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timestamp[us, tz=UTC]date 2020-02-15 11:33:14
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| downloads
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| likes
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11.7k
| library_name
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mradermacher/EEVE-Korean-kornerstone-10.8B-v1.0-GGUF
|
mradermacher
| 2024-12-16T07:18:28Z | 11 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:habin/EEVE-Korean-kornerstone-10.8B-v1.0",
"base_model:quantized:habin/EEVE-Korean-kornerstone-10.8B-v1.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-12-16T06:27:34Z |
---
base_model: habin/EEVE-Korean-kornerstone-10.8B-v1.0
language:
- en
library_name: transformers
quantized_by: mradermacher
tags: []
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/habin/EEVE-Korean-kornerstone-10.8B-v1.0
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/EEVE-Korean-kornerstone-10.8B-v1.0-GGUF/resolve/main/EEVE-Korean-kornerstone-10.8B-v1.0.Q2_K.gguf) | Q2_K | 4.1 | |
| [GGUF](https://huggingface.co/mradermacher/EEVE-Korean-kornerstone-10.8B-v1.0-GGUF/resolve/main/EEVE-Korean-kornerstone-10.8B-v1.0.Q3_K_S.gguf) | Q3_K_S | 4.8 | |
| [GGUF](https://huggingface.co/mradermacher/EEVE-Korean-kornerstone-10.8B-v1.0-GGUF/resolve/main/EEVE-Korean-kornerstone-10.8B-v1.0.Q3_K_M.gguf) | Q3_K_M | 5.3 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/EEVE-Korean-kornerstone-10.8B-v1.0-GGUF/resolve/main/EEVE-Korean-kornerstone-10.8B-v1.0.Q3_K_L.gguf) | Q3_K_L | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/EEVE-Korean-kornerstone-10.8B-v1.0-GGUF/resolve/main/EEVE-Korean-kornerstone-10.8B-v1.0.IQ4_XS.gguf) | IQ4_XS | 6.0 | |
| [GGUF](https://huggingface.co/mradermacher/EEVE-Korean-kornerstone-10.8B-v1.0-GGUF/resolve/main/EEVE-Korean-kornerstone-10.8B-v1.0.Q4_K_S.gguf) | Q4_K_S | 6.3 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/EEVE-Korean-kornerstone-10.8B-v1.0-GGUF/resolve/main/EEVE-Korean-kornerstone-10.8B-v1.0.Q4_K_M.gguf) | Q4_K_M | 6.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/EEVE-Korean-kornerstone-10.8B-v1.0-GGUF/resolve/main/EEVE-Korean-kornerstone-10.8B-v1.0.Q5_K_S.gguf) | Q5_K_S | 7.6 | |
| [GGUF](https://huggingface.co/mradermacher/EEVE-Korean-kornerstone-10.8B-v1.0-GGUF/resolve/main/EEVE-Korean-kornerstone-10.8B-v1.0.Q5_K_M.gguf) | Q5_K_M | 7.8 | |
| [GGUF](https://huggingface.co/mradermacher/EEVE-Korean-kornerstone-10.8B-v1.0-GGUF/resolve/main/EEVE-Korean-kornerstone-10.8B-v1.0.Q6_K.gguf) | Q6_K | 9.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/EEVE-Korean-kornerstone-10.8B-v1.0-GGUF/resolve/main/EEVE-Korean-kornerstone-10.8B-v1.0.Q8_0.gguf) | Q8_0 | 11.6 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/EEVE-Korean-kornerstone-10.8B-v1.0-GGUF/resolve/main/EEVE-Korean-kornerstone-10.8B-v1.0.f16.gguf) | f16 | 21.7 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
Kromtao/50_07_15
|
Kromtao
| 2024-12-16T07:17:35Z | 51 | 0 |
transformers
|
[
"transformers",
"safetensors",
"parler_tts",
"text2text-generation",
"text-to-speech",
"annotation",
"en",
"dataset:parler-tts/mls_eng",
"dataset:parler-tts/libritts_r_filtered",
"dataset:parler-tts/libritts-r-filtered-speaker-descriptions",
"dataset:parler-tts/mls-eng-speaker-descriptions",
"arxiv:2402.01912",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-to-speech
| 2024-12-16T07:16:08Z |
---
library_name: transformers
tags:
- text-to-speech
- annotation
license: apache-2.0
language:
- en
pipeline_tag: text-to-speech
inference: false
datasets:
- parler-tts/mls_eng
- parler-tts/libritts_r_filtered
- parler-tts/libritts-r-filtered-speaker-descriptions
- parler-tts/mls-eng-speaker-descriptions
---
<img src="https://huggingface.co/datasets/parler-tts/images/resolve/main/thumbnail.png" alt="Parler Logo" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
# Parler-TTS Mini v1
<a target="_blank" href="https://huggingface.co/spaces/parler-tts/parler_tts">
<img src="https://huggingface.co/datasets/huggingface/badges/raw/main/open-in-hf-spaces-sm.svg" alt="Open in HuggingFace"/>
</a>
**Parler-TTS Mini v1** is a lightweight text-to-speech (TTS) model, trained on 45K hours of audio data, that can generate high-quality, natural sounding speech with features that can be controlled using a simple text prompt (e.g. gender, background noise, speaking rate, pitch and reverberation).
With [Parler-TTS Large v1](https://huggingface.co/parler-tts/parler-tts-large-v1), this is the second set of models published as part of the [Parler-TTS](https://github.com/huggingface/parler-tts) project, which aims to provide the community with TTS training resources and dataset pre-processing code.
## π Quick Index
* [π¨βπ» Installation](#π¨βπ»-installation)
* [π² Using a random voice](#π²-random-voice)
* [π― Using a specific speaker](#π―-using-a-specific-speaker)
* [Motivation](#motivation)
* [Optimizing inference](https://github.com/huggingface/parler-tts/blob/main/INFERENCE.md)
## π οΈ Usage
### π¨βπ» Installation
Using Parler-TTS is as simple as "bonjour". Simply install the library once:
```sh
pip install git+https://github.com/huggingface/parler-tts.git
```
### π² Random voice
**Parler-TTS** has been trained to generate speech with features that can be controlled with a simple text prompt, for example:
```py
import torch
from parler_tts import ParlerTTSForConditionalGeneration
from transformers import AutoTokenizer
import soundfile as sf
device = "cuda:0" if torch.cuda.is_available() else "cpu"
model = ParlerTTSForConditionalGeneration.from_pretrained("parler-tts/parler-tts-mini-v1").to(device)
tokenizer = AutoTokenizer.from_pretrained("parler-tts/parler-tts-mini-v1")
prompt = "Hey, how are you doing today?"
description = "A female speaker delivers a slightly expressive and animated speech with a moderate speed and pitch. The recording is of very high quality, with the speaker's voice sounding clear and very close up."
input_ids = tokenizer(description, return_tensors="pt").input_ids.to(device)
prompt_input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to(device)
generation = model.generate(input_ids=input_ids, prompt_input_ids=prompt_input_ids)
audio_arr = generation.cpu().numpy().squeeze()
sf.write("parler_tts_out.wav", audio_arr, model.config.sampling_rate)
```
### π― Using a specific speaker
To ensure speaker consistency across generations, this checkpoint was also trained on 34 speakers, characterized by name (e.g. Jon, Lea, Gary, Jenna, Mike, Laura).
To take advantage of this, simply adapt your text description to specify which speaker to use: `Jon's voice is monotone yet slightly fast in delivery, with a very close recording that almost has no background noise.`
```py
import torch
from parler_tts import ParlerTTSForConditionalGeneration
from transformers import AutoTokenizer
import soundfile as sf
device = "cuda:0" if torch.cuda.is_available() else "cpu"
model = ParlerTTSForConditionalGeneration.from_pretrained("parler-tts/parler-tts-mini-v1").to(device)
tokenizer = AutoTokenizer.from_pretrained("parler-tts/parler-tts-mini-v1")
prompt = "Hey, how are you doing today?"
description = "Jon's voice is monotone yet slightly fast in delivery, with a very close recording that almost has no background noise."
input_ids = tokenizer(description, return_tensors="pt").input_ids.to(device)
prompt_input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to(device)
generation = model.generate(input_ids=input_ids, prompt_input_ids=prompt_input_ids)
audio_arr = generation.cpu().numpy().squeeze()
sf.write("parler_tts_out.wav", audio_arr, model.config.sampling_rate)
```
**Tips**:
* We've set up an [inference guide](https://github.com/huggingface/parler-tts/blob/main/INFERENCE.md) to make generation faster. Think SDPA, torch.compile, batching and streaming!
* Include the term "very clear audio" to generate the highest quality audio, and "very noisy audio" for high levels of background noise
* Punctuation can be used to control the prosody of the generations, e.g. use commas to add small breaks in speech
* The remaining speech features (gender, speaking rate, pitch and reverberation) can be controlled directly through the prompt
## Motivation
Parler-TTS is a reproduction of work from the paper [Natural language guidance of high-fidelity text-to-speech with synthetic annotations](https://www.text-description-to-speech.com) by Dan Lyth and Simon King, from Stability AI and Edinburgh University respectively.
Contrarily to other TTS models, Parler-TTS is a **fully open-source** release. All of the datasets, pre-processing, training code and weights are released publicly under permissive license, enabling the community to build on our work and develop their own powerful TTS models.
Parler-TTS was released alongside:
* [The Parler-TTS repository](https://github.com/huggingface/parler-tts) - you can train and fine-tuned your own version of the model.
* [The Data-Speech repository](https://github.com/huggingface/dataspeech) - a suite of utility scripts designed to annotate speech datasets.
* [The Parler-TTS organization](https://huggingface.co/parler-tts) - where you can find the annotated datasets as well as the future checkpoints.
## Citation
If you found this repository useful, please consider citing this work and also the original Stability AI paper:
```
@misc{lacombe-etal-2024-parler-tts,
author = {Yoach Lacombe and Vaibhav Srivastav and Sanchit Gandhi},
title = {Parler-TTS},
year = {2024},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/huggingface/parler-tts}}
}
```
```
@misc{lyth2024natural,
title={Natural language guidance of high-fidelity text-to-speech with synthetic annotations},
author={Dan Lyth and Simon King},
year={2024},
eprint={2402.01912},
archivePrefix={arXiv},
primaryClass={cs.SD}
}
```
## License
This model is permissively licensed under the Apache 2.0 license.
|
DavinciEvans/SmolLM2-FT-MyDataset
|
DavinciEvans
| 2024-12-16T07:17:09Z | 139 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"smol-course",
"module_1",
"trl",
"sft",
"conversational",
"base_model:HuggingFaceTB/SmolLM2-135M",
"base_model:finetune:HuggingFaceTB/SmolLM2-135M",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-12-16T07:13:00Z |
---
base_model: HuggingFaceTB/SmolLM2-135M
library_name: transformers
model_name: SmolLM2-FT-MyDataset
tags:
- generated_from_trainer
- smol-course
- module_1
- trl
- sft
licence: license
---
# Model Card for SmolLM2-FT-MyDataset
This model is a fine-tuned version of [HuggingFaceTB/SmolLM2-135M](https://huggingface.co/HuggingFaceTB/SmolLM2-135M).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="DavinciEvans/SmolLM2-FT-MyDataset", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.12.2
- Transformers: 4.46.3
- Pytorch: 2.5.1
- Datasets: 3.2.0
- Tokenizers: 0.20.3
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin GallouΓ©dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
AN181716/1123456545
|
AN181716
| 2024-12-16T07:16:23Z | 32 | 0 |
peft
|
[
"peft",
"pytorch",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"base_model:NousResearch/Llama-2-7b-chat-hf",
"base_model:adapter:NousResearch/Llama-2-7b-chat-hf",
"4-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2024-12-16T06:30:13Z |
---
library_name: peft
base_model:
- NousResearch/Llama-2-7b-chat-hf
pipeline_tag: text-generation
---
# 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]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.6.2
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.6.2
|
TencentARC/NVComposer
|
TencentARC
| 2024-12-16T07:05:02Z | 259 | 7 |
diffusers
|
[
"diffusers",
"image-to-3d",
"en",
"arxiv:2412.03517",
"license:other",
"region:us"
] |
image-to-3d
| 2024-12-06T08:37:02Z |
---
pipeline_tag: image-to-3d
language:
- en
library_name: diffusers
license: other
---
# NVComposer
<a href="https://arxiv.org/abs/2412.03517"><img src="https://img.shields.io/static/v1?label=Arxiv Preprint&message=NVComposer&color=red&logo=arxiv"></a>
### Abstract
Recent advancements in generative models have significantly improved novel view synthesis (NVS) from multi-view data. However, existing methods depend on external multi-view alignment processes, such as explicit pose estimation or pre-reconstruction, which limits their flexibility and accessibility, especially when alignment is unstable due to insufficient overlap or occlusions between views. In this paper, we propose NVComposer, a novel approach that eliminates the need for explicit external alignment. NVComposer enables the generative model to implicitly infer spatial and geometric relationships between multiple conditional views by introducing two key components: 1) an image-pose dual-stream diffusion model that simultaneously generates target novel views and condition camera poses, and 2) a geometry-aware feature alignment module that distills geometric priors from dense stereo models during training. Extensive experiments demonstrate that NVComposer achieves state-of-the-art performance in generative multi-view NVS tasks, removing the reliance on external alignment and thus improving model accessibility. Our approach shows substantial improvements in synthesis quality as the number of unposed input views increases, highlighting its potential for more flexible and accessible generative NVS systems.
<a href='https://lg-li.github.io/project/nvcomposer'><img src='https://img.shields.io/badge/Project-Page-green'></a>
<a href='https://huggingface.co/spaces/l-li/NVComposer'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Demo-blue'></a>
### Code
Please check our [GitHub repository](https://github.com/TencentARC/NVComposer) for code.
<a href='https://github.com/TencentARC/NVComposer'><img src='https://img.shields.io/badge/Github-Repo-blue'></a>
### Model
Download the model checkpoint using `huggingface_hub` (Version 0.1 as example):
```python
from huggingface_hub import hf_hub_download
checkpoint_path = hf_hub_download(
repo_id="TencentARC/NVComposer",
filename="NVComposer-V0.1.ckpt"
)
```
The downloaded checkpoint file can be found at `checkpoint_path`.
### Video
[Watch the introduction video](https://lg-li.github.io/project/nvcomposer) in our project page.
[<img src="https://lg-li.github.io/pub-images/li2024nvcomposer-video-cover-2.jpg" width="500">](https://lg-li.github.io/project/nvcomposer)
### Demo
You can [try the demo here](https://huggingface.co/spaces/TencentARC/NVComposer).
### Method
NVComposer contains 1) an image-pose dual-stream diffusion model that generates novel views while implicitly estimating camera poses for conditional images,
and 2) a geometry-aware feature alignment adapter that uses geometric priors distilled from pretrained dense stereo models.
<img src="https://lg-li.github.io/pub-images/li2024nvcomposer-model.jpg" width="1000">
|
VIshalChak/SmolLM2-FT-MyDataset
|
VIshalChak
| 2024-12-16T07:04:58Z | 141 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"smol-course",
"module_1",
"trl",
"sft",
"conversational",
"base_model:HuggingFaceTB/SmolLM2-135M",
"base_model:finetune:HuggingFaceTB/SmolLM2-135M",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-12-05T15:38:45Z |
---
base_model: HuggingFaceTB/SmolLM2-135M
library_name: transformers
model_name: SmolLM2-FT-MyDataset
tags:
- generated_from_trainer
- smol-course
- module_1
- trl
- sft
licence: license
---
# Model Card for SmolLM2-FT-MyDataset
This model is a fine-tuned version of [HuggingFaceTB/SmolLM2-135M](https://huggingface.co/HuggingFaceTB/SmolLM2-135M).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="VIshalChak/SmolLM2-FT-MyDataset", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.12.2
- Transformers: 4.46.3
- Pytorch: 2.5.1+cu124
- Datasets: 3.2.0
- Tokenizers: 0.20.3
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin GallouΓΒ©dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
new5558/qwen_1.5B_dummy_ft
|
new5558
| 2024-12-16T06:59:07Z | 85 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-12-16T06:49:10Z |
---
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 π€ 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]
|
MayBashendy/ArabicNewSplits5_FineTuningAraBERT_run3_AugV5_k15_task1_organization
|
MayBashendy
| 2024-12-16T06:52:13Z | 164 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:aubmindlab/bert-base-arabertv02",
"base_model:finetune:aubmindlab/bert-base-arabertv02",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-12-16T06:32:51Z |
---
library_name: transformers
base_model: aubmindlab/bert-base-arabertv02
tags:
- generated_from_trainer
model-index:
- name: ArabicNewSplits5_FineTuningAraBERT_run3_AugV5_k15_task1_organization
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. -->
# ArabicNewSplits5_FineTuningAraBERT_run3_AugV5_k15_task1_organization
This model is a fine-tuned version of [aubmindlab/bert-base-arabertv02](https://huggingface.co/aubmindlab/bert-base-arabertv02) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7188
- Qwk: 0.6861
- Mse: 0.7188
- Rmse: 0.8478
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Qwk | Mse | Rmse |
|:-------------:|:------:|:----:|:---------------:|:-------:|:------:|:------:|
| No log | 0.0213 | 2 | 5.2808 | -0.0098 | 5.2808 | 2.2980 |
| No log | 0.0426 | 4 | 3.2399 | 0.0863 | 3.2399 | 1.8000 |
| No log | 0.0638 | 6 | 2.2379 | 0.0374 | 2.2379 | 1.4960 |
| No log | 0.0851 | 8 | 1.8778 | 0.0971 | 1.8778 | 1.3703 |
| No log | 0.1064 | 10 | 1.9035 | 0.1424 | 1.9035 | 1.3797 |
| No log | 0.1277 | 12 | 1.6223 | 0.1523 | 1.6223 | 1.2737 |
| No log | 0.1489 | 14 | 1.6600 | 0.1628 | 1.6600 | 1.2884 |
| No log | 0.1702 | 16 | 1.5267 | 0.1889 | 1.5267 | 1.2356 |
