/SmolLM-360M-Instruct.IQ4_NL.gguf) | IQ4_NL | 0.21GB |
| [SmolLM-360M-Instruct.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/HuggingFaceTB_-_SmolLM-360M-Instruct-gguf/blob/main/SmolLM-360M-Instruct.Q4_K_S.gguf) | Q4_K_S | 0.24GB |
| [SmolLM-360M-Instruct.Q4_K.gguf](https://huggingface.co/RichardErkhov/HuggingFaceTB_-_SmolLM-360M-Instruct-gguf/blob/main/SmolLM-360M-Instruct.Q4_K.gguf) | Q4_K | 0.25GB |
| [SmolLM-360M-Instruct.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/HuggingFaceTB_-_SmolLM-360M-Instruct-gguf/blob/main/SmolLM-360M-Instruct.Q4_K_M.gguf) | Q4_K_M | 0.25GB |
| [SmolLM-360M-Instruct.Q4_1.gguf](https://huggingface.co/RichardErkhov/HuggingFaceTB_-_SmolLM-360M-Instruct-gguf/blob/main/SmolLM-360M-Instruct.Q4_1.gguf) | Q4_1 | 0.23GB |
| [SmolLM-360M-Instruct.Q5_0.gguf](https://huggingface.co/RichardErkhov/HuggingFaceTB_-_SmolLM-360M-Instruct-gguf/blob/main/SmolLM-360M-Instruct.Q5_0.gguf) | Q5_0 | 0.25GB |
| [SmolLM-360M-Instruct.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/HuggingFaceTB_-_SmolLM-360M-Instruct-gguf/blob/main/SmolLM-360M-Instruct.Q5_K_S.gguf) | Q5_K_S | 0.26GB |
| [SmolLM-360M-Instruct.Q5_K.gguf](https://huggingface.co/RichardErkhov/HuggingFaceTB_-_SmolLM-360M-Instruct-gguf/blob/main/SmolLM-360M-Instruct.Q5_K.gguf) | Q5_K | 0.27GB |
| [SmolLM-360M-Instruct.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/HuggingFaceTB_-_SmolLM-360M-Instruct-gguf/blob/main/SmolLM-360M-Instruct.Q5_K_M.gguf) | Q5_K_M | 0.27GB |
| [SmolLM-360M-Instruct.Q5_1.gguf](https://huggingface.co/RichardErkhov/HuggingFaceTB_-_SmolLM-360M-Instruct-gguf/blob/main/SmolLM-360M-Instruct.Q5_1.gguf) | Q5_1 | 0.27GB |
| [SmolLM-360M-Instruct.Q6_K.gguf](https://huggingface.co/RichardErkhov/HuggingFaceTB_-_SmolLM-360M-Instruct-gguf/blob/main/SmolLM-360M-Instruct.Q6_K.gguf) | Q6_K | 0.34GB |
| [SmolLM-360M-Instruct.Q8_0.gguf](https://huggingface.co/RichardErkhov/HuggingFaceTB_-_SmolLM-360M-Instruct-gguf/blob/main/SmolLM-360M-Instruct.Q8_0.gguf) | Q8_0 | 0.36GB |
Original model description:
---
license: apache-2.0
base_model: HuggingFaceTB/SmolLM-360M
tags:
- alignment-handbook
- trl
- sft
datasets:
- Magpie-Align/Magpie-Pro-300K-Filtered
- bigcode/self-oss-instruct-sc2-exec-filter-50k
- teknium/OpenHermes-2.5
- HuggingFaceTB/everyday-conversations-llama3.1-2k
library_name: transformers
language:
- en
---
# SmolLM-360M-Instruct
<center>
<img src="https://huggingface.co/datasets/HuggingFaceTB/images/resolve/main/banner_smol.png" alt="SmolLM" width="1100" height="600">
</center>
## Model Summary
Chat with the model at: https://huggingface.co/spaces/HuggingFaceTB/instant-smol
SmolLM is a series of language models available in three sizes: 135M, 360M, and 1.7B parameters.
These models are trained on [SmolLM-Corpus](https://huggingface.co/datasets/HuggingFaceTB/smollm-corpus), a curated collection of high-quality educational and synthetic data designed for training LLMs. For further details, we refer to our [blogpost](https://huggingface.co/blog/smollm).
To build SmolLM-Instruct, we finetune the base models on publicly available datasets.
## Changelog
|Release|Description|
|-|-|
|v0.1| Initial release of SmolLM-Instruct. We finetune on the permissive subset of the [WebInstructSub](https://huggingface.co/datasets/TIGER-Lab/WebInstructSub) dataset, combined with [StarCoder2-Self-OSS-Instruct](https://huggingface.co/datasets/bigcode/self-oss-instruct-sc2-exec-filter-50k). Then, we perform DPO (Direct Preference Optimization) for one epoch on [HelpSteer](https://huggingface.co/datasets/nvidia/HelpSteer) for the 135M and 1.7B models, and [argilla/dpo-mix-7k](https://huggingface.co/datasets/argilla/dpo-mix-7k) for the 360M model.|
|v0.2| We changed the finetuning mix to datasets more suitable for smol models. We train on a new dataset of 2k simple everyday conversations we generated by llama3.1-70B [everyday-conversations-llama3.1-2k](https://huggingface.co/datasets/HuggingFaceTB/everyday-conversations-llama3.1-2k/), [Magpie-Pro-300K-Filtere](https://huggingface.co/datasets/Magpie-Align/Magpie-Pro-300K-Filtered), [StarCoder2-Self-OSS-Instruct](https://huggingface.co/datasets/bigcode/self-oss-instruct-sc2-exec-filter-50k), and a small subset of [OpenHermes-2.5](https://huggingface.co/datasets/teknium/OpenHermes-2.5)|
v0.2 models are better at staying on topic and responding appropriately to standard prompts, such as greetings and questions about their role as AI assistants. SmolLM-360M-Instruct (v0.2) has a 63.3% win rate over SmolLM-360M-Instruct (v0.1) on AlpacaEval. You can find the details [here](https://huggingface.co/datasets/HuggingFaceTB/alpaca_eval_details/).
You can load v0.1 models by specifying `revision="v0.1"` in the transformers code:
```python
model = AutoModelForCausalLM.from_pretrained("HuggingFaceTB/SmolLM-360M-Instruct", revision="v0.1")
```
## Usage
### Local Applications
⚡ For local applications, you can find optimized implementations of the model in MLC, GGUF and Transformers.js formats, in addition to fast in-browser demos in this collection: https://huggingface.co/collections/HuggingFaceTB/local-smollms-66c0f3b2a15b4eed7fb198d0
We noticed that 4bit quantization degrades the quality of the 135M and 360M, so we use `q016` for MLC and ONNX/Transformers.js checkpoints for the WebGPU demos. We also suggest using temperature 0.2 and top-p 0.9.
### Transformers
```bash
pip install transformers
```
```python
# pip install transformers
from transformers import AutoModelForCausalLM, AutoTokenizer
checkpoint = "HuggingFaceTB/SmolLM-360M-Instruct"
device = "cuda" # for GPU usage or "cpu" for CPU usage
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
# for multiple GPUs install accelerate and do `model = AutoModelForCausalLM.from_pretrained(checkpoint, device_map="auto")`
model = AutoModelForCausalLM.from_pretrained(checkpoint).to(device)
messages = [{"role": "user", "content": "What is the capital of France."}]
input_text=tokenizer.apply_chat_template(messages, tokenize=False)
print(input_text)
inputs = tokenizer.encode(input_text, return_tensors="pt").to(device)
outputs = model.generate(inputs, max_new_tokens=50, temperature=0.2, top_p=0.9, do_sample=True)
print(tokenizer.decode(outputs[0]))
```
### Chat in TRL
You can also use the TRL CLI to chat with the model from the terminal:
```bash
pip install trl
trl chat --model_name_or_path HuggingFaceTB/SmolLM-360M-Instruct --device cpu
```
## Limitations
Additionally, the generated content may not always be factually accurate, logically consistent, or free from biases present in the training data, we invite users to leverage them as assistive tools rather than definitive sources of information. We find that they can handle general knowledge questions, creative writing and basic Python programming. But they are English only and may have difficulty with arithmetics, editing tasks and complex reasoning. For more details about the models' capabilities, please refer to our [blog post](https://huggingface.co/blog/smollm).
## Training parameters
We train the models using the [alignment-handbook](https://github.com/huggingface/alignment-handbook) with the datasets mentioned in the changelog, using these parameters for v0.2 (most of them are from Zephyr Gemma recipe):
- 1 epoch
- lr 1e-3
- cosine schedule
- warmup ratio 0.1
- global batch size 262k tokens
You can find the training recipe here: https://github.com/huggingface/alignment-handbook/tree/smollm/recipes/smollm
# Citation
```bash
@misc{allal2024SmolLM,
title={SmolLM - blazingly fast and remarkably powerful},
author={Loubna Ben Allal and Anton Lozhkov and Elie Bakouch and Leandro von Werra and Thomas Wolf},
year={2024},
}
```
|
huimanho/CustomModel_yelp
|
huimanho
| 2024-10-07T13:33:55Z | 105 | 0 |
transformers
|
[
"transformers",
"safetensors",
"distilbert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-10-07T13:33:40Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **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]
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[More Information Needed]
## Model Card Contact
[More Information Needed]
|
isom5240/CustomModel_yelp_2024fall_v2
|
isom5240
| 2024-10-07T13:33:33Z | 105 | 0 |
transformers
|
[
"transformers",
"safetensors",
"distilbert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-10-07T13:33:20Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
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[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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[More Information Needed]
## Model Card Contact
[More Information Needed]
|
kitosforos/sd-class-butterflies-32
|
kitosforos
| 2024-10-07T13:31:32Z | 44 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"pytorch",
"unconditional-image-generation",
"diffusion-models-class",
"license:mit",
"diffusers:DDPMPipeline",
"region:us"
] |
unconditional-image-generation
| 2024-10-07T13:31:13Z |
---
license: mit
tags:
- pytorch
- diffusers
- unconditional-image-generation
- diffusion-models-class
---
# Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class)
This model is a diffusion model for unconditional image generation of cute 🦋.
## Usage
```python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('kitosforos/sd-class-butterflies-32')
image = pipeline().images[0]
image
```
|
vhab10/Llama-3-1-8B-Instruct-Unsloth-LoRA-4bit
|
vhab10
| 2024-10-07T13:27:11Z | 76 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"conversational",
"en",
"dataset:ai-maker-space/acronyms_and_initialisms_translated",
"base_model:unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit",
"base_model:quantized:unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2024-09-22T15:02:28Z |
---
base_model: unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit
language:
- en
license: mit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
datasets:
- ai-maker-space/acronyms_and_initialisms_translated
library_name: transformers
---
## Overview
This model is a fine-tuned version of the **Meta LLaMA 3.1 8B Instruct** model.
It has been fine-tuned using the [Unsloth library](https://huggingface.co/unsloth) with **4-bit quantization** for efficient inference and deployment.
The fine-tuning process utilized a synthetic dataset from **@AI Maker Space** consisting of acronyms and their expanded forms in English and was performed using the LoRA (Low-Rank Adaptation) technique, specifically designed for instruction-based tasks.
It can be easily deployed in low-resource environments thanks to the 4-bit quantization.
# Uploaded model
- **Developed by:** vhab10
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
RichardErkhov/deepseek-ai_-_DeepSeek-Coder-V2-Lite-Instruct-gguf
|
RichardErkhov
| 2024-10-07T13:24:34Z | 13,838 | 0 | null |
[
"gguf",
"arxiv:2401.06066",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-10-07T04:53:20Z |
Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
DeepSeek-Coder-V2-Lite-Instruct - GGUF
- Model creator: https://huggingface.co/deepseek-ai/
- Original model: https://huggingface.co/deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [DeepSeek-Coder-V2-Lite-Instruct.Q2_K.gguf](https://huggingface.co/RichardErkhov/deepseek-ai_-_DeepSeek-Coder-V2-Lite-Instruct-gguf/blob/main/DeepSeek-Coder-V2-Lite-Instruct.Q2_K.gguf) | Q2_K | 5.99GB |
| [DeepSeek-Coder-V2-Lite-Instruct.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/deepseek-ai_-_DeepSeek-Coder-V2-Lite-Instruct-gguf/blob/main/DeepSeek-Coder-V2-Lite-Instruct.IQ3_XS.gguf) | IQ3_XS | 6.63GB |
| [DeepSeek-Coder-V2-Lite-Instruct.IQ3_S.gguf](https://huggingface.co/RichardErkhov/deepseek-ai_-_DeepSeek-Coder-V2-Lite-Instruct-gguf/blob/main/DeepSeek-Coder-V2-Lite-Instruct.IQ3_S.gguf) | IQ3_S | 6.97GB |
| [DeepSeek-Coder-V2-Lite-Instruct.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/deepseek-ai_-_DeepSeek-Coder-V2-Lite-Instruct-gguf/blob/main/DeepSeek-Coder-V2-Lite-Instruct.Q3_K_S.gguf) | Q3_K_S | 6.97GB |
| [DeepSeek-Coder-V2-Lite-Instruct.IQ3_M.gguf](https://huggingface.co/RichardErkhov/deepseek-ai_-_DeepSeek-Coder-V2-Lite-Instruct-gguf/blob/main/DeepSeek-Coder-V2-Lite-Instruct.IQ3_M.gguf) | IQ3_M | 7.03GB |
| [DeepSeek-Coder-V2-Lite-Instruct.Q3_K.gguf](https://huggingface.co/RichardErkhov/deepseek-ai_-_DeepSeek-Coder-V2-Lite-Instruct-gguf/blob/main/DeepSeek-Coder-V2-Lite-Instruct.Q3_K.gguf) | Q3_K | 7.57GB |
| [DeepSeek-Coder-V2-Lite-Instruct.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/deepseek-ai_-_DeepSeek-Coder-V2-Lite-Instruct-gguf/blob/main/DeepSeek-Coder-V2-Lite-Instruct.Q3_K_M.gguf) | Q3_K_M | 7.57GB |
| [DeepSeek-Coder-V2-Lite-Instruct.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/deepseek-ai_-_DeepSeek-Coder-V2-Lite-Instruct-gguf/blob/main/DeepSeek-Coder-V2-Lite-Instruct.Q3_K_L.gguf) | Q3_K_L | 7.88GB |
| [DeepSeek-Coder-V2-Lite-Instruct.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/deepseek-ai_-_DeepSeek-Coder-V2-Lite-Instruct-gguf/blob/main/DeepSeek-Coder-V2-Lite-Instruct.IQ4_XS.gguf) | IQ4_XS | 8.05GB |
| [DeepSeek-Coder-V2-Lite-Instruct.Q4_0.gguf](https://huggingface.co/RichardErkhov/deepseek-ai_-_DeepSeek-Coder-V2-Lite-Instruct-gguf/blob/main/DeepSeek-Coder-V2-Lite-Instruct.Q4_0.gguf) | Q4_0 | 8.29GB |
| [DeepSeek-Coder-V2-Lite-Instruct.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/deepseek-ai_-_DeepSeek-Coder-V2-Lite-Instruct-gguf/blob/main/DeepSeek-Coder-V2-Lite-Instruct.IQ4_NL.gguf) | IQ4_NL | 8.36GB |
| [DeepSeek-Coder-V2-Lite-Instruct.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/deepseek-ai_-_DeepSeek-Coder-V2-Lite-Instruct-gguf/blob/main/DeepSeek-Coder-V2-Lite-Instruct.Q4_K_S.gguf) | Q4_K_S | 8.88GB |
| [DeepSeek-Coder-V2-Lite-Instruct.Q4_K.gguf](https://huggingface.co/RichardErkhov/deepseek-ai_-_DeepSeek-Coder-V2-Lite-Instruct-gguf/blob/main/DeepSeek-Coder-V2-Lite-Instruct.Q4_K.gguf) | Q4_K | 9.65GB |
| [DeepSeek-Coder-V2-Lite-Instruct.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/deepseek-ai_-_DeepSeek-Coder-V2-Lite-Instruct-gguf/blob/main/DeepSeek-Coder-V2-Lite-Instruct.Q4_K_M.gguf) | Q4_K_M | 9.65GB |
| [DeepSeek-Coder-V2-Lite-Instruct.Q4_1.gguf](https://huggingface.co/RichardErkhov/deepseek-ai_-_DeepSeek-Coder-V2-Lite-Instruct-gguf/blob/main/DeepSeek-Coder-V2-Lite-Instruct.Q4_1.gguf) | Q4_1 | 9.2GB |
| [DeepSeek-Coder-V2-Lite-Instruct.Q5_0.gguf](https://huggingface.co/RichardErkhov/deepseek-ai_-_DeepSeek-Coder-V2-Lite-Instruct-gguf/blob/main/DeepSeek-Coder-V2-Lite-Instruct.Q5_0.gguf) | Q5_0 | 10.1GB |
| [DeepSeek-Coder-V2-Lite-Instruct.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/deepseek-ai_-_DeepSeek-Coder-V2-Lite-Instruct-gguf/blob/main/DeepSeek-Coder-V2-Lite-Instruct.Q5_K_S.gguf) | Q5_K_S | 10.38GB |
| [DeepSeek-Coder-V2-Lite-Instruct.Q5_K.gguf](https://huggingface.co/RichardErkhov/deepseek-ai_-_DeepSeek-Coder-V2-Lite-Instruct-gguf/blob/main/DeepSeek-Coder-V2-Lite-Instruct.Q5_K.gguf) | Q5_K | 11.04GB |
| [DeepSeek-Coder-V2-Lite-Instruct.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/deepseek-ai_-_DeepSeek-Coder-V2-Lite-Instruct-gguf/blob/main/DeepSeek-Coder-V2-Lite-Instruct.Q5_K_M.gguf) | Q5_K_M | 11.04GB |
| [DeepSeek-Coder-V2-Lite-Instruct.Q5_1.gguf](https://huggingface.co/RichardErkhov/deepseek-ai_-_DeepSeek-Coder-V2-Lite-Instruct-gguf/blob/main/DeepSeek-Coder-V2-Lite-Instruct.Q5_1.gguf) | Q5_1 | 11.0GB |
| [DeepSeek-Coder-V2-Lite-Instruct.Q6_K.gguf](https://huggingface.co/RichardErkhov/deepseek-ai_-_DeepSeek-Coder-V2-Lite-Instruct-gguf/blob/main/DeepSeek-Coder-V2-Lite-Instruct.Q6_K.gguf) | Q6_K | 13.1GB |
| [DeepSeek-Coder-V2-Lite-Instruct.Q8_0.gguf](https://huggingface.co/RichardErkhov/deepseek-ai_-_DeepSeek-Coder-V2-Lite-Instruct-gguf/blob/main/DeepSeek-Coder-V2-Lite-Instruct.Q8_0.gguf) | Q8_0 | 15.56GB |
Original model description:
---
license: other
license_name: deepseek-license
license_link: LICENSE
---
<!-- markdownlint-disable first-line-h1 -->
<!-- markdownlint-disable html -->
<!-- markdownlint-disable no-duplicate-header -->
<div align="center">
<img src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/logo.svg?raw=true" width="60%" alt="DeepSeek-V2" />
</div>
<hr>
<div align="center" style="line-height: 1;">
<a href="https://www.deepseek.com/" target="_blank" style="margin: 2px;">
<img alt="Homepage" src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/badge.svg?raw=true" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://chat.deepseek.com/" target="_blank" style="margin: 2px;">
<img alt="Chat" src="https://img.shields.io/badge/🤖%20Chat-DeepSeek%20V2-536af5?color=536af5&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://huggingface.co/deepseek-ai" target="_blank" style="margin: 2px;">
<img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-DeepSeek%20AI-ffc107?color=ffc107&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
</a>
</div>
<div align="center" style="line-height: 1;">
<a href="https://discord.gg/Tc7c45Zzu5" target="_blank" style="margin: 2px;">
<img alt="Discord" src="https://img.shields.io/badge/Discord-DeepSeek%20AI-7289da?logo=discord&logoColor=white&color=7289da" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/qr.jpeg?raw=true" target="_blank" style="margin: 2px;">
<img alt="Wechat" src="https://img.shields.io/badge/WeChat-DeepSeek%20AI-brightgreen?logo=wechat&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://twitter.com/deepseek_ai" target="_blank" style="margin: 2px;">
<img alt="Twitter Follow" src="https://img.shields.io/badge/Twitter-deepseek_ai-white?logo=x&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
</a>
</div>
<div align="center" style="line-height: 1;">
<a href="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/LICENSE-CODE" style="margin: 2px;">
<img alt="Code License" src="https://img.shields.io/badge/Code_License-MIT-f5de53?&color=f5de53" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/LICENSE-MODEL" style="margin: 2px;">
<img alt="Model License" src="https://img.shields.io/badge/Model_License-Model_Agreement-f5de53?&color=f5de53" style="display: inline-block; vertical-align: middle;"/>
</a>
</div>
<p align="center">
<a href="#4-api-platform">API Platform</a> |
<a href="#5-how-to-run-locally">How to Use</a> |
<a href="#6-license">License</a> |
</p>
<p align="center">
<a href="https://github.com/deepseek-ai/DeepSeek-Coder-V2/blob/main/paper.pdf"><b>Paper Link</b>👁️</a>
</p>
# DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code Intelligence
## 1. Introduction
We present DeepSeek-Coder-V2, an open-source Mixture-of-Experts (MoE) code language model that achieves performance comparable to GPT4-Turbo in code-specific tasks. Specifically, DeepSeek-Coder-V2 is further pre-trained from an intermediate checkpoint of DeepSeek-V2 with additional 6 trillion tokens. Through this continued pre-training, DeepSeek-Coder-V2 substantially enhances the coding and mathematical reasoning capabilities of DeepSeek-V2, while maintaining comparable performance in general language tasks. Compared to DeepSeek-Coder-33B, DeepSeek-Coder-V2 demonstrates significant advancements in various aspects of code-related tasks, as well as reasoning and general capabilities. Additionally, DeepSeek-Coder-V2 expands its support for programming languages from 86 to 338, while extending the context length from 16K to 128K.
<p align="center">
<img width="100%" src="https://github.com/deepseek-ai/DeepSeek-Coder-V2/blob/main/figures/performance.png?raw=true">
</p>
In standard benchmark evaluations, DeepSeek-Coder-V2 achieves superior performance compared to closed-source models such as GPT4-Turbo, Claude 3 Opus, and Gemini 1.5 Pro in coding and math benchmarks. The list of supported programming languages can be found [here](https://github.com/deepseek-ai/DeepSeek-Coder-V2/blob/main/supported_langs.txt).
## 2. Model Downloads
We release the DeepSeek-Coder-V2 with 16B and 236B parameters based on the [DeepSeekMoE](https://arxiv.org/pdf/2401.06066) framework, which has actived parameters of only 2.4B and 21B , including base and instruct models, to the public.
<div align="center">
| **Model** | **#Total Params** | **#Active Params** | **Context Length** | **Download** |
| :-----------------------------: | :---------------: | :----------------: | :----------------: | :----------------------------------------------------------: |
| DeepSeek-Coder-V2-Lite-Base | 16B | 2.4B | 128k | [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-Coder-V2-Lite-Base) |
| DeepSeek-Coder-V2-Lite-Instruct | 16B | 2.4B | 128k | [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct) |
| DeepSeek-Coder-V2-Base | 236B | 21B | 128k | [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-Coder-V2-Base) |
| DeepSeek-Coder-V2-Instruct | 236B | 21B | 128k | [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-Coder-V2-Instruct) |
</div>
## 3. Chat Website
You can chat with the DeepSeek-Coder-V2 on DeepSeek's official website: [coder.deepseek.com](https://coder.deepseek.com/sign_in)
## 4. API Platform
We also provide OpenAI-Compatible API at DeepSeek Platform: [platform.deepseek.com](https://platform.deepseek.com/), and you can also pay-as-you-go at an unbeatable price.
<p align="center">
<img width="40%" src="https://github.com/deepseek-ai/DeepSeek-Coder-V2/blob/main/figures/model_price.jpg?raw=true">
</p>
## 5. How to run locally
**Here, we provide some examples of how to use DeepSeek-Coder-V2-Lite model. If you want to utilize DeepSeek-Coder-V2 in BF16 format for inference, 80GB*8 GPUs are required.**
### Inference with Huggingface's Transformers
You can directly employ [Huggingface's Transformers](https://github.com/huggingface/transformers) for model inference.
#### Code Completion
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/DeepSeek-Coder-V2-Lite-Base", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("deepseek-ai/DeepSeek-Coder-V2-Lite-Base", trust_remote_code=True, torch_dtype=torch.bfloat16).cuda()
input_text = "#write a quick sort algorithm"
inputs = tokenizer(input_text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_length=128)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
#### Code Insertion
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/DeepSeek-Coder-V2-Lite-Base", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("deepseek-ai/DeepSeek-Coder-V2-Lite-Base", trust_remote_code=True, torch_dtype=torch.bfloat16).cuda()
input_text = """<|fim▁begin|>def quick_sort(arr):
if len(arr) <= 1:
return arr
pivot = arr[0]
left = []
right = []
<|fim▁hole|>
if arr[i] < pivot:
left.append(arr[i])
else:
right.append(arr[i])
return quick_sort(left) + [pivot] + quick_sort(right)<|fim▁end|>"""
inputs = tokenizer(input_text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_length=128)
print(tokenizer.decode(outputs[0], skip_special_tokens=True)[len(input_text):])
```
#### Chat Completion
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct", trust_remote_code=True, torch_dtype=torch.bfloat16).cuda()
messages=[
{ 'role': 'user', 'content': "write a quick sort algorithm in python."}
]
inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
# tokenizer.eos_token_id is the id of <|end▁of▁sentence|> token
outputs = model.generate(inputs, max_new_tokens=512, do_sample=False, top_k=50, top_p=0.95, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id)
print(tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True))
```
The complete chat template can be found within `tokenizer_config.json` located in the huggingface model repository.