| No log | 0.1915 | 18 | 1.2993 | 0.3814 | 1.2993 | 1.1399 |
| No log | 0.2128 | 20 | 1.6267 | 0.2164 | 1.6267 | 1.2754 |
| No log | 0.2340 | 22 | 2.0446 | 0.1903 | 2.0446 | 1.4299 |
| No log | 0.2553 | 24 | 2.5761 | 0.1697 | 2.5761 | 1.6050 |
| No log | 0.2766 | 26 | 2.4406 | 0.2011 | 2.4406 | 1.5622 |
| No log | 0.2979 | 28 | 1.6817 | 0.2522 | 1.6817 | 1.2968 |
| No log | 0.3191 | 30 | 1.2101 | 0.3793 | 1.2101 | 1.1000 |
| No log | 0.3404 | 32 | 1.0875 | 0.4006 | 1.0875 | 1.0428 |
| No log | 0.3617 | 34 | 1.0699 | 0.4290 | 1.0699 | 1.0344 |
| No log | 0.3830 | 36 | 1.1944 | 0.3511 | 1.1944 | 1.0929 |
| No log | 0.4043 | 38 | 1.2758 | 0.3611 | 1.2758 | 1.1295 |
| No log | 0.4255 | 40 | 1.4105 | 0.3529 | 1.4105 | 1.1876 |
| No log | 0.4468 | 42 | 1.2568 | 0.4555 | 1.2568 | 1.1211 |
| No log | 0.4681 | 44 | 0.8899 | 0.4940 | 0.8899 | 0.9434 |
| No log | 0.4894 | 46 | 0.7476 | 0.5338 | 0.7476 | 0.8646 |
| No log | 0.5106 | 48 | 0.8379 | 0.5148 | 0.8379 | 0.9154 |
| No log | 0.5319 | 50 | 0.8072 | 0.5448 | 0.8072 | 0.8985 |
| No log | 0.5532 | 52 | 1.0275 | 0.5456 | 1.0275 | 1.0137 |
| No log | 0.5745 | 54 | 1.6608 | 0.4213 | 1.6608 | 1.2887 |
| No log | 0.5957 | 56 | 1.8257 | 0.3969 | 1.8257 | 1.3512 |
| No log | 0.6170 | 58 | 1.3464 | 0.4704 | 1.3464 | 1.1604 |
| No log | 0.6383 | 60 | 0.8661 | 0.6041 | 0.8661 | 0.9307 |
| No log | 0.6596 | 62 | 1.0552 | 0.5148 | 1.0552 | 1.0272 |
| No log | 0.6809 | 64 | 1.2344 | 0.4187 | 1.2344 | 1.1110 |
| No log | 0.7021 | 66 | 1.1131 | 0.5088 | 1.1131 | 1.0551 |
| No log | 0.7234 | 68 | 0.7892 | 0.5949 | 0.7892 | 0.8884 |
| No log | 0.7447 | 70 | 0.6993 | 0.7514 | 0.6993 | 0.8362 |
| No log | 0.7660 | 72 | 0.7963 | 0.6662 | 0.7963 | 0.8924 |
| No log | 0.7872 | 74 | 0.9685 | 0.5671 | 0.9685 | 0.9841 |
| No log | 0.8085 | 76 | 1.0666 | 0.5657 | 1.0666 | 1.0328 |
| No log | 0.8298 | 78 | 1.3668 | 0.3995 | 1.3668 | 1.1691 |
| No log | 0.8511 | 80 | 1.7230 | 0.3043 | 1.7230 | 1.3126 |
| No log | 0.8723 | 82 | 1.6190 | 0.3018 | 1.6190 | 1.2724 |
| No log | 0.8936 | 84 | 1.4575 | 0.3299 | 1.4575 | 1.2073 |
| No log | 0.9149 | 86 | 0.9872 | 0.5597 | 0.9872 | 0.9936 |
| No log | 0.9362 | 88 | 0.6746 | 0.5990 | 0.6746 | 0.8213 |
| No log | 0.9574 | 90 | 0.7083 | 0.6820 | 0.7083 | 0.8416 |
| No log | 0.9787 | 92 | 0.7433 | 0.6357 | 0.7433 | 0.8622 |
| No log | 1.0 | 94 | 0.7099 | 0.5559 | 0.7099 | 0.8426 |
| No log | 1.0213 | 96 | 0.7644 | 0.5567 | 0.7644 | 0.8743 |
| No log | 1.0426 | 98 | 0.8737 | 0.5719 | 0.8737 | 0.9347 |
| No log | 1.0638 | 100 | 0.8630 | 0.5573 | 0.8630 | 0.9290 |
| No log | 1.0851 | 102 | 0.7335 | 0.6016 | 0.7335 | 0.8565 |
| No log | 1.1064 | 104 | 0.6523 | 0.6734 | 0.6523 | 0.8076 |
| No log | 1.1277 | 106 | 0.7175 | 0.6790 | 0.7175 | 0.8471 |
| No log | 1.1489 | 108 | 0.7534 | 0.6597 | 0.7534 | 0.8680 |
| No log | 1.1702 | 110 | 0.6966 | 0.7009 | 0.6966 | 0.8346 |
| No log | 1.1915 | 112 | 0.6917 | 0.6817 | 0.6917 | 0.8317 |
| No log | 1.2128 | 114 | 0.8082 | 0.6361 | 0.8082 | 0.8990 |
| No log | 1.2340 | 116 | 0.8950 | 0.6004 | 0.8950 | 0.9461 |
| No log | 1.2553 | 118 | 0.8423 | 0.6338 | 0.8423 | 0.9177 |
| No log | 1.2766 | 120 | 0.7481 | 0.6056 | 0.7481 | 0.8649 |
| No log | 1.2979 | 122 | 0.7313 | 0.6643 | 0.7313 | 0.8552 |
| No log | 1.3191 | 124 | 0.7466 | 0.6174 | 0.7466 | 0.8641 |
| No log | 1.3404 | 126 | 0.7290 | 0.6120 | 0.7290 | 0.8538 |
| No log | 1.3617 | 128 | 0.8038 | 0.6013 | 0.8038 | 0.8965 |
| No log | 1.3830 | 130 | 0.8532 | 0.6155 | 0.8532 | 0.9237 |
| No log | 1.4043 | 132 | 0.7694 | 0.6269 | 0.7694 | 0.8771 |
| No log | 1.4255 | 134 | 0.7115 | 0.6634 | 0.7115 | 0.8435 |
| No log | 1.4468 | 136 | 0.8177 | 0.6298 | 0.8177 | 0.9043 |
| No log | 1.4681 | 138 | 0.8585 | 0.5988 | 0.8585 | 0.9265 |
| No log | 1.4894 | 140 | 0.7928 | 0.6203 | 0.7928 | 0.8904 |
| No log | 1.5106 | 142 | 0.7528 | 0.5737 | 0.7528 | 0.8677 |
| No log | 1.5319 | 144 | 0.7336 | 0.6163 | 0.7336 | 0.8565 |
| No log | 1.5532 | 146 | 0.7366 | 0.6208 | 0.7366 | 0.8582 |
| No log | 1.5745 | 148 | 0.7381 | 0.6166 | 0.7381 | 0.8591 |
| No log | 1.5957 | 150 | 0.6979 | 0.6486 | 0.6979 | 0.8354 |
| No log | 1.6170 | 152 | 0.6805 | 0.6351 | 0.6805 | 0.8249 |
| No log | 1.6383 | 154 | 0.6901 | 0.6135 | 0.6901 | 0.8307 |
| No log | 1.6596 | 156 | 0.7265 | 0.6086 | 0.7265 | 0.8524 |
| No log | 1.6809 | 158 | 0.7018 | 0.6832 | 0.7018 | 0.8377 |
| No log | 1.7021 | 160 | 0.6420 | 0.7256 | 0.6420 | 0.8012 |
| No log | 1.7234 | 162 | 0.6197 | 0.7328 | 0.6197 | 0.7872 |
| No log | 1.7447 | 164 | 0.6247 | 0.7252 | 0.6247 | 0.7904 |
| No log | 1.7660 | 166 | 0.6278 | 0.7372 | 0.6278 | 0.7923 |
| No log | 1.7872 | 168 | 0.6323 | 0.7191 | 0.6323 | 0.7952 |
| No log | 1.8085 | 170 | 0.6443 | 0.6863 | 0.6443 | 0.8027 |
| No log | 1.8298 | 172 | 0.6445 | 0.6950 | 0.6445 | 0.8028 |
| No log | 1.8511 | 174 | 0.6708 | 0.6854 | 0.6708 | 0.8190 |
| No log | 1.8723 | 176 | 0.6772 | 0.6606 | 0.6772 | 0.8229 |
| No log | 1.8936 | 178 | 0.6887 | 0.6399 | 0.6887 | 0.8299 |
| No log | 1.9149 | 180 | 0.7442 | 0.6441 | 0.7442 | 0.8627 |
| No log | 1.9362 | 182 | 0.8471 | 0.6236 | 0.8471 | 0.9204 |
| No log | 1.9574 | 184 | 0.8708 | 0.6036 | 0.8708 | 0.9332 |
| No log | 1.9787 | 186 | 0.7855 | 0.6478 | 0.7855 | 0.8863 |
| No log | 2.0 | 188 | 0.6960 | 0.6985 | 0.6960 | 0.8343 |
| No log | 2.0213 | 190 | 0.7247 | 0.6448 | 0.7247 | 0.8513 |
| No log | 2.0426 | 192 | 0.7615 | 0.5981 | 0.7615 | 0.8727 |
| No log | 2.0638 | 194 | 0.7303 | 0.6192 | 0.7303 | 0.8546 |
| No log | 2.0851 | 196 | 0.6827 | 0.6366 | 0.6827 | 0.8263 |
| No log | 2.1064 | 198 | 0.6717 | 0.6690 | 0.6717 | 0.8196 |
| No log | 2.1277 | 200 | 0.6774 | 0.6684 | 0.6774 | 0.8231 |
| No log | 2.1489 | 202 | 0.6888 | 0.6768 | 0.6888 | 0.8299 |
| No log | 2.1702 | 204 | 0.6620 | 0.6997 | 0.6620 | 0.8136 |
| No log | 2.1915 | 206 | 0.6524 | 0.7091 | 0.6524 | 0.8077 |
| No log | 2.2128 | 208 | 0.6609 | 0.7205 | 0.6609 | 0.8129 |
| No log | 2.2340 | 210 | 0.6866 | 0.6957 | 0.6866 | 0.8286 |
| No log | 2.2553 | 212 | 0.6836 | 0.6911 | 0.6836 | 0.8268 |
| No log | 2.2766 | 214 | 0.6819 | 0.6634 | 0.6819 | 0.8258 |
| No log | 2.2979 | 216 | 0.6734 | 0.6689 | 0.6734 | 0.8206 |
| No log | 2.3191 | 218 | 0.6485 | 0.6490 | 0.6485 | 0.8053 |
| No log | 2.3404 | 220 | 0.6699 | 0.6580 | 0.6699 | 0.8185 |
| No log | 2.3617 | 222 | 0.6991 | 0.6566 | 0.6991 | 0.8361 |
| No log | 2.3830 | 224 | 0.6871 | 0.6588 | 0.6871 | 0.8289 |
| No log | 2.4043 | 226 | 0.6743 | 0.6436 | 0.6743 | 0.8212 |
| No log | 2.4255 | 228 | 0.6382 | 0.6489 | 0.6382 | 0.7989 |
| No log | 2.4468 | 230 | 0.6340 | 0.7222 | 0.6340 | 0.7962 |
| No log | 2.4681 | 232 | 0.6515 | 0.7175 | 0.6515 | 0.8072 |
| No log | 2.4894 | 234 | 0.6641 | 0.7125 | 0.6641 | 0.8150 |
| No log | 2.5106 | 236 | 0.7055 | 0.6701 | 0.7055 | 0.8400 |
| No log | 2.5319 | 238 | 0.7264 | 0.7083 | 0.7264 | 0.8523 |
| No log | 2.5532 | 240 | 0.7323 | 0.6984 | 0.7323 | 0.8558 |
| No log | 2.5745 | 242 | 0.7564 | 0.6881 | 0.7564 | 0.8697 |
| No log | 2.5957 | 244 | 0.7842 | 0.7018 | 0.7842 | 0.8856 |
| No log | 2.6170 | 246 | 0.7694 | 0.6979 | 0.7694 | 0.8772 |
| No log | 2.6383 | 248 | 0.7279 | 0.7060 | 0.7279 | 0.8532 |
| No log | 2.6596 | 250 | 0.7111 | 0.6803 | 0.7111 | 0.8433 |
| No log | 2.6809 | 252 | 0.7074 | 0.6490 | 0.7074 | 0.8411 |
| No log | 2.7021 | 254 | 0.7100 | 0.6830 | 0.7100 | 0.8426 |
| No log | 2.7234 | 256 | 0.7252 | 0.6838 | 0.7252 | 0.8516 |
| No log | 2.7447 | 258 | 0.7493 | 0.6836 | 0.7493 | 0.8656 |
| No log | 2.7660 | 260 | 0.7363 | 0.6799 | 0.7363 | 0.8581 |
| No log | 2.7872 | 262 | 0.7133 | 0.6952 | 0.7133 | 0.8446 |
| No log | 2.8085 | 264 | 0.7123 | 0.7271 | 0.7123 | 0.8440 |
| No log | 2.8298 | 266 | 0.7232 | 0.7103 | 0.7232 | 0.8504 |
| No log | 2.8511 | 268 | 0.7232 | 0.7183 | 0.7232 | 0.8504 |
| No log | 2.8723 | 270 | 0.7203 | 0.7028 | 0.7203 | 0.8487 |
| No log | 2.8936 | 272 | 0.7490 | 0.6783 | 0.7490 | 0.8654 |
| No log | 2.9149 | 274 | 0.7316 | 0.6661 | 0.7316 | 0.8554 |
| No log | 2.9362 | 276 | 0.6928 | 0.6389 | 0.6928 | 0.8324 |
| No log | 2.9574 | 278 | 0.7217 | 0.6396 | 0.7217 | 0.8495 |
| No log | 2.9787 | 280 | 0.7432 | 0.6538 | 0.7432 | 0.8621 |
| No log | 3.0 | 282 | 0.7296 | 0.6538 | 0.7296 | 0.8542 |
| No log | 3.0213 | 284 | 0.6992 | 0.6478 | 0.6992 | 0.8362 |
| No log | 3.0426 | 286 | 0.6994 | 0.6966 | 0.6994 | 0.8363 |
| No log | 3.0638 | 288 | 0.6984 | 0.7107 | 0.6984 | 0.8357 |
| No log | 3.0851 | 290 | 0.6862 | 0.6586 | 0.6862 | 0.8284 |
| No log | 3.1064 | 292 | 0.7350 | 0.6413 | 0.7350 | 0.8573 |
| No log | 3.1277 | 294 | 0.8033 | 0.6008 | 0.8033 | 0.8963 |
| No log | 3.1489 | 296 | 0.7779 | 0.6141 | 0.7779 | 0.8820 |
| No log | 3.1702 | 298 | 0.6956 | 0.6543 | 0.6956 | 0.8340 |
| No log | 3.1915 | 300 | 0.6508 | 0.6818 | 0.6508 | 0.8067 |
| No log | 3.2128 | 302 | 0.6522 | 0.6966 | 0.6522 | 0.8076 |
| No log | 3.2340 | 304 | 0.6511 | 0.6941 | 0.6511 | 0.8069 |
| No log | 3.2553 | 306 | 0.6477 | 0.7088 | 0.6477 | 0.8048 |
| No log | 3.2766 | 308 | 0.6609 | 0.7134 | 0.6609 | 0.8130 |
| No log | 3.2979 | 310 | 0.6536 | 0.7008 | 0.6536 | 0.8084 |
| No log | 3.3191 | 312 | 0.6396 | 0.6993 | 0.6396 | 0.7998 |
| No log | 3.3404 | 314 | 0.6796 | 0.7160 | 0.6796 | 0.8244 |
| No log | 3.3617 | 316 | 0.7039 | 0.6706 | 0.7039 | 0.8390 |
| No log | 3.3830 | 318 | 0.7143 | 0.6688 | 0.7143 | 0.8451 |
| No log | 3.4043 | 320 | 0.7075 | 0.6669 | 0.7075 | 0.8411 |
| No log | 3.4255 | 322 | 0.6661 | 0.6736 | 0.6661 | 0.8162 |
| No log | 3.4468 | 324 | 0.6498 | 0.7042 | 0.6498 | 0.8061 |
| No log | 3.4681 | 326 | 0.6737 | 0.6603 | 0.6737 | 0.8208 |
| No log | 3.4894 | 328 | 0.7000 | 0.6774 | 0.7000 | 0.8366 |
| No log | 3.5106 | 330 | 0.6867 | 0.7097 | 0.6867 | 0.8287 |
| No log | 3.5319 | 332 | 0.6828 | 0.7041 | 0.6828 | 0.8263 |
| No log | 3.5532 | 334 | 0.6779 | 0.7348 | 0.6779 | 0.8233 |
| No log | 3.5745 | 336 | 0.7324 | 0.7079 | 0.7324 | 0.8558 |
| No log | 3.5957 | 338 | 0.7566 | 0.6741 | 0.7566 | 0.8698 |
| No log | 3.6170 | 340 | 0.7442 | 0.6716 | 0.7442 | 0.8627 |
| No log | 3.6383 | 342 | 0.7037 | 0.7171 | 0.7037 | 0.8389 |
| No log | 3.6596 | 344 | 0.6520 | 0.7193 | 0.6520 | 0.8075 |
| No log | 3.6809 | 346 | 0.6559 | 0.6969 | 0.6559 | 0.8099 |
| No log | 3.7021 | 348 | 0.6580 | 0.6956 | 0.6580 | 0.8112 |
| No log | 3.7234 | 350 | 0.6492 | 0.7154 | 0.6492 | 0.8057 |
| No log | 3.7447 | 352 | 0.6719 | 0.7301 | 0.6719 | 0.8197 |
| No log | 3.7660 | 354 | 0.7533 | 0.6764 | 0.7533 | 0.8679 |
| No log | 3.7872 | 356 | 0.8305 | 0.6275 | 0.8305 | 0.9113 |
| No log | 3.8085 | 358 | 0.8455 | 0.6026 | 0.8455 | 0.9195 |
| No log | 3.8298 | 360 | 0.7909 | 0.6302 | 0.7909 | 0.8894 |
| No log | 3.8511 | 362 | 0.7494 | 0.6961 | 0.7494 | 0.8657 |
| No log | 3.8723 | 364 | 0.6888 | 0.6997 | 0.6888 | 0.8299 |
| No log | 3.8936 | 366 | 0.6804 | 0.6767 | 0.6804 | 0.8248 |
| No log | 3.9149 | 368 | 0.6967 | 0.6743 | 0.6967 | 0.8347 |
| No log | 3.9362 | 370 | 0.7181 | 0.6605 | 0.7181 | 0.8474 |
| No log | 3.9574 | 372 | 0.7304 | 0.6458 | 0.7304 | 0.8546 |
| No log | 3.9787 | 374 | 0.7205 | 0.6995 | 0.7205 | 0.8488 |
| No log | 4.0 | 376 | 0.7089 | 0.6930 | 0.7089 | 0.8420 |
| No log | 4.0213 | 378 | 0.7202 | 0.6958 | 0.7202 | 0.8486 |
| No log | 4.0426 | 380 | 0.7339 | 0.7057 | 0.7339 | 0.8567 |
| No log | 4.0638 | 382 | 0.7329 | 0.7016 | 0.7329 | 0.8561 |
| No log | 4.0851 | 384 | 0.7113 | 0.6999 | 0.7113 | 0.8434 |
| No log | 4.1064 | 386 | 0.6883 | 0.6906 | 0.6883 | 0.8296 |
| No log | 4.1277 | 388 | 0.6844 | 0.6961 | 0.6844 | 0.8273 |
| No log | 4.1489 | 390 | 0.6960 | 0.6888 | 0.6960 | 0.8343 |
| No log | 4.1702 | 392 | 0.6938 | 0.7002 | 0.6938 | 0.8329 |
| No log | 4.1915 | 394 | 0.6909 | 0.6593 | 0.6909 | 0.8312 |
| No log | 4.2128 | 396 | 0.6763 | 0.6769 | 0.6763 | 0.8223 |
| No log | 4.2340 | 398 | 0.6775 | 0.6897 | 0.6775 | 0.8231 |
| No log | 4.2553 | 400 | 0.6972 | 0.7009 | 0.6972 | 0.8350 |
| No log | 4.2766 | 402 | 0.7122 | 0.7057 | 0.7122 | 0.8439 |
| No log | 4.2979 | 404 | 0.7289 | 0.7242 | 0.7289 | 0.8537 |
| No log | 4.3191 | 406 | 0.7332 | 0.7341 | 0.7332 | 0.8563 |
| No log | 4.3404 | 408 | 0.7603 | 0.7107 | 0.7603 | 0.8719 |
| No log | 4.3617 | 410 | 0.7777 | 0.6779 | 0.7777 | 0.8819 |
| No log | 4.3830 | 412 | 0.8123 | 0.6533 | 0.8123 | 0.9013 |
| No log | 4.4043 | 414 | 0.8062 | 0.6507 | 0.8062 | 0.8979 |
| No log | 4.4255 | 416 | 0.7416 | 0.7062 | 0.7416 | 0.8612 |
| No log | 4.4468 | 418 | 0.6762 | 0.7411 | 0.6762 | 0.8223 |
| No log | 4.4681 | 420 | 0.6537 | 0.7035 | 0.6537 | 0.8085 |
| No log | 4.4894 | 422 | 0.6462 | 0.6881 | 0.6462 | 0.8039 |
| No log | 4.5106 | 424 | 0.6307 | 0.6866 | 0.6307 | 0.7942 |
| No log | 4.5319 | 426 | 0.6176 | 0.6819 | 0.6176 | 0.7859 |
| No log | 4.5532 | 428 | 0.6178 | 0.6858 | 0.6178 | 0.7860 |
| No log | 4.5745 | 430 | 0.6309 | 0.6708 | 0.6309 | 0.7943 |
| No log | 4.5957 | 432 | 0.6554 | 0.6528 | 0.6554 | 0.8096 |
| No log | 4.6170 | 434 | 0.6729 | 0.6433 | 0.6729 | 0.8203 |
| No log | 4.6383 | 436 | 0.7002 | 0.6439 | 0.7002 | 0.8368 |
| No log | 4.6596 | 438 | 0.6988 | 0.6779 | 0.6988 | 0.8360 |
| No log | 4.6809 | 440 | 0.6976 | 0.6964 | 0.6976 | 0.8353 |
| No log | 4.7021 | 442 | 0.7070 | 0.7129 | 0.7070 | 0.8408 |
| No log | 4.7234 | 444 | 0.7174 | 0.7191 | 0.7174 | 0.8470 |
| No log | 4.7447 | 446 | 0.7064 | 0.7092 | 0.7064 | 0.8405 |
| No log | 4.7660 | 448 | 0.7028 | 0.6461 | 0.7028 | 0.8383 |
| No log | 4.7872 | 450 | 0.7255 | 0.6404 | 0.7255 | 0.8518 |
| No log | 4.8085 | 452 | 0.7413 | 0.6242 | 0.7413 | 0.8610 |
| No log | 4.8298 | 454 | 0.7264 | 0.6249 | 0.7264 | 0.8523 |
| No log | 4.8511 | 456 | 0.7154 | 0.6503 | 0.7154 | 0.8458 |
| No log | 4.8723 | 458 | 0.7007 | 0.6391 | 0.7007 | 0.8371 |
| No log | 4.8936 | 460 | 0.7076 | 0.6867 | 0.7076 | 0.8412 |
| No log | 4.9149 | 462 | 0.7211 | 0.6679 | 0.7211 | 0.8492 |
| No log | 4.9362 | 464 | 0.7300 | 0.6752 | 0.7300 | 0.8544 |
| No log | 4.9574 | 466 | 0.7262 | 0.6816 | 0.7262 | 0.8522 |
| No log | 4.9787 | 468 | 0.7146 | 0.6990 | 0.7146 | 0.8453 |
| No log | 5.0 | 470 | 0.7035 | 0.6456 | 0.7035 | 0.8387 |
| No log | 5.0213 | 472 | 0.7122 | 0.6474 | 0.7122 | 0.8439 |
| No log | 5.0426 | 474 | 0.7126 | 0.6530 | 0.7126 | 0.8442 |
| No log | 5.0638 | 476 | 0.7063 | 0.6441 | 0.7063 | 0.8404 |
| No log | 5.0851 | 478 | 0.7143 | 0.6486 | 0.7143 | 0.8451 |
| No log | 5.1064 | 480 | 0.7293 | 0.6743 | 0.7293 | 0.8540 |
| No log | 5.1277 | 482 | 0.7621 | 0.6892 | 0.7621 | 0.8730 |
| No log | 5.1489 | 484 | 0.7741 | 0.6700 | 0.7741 | 0.8798 |
| No log | 5.1702 | 486 | 0.7685 | 0.6562 | 0.7685 | 0.8766 |
| No log | 5.1915 | 488 | 0.7758 | 0.6509 | 0.7758 | 0.8808 |
| No log | 5.2128 | 490 | 0.7848 | 0.6572 | 0.7848 | 0.8859 |
| No log | 5.2340 | 492 | 0.7779 | 0.6638 | 0.7779 | 0.8820 |
| No log | 5.2553 | 494 | 0.7751 | 0.6572 | 0.7751 | 0.8804 |
| No log | 5.2766 | 496 | 0.7834 | 0.6635 | 0.7834 | 0.8851 |
| No log | 5.2979 | 498 | 0.7967 | 0.6508 | 0.7967 | 0.8926 |
| 0.4043 | 5.3191 | 500 | 0.8044 | 0.6572 | 0.8044 | 0.8969 |
| 0.4043 | 5.3404 | 502 | 0.7945 | 0.6572 | 0.7945 | 0.8913 |
| 0.4043 | 5.3617 | 504 | 0.7493 | 0.6796 | 0.7493 | 0.8656 |
| 0.4043 | 5.3830 | 506 | 0.7097 | 0.7094 | 0.7097 | 0.8424 |
| 0.4043 | 5.4043 | 508 | 0.6945 | 0.7046 | 0.6945 | 0.8333 |
| 0.4043 | 5.4255 | 510 | 0.6880 | 0.6859 | 0.6880 | 0.8295 |
| 0.4043 | 5.4468 | 512 | 0.6742 | 0.6851 | 0.6742 | 0.8211 |
| 0.4043 | 5.4681 | 514 | 0.6667 | 0.6806 | 0.6667 | 0.8165 |
| 0.4043 | 5.4894 | 516 | 0.6708 | 0.6953 | 0.6708 | 0.8190 |
| 0.4043 | 5.5106 | 518 | 0.6812 | 0.7040 | 0.6812 | 0.8254 |
| 0.4043 | 5.5319 | 520 | 0.6915 | 0.7056 | 0.6915 | 0.8316 |
| 0.4043 | 5.5532 | 522 | 0.6923 | 0.6954 | 0.6923 | 0.8320 |
| 0.4043 | 5.5745 | 524 | 0.6985 | 0.6885 | 0.6985 | 0.8358 |
| 0.4043 | 5.5957 | 526 | 0.7079 | 0.6741 | 0.7079 | 0.8413 |
| 0.4043 | 5.6170 | 528 | 0.7146 | 0.6759 | 0.7146 | 0.8453 |
| 0.4043 | 5.6383 | 530 | 0.7259 | 0.6582 | 0.7259 | 0.8520 |
| 0.4043 | 5.6596 | 532 | 0.7341 | 0.6639 | 0.7341 | 0.8568 |
| 0.4043 | 5.6809 | 534 | 0.7443 | 0.6612 | 0.7443 | 0.8627 |
| 0.4043 | 5.7021 | 536 | 0.7535 | 0.6930 | 0.7535 | 0.8681 |
| 0.4043 | 5.7234 | 538 | 0.7593 | 0.6720 | 0.7593 | 0.8714 |
| 0.4043 | 5.7447 | 540 | 0.7578 | 0.6970 | 0.7578 | 0.8705 |
| 0.4043 | 5.7660 | 542 | 0.7542 | 0.6911 | 0.7542 | 0.8684 |
| 0.4043 | 5.7872 | 544 | 0.7473 | 0.6929 | 0.7473 | 0.8645 |
| 0.4043 | 5.8085 | 546 | 0.7488 | 0.6946 | 0.7488 | 0.8654 |
| 0.4043 | 5.8298 | 548 | 0.7565 | 0.7024 | 0.7565 | 0.8698 |
| 0.4043 | 5.8511 | 550 | 0.7739 | 0.6844 | 0.7739 | 0.8797 |
| 0.4043 | 5.8723 | 552 | 0.7839 | 0.6842 | 0.7839 | 0.8854 |
| 0.4043 | 5.8936 | 554 | 0.7904 | 0.6664 | 0.7904 | 0.8891 |
| 0.4043 | 5.9149 | 556 | 0.7773 | 0.6855 | 0.7773 | 0.8817 |
| 0.4043 | 5.9362 | 558 | 0.7470 | 0.7007 | 0.7470 | 0.8643 |
| 0.4043 | 5.9574 | 560 | 0.7310 | 0.6936 | 0.7310 | 0.8550 |
| 0.4043 | 5.9787 | 562 | 0.7394 | 0.6475 | 0.7394 | 0.8599 |
| 0.4043 | 6.0 | 564 | 0.7495 | 0.6445 | 0.7495 | 0.8657 |
| 0.4043 | 6.0213 | 566 | 0.7479 | 0.6315 | 0.7479 | 0.8648 |
| 0.4043 | 6.0426 | 568 | 0.7356 | 0.6461 | 0.7356 | 0.8577 |
| 0.4043 | 6.0638 | 570 | 0.7245 | 0.6530 | 0.7245 | 0.8512 |
| 0.4043 | 6.0851 | 572 | 0.7166 | 0.6592 | 0.7166 | 0.8465 |
| 0.4043 | 6.1064 | 574 | 0.7190 | 0.6753 | 0.7190 | 0.8479 |
| 0.4043 | 6.1277 | 576 | 0.7159 | 0.6679 | 0.7159 | 0.8461 |
| 0.4043 | 6.1489 | 578 | 0.7195 | 0.6345 | 0.7195 | 0.8482 |
| 0.4043 | 6.1702 | 580 | 0.7194 | 0.6567 | 0.7194 | 0.8482 |
| 0.4043 | 6.1915 | 582 | 0.7202 | 0.6567 | 0.7202 | 0.8487 |
| 0.4043 | 6.2128 | 584 | 0.7087 | 0.6488 | 0.7087 | 0.8418 |
| 0.4043 | 6.2340 | 586 | 0.6981 | 0.6782 | 0.6981 | 0.8355 |
| 0.4043 | 6.2553 | 588 | 0.7013 | 0.6741 | 0.7013 | 0.8374 |
| 0.4043 | 6.2766 | 590 | 0.7027 | 0.6741 | 0.7027 | 0.8383 |