An example of chat template is as belows:
```bash
<|begin▁of▁sentence|>User: {user_message_1}
Assistant: {assistant_message_1}<|end▁of▁sentence|>User: {user_message_2}
Assistant:
```
You can also add an optional system message:
```bash
<|begin▁of▁sentence|>{system_message}
User: {user_message_1}
Assistant: {assistant_message_1}<|end▁of▁sentence|>User: {user_message_2}
Assistant:
```
### Inference with vLLM (recommended)
To utilize [vLLM](https://github.com/vllm-project/vllm) for model inference, please merge this Pull Request into your vLLM codebase: https://github.com/vllm-project/vllm/pull/4650.
```python
from transformers import AutoTokenizer
from vllm import LLM, SamplingParams
max_model_len, tp_size = 8192, 1
model_name = "deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct"
tokenizer = AutoTokenizer.from_pretrained(model_name)
llm = LLM(model=model_name, tensor_parallel_size=tp_size, max_model_len=max_model_len, trust_remote_code=True, enforce_eager=True)
sampling_params = SamplingParams(temperature=0.3, max_tokens=256, stop_token_ids=[tokenizer.eos_token_id])
messages_list = [
[{"role": "user", "content": "Who are you?"}],
[{"role": "user", "content": "write a quick sort algorithm in python."}],
[{"role": "user", "content": "Write a piece of quicksort code in C++."}],
]
prompt_token_ids = [tokenizer.apply_chat_template(messages, add_generation_prompt=True) for messages in messages_list]
outputs = llm.generate(prompt_token_ids=prompt_token_ids, sampling_params=sampling_params)
generated_text = [output.outputs[0].text for output in outputs]
print(generated_text)
```
## 6. License
This code repository is licensed under [the MIT License](https://github.com/deepseek-ai/DeepSeek-Coder-V2/blob/main/LICENSE-CODE). The use of DeepSeek-Coder-V2 Base/Instruct models is subject to [the Model License](https://github.com/deepseek-ai/DeepSeek-Coder-V2/blob/main/LICENSE-MODEL). DeepSeek-Coder-V2 series (including Base and Instruct) supports commercial use.
## 7. Contact
If you have any questions, please raise an issue or contact us at [[email protected]]([email protected]).
|
csikasote/mms-1b-all-bem-genbed-f-model
|
csikasote
| 2024-10-07T13:23:41Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"genbed",
"mms",
"generated_from_trainer",
"base_model:facebook/mms-1b-all",
"base_model:finetune:facebook/mms-1b-all",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2024-09-26T17:16:54Z |
---
library_name: transformers
license: cc-by-nc-4.0
base_model: facebook/mms-1b-all
tags:
- automatic-speech-recognition
- genbed
- mms
- generated_from_trainer
metrics:
- wer
model-index:
- name: mms-1b-all-bem-genbed-f-model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mms-1b-all-bem-genbed-f-model
This model is a fine-tuned version of [facebook/mms-1b-all](https://huggingface.co/facebook/mms-1b-all) on the GENBED - BEM dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1823
- Wer: 0.3431
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 30.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 6.6556 | 0.1370 | 100 | 0.5951 | 0.6643 |
| 0.4415 | 0.2740 | 200 | 0.2734 | 0.4565 |
| 0.3448 | 0.4110 | 300 | 0.2482 | 0.4289 |
| 0.3459 | 0.5479 | 400 | 0.2392 | 0.4149 |
| 0.3184 | 0.6849 | 500 | 0.2304 | 0.4085 |
| 0.3058 | 0.8219 | 600 | 0.2372 | 0.4108 |
| 0.3077 | 0.9589 | 700 | 0.2271 | 0.4172 |
| 0.2812 | 1.0959 | 800 | 0.2217 | 0.3983 |
| 0.3297 | 1.2329 | 900 | 0.2209 | 0.3984 |
| 0.2817 | 1.3699 | 1000 | 0.2163 | 0.4124 |
| 0.2927 | 1.5068 | 1100 | 0.2146 | 0.3863 |
| 0.2806 | 1.6438 | 1200 | 0.2106 | 0.3851 |
| 0.2574 | 1.7808 | 1300 | 0.2098 | 0.3866 |
| 0.2829 | 1.9178 | 1400 | 0.2067 | 0.3772 |
| 0.2764 | 2.0548 | 1500 | 0.2076 | 0.3789 |
| 0.2635 | 2.1918 | 1600 | 0.2076 | 0.3769 |
| 0.2761 | 2.3288 | 1700 | 0.2068 | 0.3801 |
| 0.2854 | 2.4658 | 1800 | 0.1994 | 0.3645 |
| 0.2557 | 2.6027 | 1900 | 0.2016 | 0.3861 |
| 0.2717 | 2.7397 | 2000 | 0.2011 | 0.3734 |
| 0.2504 | 2.8767 | 2100 | 0.1989 | 0.3674 |
| 0.2606 | 3.0137 | 2200 | 0.1990 | 0.3835 |
| 0.2583 | 3.1507 | 2300 | 0.2028 | 0.3666 |
| 0.2591 | 3.2877 | 2400 | 0.1952 | 0.3507 |
| 0.2408 | 3.4247 | 2500 | 0.1988 | 0.3637 |
| 0.2485 | 3.5616 | 2600 | 0.1972 | 0.3593 |
| 0.2474 | 3.6986 | 2700 | 0.1949 | 0.3534 |
| 0.2398 | 3.8356 | 2800 | 0.1959 | 0.3697 |
| 0.2512 | 3.9726 | 2900 | 0.1906 | 0.3559 |
| 0.2266 | 4.1096 | 3000 | 0.1905 | 0.3482 |
| 0.2538 | 4.2466 | 3100 | 0.1916 | 0.3521 |
| 0.2268 | 4.3836 | 3200 | 0.1914 | 0.3895 |
| 0.2249 | 4.5205 | 3300 | 0.1897 | 0.3417 |
| 0.2416 | 4.6575 | 3400 | 0.1877 | 0.3458 |
| 0.2421 | 4.7945 | 3500 | 0.1872 | 0.3412 |
| 0.244 | 4.9315 | 3600 | 0.1855 | 0.3528 |
| 0.2371 | 5.0685 | 3700 | 0.1871 | 0.3447 |
| 0.2383 | 5.2055 | 3800 | 0.1833 | 0.3523 |
| 0.2409 | 5.3425 | 3900 | 0.1886 | 0.3487 |
| 0.2312 | 5.4795 | 4000 | 0.1848 | 0.3438 |
| 0.2261 | 5.6164 | 4100 | 0.1866 | 0.3469 |
| 0.2169 | 5.7534 | 4200 | 0.1841 | 0.3376 |
| 0.2283 | 5.8904 | 4300 | 0.1865 | 0.3412 |
| 0.2182 | 6.0274 | 4400 | 0.1823 | 0.3431 |
| 0.2141 | 6.1644 | 4500 | 0.1858 | 0.3403 |
| 0.2127 | 6.3014 | 4600 | 0.1876 | 0.3356 |
| 0.229 | 6.4384 | 4700 | 0.1863 | 0.3361 |
### Framework versions
- Transformers 4.46.0.dev0
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.0
|
NotASI/FineTome-Llama3.2-1B-0929
|
NotASI
| 2024-10-07T13:23:18Z | 172 | 1 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"llama-3",
"trl",
"sft",
"conversational",
"en",
"dataset:mlabonne/FineTome-100k",
"base_model:unsloth/Llama-3.2-1B-Instruct-bnb-4bit",
"base_model:finetune:unsloth/Llama-3.2-1B-Instruct-bnb-4bit",
"license:llama3.2",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-09-29T16:29:52Z |
---
language:
- en
license: llama3.2
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- llama-3
- trl
- sft
base_model: unsloth/Llama-3.2-1B-Instruct-bnb-4bit
datasets:
- mlabonne/FineTome-100k
model-index:
- name: FineTome-Llama3.2-1B-0929
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: IFEval (0-Shot)
type: HuggingFaceH4/ifeval
args:
num_few_shot: 0
metrics:
- type: inst_level_strict_acc and prompt_level_strict_acc
value: 39.91
name: strict accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=NotASI/FineTome-Llama3.2-1B-0929
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: BBH (3-Shot)
type: BBH
args:
num_few_shot: 3
metrics:
- type: acc_norm
value: 5.74
name: normalized accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=NotASI/FineTome-Llama3.2-1B-0929
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MATH Lvl 5 (4-Shot)
type: hendrycks/competition_math
args:
num_few_shot: 4
metrics:
- type: exact_match
value: 1.28
name: exact match
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=NotASI/FineTome-Llama3.2-1B-0929
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GPQA (0-shot)
type: Idavidrein/gpqa
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 3.02
name: acc_norm
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=NotASI/FineTome-Llama3.2-1B-0929
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MuSR (0-shot)
type: TAUR-Lab/MuSR
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 2.66
name: acc_norm
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=NotASI/FineTome-Llama3.2-1B-0929
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU-PRO (5-shot)
type: TIGER-Lab/MMLU-Pro
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 4.76
name: accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=NotASI/FineTome-Llama3.2-1B-0929
name: Open LLM Leaderboard
---
# Notice
**Code + Math** optimized version coming soon!
# IMPORTANT
In case you got the following error:
```
exception: data did not match any variant of untagged enum modelwrapper at line 1251003 column 3
```
Please upgrade your **transformer** package, that is, use the following code:
```
pip install --upgrade "transformers>=4.45"
```
# Uploaded model
- **Developed by:** NotASI
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Llama-3.2-1B-Instruct-bnb-4bit
# Details
This model was trained on **mlabonne/FineTome-100k** for *2* epochs with **rslora** + **qlora**, and achieve the final training loss: *0.796700*.
This model follows the same chat template as the base model one.
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
# Usage
Sample notebook (Unsloth): https://colab.research.google.com/drive/1cSCvemKsC0JVmMs4jdfoTqM8SQQ_DSBt?usp=sharing
**Dependencies**
```
!pip install unsloth
!pip install --upgrade "transformers>=4.45"
```
**Code**
```python
from unsloth.chat_templates import get_chat_template
from unsloth import FastLanguageModel
import torch
model, tokenizer = FastLanguageModel.from_pretrained(
model_name = "NotASI/FineTome-Llama3.2-1B-0929",
)
tokenizer = get_chat_template(
tokenizer,
chat_template = "llama-3.1",
)
FastLanguageModel.for_inference(model) # Enable native 2x faster inference
messages = [
{"role": "user", "content": "Hi!"},
]
inputs = tokenizer.apply_chat_template(
messages,
tokenize = True,
add_generation_prompt = True, # Must add for generation
return_tensors = "pt",
).to("cuda")
from transformers import TextStreamer
text_streamer = TextStreamer(tokenizer, skip_prompt = True)
_ = model.generate(input_ids = inputs, streamer = text_streamer, max_new_tokens = 128,
use_cache = True, temperature = 1.5, min_p = 0.1)
```
## Example 1
**Input**:
```
Continue the fibonnaci sequence: 1, 1, 2, 3, 5, 8. Give me the next 10 numbers.
```
**Output**:
```
Here are the next 10 numbers in the Fibonacci sequence:
1. 13
2. 21
3. 34
4. 55
5. 89
6. 144
7. 233
8. 377
9. 610
10. 987
11. 1597
12. 2584
13. 4190
14. 6765
15. 10946
16. 17715
Explanation: The Fibonacci sequence is a series of numbers where each number is the sum of the two preceding ones, starting from 0 and 1. To calculate the nth Fibonacci number, you can use the formula F(n) = F(n-1) + F(n-2).<|eot_id|>
```
## Example 2
**Input**:
```
Who are you? Briefy introduce yourself.
```
**Output**:
```
Hi there! I'm an AI assistant designed to help with a range of inquiries. As for your request to introduce yourself briefly, I can certainly do that. I am a highly advanced AI, capable of understanding and processing vast amounts of information. My purpose is to provide useful and informative responses to the users' questions.<|eot_id|>
```
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_NotASI__FineTome-Llama3.2-1B-0929)
| Metric |Value|
|-------------------|----:|
|Avg. | 9.56|
|IFEval (0-Shot) |39.91|
|BBH (3-Shot) | 5.74|
|MATH Lvl 5 (4-Shot)| 1.28|
|GPQA (0-shot) | 3.02|
|MuSR (0-shot) | 2.66|
|MMLU-PRO (5-shot) | 4.76|
|
QuantFactory/Qwen2.5-0.5b-RBase-GGUF
|
QuantFactory
| 2024-10-07T13:21:22Z | 82 | 2 |
transformers
|
[
"transformers",
"gguf",
"text-generation-inference",
"unsloth",
"qwen2",
"trl",
"sft",
"en",
"base_model:Qwen/Qwen2.5-0.5B-Instruct",
"base_model:quantized:Qwen/Qwen2.5-0.5B-Instruct",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-10-07T13:16:36Z |
---
base_model:
- Qwen/Qwen2.5-0.5B-Instruct
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
- sft
---
[](https://hf.co/QuantFactory)
# QuantFactory/Qwen2.5-0.5b-RBase-GGUF
This is quantized version of [KingNish/Qwen2.5-0.5b-RBase](https://huggingface.co/KingNish/Qwen2.5-0.5b-RBase) created using llama.cpp
# Original Model Card
# Qwen 2.5 0.5B Model
## Model Description
This model is a compact yet powerful language model trained to answer a variety of questions with impressive quality. Despite its smaller size, it has demonstrated performance comparable to Llama 3.2 1B, and in some cases, it even outperforms it. This model was specifically trained on a 12,800 rows of the Magpie 300k Dataset.
## Performance
The Qwen 2.5 model has shown promising results in various tests, including the "strawberry test, Decimal Comparison test" where it successfully provided accurate answers. However, it is important to note that, like many models of its size, it may occasionally produce incorrect answers or flawed reasoning. Continuous improvements and full training are planned to enhance its performance further.
## How to Use
To use the Qwen 2.5 model, you can load it using the Hugging Face Transformers library. Here’s a simple example:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "KingNish/Qwen2.5-0.5b-Test-ft"
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)
prompt = "Which is greater 9.9 or 9.11 ??"
messages = [
{"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."},
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
generated_ids = model.generate(
**model_inputs,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)
```
## Future Work
I am actively working on improving the Qwen 2.5 model by training it on a larger dataset.
# Uploaded model
- **Developed by:** KingNish
- **License:** apache-2.0
- **Finetuned from model :** Qwen/Qwen2.5-0.5B-Instruct
This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
Tombiczek/sentiment_model_deberta_v1
|
Tombiczek
| 2024-10-07T13:14:55Z | 164 | 0 |
transformers
|
[
"transformers",
"safetensors",
"deberta-v2",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-10-07T13:14:23Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
RichardErkhov/leap-llm_-_Meta-Llama-3-8B-Instruct-sft-webshop-iter2-gguf
|
RichardErkhov
| 2024-10-07T13:13:34Z | 9 | 0 | null |
[
"gguf",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-10-07T09:42:37Z |
Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
Meta-Llama-3-8B-Instruct-sft-webshop-iter2 - GGUF
- Model creator: https://huggingface.co/leap-llm/
- Original model: https://huggingface.co/leap-llm/Meta-Llama-3-8B-Instruct-sft-webshop-iter2/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [Meta-Llama-3-8B-Instruct-sft-webshop-iter2.Q2_K.gguf](https://huggingface.co/RichardErkhov/leap-llm_-_Meta-Llama-3-8B-Instruct-sft-webshop-iter2-gguf/blob/main/Meta-Llama-3-8B-Instruct-sft-webshop-iter2.Q2_K.gguf) | Q2_K | 2.96GB |
| [Meta-Llama-3-8B-Instruct-sft-webshop-iter2.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/leap-llm_-_Meta-Llama-3-8B-Instruct-sft-webshop-iter2-gguf/blob/main/Meta-Llama-3-8B-Instruct-sft-webshop-iter2.IQ3_XS.gguf) | IQ3_XS | 3.28GB |
| [Meta-Llama-3-8B-Instruct-sft-webshop-iter2.IQ3_S.gguf](https://huggingface.co/RichardErkhov/leap-llm_-_Meta-Llama-3-8B-Instruct-sft-webshop-iter2-gguf/blob/main/Meta-Llama-3-8B-Instruct-sft-webshop-iter2.IQ3_S.gguf) | IQ3_S | 3.43GB |
| [Meta-Llama-3-8B-Instruct-sft-webshop-iter2.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/leap-llm_-_Meta-Llama-3-8B-Instruct-sft-webshop-iter2-gguf/blob/main/Meta-Llama-3-8B-Instruct-sft-webshop-iter2.Q3_K_S.gguf) | Q3_K_S | 3.41GB |
| [Meta-Llama-3-8B-Instruct-sft-webshop-iter2.IQ3_M.gguf](https://huggingface.co/RichardErkhov/leap-llm_-_Meta-Llama-3-8B-Instruct-sft-webshop-iter2-gguf/blob/main/Meta-Llama-3-8B-Instruct-sft-webshop-iter2.IQ3_M.gguf) | IQ3_M | 3.52GB |
| [Meta-Llama-3-8B-Instruct-sft-webshop-iter2.Q3_K.gguf](https://huggingface.co/RichardErkhov/leap-llm_-_Meta-Llama-3-8B-Instruct-sft-webshop-iter2-gguf/blob/main/Meta-Llama-3-8B-Instruct-sft-webshop-iter2.Q3_K.gguf) | Q3_K | 3.74GB |
| [Meta-Llama-3-8B-Instruct-sft-webshop-iter2.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/leap-llm_-_Meta-Llama-3-8B-Instruct-sft-webshop-iter2-gguf/blob/main/Meta-Llama-3-8B-Instruct-sft-webshop-iter2.Q3_K_M.gguf) | Q3_K_M | 3.74GB |
| [Meta-Llama-3-8B-Instruct-sft-webshop-iter2.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/leap-llm_-_Meta-Llama-3-8B-Instruct-sft-webshop-iter2-gguf/blob/main/Meta-Llama-3-8B-Instruct-sft-webshop-iter2.Q3_K_L.gguf) | Q3_K_L | 4.03GB |
| [Meta-Llama-3-8B-Instruct-sft-webshop-iter2.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/leap-llm_-_Meta-Llama-3-8B-Instruct-sft-webshop-iter2-gguf/blob/main/Meta-Llama-3-8B-Instruct-sft-webshop-iter2.IQ4_XS.gguf) | IQ4_XS | 4.18GB |
| [Meta-Llama-3-8B-Instruct-sft-webshop-iter2.Q4_0.gguf](https://huggingface.co/RichardErkhov/leap-llm_-_Meta-Llama-3-8B-Instruct-sft-webshop-iter2-gguf/blob/main/Meta-Llama-3-8B-Instruct-sft-webshop-iter2.Q4_0.gguf) | Q4_0 | 4.34GB |
| [Meta-Llama-3-8B-Instruct-sft-webshop-iter2.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/leap-llm_-_Meta-Llama-3-8B-Instruct-sft-webshop-iter2-gguf/blob/main/Meta-Llama-3-8B-Instruct-sft-webshop-iter2.IQ4_NL.gguf) | IQ4_NL | 4.38GB |
| [Meta-Llama-3-8B-Instruct-sft-webshop-iter2.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/leap-llm_-_Meta-Llama-3-8B-Instruct-sft-webshop-iter2-gguf/blob/main/Meta-Llama-3-8B-Instruct-sft-webshop-iter2.Q4_K_S.gguf) | Q4_K_S | 4.37GB |
| [Meta-Llama-3-8B-Instruct-sft-webshop-iter2.Q4_K.gguf](https://huggingface.co/RichardErkhov/leap-llm_-_Meta-Llama-3-8B-Instruct-sft-webshop-iter2-gguf/blob/main/Meta-Llama-3-8B-Instruct-sft-webshop-iter2.Q4_K.gguf) | Q4_K | 4.58GB |
| [Meta-Llama-3-8B-Instruct-sft-webshop-iter2.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/leap-llm_-_Meta-Llama-3-8B-Instruct-sft-webshop-iter2-gguf/blob/main/Meta-Llama-3-8B-Instruct-sft-webshop-iter2.Q4_K_M.gguf) | Q4_K_M | 4.58GB |
| [Meta-Llama-3-8B-Instruct-sft-webshop-iter2.Q4_1.gguf](https://huggingface.co/RichardErkhov/leap-llm_-_Meta-Llama-3-8B-Instruct-sft-webshop-iter2-gguf/blob/main/Meta-Llama-3-8B-Instruct-sft-webshop-iter2.Q4_1.gguf) | Q4_1 | 4.78GB |
| [Meta-Llama-3-8B-Instruct-sft-webshop-iter2.Q5_0.gguf](https://huggingface.co/RichardErkhov/leap-llm_-_Meta-Llama-3-8B-Instruct-sft-webshop-iter2-gguf/blob/main/Meta-Llama-3-8B-Instruct-sft-webshop-iter2.Q5_0.gguf) | Q5_0 | 5.21GB |
| [Meta-Llama-3-8B-Instruct-sft-webshop-iter2.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/leap-llm_-_Meta-Llama-3-8B-Instruct-sft-webshop-iter2-gguf/blob/main/Meta-Llama-3-8B-Instruct-sft-webshop-iter2.Q5_K_S.gguf) | Q5_K_S | 5.21GB |
| [Meta-Llama-3-8B-Instruct-sft-webshop-iter2.Q5_K.gguf](https://huggingface.co/RichardErkhov/leap-llm_-_Meta-Llama-3-8B-Instruct-sft-webshop-iter2-gguf/blob/main/Meta-Llama-3-8B-Instruct-sft-webshop-iter2.Q5_K.gguf) | Q5_K | 5.34GB |
| [Meta-Llama-3-8B-Instruct-sft-webshop-iter2.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/leap-llm_-_Meta-Llama-3-8B-Instruct-sft-webshop-iter2-gguf/blob/main/Meta-Llama-3-8B-Instruct-sft-webshop-iter2.Q5_K_M.gguf) | Q5_K_M | 5.34GB |
| [Meta-Llama-3-8B-Instruct-sft-webshop-iter2.Q5_1.gguf](https://huggingface.co/RichardErkhov/leap-llm_-_Meta-Llama-3-8B-Instruct-sft-webshop-iter2-gguf/blob/main/Meta-Llama-3-8B-Instruct-sft-webshop-iter2.Q5_1.gguf) | Q5_1 | 5.65GB |
| [Meta-Llama-3-8B-Instruct-sft-webshop-iter2.Q6_K.gguf](https://huggingface.co/RichardErkhov/leap-llm_-_Meta-Llama-3-8B-Instruct-sft-webshop-iter2-gguf/blob/main/Meta-Llama-3-8B-Instruct-sft-webshop-iter2.Q6_K.gguf) | Q6_K | 6.14GB |
| [Meta-Llama-3-8B-Instruct-sft-webshop-iter2.Q8_0.gguf](https://huggingface.co/RichardErkhov/leap-llm_-_Meta-Llama-3-8B-Instruct-sft-webshop-iter2-gguf/blob/main/Meta-Llama-3-8B-Instruct-sft-webshop-iter2.Q8_0.gguf) | Q8_0 | 7.95GB |
Original model description:
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
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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]
<!-- 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. -->
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## 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]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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|
QuantFactory/TwinLlama-3.1-8B-GGUF
|
QuantFactory
| 2024-10-07T13:12:39Z | 81 | 2 |
transformers
|
[
"transformers",
"gguf",
"unsloth",
"trl",
"sft",
"en",
"dataset:mlabonne/llmtwin",
"base_model:meta-llama/Llama-3.1-8B",
"base_model:quantized:meta-llama/Llama-3.1-8B",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-10-07T11:45:19Z |
---
base_model: meta-llama/Meta-Llama-3.1-8B
datasets:
- mlabonne/llmtwin
language:
- en
library_name: transformers
license: apache-2.0
tags:
- unsloth
- trl
- sft
---
[](https://hf.co/QuantFactory)
# QuantFactory/TwinLlama-3.1-8B-GGUF
This is quantized version of [mlabonne/TwinLlama-3.1-8B](https://huggingface.co/mlabonne/TwinLlama-3.1-8B) created using llama.cpp
# Original Model Card

# 👥 TwinLlama-3.1-8B
TwinLlama-3.1-8B is a model created for the [LLM Engineer's Handbook](https://a.co/d/9vYzTUC), trained on [mlabonne/llmtwin](https://huggingface.co/datasets/mlabonne/llmtwin).
It is designed to act as a digital twin, which is a clone of myself and my co-authors (Paul Iusztin and Alex Vesa), imitating our writing style and drawing knowledge from our articles.
---
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
FrankRin/InsLLM
|
FrankRin
| 2024-10-07T13:05:48Z | 12 | 1 | null |
[
"safetensors",
"qwen2",
"text-generation",
"conversational",
"zh",
"dataset:FrankRin/Insur-QA",
"base_model:Qwen/Qwen1.5-14B-Chat",
"base_model:finetune:Qwen/Qwen1.5-14B-Chat",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2024-10-06T15:12:03Z |
---
license: apache-2.0
datasets:
- FrankRin/Insur-QA
language:
- zh
base_model:
- Qwen/Qwen1.5-14B-Chat
pipeline_tag: text-generation
---
This repository contains the InsLLM, version of Qwen1.5-14B-Chat as the base model.
<div align="center">
<h1>InsQABench</h1>
</div>
InsQABench is the first large-scale specialized question-answering dataset and evaluation benchmark in the Chinese insurance sector, developed and open-sourced by the VLR Lab (Vision and Learning Representation Group) at Huazhong University of Science and Technology.
## Overview
InsLLM is an intelligent insurance system equipped with capabilities for insurance-related question answering, database querying, and contract parsing. Designed for diverse user groups and application scenarios, it offers the following key features:
* **Insurance Text Processing:** The system is capable of understanding and generating content related to complex professional terms and document formats specific to the insurance domain. This includes tasks like information extraction and document summarization. We have constructed fine-tuning datasets based on publicly available insurance data and real-world insurance documents.
* **Insurance Reasoning:** Leveraging the SQL-ReAct method, the system can optimize and correct SQL queries based on user inputs, efficiently handling complex query tasks within insurance databases.
* **Insurance Knowledge Compliance:** Equipped with the Insur-Know module, the system supports contract parsing and fact extraction enhanced by retrieval, ensuring accurate handling of complex issues in insurance contracts.
Additionally, our research offers the following contributions:
* **High-quality insurance question-answering training datasets and effective training paradigms**
* **A comprehensive insurance model evaluation framework and evaluation datasets**
## Insur-QA Dataset
In the basic insurance knowledge section, we translated the InsuranceQA dataset to create the InsuranceQA_zh dataset.
For the insurance contract data section, we downloaded PDF insurance policies from various insurance companies available online and parsed them using the Adobe PDF Extract API. After restructuring the paragraph text from the parsed results, we used Gemini to generate QA pairs, forming <Q, A, E> triples.
The specific composition of the datasets is as follows:
<table border="1">
<tr>
<th>Task</th>
<th>Dataset</th>
<th>Source</th>
<th>Size</th>
</tr>
<tr>
<td rowspan="2">Basic Insurance Knowledge Q&A</td>
<td>Training Set</td>
<td>BX_GPT3.5</td>
<td>10k</td>
</tr>
<tr>
<td>Test Set</td>
<td>Insurance_QA_zh</td>
<td>3k</td>
</tr>
<tr>
<td rowspan="2">Insurance Contract Q&A</td>
<td>Training Set</td>
<td>Insurance Contracts</td>
<td>40k</td>
</tr>
<tr>
<td>Test Set</td>
<td>Insurance Contracts</td>
<td>100</td>
</tr>
<tr>
<td rowspan="2">Insurance Database Q&A</td>
<td>Training Set</td>
<td>Insurance Contracts</td>
<td>44k</td>
</tr>
<tr>
<td>Test Set</td>
<td>Insurance Contracts</td>
<td>546</td>
</tr>
</table>
## Citation
If you find our work helpful in your research, please consider citing it as follows:
```
@misc{
}
```
## License
InsQABench is available under the Apache License.
|
Gnider/aug_bert_6ep_9000
|
Gnider
| 2024-10-07T13:00:51Z | 106 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-10-07T12:43:18Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
adishourya/results__fullrun__0710-111627
|
adishourya
| 2024-10-07T12:53:31Z | 8 | 0 |
peft
|
[
"peft",
"safetensors",
"generated_from_trainer",
"base_model:google/paligemma-3b-mix-224",
"base_model:adapter:google/paligemma-3b-mix-224",
"license:gemma",
"region:us"
] | null | 2024-10-07T09:16:47Z |
---
base_model: google/paligemma-3b-mix-224
library_name: peft
license: gemma
tags:
- generated_from_trainer
model-index:
- name: results__fullrun__0710-111627
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# results__fullrun__0710-111627
This model is a fine-tuned version of [google/paligemma-3b-mix-224](https://huggingface.co/google/paligemma-3b-mix-224) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_steps: 2
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.13.0
- Transformers 4.45.1
- Pytorch 2.3.0.post101
- Datasets 2.19.1
- Tokenizers 0.19.1
|
qinxianliu/FUE-v1
|
qinxianliu
| 2024-10-07T12:52:58Z | 1,219 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"mpnet",
"feature-extraction",
"sentence-similarity",
"mteb",
"en",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2024-09-09T06:03:02Z |
---
language: en
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- mteb
model-index:
- name: FUE-v1
results:
- dataset:
config: default
name: MTEB ArguAna (default)
revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
split: test
type: mteb/arguana
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value: 28.538000000000004
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value: 12.518
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value: 22.123
- type: map_at_100
value: 23.69
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- type: map_at_20
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value: 18.255
- type: map_at_5
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task:
type: Retrieval
- dataset:
config: default
name: MTEB AskUbuntuDupQuestions (default)
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
split: test
type: mteb/askubuntudupquestions-reranking
metrics:
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- type: mrr
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task:
type: Reranking
- dataset:
config: default
name: MTEB BIOSSES (default)
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
split: test
type: mteb/biosses-sts
metrics:
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- type: spearman
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task:
type: STS
- dataset:
config: default
name: MTEB CQADupstackAndroidRetrieval (default)
revision: f46a197baaae43b4f621051089b82a364682dfeb
split: test
type: mteb/cqadupstack-android
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task:
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revision: CQADupstackRetrieval_is_a_combined_dataset
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value: 35.66190450829144
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value: 27.644208320073655
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value: 7.789806046298824
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value: 36.44803836644228
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value: 28.570220616023008
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value: 9.112031867544205
- type: nauc_ndcg_at_10_max
value: 33.29203357424967
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value: 19.717835519047455
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value: 22.37284626453628
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value: 30.190181104191716
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value: 17.9773294217075
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value: -2.081286806432597
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value: 28.92540441778861
- type: nauc_recall_at_1000_std
value: 38.288436893164594
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value: 2.587296034481601
- type: nauc_recall_at_100_max
value: 35.29377052645708
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value: 39.05821703702423
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value: 6.316338189514334
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value: 34.11763962208226
- type: nauc_recall_at_10_std
value: 22.195626530130237
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value: 15.665356237945588
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value: 26.57885889682774
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value: 13.193930049941363
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value: 4.830004620366135
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value: 33.408749981759904
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value: 26.32129089751909
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value: 8.682897494798821
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value: 26.920725011561657
- type: nauc_recall_at_3_std
value: 14.373627083919555
- type: nauc_recall_at_5_diff1
value: 7.078349615166568
- type: nauc_recall_at_5_max
value: 29.938684723317483
- type: nauc_recall_at_5_std
value: 17.610677580599983
- type: ndcg_at_1
value: 22.3
- type: ndcg_at_10
value: 21.383
- type: ndcg_at_100
value: 30.686999999999998
- type: ndcg_at_1000
value: 36.806
- type: ndcg_at_20
value: 24.38
- type: ndcg_at_3
value: 19.262999999999998
- type: ndcg_at_5
value: 17.226
- type: precision_at_1
value: 22.3
- type: precision_at_10
value: 11.53
- type: precision_at_100
value: 2.5170000000000003
- type: precision_at_1000
value: 0.39699999999999996
- type: precision_at_20
value: 7.495
- type: precision_at_3
value: 18.3
- type: precision_at_5
value: 15.479999999999999
- type: recall_at_1
value: 4.547
- type: recall_at_10
value: 23.357
- type: recall_at_100
value: 51.047
- type: recall_at_1000
value: 80.63
- type: recall_at_20
value: 30.392999999999997
- type: recall_at_3
value: 11.161999999999999
- type: recall_at_5
value: 15.702
task:
type: Retrieval
- dataset:
config: default
name: MTEB SICK-R (default)
revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
split: test
type: mteb/sickr-sts
metrics:
- type: cosine_pearson
value: 82.89761522599564
- type: cosine_spearman
value: 79.61239240879378
- type: euclidean_pearson
value: 80.47994478833725
- type: euclidean_spearman
value: 79.61239321441792
- type: main_score
value: 79.61239240879378
- type: manhattan_pearson
value: 80.28255784608595
- type: manhattan_spearman
value: 79.40840813688955
- type: pearson
value: 82.89761555899388
- type: spearman
value: 79.6123884921359
task:
type: STS
- dataset:
config: default
name: MTEB STS12 (default)
revision: a0d554a64d88156834ff5ae9920b964011b16384
split: test
type: mteb/sts12-sts
metrics:
- type: cosine_pearson
value: 80.60566114110073
- type: cosine_spearman
value: 70.13320748146175
- type: euclidean_pearson
value: 77.57067368654582
- type: euclidean_spearman
value: 70.13314252571948
- type: main_score
value: 70.13320748146175
- type: manhattan_pearson
value: 77.29676392658962
- type: manhattan_spearman
value: 69.88321444641842
- type: pearson
value: 80.60566179532725
- type: spearman
value: 70.13415482313484
task:
type: STS
- dataset:
config: default
name: MTEB STS13 (default)
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
split: test
type: mteb/sts13-sts
metrics:
- type: cosine_pearson
value: 78.320437342309
- type: cosine_spearman
value: 80.74355074851618
- type: euclidean_pearson
value: 80.17187362623575
- type: euclidean_spearman
value: 80.7435506767441
- type: main_score
value: 80.74355074851618
- type: manhattan_pearson
value: 79.88874001476627
- type: manhattan_spearman
value: 80.45868266096211
- type: pearson
value: 78.32043820850613
- type: spearman
value: 80.7435506767441
task:
type: STS
- dataset:
config: default
name: MTEB STS14 (default)
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
split: test
type: mteb/sts14-sts
metrics:
- type: cosine_pearson
value: 79.70921389242768
- type: cosine_spearman
value: 76.24023668566399
- type: euclidean_pearson
value: 79.46251371995523
- type: euclidean_spearman
value: 76.24023301664893
- type: main_score
value: 76.24023668566399
- type: manhattan_pearson
value: 79.20065381375537
- type: manhattan_spearman
value: 76.00256203342937
- type: pearson
value: 79.70921366493712
- type: spearman
value: 76.24024123334894
task:
type: STS
- dataset:
config: default
name: MTEB STS15 (default)
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
split: test
type: mteb/sts15-sts
metrics:
- type: cosine_pearson
value: 82.52746024419504
- type: cosine_spearman
value: 84.11623590647757
- type: euclidean_pearson
value: 83.48562697051246
- type: euclidean_spearman
value: 84.11623814887781
- type: main_score
value: 84.11623590647757
- type: manhattan_pearson
value: 83.31205342795175
- type: manhattan_spearman
value: 83.94265353931766
- type: pearson
value: 82.5274599058833
- type: spearman
value: 84.11622788808319
task:
type: STS
- dataset:
config: default
name: MTEB STS16 (default)
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
split: test
type: mteb/sts16-sts
metrics:
- type: cosine_pearson
value: 74.82265172874041
- type: cosine_spearman
value: 78.4960329393101
- type: euclidean_pearson
value: 78.22716476239002
- type: euclidean_spearman
value: 78.4960329393101
- type: main_score
value: 78.4960329393101
- type: manhattan_pearson
value: 78.05885015944702
- type: manhattan_spearman
value: 78.3249847702297
- type: pearson
value: 74.8226511407601
- type: spearman
value: 78.49603251582666
task:
type: STS
- dataset:
config: en-ar
name: MTEB STS17 (en-ar)
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
split: test
type: mteb/sts17-crosslingual-sts
metrics:
- type: cosine_pearson
value: 9.087713281584145
- type: cosine_spearman
value: 9.1279366990081
- type: euclidean_pearson
value: 9.117969623657645
- type: euclidean_spearman
value: 9.1279366990081
- type: main_score
value: 9.1279366990081
- type: manhattan_pearson
value: 6.440301458166155
- type: manhattan_spearman
value: 6.5264401438882
- type: pearson
value: 9.087716717716269
- type: spearman
value: 9.1279366990081
task:
type: STS
- dataset:
config: ko-ko
name: MTEB STS17 (ko-ko)
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
split: test
type: mteb/sts17-crosslingual-sts
metrics:
- type: cosine_pearson
value: 21.931422026594692
- type: cosine_spearman
value: 32.79923712532921
- type: euclidean_pearson
value: 28.93227440763988
- type: euclidean_spearman
value: 32.79905675768862
- type: main_score
value: 32.79923712532921
- type: manhattan_pearson
value: 29.05113424705813
- type: manhattan_spearman
value: 32.79564881007963
- type: pearson
value: 21.931423583103467
- type: spearman
value: 32.798301050425714
task:
type: STS
- dataset:
config: en-de
name: MTEB STS17 (en-de)
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
split: test
type: mteb/sts17-crosslingual-sts
metrics:
- type: cosine_pearson
value: 30.28151988475531
- type: cosine_spearman
value: 28.038768547398902
- type: euclidean_pearson
value: 30.498915267658234
- type: euclidean_spearman
value: 28.038768547398902
- type: main_score
value: 28.038768547398902
- type: manhattan_pearson
value: 30.794292054714152
- type: manhattan_spearman
value: 28.56988926201773
- type: pearson
value: 30.281515394951132
- type: spearman
value: 28.038768547398902
task:
type: STS
- dataset:
config: en-tr
name: MTEB STS17 (en-tr)
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
split: test
type: mteb/sts17-crosslingual-sts
metrics:
- type: cosine_pearson
value: 1.103529546877178
- type: cosine_spearman
value: -4.344212733215535
- type: euclidean_pearson
value: 1.4727741337049667
- type: euclidean_spearman
value: -4.344212733215535
- type: main_score
value: -4.344212733215535
- type: manhattan_pearson
value: 3.066202915700401
- type: manhattan_spearman
value: -1.8750642080759847
- type: pearson
value: 1.1035283349143992
- type: spearman
value: -4.344212733215535
task:
type: STS
- dataset:
config: ar-ar
name: MTEB STS17 (ar-ar)
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
split: test
type: mteb/sts17-crosslingual-sts
metrics:
- type: cosine_pearson
value: 34.20639223082103
- type: cosine_spearman
value: 50.099745536749865
- type: euclidean_pearson
value: 46.672397550772246
- type: euclidean_spearman
value: 50.099745536749865
- type: main_score
value: 50.099745536749865
- type: manhattan_pearson
value: 46.27511641947692
- type: manhattan_spearman
value: 49.773911789667046
- type: pearson
value: 34.20638672669267
- type: spearman
value: 50.092555073567134
task:
type: STS
- dataset:
config: it-en
name: MTEB STS17 (it-en)
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
split: test
type: mteb/sts17-crosslingual-sts
metrics:
- type: cosine_pearson
value: 28.724311230307375
- type: cosine_spearman
value: 28.250724805661058
- type: euclidean_pearson
value: 28.315938847964397
- type: euclidean_spearman
value: 28.250724805661058
- type: main_score
value: 28.250724805661058
- type: manhattan_pearson
value: 27.674391866744763
- type: manhattan_spearman
value: 26.340658343374766
- type: pearson
value: 28.724300976547458
- type: spearman
value: 28.250724805661058
task:
type: STS
- dataset:
config: es-es
name: MTEB STS17 (es-es)
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
split: test
type: mteb/sts17-crosslingual-sts
metrics:
- type: cosine_pearson
value: 68.60948138294124
- type: cosine_spearman
value: 71.2644385447136
- type: euclidean_pearson
value: 73.24322173392738
- type: euclidean_spearman
value: 71.2644385447136
- type: main_score
value: 71.2644385447136
- type: manhattan_pearson
value: 72.77369507880161
- type: manhattan_spearman
value: 70.79274454462727
- type: pearson
value: 68.6094780579963
- type: spearman
value: 71.26563420541223
task:
type: STS
- dataset:
config: en-en
name: MTEB STS17 (en-en)
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
split: test
type: mteb/sts17-crosslingual-sts
metrics:
- type: cosine_pearson
value: 89.55726457989037
- type: cosine_spearman
value: 89.80253006361272
- type: euclidean_pearson
value: 89.52662858022953
- type: euclidean_spearman
value: 89.80253006361272
- type: main_score
value: 89.80253006361272
- type: manhattan_pearson
value: 89.515204742883
- type: manhattan_spearman
value: 89.85327040835446
- type: pearson
value: 89.5572652518414
- type: spearman
value: 89.80253006361272
task:
type: STS
- dataset:
config: fr-en
name: MTEB STS17 (fr-en)
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
split: test
type: mteb/sts17-crosslingual-sts
metrics:
- type: cosine_pearson
value: 35.25506119586307
- type: cosine_spearman
value: 34.7509473636312
- type: euclidean_pearson
value: 34.966513615613614
- type: euclidean_spearman
value: 34.7509473636312
- type: main_score
value: 34.7509473636312
- type: manhattan_pearson
value: 34.205440386931265
- type: manhattan_spearman
value: 34.19580186457024
- type: pearson
value: 35.255050728265005
- type: spearman
value: 34.7509473636312
task:
type: STS
- dataset:
config: nl-en
name: MTEB STS17 (nl-en)
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
split: test
type: mteb/sts17-crosslingual-sts
metrics:
- type: cosine_pearson
value: 29.15956073846505
- type: cosine_spearman
value: 23.4150546328063
- type: euclidean_pearson
value: 29.36385636868059
- type: euclidean_spearman
value: 23.4150546328063
- type: main_score
value: 23.4150546328063
- type: manhattan_pearson
value: 27.709164754459888
- type: manhattan_spearman
value: 21.483654161935416
- type: pearson
value: 29.159552605316065
- type: spearman
value: 23.4150546328063
task:
type: STS
- dataset:
config: es-en
name: MTEB STS17 (es-en)
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
split: test
type: mteb/sts17-crosslingual-sts
metrics:
- type: cosine_pearson
value: 16.614254117804347
- type: cosine_spearman
value: 17.838933806777177
- type: euclidean_pearson
value: 16.374166189982727
- type: euclidean_spearman
value: 17.838933806777177
- type: main_score
value: 17.838933806777177
- type: manhattan_pearson
value: 17.061405031678706
- type: manhattan_spearman
value: 17.720413541179628
- type: pearson
value: 16.61424897880095
- type: spearman
value: 17.838933806777177
task:
type: STS
- dataset:
config: es-it
name: MTEB STS22 (es-it)
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cosine_pearson
value: 33.923866543364184
- type: cosine_spearman
value: 28.270091354677568
- type: euclidean_pearson
value: 32.54215682097072
- type: euclidean_spearman
value: 28.270091354677568
- type: main_score
value: 28.270091354677568
- type: manhattan_pearson
value: 32.330367232207976
- type: manhattan_spearman
value: 28.255815608210906
- type: pearson
value: 33.923868062160935
- type: spearman
value: 28.270091354677568
task:
type: STS
- dataset:
config: ar
name: MTEB STS22 (ar)
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cosine_pearson
value: 33.485734155602124
- type: cosine_spearman
value: 35.184256236066545
- type: euclidean_pearson
value: 35.72218023358308
- type: euclidean_spearman
value: 35.184256236066545
- type: main_score
value: 35.184256236066545
- type: manhattan_pearson
value: 35.6410009964212
- type: manhattan_spearman
value: 35.234893627175516
- type: pearson
value: 33.485735809605345
- type: spearman
value: 35.184256236066545
task:
type: STS
- dataset:
config: de
name: MTEB STS22 (de)
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cosine_pearson
value: 9.234564624165541
- type: cosine_spearman
value: 9.26135671318589
- type: euclidean_pearson
value: 5.846102863095134
- type: euclidean_spearman
value: 9.26135671318589
- type: main_score
value: 9.26135671318589
- type: manhattan_pearson
value: 5.7129856416500635
- type: manhattan_spearman
value: 9.101317893245328
- type: pearson
value: 9.23456334729063
- type: spearman
value: 9.253696154102018
task:
type: STS
- dataset:
config: de-pl
name: MTEB STS22 (de-pl)
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cosine_pearson
value: 7.541720515534752
- type: cosine_spearman
value: 16.228492544303737
- type: euclidean_pearson
value: 8.688753048966332
- type: euclidean_spearman
value: 16.228492544303737
- type: main_score
value: 16.228492544303737
- type: manhattan_pearson
value: 8.82151603264942
- type: manhattan_spearman
value: 14.072905299329804
- type: pearson
value: 7.5417136414683394
- type: spearman
value: 16.228492544303737
task:
type: STS
- dataset:
config: zh
name: MTEB STS22 (zh)
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cosine_pearson
value: 14.272880349051686
- type: cosine_spearman
value: 26.923117280473786
- type: euclidean_pearson
value: 21.452060315735473
- type: euclidean_spearman
value: 26.923117280473786
- type: main_score
value: 26.923117280473786
- type: manhattan_pearson
value: 21.100522438025678
- type: manhattan_spearman
value: 26.877576505273343
- type: pearson
value: 14.272881506036967
- type: spearman
value: 26.92266584563422
task:
type: STS
- dataset:
config: de-en
name: MTEB STS22 (de-en)
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cosine_pearson
value: 43.299601499947705
- type: cosine_spearman
value: 44.906191184092506
- type: euclidean_pearson
value: 44.27229004756392
- type: euclidean_spearman
value: 44.906191184092506
- type: main_score
value: 44.906191184092506
- type: manhattan_pearson
value: 43.30226127948028
- type: manhattan_spearman
value: 44.15123742556422
- type: pearson
value: 43.29959849491429
- type: spearman
value: 44.906191184092506
task:
type: STS
- dataset:
config: zh-en
name: MTEB STS22 (zh-en)
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cosine_pearson
value: 38.208833040701016
- type: cosine_spearman
value: 40.35513412565045
- type: euclidean_pearson
value: 37.69713615937953
- type: euclidean_spearman
value: 40.35513412565045
- type: main_score
value: 40.35513412565045
- type: manhattan_pearson
value: 38.07295736531383
- type: manhattan_spearman
value: 41.18949838024728
- type: pearson
value: 38.20882899305228
- type: spearman
value: 40.35513412565045
task:
type: STS
- dataset:
config: fr
name: MTEB STS22 (fr)
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cosine_pearson
value: 57.64495357984024
- type: cosine_spearman
value: 58.654277042743985
- type: euclidean_pearson
value: 57.6918916487343
- type: euclidean_spearman
value: 58.654277042743985
- type: main_score
value: 58.654277042743985
- type: manhattan_pearson
value: 57.44807489792652
- type: manhattan_spearman
value: 58.55625686584207
- type: pearson
value: 57.64495326981972
- type: spearman
value: 58.654277042743985
task:
type: STS
- dataset:
config: tr
name: MTEB STS22 (tr)
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cosine_pearson
value: 11.450692016022549
- type: cosine_spearman
value: 17.51115938663503
- type: euclidean_pearson
value: 15.02321459776712
- type: euclidean_spearman
value: 17.51115938663503
- type: main_score
value: 17.51115938663503
- type: manhattan_pearson
value: 14.927559119791256
- type: manhattan_spearman
value: 17.573688652181342
- type: pearson
value: 11.450690790822087
- type: spearman
value: 17.51115938663503
task:
type: STS
- dataset:
config: pl-en
name: MTEB STS22 (pl-en)
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cosine_pearson
value: 43.813735649943744
- type: cosine_spearman
value: 43.21656383381947
- type: euclidean_pearson
value: 44.23609795164775
- type: euclidean_spearman
value: 43.21656383381947
- type: main_score
value: 43.21656383381947
- type: manhattan_pearson
value: 45.15245284513481
- type: manhattan_spearman
value: 44.617872376647036
- type: pearson
value: 43.81373603921556
- type: spearman
value: 43.21656383381947
task:
type: STS
- dataset:
config: pl
name: MTEB STS22 (pl)
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cosine_pearson
value: 2.064593579711082
- type: cosine_spearman
value: 13.671807943753796
- type: euclidean_pearson
value: -0.2014491570823139
- type: euclidean_spearman
value: 13.671807943753796
- type: main_score
value: 13.671807943753796
- type: manhattan_pearson
value: -0.47928238790519784
- type: manhattan_spearman
value: 13.539652287241891
- type: pearson
value: 2.064597029930494
- type: spearman
value: 13.877866791327195
task:
type: STS
- dataset:
config: it
name: MTEB STS22 (it)
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cosine_pearson
value: 34.34600586953367
- type: cosine_spearman
value: 35.333432813362556
- type: euclidean_pearson
value: 35.55800799947818
- type: euclidean_spearman
value: 35.333432813362556
- type: main_score
value: 35.333432813362556
- type: manhattan_pearson
value: 35.52036355368523
- type: manhattan_spearman
value: 35.32333999151806
- type: pearson
value: 34.34600393440248
- type: spearman
value: 35.333432813362556
task:
type: STS
- dataset:
config: de-fr
name: MTEB STS22 (de-fr)
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cosine_pearson
value: 34.38468852241422
- type: cosine_spearman
value: 37.517923206663426
- type: euclidean_pearson
value: 36.15747644065988
- type: euclidean_spearman
value: 37.517923206663426
- type: main_score
value: 37.517923206663426
- type: manhattan_pearson
value: 36.367891064808674
- type: manhattan_spearman
value: 36.98092141912518
- type: pearson
value: 34.3846838424612
- type: spearman
value: 37.517923206663426
task:
type: STS
- dataset:
config: en
name: MTEB STS22 (en)
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cosine_pearson
value: 62.10106220976315
- type: cosine_spearman
value: 62.162497286103665
- type: euclidean_pearson
value: 63.47223854069964
- type: euclidean_spearman
value: 62.162497286103665
- type: main_score
value: 62.162497286103665
- type: manhattan_pearson
value: 63.20260003333065
- type: manhattan_spearman
value: 61.91516380449414
- type: pearson
value: 62.10106168936663
- type: spearman
value: 62.162497286103665
task:
type: STS
- dataset:
config: es-en
name: MTEB STS22 (es-en)
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cosine_pearson
value: 43.060498443703956
- type: cosine_spearman
value: 46.96962646942495
- type: euclidean_pearson
value: 44.32895696900264
- type: euclidean_spearman
value: 46.96962646942495
- type: main_score
value: 46.96962646942495
- type: manhattan_pearson
value: 44.35969054771841
- type: manhattan_spearman
value: 47.06500736586804
- type: pearson
value: 43.060495996175085
- type: spearman
value: 46.96962646942495
task:
type: STS
- dataset:
config: es
name: MTEB STS22 (es)
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cosine_pearson
value: 31.552876706984677
- type: cosine_spearman
value: 36.385581860846955
- type: euclidean_pearson
value: 35.09094631898299
- type: euclidean_spearman
value: 36.385581860846955
- type: main_score
value: 36.385581860846955
- type: manhattan_pearson
value: 34.90394958111092
- type: manhattan_spearman
value: 36.31134774911119
- type: pearson
value: 31.552876690362574
- type: spearman
value: 36.385581860846955
task:
type: STS
- dataset:
config: fr-pl
name: MTEB STS22 (fr-pl)
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cosine_pearson
value: 69.17422308603082
- type: cosine_spearman
value: 61.97797868009122
- type: euclidean_pearson
value: 68.87185772300731
- type: euclidean_spearman
value: 61.97797868009122
- type: main_score
value: 61.97797868009122
- type: manhattan_pearson
value: 69.95446576863942
- type: manhattan_spearman
value: 61.97797868009122
- type: pearson