| 0.4043 | 6.2979 | 592 | 0.7053 | 0.6709 | 0.7053 | 0.8398 |
| 0.4043 | 6.3191 | 594 | 0.7059 | 0.6807 | 0.7059 | 0.8402 |
| 0.4043 | 6.3404 | 596 | 0.6953 | 0.6760 | 0.6953 | 0.8339 |
| 0.4043 | 6.3617 | 598 | 0.6907 | 0.6760 | 0.6907 | 0.8311 |
| 0.4043 | 6.3830 | 600 | 0.6913 | 0.6735 | 0.6913 | 0.8314 |
| 0.4043 | 6.4043 | 602 | 0.6976 | 0.6735 | 0.6976 | 0.8352 |
| 0.4043 | 6.4255 | 604 | 0.7045 | 0.6717 | 0.7045 | 0.8393 |
| 0.4043 | 6.4468 | 606 | 0.7174 | 0.6795 | 0.7174 | 0.8470 |
| 0.4043 | 6.4681 | 608 | 0.7324 | 0.6957 | 0.7324 | 0.8558 |
| 0.4043 | 6.4894 | 610 | 0.7341 | 0.6833 | 0.7341 | 0.8568 |
| 0.4043 | 6.5106 | 612 | 0.7376 | 0.6957 | 0.7376 | 0.8589 |
| 0.4043 | 6.5319 | 614 | 0.7344 | 0.6964 | 0.7344 | 0.8570 |
| 0.4043 | 6.5532 | 616 | 0.7325 | 0.6989 | 0.7325 | 0.8559 |
| 0.4043 | 6.5745 | 618 | 0.7263 | 0.6873 | 0.7263 | 0.8522 |
| 0.4043 | 6.5957 | 620 | 0.7204 | 0.6681 | 0.7204 | 0.8488 |
| 0.4043 | 6.6170 | 622 | 0.7208 | 0.6776 | 0.7208 | 0.8490 |
| 0.4043 | 6.6383 | 624 | 0.7271 | 0.6795 | 0.7271 | 0.8527 |
| 0.4043 | 6.6596 | 626 | 0.7408 | 0.6964 | 0.7408 | 0.8607 |
| 0.4043 | 6.6809 | 628 | 0.7482 | 0.7007 | 0.7482 | 0.8650 |
| 0.4043 | 6.7021 | 630 | 0.7539 | 0.6971 | 0.7539 | 0.8683 |
| 0.4043 | 6.7234 | 632 | 0.7579 | 0.7003 | 0.7579 | 0.8706 |
| 0.4043 | 6.7447 | 634 | 0.7648 | 0.6840 | 0.7648 | 0.8745 |
| 0.4043 | 6.7660 | 636 | 0.7605 | 0.6768 | 0.7605 | 0.8721 |
| 0.4043 | 6.7872 | 638 | 0.7481 | 0.6985 | 0.7481 | 0.8649 |
| 0.4043 | 6.8085 | 640 | 0.7376 | 0.6990 | 0.7376 | 0.8589 |
| 0.4043 | 6.8298 | 642 | 0.7310 | 0.6990 | 0.7310 | 0.8550 |
| 0.4043 | 6.8511 | 644 | 0.7238 | 0.6998 | 0.7238 | 0.8508 |
| 0.4043 | 6.8723 | 646 | 0.7246 | 0.6698 | 0.7246 | 0.8512 |
| 0.4043 | 6.8936 | 648 | 0.7302 | 0.6778 | 0.7302 | 0.8545 |
| 0.4043 | 6.9149 | 650 | 0.7310 | 0.6594 | 0.7310 | 0.8550 |
| 0.4043 | 6.9362 | 652 | 0.7180 | 0.6498 | 0.7180 | 0.8474 |
| 0.4043 | 6.9574 | 654 | 0.7031 | 0.6730 | 0.7031 | 0.8385 |
| 0.4043 | 6.9787 | 656 | 0.6977 | 0.6663 | 0.6977 | 0.8353 |
| 0.4043 | 7.0 | 658 | 0.7098 | 0.6721 | 0.7098 | 0.8425 |
| 0.4043 | 7.0213 | 660 | 0.7370 | 0.6893 | 0.7370 | 0.8585 |
| 0.4043 | 7.0426 | 662 | 0.7584 | 0.6736 | 0.7584 | 0.8708 |
| 0.4043 | 7.0638 | 664 | 0.7650 | 0.6832 | 0.7650 | 0.8746 |
| 0.4043 | 7.0851 | 666 | 0.7639 | 0.6832 | 0.7639 | 0.8740 |
| 0.4043 | 7.1064 | 668 | 0.7577 | 0.6809 | 0.7577 | 0.8705 |
| 0.4043 | 7.1277 | 670 | 0.7511 | 0.6611 | 0.7511 | 0.8666 |
| 0.4043 | 7.1489 | 672 | 0.7495 | 0.6611 | 0.7495 | 0.8657 |
| 0.4043 | 7.1702 | 674 | 0.7422 | 0.6884 | 0.7422 | 0.8615 |
| 0.4043 | 7.1915 | 676 | 0.7327 | 0.6934 | 0.7327 | 0.8560 |
| 0.4043 | 7.2128 | 678 | 0.7207 | 0.6810 | 0.7207 | 0.8489 |
| 0.4043 | 7.2340 | 680 | 0.7088 | 0.6890 | 0.7088 | 0.8419 |
| 0.4043 | 7.2553 | 682 | 0.7020 | 0.6689 | 0.7020 | 0.8378 |
| 0.4043 | 7.2766 | 684 | 0.6973 | 0.6689 | 0.6973 | 0.8351 |
| 0.4043 | 7.2979 | 686 | 0.6939 | 0.6668 | 0.6939 | 0.8330 |
| 0.4043 | 7.3191 | 688 | 0.6950 | 0.6790 | 0.6950 | 0.8337 |
| 0.4043 | 7.3404 | 690 | 0.6970 | 0.6720 | 0.6970 | 0.8349 |
| 0.4043 | 7.3617 | 692 | 0.6923 | 0.6790 | 0.6923 | 0.8320 |
| 0.4043 | 7.3830 | 694 | 0.6842 | 0.6714 | 0.6842 | 0.8271 |
| 0.4043 | 7.4043 | 696 | 0.6810 | 0.6674 | 0.6810 | 0.8252 |
| 0.4043 | 7.4255 | 698 | 0.6938 | 0.6788 | 0.6938 | 0.8329 |
| 0.4043 | 7.4468 | 700 | 0.7100 | 0.6754 | 0.7100 | 0.8426 |
| 0.4043 | 7.4681 | 702 | 0.7129 | 0.6957 | 0.7129 | 0.8443 |
| 0.4043 | 7.4894 | 704 | 0.7101 | 0.6721 | 0.7101 | 0.8426 |
| 0.4043 | 7.5106 | 706 | 0.7159 | 0.6850 | 0.7159 | 0.8461 |
| 0.4043 | 7.5319 | 708 | 0.7248 | 0.6750 | 0.7248 | 0.8513 |
| 0.4043 | 7.5532 | 710 | 0.7456 | 0.6795 | 0.7456 | 0.8635 |
| 0.4043 | 7.5745 | 712 | 0.7697 | 0.6717 | 0.7697 | 0.8773 |
| 0.4043 | 7.5957 | 714 | 0.7804 | 0.6543 | 0.7804 | 0.8834 |
| 0.4043 | 7.6170 | 716 | 0.7787 | 0.6543 | 0.7787 | 0.8825 |
| 0.4043 | 7.6383 | 718 | 0.7821 | 0.6630 | 0.7821 | 0.8844 |
| 0.4043 | 7.6596 | 720 | 0.7727 | 0.6807 | 0.7727 | 0.8790 |
| 0.4043 | 7.6809 | 722 | 0.7630 | 0.6924 | 0.7630 | 0.8735 |
| 0.4043 | 7.7021 | 724 | 0.7446 | 0.6734 | 0.7446 | 0.8629 |
| 0.4043 | 7.7234 | 726 | 0.7265 | 0.6958 | 0.7265 | 0.8523 |
| 0.4043 | 7.7447 | 728 | 0.7144 | 0.6760 | 0.7144 | 0.8452 |
| 0.4043 | 7.7660 | 730 | 0.7099 | 0.6465 | 0.7099 | 0.8425 |
| 0.4043 | 7.7872 | 732 | 0.7124 | 0.6586 | 0.7124 | 0.8441 |
| 0.4043 | 7.8085 | 734 | 0.7173 | 0.6760 | 0.7173 | 0.8469 |
| 0.4043 | 7.8298 | 736 | 0.7189 | 0.6893 | 0.7189 | 0.8479 |
| 0.4043 | 7.8511 | 738 | 0.7177 | 0.6758 | 0.7177 | 0.8471 |
| 0.4043 | 7.8723 | 740 | 0.7199 | 0.6758 | 0.7199 | 0.8485 |
| 0.4043 | 7.8936 | 742 | 0.7195 | 0.6692 | 0.7195 | 0.8483 |
| 0.4043 | 7.9149 | 744 | 0.7179 | 0.6586 | 0.7179 | 0.8473 |
| 0.4043 | 7.9362 | 746 | 0.7162 | 0.6431 | 0.7162 | 0.8463 |
| 0.4043 | 7.9574 | 748 | 0.7173 | 0.6605 | 0.7173 | 0.8469 |
| 0.4043 | 7.9787 | 750 | 0.7220 | 0.6827 | 0.7220 | 0.8497 |
| 0.4043 | 8.0 | 752 | 0.7285 | 0.6893 | 0.7285 | 0.8535 |
| 0.4043 | 8.0213 | 754 | 0.7323 | 0.6958 | 0.7323 | 0.8557 |
| 0.4043 | 8.0426 | 756 | 0.7346 | 0.7022 | 0.7346 | 0.8571 |
| 0.4043 | 8.0638 | 758 | 0.7267 | 0.6827 | 0.7267 | 0.8525 |
| 0.4043 | 8.0851 | 760 | 0.7229 | 0.6674 | 0.7229 | 0.8503 |
| 0.4043 | 8.1064 | 762 | 0.7183 | 0.6595 | 0.7183 | 0.8476 |
| 0.4043 | 8.1277 | 764 | 0.7131 | 0.6613 | 0.7131 | 0.8445 |
| 0.4043 | 8.1489 | 766 | 0.7092 | 0.6482 | 0.7092 | 0.8421 |
| 0.4043 | 8.1702 | 768 | 0.7082 | 0.6316 | 0.7082 | 0.8415 |
| 0.4043 | 8.1915 | 770 | 0.7109 | 0.6448 | 0.7109 | 0.8432 |
| 0.4043 | 8.2128 | 772 | 0.7152 | 0.6499 | 0.7152 | 0.8457 |
| 0.4043 | 8.2340 | 774 | 0.7153 | 0.6648 | 0.7153 | 0.8458 |
| 0.4043 | 8.2553 | 776 | 0.7206 | 0.6626 | 0.7206 | 0.8489 |
| 0.4043 | 8.2766 | 778 | 0.7275 | 0.6757 | 0.7275 | 0.8530 |
| 0.4043 | 8.2979 | 780 | 0.7329 | 0.7011 | 0.7329 | 0.8561 |
| 0.4043 | 8.3191 | 782 | 0.7295 | 0.6950 | 0.7295 | 0.8541 |
| 0.4043 | 8.3404 | 784 | 0.7262 | 0.6969 | 0.7262 | 0.8522 |
| 0.4043 | 8.3617 | 786 | 0.7243 | 0.6969 | 0.7243 | 0.8510 |
| 0.4043 | 8.3830 | 788 | 0.7146 | 0.6861 | 0.7146 | 0.8453 |
| 0.4043 | 8.4043 | 790 | 0.7112 | 0.6958 | 0.7112 | 0.8433 |
| 0.4043 | 8.4255 | 792 | 0.7167 | 0.7022 | 0.7167 | 0.8466 |
| 0.4043 | 8.4468 | 794 | 0.7293 | 0.7036 | 0.7293 | 0.8540 |
| 0.4043 | 8.4681 | 796 | 0.7386 | 0.6892 | 0.7386 | 0.8594 |
| 0.4043 | 8.4894 | 798 | 0.7399 | 0.6892 | 0.7399 | 0.8602 |
| 0.4043 | 8.5106 | 800 | 0.7399 | 0.6880 | 0.7399 | 0.8601 |
| 0.4043 | 8.5319 | 802 | 0.7391 | 0.6899 | 0.7391 | 0.8597 |
| 0.4043 | 8.5532 | 804 | 0.7330 | 0.7022 | 0.7330 | 0.8561 |
| 0.4043 | 8.5745 | 806 | 0.7251 | 0.6958 | 0.7251 | 0.8515 |
| 0.4043 | 8.5957 | 808 | 0.7193 | 0.6958 | 0.7193 | 0.8481 |
| 0.4043 | 8.6170 | 810 | 0.7202 | 0.6958 | 0.7202 | 0.8486 |
| 0.4043 | 8.6383 | 812 | 0.7218 | 0.6958 | 0.7218 | 0.8496 |
| 0.4043 | 8.6596 | 814 | 0.7193 | 0.6958 | 0.7193 | 0.8481 |
| 0.4043 | 8.6809 | 816 | 0.7148 | 0.6893 | 0.7148 | 0.8455 |
| 0.4043 | 8.7021 | 818 | 0.7108 | 0.6827 | 0.7108 | 0.8431 |
| 0.4043 | 8.7234 | 820 | 0.7109 | 0.6827 | 0.7109 | 0.8431 |
| 0.4043 | 8.7447 | 822 | 0.7175 | 0.6958 | 0.7175 | 0.8471 |
| 0.4043 | 8.7660 | 824 | 0.7295 | 0.6835 | 0.7295 | 0.8541 |
| 0.4043 | 8.7872 | 826 | 0.7423 | 0.6899 | 0.7423 | 0.8616 |
| 0.4043 | 8.8085 | 828 | 0.7529 | 0.6873 | 0.7529 | 0.8677 |
| 0.4043 | 8.8298 | 830 | 0.7495 | 0.6809 | 0.7495 | 0.8658 |
| 0.4043 | 8.8511 | 832 | 0.7404 | 0.6835 | 0.7404 | 0.8605 |
| 0.4043 | 8.8723 | 834 | 0.7288 | 0.6770 | 0.7288 | 0.8537 |
| 0.4043 | 8.8936 | 836 | 0.7194 | 0.6893 | 0.7194 | 0.8482 |
| 0.4043 | 8.9149 | 838 | 0.7141 | 0.6760 | 0.7141 | 0.8451 |
| 0.4043 | 8.9362 | 840 | 0.7121 | 0.6760 | 0.7121 | 0.8439 |
| 0.4043 | 8.9574 | 842 | 0.7123 | 0.6605 | 0.7123 | 0.8440 |
| 0.4043 | 8.9787 | 844 | 0.7135 | 0.6760 | 0.7135 | 0.8447 |
| 0.4043 | 9.0 | 846 | 0.7154 | 0.6760 | 0.7154 | 0.8458 |
| 0.4043 | 9.0213 | 848 | 0.7165 | 0.6827 | 0.7165 | 0.8465 |
| 0.4043 | 9.0426 | 850 | 0.7169 | 0.6758 | 0.7169 | 0.8467 |
| 0.4043 | 9.0638 | 852 | 0.7162 | 0.6758 | 0.7162 | 0.8463 |
| 0.4043 | 9.0851 | 854 | 0.7158 | 0.6758 | 0.7158 | 0.8460 |
| 0.4043 | 9.1064 | 856 | 0.7138 | 0.6804 | 0.7138 | 0.8449 |
| 0.4043 | 9.1277 | 858 | 0.7124 | 0.6907 | 0.7124 | 0.8440 |
| 0.4043 | 9.1489 | 860 | 0.7116 | 0.6907 | 0.7116 | 0.8436 |
| 0.4043 | 9.1702 | 862 | 0.7124 | 0.6804 | 0.7124 | 0.8440 |
| 0.4043 | 9.1915 | 864 | 0.7154 | 0.6842 | 0.7154 | 0.8458 |
| 0.4043 | 9.2128 | 866 | 0.7167 | 0.6796 | 0.7167 | 0.8466 |
| 0.4043 | 9.2340 | 868 | 0.7180 | 0.6796 | 0.7180 | 0.8473 |
| 0.4043 | 9.2553 | 870 | 0.7170 | 0.6796 | 0.7170 | 0.8468 |
| 0.4043 | 9.2766 | 872 | 0.7175 | 0.6861 | 0.7175 | 0.8470 |
| 0.4043 | 9.2979 | 874 | 0.7153 | 0.6842 | 0.7153 | 0.8457 |
| 0.4043 | 9.3191 | 876 | 0.7113 | 0.6842 | 0.7113 | 0.8434 |
| 0.4043 | 9.3404 | 878 | 0.7078 | 0.6804 | 0.7078 | 0.8413 |
| 0.4043 | 9.3617 | 880 | 0.7058 | 0.6876 | 0.7058 | 0.8401 |
| 0.4043 | 9.3830 | 882 | 0.7043 | 0.6940 | 0.7043 | 0.8392 |
| 0.4043 | 9.4043 | 884 | 0.7043 | 0.6837 | 0.7043 | 0.8392 |
| 0.4043 | 9.4255 | 886 | 0.7044 | 0.6804 | 0.7044 | 0.8393 |
| 0.4043 | 9.4468 | 888 | 0.7065 | 0.6804 | 0.7065 | 0.8405 |
| 0.4043 | 9.4681 | 890 | 0.7090 | 0.6804 | 0.7090 | 0.8420 |
| 0.4043 | 9.4894 | 892 | 0.7117 | 0.6907 | 0.7117 | 0.8436 |
| 0.4043 | 9.5106 | 894 | 0.7141 | 0.6907 | 0.7141 | 0.8450 |
| 0.4043 | 9.5319 | 896 | 0.7161 | 0.6861 | 0.7161 | 0.8462 |
| 0.4043 | 9.5532 | 898 | 0.7162 | 0.6861 | 0.7162 | 0.8463 |
| 0.4043 | 9.5745 | 900 | 0.7161 | 0.6861 | 0.7161 | 0.8462 |
| 0.4043 | 9.5957 | 902 | 0.7153 | 0.6861 | 0.7153 | 0.8457 |
| 0.4043 | 9.6170 | 904 | 0.7148 | 0.6861 | 0.7148 | 0.8455 |
| 0.4043 | 9.6383 | 906 | 0.7162 | 0.6861 | 0.7162 | 0.8463 |
| 0.4043 | 9.6596 | 908 | 0.7162 | 0.6861 | 0.7162 | 0.8463 |
| 0.4043 | 9.6809 | 910 | 0.7158 | 0.6861 | 0.7158 | 0.8460 |
| 0.4043 | 9.7021 | 912 | 0.7157 | 0.6861 | 0.7157 | 0.8460 |
| 0.4043 | 9.7234 | 914 | 0.7157 | 0.6861 | 0.7157 | 0.8460 |
| 0.4043 | 9.7447 | 916 | 0.7155 | 0.6861 | 0.7155 | 0.8459 |
| 0.4043 | 9.7660 | 918 | 0.7160 | 0.6861 | 0.7160 | 0.8462 |
| 0.4043 | 9.7872 | 920 | 0.7171 | 0.6861 | 0.7171 | 0.8468 |
| 0.4043 | 9.8085 | 922 | 0.7182 | 0.6861 | 0.7182 | 0.8475 |
| 0.4043 | 9.8298 | 924 | 0.7188 | 0.6861 | 0.7188 | 0.8478 |
| 0.4043 | 9.8511 | 926 | 0.7192 | 0.6676 | 0.7192 | 0.8480 |
| 0.4043 | 9.8723 | 928 | 0.7195 | 0.6676 | 0.7195 | 0.8483 |
| 0.4043 | 9.8936 | 930 | 0.7199 | 0.6676 | 0.7199 | 0.8485 |
| 0.4043 | 9.9149 | 932 | 0.7198 | 0.6676 | 0.7198 | 0.8484 |
| 0.4043 | 9.9362 | 934 | 0.7195 | 0.6676 | 0.7195 | 0.8482 |
| 0.4043 | 9.9574 | 936 | 0.7192 | 0.6861 | 0.7192 | 0.8480 |
| 0.4043 | 9.9787 | 938 | 0.7189 | 0.6861 | 0.7189 | 0.8479 |
| 0.4043 | 10.0 | 940 | 0.7188 | 0.6861 | 0.7188 | 0.8478 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu118
- Datasets 2.21.0
- Tokenizers 0.19.1
|
annofung/Dmeta-embedding-zh-Q4_0-GGUF
|
annofung
| 2024-12-16T06:51:59Z | 36 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"gguf",
"feature-extraction",
"sentence-similarity",
"mteb",
"RAG",
"llama-cpp",
"gguf-my-repo",
"zh",
"en",
"base_model:DMetaSoul/Dmeta-embedding-zh",
"base_model:quantized:DMetaSoul/Dmeta-embedding-zh",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2024-12-16T06:51:57Z |
---
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- mteb
- RAG
- llama-cpp
- gguf-my-repo
license: apache-2.0
language:
- zh
- en
pipeline_tag: feature-extraction
base_model: DMetaSoul/Dmeta-embedding-zh
model-index:
- name: Dmeta-embedding
results:
- task:
type: STS
dataset:
name: MTEB AFQMC
type: C-MTEB/AFQMC
config: default
split: validation
revision: None
metrics:
- type: cos_sim_pearson
value: 65.60825224706932
- type: cos_sim_spearman
value: 71.12862586297193
- type: euclidean_pearson
value: 70.18130275750404
- type: euclidean_spearman
value: 71.12862586297193
- type: manhattan_pearson
value: 70.14470398075396
- type: manhattan_spearman
value: 71.05226975911737
- task:
type: STS
dataset:
name: MTEB ATEC
type: C-MTEB/ATEC
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson
value: 65.52386345655479
- type: cos_sim_spearman
value: 64.64245253181382
- type: euclidean_pearson
value: 73.20157662981914
- type: euclidean_spearman
value: 64.64245253178956
- type: manhattan_pearson
value: 73.22837571756348
- type: manhattan_spearman
value: 64.62632334391418
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (zh)
type: mteb/amazon_reviews_multi
config: zh
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 44.925999999999995
- type: f1
value: 42.82555191308971
- task:
type: STS
dataset:
name: MTEB BQ
type: C-MTEB/BQ
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson
value: 71.35236446393156
- type: cos_sim_spearman
value: 72.29629643702184
- type: euclidean_pearson
value: 70.94570179874498
- type: euclidean_spearman
value: 72.29629297226953
- type: manhattan_pearson
value: 70.84463025501125
- type: manhattan_spearman
value: 72.24527021975821
- task:
type: Clustering
dataset:
name: MTEB CLSClusteringP2P
type: C-MTEB/CLSClusteringP2P
config: default
split: test
revision: None
metrics:
- type: v_measure
value: 40.24232916894152
- task:
type: Clustering
dataset:
name: MTEB CLSClusteringS2S
type: C-MTEB/CLSClusteringS2S
config: default
split: test
revision: None
metrics:
- type: v_measure
value: 39.167806226929706
- task:
type: Reranking
dataset:
name: MTEB CMedQAv1
type: C-MTEB/CMedQAv1-reranking
config: default
split: test
revision: None
metrics:
- type: map
value: 88.48837920106357
- type: mrr
value: 90.36861111111111
- task:
type: Reranking
dataset:
name: MTEB CMedQAv2
type: C-MTEB/CMedQAv2-reranking
config: default
split: test
revision: None
metrics:
- type: map
value: 89.17878171657071
- type: mrr
value: 91.35805555555555
- task:
type: Retrieval
dataset:
name: MTEB CmedqaRetrieval
type: C-MTEB/CmedqaRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 25.751
- type: map_at_10
value: 38.946
- type: map_at_100
value: 40.855000000000004
- type: map_at_1000
value: 40.953
- type: map_at_3
value: 34.533
- type: map_at_5
value: 36.905
- type: mrr_at_1
value: 39.235
- type: mrr_at_10
value: 47.713
- type: mrr_at_100
value: 48.71
- type: mrr_at_1000
value: 48.747
- type: mrr_at_3
value: 45.086
- type: mrr_at_5
value: 46.498
- type: ndcg_at_1
value: 39.235
- type: ndcg_at_10
value: 45.831
- type: ndcg_at_100
value: 53.162
- type: ndcg_at_1000
value: 54.800000000000004
- type: ndcg_at_3
value: 40.188
- type: ndcg_at_5
value: 42.387
- type: precision_at_1
value: 39.235
- type: precision_at_10
value: 10.273
- type: precision_at_100
value: 1.627
- type: precision_at_1000
value: 0.183
- type: precision_at_3
value: 22.772000000000002
- type: precision_at_5
value: 16.524
- type: recall_at_1
value: 25.751
- type: recall_at_10
value: 57.411
- type: recall_at_100
value: 87.44
- type: recall_at_1000
value: 98.386
- type: recall_at_3
value: 40.416000000000004
- type: recall_at_5
value: 47.238
- task:
type: PairClassification
dataset:
name: MTEB Cmnli
type: C-MTEB/CMNLI
config: default
split: validation
revision: None
metrics:
- type: cos_sim_accuracy
value: 83.59591100420926
- type: cos_sim_ap
value: 90.65538153970263
- type: cos_sim_f1
value: 84.76466651795673
- type: cos_sim_precision
value: 81.04073363190446
- type: cos_sim_recall
value: 88.84732288987608
- type: dot_accuracy
value: 83.59591100420926
- type: dot_ap
value: 90.64355541781003
- type: dot_f1
value: 84.76466651795673
- type: dot_precision
value: 81.04073363190446
- type: dot_recall
value: 88.84732288987608
- type: euclidean_accuracy
value: 83.59591100420926
- type: euclidean_ap
value: 90.6547878194287
- type: euclidean_f1
value: 84.76466651795673
- type: euclidean_precision
value: 81.04073363190446
- type: euclidean_recall
value: 88.84732288987608
- type: manhattan_accuracy
value: 83.51172579675286
- type: manhattan_ap
value: 90.59941589844144
- type: manhattan_f1
value: 84.51827242524917
- type: manhattan_precision
value: 80.28613507258574
- type: manhattan_recall
value: 89.22141688099134
- type: max_accuracy
value: 83.59591100420926
- type: max_ap
value: 90.65538153970263
- type: max_f1
value: 84.76466651795673
- task:
type: Retrieval
dataset:
name: MTEB CovidRetrieval
type: C-MTEB/CovidRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 63.251000000000005
- type: map_at_10
value: 72.442
- type: map_at_100
value: 72.79299999999999
- type: map_at_1000
value: 72.80499999999999
- type: map_at_3
value: 70.293
- type: map_at_5
value: 71.571
- type: mrr_at_1
value: 63.541000000000004
- type: mrr_at_10
value: 72.502
- type: mrr_at_100
value: 72.846
- type: mrr_at_1000
value: 72.858
- type: mrr_at_3
value: 70.39
- type: mrr_at_5
value: 71.654
- type: ndcg_at_1
value: 63.541000000000004
- type: ndcg_at_10
value: 76.774
- type: ndcg_at_100
value: 78.389
- type: ndcg_at_1000
value: 78.678
- type: ndcg_at_3
value: 72.47
- type: ndcg_at_5
value: 74.748
- type: precision_at_1
value: 63.541000000000004
- type: precision_at_10
value: 9.115
- type: precision_at_100
value: 0.9860000000000001
- type: precision_at_1000
value: 0.101
- type: precision_at_3
value: 26.379
- type: precision_at_5
value: 16.965
- type: recall_at_1
value: 63.251000000000005
- type: recall_at_10
value: 90.253
- type: recall_at_100
value: 97.576
- type: recall_at_1000
value: 99.789
- type: recall_at_3
value: 78.635
- type: recall_at_5
value: 84.141
- task:
type: Retrieval
dataset:
name: MTEB DuRetrieval
type: C-MTEB/DuRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 23.597
- type: map_at_10
value: 72.411
- type: map_at_100
value: 75.58500000000001
- type: map_at_1000
value: 75.64800000000001
- type: map_at_3
value: 49.61
- type: map_at_5
value: 62.527
- type: mrr_at_1
value: 84.65
- type: mrr_at_10
value: 89.43900000000001
- type: mrr_at_100
value: 89.525
- type: mrr_at_1000
value: 89.529
- type: mrr_at_3
value: 89
- type: mrr_at_5
value: 89.297
- type: ndcg_at_1
value: 84.65
- type: ndcg_at_10
value: 81.47
- type: ndcg_at_100
value: 85.198
- type: ndcg_at_1000
value: 85.828
- type: ndcg_at_3
value: 79.809
- type: ndcg_at_5
value: 78.55
- type: precision_at_1
value: 84.65
- type: precision_at_10
value: 39.595
- type: precision_at_100
value: 4.707
- type: precision_at_1000
value: 0.485
- type: precision_at_3
value: 71.61699999999999
- type: precision_at_5
value: 60.45
- type: recall_at_1
value: 23.597
- type: recall_at_10
value: 83.34
- type: recall_at_100
value: 95.19800000000001
- type: recall_at_1000
value: 98.509
- type: recall_at_3
value: 52.744
- type: recall_at_5
value: 68.411
- task:
type: Retrieval
dataset:
name: MTEB EcomRetrieval
type: C-MTEB/EcomRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 53.1
- type: map_at_10
value: 63.359
- type: map_at_100
value: 63.9
- type: map_at_1000
value: 63.909000000000006
- type: map_at_3
value: 60.95
- type: map_at_5
value: 62.305
- type: mrr_at_1
value: 53.1
- type: mrr_at_10
value: 63.359
- type: mrr_at_100
value: 63.9
- type: mrr_at_1000
value: 63.909000000000006
- type: mrr_at_3
value: 60.95
- type: mrr_at_5
value: 62.305
- type: ndcg_at_1
value: 53.1
- type: ndcg_at_10
value: 68.418
- type: ndcg_at_100
value: 70.88499999999999
- type: ndcg_at_1000
value: 71.135
- type: ndcg_at_3
value: 63.50599999999999
- type: ndcg_at_5
value: 65.92
- type: precision_at_1
value: 53.1
- type: precision_at_10
value: 8.43
- type: precision_at_100
value: 0.955
- type: precision_at_1000
value: 0.098
- type: precision_at_3
value: 23.633000000000003
- type: precision_at_5
value: 15.340000000000002
- type: recall_at_1
value: 53.1
- type: recall_at_10
value: 84.3
- type: recall_at_100
value: 95.5
- type: recall_at_1000
value: 97.5
- type: recall_at_3
value: 70.89999999999999
- type: recall_at_5
value: 76.7
- task:
type: Classification
dataset:
name: MTEB IFlyTek
type: C-MTEB/IFlyTek-classification
config: default
split: validation
revision: None
metrics:
- type: accuracy
value: 48.303193535975375
- type: f1
value: 35.96559358693866
- task:
type: Classification
dataset:
name: MTEB JDReview
type: C-MTEB/JDReview-classification
config: default
split: test
revision: None
metrics:
- type: accuracy
value: 85.06566604127579
- type: ap
value: 52.0596483757231
- type: f1
value: 79.5196835127668
- task:
type: STS
dataset:
name: MTEB LCQMC
type: C-MTEB/LCQMC
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson
value: 74.48499423626059
- type: cos_sim_spearman
value: 78.75806756061169
- type: euclidean_pearson
value: 78.47917601852879
- type: euclidean_spearman
value: 78.75807199272622
- type: manhattan_pearson
value: 78.40207586289772
- type: manhattan_spearman
value: 78.6911776964119
- task:
type: Reranking
dataset:
name: MTEB MMarcoReranking
type: C-MTEB/Mmarco-reranking
config: default
split: dev
revision: None
metrics:
- type: map
value: 24.75987466552363
- type: mrr
value: 23.40515873015873
- task:
type: Retrieval
dataset:
name: MTEB MMarcoRetrieval
type: C-MTEB/MMarcoRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 58.026999999999994
- type: map_at_10
value: 67.50699999999999
- type: map_at_100
value: 67.946
- type: map_at_1000
value: 67.96600000000001
- type: map_at_3
value: 65.503
- type: map_at_5
value: 66.649
- type: mrr_at_1
value: 60.20100000000001
- type: mrr_at_10
value: 68.271
- type: mrr_at_100
value: 68.664
- type: mrr_at_1000
value: 68.682
- type: mrr_at_3
value: 66.47800000000001
- type: mrr_at_5
value: 67.499
- type: ndcg_at_1
value: 60.20100000000001
- type: ndcg_at_10
value: 71.697
- type: ndcg_at_100
value: 73.736
- type: ndcg_at_1000
value: 74.259
- type: ndcg_at_3
value: 67.768
- type: ndcg_at_5
value: 69.72
- type: precision_at_1
value: 60.20100000000001
- type: precision_at_10
value: 8.927999999999999
- type: precision_at_100
value: 0.9950000000000001
- type: precision_at_1000
value: 0.104
- type: precision_at_3
value: 25.883
- type: precision_at_5
value: 16.55
- type: recall_at_1
value: 58.026999999999994
- type: recall_at_10
value: 83.966
- type: recall_at_100
value: 93.313
- type: recall_at_1000
value: 97.426
- type: recall_at_3
value: 73.342
- type: recall_at_5
value: 77.997
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (zh-CN)
type: mteb/amazon_massive_intent
config: zh-CN
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 71.1600537995965
- type: f1
value: 68.8126216609964
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (zh-CN)
type: mteb/amazon_massive_scenario
config: zh-CN
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 73.54068594485541
- type: f1
value: 73.46845879869848
- task:
type: Retrieval
dataset:
name: MTEB MedicalRetrieval
type: C-MTEB/MedicalRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 54.900000000000006
- type: map_at_10
value: 61.363
- type: map_at_100
value: 61.924
- type: map_at_1000
value: 61.967000000000006
- type: map_at_3
value: 59.767
- type: map_at_5
value: 60.802
- type: mrr_at_1
value: 55.1
- type: mrr_at_10
value: 61.454
- type: mrr_at_100
value: 62.016000000000005
- type: mrr_at_1000
value: 62.059
- type: mrr_at_3
value: 59.882999999999996
- type: mrr_at_5
value: 60.893
- type: ndcg_at_1
value: 54.900000000000006
- type: ndcg_at_10
value: 64.423
- type: ndcg_at_100
value: 67.35900000000001
- type: ndcg_at_1000
value: 68.512
- type: ndcg_at_3
value: 61.224000000000004
- type: ndcg_at_5
value: 63.083
- type: precision_at_1
value: 54.900000000000006
- type: precision_at_10
value: 7.3999999999999995
- type: precision_at_100
value: 0.882
- type: precision_at_1000
value: 0.097
- type: precision_at_3
value: 21.8
- type: precision_at_5
value: 13.98
- type: recall_at_1
value: 54.900000000000006
- type: recall_at_10
value: 74
- type: recall_at_100
value: 88.2
- type: recall_at_1000
value: 97.3
- type: recall_at_3
value: 65.4
- type: recall_at_5
value: 69.89999999999999
- task:
type: Classification
dataset:
name: MTEB MultilingualSentiment
type: C-MTEB/MultilingualSentiment-classification
config: default
split: validation
revision: None
metrics:
- type: accuracy
value: 75.15666666666667
- type: f1
value: 74.8306375354435
- task:
type: PairClassification
dataset:
name: MTEB Ocnli
type: C-MTEB/OCNLI
config: default
split: validation
revision: None
metrics:
- type: cos_sim_accuracy
value: 83.10774228478614
- type: cos_sim_ap
value: 87.17679348388666
- type: cos_sim_f1
value: 84.59302325581395
- type: cos_sim_precision
value: 78.15577439570276
- type: cos_sim_recall
value: 92.18585005279832
- type: dot_accuracy
value: 83.10774228478614
- type: dot_ap
value: 87.17679348388666
- type: dot_f1
value: 84.59302325581395
- type: dot_precision
value: 78.15577439570276
- type: dot_recall
value: 92.18585005279832
- type: euclidean_accuracy
value: 83.10774228478614
- type: euclidean_ap
value: 87.17679348388666
- type: euclidean_f1
value: 84.59302325581395
- type: euclidean_precision
value: 78.15577439570276
- type: euclidean_recall
value: 92.18585005279832
- type: manhattan_accuracy
value: 82.67460747157553
- type: manhattan_ap
value: 86.94296334435238
- type: manhattan_f1
value: 84.32327166504382
- type: manhattan_precision
value: 78.22944896115628
- type: manhattan_recall
value: 91.4466737064414
- type: max_accuracy
value: 83.10774228478614
- type: max_ap
value: 87.17679348388666
- type: max_f1
value: 84.59302325581395
- task:
type: Classification
dataset:
name: MTEB OnlineShopping
type: C-MTEB/OnlineShopping-classification
config: default
split: test
revision: None
metrics:
- type: accuracy
value: 93.24999999999999
- type: ap
value: 90.98617641063584
- type: f1
value: 93.23447883650289
- task:
type: STS
dataset:
name: MTEB PAWSX
type: C-MTEB/PAWSX
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson
value: 41.071417937737856
- type: cos_sim_spearman
value: 45.049199344455424
- type: euclidean_pearson
value: 44.913450096830786
- type: euclidean_spearman
value: 45.05733424275291
- type: manhattan_pearson
value: 44.881623825912065
- type: manhattan_spearman
value: 44.989923561416596
- task:
type: STS
dataset:
name: MTEB QBQTC
type: C-MTEB/QBQTC
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson
value: 41.38238052689359
- type: cos_sim_spearman
value: 42.61949690594399
- type: euclidean_pearson
value: 40.61261500356766
- type: euclidean_spearman
value: 42.619626605620724
- type: manhattan_pearson
value: 40.8886109204474
- type: manhattan_spearman
value: 42.75791523010463
- task:
type: STS
dataset:
name: MTEB STS22 (zh)
type: mteb/sts22-crosslingual-sts
config: zh
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 62.10977863727196
- type: cos_sim_spearman
value: 63.843727112473225
- type: euclidean_pearson
value: 63.25133487817196
- type: euclidean_spearman
value: 63.843727112473225
- type: manhattan_pearson
value: 63.58749018644103
- type: manhattan_spearman
value: 63.83820575456674
- task:
type: STS
dataset:
name: MTEB STSB
type: C-MTEB/STSB
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson
value: 79.30616496720054
- type: cos_sim_spearman
value: 80.767935782436
- type: euclidean_pearson
value: 80.4160642670106
- type: euclidean_spearman
value: 80.76820284024356
- type: manhattan_pearson
value: 80.27318714580251
- type: manhattan_spearman
value: 80.61030164164964
- task:
type: Reranking
dataset:
name: MTEB T2Reranking
type: C-MTEB/T2Reranking
config: default
split: dev
revision: None
metrics:
- type: map
value: 66.26242871142425
- type: mrr
value: 76.20689863623174
- task:
type: Retrieval
dataset:
name: MTEB T2Retrieval
type: C-MTEB/T2Retrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 26.240999999999996
- type: map_at_10
value: 73.009
- type: map_at_100
value: 76.893
- type: map_at_1000
value: 76.973
- type: map_at_3
value: 51.339
- type: map_at_5
value: 63.003
- type: mrr_at_1
value: 87.458
- type: mrr_at_10
value: 90.44
- type: mrr_at_100
value: 90.558
- type: mrr_at_1000
value: 90.562
- type: mrr_at_3
value: 89.89
- type: mrr_at_5
value: 90.231
- type: ndcg_at_1
value: 87.458
- type: ndcg_at_10
value: 81.325
- type: ndcg_at_100
value: 85.61999999999999
- type: ndcg_at_1000
value: 86.394
- type: ndcg_at_3
value: 82.796
- type: ndcg_at_5
value: 81.219
- type: precision_at_1
value: 87.458
- type: precision_at_10
value: 40.534
- type: precision_at_100
value: 4.96
- type: precision_at_1000
value: 0.514
- type: precision_at_3
value: 72.444
- type: precision_at_5
value: 60.601000000000006
- type: recall_at_1
value: 26.240999999999996
- type: recall_at_10
value: 80.42
- type: recall_at_100
value: 94.118
- type: recall_at_1000
value: 98.02199999999999
- type: recall_at_3
value: 53.174
- type: recall_at_5
value: 66.739
- task:
type: Classification
dataset:
name: MTEB TNews
type: C-MTEB/TNews-classification
config: default
split: validation
revision: None
metrics:
- type: accuracy
value: 52.40899999999999
- type: f1
value: 50.68532128056062
- task:
type: Clustering
dataset:
name: MTEB ThuNewsClusteringP2P
type: C-MTEB/ThuNewsClusteringP2P
config: default
split: test
revision: None
metrics:
- type: v_measure
value: 65.57616085176686
- task:
type: Clustering
dataset:
name: MTEB ThuNewsClusteringS2S
type: C-MTEB/ThuNewsClusteringS2S
config: default
split: test
revision: None
metrics:
- type: v_measure
value: 58.844999922904925
- task:
type: Retrieval
dataset:
name: MTEB VideoRetrieval
type: C-MTEB/VideoRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 58.4
- type: map_at_10
value: 68.64
- type: map_at_100
value: 69.062
- type: map_at_1000
value: 69.073
- type: map_at_3
value: 66.567
- type: map_at_5
value: 67.89699999999999
- type: mrr_at_1
value: 58.4
- type: mrr_at_10
value: 68.64
- type: mrr_at_100
value: 69.062
- type: mrr_at_1000
value: 69.073
- type: mrr_at_3
value: 66.567
- type: mrr_at_5
value: 67.89699999999999
- type: ndcg_at_1
value: 58.4
- type: ndcg_at_10
value: 73.30600000000001
- type: ndcg_at_100
value: 75.276
- type: ndcg_at_1000
value: 75.553
- type: ndcg_at_3
value: 69.126
- type: ndcg_at_5
value: 71.519
- type: precision_at_1
value: 58.4
- type: precision_at_10
value: 8.780000000000001
- type: precision_at_100
value: 0.968
- type: precision_at_1000
value: 0.099
- type: precision_at_3
value: 25.5
- type: precision_at_5
value: 16.46
- type: recall_at_1
value: 58.4
- type: recall_at_10
value: 87.8
- type: recall_at_100
value: 96.8
- type: recall_at_1000
value: 99
- type: recall_at_3
value: 76.5
- type: recall_at_5
value: 82.3
- task:
type: Classification
dataset:
name: MTEB Waimai
type: C-MTEB/waimai-classification
config: default
split: test
revision: None
metrics:
- type: accuracy
value: 86.21000000000001
- type: ap
value: 69.17460264576461
- type: f1
value: 84.68032984659226
---
# annofung/Dmeta-embedding-zh-Q4_0-GGUF
This model was converted to GGUF format from [`DMetaSoul/Dmeta-embedding-zh`](https://huggingface.co/DMetaSoul/Dmeta-embedding-zh) 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/DMetaSoul/Dmeta-embedding-zh) 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 annofung/Dmeta-embedding-zh-Q4_0-GGUF --hf-file dmeta-embedding-zh-q4_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo annofung/Dmeta-embedding-zh-Q4_0-GGUF --hf-file dmeta-embedding-zh-q4_0.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo annofung/Dmeta-embedding-zh-Q4_0-GGUF --hf-file dmeta-embedding-zh-q4_0.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo annofung/Dmeta-embedding-zh-Q4_0-GGUF --hf-file dmeta-embedding-zh-q4_0.gguf -c 2048
```
|
luaqi/sn29_12152
|
luaqi
| 2024-12-16T06:43:04Z | 43 | 0 |
transformers
|
[
"transformers",
"safetensors",
"phi3",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-12-16T06:36:22Z |
---
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 π€ 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]
|
DeepDream2045/b9169e8b-bee3-4209-817e-87cc201f6be8
|
DeepDream2045
| 2024-12-16T06:34:35Z | 14 | 0 |
peft
|
[
"peft",
"safetensors",
"qwen2",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/Qwen2.5-Coder-1.5B-Instruct",
"base_model:adapter:unsloth/Qwen2.5-Coder-1.5B-Instruct",
"license:apache-2.0",
"region:us"
] | null | 2024-12-16T06:29:16Z |
---
library_name: peft
license: apache-2.0
base_model: unsloth/Qwen2.5-Coder-1.5B-Instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: b9169e8b-bee3-4209-817e-87cc201f6be8
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.5.2`
```yaml
adapter: lora
base_model: unsloth/Qwen2.5-Coder-1.5B-Instruct
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 7693725e9f88ce58_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/7693725e9f88ce58_train_data.json
type:
field_instruction: text
field_output: transcript
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: 1
eval_max_new_tokens: 128
eval_steps: 25
eval_table_size: null
flash_attention: false
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 16
gradient_checkpointing: true
group_by_length: true
hub_model_id: DeepDream2045/b9169e8b-bee3-4209-817e-87cc201f6be8
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0001
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_steps: 50
micro_batch_size: 2
mlflow_experiment_name: /tmp/7693725e9f88ce58_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
optimizer: adamw_torch
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 25
sequence_len: 2048
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: b9169e8b-bee3-4209-817e-87cc201f6be8
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: b9169e8b-bee3-4209-817e-87cc201f6be8
warmup_ratio: 0.05
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# b9169e8b-bee3-4209-817e-87cc201f6be8
This model is a fine-tuned version of [unsloth/Qwen2.5-Coder-1.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-Coder-1.5B-Instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2240
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- total_eval_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 2
- training_steps: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.0219 | 0.0099 | 1 | 1.3110 |
| 0.4663 | 0.2472 | 25 | 0.2859 |
| 0.4577 | 0.4944 | 50 | 0.2240 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.3
- Pytorch 2.3.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
|
Manasa1/model_name
|
Manasa1
| 2024-12-16T06:34:17Z | 140 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gpt2",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-12-16T01:52:21Z |
---
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 π€ 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]
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## Model Card Authors [optional]
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## Model Card Contact
[More Information Needed]
|
QuantFactory/Albatross2.1-8B-Instruct-GGUF
|
QuantFactory
| 2024-12-16T06:33:36Z | 229 | 2 |
transformers
|
[
"transformers",
"gguf",
"mergekit",
"merge",
"base_model:NousResearch/Hermes-3-Llama-3.1-8B",
"base_model:merge:NousResearch/Hermes-3-Llama-3.1-8B",
"base_model:qingy2024/NaturalLM3-8B-Instruct-v0.1",
"base_model:merge:qingy2024/NaturalLM3-8B-Instruct-v0.1",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-12-16T05:47:05Z |
---
base_model:
- NousResearch/Hermes-3-Llama-3.1-8B
- qingy2024/NaturalLM3-8B-Instruct-v0.1
library_name: transformers
tags:
- mergekit
- merge
---
[](https://hf.co/QuantFactory)
# QuantFactory/Albatross2.1-8B-Instruct-GGUF
This is quantized version of [qingy2024/Albatross2.1-8B-Instruct](https://huggingface.co/qingy2024/Albatross2.1-8B-Instruct) created using llama.cpp