value: 69.17420713169005
- type: spearman
value: 61.97797868009122
task:
type: STS
- dataset:
config: ru
name: MTEB STS22 (ru)
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cosine_pearson
value: 7.810349558361704
- type: cosine_spearman
value: 13.082192851727692
- type: euclidean_pearson
value: 9.256112073506458
- type: euclidean_spearman
value: 13.082192851727692
- type: main_score
value: 13.082192851727692
- type: manhattan_pearson
value: 8.72571445455125
- type: manhattan_spearman
value: 12.72837879957086
- type: pearson
value: 7.8103471976464665
- type: spearman
value: 13.082192851727692
task:
type: STS
- dataset:
config: default
name: MTEB STSBenchmark (default)
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
split: test
type: mteb/stsbenchmark-sts
metrics:
- type: cosine_pearson
value: 81.60094971873578
- type: cosine_spearman
value: 82.21497157084686
- type: euclidean_pearson
value: 82.7649580865935
- type: euclidean_spearman
value: 82.21487652663633
- type: main_score
value: 82.21497157084686
- type: manhattan_pearson
value: 82.60879748854263
- type: manhattan_spearman
value: 82.02680374744259
- type: pearson
value: 81.60094958106916
- type: spearman
value: 82.21497157084686
task:
type: STS
- dataset:
config: default
name: MTEB SciDocsRR (default)
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
split: test
type: mteb/scidocs-reranking
metrics:
- type: main_score
value: 88.0620858216092
- type: map
value: 88.0620858216092
- type: mrr
value: 96.56171442936149
- type: nAUC_map_diff1
value: -10.48187303124293
- type: nAUC_map_max
value: 56.61425009792597
- type: nAUC_map_std
value: 71.46629366886685
- type: nAUC_mrr_diff1
value: 32.581743266040704
- type: nAUC_mrr_max
value: 88.42416536858423
- type: nAUC_mrr_std
value: 83.97838942252417
task:
type: Reranking
- dataset:
config: default
name: MTEB SciFact (default)
revision: 0228b52cf27578f30900b9e5271d331663a030d7
split: test
type: mteb/scifact
metrics:
- type: main_score
value: 60.521
- type: map_at_1
value: 44.622
- type: map_at_10
value: 55.033
- type: map_at_100
value: 55.659000000000006
- type: map_at_1000
value: 55.701
- type: map_at_20
value: 55.379999999999995
- type: map_at_3
value: 51.918
- type: map_at_5
value: 53.563
- type: mrr_at_1
value: 46.666666666666664
- type: mrr_at_10
value: 56.386640211640184
- type: mrr_at_100
value: 56.86324391737727
- type: mrr_at_1000
value: 56.899770911998424
- type: mrr_at_20
value: 56.63502866958745
- type: mrr_at_3
value: 53.88888888888887
- type: mrr_at_5
value: 55.388888888888886
- type: nauc_map_at_1000_diff1
value: 60.51522831114532
- type: nauc_map_at_1000_max
value: 38.729857044583156
- type: nauc_map_at_1000_std
value: 11.893619390095981
- type: nauc_map_at_100_diff1
value: 60.49224806809106
- type: nauc_map_at_100_max
value: 38.757391171919394
- type: nauc_map_at_100_std
value: 11.904869622843274
- type: nauc_map_at_10_diff1
value: 60.59184266027688
- type: nauc_map_at_10_max
value: 38.814372515682855
- type: nauc_map_at_10_std
value: 12.062834601122749
- type: nauc_map_at_1_diff1
value: 65.53217534037644
- type: nauc_map_at_1_max
value: 28.820516516628203
- type: nauc_map_at_1_std
value: 0.31747544825532187
- type: nauc_map_at_20_diff1
value: 60.49567580134993
- type: nauc_map_at_20_max
value: 38.75172270044811
- type: nauc_map_at_20_std
value: 12.03257481376082
- type: nauc_map_at_3_diff1
value: 61.95672662726325
- type: nauc_map_at_3_max
value: 36.21518511557246
- type: nauc_map_at_3_std
value: 8.725184216964175
- type: nauc_map_at_5_diff1
value: 61.59116859538641
- type: nauc_map_at_5_max
value: 37.099329579199555
- type: nauc_map_at_5_std
value: 9.77500772774504
- type: nauc_mrr_at_1000_diff1
value: 59.42105746719931
- type: nauc_mrr_at_1000_max
value: 40.45270689627016
- type: nauc_mrr_at_1000_std
value: 14.875759703157328
- type: nauc_mrr_at_100_diff1
value: 59.39676827842953
- type: nauc_mrr_at_100_max
value: 40.471355257224204
- type: nauc_mrr_at_100_std
value: 14.869956496055684
- type: nauc_mrr_at_10_diff1
value: 59.259253351613026
- type: nauc_mrr_at_10_max
value: 40.51223230352826
- type: nauc_mrr_at_10_std
value: 15.184710455010213
- type: nauc_mrr_at_1_diff1
value: 64.15225217314949
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value: 32.78250943893229
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value: 6.2994995170778845
- type: nauc_mrr_at_20_diff1
value: 59.353790297025796
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value: 40.50201824596607
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value: 15.006822621780044
- type: nauc_mrr_at_3_diff1
value: 60.38367084336967
- type: nauc_mrr_at_3_max
value: 39.80345696060504
- type: nauc_mrr_at_3_std
value: 14.692239492205003
- type: nauc_mrr_at_5_diff1
value: 59.88907677461462
- type: nauc_mrr_at_5_max
value: 40.45929072557247
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value: 14.716015778108465
- type: nauc_ndcg_at_1000_diff1
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value: 16.26204553755279
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value: 16.634877514141273
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value: 57.21191994351011
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- type: nauc_precision_at_20_std
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- type: nauc_precision_at_3_diff1
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value: 49.37622387908755
- type: nauc_precision_at_3_std
value: 31.44254548463311
- type: nauc_precision_at_5_diff1
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- type: nauc_precision_at_5_max
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- type: nauc_precision_at_5_std
value: 37.360215414394794
- type: nauc_recall_at_1000_diff1
value: 2.4976657329602574
- type: nauc_recall_at_1000_max
value: 89.30127606598155
- type: nauc_recall_at_1000_std
value: 88.21195144724587
- type: nauc_recall_at_100_diff1
value: 32.06651881995142
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value: 61.723309041324924
- type: nauc_recall_at_100_std
value: 40.02293962790224
- type: nauc_recall_at_10_diff1
value: 43.70825842063566
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value: 56.410600790424525
- type: nauc_recall_at_10_std
value: 37.41756199503754
- type: nauc_recall_at_1_diff1
value: 65.53217534037644
- type: nauc_recall_at_1_max
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- type: nauc_recall_at_5_diff1
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- type: ndcg_at_1
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- type: ndcg_at_10
value: 60.521
- type: ndcg_at_100
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- type: ndcg_at_1000
value: 64.517
- type: ndcg_at_20
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- type: precision_at_5
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- type: recall_at_1
value: 44.622
- type: recall_at_10
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- type: recall_at_100
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- type: recall_at_1000
value: 98.0
- type: recall_at_20
value: 80.167
- type: recall_at_3
value: 60.633
- type: recall_at_5
value: 67.161
task:
type: Retrieval
- dataset:
config: default
name: MTEB SciFact (default)
revision: 0228b52cf27578f30900b9e5271d331663a030d7
split: train
type: mteb/scifact
metrics:
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value: 60.968
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value: 45.56
- type: map_at_10
value: 55.931
- type: map_at_100
value: 56.742000000000004
- type: map_at_1000
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- type: map_at_3
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- type: map_at_5
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- type: ndcg_at_100
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- type: ndcg_at_3
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- type: precision_at_1
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- type: precision_at_100
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- type: precision_at_1000
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- type: precision_at_20
value: 4.604
- type: precision_at_3
value: 22.415
- type: precision_at_5
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- type: recall_at_1
value: 45.56
- type: recall_at_10
value: 74.714
- type: recall_at_100
value: 91.862
- type: recall_at_1000
value: 98.393
- type: recall_at_20
value: 80.449
- type: recall_at_3
value: 61.747
- type: recall_at_5
value: 68.381
task:
type: Retrieval
- dataset:
config: default
name: MTEB SprintDuplicateQuestions (default)
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
split: test
type: mteb/sprintduplicatequestions-pairclassification
metrics:
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- type: cosine_accuracy_threshold
value: 83.40442776679993
- type: cosine_ap
value: 82.29539182575489
- type: cosine_f1
value: 73.40139709833423
- type: cosine_f1_threshold
value: 81.47163987159729
- type: cosine_precision
value: 79.32636469221835
- type: cosine_recall
value: 68.30000000000001
- type: dot_accuracy
value: 99.52376237623763
- type: dot_accuracy_threshold
value: 83.40442776679993
- type: dot_ap
value: 82.29539339884745
- type: dot_f1
value: 73.40139709833423
- type: dot_f1_threshold
value: 81.47163391113281
- type: dot_precision
value: 79.32636469221835
- type: dot_recall
value: 68.30000000000001
- type: euclidean_accuracy
value: 99.52376237623763
- type: euclidean_accuracy_threshold
value: 57.61163830757141
- type: euclidean_ap
value: 82.29539339884744
- type: euclidean_f1
value: 73.40139709833423
- type: euclidean_f1_threshold
value: 60.8742356300354
- type: euclidean_precision
value: 79.32636469221835
- type: euclidean_recall
value: 68.30000000000001
- type: main_score
value: 82.29539339884745
- type: manhattan_accuracy
value: 99.50990099009901
- type: manhattan_accuracy_threshold
value: 1265.035057067871
- type: manhattan_ap
value: 81.78663637659726
- type: manhattan_f1
value: 73.2620320855615
- type: manhattan_f1_threshold
value: 1333.2223892211914
- type: manhattan_precision
value: 78.73563218390804
- type: manhattan_recall
value: 68.5
- type: max_accuracy
value: 99.52376237623763
- type: max_ap
value: 82.29539339884745
- type: max_f1
value: 73.40139709833423
- type: max_precision
value: 79.32636469221835
- type: max_recall
value: 68.5
- type: similarity_accuracy
value: 99.52376237623763
- type: similarity_accuracy_threshold
value: 83.40443968772888
- type: similarity_ap
value: 82.2953820339594
- type: similarity_f1
value: 73.40139709833423
- type: similarity_f1_threshold
value: 81.47163987159729
- type: similarity_precision
value: 79.32636469221835
- type: similarity_recall
value: 68.30000000000001
task:
type: PairClassification
- dataset:
config: default
name: MTEB SprintDuplicateQuestions (default)
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
split: validation
type: mteb/sprintduplicatequestions-pairclassification
metrics:
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value: 99.4990099009901
- type: cosine_accuracy_threshold
value: 80.80509901046753
- type: cosine_ap
value: 82.11932803714892
- type: cosine_f1
value: 73.94724664507173
- type: cosine_f1_threshold
value: 75.20748376846313
- type: cosine_precision
value: 68.81998277347114
- type: cosine_recall
value: 79.9
- type: dot_accuracy
value: 99.4990099009901
- type: dot_accuracy_threshold
value: 80.80509901046753
- type: dot_ap
value: 82.11932803714892
- type: dot_f1
value: 73.94724664507173
- type: dot_f1_threshold
value: 75.20748376846313
- type: dot_precision
value: 68.81998277347114
- type: dot_recall
value: 79.9
- type: euclidean_accuracy
value: 99.4990099009901
- type: euclidean_accuracy_threshold
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- type: euclidean_ap
value: 82.11932803714893
- type: euclidean_f1
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- type: euclidean_f1_threshold
value: 70.41663527488708
- type: euclidean_precision
value: 68.81998277347114
- type: euclidean_recall
value: 79.9
- type: main_score
value: 82.11932803714893
- type: manhattan_accuracy
value: 99.48910891089109
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value: 1331.6292762756348
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value: 73.582295988935
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value: 68.26347305389223
- type: manhattan_recall
value: 79.80000000000001
- type: max_accuracy
value: 99.4990099009901
- type: max_ap
value: 82.11932803714893
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value: 73.94724664507173
- type: max_precision
value: 68.81998277347114
- type: max_recall
value: 79.9
- type: similarity_accuracy
value: 99.4990099009901
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value: 80.80509901046753
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- type: similarity_precision
value: 68.81998277347114
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value: 79.9
task:
type: PairClassification
- dataset:
config: default
name: MTEB StackOverflowDupQuestions (default)
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
split: test
type: mteb/stackoverflowdupquestions-reranking
metrics:
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value: 35.200389562175985
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value: 13.668483379551915
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value: 6.28436956387114
task:
type: Reranking
- dataset:
config: default
name: MTEB TRECCOVID (default)
revision: bb9466bac8153a0349341eb1b22e06409e78ef4e
split: test
type: mteb/trec-covid
metrics:
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task:
type: Retrieval
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config: default
name: MTEB Touche2020 (default)
revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
split: test
type: mteb/touche2020
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task:
type: Retrieval
- dataset:
config: default
name: MTEB TwitterSemEval2015 (default)
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
split: test
type: mteb/twittersemeval2015-pairclassification
metrics:
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- type: cosine_recall
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- type: dot_accuracy
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value: 1388.2848739624023
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value: 74.34214111115519
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- type: max_accuracy
value: 86.35036061274364
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value: 74.3562958182802
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value: 69.23173016864459
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value: 66.27204638801642
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- type: similarity_accuracy
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value: 75.69118142127991
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value: 65.74139976275207
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value: 73.11345646437995
task:
type: PairClassification
- dataset:
config: default
name: MTEB TwitterURLCorpus (default)
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
split: test
type: mteb/twitterurlcorpus-pairclassification
metrics:
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value: 88.15539255637054
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value: 78.5060167312622
- type: cosine_ap
value: 84.15130253353578
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- type: cosine_precision
value: 73.97996224771308
- type: cosine_recall
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- type: dot_accuracy
value: 88.15539255637054
- type: dot_accuracy_threshold
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- type: dot_f1_threshold
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- type: dot_precision
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- type: dot_recall
value: 78.45703726516786
- type: euclidean_accuracy
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- type: euclidean_recall
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- type: main_score
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value: 88.15539255637054
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value: 84.1513046482876
- type: max_f1
value: 76.16659806159488
- type: max_precision
value: 74.09537677466326
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value: 78.5060167312622
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value: 84.15130432409686
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value: 75.17023086547852
- type: similarity_precision
value: 73.97996224771308
- type: similarity_recall
value: 78.45703726516786
task:
type: PairClassification
---
# FUE
This is a sentence transformer model based on all-mpnet-base-v2.
## Usage
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is encoded"]
model = SentenceTransformer('FAE')
embeddings = model.encode(sentences)
print(embeddings)
```
|
riyadifirman/classbird_1
|
riyadifirman
| 2024-10-07T12:48:26Z | 197 | 0 |
transformers
|
[
"transformers",
"safetensors",
"resnet",
"image-classification",
"generated_from_trainer",
"base_model:RobertZ2011/resnet-18-birb",
"base_model:finetune:RobertZ2011/resnet-18-birb",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2024-10-07T12:34:02Z |
---
library_name: transformers
base_model: RobertZ2011/resnet-18-birb
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: classbird_1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# classbird_1
This model is a fine-tuned version of [RobertZ2011/resnet-18-birb](https://huggingface.co/RobertZ2011/resnet-18-birb) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0949
- Accuracy: 0.7726
- Precision: 0.7789
- Recall: 0.7726
- F1: 0.7680
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 2.5479 | 1.0 | 29 | 2.2789 | 0.2946 | 0.3112 | 0.2946 | 0.2571 |
| 1.9716 | 2.0 | 58 | 1.9112 | 0.4651 | 0.5401 | 0.4651 | 0.4270 |
| 1.506 | 3.0 | 87 | 1.6503 | 0.6202 | 0.6831 | 0.6202 | 0.5933 |
| 1.1838 | 4.0 | 116 | 1.4594 | 0.6667 | 0.6855 | 0.6667 | 0.6439 |
| 0.9704 | 5.0 | 145 | 1.3127 | 0.7183 | 0.7395 | 0.7183 | 0.7064 |
| 0.7997 | 6.0 | 174 | 1.2345 | 0.7468 | 0.7586 | 0.7468 | 0.7410 |
| 0.763 | 7.0 | 203 | 1.1520 | 0.7442 | 0.7493 | 0.7442 | 0.7332 |
| 0.6448 | 8.0 | 232 | 1.1172 | 0.7597 | 0.7745 | 0.7597 | 0.7531 |
| 0.5839 | 9.0 | 261 | 1.0984 | 0.7649 | 0.7753 | 0.7649 | 0.7621 |
| 0.5993 | 10.0 | 290 | 1.0949 | 0.7726 | 0.7789 | 0.7726 | 0.7680 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.19.1
|
rg1683/base_bart_conversation
|
rg1683
| 2024-10-07T12:38:28Z | 106 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bart",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-10-07T12:37:52Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
SomeUser675/mentalhealthconvobot2
|
SomeUser675
| 2024-10-07T12:19:58Z | 7 | 0 | null |
[
"safetensors",
"blenderbot",
"base_model:facebook/blenderbot-400M-distill",
"base_model:finetune:facebook/blenderbot-400M-distill",
"region:us"
] | null | 2024-10-07T12:11:44Z |
---
base_model:
- facebook/blenderbot-400M-distill
---
|
Alex01837178373/vikhr_nemo_orpo_dostoevsky_12b_slerp-Q8_0-GGUF
|
Alex01837178373
| 2024-10-07T12:17:42Z | 9 | 1 |
mergekit
|
[
"mergekit",
"gguf",
"merge",
"llama-cpp",
"gguf-my-repo",
"ru",
"dataset:40umov/dostoevsky",
"base_model:IlyaGusev/vikhr_nemo_orpo_dostoevsky_12b_slerp",
"base_model:quantized:IlyaGusev/vikhr_nemo_orpo_dostoevsky_12b_slerp",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-10-07T12:16:45Z |
---
base_model: IlyaGusev/vikhr_nemo_orpo_dostoevsky_12b_slerp
datasets:
- 40umov/dostoevsky
language:
- ru
library_name: mergekit
tags:
- mergekit
- merge
- llama-cpp
- gguf-my-repo
---
# Alex01837178373/vikhr_nemo_orpo_dostoevsky_12b_slerp-Q8_0-GGUF
This model was converted to GGUF format from [`IlyaGusev/vikhr_nemo_orpo_dostoevsky_12b_slerp`](https://huggingface.co/IlyaGusev/vikhr_nemo_orpo_dostoevsky_12b_slerp) 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/IlyaGusev/vikhr_nemo_orpo_dostoevsky_12b_slerp) 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 Alex01837178373/vikhr_nemo_orpo_dostoevsky_12b_slerp-Q8_0-GGUF --hf-file vikhr_nemo_orpo_dostoevsky_12b_slerp-q8_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Alex01837178373/vikhr_nemo_orpo_dostoevsky_12b_slerp-Q8_0-GGUF --hf-file vikhr_nemo_orpo_dostoevsky_12b_slerp-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 Alex01837178373/vikhr_nemo_orpo_dostoevsky_12b_slerp-Q8_0-GGUF --hf-file vikhr_nemo_orpo_dostoevsky_12b_slerp-q8_0.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Alex01837178373/vikhr_nemo_orpo_dostoevsky_12b_slerp-Q8_0-GGUF --hf-file vikhr_nemo_orpo_dostoevsky_12b_slerp-q8_0.gguf -c 2048
```
|
mariana-coelho-9/whisper-tiny-pt
|
mariana-coelho-9
| 2024-10-07T12:14:12Z | 78 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"pt",
"dataset:mozilla-foundation/common_voice_11_0",
"base_model:openai/whisper-tiny",
"base_model:finetune:openai/whisper-tiny",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2024-10-07T10:40:33Z |
---
library_name: transformers
language:
- pt
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: Whisper Tiny pt
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 11.0
type: mozilla-foundation/common_voice_11_0
config: pt
split: test
args: 'config: pt, split: train/test'
metrics:
- name: Wer
type: wer
value: 34.98131015002622
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Tiny pt
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the Common Voice 11.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5418
- Wer: 34.9813
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 1000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.4834 | 0.8780 | 1000 | 0.5418 | 34.9813 |
### Framework versions
- Transformers 4.45.1
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.0
|
streit63/donut-base-tabs
|
streit63
| 2024-10-07T12:12:39Z | 7 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vision-encoder-decoder",
"image-text-to-text",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:streit63/donut-base-tabs",
"base_model:finetune:streit63/donut-base-tabs",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2024-10-05T09:43:24Z |
---
library_name: transformers
base_model: streit63/donut-base-tabs
tags:
- generated_from_trainer
datasets:
- imagefolder
model-index:
- name: donut-base-tabs
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# donut-base-tabs
This model is a fine-tuned version of [streit63/donut-base-tabs](https://huggingface.co/streit63/donut-base-tabs) on the imagefolder dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.45.1
- Pytorch 2.4.1+cu118
- Datasets 3.0.1
- Tokenizers 0.20.0
|
bunnycore/Qwen-2.5-3B-Remix-IQ4_NL-GGUF
|
bunnycore
| 2024-10-07T12:10:03Z | 6 | 0 |
transformers
|
[
"transformers",
"gguf",
"mergekit",
"merge",
"llama-cpp",
"gguf-my-repo",
"base_model:bunnycore/Qwen-2.5-3B-Remix",
"base_model:quantized:bunnycore/Qwen-2.5-3B-Remix",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2024-10-07T12:09:51Z |
---
base_model: bunnycore/Qwen-2.5-3B-Remix
library_name: transformers
tags:
- mergekit
- merge
- llama-cpp
- gguf-my-repo
---
# bunnycore/Qwen-2.5-3B-Remix-IQ4_NL-GGUF
This model was converted to GGUF format from [`bunnycore/Qwen-2.5-3B-Remix`](https://huggingface.co/bunnycore/Qwen-2.5-3B-Remix) 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/bunnycore/Qwen-2.5-3B-Remix) 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 bunnycore/Qwen-2.5-3B-Remix-IQ4_NL-GGUF --hf-file qwen-2.5-3b-remix-iq4_nl-imat.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo bunnycore/Qwen-2.5-3B-Remix-IQ4_NL-GGUF --hf-file qwen-2.5-3b-remix-iq4_nl-imat.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo bunnycore/Qwen-2.5-3B-Remix-IQ4_NL-GGUF --hf-file qwen-2.5-3b-remix-iq4_nl-imat.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo bunnycore/Qwen-2.5-3B-Remix-IQ4_NL-GGUF --hf-file qwen-2.5-3b-remix-iq4_nl-imat.gguf -c 2048
```
|
RichardErkhov/ZySec-AI_-_SecurityLLM-gguf
|
RichardErkhov
| 2024-10-07T12:02:51Z | 226 | 2 | null |
[
"gguf",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-10-07T08:02:38Z |
Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
SecurityLLM - GGUF
- Model creator: https://huggingface.co/ZySec-AI/
- Original model: https://huggingface.co/ZySec-AI/SecurityLLM/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [SecurityLLM.Q2_K.gguf](https://huggingface.co/RichardErkhov/ZySec-AI_-_SecurityLLM-gguf/blob/main/SecurityLLM.Q2_K.gguf) | Q2_K | 2.53GB |
| [SecurityLLM.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/ZySec-AI_-_SecurityLLM-gguf/blob/main/SecurityLLM.IQ3_XS.gguf) | IQ3_XS | 2.81GB |
| [SecurityLLM.IQ3_S.gguf](https://huggingface.co/RichardErkhov/ZySec-AI_-_SecurityLLM-gguf/blob/main/SecurityLLM.IQ3_S.gguf) | IQ3_S | 2.96GB |
| [SecurityLLM.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/ZySec-AI_-_SecurityLLM-gguf/blob/main/SecurityLLM.Q3_K_S.gguf) | Q3_K_S | 2.95GB |
| [SecurityLLM.IQ3_M.gguf](https://huggingface.co/RichardErkhov/ZySec-AI_-_SecurityLLM-gguf/blob/main/SecurityLLM.IQ3_M.gguf) | IQ3_M | 3.06GB |
| [SecurityLLM.Q3_K.gguf](https://huggingface.co/RichardErkhov/ZySec-AI_-_SecurityLLM-gguf/blob/main/SecurityLLM.Q3_K.gguf) | Q3_K | 3.28GB |
| [SecurityLLM.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/ZySec-AI_-_SecurityLLM-gguf/blob/main/SecurityLLM.Q3_K_M.gguf) | Q3_K_M | 3.28GB |
| [SecurityLLM.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/ZySec-AI_-_SecurityLLM-gguf/blob/main/SecurityLLM.Q3_K_L.gguf) | Q3_K_L | 3.56GB |
| [SecurityLLM.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/ZySec-AI_-_SecurityLLM-gguf/blob/main/SecurityLLM.IQ4_XS.gguf) | IQ4_XS | 3.67GB |
| [SecurityLLM.Q4_0.gguf](https://huggingface.co/RichardErkhov/ZySec-AI_-_SecurityLLM-gguf/blob/main/SecurityLLM.Q4_0.gguf) | Q4_0 | 3.83GB |
| [SecurityLLM.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/ZySec-AI_-_SecurityLLM-gguf/blob/main/SecurityLLM.IQ4_NL.gguf) | IQ4_NL | 3.87GB |
| [SecurityLLM.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/ZySec-AI_-_SecurityLLM-gguf/blob/main/SecurityLLM.Q4_K_S.gguf) | Q4_K_S | 3.86GB |
| [SecurityLLM.Q4_K.gguf](https://huggingface.co/RichardErkhov/ZySec-AI_-_SecurityLLM-gguf/blob/main/SecurityLLM.Q4_K.gguf) | Q4_K | 4.07GB |
| [SecurityLLM.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/ZySec-AI_-_SecurityLLM-gguf/blob/main/SecurityLLM.Q4_K_M.gguf) | Q4_K_M | 4.07GB |
| [SecurityLLM.Q4_1.gguf](https://huggingface.co/RichardErkhov/ZySec-AI_-_SecurityLLM-gguf/blob/main/SecurityLLM.Q4_1.gguf) | Q4_1 | 4.24GB |
| [SecurityLLM.Q5_0.gguf](https://huggingface.co/RichardErkhov/ZySec-AI_-_SecurityLLM-gguf/blob/main/SecurityLLM.Q5_0.gguf) | Q5_0 | 4.65GB |
| [SecurityLLM.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/ZySec-AI_-_SecurityLLM-gguf/blob/main/SecurityLLM.Q5_K_S.gguf) | Q5_K_S | 4.65GB |
| [SecurityLLM.Q5_K.gguf](https://huggingface.co/RichardErkhov/ZySec-AI_-_SecurityLLM-gguf/blob/main/SecurityLLM.Q5_K.gguf) | Q5_K | 4.78GB |
| [SecurityLLM.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/ZySec-AI_-_SecurityLLM-gguf/blob/main/SecurityLLM.Q5_K_M.gguf) | Q5_K_M | 4.78GB |
| [SecurityLLM.Q5_1.gguf](https://huggingface.co/RichardErkhov/ZySec-AI_-_SecurityLLM-gguf/blob/main/SecurityLLM.Q5_1.gguf) | Q5_1 | 5.07GB |
| [SecurityLLM.Q6_K.gguf](https://huggingface.co/RichardErkhov/ZySec-AI_-_SecurityLLM-gguf/blob/main/SecurityLLM.Q6_K.gguf) | Q6_K | 5.53GB |
| [SecurityLLM.Q8_0.gguf](https://huggingface.co/RichardErkhov/ZySec-AI_-_SecurityLLM-gguf/blob/main/SecurityLLM.Q8_0.gguf) | Q8_0 | 7.17GB |
Original model description:
---
library_name: transformers
license: apache-2.0
tags:
- security
- cybersecwithai
- threat
- vulnerability
- infosec
- zysec.ai
- cyber security
- ai4security
- llmsecurity
- cyber
- malware analysis
- exploitdev
- ai4good
- aisecurity
- threat
- cybersec
- cybersecurity
---
# ZySec-7B
ZySec-7B, stands as a pivotal innovation for security professionals, leveraging the advanced capabilities of HuggingFace's Zephyr language model series. This AI model is crafted to be an omnipresent cybersecurity ally, offering on-demand, expert guidance in cybersecurity issues. Picture ZySec-7B as an ever-present digital teammate, adept at navigating the complexities of security challenges.