# Original Model Card
# merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the SLERP merge method.
### Models Merged
The following models were included in the merge:
* [NousResearch/Hermes-3-Llama-3.1-8B](https://huggingface.co/NousResearch/Hermes-3-Llama-3.1-8B)
* [qingy2024/NaturalLM3-8B-Instruct-v0.1](https://huggingface.co/qingy2024/NaturalLM3-8B-Instruct-v0.1)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: qingy2024/NaturalLM3-8B-Instruct-v0.1
- model: NousResearch/Hermes-3-Llama-3.1-8B
merge_method: slerp
base_model: qingy2024/NaturalLM3-8B-Instruct-v0.1
dtype: bfloat16
parameters:
t: [0, 0.5, 1, 0.5, 0] # V shaped curve: Hermes for input & output, WizardMath in the middle layers
```
|
ganga4364/whipser-small-reft
|
ganga4364
| 2024-12-16T06:33:17Z | 92 | 0 |
transformers
|
[
"transformers",
"safetensors",
"whisper",
"automatic-speech-recognition",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2024-12-16T06:32:43Z |
---
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 π€ 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]
|
MayBashendy/ArabicNewSplits5_FineTuningAraBERT_run3_AugV5_k14_task1_organization
|
MayBashendy
| 2024-12-16T06:32:25Z | 164 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:aubmindlab/bert-base-arabertv02",
"base_model:finetune:aubmindlab/bert-base-arabertv02",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-12-16T06:14:21Z |
---
library_name: transformers
base_model: aubmindlab/bert-base-arabertv02
tags:
- generated_from_trainer
model-index:
- name: ArabicNewSplits5_FineTuningAraBERT_run3_AugV5_k14_task1_organization
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. -->
# ArabicNewSplits5_FineTuningAraBERT_run3_AugV5_k14_task1_organization
This model is a fine-tuned version of [aubmindlab/bert-base-arabertv02](https://huggingface.co/aubmindlab/bert-base-arabertv02) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8429
- Qwk: 0.5855
- Mse: 0.8429
- Rmse: 0.9181
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Qwk | Mse | Rmse |
|:-------------:|:------:|:----:|:---------------:|:-------:|:------:|:------:|
| No log | 0.0227 | 2 | 5.3490 | -0.0558 | 5.3490 | 2.3128 |
| No log | 0.0455 | 4 | 3.3647 | 0.0377 | 3.3647 | 1.8343 |
| No log | 0.0682 | 6 | 3.0748 | -0.0390 | 3.0748 | 1.7535 |
| No log | 0.0909 | 8 | 2.7229 | -0.0911 | 2.7229 | 1.6501 |
| No log | 0.1136 | 10 | 1.8212 | 0.0306 | 1.8212 | 1.3495 |
| No log | 0.1364 | 12 | 1.6693 | 0.0518 | 1.6693 | 1.2920 |
| No log | 0.1591 | 14 | 1.6198 | 0.0331 | 1.6198 | 1.2727 |
| No log | 0.1818 | 16 | 1.3786 | 0.1063 | 1.3786 | 1.1741 |
| No log | 0.2045 | 18 | 1.3382 | 0.1686 | 1.3382 | 1.1568 |
| No log | 0.2273 | 20 | 1.6306 | 0.0339 | 1.6306 | 1.2769 |
| No log | 0.25 | 22 | 1.9945 | 0.0323 | 1.9945 | 1.4123 |
| No log | 0.2727 | 24 | 2.0301 | 0.0950 | 2.0301 | 1.4248 |
| No log | 0.2955 | 26 | 1.9271 | 0.0741 | 1.9271 | 1.3882 |
| No log | 0.3182 | 28 | 1.9947 | 0.1494 | 1.9947 | 1.4123 |
| No log | 0.3409 | 30 | 1.9236 | 0.1507 | 1.9236 | 1.3869 |
| No log | 0.3636 | 32 | 1.7300 | 0.1055 | 1.7300 | 1.3153 |
| No log | 0.3864 | 34 | 1.5218 | 0.1202 | 1.5218 | 1.2336 |
| No log | 0.4091 | 36 | 1.4265 | 0.1380 | 1.4265 | 1.1944 |
| No log | 0.4318 | 38 | 1.3516 | 0.1344 | 1.3516 | 1.1626 |
| No log | 0.4545 | 40 | 1.3813 | 0.1505 | 1.3813 | 1.1753 |
| No log | 0.4773 | 42 | 1.3899 | 0.1687 | 1.3899 | 1.1789 |
| No log | 0.5 | 44 | 1.4658 | 0.1646 | 1.4658 | 1.2107 |
| No log | 0.5227 | 46 | 1.4194 | 0.1646 | 1.4194 | 1.1914 |
| No log | 0.5455 | 48 | 1.4911 | 0.1434 | 1.4911 | 1.2211 |
| No log | 0.5682 | 50 | 1.5361 | 0.1602 | 1.5361 | 1.2394 |
| No log | 0.5909 | 52 | 1.5427 | 0.1212 | 1.5427 | 1.2420 |
| No log | 0.6136 | 54 | 1.4824 | 0.1474 | 1.4824 | 1.2175 |
| No log | 0.6364 | 56 | 1.3233 | 0.2217 | 1.3233 | 1.1503 |
| No log | 0.6591 | 58 | 1.1757 | 0.3647 | 1.1757 | 1.0843 |
| No log | 0.6818 | 60 | 1.0991 | 0.4008 | 1.0991 | 1.0484 |
| No log | 0.7045 | 62 | 1.2582 | 0.4146 | 1.2582 | 1.1217 |
| No log | 0.7273 | 64 | 1.3722 | 0.3928 | 1.3722 | 1.1714 |
| No log | 0.75 | 66 | 1.4696 | 0.3575 | 1.4696 | 1.2123 |
| No log | 0.7727 | 68 | 1.7737 | 0.3087 | 1.7737 | 1.3318 |
| No log | 0.7955 | 70 | 1.7612 | 0.2977 | 1.7612 | 1.3271 |
| No log | 0.8182 | 72 | 1.6032 | 0.3092 | 1.6032 | 1.2662 |
| No log | 0.8409 | 74 | 1.2334 | 0.3736 | 1.2334 | 1.1106 |
| No log | 0.8636 | 76 | 0.9149 | 0.4912 | 0.9149 | 0.9565 |
| No log | 0.8864 | 78 | 0.8518 | 0.5093 | 0.8518 | 0.9229 |
| No log | 0.9091 | 80 | 0.8657 | 0.4980 | 0.8657 | 0.9304 |
| No log | 0.9318 | 82 | 0.9931 | 0.4501 | 0.9931 | 0.9965 |
| No log | 0.9545 | 84 | 1.2677 | 0.3501 | 1.2677 | 1.1259 |
| No log | 0.9773 | 86 | 1.3564 | 0.3485 | 1.3564 | 1.1647 |
| No log | 1.0 | 88 | 1.2550 | 0.3468 | 1.2550 | 1.1203 |
| No log | 1.0227 | 90 | 1.1234 | 0.3547 | 1.1234 | 1.0599 |
| No log | 1.0455 | 92 | 1.0306 | 0.4787 | 1.0306 | 1.0152 |
| No log | 1.0682 | 94 | 0.9733 | 0.5020 | 0.9733 | 0.9865 |
| No log | 1.0909 | 96 | 0.9634 | 0.4805 | 0.9634 | 0.9815 |
| No log | 1.1136 | 98 | 0.9319 | 0.5437 | 0.9319 | 0.9654 |
| No log | 1.1364 | 100 | 0.8912 | 0.5341 | 0.8912 | 0.9441 |
| No log | 1.1591 | 102 | 0.7748 | 0.6124 | 0.7748 | 0.8802 |
| No log | 1.1818 | 104 | 0.7571 | 0.6117 | 0.7571 | 0.8701 |
| No log | 1.2045 | 106 | 0.7243 | 0.6448 | 0.7243 | 0.8511 |
| No log | 1.2273 | 108 | 0.7508 | 0.6396 | 0.7508 | 0.8665 |
| No log | 1.25 | 110 | 0.7692 | 0.6380 | 0.7692 | 0.8771 |
| No log | 1.2727 | 112 | 0.8163 | 0.6226 | 0.8163 | 0.9035 |
| No log | 1.2955 | 114 | 0.8010 | 0.6504 | 0.8010 | 0.8950 |
| No log | 1.3182 | 116 | 0.9234 | 0.5272 | 0.9234 | 0.9609 |
| No log | 1.3409 | 118 | 0.9446 | 0.5006 | 0.9446 | 0.9719 |
| No log | 1.3636 | 120 | 0.8559 | 0.5434 | 0.8559 | 0.9252 |
| No log | 1.3864 | 122 | 0.7797 | 0.6561 | 0.7797 | 0.8830 |
| No log | 1.4091 | 124 | 0.7738 | 0.6426 | 0.7738 | 0.8797 |
| No log | 1.4318 | 126 | 0.8801 | 0.5040 | 0.8801 | 0.9381 |
| No log | 1.4545 | 128 | 0.9400 | 0.4588 | 0.9400 | 0.9695 |
| No log | 1.4773 | 130 | 0.9120 | 0.5240 | 0.9120 | 0.9550 |
| No log | 1.5 | 132 | 0.9781 | 0.5193 | 0.9781 | 0.9890 |
| No log | 1.5227 | 134 | 1.0521 | 0.5047 | 1.0521 | 1.0257 |
| No log | 1.5455 | 136 | 1.0245 | 0.5038 | 1.0245 | 1.0122 |
| No log | 1.5682 | 138 | 1.0085 | 0.5055 | 1.0085 | 1.0042 |
| No log | 1.5909 | 140 | 0.9524 | 0.4857 | 0.9524 | 0.9759 |
| No log | 1.6136 | 142 | 0.8927 | 0.4602 | 0.8927 | 0.9448 |
| No log | 1.6364 | 144 | 0.8421 | 0.5755 | 0.8421 | 0.9177 |
| No log | 1.6591 | 146 | 0.8646 | 0.5521 | 0.8646 | 0.9298 |
| No log | 1.6818 | 148 | 0.9441 | 0.4908 | 0.9441 | 0.9716 |
| No log | 1.7045 | 150 | 1.0735 | 0.4050 | 1.0735 | 1.0361 |
| No log | 1.7273 | 152 | 1.1674 | 0.4117 | 1.1674 | 1.0805 |
| No log | 1.75 | 154 | 1.0499 | 0.4228 | 1.0499 | 1.0247 |
| No log | 1.7727 | 156 | 0.9961 | 0.5117 | 0.9961 | 0.9980 |
| No log | 1.7955 | 158 | 0.9469 | 0.5705 | 0.9469 | 0.9731 |
| No log | 1.8182 | 160 | 0.9150 | 0.5931 | 0.9150 | 0.9566 |
| No log | 1.8409 | 162 | 0.9123 | 0.5854 | 0.9123 | 0.9551 |
| No log | 1.8636 | 164 | 0.9224 | 0.6013 | 0.9224 | 0.9604 |
| No log | 1.8864 | 166 | 0.9125 | 0.6092 | 0.9125 | 0.9553 |
| No log | 1.9091 | 168 | 0.8615 | 0.6130 | 0.8615 | 0.9282 |
| No log | 1.9318 | 170 | 0.8208 | 0.6761 | 0.8208 | 0.9060 |
| No log | 1.9545 | 172 | 0.8279 | 0.6859 | 0.8279 | 0.9099 |
| No log | 1.9773 | 174 | 0.8355 | 0.6467 | 0.8355 | 0.9140 |
| No log | 2.0 | 176 | 0.8344 | 0.6626 | 0.8344 | 0.9134 |
| No log | 2.0227 | 178 | 0.8528 | 0.6084 | 0.8528 | 0.9235 |
| No log | 2.0455 | 180 | 0.8779 | 0.5850 | 0.8779 | 0.9370 |
| No log | 2.0682 | 182 | 0.9089 | 0.5540 | 0.9089 | 0.9534 |
| No log | 2.0909 | 184 | 0.9244 | 0.6034 | 0.9244 | 0.9615 |
| No log | 2.1136 | 186 | 0.9206 | 0.5623 | 0.9206 | 0.9595 |
| No log | 2.1364 | 188 | 0.8940 | 0.6014 | 0.8940 | 0.9455 |
| No log | 2.1591 | 190 | 0.8336 | 0.6528 | 0.8336 | 0.9130 |
| No log | 2.1818 | 192 | 0.8470 | 0.5902 | 0.8470 | 0.9204 |
| No log | 2.2045 | 194 | 0.8415 | 0.5902 | 0.8415 | 0.9173 |
| No log | 2.2273 | 196 | 0.8191 | 0.6417 | 0.8191 | 0.9051 |
| No log | 2.25 | 198 | 0.7806 | 0.6673 | 0.7806 | 0.8835 |
| No log | 2.2727 | 200 | 0.7790 | 0.6545 | 0.7790 | 0.8826 |
| No log | 2.2955 | 202 | 0.8361 | 0.5997 | 0.8361 | 0.9144 |
| No log | 2.3182 | 204 | 0.8199 | 0.6020 | 0.8199 | 0.9055 |
| No log | 2.3409 | 206 | 0.8332 | 0.5707 | 0.8332 | 0.9128 |
| No log | 2.3636 | 208 | 0.8332 | 0.5779 | 0.8332 | 0.9128 |
| No log | 2.3864 | 210 | 0.7935 | 0.6298 | 0.7935 | 0.8908 |
| No log | 2.4091 | 212 | 0.7827 | 0.6222 | 0.7827 | 0.8847 |
| No log | 2.4318 | 214 | 0.8025 | 0.5887 | 0.8025 | 0.8958 |
| No log | 2.4545 | 216 | 0.8632 | 0.5574 | 0.8632 | 0.9291 |
| No log | 2.4773 | 218 | 0.9495 | 0.5492 | 0.9495 | 0.9744 |
| No log | 2.5 | 220 | 0.9502 | 0.5837 | 0.9502 | 0.9748 |
| No log | 2.5227 | 222 | 0.8187 | 0.6224 | 0.8187 | 0.9048 |
| No log | 2.5455 | 224 | 0.7673 | 0.6386 | 0.7673 | 0.8759 |
| No log | 2.5682 | 226 | 0.8094 | 0.6288 | 0.8094 | 0.8997 |
| No log | 2.5909 | 228 | 0.8222 | 0.6328 | 0.8222 | 0.9067 |
| No log | 2.6136 | 230 | 0.8201 | 0.6272 | 0.8201 | 0.9056 |
| No log | 2.6364 | 232 | 0.9148 | 0.6156 | 0.9148 | 0.9565 |
| No log | 2.6591 | 234 | 0.9762 | 0.5715 | 0.9762 | 0.9880 |
| No log | 2.6818 | 236 | 0.9258 | 0.5424 | 0.9258 | 0.9622 |
| No log | 2.7045 | 238 | 0.8677 | 0.5599 | 0.8677 | 0.9315 |
| No log | 2.7273 | 240 | 0.8569 | 0.6053 | 0.8569 | 0.9257 |
| No log | 2.75 | 242 | 0.8847 | 0.6028 | 0.8847 | 0.9406 |
| No log | 2.7727 | 244 | 0.8689 | 0.5893 | 0.8689 | 0.9322 |
| No log | 2.7955 | 246 | 0.8324 | 0.5908 | 0.8324 | 0.9124 |
| No log | 2.8182 | 248 | 0.8371 | 0.5889 | 0.8371 | 0.9149 |
| No log | 2.8409 | 250 | 0.9214 | 0.5205 | 0.9214 | 0.9599 |
| No log | 2.8636 | 252 | 1.0487 | 0.5607 | 1.0487 | 1.0241 |
| No log | 2.8864 | 254 | 1.1339 | 0.5440 | 1.1339 | 1.0648 |
| No log | 2.9091 | 256 | 1.0747 | 0.5547 | 1.0747 | 1.0367 |
| No log | 2.9318 | 258 | 0.9656 | 0.5521 | 0.9656 | 0.9826 |
| No log | 2.9545 | 260 | 0.9127 | 0.5494 | 0.9127 | 0.9554 |
| No log | 2.9773 | 262 | 0.8701 | 0.5704 | 0.8701 | 0.9328 |
| No log | 3.0 | 264 | 0.8570 | 0.5271 | 0.8570 | 0.9258 |
| No log | 3.0227 | 266 | 0.8699 | 0.5343 | 0.8699 | 0.9327 |
| No log | 3.0455 | 268 | 0.9099 | 0.5532 | 0.9099 | 0.9539 |
| No log | 3.0682 | 270 | 0.9781 | 0.5568 | 0.9781 | 0.9890 |
| No log | 3.0909 | 272 | 1.0299 | 0.5765 | 1.0299 | 1.0148 |
| No log | 3.1136 | 274 | 0.9600 | 0.5607 | 0.9600 | 0.9798 |
| No log | 3.1364 | 276 | 0.8494 | 0.5453 | 0.8494 | 0.9216 |
| No log | 3.1591 | 278 | 0.7874 | 0.5882 | 0.7874 | 0.8873 |
| No log | 3.1818 | 280 | 0.7949 | 0.5949 | 0.7949 | 0.8916 |
| No log | 3.2045 | 282 | 0.8750 | 0.5847 | 0.8750 | 0.9354 |
| No log | 3.2273 | 284 | 0.9808 | 0.6018 | 0.9808 | 0.9904 |
| No log | 3.25 | 286 | 0.9217 | 0.5823 | 0.9217 | 0.9600 |
| No log | 3.2727 | 288 | 0.8084 | 0.5893 | 0.8084 | 0.8991 |
| No log | 3.2955 | 290 | 0.7727 | 0.5894 | 0.7727 | 0.8790 |
| No log | 3.3182 | 292 | 0.7706 | 0.5904 | 0.7706 | 0.8778 |
| No log | 3.3409 | 294 | 0.7691 | 0.5935 | 0.7691 | 0.8770 |
| No log | 3.3636 | 296 | 0.7999 | 0.5753 | 0.7999 | 0.8944 |
| No log | 3.3864 | 298 | 0.8449 | 0.5365 | 0.8449 | 0.9192 |
| No log | 3.4091 | 300 | 0.8257 | 0.5556 | 0.8257 | 0.9087 |
| No log | 3.4318 | 302 | 0.7759 | 0.5607 | 0.7759 | 0.8809 |
| No log | 3.4545 | 304 | 0.7411 | 0.5906 | 0.7411 | 0.8608 |
| No log | 3.4773 | 306 | 0.7348 | 0.5946 | 0.7348 | 0.8572 |
| No log | 3.5 | 308 | 0.7354 | 0.6072 | 0.7354 | 0.8576 |
| No log | 3.5227 | 310 | 0.7593 | 0.6022 | 0.7593 | 0.8714 |
| No log | 3.5455 | 312 | 0.7859 | 0.5919 | 0.7859 | 0.8865 |
| No log | 3.5682 | 314 | 0.8208 | 0.6195 | 0.8208 | 0.9060 |
| No log | 3.5909 | 316 | 0.8696 | 0.6556 | 0.8696 | 0.9325 |
| No log | 3.6136 | 318 | 0.8919 | 0.6396 | 0.8919 | 0.9444 |
| No log | 3.6364 | 320 | 0.8929 | 0.6368 | 0.8929 | 0.9449 |
| No log | 3.6591 | 322 | 0.8151 | 0.6348 | 0.8151 | 0.9028 |
| No log | 3.6818 | 324 | 0.7653 | 0.6343 | 0.7653 | 0.8748 |
| No log | 3.7045 | 326 | 0.7700 | 0.6270 | 0.7700 | 0.8775 |
| No log | 3.7273 | 328 | 0.7750 | 0.6069 | 0.7750 | 0.8804 |
| No log | 3.75 | 330 | 0.7438 | 0.6087 | 0.7438 | 0.8624 |
| No log | 3.7727 | 332 | 0.7373 | 0.6024 | 0.7373 | 0.8586 |
| No log | 3.7955 | 334 | 0.7536 | 0.5963 | 0.7536 | 0.8681 |
| No log | 3.8182 | 336 | 0.7562 | 0.5883 | 0.7562 | 0.8696 |
| No log | 3.8409 | 338 | 0.7647 | 0.5977 | 0.7647 | 0.8745 |
| No log | 3.8636 | 340 | 0.7834 | 0.6039 | 0.7834 | 0.8851 |
| No log | 3.8864 | 342 | 0.8190 | 0.6316 | 0.8190 | 0.9050 |
| No log | 3.9091 | 344 | 0.8879 | 0.6230 | 0.8879 | 0.9423 |
| No log | 3.9318 | 346 | 0.9013 | 0.6247 | 0.9013 | 0.9494 |
| No log | 3.9545 | 348 | 0.9038 | 0.6266 | 0.9038 | 0.9507 |
| No log | 3.9773 | 350 | 0.9395 | 0.5920 | 0.9395 | 0.9693 |
| No log | 4.0 | 352 | 0.9352 | 0.5333 | 0.9352 | 0.9671 |
| No log | 4.0227 | 354 | 0.8797 | 0.5524 | 0.8797 | 0.9379 |
| No log | 4.0455 | 356 | 0.8672 | 0.5527 | 0.8672 | 0.9312 |
| No log | 4.0682 | 358 | 0.8654 | 0.5507 | 0.8654 | 0.9303 |
| No log | 4.0909 | 360 | 0.8693 | 0.5345 | 0.8693 | 0.9323 |
| No log | 4.1136 | 362 | 0.8652 | 0.5633 | 0.8652 | 0.9302 |
| No log | 4.1364 | 364 | 0.8463 | 0.5788 | 0.8463 | 0.9199 |
| No log | 4.1591 | 366 | 0.8254 | 0.6007 | 0.8254 | 0.9085 |
| No log | 4.1818 | 368 | 0.8381 | 0.5976 | 0.8381 | 0.9155 |
| No log | 4.2045 | 370 | 0.8500 | 0.6135 | 0.8500 | 0.9220 |
| No log | 4.2273 | 372 | 0.8195 | 0.6185 | 0.8195 | 0.9053 |
| No log | 4.25 | 374 | 0.7750 | 0.5898 | 0.7750 | 0.8803 |
| No log | 4.2727 | 376 | 0.7754 | 0.5891 | 0.7754 | 0.8806 |
| No log | 4.2955 | 378 | 0.7843 | 0.5789 | 0.7843 | 0.8856 |
| No log | 4.3182 | 380 | 0.7901 | 0.5789 | 0.7901 | 0.8889 |
| No log | 4.3409 | 382 | 0.7907 | 0.5706 | 0.7907 | 0.8892 |
| No log | 4.3636 | 384 | 0.7706 | 0.5668 | 0.7706 | 0.8779 |
| No log | 4.3864 | 386 | 0.7878 | 0.5828 | 0.7878 | 0.8876 |
| No log | 4.4091 | 388 | 0.8079 | 0.6061 | 0.8079 | 0.8988 |
| No log | 4.4318 | 390 | 0.8240 | 0.6061 | 0.8240 | 0.9077 |
| No log | 4.4545 | 392 | 0.7922 | 0.5986 | 0.7922 | 0.8900 |
| No log | 4.4773 | 394 | 0.7625 | 0.6138 | 0.7625 | 0.8732 |
| No log | 4.5 | 396 | 0.7759 | 0.6327 | 0.7759 | 0.8808 |
| No log | 4.5227 | 398 | 0.8264 | 0.6254 | 0.8264 | 0.9091 |
| No log | 4.5455 | 400 | 0.9013 | 0.6112 | 0.9013 | 0.9493 |
| No log | 4.5682 | 402 | 0.9969 | 0.5820 | 0.9969 | 0.9985 |
| No log | 4.5909 | 404 | 1.0017 | 0.5464 | 1.0017 | 1.0009 |
| No log | 4.6136 | 406 | 0.9145 | 0.5850 | 0.9145 | 0.9563 |
| No log | 4.6364 | 408 | 0.8532 | 0.5989 | 0.8532 | 0.9237 |
| No log | 4.6591 | 410 | 0.8481 | 0.5952 | 0.8481 | 0.9209 |
| No log | 4.6818 | 412 | 0.8722 | 0.5781 | 0.8722 | 0.9339 |
| No log | 4.7045 | 414 | 0.9085 | 0.5788 | 0.9085 | 0.9531 |
| No log | 4.7273 | 416 | 0.9709 | 0.5864 | 0.9709 | 0.9853 |
| No log | 4.75 | 418 | 0.9881 | 0.5892 | 0.9881 | 0.9940 |
| No log | 4.7727 | 420 | 1.0214 | 0.5899 | 1.0214 | 1.0107 |
| No log | 4.7955 | 422 | 1.0608 | 0.5507 | 1.0608 | 1.0300 |
| No log | 4.8182 | 424 | 1.0820 | 0.5450 | 1.0820 | 1.0402 |
| No log | 4.8409 | 426 | 1.0083 | 0.5765 | 1.0083 | 1.0042 |
| No log | 4.8636 | 428 | 0.9129 | 0.6067 | 0.9129 | 0.9555 |
| No log | 4.8864 | 430 | 0.8184 | 0.5878 | 0.8184 | 0.9046 |
| No log | 4.9091 | 432 | 0.7680 | 0.5810 | 0.7680 | 0.8763 |
| No log | 4.9318 | 434 | 0.7605 | 0.5918 | 0.7605 | 0.8720 |
| No log | 4.9545 | 436 | 0.7627 | 0.5782 | 0.7627 | 0.8733 |
| No log | 4.9773 | 438 | 0.7763 | 0.5707 | 0.7763 | 0.8811 |
| No log | 5.0 | 440 | 0.7793 | 0.5719 | 0.7793 | 0.8828 |
| No log | 5.0227 | 442 | 0.7733 | 0.5599 | 0.7733 | 0.8794 |
| No log | 5.0455 | 444 | 0.7739 | 0.5960 | 0.7739 | 0.8797 |
| No log | 5.0682 | 446 | 0.7711 | 0.5960 | 0.7711 | 0.8781 |
| No log | 5.0909 | 448 | 0.7635 | 0.5799 | 0.7635 | 0.8738 |
| No log | 5.1136 | 450 | 0.7611 | 0.5796 | 0.7611 | 0.8724 |
| No log | 5.1364 | 452 | 0.7513 | 0.5704 | 0.7513 | 0.8668 |
| No log | 5.1591 | 454 | 0.7456 | 0.5858 | 0.7456 | 0.8635 |
| No log | 5.1818 | 456 | 0.7744 | 0.5736 | 0.7744 | 0.8800 |