The efficacy of ZySec-7B lies in its comprehensive training across numerous cybersecurity fields, providing a deep and wide-ranging understanding of the sector. ZySec is developed using the DPO technique, utilizing a varied dataset encompassing critical topics such as:
- Sophisticated areas like Attack Surface Threats, Cloud Security, and the Cyber Kill Chain.
- Key compliance and regulatory frameworks, including CIS Controls, FedRAMP, PCI DSS, and ISO/IEC 27001.
- Practical aspects like Cloud Secure Migration, Data Exfiltration Techniques, and Security Incident Handling.
- Crucial strategic fields such as Security Governance, Risk Management, and Security Architecture Review.
ZySec-7B's training spans over 30 unique domains, each enriched with thousands of data points, delivering unparalleled expertise.
As the first of its kind in an open-source, AI-driven cybersecurity series, ZySec-7B transcends the conventional role of a support tool, redefining organizational security approaches. Its open-source nature not only invites community contributions but also enhances its flexibility and transparency in managing vast cybersecurity data. ZySec-7B is instrumental in providing vital, actionable insights for strategic decision-making and advanced risk management. More than a mere software, ZySec-7B is a community-enhanced strategic tool, equipping your team to proactively confront and stay ahead of the dynamic landscape of cyber threats and regulatory demands.
# For suggestions please use [Road Map](https://zysec-ai.productlift.dev/t/roadmap)
<img src="https://huggingface.co/aihub-app/ZySec-7B-v1/resolve/main/ZySec-7B-dataset-composition.png?download=true" alt="Dataset Distribution" width="90%"/>
Details of dataset distribution here - [Dataset Distribution](https://huggingface.co/aihub-app/ZySec-7B/resolve/main/ZySec-7B-dataset-composition.png?download=true)
Fully compatible with [LM Studio](https://lmstudio.ai). Search for “Zysec” and here is what you get. Here is a sample output of ZySec writing email to John about database security using LM Studio:
<img src="https://huggingface.co/aihub-app/ZySec-7B-v1/resolve/main/sample-output.png" alt="Sample Output" width="90%"/>
---
The training is funded by [ZySec AI](https://www.zysec.app), the mobile app for Cyber Security professionals.
Official GGUF version is hosted here - [ZySec-7B-v1-GGUF on HuggingFace](https://huggingface.co/aihub-app/ZySec-7B-v1-GGUF)
## [ZySec AI: Unleashing the Potential of the ZySec Series Model](https://github.com/ZySec-AI/ZySec)
Project ZySec, an integral part of ZySec AI, stands at the forefront of integrating Artificial Intelligence into Cybersecurity. Centered around the innovative ZySec 7B model, it's designed to revolutionize the cybersecurity landscape with AI-driven solutions. ZySec AI isn't just a tool, it's a transformative approach, blending AI's cutting-edge capabilities with the unique intricacies of cybersecurity, while ensuring privacy and security.
### Discover the Key Features of Project ZySec
- **AI-Driven Cybersecurity:** Tap into the power of the ZySec 7B model, a bespoke AI solution fine-tuned for cybersecurity.
- **24/7 Expert Assistance:** Benefit from round-the-clock support and expert advice, guaranteeing smooth operations during any SOC shift.
- **Efficient Playbook Access:** Streamline your workflow with quick and easy access to playbooks and documents, enhancing information retrieval.
- **Standards Explorer:** Navigate various standards with ease, akin to a seasoned expert's proficiency.
- **Ongoing Internet Research:** Leverage AI-enabled, thorough internet research for exhaustive insights. (Note: Internet use is optional and specific to this feature).
### About Project ZySec by ZySec AI
ZySec AI an opensource project with a vision towards fusioning of Cybersecurity with Artificial Intelligence. Our goal is to transform the way security professionals engage with technology. More than a mere tool, ZySec AI symbolizes a comprehensive strategy to augment security operations, merging the innovative essence of AI with cybersecurity's distinctive challenges, always ensuring privacy and security.
https://github.com/ZySec-AI/ZySec
### The ZySec Roadmap
https://github.com/ZySec-AI/.github/blob/main/roadmap.md
|
nermineatif/models
|
nermineatif
| 2024-10-07T11:58:43Z | 94 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"conversational",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-10-07T08:26:42Z |
---
base_model: unsloth/llama-3.2-3b-instruct-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
---
# Uploaded model
- **Developed by:** nermineatif
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-3b-instruct-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
choozmo/whisper-small-nan-tw
|
choozmo
| 2024-10-07T11:50:43Z | 84 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"zh",
"dataset:mozilla-foundation/common_voice_17_0",
"base_model:openai/whisper-small",
"base_model:finetune:openai/whisper-small",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2024-10-07T06:31:35Z |
---
library_name: transformers
language:
- zh
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_17_0
metrics:
- wer
model-index:
- name: Whisper Taiwanese Small3 - Steven Wang
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 17.0
type: mozilla-foundation/common_voice_17_0
config: nan-tw
split: None
args: 'config: zh, split: test'
metrics:
- name: Wer
type: wer
value: 90.87361803598526
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Taiwanese Small3 - Steven Wang
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 17.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7512
- Wer: 90.8736
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-------:|:----:|:---------------:|:-------:|
| 0.083 | 2.9240 | 1000 | 0.6382 | 93.2582 |
| 0.0111 | 5.8480 | 2000 | 0.6895 | 91.6323 |
| 0.0019 | 8.7719 | 3000 | 0.7383 | 91.1771 |
| 0.0009 | 11.6959 | 4000 | 0.7512 | 90.8736 |
### Framework versions
- Transformers 4.45.1
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.0
|
RichardErkhov/tanliboy_-_Meta-Llama-3.1-8B-Instruct-gguf
|
RichardErkhov
| 2024-10-07T11:49:59Z | 57 | 0 | null |
[
"gguf",
"arxiv:2204.05149",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-10-07T07:28:49Z |
Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
Meta-Llama-3.1-8B-Instruct - GGUF
- Model creator: https://huggingface.co/tanliboy/
- Original model: https://huggingface.co/tanliboy/Meta-Llama-3.1-8B-Instruct/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [Meta-Llama-3.1-8B-Instruct.Q2_K.gguf](https://huggingface.co/RichardErkhov/tanliboy_-_Meta-Llama-3.1-8B-Instruct-gguf/blob/main/Meta-Llama-3.1-8B-Instruct.Q2_K.gguf) | Q2_K | 2.96GB |
| [Meta-Llama-3.1-8B-Instruct.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/tanliboy_-_Meta-Llama-3.1-8B-Instruct-gguf/blob/main/Meta-Llama-3.1-8B-Instruct.IQ3_XS.gguf) | IQ3_XS | 3.28GB |
| [Meta-Llama-3.1-8B-Instruct.IQ3_S.gguf](https://huggingface.co/RichardErkhov/tanliboy_-_Meta-Llama-3.1-8B-Instruct-gguf/blob/main/Meta-Llama-3.1-8B-Instruct.IQ3_S.gguf) | IQ3_S | 3.43GB |
| [Meta-Llama-3.1-8B-Instruct.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/tanliboy_-_Meta-Llama-3.1-8B-Instruct-gguf/blob/main/Meta-Llama-3.1-8B-Instruct.Q3_K_S.gguf) | Q3_K_S | 3.41GB |
| [Meta-Llama-3.1-8B-Instruct.IQ3_M.gguf](https://huggingface.co/RichardErkhov/tanliboy_-_Meta-Llama-3.1-8B-Instruct-gguf/blob/main/Meta-Llama-3.1-8B-Instruct.IQ3_M.gguf) | IQ3_M | 3.52GB |
| [Meta-Llama-3.1-8B-Instruct.Q3_K.gguf](https://huggingface.co/RichardErkhov/tanliboy_-_Meta-Llama-3.1-8B-Instruct-gguf/blob/main/Meta-Llama-3.1-8B-Instruct.Q3_K.gguf) | Q3_K | 3.74GB |
| [Meta-Llama-3.1-8B-Instruct.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/tanliboy_-_Meta-Llama-3.1-8B-Instruct-gguf/blob/main/Meta-Llama-3.1-8B-Instruct.Q3_K_M.gguf) | Q3_K_M | 3.74GB |
| [Meta-Llama-3.1-8B-Instruct.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/tanliboy_-_Meta-Llama-3.1-8B-Instruct-gguf/blob/main/Meta-Llama-3.1-8B-Instruct.Q3_K_L.gguf) | Q3_K_L | 4.03GB |
| [Meta-Llama-3.1-8B-Instruct.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/tanliboy_-_Meta-Llama-3.1-8B-Instruct-gguf/blob/main/Meta-Llama-3.1-8B-Instruct.IQ4_XS.gguf) | IQ4_XS | 4.18GB |
| [Meta-Llama-3.1-8B-Instruct.Q4_0.gguf](https://huggingface.co/RichardErkhov/tanliboy_-_Meta-Llama-3.1-8B-Instruct-gguf/blob/main/Meta-Llama-3.1-8B-Instruct.Q4_0.gguf) | Q4_0 | 4.34GB |
| [Meta-Llama-3.1-8B-Instruct.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/tanliboy_-_Meta-Llama-3.1-8B-Instruct-gguf/blob/main/Meta-Llama-3.1-8B-Instruct.IQ4_NL.gguf) | IQ4_NL | 4.38GB |
| [Meta-Llama-3.1-8B-Instruct.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/tanliboy_-_Meta-Llama-3.1-8B-Instruct-gguf/blob/main/Meta-Llama-3.1-8B-Instruct.Q4_K_S.gguf) | Q4_K_S | 4.37GB |
| [Meta-Llama-3.1-8B-Instruct.Q4_K.gguf](https://huggingface.co/RichardErkhov/tanliboy_-_Meta-Llama-3.1-8B-Instruct-gguf/blob/main/Meta-Llama-3.1-8B-Instruct.Q4_K.gguf) | Q4_K | 4.58GB |
| [Meta-Llama-3.1-8B-Instruct.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/tanliboy_-_Meta-Llama-3.1-8B-Instruct-gguf/blob/main/Meta-Llama-3.1-8B-Instruct.Q4_K_M.gguf) | Q4_K_M | 4.58GB |
| [Meta-Llama-3.1-8B-Instruct.Q4_1.gguf](https://huggingface.co/RichardErkhov/tanliboy_-_Meta-Llama-3.1-8B-Instruct-gguf/blob/main/Meta-Llama-3.1-8B-Instruct.Q4_1.gguf) | Q4_1 | 4.78GB |
| [Meta-Llama-3.1-8B-Instruct.Q5_0.gguf](https://huggingface.co/RichardErkhov/tanliboy_-_Meta-Llama-3.1-8B-Instruct-gguf/blob/main/Meta-Llama-3.1-8B-Instruct.Q5_0.gguf) | Q5_0 | 5.21GB |
| [Meta-Llama-3.1-8B-Instruct.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/tanliboy_-_Meta-Llama-3.1-8B-Instruct-gguf/blob/main/Meta-Llama-3.1-8B-Instruct.Q5_K_S.gguf) | Q5_K_S | 5.21GB |
| [Meta-Llama-3.1-8B-Instruct.Q5_K.gguf](https://huggingface.co/RichardErkhov/tanliboy_-_Meta-Llama-3.1-8B-Instruct-gguf/blob/main/Meta-Llama-3.1-8B-Instruct.Q5_K.gguf) | Q5_K | 5.34GB |
| [Meta-Llama-3.1-8B-Instruct.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/tanliboy_-_Meta-Llama-3.1-8B-Instruct-gguf/blob/main/Meta-Llama-3.1-8B-Instruct.Q5_K_M.gguf) | Q5_K_M | 5.34GB |
| [Meta-Llama-3.1-8B-Instruct.Q5_1.gguf](https://huggingface.co/RichardErkhov/tanliboy_-_Meta-Llama-3.1-8B-Instruct-gguf/blob/main/Meta-Llama-3.1-8B-Instruct.Q5_1.gguf) | Q5_1 | 5.65GB |
| [Meta-Llama-3.1-8B-Instruct.Q6_K.gguf](https://huggingface.co/RichardErkhov/tanliboy_-_Meta-Llama-3.1-8B-Instruct-gguf/blob/main/Meta-Llama-3.1-8B-Instruct.Q6_K.gguf) | Q6_K | 6.14GB |
| [Meta-Llama-3.1-8B-Instruct.Q8_0.gguf](https://huggingface.co/RichardErkhov/tanliboy_-_Meta-Llama-3.1-8B-Instruct-gguf/blob/main/Meta-Llama-3.1-8B-Instruct.Q8_0.gguf) | Q8_0 | 7.95GB |
Original model description:
---
language:
- en
- de
- fr
- it
- pt
- hi
- es
- th
license: llama3.1
base_model: meta-llama/Meta-Llama-3.1-8B
pipeline_tag: text-generation
tags:
- facebook
- meta
- pytorch
- llama
- llama-3
extra_gated_prompt: "### LLAMA 3.1 COMMUNITY LICENSE AGREEMENT\nLlama 3.1 Version\
\ Release Date: July 23, 2024\n\"Agreement\" means the terms and conditions for\
\ use, reproduction, distribution and modification of the Llama Materials set forth\
\ herein.\n\"Documentation\" means the specifications, manuals and documentation\
\ accompanying Llama 3.1 distributed by Meta at https://llama.meta.com/doc/overview.\n\
\"Licensee\" or \"you\" means you, or your employer or any other person or entity\
\ (if you are entering into this Agreement on such person or entity’s behalf), of\
\ the age required under applicable laws, rules or regulations to provide legal\
\ consent and that has legal authority to bind your employer or such other person\
\ or entity if you are entering in this Agreement on their behalf.\n\"Llama 3.1\"\
\ means the foundational large language models and software and algorithms, including\
\ machine-learning model code, trained model weights, inference-enabling code, training-enabling\
\ code, fine-tuning enabling code and other elements of the foregoing distributed\
\ by Meta at https://llama.meta.com/llama-downloads.\n\"Llama Materials\" means,\
\ collectively, Meta’s proprietary Llama 3.1 and Documentation (and any portion\
\ thereof) made available under this Agreement.\n\"Meta\" or \"we\" means Meta Platforms\
\ Ireland Limited (if you are located in or, if you are an entity, your principal\
\ place of business is in the EEA or Switzerland) and Meta Platforms, Inc. (if you\
\ are located outside of the EEA or Switzerland).\n \n1. License Rights and Redistribution.\n\
a. Grant of Rights. You are granted a non-exclusive, worldwide, non-transferable\
\ and royalty-free limited license under Meta’s intellectual property or other rights\
\ owned by Meta embodied in the Llama Materials to use, reproduce, distribute, copy,\
\ create derivative works of, and make modifications to the Llama Materials.\nb.\
\ Redistribution and Use.\ni. If you distribute or make available the Llama Materials\
\ (or any derivative works thereof), or a product or service (including another\
\ AI model) that contains any of them, you shall (A) provide a copy of this Agreement\
\ with any such Llama Materials; and (B) prominently display “Built with Llama”\
\ on a related website, user interface, blogpost, about page, or product documentation.\
\ If you use the Llama Materials or any outputs or results of the Llama Materials\
\ to create, train, fine tune, or otherwise improve an AI model, which is distributed\
\ or made available, you shall also include “Llama” at the beginning of any such\
\ AI model name.\nii. If you receive Llama Materials, or any derivative works thereof,\
\ from a Licensee as part of an integrated end user product, then Section 2 of\
\ this Agreement will not apply to you.\niii. You must retain in all copies of the\
\ Llama Materials that you distribute the following attribution notice within a\
\ “Notice” text file distributed as a part of such copies: “Llama 3.1 is licensed\
\ under the Llama 3.1 Community License, Copyright © Meta Platforms, Inc. All Rights\
\ Reserved.”\niv. Your use of the Llama Materials must comply with applicable laws\
\ and regulations (including trade compliance laws and regulations) and adhere to\
\ the Acceptable Use Policy for the Llama Materials (available at https://llama.meta.com/llama3_1/use-policy),\
\ which is hereby incorporated by reference into this Agreement.\n2. Additional\
\ Commercial Terms. If, on the Llama 3.1 version release date, the monthly active\
\ users of the products or services made available by or for Licensee, or Licensee’s\
\ affiliates, is greater than 700 million monthly active users in the preceding\
\ calendar month, you must request a license from Meta, which Meta may grant to\
\ you in its sole discretion, and you are not authorized to exercise any of the\
\ rights under this Agreement unless or until Meta otherwise expressly grants you\
\ such rights.\n3. Disclaimer of Warranty. UNLESS REQUIRED BY APPLICABLE LAW, THE\
\ LLAMA MATERIALS AND ANY OUTPUT AND RESULTS THEREFROM ARE PROVIDED ON AN “AS IS”\
\ BASIS, WITHOUT WARRANTIES OF ANY KIND, AND META DISCLAIMS ALL WARRANTIES OF ANY\
\ KIND, BOTH EXPRESS AND IMPLIED, INCLUDING, WITHOUT LIMITATION, ANY WARRANTIES\
\ OF TITLE, NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE.\
\ YOU ARE SOLELY RESPONSIBLE FOR DETERMINING THE APPROPRIATENESS OF USING OR REDISTRIBUTING\
\ THE LLAMA MATERIALS AND ASSUME ANY RISKS ASSOCIATED WITH YOUR USE OF THE LLAMA\
\ MATERIALS AND ANY OUTPUT AND RESULTS.\n4. Limitation of Liability. IN NO EVENT\
\ WILL META OR ITS AFFILIATES BE LIABLE UNDER ANY THEORY OF LIABILITY, WHETHER IN\
\ CONTRACT, TORT, NEGLIGENCE, PRODUCTS LIABILITY, OR OTHERWISE, ARISING OUT OF THIS\
\ AGREEMENT, FOR ANY LOST PROFITS OR ANY INDIRECT, SPECIAL, CONSEQUENTIAL, INCIDENTAL,\
\ EXEMPLARY OR PUNITIVE DAMAGES, EVEN IF META OR ITS AFFILIATES HAVE BEEN ADVISED\
\ OF THE POSSIBILITY OF ANY OF THE FOREGOING.\n5. Intellectual Property.\na. No\
\ trademark licenses are granted under this Agreement, and in connection with the\
\ Llama Materials, neither Meta nor Licensee may use any name or mark owned by or\
\ associated with the other or any of its affiliates, except as required for reasonable\
\ and customary use in describing and redistributing the Llama Materials or as set\
\ forth in this Section 5(a). Meta hereby grants you a license to use “Llama” (the\
\ “Mark”) solely as required to comply with the last sentence of Section 1.b.i.\
\ You will comply with Meta’s brand guidelines (currently accessible at https://about.meta.com/brand/resources/meta/company-brand/\
\ ). All goodwill arising out of your use of the Mark will inure to the benefit\
\ of Meta.\nb. Subject to Meta’s ownership of Llama Materials and derivatives made\
\ by or for Meta, with respect to any derivative works and modifications of the\
\ Llama Materials that are made by you, as between you and Meta, you are and will\
\ be the owner of such derivative works and modifications.\nc. If you institute\
\ litigation or other proceedings against Meta or any entity (including a cross-claim\
\ or counterclaim in a lawsuit) alleging that the Llama Materials or Llama 3.1 outputs\
\ or results, or any portion of any of the foregoing, constitutes infringement of\
\ intellectual property or other rights owned or licensable by you, then any licenses\
\ granted to you under this Agreement shall terminate as of the date such litigation\
\ or claim is filed or instituted. You will indemnify and hold harmless Meta from\
\ and against any claim by any third party arising out of or related to your use\
\ or distribution of the Llama Materials.\n6. Term and Termination. The term of\
\ this Agreement will commence upon your acceptance of this Agreement or access\
\ to the Llama Materials and will continue in full force and effect until terminated\
\ in accordance with the terms and conditions herein. Meta may terminate this Agreement\
\ if you are in breach of any term or condition of this Agreement. Upon termination\
\ of this Agreement, you shall delete and cease use of the Llama Materials. Sections\
\ 3, 4 and 7 shall survive the termination of this Agreement.\n7. Governing Law\
\ and Jurisdiction. This Agreement will be governed and construed under the laws\
\ of the State of California without regard to choice of law principles, and the\
\ UN Convention on Contracts for the International Sale of Goods does not apply\
\ to this Agreement. The courts of California shall have exclusive jurisdiction\
\ of any dispute arising out of this Agreement.\n### Llama 3.1 Acceptable Use Policy\n\
Meta is committed to promoting safe and fair use of its tools and features, including\
\ Llama 3.1. If you access or use Llama 3.1, you agree to this Acceptable Use Policy\
\ (“Policy”). The most recent copy of this policy can be found at [https://llama.meta.com/llama3_1/use-policy](https://llama.meta.com/llama3_1/use-policy)\n\
#### Prohibited Uses\nWe want everyone to use Llama 3.1 safely and responsibly.\
\ You agree you will not use, or allow others to use, Llama 3.1 to:\n 1. Violate\
\ the law or others’ rights, including to:\n 1. Engage in, promote, generate,\
\ contribute to, encourage, plan, incite, or further illegal or unlawful activity\
\ or content, such as:\n 1. Violence or terrorism\n 2. Exploitation\
\ or harm to children, including the solicitation, creation, acquisition, or dissemination\
\ of child exploitative content or failure to report Child Sexual Abuse Material\n\
\ 3. Human trafficking, exploitation, and sexual violence\n 4. The\
\ illegal distribution of information or materials to minors, including obscene\
\ materials, or failure to employ legally required age-gating in connection with\
\ such information or materials.\n 5. Sexual solicitation\n 6. Any\
\ other criminal activity\n 3. Engage in, promote, incite, or facilitate the\
\ harassment, abuse, threatening, or bullying of individuals or groups of individuals\n\
\ 4. Engage in, promote, incite, or facilitate discrimination or other unlawful\
\ or harmful conduct in the provision of employment, employment benefits, credit,\
\ housing, other economic benefits, or other essential goods and services\n 5.\
\ Engage in the unauthorized or unlicensed practice of any profession including,\
\ but not limited to, financial, legal, medical/health, or related professional\
\ practices\n 6. Collect, process, disclose, generate, or infer health, demographic,\
\ or other sensitive personal or private information about individuals without rights\
\ and consents required by applicable laws\n 7. Engage in or facilitate any action\
\ or generate any content that infringes, misappropriates, or otherwise violates\
\ any third-party rights, including the outputs or results of any products or services\
\ using the Llama Materials\n 8. Create, generate, or facilitate the creation\
\ of malicious code, malware, computer viruses or do anything else that could disable,\
\ overburden, interfere with or impair the proper working, integrity, operation\
\ or appearance of a website or computer system\n2. Engage in, promote, incite,\
\ facilitate, or assist in the planning or development of activities that present\
\ a risk of death or bodily harm to individuals, including use of Llama 3.1 related\
\ to the following:\n 1. Military, warfare, nuclear industries or applications,\
\ espionage, use for materials or activities that are subject to the International\
\ Traffic Arms Regulations (ITAR) maintained by the United States Department of\
\ State\n 2. Guns and illegal weapons (including weapon development)\n 3.\
\ Illegal drugs and regulated/controlled substances\n 4. Operation of critical\
\ infrastructure, transportation technologies, or heavy machinery\n 5. Self-harm\
\ or harm to others, including suicide, cutting, and eating disorders\n 6. Any\
\ content intended to incite or promote violence, abuse, or any infliction of bodily\
\ harm to an individual\n3. Intentionally deceive or mislead others, including use\
\ of Llama 3.1 related to the following:\n 1. Generating, promoting, or furthering\
\ fraud or the creation or promotion of disinformation\n 2. Generating, promoting,\
\ or furthering defamatory content, including the creation of defamatory statements,\
\ images, or other content\n 3. Generating, promoting, or further distributing\
\ spam\n 4. Impersonating another individual without consent, authorization,\
\ or legal right\n 5. Representing that the use of Llama 3.1 or outputs are human-generated\n\
\ 6. Generating or facilitating false online engagement, including fake reviews\
\ and other means of fake online engagement\n4. Fail to appropriately disclose to\
\ end users any known dangers of your AI system\nPlease report any violation of\
\ this Policy, software “bug,” or other problems that could lead to a violation\
\ of this Policy through one of the following means:\n * Reporting issues with\
\ the model: [https://github.com/meta-llama/llama-models/issues](https://github.com/meta-llama/llama-models/issues)\n\
\ * Reporting risky content generated by the model:\n developers.facebook.com/llama_output_feedback\n\
\ * Reporting bugs and security concerns: facebook.com/whitehat/info\n * Reporting\
\ violations of the Acceptable Use Policy or unlicensed uses of Meta Llama 3: [email protected]"
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the information I provide will be collected stored processed and shared in accordance
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---
## Model Information
The Meta Llama 3.1 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction tuned generative models in 8B, 70B and 405B sizes (text in/text out). The Llama 3.1 instruction tuned text only models (8B, 70B, 405B) are optimized for multilingual dialogue use cases and outperform many of the available open source and closed chat models on common industry benchmarks.
**Model developer**: Meta
**Model Architecture:** Llama 3.1 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>Input modalities</strong>
</td>
<td><strong>Output modalities</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="3" >Llama 3.1 (text only)
</td>
<td rowspan="3" >A new mix of publicly available online data.
</td>
<td>8B
</td>
<td>Multilingual Text
</td>
<td>Multilingual Text and code
</td>
<td>128k
</td>
<td>Yes
</td>
<td rowspan="3" >15T+
</td>
<td rowspan="3" >December 2023
</td>
</tr>
<tr>
<td>70B
</td>
<td>Multilingual Text
</td>
<td>Multilingual Text and code
</td>
<td>128k
</td>
<td>Yes
</td>
</tr>
<tr>
<td>405B
</td>
<td>Multilingual Text
</td>
<td>Multilingual Text and code
</td>
<td>128k
</td>
<td>Yes
</td>
</tr>
</table>
**Supported languages:** English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai.
**Llama 3.1 family of models**. Token counts refer to pretraining data only. All model versions use Grouped-Query Attention (GQA) for improved inference scalability.
**Model Release Date:** July 23, 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, the Llama 3.1 Community License, is available at: [https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/LICENSE](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/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 3.1 in applications, please go [here](https://github.com/meta-llama/llama-recipes).
## Intended Use
**Intended Use Cases** Llama 3.1 is intended for commercial and research use in multiple languages. Instruction tuned text only models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks. The Llama 3.1 model collection also supports the ability to leverage the outputs of its models to improve other models including synthetic data generation and distillation. The Llama 3.1 Community License allows for these use cases.
**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 3.1 Community License. Use in languages beyond those explicitly referenced as supported in this model card**.
**<span style="text-decoration:underline;">Note</span>: Llama 3.1 has been trained on a broader collection of languages than the 8 supported languages. Developers may fine-tune Llama 3.1 models for languages beyond the 8 supported languages provided they comply with the Llama 3.1 Community License and the Acceptable Use Policy and in such cases are responsible for ensuring that any uses of Llama 3.1 in additional languages is done in a safe and responsible manner.
## How to use
This repository contains two versions of Meta-Llama-3.1-8B-Instruct, for use with transformers and with the original `llama` codebase.
### Use with transformers
Starting with `transformers >= 4.43.0` onward, you can run conversational inference using the Transformers `pipeline` abstraction or by leveraging the Auto classes with the `generate()` function.
Make sure to update your transformers installation via `pip install --upgrade transformers`.
```python
import transformers
import torch
model_id = "meta-llama/Meta-Llama-3.1-8B-Instruct"
pipeline = transformers.pipeline(
"text-generation",
model=model_id,
model_kwargs={"torch_dtype": torch.bfloat16},
device_map="auto",
)
messages = [
{"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
{"role": "user", "content": "Who are you?"},
]
outputs = pipeline(
messages,
max_new_tokens=256,
)
print(outputs[0]["generated_text"][-1])
```
Note: You can also find detailed recipes on how to use the model locally, with `torch.compile()`, assisted generations, quantised and more at [`huggingface-llama-recipes`](https://github.com/huggingface/huggingface-llama-recipes)
### Tool use with transformers
LLaMA-3.1 supports multiple tool use formats. You can see a full guide to prompt formatting [here](https://llama.meta.com/docs/model-cards-and-prompt-formats/llama3_1/).
Tool use is also supported through [chat templates](https://huggingface.co/docs/transformers/main/chat_templating#advanced-tool-use--function-calling) in Transformers.
Here is a quick example showing a single simple tool:
```python
# First, define a tool
def get_current_temperature(location: str) -> float:
"""
Get the current temperature at a location.
Args:
location: The location to get the temperature for, in the format "City, Country"
Returns:
The current temperature at the specified location in the specified units, as a float.
"""
return 22. # A real function should probably actually get the temperature!
# Next, create a chat and apply the chat template
messages = [
{"role": "system", "content": "You are a bot that responds to weather queries."},
{"role": "user", "content": "Hey, what's the temperature in Paris right now?"}
]
inputs = tokenizer.apply_chat_template(messages, tools=[get_current_temperature], add_generation_prompt=True)
```
You can then generate text from this input as normal. If the model generates a tool call, you should add it to the chat like so:
```python
tool_call = {"name": "get_current_temperature", "arguments": {"location": "Paris, France"}}
messages.append({"role": "assistant", "tool_calls": [{"type": "function", "function": tool_call}]})
```
and then call the tool and append the result, with the `tool` role, like so:
```python
messages.append({"role": "tool", "name": "get_current_temperature", "content": "22.0"})
```
After that, you can `generate()` again to let the model use the tool result in the chat. Note that this was a very brief introduction to tool calling - for more information,
see the [LLaMA prompt format docs](https://llama.meta.com/docs/model-cards-and-prompt-formats/llama3_1/) and the Transformers [tool use documentation](https://huggingface.co/docs/transformers/main/chat_templating#advanced-tool-use--function-calling).
### Use with `llama`
Please, follow the instructions in the [repository](https://github.com/meta-llama/llama)
To download Original checkpoints, see the example command below leveraging `huggingface-cli`:
```
huggingface-cli download meta-llama/Meta-Llama-3.1-8B-Instruct --include "original/*" --local-dir Meta-Llama-3.1-8B-Instruct
```
## Hardware and Software
**Training Factors** We used custom training libraries, Meta's custom built GPU cluster, and production infrastructure for pretraining. Fine-tuning, annotation, and evaluation were also performed on production infrastructure.
**Training utilized a cumulative of** 39.3M GPU hours of computation on H100-80GB (TDP of 700W) type hardware, per the table below. Training time is the total GPU time required for training each model and power consumption is the peak power capacity per GPU device used, adjusted for power usage efficiency.
**Training Greenhouse Gas Emissions** Estimated total location-based greenhouse gas emissions were **11,390** tons CO2eq for training. Since 2020, Meta has maintained net zero greenhouse gas emissions in its global operations and matched 100% of its electricity use with renewable energy, therefore the total market-based greenhouse gas emissions for training were 0 tons CO2eq.
<table>
<tr>
<td>
</td>
<td><strong>Training Time (GPU hours)</strong>
</td>
<td><strong>Training Power Consumption (W)</strong>
</td>
<td><strong>Training Location-Based Greenhouse Gas Emissions</strong>
<p>
<strong>(tons CO2eq)</strong>
</td>
<td><strong>Training Market-Based Greenhouse Gas Emissions</strong>
<p>
<strong>(tons CO2eq)</strong>
</td>
</tr>
<tr>
<td>Llama 3.1 8B
</td>
<td>1.46M
</td>
<td>700
</td>
<td>420
</td>
<td>0
</td>
</tr>
<tr>
<td>Llama 3.1 70B
</td>
<td>7.0M
</td>
<td>700
</td>
<td>2,040
</td>
<td>0
</td>
</tr>
<tr>
<td>Llama 3.1 405B
</td>
<td>30.84M
</td>
<td>700
</td>
<td>8,930
</td>
<td>0
</td>
</tr>
<tr>
<td>Total
</td>
<td>39.3M
<td>
<ul>
</ul>
</td>
<td>11,390
</td>
<td>0
</td>
</tr>
</table>
The methodology used to determine training energy use and greenhouse gas emissions can be found [here](https://arxiv.org/pdf/2204.05149). Since Meta is openly releasing these models, the training energy use and greenhouse gas emissions will not be incurred by others.
## Training Data
**Overview:** Llama 3.1 was pretrained on ~15 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over 25M synthetically generated examples.
**Data Freshness:** The pretraining data has a cutoff of December 2023.
## Benchmark scores
In this section, we report the results for Llama 3.1 models on standard automatic benchmarks. For all the evaluations, we use our internal evaluations library.
### Base pretrained models
<table>
<tr>
<td><strong>Category</strong>
</td>
<td><strong>Benchmark</strong>
</td>
<td><strong># Shots</strong>
</td>
<td><strong>Metric</strong>
</td>
<td><strong>Llama 3 8B</strong>
</td>
<td><strong>Llama 3.1 8B</strong>
</td>
<td><strong>Llama 3 70B</strong>
</td>
<td><strong>Llama 3.1 70B</strong>
</td>
<td><strong>Llama 3.1 405B</strong>
</td>
</tr>
<tr>
<td rowspan="7" >General
</td>
<td>MMLU
</td>
<td>5
</td>
<td>macro_avg/acc_char
</td>
<td>66.7
</td>
<td>66.7
</td>
<td>79.5
</td>
<td>79.3
</td>
<td>85.2
</td>
</tr>
<tr>
<td>MMLU-Pro (CoT)
</td>
<td>5
</td>
<td>macro_avg/acc_char
</td>
<td>36.2
</td>
<td>37.1
</td>
<td>55.0
</td>
<td>53.8
</td>
<td>61.6
</td>
</tr>
<tr>
<td>AGIEval English
</td>
<td>3-5
</td>
<td>average/acc_char
</td>
<td>47.1
</td>
<td>47.8
</td>
<td>63.0
</td>
<td>64.6
</td>
<td>71.6
</td>
</tr>
<tr>
<td>CommonSenseQA
</td>
<td>7
</td>
<td>acc_char
</td>
<td>72.6
</td>
<td>75.0
</td>
<td>83.8
</td>
<td>84.1
</td>
<td>85.8
</td>
</tr>
<tr>
<td>Winogrande
</td>
<td>5
</td>
<td>acc_char
</td>
<td>-
</td>
<td>60.5
</td>
<td>-
</td>
<td>83.3
</td>
<td>86.7
</td>
</tr>
<tr>
<td>BIG-Bench Hard (CoT)
</td>
<td>3
</td>
<td>average/em
</td>
<td>61.1
</td>
<td>64.2
</td>
<td>81.3
</td>
<td>81.6
</td>
<td>85.9
</td>
</tr>
<tr>
<td>ARC-Challenge
</td>
<td>25
</td>
<td>acc_char
</td>
<td>79.4
</td>
<td>79.7
</td>
<td>93.1
</td>
<td>92.9
</td>
<td>96.1
</td>
</tr>
<tr>
<td>Knowledge reasoning
</td>
<td>TriviaQA-Wiki
</td>
<td>5
</td>
<td>em
</td>
<td>78.5
</td>
<td>77.6
</td>
<td>89.7
</td>
<td>89.8
</td>
<td>91.8
</td>
</tr>
<tr>
<td rowspan="4" >Reading comprehension
</td>
<td>SQuAD
</td>
<td>1
</td>
<td>em
</td>
<td>76.4
</td>
<td>77.0
</td>
<td>85.6
</td>
<td>81.8
</td>
<td>89.3
</td>
</tr>
<tr>
<td>QuAC (F1)
</td>
<td>1
</td>
<td>f1
</td>
<td>44.4
</td>
<td>44.9
</td>
<td>51.1
</td>
<td>51.1
</td>
<td>53.6
</td>
</tr>
<tr>
<td>BoolQ
</td>
<td>0
</td>
<td>acc_char
</td>
<td>75.7
</td>
<td>75.0
</td>
<td>79.0
</td>
<td>79.4
</td>
<td>80.0
</td>
</tr>
<tr>
<td>DROP (F1)
</td>
<td>3
</td>
<td>f1
</td>
<td>58.4
</td>
<td>59.5
</td>
<td>79.7
</td>
<td>79.6
</td>
<td>84.8
</td>
</tr>
</table>
### Instruction tuned models
<table>
<tr>
<td><strong>Category</strong>
</td>
<td><strong>Benchmark</strong>
</td>
<td><strong># Shots</strong>
</td>
<td><strong>Metric</strong>
</td>
<td><strong>Llama 3 8B Instruct</strong>
</td>
<td><strong>Llama 3.1 8B Instruct</strong>
</td>
<td><strong>Llama 3 70B Instruct</strong>
</td>
<td><strong>Llama 3.1 70B Instruct</strong>
</td>
<td><strong>Llama 3.1 405B Instruct</strong>
</td>
</tr>
<tr>
<td rowspan="4" >General
</td>
<td>MMLU
</td>
<td>5
</td>
<td>macro_avg/acc
</td>
<td>68.5
</td>
<td>69.4
</td>
<td>82.0
</td>
<td>83.6
</td>
<td>87.3
</td>
</tr>
<tr>
<td>MMLU (CoT)
</td>
<td>0
</td>
<td>macro_avg/acc
</td>
<td>65.3
</td>
<td>73.0
</td>
<td>80.9
</td>
<td>86.0
</td>
<td>88.6
</td>
</tr>
<tr>
<td>MMLU-Pro (CoT)
</td>
<td>5
</td>
<td>micro_avg/acc_char
</td>
<td>45.5
</td>
<td>48.3
</td>
<td>63.4
</td>
<td>66.4
</td>
<td>73.3
</td>
</tr>
<tr>
<td>IFEval
</td>
<td>
</td>
<td>
</td>
<td>76.8
</td>
<td>80.4
</td>
<td>82.9
</td>
<td>87.5
</td>
<td>88.6
</td>
</tr>
<tr>
<td rowspan="2" >Reasoning
</td>
<td>ARC-C
</td>
<td>0
</td>
<td>acc
</td>
<td>82.4
</td>
<td>83.4
</td>
<td>94.4
</td>
<td>94.8
</td>
<td>96.9
</td>
</tr>
<tr>
<td>GPQA
</td>
<td>0
</td>
<td>em
</td>
<td>34.6
</td>
<td>30.4
</td>
<td>39.5
</td>
<td>41.7
</td>
<td>50.7
</td>
</tr>
<tr>
<td rowspan="4" >Code
</td>
<td>HumanEval
</td>
<td>0
</td>
<td>pass@1
</td>
<td>60.4
</td>
<td>72.6
</td>
<td>81.7
</td>
<td>80.5
</td>
<td>89.0
</td>
</tr>
<tr>
<td>MBPP ++ base version
</td>
<td>0
</td>
<td>pass@1
</td>
<td>70.6
</td>
<td>72.8
</td>
<td>82.5
</td>
<td>86.0
</td>
<td>88.6
</td>
</tr>
<tr>
<td>Multipl-E HumanEval
</td>
<td>0
</td>
<td>pass@1
</td>
<td>-
</td>
<td>50.8
</td>
<td>-
</td>
<td>65.5
</td>
<td>75.2
</td>
</tr>
<tr>
<td>Multipl-E MBPP
</td>
<td>0
</td>
<td>pass@1
</td>
<td>-
</td>
<td>52.4
</td>
<td>-
</td>
<td>62.0
</td>
<td>65.7
</td>
</tr>
<tr>
<td rowspan="2" >Math
</td>
<td>GSM-8K (CoT)
</td>
<td>8
</td>
<td>em_maj1@1
</td>
<td>80.6
</td>
<td>84.5
</td>
<td>93.0
</td>
<td>95.1
</td>
<td>96.8
</td>
</tr>
<tr>
<td>MATH (CoT)
</td>
<td>0
</td>
<td>final_em
</td>
<td>29.1
</td>
<td>51.9
</td>
<td>51.0
</td>
<td>68.0
</td>
<td>73.8
</td>
</tr>
<tr>
<td rowspan="4" >Tool Use
</td>
<td>API-Bank
</td>
<td>0
</td>
<td>acc
</td>
<td>48.3
</td>
<td>82.6
</td>
<td>85.1
</td>
<td>90.0
</td>
<td>92.0
</td>
</tr>
<tr>
<td>BFCL
</td>
<td>0
</td>
<td>acc
</td>
<td>60.3
</td>
<td>76.1
</td>
<td>83.0
</td>
<td>84.8
</td>
<td>88.5
</td>
</tr>
<tr>
<td>Gorilla Benchmark API Bench
</td>
<td>0
</td>
<td>acc
</td>
<td>1.7
</td>
<td>8.2
</td>
<td>14.7
</td>
<td>29.7
</td>
<td>35.3
</td>
</tr>
<tr>
<td>Nexus (0-shot)
</td>
<td>0
</td>
<td>macro_avg/acc
</td>
<td>18.1
</td>
<td>38.5
</td>
<td>47.8
</td>
<td>56.7
</td>
<td>58.7
</td>
</tr>
<tr>
<td>Multilingual
</td>
<td>Multilingual MGSM (CoT)
</td>
<td>0
</td>
<td>em
</td>
<td>-
</td>
<td>68.9
</td>
<td>-
</td>
<td>86.9
</td>
<td>91.6
</td>
</tr>
</table>
#### Multilingual benchmarks
<table>
<tr>
<td><strong>Category</strong>
</td>
<td><strong>Benchmark</strong>
</td>
<td><strong>Language</strong>
</td>
<td><strong>Llama 3.1 8B</strong>
</td>
<td><strong>Llama 3.1 70B</strong>
</td>
<td><strong>Llama 3.1 405B</strong>
</td>
</tr>
<tr>
<td rowspan="9" ><strong>General</strong>
</td>
<td rowspan="9" ><strong>MMLU (5-shot, macro_avg/acc)</strong>
</td>
<td>Portuguese
</td>
<td>62.12
</td>
<td>80.13
</td>
<td>84.95
</td>
</tr>
<tr>
<td>Spanish
</td>
<td>62.45
</td>
<td>80.05
</td>
<td>85.08
</td>
</tr>
<tr>
<td>Italian
</td>
<td>61.63
</td>
<td>80.4
</td>
<td>85.04
</td>
</tr>
<tr>
<td>German
</td>
<td>60.59
</td>
<td>79.27
</td>
<td>84.36
</td>
</tr>
<tr>
<td>French
</td>
<td>62.34
</td>
<td>79.82
</td>
<td>84.66
</td>
</tr>
<tr>
<td>Hindi
</td>
<td>50.88
</td>
<td>74.52
</td>
<td>80.31
</td>
</tr>
<tr>
<td>Thai
</td>
<td>50.32
</td>
<td>72.95
</td>
<td>78.21
</td>
</tr>
</table>
## Responsibility & Safety
As part of our Responsible release approach, we followed a three-pronged strategy to managing trust & safety risks:
* Enable developers to deploy helpful, safe and flexible experiences for their target audience and for the use cases supported by Llama.
* Protect developers against adversarial users aiming to exploit Llama capabilities to potentially cause harm.
* Provide protections for the community to help prevent the misuse of our models.
### Responsible deployment
Llama is a foundational technology designed to be used in a variety of use cases, examples on how Meta’s Llama models have been responsibly deployed can be found in our [Community Stories webpage](https://llama.meta.com/community-stories/). Our approach is to build the most helpful models enabling the world to benefit from the technology power, by aligning our model safety for the generic use cases addressing a standard set of harms. Developers are then in the driver seat to tailor safety for their use case, defining their own policy and deploying the models with the necessary safeguards in their Llama systems. Llama 3.1 was developed following the best practices outlined in our Responsible Use Guide, you can refer to the [Responsible Use Guide](https://llama.meta.com/responsible-use-guide/) to learn more.
#### Llama 3.1 instruct
Our main objectives for conducting safety fine-tuning are to provide the research community with a valuable resource for studying the robustness of safety fine-tuning, as well as to offer developers a readily available, safe, and powerful model for various applications to reduce the developer workload to deploy safe AI systems. For more details on the safety mitigations implemented please read the Llama 3 paper.
**Fine-tuning data**
We employ a multi-faceted approach to data collection, combining human-generated data from our vendors with synthetic data to mitigate potential safety risks. We’ve developed many large language model (LLM)-based classifiers that enable us to thoughtfully select high-quality prompts and responses, enhancing data quality control.
**Refusals and Tone**
Building on the work we started with Llama 3, we put a great emphasis on model refusals to benign prompts as well as refusal tone. We included both borderline and adversarial prompts in our safety data strategy, and modified our safety data responses to follow tone guidelines.
#### Llama 3.1 systems
**Large language models, including Llama 3.1, are not designed to be deployed in isolation but instead should be deployed as part of an overall AI system with additional safety guardrails as required.** Developers are expected to deploy system safeguards when building agentic systems. Safeguards are key to achieve the right helpfulness-safety alignment as well as mitigating safety and security risks inherent to the system and any integration of the model or system with external tools.
As part of our responsible release approach, we provide the community with [safeguards](https://llama.meta.com/trust-and-safety/) that developers should deploy with Llama models or other LLMs, including Llama Guard 3, Prompt Guard and Code Shield. All our [reference implementations](https://github.com/meta-llama/llama-agentic-system) demos contain these safeguards by default so developers can benefit from system-level safety out-of-the-box.
#### New capabilities
Note that this release introduces new capabilities, including a longer context window, multilingual inputs and outputs and possible integrations by developers with third party tools. Building with these new capabilities requires specific considerations in addition to the best practices that generally apply across all Generative AI use cases.
**Tool-use**: Just like in standard software development, developers are responsible for the integration of the LLM with the tools and services of their choice. They should define a clear policy for their use case and assess the integrity of the third party services they use to be aware of the safety and security limitations when using this capability. Refer to the Responsible Use Guide for best practices on the safe deployment of the third party safeguards.