| No log | 5.2045 | 458 | 0.8393 | 0.5760 | 0.8393 | 0.9161 |
| No log | 5.2273 | 460 | 0.8511 | 0.5655 | 0.8511 | 0.9225 |
| No log | 5.25 | 462 | 0.8170 | 0.5929 | 0.8170 | 0.9039 |
| No log | 5.2727 | 464 | 0.7895 | 0.5858 | 0.7895 | 0.8885 |
| No log | 5.2955 | 466 | 0.7679 | 0.5723 | 0.7679 | 0.8763 |
| No log | 5.3182 | 468 | 0.7676 | 0.5626 | 0.7676 | 0.8761 |
| No log | 5.3409 | 470 | 0.7702 | 0.5517 | 0.7702 | 0.8776 |
| No log | 5.3636 | 472 | 0.7998 | 0.6062 | 0.7998 | 0.8943 |
| No log | 5.3864 | 474 | 0.8072 | 0.6303 | 0.8072 | 0.8984 |
| No log | 5.4091 | 476 | 0.8149 | 0.6226 | 0.8149 | 0.9027 |
| No log | 5.4318 | 478 | 0.8101 | 0.6068 | 0.8101 | 0.9000 |
| No log | 5.4545 | 480 | 0.7918 | 0.5833 | 0.7918 | 0.8898 |
| No log | 5.4773 | 482 | 0.7810 | 0.6016 | 0.7810 | 0.8837 |
| No log | 5.5 | 484 | 0.7797 | 0.5855 | 0.7797 | 0.8830 |
| No log | 5.5227 | 486 | 0.7854 | 0.5984 | 0.7854 | 0.8863 |
| No log | 5.5455 | 488 | 0.7932 | 0.6077 | 0.7932 | 0.8906 |
| No log | 5.5682 | 490 | 0.7940 | 0.6165 | 0.7940 | 0.8911 |
| No log | 5.5909 | 492 | 0.7812 | 0.6072 | 0.7812 | 0.8839 |
| No log | 5.6136 | 494 | 0.7886 | 0.6133 | 0.7886 | 0.8880 |
| No log | 5.6364 | 496 | 0.8081 | 0.6246 | 0.8081 | 0.8989 |
| No log | 5.6591 | 498 | 0.8125 | 0.6246 | 0.8125 | 0.9014 |
| 0.4243 | 5.6818 | 500 | 0.7945 | 0.6246 | 0.7945 | 0.8914 |
| 0.4243 | 5.7045 | 502 | 0.7790 | 0.6178 | 0.7790 | 0.8826 |
| 0.4243 | 5.7273 | 504 | 0.7719 | 0.6118 | 0.7719 | 0.8786 |
| 0.4243 | 5.75 | 506 | 0.7595 | 0.6061 | 0.7595 | 0.8715 |
| 0.4243 | 5.7727 | 508 | 0.7688 | 0.5798 | 0.7688 | 0.8768 |
| 0.4243 | 5.7955 | 510 | 0.7932 | 0.5847 | 0.7932 | 0.8906 |
| 0.4243 | 5.8182 | 512 | 0.8209 | 0.5724 | 0.8209 | 0.9060 |
| 0.4243 | 5.8409 | 514 | 0.8495 | 0.6075 | 0.8495 | 0.9217 |
| 0.4243 | 5.8636 | 516 | 0.8626 | 0.6044 | 0.8626 | 0.9287 |
| 0.4243 | 5.8864 | 518 | 0.8431 | 0.6013 | 0.8431 | 0.9182 |
| 0.4243 | 5.9091 | 520 | 0.7967 | 0.6050 | 0.7967 | 0.8926 |
| 0.4243 | 5.9318 | 522 | 0.7600 | 0.5680 | 0.7600 | 0.8718 |
| 0.4243 | 5.9545 | 524 | 0.7482 | 0.5595 | 0.7482 | 0.8650 |
| 0.4243 | 5.9773 | 526 | 0.7447 | 0.5649 | 0.7447 | 0.8630 |
| 0.4243 | 6.0 | 528 | 0.7587 | 0.5972 | 0.7587 | 0.8710 |
| 0.4243 | 6.0227 | 530 | 0.7800 | 0.6184 | 0.7800 | 0.8832 |
| 0.4243 | 6.0455 | 532 | 0.7987 | 0.6184 | 0.7987 | 0.8937 |
| 0.4243 | 6.0682 | 534 | 0.8039 | 0.6109 | 0.8039 | 0.8966 |
| 0.4243 | 6.0909 | 536 | 0.8150 | 0.6020 | 0.8150 | 0.9028 |
| 0.4243 | 6.1136 | 538 | 0.7892 | 0.5677 | 0.7892 | 0.8884 |
| 0.4243 | 6.1364 | 540 | 0.7663 | 0.5428 | 0.7663 | 0.8754 |
| 0.4243 | 6.1591 | 542 | 0.7484 | 0.5699 | 0.7484 | 0.8651 |
| 0.4243 | 6.1818 | 544 | 0.7485 | 0.5781 | 0.7485 | 0.8652 |
| 0.4243 | 6.2045 | 546 | 0.7559 | 0.5800 | 0.7559 | 0.8694 |
| 0.4243 | 6.2273 | 548 | 0.7529 | 0.5844 | 0.7529 | 0.8677 |
| 0.4243 | 6.25 | 550 | 0.7541 | 0.5741 | 0.7541 | 0.8684 |
| 0.4243 | 6.2727 | 552 | 0.7700 | 0.5776 | 0.7700 | 0.8775 |
| 0.4243 | 6.2955 | 554 | 0.7909 | 0.5988 | 0.7909 | 0.8893 |
| 0.4243 | 6.3182 | 556 | 0.7916 | 0.6026 | 0.7916 | 0.8897 |
| 0.4243 | 6.3409 | 558 | 0.7927 | 0.6181 | 0.7927 | 0.8903 |
| 0.4243 | 6.3636 | 560 | 0.7844 | 0.5841 | 0.7844 | 0.8857 |
| 0.4243 | 6.3864 | 562 | 0.7642 | 0.5865 | 0.7642 | 0.8742 |
| 0.4243 | 6.4091 | 564 | 0.7586 | 0.5883 | 0.7586 | 0.8710 |
| 0.4243 | 6.4318 | 566 | 0.7760 | 0.5831 | 0.7760 | 0.8809 |
| 0.4243 | 6.4545 | 568 | 0.8067 | 0.5977 | 0.8067 | 0.8982 |
| 0.4243 | 6.4773 | 570 | 0.8468 | 0.5817 | 0.8468 | 0.9202 |
| 0.4243 | 6.5 | 572 | 0.9096 | 0.5589 | 0.9096 | 0.9537 |
| 0.4243 | 6.5227 | 574 | 0.9367 | 0.5824 | 0.9367 | 0.9678 |
| 0.4243 | 6.5455 | 576 | 0.9166 | 0.5949 | 0.9166 | 0.9574 |
| 0.4243 | 6.5682 | 578 | 0.8527 | 0.6009 | 0.8527 | 0.9234 |
| 0.4243 | 6.5909 | 580 | 0.7932 | 0.6126 | 0.7932 | 0.8906 |
| 0.4243 | 6.6136 | 582 | 0.7552 | 0.5936 | 0.7552 | 0.8690 |
| 0.4243 | 6.6364 | 584 | 0.7509 | 0.5657 | 0.7509 | 0.8665 |
| 0.4243 | 6.6591 | 586 | 0.7626 | 0.6114 | 0.7626 | 0.8733 |
| 0.4243 | 6.6818 | 588 | 0.7951 | 0.6136 | 0.7951 | 0.8917 |
| 0.4243 | 6.7045 | 590 | 0.8322 | 0.6021 | 0.8322 | 0.9122 |
| 0.4243 | 6.7273 | 592 | 0.8721 | 0.5832 | 0.8721 | 0.9339 |
| 0.4243 | 6.75 | 594 | 0.9063 | 0.5962 | 0.9063 | 0.9520 |
| 0.4243 | 6.7727 | 596 | 0.9052 | 0.5822 | 0.9052 | 0.9514 |
| 0.4243 | 6.7955 | 598 | 0.8691 | 0.5698 | 0.8691 | 0.9323 |
| 0.4243 | 6.8182 | 600 | 0.8150 | 0.5862 | 0.8150 | 0.9028 |
| 0.4243 | 6.8409 | 602 | 0.7727 | 0.5744 | 0.7727 | 0.8790 |
| 0.4243 | 6.8636 | 604 | 0.7507 | 0.5681 | 0.7507 | 0.8665 |
| 0.4243 | 6.8864 | 606 | 0.7425 | 0.5732 | 0.7425 | 0.8617 |
| 0.4243 | 6.9091 | 608 | 0.7429 | 0.5668 | 0.7429 | 0.8619 |
| 0.4243 | 6.9318 | 610 | 0.7378 | 0.5722 | 0.7378 | 0.8589 |
| 0.4243 | 6.9545 | 612 | 0.7520 | 0.5698 | 0.7520 | 0.8672 |
| 0.4243 | 6.9773 | 614 | 0.7689 | 0.6135 | 0.7689 | 0.8769 |
| 0.4243 | 7.0 | 616 | 0.7776 | 0.6125 | 0.7776 | 0.8818 |
| 0.4243 | 7.0227 | 618 | 0.7952 | 0.6028 | 0.7952 | 0.8917 |
| 0.4243 | 7.0455 | 620 | 0.7984 | 0.6091 | 0.7984 | 0.8935 |
| 0.4243 | 7.0682 | 622 | 0.7826 | 0.6171 | 0.7826 | 0.8846 |
| 0.4243 | 7.0909 | 624 | 0.7715 | 0.6257 | 0.7715 | 0.8783 |
| 0.4243 | 7.1136 | 626 | 0.7739 | 0.6283 | 0.7739 | 0.8797 |
| 0.4243 | 7.1364 | 628 | 0.8043 | 0.6338 | 0.8043 | 0.8968 |
| 0.4243 | 7.1591 | 630 | 0.8461 | 0.6430 | 0.8461 | 0.9198 |
| 0.4243 | 7.1818 | 632 | 0.8513 | 0.6498 | 0.8513 | 0.9226 |
| 0.4243 | 7.2045 | 634 | 0.8233 | 0.6444 | 0.8233 | 0.9073 |
| 0.4243 | 7.2273 | 636 | 0.8110 | 0.6489 | 0.8110 | 0.9005 |
| 0.4243 | 7.25 | 638 | 0.7895 | 0.6414 | 0.7895 | 0.8886 |
| 0.4243 | 7.2727 | 640 | 0.7687 | 0.6513 | 0.7687 | 0.8767 |
| 0.4243 | 7.2955 | 642 | 0.7467 | 0.6598 | 0.7467 | 0.8641 |
| 0.4243 | 7.3182 | 644 | 0.7315 | 0.6530 | 0.7315 | 0.8553 |
| 0.4243 | 7.3409 | 646 | 0.7314 | 0.6468 | 0.7314 | 0.8552 |
| 0.4243 | 7.3636 | 648 | 0.7430 | 0.6468 | 0.7430 | 0.8620 |
| 0.4243 | 7.3864 | 650 | 0.7801 | 0.6471 | 0.7801 | 0.8832 |
| 0.4243 | 7.4091 | 652 | 0.8371 | 0.6444 | 0.8371 | 0.9149 |
| 0.4243 | 7.4318 | 654 | 0.8929 | 0.6207 | 0.8929 | 0.9449 |
| 0.4243 | 7.4545 | 656 | 0.9030 | 0.6207 | 0.9030 | 0.9503 |
| 0.4243 | 7.4773 | 658 | 0.8800 | 0.6137 | 0.8800 | 0.9381 |
| 0.4243 | 7.5 | 660 | 0.8543 | 0.6276 | 0.8543 | 0.9243 |
| 0.4243 | 7.5227 | 662 | 0.8249 | 0.6299 | 0.8249 | 0.9083 |
| 0.4243 | 7.5455 | 664 | 0.8101 | 0.6282 | 0.8101 | 0.9001 |
| 0.4243 | 7.5682 | 666 | 0.8135 | 0.6234 | 0.8135 | 0.9020 |
| 0.4243 | 7.5909 | 668 | 0.8248 | 0.6186 | 0.8248 | 0.9082 |
| 0.4243 | 7.6136 | 670 | 0.8226 | 0.6186 | 0.8226 | 0.9070 |
| 0.4243 | 7.6364 | 672 | 0.8463 | 0.6104 | 0.8463 | 0.9200 |
| 0.4243 | 7.6591 | 674 | 0.8469 | 0.6104 | 0.8469 | 0.9203 |
| 0.4243 | 7.6818 | 676 | 0.8269 | 0.6059 | 0.8269 | 0.9093 |
| 0.4243 | 7.7045 | 678 | 0.8234 | 0.6059 | 0.8234 | 0.9074 |
| 0.4243 | 7.7273 | 680 | 0.8111 | 0.6006 | 0.8111 | 0.9006 |
| 0.4243 | 7.75 | 682 | 0.8053 | 0.6213 | 0.8053 | 0.8974 |
| 0.4243 | 7.7727 | 684 | 0.8003 | 0.6263 | 0.8003 | 0.8946 |
| 0.4243 | 7.7955 | 686 | 0.7967 | 0.6263 | 0.7967 | 0.8926 |
| 0.4243 | 7.8182 | 688 | 0.7919 | 0.6263 | 0.7919 | 0.8899 |
| 0.4243 | 7.8409 | 690 | 0.8011 | 0.6142 | 0.8011 | 0.8950 |
| 0.4243 | 7.8636 | 692 | 0.8076 | 0.6142 | 0.8076 | 0.8987 |
| 0.4243 | 7.8864 | 694 | 0.8271 | 0.6225 | 0.8271 | 0.9095 |
| 0.4243 | 7.9091 | 696 | 0.8340 | 0.6336 | 0.8340 | 0.9132 |
| 0.4243 | 7.9318 | 698 | 0.8333 | 0.6264 | 0.8333 | 0.9129 |
| 0.4243 | 7.9545 | 700 | 0.8306 | 0.6264 | 0.8306 | 0.9114 |
| 0.4243 | 7.9773 | 702 | 0.8144 | 0.6305 | 0.8144 | 0.9024 |
| 0.4243 | 8.0 | 704 | 0.8037 | 0.6096 | 0.8037 | 0.8965 |
| 0.4243 | 8.0227 | 706 | 0.7985 | 0.6347 | 0.7985 | 0.8936 |
| 0.4243 | 8.0455 | 708 | 0.7985 | 0.6271 | 0.7985 | 0.8936 |
| 0.4243 | 8.0682 | 710 | 0.7974 | 0.6347 | 0.7974 | 0.8930 |
| 0.4243 | 8.0909 | 712 | 0.7933 | 0.6254 | 0.7933 | 0.8906 |
| 0.4243 | 8.1136 | 714 | 0.7975 | 0.6279 | 0.7975 | 0.8930 |
| 0.4243 | 8.1364 | 716 | 0.8014 | 0.6371 | 0.8014 | 0.8952 |
| 0.4243 | 8.1591 | 718 | 0.8212 | 0.6147 | 0.8212 | 0.9062 |
| 0.4243 | 8.1818 | 720 | 0.8361 | 0.6222 | 0.8361 | 0.9144 |
| 0.4243 | 8.2045 | 722 | 0.8488 | 0.6423 | 0.8488 | 0.9213 |
| 0.4243 | 8.2273 | 724 | 0.8652 | 0.6225 | 0.8652 | 0.9301 |
| 0.4243 | 8.25 | 726 | 0.8622 | 0.6225 | 0.8622 | 0.9285 |
| 0.4243 | 8.2727 | 728 | 0.8443 | 0.6207 | 0.8443 | 0.9189 |
| 0.4243 | 8.2955 | 730 | 0.8108 | 0.6296 | 0.8108 | 0.9005 |
| 0.4243 | 8.3182 | 732 | 0.7741 | 0.6193 | 0.7741 | 0.8798 |
| 0.4243 | 8.3409 | 734 | 0.7526 | 0.6213 | 0.7526 | 0.8675 |
| 0.4243 | 8.3636 | 736 | 0.7469 | 0.6187 | 0.7469 | 0.8643 |
| 0.4243 | 8.3864 | 738 | 0.7478 | 0.6125 | 0.7478 | 0.8647 |
| 0.4243 | 8.4091 | 740 | 0.7543 | 0.6109 | 0.7543 | 0.8685 |
| 0.4243 | 8.4318 | 742 | 0.7626 | 0.6280 | 0.7626 | 0.8733 |
| 0.4243 | 8.4545 | 744 | 0.7815 | 0.6347 | 0.7815 | 0.8840 |
| 0.4243 | 8.4773 | 746 | 0.8052 | 0.6338 | 0.8052 | 0.8973 |
| 0.4243 | 8.5 | 748 | 0.8179 | 0.6411 | 0.8179 | 0.9044 |
| 0.4243 | 8.5227 | 750 | 0.8327 | 0.6231 | 0.8327 | 0.9125 |
| 0.4243 | 8.5455 | 752 | 0.8407 | 0.6201 | 0.8407 | 0.9169 |
| 0.4243 | 8.5682 | 754 | 0.8513 | 0.6046 | 0.8513 | 0.9226 |
| 0.4243 | 8.5909 | 756 | 0.8691 | 0.6125 | 0.8691 | 0.9323 |
| 0.4243 | 8.6136 | 758 | 0.8774 | 0.6196 | 0.8774 | 0.9367 |
| 0.4243 | 8.6364 | 760 | 0.8984 | 0.6233 | 0.8984 | 0.9478 |
| 0.4243 | 8.6591 | 762 | 0.9071 | 0.6233 | 0.9071 | 0.9524 |
| 0.4243 | 8.6818 | 764 | 0.9015 | 0.6233 | 0.9015 | 0.9495 |
| 0.4243 | 8.7045 | 766 | 0.8803 | 0.6196 | 0.8803 | 0.9382 |
| 0.4243 | 8.7273 | 768 | 0.8634 | 0.6219 | 0.8634 | 0.9292 |
| 0.4243 | 8.75 | 770 | 0.8443 | 0.6289 | 0.8443 | 0.9189 |
| 0.4243 | 8.7727 | 772 | 0.8380 | 0.6476 | 0.8380 | 0.9154 |
| 0.4243 | 8.7955 | 774 | 0.8276 | 0.6493 | 0.8276 | 0.9097 |
| 0.4243 | 8.8182 | 776 | 0.8322 | 0.6476 | 0.8322 | 0.9122 |
| 0.4243 | 8.8409 | 778 | 0.8406 | 0.6544 | 0.8406 | 0.9169 |
| 0.4243 | 8.8636 | 780 | 0.8351 | 0.6544 | 0.8351 | 0.9138 |
| 0.4243 | 8.8864 | 782 | 0.8267 | 0.6607 | 0.8267 | 0.9092 |
| 0.4243 | 8.9091 | 784 | 0.8274 | 0.6607 | 0.8274 | 0.9096 |
| 0.4243 | 8.9318 | 786 | 0.8290 | 0.6476 | 0.8290 | 0.9105 |
| 0.4243 | 8.9545 | 788 | 0.8339 | 0.6446 | 0.8339 | 0.9132 |
| 0.4243 | 8.9773 | 790 | 0.8449 | 0.6320 | 0.8449 | 0.9192 |
| 0.4243 | 9.0 | 792 | 0.8680 | 0.6158 | 0.8680 | 0.9316 |
| 0.4243 | 9.0227 | 794 | 0.8876 | 0.6196 | 0.8876 | 0.9421 |
| 0.4243 | 9.0455 | 796 | 0.8960 | 0.6196 | 0.8960 | 0.9466 |
| 0.4243 | 9.0682 | 798 | 0.9075 | 0.6233 | 0.9075 | 0.9526 |
| 0.4243 | 9.0909 | 800 | 0.9084 | 0.6233 | 0.9084 | 0.9531 |
| 0.4243 | 9.1136 | 802 | 0.9115 | 0.6233 | 0.9115 | 0.9547 |
| 0.4243 | 9.1364 | 804 | 0.9122 | 0.6163 | 0.9122 | 0.9551 |
| 0.4243 | 9.1591 | 806 | 0.9145 | 0.6058 | 0.9145 | 0.9563 |
| 0.4243 | 9.1818 | 808 | 0.9220 | 0.6058 | 0.9220 | 0.9602 |
| 0.4243 | 9.2045 | 810 | 0.9185 | 0.6058 | 0.9185 | 0.9584 |
| 0.4243 | 9.2273 | 812 | 0.9091 | 0.6058 | 0.9091 | 0.9534 |
| 0.4243 | 9.25 | 814 | 0.8922 | 0.6159 | 0.8922 | 0.9446 |
| 0.4243 | 9.2727 | 816 | 0.8776 | 0.6134 | 0.8776 | 0.9368 |
| 0.4243 | 9.2955 | 818 | 0.8573 | 0.5995 | 0.8573 | 0.9259 |
| 0.4243 | 9.3182 | 820 | 0.8352 | 0.6009 | 0.8352 | 0.9139 |
| 0.4243 | 9.3409 | 822 | 0.8204 | 0.5998 | 0.8204 | 0.9058 |
| 0.4243 | 9.3636 | 824 | 0.8091 | 0.6050 | 0.8091 | 0.8995 |
| 0.4243 | 9.3864 | 826 | 0.8039 | 0.6050 | 0.8039 | 0.8966 |
| 0.4243 | 9.4091 | 828 | 0.8052 | 0.6050 | 0.8052 | 0.8973 |
| 0.4243 | 9.4318 | 830 | 0.8097 | 0.6050 | 0.8097 | 0.8998 |
| 0.4243 | 9.4545 | 832 | 0.8168 | 0.5908 | 0.8168 | 0.9038 |
| 0.4243 | 9.4773 | 834 | 0.8237 | 0.5908 | 0.8237 | 0.9076 |
| 0.4243 | 9.5 | 836 | 0.8278 | 0.5908 | 0.8278 | 0.9098 |
| 0.4243 | 9.5227 | 838 | 0.8310 | 0.5801 | 0.8310 | 0.9116 |
| 0.4243 | 9.5455 | 840 | 0.8381 | 0.5776 | 0.8381 | 0.9155 |
| 0.4243 | 9.5682 | 842 | 0.8488 | 0.6009 | 0.8488 | 0.9213 |
| 0.4243 | 9.5909 | 844 | 0.8619 | 0.5995 | 0.8619 | 0.9284 |
| 0.4243 | 9.6136 | 846 | 0.8694 | 0.6035 | 0.8694 | 0.9324 |
| 0.4243 | 9.6364 | 848 | 0.8780 | 0.6100 | 0.8780 | 0.9370 |
| 0.4243 | 9.6591 | 850 | 0.8812 | 0.6100 | 0.8812 | 0.9387 |
| 0.4243 | 9.6818 | 852 | 0.8795 | 0.6100 | 0.8795 | 0.9378 |
| 0.4243 | 9.7045 | 854 | 0.8758 | 0.6149 | 0.8758 | 0.9358 |
| 0.4243 | 9.7273 | 856 | 0.8694 | 0.6109 | 0.8694 | 0.9324 |
| 0.4243 | 9.75 | 858 | 0.8636 | 0.6035 | 0.8636 | 0.9293 |
| 0.4243 | 9.7727 | 860 | 0.8585 | 0.5995 | 0.8585 | 0.9266 |
| 0.4243 | 9.7955 | 862 | 0.8538 | 0.6009 | 0.8538 | 0.9240 |
| 0.4243 | 9.8182 | 864 | 0.8511 | 0.6009 | 0.8511 | 0.9225 |
| 0.4243 | 9.8409 | 866 | 0.8497 | 0.6009 | 0.8497 | 0.9218 |
| 0.4243 | 9.8636 | 868 | 0.8478 | 0.5933 | 0.8478 | 0.9208 |
| 0.4243 | 9.8864 | 870 | 0.8459 | 0.5855 | 0.8459 | 0.9197 |
| 0.4243 | 9.9091 | 872 | 0.8452 | 0.5855 | 0.8452 | 0.9193 |
| 0.4243 | 9.9318 | 874 | 0.8441 | 0.5855 | 0.8441 | 0.9188 |
| 0.4243 | 9.9545 | 876 | 0.8432 | 0.5855 | 0.8432 | 0.9182 |
| 0.4243 | 9.9773 | 878 | 0.8430 | 0.5855 | 0.8430 | 0.9181 |
| 0.4243 | 10.0 | 880 | 0.8429 | 0.5855 | 0.8429 | 0.9181 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu118
- Datasets 2.21.0
- Tokenizers 0.19.1
|
kimyc02/llmsft
|
kimyc02
| 2024-12-16T06:30:13Z | 139 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-12-16T02:35:52Z |
---
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 π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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|
wangjiarui/Llama-3.2-11B-Vision-Instruct
|
wangjiarui
| 2024-12-16T06:29:54Z | 15 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mllama",
"image-text-to-text",
"facebook",
"meta",
"pytorch",
"llama",
"llama-3",
"conversational",
"en",
"de",
"fr",
"it",
"pt",
"hi",
"es",
"th",
"arxiv:2204.05149",
"license:llama3.2",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2024-12-14T15:46:28Z |
---
language:
- en
- de
- fr
- it
- pt
- hi
- es
- th
library_name: transformers
pipeline_tag: image-text-to-text
tags:
- facebook
- meta
- pytorch
- llama
- llama-3
license: llama3.2
extra_gated_prompt: >-
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* Reporting issues with the model: [https://github.com/meta-llama/llama-models/issues](https://l.workplace.com/l.php?u=https%3A%2F%2Fgithub.com%2Fmeta-llama%2Fllama-models%2Fissues&h=AT0qV8W9BFT6NwihiOHRuKYQM_UnkzN_NmHMy91OT55gkLpgi4kQupHUl0ssR4dQsIQ8n3tfd0vtkobvsEvt1l4Ic6GXI2EeuHV8N08OG2WnbAmm0FL4ObkazC6G_256vN0lN9DsykCvCqGZ)
* Reporting risky content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback)
* Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info)
* Reporting violations of the Acceptable Use Policy or unlicensed uses of Llama 3.2: [email protected]
extra_gated_fields:
First Name: text
Last Name: text
Date of birth: date_picker
Country: country
Affiliation: text
Job title:
type: select
options:
- Student
- Research Graduate
- AI researcher
- AI developer/engineer
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geo: ip_location
By clicking Submit below I accept the terms of the license and acknowledge that the information I provide will be collected stored processed and shared in accordance with the Meta Privacy Policy: checkbox
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---
## Model Information
The Llama 3.2-Vision collection of multimodal large language models (LLMs) is a collection of pretrained and instruction-tuned image reasoning generative models in 11B and 90B sizes (text \+ images in / text out). The Llama 3.2-Vision instruction-tuned models are optimized for visual recognition, image reasoning, captioning, and answering general questions about an image. The models outperform many of the available open source and closed multimodal models on common industry benchmarks.
**Model Developer**: Meta
**Model Architecture:** Llama 3.2-Vision is built on top of Llama 3.1 text-only model, which 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. To support image recognition tasks, the Llama 3.2-Vision model uses a separately trained vision adapter that integrates with the pre-trained Llama 3.1 language model. The adapter consists of a series of cross-attention layers that feed image encoder representations into the core LLM.
| | Training Data | Params | Input modalities | Output modalities | Context length | GQA | Data volume | Knowledge cutoff |