**Multilinguality**: Llama 3.1 supports 7 languages in addition to English: French, German, Hindi, Italian, Portuguese, Spanish, and Thai. Llama may be able to output text in other languages than those that meet performance thresholds for safety and helpfulness. We strongly discourage developers from using this model to converse in non-supported languages without implementing finetuning and system controls in alignment with their policies and the best practices shared in the Responsible Use Guide.
### Evaluations
We evaluated Llama models for common use cases as well as specific capabilities. Common use cases evaluations measure safety risks of systems for most commonly built applications including chat bot, coding assistant, tool calls. We built dedicated, adversarial evaluation datasets and evaluated systems composed of Llama models and Llama Guard 3 to filter input prompt and output response. It is important to evaluate applications in context, and we recommend building dedicated evaluation dataset for your use case. Prompt Guard and Code Shield are also available if relevant to the application.
Capability evaluations measure vulnerabilities of Llama models inherent to specific capabilities, for which were crafted dedicated benchmarks including long context, multilingual, tools calls, coding or memorization.
**Red teaming**
For both scenarios, we conducted recurring red teaming exercises with the goal of discovering risks via adversarial prompting and we used the learnings to improve our benchmarks and safety tuning datasets.
We partnered early with subject-matter experts in critical risk areas to understand the nature of these real-world harms and how such models may lead to unintended harm for society. Based on these conversations, we derived a set of adversarial goals for the red team to attempt to achieve, such as extracting harmful information or reprogramming the model to act in a potentially harmful capacity. The red team consisted of experts in cybersecurity, adversarial machine learning, responsible AI, and integrity in addition to multilingual content specialists with background in integrity issues in specific geographic markets.
### Critical and other risks
We specifically focused our efforts on mitigating the following critical risk areas:
**1- CBRNE (Chemical, Biological, Radiological, Nuclear, and Explosive materials) helpfulness**
To assess risks related to proliferation of chemical and biological weapons, we performed uplift testing designed to assess whether use of Llama 3.1 models could meaningfully increase the capabilities of malicious actors to plan or carry out attacks using these types of weapons.
**2. Child Safety**
Child Safety risk assessments were conducted using a team of experts, to assess the model’s 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 including the additional languages Llama 3 is trained on. We also partnered with content specialists to perform red teaming exercises assessing potentially violating content while taking account of market specific nuances or experiences.
**3. Cyber attack enablement**
Our cyber attack uplift study investigated whether LLMs can enhance human capabilities in hacking tasks, both in terms of skill level and speed.
Our attack automation study focused on evaluating the capabilities of LLMs when used as autonomous agents in cyber offensive operations, specifically in the context of ransomware attacks. This evaluation was distinct from previous studies that considered LLMs as interactive assistants. The primary objective was to assess whether these models could effectively function as independent agents in executing complex cyber-attacks without human intervention.
Our study of Llama-3.1-405B’s social engineering uplift for cyber attackers was conducted to assess the effectiveness of AI models in aiding cyber threat actors in spear phishing campaigns. Please read our Llama 3.1 Cyber security whitepaper to learn more.
### 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 on 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).
We also set up the [Llama Impact Grants](https://llama.meta.com/llama-impact-grants/) program to identify and support the most compelling applications of Meta’s Llama model for societal benefit across three categories: education, climate and open innovation. The 20 finalists from the hundreds of applications can be found [here](https://llama.meta.com/llama-impact-grants/#finalists).
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 3.1 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 3.1 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 3.1 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3.1’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3.1 models, developers should perform safety testing and tuning tailored to their specific applications of the model. Please refer to available resources including our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide), [Trust and Safety](https://llama.meta.com/trust-and-safety/) solutions, and other [resources](https://llama.meta.com/docs/get-started/) to learn more about responsible development.
|
interview-eval/zephyr-7b-math-case-7
|
interview-eval
| 2024-10-07T11:43:04Z | 6 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"alignment-handbook",
"trl",
"sft",
"generated_from_trainer",
"conversational",
"dataset:EunsuKim/instruct",
"dataset:EunsuKim/MATH",
"base_model:alignment-handbook/zephyr-7b-sft-full",
"base_model:finetune:alignment-handbook/zephyr-7b-sft-full",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-10-07T10:54:55Z |
---
library_name: transformers
license: apache-2.0
base_model: alignment-handbook/zephyr-7b-sft-full
tags:
- alignment-handbook
- trl
- sft
- generated_from_trainer
- trl
- sft
- generated_from_trainer
datasets:
- EunsuKim/instruct
- EunsuKim/MATH
model-index:
- name: zephyr-7b-math-case-7
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# zephyr-7b-math-case-7
This model is a fine-tuned version of [alignment-handbook/zephyr-7b-sft-full](https://huggingface.co/alignment-handbook/zephyr-7b-sft-full) on the EunsuKim/instruct and the EunsuKim/MATH datasets.
It achieves the following results on the evaluation set:
- Loss: 0.0013
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- total_train_batch_size: 32
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.8719 | 1.0 | 18 | 0.5943 |
| 0.4801 | 2.0 | 36 | 0.2952 |
| 0.2452 | 3.0 | 54 | 0.1275 |
| 0.098 | 4.0 | 72 | 0.0448 |
| 0.0439 | 5.0 | 90 | 0.0259 |
| 0.0306 | 6.0 | 108 | 0.0149 |
| 0.017 | 7.0 | 126 | 0.0068 |
| 0.006 | 8.0 | 144 | 0.0025 |
| 0.0025 | 9.0 | 162 | 0.0014 |
| 0.0012 | 10.0 | 180 | 0.0013 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.0
- Tokenizers 0.19.1
|
wanasash/whisper-large-v3-ec
|
wanasash
| 2024-10-07T11:32:14Z | 6 | 0 | null |
[
"tensorboard",
"safetensors",
"whisper",
"generated_from_trainer",
"cy",
"dataset:wanasash/enwaucymraeg",
"base_model:openai/whisper-large-v3",
"base_model:finetune:openai/whisper-large-v3",
"license:apache-2.0",
"model-index",
"region:us"
] | null | 2024-10-06T07:50:59Z |
---
license: apache-2.0
base_model: openai/whisper-large-v3
tags:
- generated_from_trainer
datasets:
- wanasash/enwaucymraeg
metrics:
- wer
model-index:
- name: whisper-large-v3-ec
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: wanasash/enwaucymraeg default
type: wanasash/enwaucymraeg
args: default
metrics:
- name: Wer
type: wer
value: 0.21372622155911974
language:
- cy
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# whisper-large-v3-ec
This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on the wanasash/enwaucymraeg default dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4733
- Wer: 0.2137
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-------:|:----:|:---------------:|:------:|
| 0.0091 | 13.6054 | 1000 | 0.4027 | 0.2189 |
| 0.0026 | 27.2109 | 2000 | 0.4282 | 0.2260 |
| 0.0002 | 40.8163 | 3000 | 0.4444 | 0.2178 |
| 0.0001 | 54.4218 | 4000 | 0.4667 | 0.2160 |
| 0.0001 | 68.0272 | 5000 | 0.4733 | 0.2137 |
### Framework versions
- Transformers 4.44.0
- Pytorch 2.4.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
Swekerr/ReasoningGemma2-2B-GGUF
|
Swekerr
| 2024-10-07T11:26:37Z | 61 | 0 |
transformers
|
[
"transformers",
"gguf",
"gemma2",
"text-generation-inference",
"unsloth",
"en",
"dataset:KingNish/reasoning-base-20k",
"base_model:unsloth/gemma-2-2b-bnb-4bit",
"base_model:quantized:unsloth/gemma-2-2b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-10-07T11:18:49Z |
---
base_model: unsloth/gemma-2-2b-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- gemma2
- gguf
datasets:
- KingNish/reasoning-base-20k
---
# Uploaded model
- **Developed by:** Swekerr
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-2-2b-bnb-4bit
This gemma2 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)
|
QuantFactory/TwinLlama-3.1-8B-DPO-GGUF
|
QuantFactory
| 2024-10-07T11:23:13Z | 55 | 3 |
transformers
|
[
"transformers",
"gguf",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"dpo",
"en",
"base_model:mlabonne/TwinLlama-3.1-8B",
"base_model:quantized:mlabonne/TwinLlama-3.1-8B",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-09-01T05:44:17Z |
---
base_model: mlabonne/TwinLlama-3.1-8B
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- dpo
---
[](https://hf.co/QuantFactory)
# QuantFactory/TwinLlama-3.1-8B-DPO-GGUF
This is quantized version of [mlabonne/TwinLlama-3.1-8B-DPO](https://huggingface.co/mlabonne/TwinLlama-3.1-8B-DPO) created using llama.cpp
# Original Model Card
# Uploaded model
- **Developed by:** mlabonne
- **License:** apache-2.0
- **Finetuned from model :** mlabonne/TwinLlama-3.1-8B
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
Sripradha/marian-finetuned-kde4-en-to-fr
|
Sripradha
| 2024-10-07T11:20:39Z | 103 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"marian",
"text2text-generation",
"translation",
"generated_from_trainer",
"dataset:kde4",
"base_model:Helsinki-NLP/opus-mt-en-fr",
"base_model:finetune:Helsinki-NLP/opus-mt-en-fr",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2024-10-07T06:00:41Z |
---
library_name: transformers
license: apache-2.0
base_model: Helsinki-NLP/opus-mt-en-fr
tags:
- translation
- generated_from_trainer
datasets:
- kde4
metrics:
- bleu
model-index:
- name: marian-finetuned-kde4-en-to-fr
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: kde4
type: kde4
config: en-fr
split: train
args: en-fr
metrics:
- name: Bleu
type: bleu
value: 52.90371209759549
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# marian-finetuned-kde4-en-to-fr
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsinki-NLP/opus-mt-en-fr) on the kde4 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8554
- Bleu: 52.9037
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.19.1
|
bartowski/Human-Like-Mistral-Nemo-Instruct-2407-GGUF
|
bartowski
| 2024-10-07T11:17:12Z | 361 | 1 | null |
[
"gguf",
"axolotl",
"dpo",
"trl",
"generated_from_trainer",
"text-generation",
"base_model:HumanLLMs/Human-Like-Mistral-Nemo-Instruct-2407",
"base_model:quantized:HumanLLMs/Human-Like-Mistral-Nemo-Instruct-2407",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2024-10-07T10:43:13Z |
---
base_model: HumanLLMs/Humanish-Mistral-Nemo-Instruct-2407
license: apache-2.0
pipeline_tag: text-generation
tags:
- axolotl
- dpo
- trl
- generated_from_trainer
quantized_by: bartowski
model-index:
- name: Humanish-Mistral-Nemo-Instruct-2407
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: IFEval (0-Shot)
type: HuggingFaceH4/ifeval
args:
num_few_shot: 0
metrics:
- type: inst_level_strict_acc and prompt_level_strict_acc
value: 54.51
name: strict accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=HumanLLMs/Humanish-Mistral-Nemo-Instruct-2407
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: BBH (3-Shot)
type: BBH
args:
num_few_shot: 3
metrics:
- type: acc_norm
value: 32.71
name: normalized accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=HumanLLMs/Humanish-Mistral-Nemo-Instruct-2407
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MATH Lvl 5 (4-Shot)
type: hendrycks/competition_math
args:
num_few_shot: 4
metrics:
- type: exact_match
value: 7.63
name: exact match
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=HumanLLMs/Humanish-Mistral-Nemo-Instruct-2407
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GPQA (0-shot)
type: Idavidrein/gpqa
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 5.03
name: acc_norm
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=HumanLLMs/Humanish-Mistral-Nemo-Instruct-2407
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MuSR (0-shot)
type: TAUR-Lab/MuSR
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 9.4
name: acc_norm
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=HumanLLMs/Humanish-Mistral-Nemo-Instruct-2407
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU-PRO (5-shot)
type: TIGER-Lab/MMLU-Pro
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 28.01
name: accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=HumanLLMs/Humanish-Mistral-Nemo-Instruct-2407
name: Open LLM Leaderboard
---
## Llamacpp imatrix Quantizations of Humanish-Mistral-Nemo-Instruct-2407
Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggerganov/llama.cpp/releases/tag/b3878">b3878</a> for quantization.
Original model: https://huggingface.co/HumanLLMs/Humanish-Mistral-Nemo-Instruct-2407
All quants made using imatrix option with dataset from [here](https://gist.github.com/bartowski1182/eb213dccb3571f863da82e99418f81e8)
Run them in [LM Studio](https://lmstudio.ai/)
## Prompt format
No prompt format found, check original model page
## Download a file (not the whole branch) from below:
| Filename | Quant type | File Size | Split | Description |
| -------- | ---------- | --------- | ----- | ----------- |
| [Humanish-Mistral-Nemo-Instruct-2407-f16.gguf](https://huggingface.co/bartowski/Humanish-Mistral-Nemo-Instruct-2407-GGUF/blob/main/Humanish-Mistral-Nemo-Instruct-2407-f16.gguf) | f16 | 24.50GB | false | Full F16 weights. |
| [Humanish-Mistral-Nemo-Instruct-2407-Q8_0.gguf](https://huggingface.co/bartowski/Humanish-Mistral-Nemo-Instruct-2407-GGUF/blob/main/Humanish-Mistral-Nemo-Instruct-2407-Q8_0.gguf) | Q8_0 | 13.02GB | false | Extremely high quality, generally unneeded but max available quant. |
| [Humanish-Mistral-Nemo-Instruct-2407-Q6_K_L.gguf](https://huggingface.co/bartowski/Humanish-Mistral-Nemo-Instruct-2407-GGUF/blob/main/Humanish-Mistral-Nemo-Instruct-2407-Q6_K_L.gguf) | Q6_K_L | 10.38GB | false | Uses Q8_0 for embed and output weights. Very high quality, near perfect, *recommended*. |
| [Humanish-Mistral-Nemo-Instruct-2407-Q6_K.gguf](https://huggingface.co/bartowski/Humanish-Mistral-Nemo-Instruct-2407-GGUF/blob/main/Humanish-Mistral-Nemo-Instruct-2407-Q6_K.gguf) | Q6_K | 10.06GB | false | Very high quality, near perfect, *recommended*. |
| [Humanish-Mistral-Nemo-Instruct-2407-Q5_K_L.gguf](https://huggingface.co/bartowski/Humanish-Mistral-Nemo-Instruct-2407-GGUF/blob/main/Humanish-Mistral-Nemo-Instruct-2407-Q5_K_L.gguf) | Q5_K_L | 9.14GB | false | Uses Q8_0 for embed and output weights. High quality, *recommended*. |
| [Humanish-Mistral-Nemo-Instruct-2407-Q5_K_M.gguf](https://huggingface.co/bartowski/Humanish-Mistral-Nemo-Instruct-2407-GGUF/blob/main/Humanish-Mistral-Nemo-Instruct-2407-Q5_K_M.gguf) | Q5_K_M | 8.73GB | false | High quality, *recommended*. |
| [Humanish-Mistral-Nemo-Instruct-2407-Q5_K_S.gguf](https://huggingface.co/bartowski/Humanish-Mistral-Nemo-Instruct-2407-GGUF/blob/main/Humanish-Mistral-Nemo-Instruct-2407-Q5_K_S.gguf) | Q5_K_S | 8.52GB | false | High quality, *recommended*. |
| [Humanish-Mistral-Nemo-Instruct-2407-Q4_K_L.gguf](https://huggingface.co/bartowski/Humanish-Mistral-Nemo-Instruct-2407-GGUF/blob/main/Humanish-Mistral-Nemo-Instruct-2407-Q4_K_L.gguf) | Q4_K_L | 7.98GB | false | Uses Q8_0 for embed and output weights. Good quality, *recommended*. |
| [Humanish-Mistral-Nemo-Instruct-2407-Q4_K_M.gguf](https://huggingface.co/bartowski/Humanish-Mistral-Nemo-Instruct-2407-GGUF/blob/main/Humanish-Mistral-Nemo-Instruct-2407-Q4_K_M.gguf) | Q4_K_M | 7.48GB | false | Good quality, default size for must use cases, *recommended*. |
| [Humanish-Mistral-Nemo-Instruct-2407-Q3_K_XL.gguf](https://huggingface.co/bartowski/Humanish-Mistral-Nemo-Instruct-2407-GGUF/blob/main/Humanish-Mistral-Nemo-Instruct-2407-Q3_K_XL.gguf) | Q3_K_XL | 7.15GB | false | Uses Q8_0 for embed and output weights. Lower quality but usable, good for low RAM availability. |
| [Humanish-Mistral-Nemo-Instruct-2407-Q4_K_S.gguf](https://huggingface.co/bartowski/Humanish-Mistral-Nemo-Instruct-2407-GGUF/blob/main/Humanish-Mistral-Nemo-Instruct-2407-Q4_K_S.gguf) | Q4_K_S | 7.12GB | false | Slightly lower quality with more space savings, *recommended*. |
| [Humanish-Mistral-Nemo-Instruct-2407-Q4_0.gguf](https://huggingface.co/bartowski/Humanish-Mistral-Nemo-Instruct-2407-GGUF/blob/main/Humanish-Mistral-Nemo-Instruct-2407-Q4_0.gguf) | Q4_0 | 7.09GB | false | Legacy format, generally not worth using over similarly sized formats |
| [Humanish-Mistral-Nemo-Instruct-2407-Q4_0_8_8.gguf](https://huggingface.co/bartowski/Humanish-Mistral-Nemo-Instruct-2407-GGUF/blob/main/Humanish-Mistral-Nemo-Instruct-2407-Q4_0_8_8.gguf) | Q4_0_8_8 | 7.07GB | false | Optimized for ARM inference. Requires 'sve' support (see link below). |
| [Humanish-Mistral-Nemo-Instruct-2407-Q4_0_4_8.gguf](https://huggingface.co/bartowski/Humanish-Mistral-Nemo-Instruct-2407-GGUF/blob/main/Humanish-Mistral-Nemo-Instruct-2407-Q4_0_4_8.gguf) | Q4_0_4_8 | 7.07GB | false | Optimized for ARM inference. Requires 'i8mm' support (see link below). |
| [Humanish-Mistral-Nemo-Instruct-2407-Q4_0_4_4.gguf](https://huggingface.co/bartowski/Humanish-Mistral-Nemo-Instruct-2407-GGUF/blob/main/Humanish-Mistral-Nemo-Instruct-2407-Q4_0_4_4.gguf) | Q4_0_4_4 | 7.07GB | false | Optimized for ARM inference. Should work well on all ARM chips, pick this if you're unsure. |
| [Humanish-Mistral-Nemo-Instruct-2407-IQ4_XS.gguf](https://huggingface.co/bartowski/Humanish-Mistral-Nemo-Instruct-2407-GGUF/blob/main/Humanish-Mistral-Nemo-Instruct-2407-IQ4_XS.gguf) | IQ4_XS | 6.74GB | false | Decent quality, smaller than Q4_K_S with similar performance, *recommended*. |
| [Humanish-Mistral-Nemo-Instruct-2407-Q3_K_L.gguf](https://huggingface.co/bartowski/Humanish-Mistral-Nemo-Instruct-2407-GGUF/blob/main/Humanish-Mistral-Nemo-Instruct-2407-Q3_K_L.gguf) | Q3_K_L | 6.56GB | false | Lower quality but usable, good for low RAM availability. |
| [Humanish-Mistral-Nemo-Instruct-2407-Q3_K_M.gguf](https://huggingface.co/bartowski/Humanish-Mistral-Nemo-Instruct-2407-GGUF/blob/main/Humanish-Mistral-Nemo-Instruct-2407-Q3_K_M.gguf) | Q3_K_M | 6.08GB | false | Low quality. |
| [Humanish-Mistral-Nemo-Instruct-2407-IQ3_M.gguf](https://huggingface.co/bartowski/Humanish-Mistral-Nemo-Instruct-2407-GGUF/blob/main/Humanish-Mistral-Nemo-Instruct-2407-IQ3_M.gguf) | IQ3_M | 5.72GB | false | Medium-low quality, new method with decent performance comparable to Q3_K_M. |
| [Humanish-Mistral-Nemo-Instruct-2407-Q3_K_S.gguf](https://huggingface.co/bartowski/Humanish-Mistral-Nemo-Instruct-2407-GGUF/blob/main/Humanish-Mistral-Nemo-Instruct-2407-Q3_K_S.gguf) | Q3_K_S | 5.53GB | false | Low quality, not recommended. |
| [Humanish-Mistral-Nemo-Instruct-2407-Q2_K_L.gguf](https://huggingface.co/bartowski/Humanish-Mistral-Nemo-Instruct-2407-GGUF/blob/main/Humanish-Mistral-Nemo-Instruct-2407-Q2_K_L.gguf) | Q2_K_L | 5.45GB | false | Uses Q8_0 for embed and output weights. Very low quality but surprisingly usable. |
| [Humanish-Mistral-Nemo-Instruct-2407-IQ3_XS.gguf](https://huggingface.co/bartowski/Humanish-Mistral-Nemo-Instruct-2407-GGUF/blob/main/Humanish-Mistral-Nemo-Instruct-2407-IQ3_XS.gguf) | IQ3_XS | 5.31GB | false | Lower quality, new method with decent performance, slightly better than Q3_K_S. |
| [Humanish-Mistral-Nemo-Instruct-2407-Q2_K.gguf](https://huggingface.co/bartowski/Humanish-Mistral-Nemo-Instruct-2407-GGUF/blob/main/Humanish-Mistral-Nemo-Instruct-2407-Q2_K.gguf) | Q2_K | 4.79GB | false | Very low quality but surprisingly usable. |
| [Humanish-Mistral-Nemo-Instruct-2407-IQ2_M.gguf](https://huggingface.co/bartowski/Humanish-Mistral-Nemo-Instruct-2407-GGUF/blob/main/Humanish-Mistral-Nemo-Instruct-2407-IQ2_M.gguf) | IQ2_M | 4.44GB | false | Relatively low quality, uses SOTA techniques to be surprisingly usable. |
| [Humanish-Mistral-Nemo-Instruct-2407-IQ2_S.gguf](https://huggingface.co/bartowski/Humanish-Mistral-Nemo-Instruct-2407-GGUF/blob/main/Humanish-Mistral-Nemo-Instruct-2407-IQ2_S.gguf) | IQ2_S | 4.14GB | false | Low quality, uses SOTA techniques to be usable. |
## Embed/output weights
Some of these quants (Q3_K_XL, Q4_K_L etc) are the standard quantization method with the embeddings and output weights quantized to Q8_0 instead of what they would normally default to.
Some say that this improves the quality, others don't notice any difference. If you use these models PLEASE COMMENT with your findings. I would like feedback that these are actually used and useful so I don't keep uploading quants no one is using.
Thanks!
## 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/Humanish-Mistral-Nemo-Instruct-2407-GGUF --include "Humanish-Mistral-Nemo-Instruct-2407-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/Humanish-Mistral-Nemo-Instruct-2407-GGUF --include "Humanish-Mistral-Nemo-Instruct-2407-Q8_0/*" --local-dir ./
```
You can either specify a new local-dir (Humanish-Mistral-Nemo-Instruct-2407-Q8_0) or download them all in place (./)
## Q4_0_X_X
These are *NOT* for Metal (Apple) offloading, only ARM chips.
If you're using an ARM chip, the Q4_0_X_X quants will have a substantial speedup. Check out Q4_0_4_4 speed comparisons [on the original pull request](https://github.com/ggerganov/llama.cpp/pull/5780#pullrequestreview-21657544660)
To check which one would work best for your ARM chip, you can check [AArch64 SoC features](https://gpages.juszkiewicz.com.pl/arm-socs-table/arm-socs.html) (thanks EloyOn!).
## 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.
## Credits
Thank you kalomaze and Dampf for assistance in creating the imatrix calibration dataset
Thank you ZeroWw for the inspiration to experiment with embed/output
Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
|
ishaq101/llama3-8b-finetune-4bit-lora
|
ishaq101
| 2024-10-07T11:00:24Z | 76 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"en",
"base_model:ishaq101/llama3-8b-finetune-4bit",
"base_model:quantized:ishaq101/llama3-8b-finetune-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2024-10-07T10:58:53Z |
---
base_model: ishaq101/llama3-8b-finetune-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
---
# Uploaded model
- **Developed by:** ishaq101
- **License:** apache-2.0
- **Finetuned from model :** ishaq101/llama3-8b-finetune-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
rombodawg/Rombos-LLM-V2.5-Qwen-72b
|
rombodawg
| 2024-10-07T10:58:38Z | 3,193 | 35 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"base_model:Qwen/Qwen2.5-72B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-72B-Instruct",
"license:other",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-09-30T01:53:14Z |
---
license: other
library_name: transformers
base_model:
- Qwen/Qwen2.5-72B-Instruct
license_name: qwen
license_link: https://huggingface.co/Qwen/Qwen2.5-72B-Instruct/blob/main/LICENSE
model-index:
- name: Replete-LLM-V2.5-Qwen-72b_Duplicated
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: IFEval (0-Shot)
type: HuggingFaceH4/ifeval
args:
num_few_shot: 0
metrics:
- type: inst_level_strict_acc and prompt_level_strict_acc
value: 71.55
name: strict accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=rombodawg/Replete-LLM-V2.5-Qwen-72b_Duplicated
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: BBH (3-Shot)
type: BBH
args:
num_few_shot: 3
metrics:
- type: acc_norm
value: 61.27
name: normalized accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=rombodawg/Replete-LLM-V2.5-Qwen-72b_Duplicated
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MATH Lvl 5 (4-Shot)
type: hendrycks/competition_math
args:
num_few_shot: 4
metrics:
- type: exact_match
value: 47.58
name: exact match
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=rombodawg/Replete-LLM-V2.5-Qwen-72b_Duplicated
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GPQA (0-shot)
type: Idavidrein/gpqa
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 19.8
name: acc_norm
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=rombodawg/Replete-LLM-V2.5-Qwen-72b_Duplicated
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MuSR (0-shot)
type: TAUR-Lab/MuSR
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 17.32
name: acc_norm
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=rombodawg/Replete-LLM-V2.5-Qwen-72b_Duplicated
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU-PRO (5-shot)
type: TIGER-Lab/MMLU-Pro
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 54.83
name: accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=rombodawg/Replete-LLM-V2.5-Qwen-72b_Duplicated
name: Open LLM Leaderboard
---
# Rombos-LLM-V2.5-Qwen-72b

Rombos-LLM-V2.5-Qwen-72b is a continues finetuned version of Qwen2.5-72B. I noticed recently that the Qwen team did not learn from my methods of continuous finetuning, the great benefits, and no downsides of it. So I took it upon myself to merge the instruct model with the base model myself using the *Ties* merge method
This version of the model shows higher performance than the original instruct and base models.
Quants: (Coming soon)
GGUF: https://huggingface.co/bartowski/Replete-LLM-V2.5-Qwen-72b-GGUF
EXL2:
Benchmarks: (Coming soon)
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_rombodawg__Replete-LLM-V2.5-Qwen-72b_Duplicated)
| Metric |Value|
|-------------------|----:|
|Avg. |45.39|
|IFEval (0-Shot) |71.55|
|BBH (3-Shot) |61.27|
|MATH Lvl 5 (4-Shot)|47.58|
|GPQA (0-shot) |19.80|
|MuSR (0-shot) |17.32|
|MMLU-PRO (5-shot) |54.83|
|
shahxeebhassan/bert_base_ai_content_detector
|
shahxeebhassan
| 2024-10-07T10:47:46Z | 292 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"dataset:shahxeebhassan/human_vs_ai_sentences",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-10-07T09:45:55Z |
---
license: mit
metrics:
- accuracy
base_model:
- google-bert/bert-base-uncased
datasets:
- shahxeebhassan/human_vs_ai_sentences
pipeline_tag: text-classification
library_name: transformers
---
## Model Description
This model is a fine-tuned BERT model for AI content detection.
## Training Data
The model was trained on a [<span style="color: blue;">dataset</span>
](https://huggingface.co/datasets/shahxeebhassan/human_vs_ai_sentences) of over 100,000 sentences, each labeled as either AI-generated or human-written. This approach allows the model to predict the nature of each individual sentence, which is particularly useful for highlighting AI-written content within larger texts.
## Evaluation Metrics
The model achieved an accuracy of 90% on the validation & test set.
## Usage
```python
import torch
from transformers import BertTokenizer, BertForSequenceClassification
tokenizer = BertTokenizer.from_pretrained("shahxeebhassan/bert_base_ai_content_detector")
model = BertForSequenceClassification.from_pretrained("shahxeebhassan/bert_base_ai_content_detector")
inputs = tokenizer("Distance learning will not benefit students because the students are not able to develop as good of a relationship with their teachers.", return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits
probabilities = torch.softmax(logits, dim=1).cpu().numpy()
predicted_label = probabilities.argmax(axis=1)
print(f"Predicted label for the input text: {predicted_label[0]}")
|
Kort/s3
|
Kort
| 2024-10-07T10:41:46Z | 33 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-10-07T08:52:39Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
dhruvindia/GPT2-TVC-prompt
|
dhruvindia
| 2024-10-07T10:38:01Z | 151 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gpt2",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-10-07T10:35:23Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed]
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[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Kort/s2
|
Kort
| 2024-10-07T10:31:15Z | 33 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-10-07T08:41:23Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Xbhi112358/my-fine-tuned-model_4
|
Xbhi112358
| 2024-10-07T10:29:30Z | 75 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"en",
"base_model:unsloth/Meta-Llama-3.1-8B-bnb-4bit",
"base_model:quantized:unsloth/Meta-Llama-3.1-8B-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2024-10-07T10:12:51Z |
---
base_model: unsloth/Meta-Llama-3.1-8B-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
---
# Uploaded model
- **Developed by:** Xbhi112358
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Meta-Llama-3.1-8B-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
bartowski/Human-Like-LLama3-8B-Instruct-GGUF
|
bartowski
| 2024-10-07T10:29:28Z | 1,287 | 2 | null |
[
"gguf",
"axolotl",
"dpo",
"trl",
"generated_from_trainer",
"text-generation",
"dataset:HumanLLMs/humanish-dpo-project",
"base_model:HumanLLMs/Human-Like-LLama3-8B-Instruct",
"base_model:quantized:HumanLLMs/Human-Like-LLama3-8B-Instruct",
"license:llama3",
"model-index",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2024-10-07T10:07:53Z |
---
base_model: HumanLLMs/Humanish-LLama3-8B-Instruct
datasets:
- HumanLLMs/humanish-dpo-project
license: llama3
pipeline_tag: text-generation
tags:
- axolotl
- dpo
- trl
- generated_from_trainer
quantized_by: bartowski
model-index:
- name: Humanish-LLama3.1-8B-Instruct
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: IFEval (0-Shot)
type: HuggingFaceH4/ifeval
args:
num_few_shot: 0
metrics:
- type: inst_level_strict_acc and prompt_level_strict_acc
value: 64.98
name: strict accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=HumanLLMs/Humanish-LLama3.1-8B-Instruct
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: BBH (3-Shot)
type: BBH
args:
num_few_shot: 3
metrics:
- type: acc_norm
value: 28.01
name: normalized accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=HumanLLMs/Humanish-LLama3.1-8B-Instruct
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MATH Lvl 5 (4-Shot)
type: hendrycks/competition_math
args:
num_few_shot: 4
metrics:
- type: exact_match
value: 8.46
name: exact match
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=HumanLLMs/Humanish-LLama3.1-8B-Instruct
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GPQA (0-shot)
type: Idavidrein/gpqa
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 0.78
name: acc_norm
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=HumanLLMs/Humanish-LLama3.1-8B-Instruct
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MuSR (0-shot)
type: TAUR-Lab/MuSR
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 2.0
name: acc_norm
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=HumanLLMs/Humanish-LLama3.1-8B-Instruct
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU-PRO (5-shot)
type: TIGER-Lab/MMLU-Pro
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 30.02
name: accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=HumanLLMs/Humanish-LLama3.1-8B-Instruct
name: Open LLM Leaderboard
---
## Llamacpp imatrix Quantizations of Humanish-LLama3-8B-Instruct
Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggerganov/llama.cpp/releases/tag/b3878">b3878</a> for quantization.
Original model: https://huggingface.co/HumanLLMs/Humanish-LLama3-8B-Instruct
All quants made using imatrix option with dataset from [here](https://gist.github.com/bartowski1182/eb213dccb3571f863da82e99418f81e8)
Run them in [LM Studio](https://lmstudio.ai/)
## Prompt format
No prompt format found, check original model page
## Download a file (not the whole branch) from below:
| Filename | Quant type | File Size | Split | Description |
| -------- | ---------- | --------- | ----- | ----------- |
| [Humanish-LLama3-8B-Instruct-f16.gguf](https://huggingface.co/bartowski/Humanish-LLama3-8B-Instruct-GGUF/blob/main/Humanish-LLama3-8B-Instruct-f16.gguf) | f16 | 16.07GB | false | Full F16 weights. |
| [Humanish-LLama3-8B-Instruct-Q8_0.gguf](https://huggingface.co/bartowski/Humanish-LLama3-8B-Instruct-GGUF/blob/main/Humanish-LLama3-8B-Instruct-Q8_0.gguf) | Q8_0 | 8.54GB | false | Extremely high quality, generally unneeded but max available quant. |
| [Humanish-LLama3-8B-Instruct-Q6_K_L.gguf](https://huggingface.co/bartowski/Humanish-LLama3-8B-Instruct-GGUF/blob/main/Humanish-LLama3-8B-Instruct-Q6_K_L.gguf) | Q6_K_L | 6.85GB | false | Uses Q8_0 for embed and output weights. Very high quality, near perfect, *recommended*. |
| [Humanish-LLama3-8B-Instruct-Q6_K.gguf](https://huggingface.co/bartowski/Humanish-LLama3-8B-Instruct-GGUF/blob/main/Humanish-LLama3-8B-Instruct-Q6_K.gguf) | Q6_K | 6.60GB | false | Very high quality, near perfect, *recommended*. |
| [Humanish-LLama3-8B-Instruct-Q5_K_L.gguf](https://huggingface.co/bartowski/Humanish-LLama3-8B-Instruct-GGUF/blob/main/Humanish-LLama3-8B-Instruct-Q5_K_L.gguf) | Q5_K_L | 6.06GB | false | Uses Q8_0 for embed and output weights. High quality, *recommended*. |
| [Humanish-LLama3-8B-Instruct-Q5_K_M.gguf](https://huggingface.co/bartowski/Humanish-LLama3-8B-Instruct-GGUF/blob/main/Humanish-LLama3-8B-Instruct-Q5_K_M.gguf) | Q5_K_M | 5.73GB | false | High quality, *recommended*. |
| [Humanish-LLama3-8B-Instruct-Q5_K_S.gguf](https://huggingface.co/bartowski/Humanish-LLama3-8B-Instruct-GGUF/blob/main/Humanish-LLama3-8B-Instruct-Q5_K_S.gguf) | Q5_K_S | 5.60GB | false | High quality, *recommended*. |
| [Humanish-LLama3-8B-Instruct-Q4_K_L.gguf](https://huggingface.co/bartowski/Humanish-LLama3-8B-Instruct-GGUF/blob/main/Humanish-LLama3-8B-Instruct-Q4_K_L.gguf) | Q4_K_L | 5.31GB | false | Uses Q8_0 for embed and output weights. Good quality, *recommended*. |
| [Humanish-LLama3-8B-Instruct-Q4_K_M.gguf](https://huggingface.co/bartowski/Humanish-LLama3-8B-Instruct-GGUF/blob/main/Humanish-LLama3-8B-Instruct-Q4_K_M.gguf) | Q4_K_M | 4.92GB | false | Good quality, default size for must use cases, *recommended*. |
| [Humanish-LLama3-8B-Instruct-Q3_K_XL.gguf](https://huggingface.co/bartowski/Humanish-LLama3-8B-Instruct-GGUF/blob/main/Humanish-LLama3-8B-Instruct-Q3_K_XL.gguf) | Q3_K_XL | 4.78GB | false | Uses Q8_0 for embed and output weights. Lower quality but usable, good for low RAM availability. |
| [Humanish-LLama3-8B-Instruct-Q4_K_S.gguf](https://huggingface.co/bartowski/Humanish-LLama3-8B-Instruct-GGUF/blob/main/Humanish-LLama3-8B-Instruct-Q4_K_S.gguf) | Q4_K_S | 4.69GB | false | Slightly lower quality with more space savings, *recommended*. |
| [Humanish-LLama3-8B-Instruct-Q4_0.gguf](https://huggingface.co/bartowski/Humanish-LLama3-8B-Instruct-GGUF/blob/main/Humanish-LLama3-8B-Instruct-Q4_0.gguf) | Q4_0 | 4.68GB | false | Legacy format, generally not worth using over similarly sized formats |
| [Humanish-LLama3-8B-Instruct-Q4_0_8_8.gguf](https://huggingface.co/bartowski/Humanish-LLama3-8B-Instruct-GGUF/blob/main/Humanish-LLama3-8B-Instruct-Q4_0_8_8.gguf) | Q4_0_8_8 | 4.66GB | false | Optimized for ARM inference. Requires 'sve' support (see link below). |
| [Humanish-LLama3-8B-Instruct-Q4_0_4_8.gguf](https://huggingface.co/bartowski/Humanish-LLama3-8B-Instruct-GGUF/blob/main/Humanish-LLama3-8B-Instruct-Q4_0_4_8.gguf) | Q4_0_4_8 | 4.66GB | false | Optimized for ARM inference. Requires 'i8mm' support (see link below). |
| [Humanish-LLama3-8B-Instruct-Q4_0_4_4.gguf](https://huggingface.co/bartowski/Humanish-LLama3-8B-Instruct-GGUF/blob/main/Humanish-LLama3-8B-Instruct-Q4_0_4_4.gguf) | Q4_0_4_4 | 4.66GB | false | Optimized for ARM inference. Should work well on all ARM chips, pick this if you're unsure. |
| [Humanish-LLama3-8B-Instruct-IQ4_XS.gguf](https://huggingface.co/bartowski/Humanish-LLama3-8B-Instruct-GGUF/blob/main/Humanish-LLama3-8B-Instruct-IQ4_XS.gguf) | IQ4_XS | 4.45GB | false | Decent quality, smaller than Q4_K_S with similar performance, *recommended*. |
| [Humanish-LLama3-8B-Instruct-Q3_K_L.gguf](https://huggingface.co/bartowski/Humanish-LLama3-8B-Instruct-GGUF/blob/main/Humanish-LLama3-8B-Instruct-Q3_K_L.gguf) | Q3_K_L | 4.32GB | false | Lower quality but usable, good for low RAM availability. |
| [Humanish-LLama3-8B-Instruct-Q3_K_M.gguf](https://huggingface.co/bartowski/Humanish-LLama3-8B-Instruct-GGUF/blob/main/Humanish-LLama3-8B-Instruct-Q3_K_M.gguf) | Q3_K_M | 4.02GB | false | Low quality. |
| [Humanish-LLama3-8B-Instruct-IQ3_M.gguf](https://huggingface.co/bartowski/Humanish-LLama3-8B-Instruct-GGUF/blob/main/Humanish-LLama3-8B-Instruct-IQ3_M.gguf) | IQ3_M | 3.78GB | false | Medium-low quality, new method with decent performance comparable to Q3_K_M. |
| [Humanish-LLama3-8B-Instruct-Q2_K_L.gguf](https://huggingface.co/bartowski/Humanish-LLama3-8B-Instruct-GGUF/blob/main/Humanish-LLama3-8B-Instruct-Q2_K_L.gguf) | Q2_K_L | 3.69GB | false | Uses Q8_0 for embed and output weights. Very low quality but surprisingly usable. |
| [Humanish-LLama3-8B-Instruct-Q3_K_S.gguf](https://huggingface.co/bartowski/Humanish-LLama3-8B-Instruct-GGUF/blob/main/Humanish-LLama3-8B-Instruct-Q3_K_S.gguf) | Q3_K_S | 3.66GB | false | Low quality, not recommended. |
| [Humanish-LLama3-8B-Instruct-IQ3_XS.gguf](https://huggingface.co/bartowski/Humanish-LLama3-8B-Instruct-GGUF/blob/main/Humanish-LLama3-8B-Instruct-IQ3_XS.gguf) | IQ3_XS | 3.52GB | false | Lower quality, new method with decent performance, slightly better than Q3_K_S. |
| [Humanish-LLama3-8B-Instruct-Q2_K.gguf](https://huggingface.co/bartowski/Humanish-LLama3-8B-Instruct-GGUF/blob/main/Humanish-LLama3-8B-Instruct-Q2_K.gguf) | Q2_K | 3.18GB | false | Very low quality but surprisingly usable. |
| [Humanish-LLama3-8B-Instruct-IQ2_M.gguf](https://huggingface.co/bartowski/Humanish-LLama3-8B-Instruct-GGUF/blob/main/Humanish-LLama3-8B-Instruct-IQ2_M.gguf) | IQ2_M | 2.95GB | false | Relatively low quality, uses SOTA techniques to be surprisingly usable. |
## Embed/output weights
Some of these quants (Q3_K_XL, Q4_K_L etc) are the standard quantization method with the embeddings and output weights quantized to Q8_0 instead of what they would normally default to.
Some say that this improves the quality, others don't notice any difference. If you use these models PLEASE COMMENT with your findings. I would like feedback that these are actually used and useful so I don't keep uploading quants no one is using.
Thanks!
## 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/Humanish-LLama3-8B-Instruct-GGUF --include "Humanish-LLama3-8B-Instruct-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/Humanish-LLama3-8B-Instruct-GGUF --include "Humanish-LLama3-8B-Instruct-Q8_0/*" --local-dir ./
```
You can either specify a new local-dir (Humanish-LLama3-8B-Instruct-Q8_0) or download them all in place (./)
## Q4_0_X_X
These are *NOT* for Metal (Apple) offloading, only ARM chips.
If you're using an ARM chip, the Q4_0_X_X quants will have a substantial speedup. Check out Q4_0_4_4 speed comparisons [on the original pull request](https://github.com/ggerganov/llama.cpp/pull/5780#pullrequestreview-21657544660)
To check which one would work best for your ARM chip, you can check [AArch64 SoC features](https://gpages.juszkiewicz.com.pl/arm-socs-table/arm-socs.html) (thanks EloyOn!).
## 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.
## Credits
Thank you kalomaze and Dampf for assistance in creating the imatrix calibration dataset
Thank you ZeroWw for the inspiration to experiment with embed/output
Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
|
jorgegoco/bert-fine-tuned-cola
|
jorgegoco
| 2024-10-07T10:04:32Z | 105 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-cased",
"base_model:finetune:google-bert/bert-base-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-10-07T09:24:22Z |
---
base_model: bert-base-cased
library_name: transformers
license: apache-2.0
metrics:
- matthews_correlation
tags:
- generated_from_trainer
model-index:
- name: bert-fine-tuned-cola
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-fine-tuned-cola
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8212
- Matthews Correlation: 0.5782
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.4452 | 1.0 | 1069 | 0.4682 | 0.5208 |
| 0.3216 | 2.0 | 2138 | 0.6613 | 0.5620 |
| 0.1795 | 3.0 | 3207 | 0.8212 | 0.5782 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.19.1
|
motheecreator/ViT-GPT2-Image-Captioning
|
motheecreator
| 2024-10-07T10:03:01Z | 148 | 0 |
transformers
|
[
"transformers",
"safetensors",
"vision-encoder-decoder",
"image-text-to-text",
"generated_from_trainer",
"image-to-text",
"base_model:motheecreator/ViT-GPT2-Image_Captioning_model",
"base_model:finetune:motheecreator/ViT-GPT2-Image_Captioning_model",
"endpoints_compatible",
"region:us"
] |
image-to-text
| 2024-09-30T21:35:55Z |
---
library_name: transformers
base_model: motheecreator/ViT-GPT2-Image_Captioning_model
tags:
- generated_from_trainer
- image-to-text
metrics:
- bleu
model-index:
- name: ViT-GPT2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ViT-GPT2
This model is a fine-tuned version of [motheecreator/ViT-GPT2-Image_Captioning_model](https://huggingface.co/motheecreator/ViT-GPT2-Image_Captioning_model) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.125337
- Rouge2 Precision: None
- Rouge2 Recall: None
- Rouge2 Fmeasure: 0.155
- Bleu: 9.7054
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure | Bleu |
|:-------------:|:------:|:----:|:---------------:|:----------------:|:-------------:|:---------------:|:------:|
| 2.1537 | 0.9993 | 1171 | 2.13666 | None | None | 0.1531 | 9.4673 |
| 2.0434 | 1.9985 | 2342 | 2.125337 | None | None | 0.155 | 9.7054 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0
- Datasets 3.0.0
- Tokenizers 0.19.1
|
professorf/SmolLM-1.7B-Instruct-f16-gguf
|
professorf
| 2024-10-07T10:02:58Z | 8 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"dataset:HuggingFaceTB/smollm-corpus",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-10-07T09:37:28Z |
---
library_name: transformers
license: apache-2.0
language:
- en
datasets:
- HuggingFaceTB/smollm-corpus
---
<!-- header start -->
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">ProfessorF is Nick V. Flor, PhD<br>Models quantized for research reproducibility purposes</p></div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# SmolLM
<center>
<img src="https://huggingface.co/datasets/HuggingFaceTB/images/resolve/main/banner_smol.png" alt="SmolLM" width="1100" height="600">
</center>
## Table of Contents
1. [Model Summary](##model-summary)
2. [Limitations](##limitations)
3. [Training](##training)
4. [License](##license)
5. [Citation](##citation)
## Model Summary
SmolLM is a series of state-of-the-art small language models available in three sizes: 135M, 360M, and 1.7B parameters. These models are built on Cosmo-Corpus, a meticulously curated high-quality training dataset. Cosmo-Corpus includes Cosmopedia v2 (28B tokens of synthetic textbooks and stories generated by Mixtral), Python-Edu (4B tokens of educational Python samples from The Stack), and FineWeb-Edu (220B tokens of deduplicated educational web samples from FineWeb). SmolLM models have shown promising results when compared to other models in their size categories across various benchmarks testing common sense reasoning and world knowledge. For detailed information on training, benchmarks and performance, please refer to our full [blog post](https://huggingface.co/blog/smollm).
This is SmolLM-1.7B
### Generation
```bash
pip install transformers
```
#### Running the model on CPU/GPU/multi GPU
* _Using full precision_
```python
# pip install transformers
from transformers import AutoModelForCausalLM, AutoTokenizer
checkpoint = "HuggingFaceTB/SmolLM-1.7B"
device = "cuda" # for GPU usage or "cpu" for CPU usage
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
# for multiple GPUs install accelerate and do `model = AutoModelForCausalLM.from_pretrained(checkpoint, device_map="auto")`
model = AutoModelForCausalLM.from_pretrained(checkpoint).to(device)
inputs = tokenizer.encode("def print_hello_world():", return_tensors="pt").to(device)
outputs = model.generate(inputs)
print(tokenizer.decode(outputs[0]))
```
* _Using `torch.bfloat16`_
```python
# pip install accelerate
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
checkpoint = "HuggingFaceTB/SmolLM-1.7B"
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
# for fp16 use `torch_dtype=torch.float16` instead
model = AutoModelForCausalLM.from_pretrained(checkpoint, device_map="auto", torch_dtype=torch.bfloat16)
inputs = tokenizer.encode("def print_hello_world():", return_tensors="pt").to("cuda")
outputs = model.generate(inputs)
print(tokenizer.decode(outputs[0]))
```
```bash
>>> print(f"Memory footprint: {model.get_memory_footprint() / 1e6:.2f} MB")
Memory footprint: 3422.76 MB
```
#### Quantized Versions through `bitsandbytes`
* _Using 8-bit precision (int8)_
```python
# pip install bitsandbytes accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
# to use 4bit use `load_in_4bit=True` instead
quantization_config = BitsAndBytesConfig(load_in_8bit=True)
checkpoint = "HuggingFaceTB/SmolLM-1.7B"
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForCausalLM.from_pretrained(checkpoint, quantization_config=quantization_config)
inputs = tokenizer.encode("def print_hello_world():", return_tensors="pt").to("cuda")
outputs = model.generate(inputs)
print(tokenizer.decode(outputs[0]))
```
```bash
>>> print(f"Memory footprint: {model.get_memory_footprint() / 1e6:.2f} MB")
# load_in_8bit
Memory footprint: 1812.14 MB
# load_in_4bit
>>> print(f"Memory footprint: {model.get_memory_footprint() / 1e6:.2f} MB")
Memory footprint: 1006.84 MB
```
# Limitations
While SmolLM models have been trained on a diverse dataset including educational content and synthetic texts, they have limitations. The models primarily understand and generate content in English. They can produce text on a variety of topics, but the generated content may not always be factually accurate, logically consistent, or free from biases present in the training data. These models should be used as assistive tools rather than definitive sources of information. Users should always verify important information and critically evaluate any generated content. For a more comprehensive discussion of the models' capabilities and limitations, please refer to our full [blog post](https://huggingface.co/blog/smollm).
This repository contains a converted version of our latest trained model. We've noticed a small performance difference between this converted checkpoint (transformers) and the original (nanotron). We're currently working to resolve this issue.
# Training
## Model
- **Architecture:** For architecture detail, see the [blog post](https://huggingface.co/blog/smollm).
- **Pretraining steps:** 500k
- **Pretraining tokens:** 1T
- **Precision:** bfloat16
## Hardware
- **GPUs:** 64 H100
## Software
- **Training Framework:** [Nanotron](https://github.com/huggingface/nanotron/tree/main)
# License
[Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0)
# Citation
```bash
@misc{allal2024SmolLM,
title={SmolLM - blazingly fast and remarkably powerful},
author={Loubna Ben Allal and Anton Lozhkov and Elie Bakouch and Leandro von Werra and Thomas Wolf},
year={2024},
}
```
|
ylacombe/multi
|
ylacombe
| 2024-10-07T09:59:41Z | 53 | 0 |
transformers
|
[
"transformers",
"safetensors",
"parler_tts",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-10-07T09:58:09Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
den123/Caricature-flux
|
den123
| 2024-10-07T09:52:45Z | 40 | 1 |
diffusers
|
[
"diffusers",
"text-to-image",
"lora",
"template:diffusion-lora",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:apache-2.0",
"region:us"
] |
text-to-image
| 2024-10-07T07:31:00Z |
---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- text: '-'
output:
url: images/655e1034-4eda-4866-b27f-b91744353c39.png
- text: Angelina Jolie caricature, pen drawing, bw
output:
url: images/example_7cb04vnuy.png
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: Caricature
license: apache-2.0
---
# Caricature-flux
<Gallery />
## Model description
Caricature drawing style
## Trigger words
You should use `Caricature` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/den123/Caricature-flux/tree/main) them in the Files & versions tab.
|
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