| :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- |
| Llama 3.2-Vision | (Image, text) pairs | 11B (10.6) | Text \+ Image | Text | 128k | Yes | 6B (image, text) pairs | December 2023 |
| Llama 3.2-Vision | (Image, text) pairs | 90B (88.8) | Text \+ Image | Text | 128k | Yes | 6B (image, text) pairs | December 2023 |
**Supported Languages:** For text only tasks, English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai are officially supported. Llama 3.2 has been trained on a broader collection of languages than these 8 supported languages. Note for image+text applications, English is the only language supported.
Developers may fine-tune Llama 3.2 models for languages beyond these supported languages, provided they comply with the Llama 3.2 Community License and the Acceptable Use Policy. Developers are always expected to ensure that their deployments, including those that involve additional languages, are completed safely and responsibly.
**Llama 3.2 Model Family:** Token counts refer to pretraining data only. All model versions use Grouped-Query Attention (GQA) for improved inference scalability.
**Model Release Date:** Sept 25, 2024
**Status:** This is a static model trained on an offline dataset. Future versions may be released that improve model capabilities and safety.
**License:** Use of Llama 3.2 is governed by the [Llama 3.2 Community License](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/LICENSE) (a custom, commercial license agreement).
**Feedback:** 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/llama-models/tree/main/models/llama3_2). For more technical information about generation parameters and recipes for how to use Llama 3.2-Vision in applications, please go [here](https://github.com/meta-llama/llama-recipes).
## Intended Use
**Intended Use Cases:** Llama 3.2-Vision is intended for commercial and research use. Instruction tuned models are intended for visual recognition, image reasoning, captioning, and assistant-like chat with images, whereas pretrained models can be adapted for a variety of image reasoning tasks. Additionally, because of Llama 3.2-Visionβs ability to take images and text as inputs, additional use cases could include:
1. Visual Question Answering (VQA) and Visual Reasoning: Imagine a machine that looks at a picture and understands your questions about it.
2. Document Visual Question Answering (DocVQA): Imagine a computer understanding both the text and layout of a document, like a map or contract, and then answering questions about it directly from the image.
3. Image Captioning: Image captioning bridges the gap between vision and language, extracting details, understanding the scene, and then crafting a sentence or two that tells the story.
4. Image-Text Retrieval: Image-text retrieval is like a matchmaker for images and their descriptions. Similar to a search engine but one that understands both pictures and words.
5. Visual Grounding: Visual grounding is like connecting the dots between what we see and say. Itβs about understanding how language references specific parts of an image, allowing AI models to pinpoint objects or regions based on natural language descriptions.
The Llama 3.2 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 3.2 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 3.2 Community License. Use in languages beyond those explicitly referenced as supported in this model card.
## How to use
This repository contains two versions of Llama-3.2-11B-Vision-Instruct, for use with transformers and with the original `llama` codebase.
### Use with transformers
Starting with transformers >= 4.45.0 onward, you can run inference using conversational messages that may include an image you can query about.
Make sure to update your transformers installation via `pip install --upgrade transformers`.
```python
import requests
import torch
from PIL import Image
from transformers import MllamaForConditionalGeneration, AutoProcessor
from modelscope import snapshot_download
model_id = "LLM-Research/Llama-3.2-11B-Vision-Instruct"
model_dir = snapshot_download(model_id, ignore_file_pattern=['*.pth'])
model = MllamaForConditionalGeneration.from_pretrained(
model_dir,
torch_dtype=torch.bfloat16,
device_map="auto",
)
processor = AutoProcessor.from_pretrained(model_dir)
url = "https://www.modelscope.cn/models/LLM-Research/Llama-3.2-11B-Vision/resolve/master/rabbit.jpg"
image = Image.open(requests.get(url, stream=True).raw)
messages = [
{"role": "user", "content": [
{"type": "image"},
{"type": "text", "text": "If I had to write a haiku for this one, it would be: "}
]}
]
input_text = processor.apply_chat_template(messages, add_generation_prompt=True)
inputs = processor(image, input_text, return_tensors="pt").to(model.device)
output = model.generate(**inputs, max_new_tokens=30)
print(processor.decode(output[0]))
```
### Use with `llama`
Please, follow the instructions in the [repository](https://github.com/meta-llama/llama).
To download the original checkpoints, you can use `modelscope cli` as follows:
```
modelscope download --model LLM-Research/Llama-3.2-11B-Vision-Instruct --include "original/*" --local_dir Llama-3.2-11B-Vision-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 Energy Use:** Training utilized a cumulative of **2.02M** 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 **584** 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.
| | Training Time (GPU hours) | Training Power Consumption (W) | Training Location-Based Greenhouse Gas Emissions (tons CO2eq) | Training Market-Based Greenhouse Gas Emissions (tons CO2eq) |
| :---- | :---: | :---: | :---: | :---: |
| Llama 3.2-vision 11B | Stage 1 pretraining: 147K H100 hours Stage 2 annealing: 98K H100 hours SFT: 896 H100 hours RLHF: 224 H100 hours | 700 | 71 | 0 |
| Llama 3.2-vision 90B | Stage 1 pretraining: 885K H100 hours Stage 2 annealing: 885K H100 hours SFT: 3072 H100 hours RLHF: 2048 H100 hours | 700 | 513 | 0 |
| Total | 2.02M | | 584 | 0 |
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 3.2-Vision was pretrained on 6B image and text pairs. The instruction tuning data includes publicly available vision instruction datasets, as well as over 3M synthetically generated examples.
**Data Freshness:** The pretraining data has a cutoff of December 2023\.
## Benchmarks \- Image Reasoning
In this section, we report the results for Llama 3.2-Vision models on standard automatic benchmarks. For all these evaluations, we used our internal evaluations library.
### Base Pretrained Models
| Category | Benchmark | \# Shots | Metric | Llama 3.2 11B | Llama 3.2 90B |
| ----- | ----- | ----- | ----- | ----- | ----- |
| Image Understanding | VQAv2 (val) | 0 | Accuracy | 66.8 | 73.6 |
| | Text VQA (val) | 0 | Relaxed accuracy | 73.1 | 73.5 |
| | DocVQA (val, unseen) | 0 | ANLS | 62.3 | 70.7 |
| Visual Reasoning | MMMU (val, 0-shot) | 0 | Micro average accuracy | 41.7 | 49.3 |
| | ChartQA (test) | 0 | Accuracy | 39.4 | 54.2 |
| | InfographicsQA (val, unseen) | 0 | ANLS | 43.2 | 56.8 |
| | AI2 Diagram (test) | 0 | Accuracy | 62.4 | 75.3 |
### Instruction Tuned Models
| Modality | Capability | Benchmark | \# Shots | Metric | Llama 3.2 11B | Llama 3.2 90B |
| ----- | :---: | ----- | :---: | :---: | ----- | ----- |
| Image | College-level Problems and Mathematical Reasoning | MMMU (val, CoT) | 0 | Micro average accuracy | 50.7 | 60.3 |
| | | MMMU-Pro, Standard (10 opts, test) | 0 | Accuracy | 33.0 | 45.2 |
| | | MMMU-Pro, Vision (test) | 0 | Accuracy | 23.7 | 33.8 |
| | | MathVista (testmini) | 0 | Accuracy | 51.5 | 57.3 |
| | Charts and Diagram Understanding | ChartQA (test, CoT) | 0 | Relaxed accuracy | 83.4 | 85.5 |
| | | AI2 Diagram (test) | 0 | Accuracy | 91.1 | 92.3 |
| | | DocVQA (test) | 0 | ANLS | 88.4 | 90.1 |
| | General Visual Question Answering | VQAv2 (test) | 0 | Accuracy | 75.2 | 78.1 |
| | | | | | | |
| Text | General | MMLU (CoT) | 0 | Macro\_avg/acc | 73.0 | 86.0 |
| | Math | MATH (CoT) | 0 | Final\_em | 51.9 | 68.0 |
| | Reasoning | GPQA | 0 | Accuracy | 32.8 | 46.7 |
| | Multilingual | MGSM (CoT) | 0 | em | 68.9 | 86.9 |
## Responsibility & Safety
As part of our Responsible release approach, we followed a three-pronged strategy to managing trust & safety risks:
1. Enable developers to deploy helpful, safe and flexible experiences for their target audience and for the use cases supported by Llama.
2. Protect developers against adversarial users aiming to exploit Llama capabilities to potentially cause harm.
3. Provide protections for the community to help prevent the misuse of our models.
### Responsible Deployment
**Approach:** Llama is a foundational technology designed to be used in a variety of use cases, examples on how Metaβs 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 3.2 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 3.2 Instruct
**Objective:** 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. We implemented the same set of safety mitigations as in Llama 3, and you can learn more about these in the Llama 3 [paper](https://ai.meta.com/research/publications/the-llama-3-herd-of-models/).
**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. Weβve 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 3.2 Systems
**Safety as a System:** Large language models, including Llama 3.2, **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, 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 and Use Cases
**Technological Advancement:** Llama releases usually introduce new capabilities that require specific considerations in addition to the best practices that generally apply across all Generative AI use cases. For prior release capabilities also supported by Llama 3.2, see [Llama 3.1 Model Card](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/MODEL_CARD.md), as the same considerations apply here as well.,
**Image Reasoning:** Llama 3.2-Vision models come with multimodal (text and image) input capabilities enabling image reasoning applications. As part of our responsible release process, we took dedicated measures including evaluations and mitigations to address the risk of the models uniquely identifying individuals in images. As with other LLM risks, models may not always be robust to adversarial prompts, and developers should evaluate identification and other applicable risks in the context of their applications as well as consider deploying Llama Guard 3-11B-Vision as part of their system or other mitigations as appropriate to detect and mitigate such risks.
### Evaluations
**Scaled Evaluations:** We built dedicated, adversarial evaluation datasets and evaluated systems composed of Llama models and Purple Llama safeguards 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.
**Red teaming:** 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 Risks
In addition to our safety work above, we took extra care on measuring and/or mitigating the following critical risk areas:
**1\. CBRNE (Chemical, Biological, Radiological, Nuclear, and Explosive Weapons):** For Llama 3.1, 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. For Llama 3.2-Vision models, we conducted additional targeted evaluations and found that it was unlikely Llama 3.2 presented an increase in scientific capabilities due to its added image understanding capability as compared to Llama 3.1.
**2\. Child Safety:** Child Safety risk assessments were conducted using a team of experts, to assess the modelβs 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 Attacks:** For Llama 3.1 405B, 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. Because Llama 3.2βs vision capabilities are not generally germane to cyber uplift, we believe that the testing conducted for Llama 3.1 also applies to Llama 3.2.
### Community
**Industry Partnerships:** 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).
**Grants:** 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 Metaβs 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).
**Reporting:** 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
**Values:** The core values of Llama 3.2 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 3.2 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.
**Testing:** But Llama 3.2 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 3.2βs 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 3.2 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.
|
mradermacher/Fimbulvetr-Holodeck-Erebus-Westlake-10.7B-GGUF
|
mradermacher
| 2024-12-16T06:27:20Z | 66 | 1 |
transformers
|
[
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:backyardai/Fimbulvetr-Holodeck-Erebus-Westlake-10.7B",
"base_model:quantized:backyardai/Fimbulvetr-Holodeck-Erebus-Westlake-10.7B",
"endpoints_compatible",
"region:us"
] | null | 2024-12-16T03:01:27Z |
---
base_model: backyardai/Fimbulvetr-Holodeck-Erebus-Westlake-10.7B
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/backyardai/Fimbulvetr-Holodeck-Erebus-Westlake-10.7B
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Fimbulvetr-Holodeck-Erebus-Westlake-10.7B-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Fimbulvetr-Holodeck-Erebus-Westlake-10.7B-GGUF/resolve/main/Fimbulvetr-Holodeck-Erebus-Westlake-10.7B.Q2_K.gguf) | Q2_K | 4.1 | |
| [GGUF](https://huggingface.co/mradermacher/Fimbulvetr-Holodeck-Erebus-Westlake-10.7B-GGUF/resolve/main/Fimbulvetr-Holodeck-Erebus-Westlake-10.7B.Q3_K_S.gguf) | Q3_K_S | 4.8 | |
| [GGUF](https://huggingface.co/mradermacher/Fimbulvetr-Holodeck-Erebus-Westlake-10.7B-GGUF/resolve/main/Fimbulvetr-Holodeck-Erebus-Westlake-10.7B.Q3_K_M.gguf) | Q3_K_M | 5.3 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Fimbulvetr-Holodeck-Erebus-Westlake-10.7B-GGUF/resolve/main/Fimbulvetr-Holodeck-Erebus-Westlake-10.7B.Q3_K_L.gguf) | Q3_K_L | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/Fimbulvetr-Holodeck-Erebus-Westlake-10.7B-GGUF/resolve/main/Fimbulvetr-Holodeck-Erebus-Westlake-10.7B.IQ4_XS.gguf) | IQ4_XS | 5.9 | |
| [GGUF](https://huggingface.co/mradermacher/Fimbulvetr-Holodeck-Erebus-Westlake-10.7B-GGUF/resolve/main/Fimbulvetr-Holodeck-Erebus-Westlake-10.7B.Q4_K_S.gguf) | Q4_K_S | 6.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Fimbulvetr-Holodeck-Erebus-Westlake-10.7B-GGUF/resolve/main/Fimbulvetr-Holodeck-Erebus-Westlake-10.7B.Q4_K_M.gguf) | Q4_K_M | 6.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Fimbulvetr-Holodeck-Erebus-Westlake-10.7B-GGUF/resolve/main/Fimbulvetr-Holodeck-Erebus-Westlake-10.7B.Q5_K_S.gguf) | Q5_K_S | 7.5 | |
| [GGUF](https://huggingface.co/mradermacher/Fimbulvetr-Holodeck-Erebus-Westlake-10.7B-GGUF/resolve/main/Fimbulvetr-Holodeck-Erebus-Westlake-10.7B.Q5_K_M.gguf) | Q5_K_M | 7.7 | |
| [GGUF](https://huggingface.co/mradermacher/Fimbulvetr-Holodeck-Erebus-Westlake-10.7B-GGUF/resolve/main/Fimbulvetr-Holodeck-Erebus-Westlake-10.7B.Q6_K.gguf) | Q6_K | 8.9 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Fimbulvetr-Holodeck-Erebus-Westlake-10.7B-GGUF/resolve/main/Fimbulvetr-Holodeck-Erebus-Westlake-10.7B.Q8_0.gguf) | Q8_0 | 11.5 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Fimbulvetr-Holodeck-Erebus-Westlake-10.7B-GGUF/resolve/main/Fimbulvetr-Holodeck-Erebus-Westlake-10.7B.f16.gguf) | f16 | 21.6 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
fadelfitrah/Mistrall-Python-Codegent
|
fadelfitrah
| 2024-12-16T06:16:41Z | 74 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2024-12-02T03:37:36Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
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|
MedCat/MedCAT-PT-BioGPT-Large-v1-cosine_lr-checkpoint-260000
|
MedCat
| 2024-12-16T06:14:24Z | 92 | 0 |
transformers
|
[
"transformers",
"safetensors",
"biogpt",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-12-16T06:04:02Z |
---
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 π€ 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
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[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).
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|
MayBashendy/ArabicNewSplits5_FineTuningAraBERT_run3_AugV5_k13_task1_organization
|
MayBashendy
| 2024-12-16T06:13:53Z | 182 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:aubmindlab/bert-base-arabertv02",
"base_model:finetune:aubmindlab/bert-base-arabertv02",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-12-16T05:56:02Z |
---
library_name: transformers
base_model: aubmindlab/bert-base-arabertv02
tags:
- generated_from_trainer
model-index:
- name: ArabicNewSplits5_FineTuningAraBERT_run3_AugV5_k13_task1_organization
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. -->
# ArabicNewSplits5_FineTuningAraBERT_run3_AugV5_k13_task1_organization
This model is a fine-tuned version of [aubmindlab/bert-base-arabertv02](https://huggingface.co/aubmindlab/bert-base-arabertv02) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5852
- Qwk: 0.7396
- Mse: 0.5852
- Rmse: 0.7650
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Qwk | Mse | Rmse |
|:-------------:|:------:|:----:|:---------------:|:-------:|:------:|:------:|
| No log | 0.0244 | 2 | 5.4091 | -0.0111 | 5.4091 | 2.3257 |
| No log | 0.0488 | 4 | 3.6820 | 0.0425 | 3.6820 | 1.9189 |
| No log | 0.0732 | 6 | 2.3777 | 0.0304 | 2.3777 | 1.5420 |
| No log | 0.0976 | 8 | 2.2442 | -0.0939 | 2.2442 | 1.4981 |
| No log | 0.1220 | 10 | 1.9541 | -0.0040 | 1.9541 | 1.3979 |
| No log | 0.1463 | 12 | 1.5813 | 0.0800 | 1.5813 | 1.2575 |
| No log | 0.1707 | 14 | 1.5360 | 0.0359 | 1.5360 | 1.2394 |
| No log | 0.1951 | 16 | 1.6016 | 0.0085 | 1.6016 | 1.2655 |
| No log | 0.2195 | 18 | 1.4532 | 0.0329 | 1.4532 | 1.2055 |
| No log | 0.2439 | 20 | 1.4724 | 0.0500 | 1.4724 | 1.2134 |
| No log | 0.2683 | 22 | 1.4271 | 0.0720 | 1.4271 | 1.1946 |
| No log | 0.2927 | 24 | 1.4113 | 0.0824 | 1.4113 | 1.1880 |
| No log | 0.3171 | 26 | 1.2311 | 0.3053 | 1.2311 | 1.1096 |
| No log | 0.3415 | 28 | 1.0737 | 0.3834 | 1.0737 | 1.0362 |
| No log | 0.3659 | 30 | 1.0641 | 0.3956 | 1.0641 | 1.0315 |
| No log | 0.3902 | 32 | 1.2699 | 0.2278 | 1.2699 | 1.1269 |
| No log | 0.4146 | 34 | 1.6243 | 0.0892 | 1.6243 | 1.2745 |
| No log | 0.4390 | 36 | 2.1665 | 0.1591 | 2.1665 | 1.4719 |
| No log | 0.4634 | 38 | 2.4553 | 0.1854 | 2.4553 | 1.5669 |
| No log | 0.4878 | 40 | 1.9220 | 0.2606 | 1.9220 | 1.3863 |
| No log | 0.5122 | 42 | 1.4810 | 0.3097 | 1.4810 | 1.2170 |
| No log | 0.5366 | 44 | 1.4959 | 0.25 | 1.4959 | 1.2231 |
| No log | 0.5610 | 46 | 1.5674 | 0.2967 | 1.5674 | 1.2519 |
| No log | 0.5854 | 48 | 1.9688 | 0.2696 | 1.9688 | 1.4032 |
| No log | 0.6098 | 50 | 1.8817 | 0.2938 | 1.8817 | 1.3717 |
| No log | 0.6341 | 52 | 1.4417 | 0.3228 | 1.4417 | 1.2007 |
| No log | 0.6585 | 54 | 1.1867 | 0.3461 | 1.1867 | 1.0894 |
| No log | 0.6829 | 56 | 1.0194 | 0.4670 | 1.0194 | 1.0097 |
| No log | 0.7073 | 58 | 0.9414 | 0.4405 | 0.9414 | 0.9703 |
| No log | 0.7317 | 60 | 1.0233 | 0.4817 | 1.0233 | 1.0116 |
| No log | 0.7561 | 62 | 1.1638 | 0.4240 | 1.1638 | 1.0788 |
| No log | 0.7805 | 64 | 1.3070 | 0.4069 | 1.3070 | 1.1432 |
| No log | 0.8049 | 66 | 1.1232 | 0.4655 | 1.1232 | 1.0598 |
| No log | 0.8293 | 68 | 1.1243 | 0.4716 | 1.1243 | 1.0603 |
| No log | 0.8537 | 70 | 1.1402 | 0.4495 | 1.1402 | 1.0678 |
| No log | 0.8780 | 72 | 1.0739 | 0.4496 | 1.0739 | 1.0363 |
| No log | 0.9024 | 74 | 1.0324 | 0.4683 | 1.0324 | 1.0161 |
| No log | 0.9268 | 76 | 1.2340 | 0.4666 | 1.2340 | 1.1109 |
| No log | 0.9512 | 78 | 1.4338 | 0.4534 | 1.4338 | 1.1974 |
| No log | 0.9756 | 80 | 1.6928 | 0.3647 | 1.6928 | 1.3011 |
| No log | 1.0 | 82 | 1.5565 | 0.3464 | 1.5565 | 1.2476 |
| No log | 1.0244 | 84 | 1.4545 | 0.3836 | 1.4545 | 1.2060 |
| No log | 1.0488 | 86 | 1.0954 | 0.4952 | 1.0954 | 1.0466 |
| No log | 1.0732 | 88 | 0.7918 | 0.5843 | 0.7918 | 0.8899 |
| No log | 1.0976 | 90 | 0.7253 | 0.6109 | 0.7253 | 0.8516 |
| No log | 1.1220 | 92 | 0.7277 | 0.5838 | 0.7277 | 0.8530 |
| No log | 1.1463 | 94 | 0.7408 | 0.5896 | 0.7408 | 0.8607 |
| No log | 1.1707 | 96 | 0.7075 | 0.5638 | 0.7075 | 0.8411 |
| No log | 1.1951 | 98 | 0.7414 | 0.5433 | 0.7414 | 0.8610 |
| No log | 1.2195 | 100 | 0.7495 | 0.5510 | 0.7495 | 0.8657 |
| No log | 1.2439 | 102 | 0.7330 | 0.6279 | 0.7330 | 0.8562 |
| No log | 1.2683 | 104 | 0.7337 | 0.6315 | 0.7337 | 0.8566 |
| No log | 1.2927 | 106 | 0.7109 | 0.6402 | 0.7109 | 0.8432 |
| No log | 1.3171 | 108 | 0.6509 | 0.6778 | 0.6509 | 0.8068 |
| No log | 1.3415 | 110 | 0.6524 | 0.5976 | 0.6524 | 0.8077 |
| No log | 1.3659 | 112 | 0.7842 | 0.4906 | 0.7842 | 0.8855 |
| No log | 1.3902 | 114 | 0.7915 | 0.4589 | 0.7915 | 0.8897 |
| No log | 1.4146 | 116 | 0.6859 | 0.5369 | 0.6859 | 0.8282 |
| No log | 1.4390 | 118 | 0.6318 | 0.6109 | 0.6318 | 0.7948 |
| No log | 1.4634 | 120 | 0.7223 | 0.6861 | 0.7223 | 0.8499 |
| No log | 1.4878 | 122 | 0.9051 | 0.5733 | 0.9051 | 0.9514 |
| No log | 1.5122 | 124 | 0.8670 | 0.5826 | 0.8670 | 0.9311 |
| No log | 1.5366 | 126 | 0.7393 | 0.6231 | 0.7393 | 0.8598 |
| No log | 1.5610 | 128 | 0.6482 | 0.6559 | 0.6482 | 0.8051 |
| No log | 1.5854 | 130 | 0.6772 | 0.6401 | 0.6772 | 0.8229 |
| No log | 1.6098 | 132 | 0.7100 | 0.6421 | 0.7100 | 0.8426 |
| No log | 1.6341 | 134 | 0.7519 | 0.6113 | 0.7519 | 0.8671 |
| No log | 1.6585 | 136 | 0.7875 | 0.5897 | 0.7875 | 0.8874 |
| No log | 1.6829 | 138 | 0.6987 | 0.6536 | 0.6987 | 0.8359 |
| No log | 1.7073 | 140 | 0.5360 | 0.7484 | 0.5360 | 0.7321 |
| No log | 1.7317 | 142 | 0.5463 | 0.7620 | 0.5463 | 0.7391 |
| No log | 1.7561 | 144 | 0.5998 | 0.7352 | 0.5998 | 0.7745 |
| No log | 1.7805 | 146 | 0.5578 | 0.7243 | 0.5578 | 0.7468 |
| No log | 1.8049 | 148 | 0.5769 | 0.7424 | 0.5769 | 0.7595 |
| No log | 1.8293 | 150 | 0.6215 | 0.7133 | 0.6215 | 0.7884 |
| No log | 1.8537 | 152 | 0.6803 | 0.6850 | 0.6803 | 0.8248 |
| No log | 1.8780 | 154 | 0.6644 | 0.6924 | 0.6644 | 0.8151 |
| No log | 1.9024 | 156 | 0.6113 | 0.7179 | 0.6113 | 0.7819 |
| No log | 1.9268 | 158 | 0.5890 | 0.7475 | 0.5890 | 0.7674 |
| No log | 1.9512 | 160 | 0.5977 | 0.7551 | 0.5977 | 0.7731 |
| No log | 1.9756 | 162 | 0.6070 | 0.7070 | 0.6070 | 0.7791 |
| No log | 2.0 | 164 | 0.5988 | 0.6639 | 0.5988 | 0.7738 |
| No log | 2.0244 | 166 | 0.6108 | 0.6502 | 0.6108 | 0.7815 |
| No log | 2.0488 | 168 | 0.5973 | 0.6589 | 0.5973 | 0.7729 |
| No log | 2.0732 | 170 | 0.6184 | 0.6498 | 0.6184 | 0.7864 |
| No log | 2.0976 | 172 | 0.6381 | 0.6668 | 0.6381 | 0.7988 |
| No log | 2.1220 | 174 | 0.6717 | 0.6723 | 0.6717 | 0.8196 |
| No log | 2.1463 | 176 | 0.6244 | 0.6916 | 0.6244 | 0.7902 |
| No log | 2.1707 | 178 | 0.6034 | 0.7136 | 0.6034 | 0.7768 |
| No log | 2.1951 | 180 | 0.7075 | 0.6641 | 0.7075 | 0.8411 |
| No log | 2.2195 | 182 | 0.6912 | 0.6869 | 0.6912 | 0.8314 |
| No log | 2.2439 | 184 | 0.6687 | 0.6959 | 0.6687 | 0.8178 |
| No log | 2.2683 | 186 | 0.6118 | 0.7111 | 0.6118 | 0.7822 |
| No log | 2.2927 | 188 | 0.5829 | 0.7371 | 0.5829 | 0.7635 |
| No log | 2.3171 | 190 | 0.6061 | 0.6748 | 0.6061 | 0.7785 |
| No log | 2.3415 | 192 | 0.6586 | 0.6374 | 0.6586 | 0.8115 |
| No log | 2.3659 | 194 | 0.6702 | 0.6316 | 0.6702 | 0.8187 |
| No log | 2.3902 | 196 | 0.6928 | 0.5755 | 0.6928 | 0.8323 |
| No log | 2.4146 | 198 | 0.6712 | 0.6269 | 0.6712 | 0.8193 |
| No log | 2.4390 | 200 | 0.6238 | 0.6543 | 0.6238 | 0.7898 |
| No log | 2.4634 | 202 | 0.5877 | 0.6925 | 0.5877 | 0.7666 |
| No log | 2.4878 | 204 | 0.5994 | 0.7381 | 0.5994 | 0.7742 |
| No log | 2.5122 | 206 | 0.6102 | 0.7244 | 0.6102 | 0.7811 |
| No log | 2.5366 | 208 | 0.5809 | 0.7238 | 0.5809 | 0.7622 |
| No log | 2.5610 | 210 | 0.6201 | 0.6913 | 0.6201 | 0.7875 |
| No log | 2.5854 | 212 | 0.7025 | 0.6725 | 0.7025 | 0.8381 |
| No log | 2.6098 | 214 | 0.6638 | 0.6846 | 0.6638 | 0.8148 |
| No log | 2.6341 | 216 | 0.6309 | 0.6882 | 0.6309 | 0.7943 |
| No log | 2.6585 | 218 | 0.6645 | 0.6843 | 0.6645 | 0.8152 |
| No log | 2.6829 | 220 | 0.6669 | 0.6761 | 0.6669 | 0.8166 |
| No log | 2.7073 | 222 | 0.6231 | 0.7051 | 0.6231 | 0.7894 |
| No log | 2.7317 | 224 | 0.6251 | 0.7259 | 0.6251 | 0.7906 |
| No log | 2.7561 | 226 | 0.7249 | 0.7090 | 0.7249 | 0.8514 |
| No log | 2.7805 | 228 | 0.7095 | 0.7164 | 0.7095 | 0.8423 |
| No log | 2.8049 | 230 | 0.6130 | 0.7203 | 0.6130 | 0.7829 |
| No log | 2.8293 | 232 | 0.5879 | 0.7292 | 0.5879 | 0.7668 |
| No log | 2.8537 | 234 | 0.6025 | 0.7113 | 0.6025 | 0.7762 |
| No log | 2.8780 | 236 | 0.5706 | 0.7082 | 0.5706 | 0.7554 |
| No log | 2.9024 | 238 | 0.5531 | 0.7346 | 0.5531 | 0.7437 |
| No log | 2.9268 | 240 | 0.5560 | 0.7327 | 0.5560 | 0.7456 |
| No log | 2.9512 | 242 | 0.5676 | 0.7125 | 0.5676 | 0.7534 |
| No log | 2.9756 | 244 | 0.5670 | 0.7356 | 0.5670 | 0.7530 |
| No log | 3.0 | 246 | 0.5768 | 0.7379 | 0.5768 | 0.7595 |
| No log | 3.0244 | 248 | 0.5952 | 0.7470 | 0.5952 | 0.7715 |
| No log | 3.0488 | 250 | 0.6125 | 0.7350 | 0.6125 | 0.7827 |
| No log | 3.0732 | 252 | 0.6413 | 0.7423 | 0.6413 | 0.8008 |
| No log | 3.0976 | 254 | 0.6349 | 0.7233 | 0.6349 | 0.7968 |
| No log | 3.1220 | 256 | 0.6242 | 0.7224 | 0.6242 | 0.7901 |
| No log | 3.1463 | 258 | 0.6302 | 0.7185 | 0.6302 | 0.7939 |
| No log | 3.1707 | 260 | 0.6284 | 0.7028 | 0.6284 | 0.7927 |
| No log | 3.1951 | 262 | 0.6257 | 0.7021 | 0.6257 | 0.7910 |
| No log | 3.2195 | 264 | 0.6213 | 0.7136 | 0.6213 | 0.7883 |
| No log | 3.2439 | 266 | 0.6048 | 0.7234 | 0.6048 | 0.7777 |
| No log | 3.2683 | 268 | 0.5878 | 0.7286 | 0.5878 | 0.7667 |
| No log | 3.2927 | 270 | 0.5719 | 0.7368 | 0.5719 | 0.7562 |
| No log | 3.3171 | 272 | 0.5664 | 0.7415 | 0.5664 | 0.7526 |
| No log | 3.3415 | 274 | 0.6255 | 0.7496 | 0.6255 | 0.7909 |
| No log | 3.3659 | 276 | 0.6330 | 0.7407 | 0.6330 | 0.7956 |
| No log | 3.3902 | 278 | 0.5688 | 0.7485 | 0.5688 | 0.7542 |
| No log | 3.4146 | 280 | 0.5622 | 0.7504 | 0.5622 | 0.7498 |
| No log | 3.4390 | 282 | 0.5675 | 0.7461 | 0.5675 | 0.7533 |
| No log | 3.4634 | 284 | 0.5976 | 0.7536 | 0.5976 | 0.7731 |
| No log | 3.4878 | 286 | 0.6485 | 0.7631 | 0.6485 | 0.8053 |
| No log | 3.5122 | 288 | 0.6456 | 0.7636 | 0.6456 | 0.8035 |
| No log | 3.5366 | 290 | 0.6217 | 0.7409 | 0.6217 | 0.7885 |
| No log | 3.5610 | 292 | 0.6057 | 0.7501 | 0.6057 | 0.7783 |
| No log | 3.5854 | 294 | 0.5963 | 0.7510 | 0.5963 | 0.7722 |
| No log | 3.6098 | 296 | 0.5961 | 0.7540 | 0.5961 | 0.7721 |
| No log | 3.6341 | 298 | 0.5824 | 0.7531 | 0.5824 | 0.7631 |
| No log | 3.6585 | 300 | 0.5681 | 0.7067 | 0.5681 | 0.7537 |
| No log | 3.6829 | 302 | 0.5672 | 0.7229 | 0.5672 | 0.7532 |
| No log | 3.7073 | 304 | 0.5705 | 0.7337 | 0.5705 | 0.7553 |
| No log | 3.7317 | 306 | 0.6017 | 0.6902 | 0.6017 | 0.7757 |
| No log | 3.7561 | 308 | 0.5924 | 0.6916 | 0.5924 | 0.7697 |
| No log | 3.7805 | 310 | 0.5668 | 0.7041 | 0.5668 | 0.7529 |
| No log | 3.8049 | 312 | 0.5623 | 0.7133 | 0.5623 | 0.7499 |
| No log | 3.8293 | 314 | 0.5706 | 0.6765 | 0.5706 | 0.7554 |
| No log | 3.8537 | 316 | 0.5746 | 0.6575 | 0.5746 | 0.7580 |
| No log | 3.8780 | 318 | 0.5718 | 0.6788 | 0.5718 | 0.7562 |
| No log | 3.9024 | 320 | 0.5631 | 0.7007 | 0.5631 | 0.7504 |
| No log | 3.9268 | 322 | 0.5599 | 0.7297 | 0.5599 | 0.7482 |
| No log | 3.9512 | 324 | 0.5463 | 0.7273 | 0.5463 | 0.7391 |
| No log | 3.9756 | 326 | 0.5640 | 0.7480 | 0.5640 | 0.7510 |
| No log | 4.0 | 328 | 0.6338 | 0.7481 | 0.6338 | 0.7961 |
| No log | 4.0244 | 330 | 0.6630 | 0.7403 | 0.6630 | 0.8143 |
| No log | 4.0488 | 332 | 0.6210 | 0.7481 | 0.6210 | 0.7881 |
| No log | 4.0732 | 334 | 0.5823 | 0.7532 | 0.5823 | 0.7631 |
| No log | 4.0976 | 336 | 0.5655 | 0.7399 | 0.5655 | 0.7520 |
| No log | 4.1220 | 338 | 0.5616 | 0.7443 | 0.5616 | 0.7494 |
| No log | 4.1463 | 340 | 0.5674 | 0.7133 | 0.5674 | 0.7533 |
| No log | 4.1707 | 342 | 0.5636 | 0.7323 | 0.5636 | 0.7507 |
| No log | 4.1951 | 344 | 0.5699 | 0.7269 | 0.5699 | 0.7549 |
| No log | 4.2195 | 346 | 0.5731 | 0.7269 | 0.5731 | 0.7570 |
| No log | 4.2439 | 348 | 0.5710 | 0.7215 | 0.5710 | 0.7556 |
| No log | 4.2683 | 350 | 0.5755 | 0.6968 | 0.5755 | 0.7586 |
| No log | 4.2927 | 352 | 0.5819 | 0.6874 | 0.5819 | 0.7628 |
| No log | 4.3171 | 354 | 0.5674 | 0.7030 | 0.5674 | 0.7533 |
| No log | 4.3415 | 356 | 0.5683 | 0.7411 | 0.5683 | 0.7539 |
| No log | 4.3659 | 358 | 0.5809 | 0.7354 | 0.5809 | 0.7622 |
| No log | 4.3902 | 360 | 0.5855 | 0.7428 | 0.5855 | 0.7652 |
| No log | 4.4146 | 362 | 0.5919 | 0.7412 | 0.5919 | 0.7694 |
| No log | 4.4390 | 364 | 0.5875 | 0.7370 | 0.5875 | 0.7665 |
| No log | 4.4634 | 366 | 0.5867 | 0.7403 | 0.5867 | 0.7660 |
| No log | 4.4878 | 368 | 0.5909 | 0.7403 | 0.5909 | 0.7687 |
| No log | 4.5122 | 370 | 0.5854 | 0.7436 | 0.5854 | 0.7651 |
| No log | 4.5366 | 372 | 0.5700 | 0.7355 | 0.5700 | 0.7550 |
| No log | 4.5610 | 374 | 0.5800 | 0.7479 | 0.5800 | 0.7616 |
| No log | 4.5854 | 376 | 0.5818 | 0.7540 | 0.5818 | 0.7628 |
| No log | 4.6098 | 378 | 0.5739 | 0.7540 | 0.5739 | 0.7576 |
| No log | 4.6341 | 380 | 0.5654 | 0.7573 | 0.5654 | 0.7520 |
| No log | 4.6585 | 382 | 0.5581 | 0.7588 | 0.5581 | 0.7471 |
| No log | 4.6829 | 384 | 0.5590 | 0.7477 | 0.5590 | 0.7477 |
| No log | 4.7073 | 386 | 0.5699 | 0.7401 | 0.5699 | 0.7549 |
| No log | 4.7317 | 388 | 0.5717 | 0.7437 | 0.5717 | 0.7561 |
| No log | 4.7561 | 390 | 0.5706 | 0.7583 | 0.5706 | 0.7554 |
| No log | 4.7805 | 392 | 0.5699 | 0.7591 | 0.5699 | 0.7549 |
| No log | 4.8049 | 394 | 0.5975 | 0.7400 | 0.5975 | 0.7730 |
| No log | 4.8293 | 396 | 0.6326 | 0.7313 | 0.6326 | 0.7954 |
| No log | 4.8537 | 398 | 0.6414 | 0.7269 | 0.6414 | 0.8009 |
| No log | 4.8780 | 400 | 0.5877 | 0.7279 | 0.5877 | 0.7666 |
| No log | 4.9024 | 402 | 0.5572 | 0.7405 | 0.5572 | 0.7465 |
| No log | 4.9268 | 404 | 0.5660 | 0.7211 | 0.5660 | 0.7524 |
| No log | 4.9512 | 406 | 0.5841 | 0.7286 | 0.5841 | 0.7643 |
| No log | 4.9756 | 408 | 0.5588 | 0.7234 | 0.5588 | 0.7476 |
| No log | 5.0 | 410 | 0.5400 | 0.7363 | 0.5400 | 0.7348 |
| No log | 5.0244 | 412 | 0.5546 | 0.7269 | 0.5546 | 0.7447 |
| No log | 5.0488 | 414 | 0.5730 | 0.7194 | 0.5730 | 0.7570 |
| No log | 5.0732 | 416 | 0.5528 | 0.7076 | 0.5528 | 0.7435 |
| No log | 5.0976 | 418 | 0.5455 | 0.7204 | 0.5455 | 0.7386 |
| No log | 5.1220 | 420 | 0.5774 | 0.7384 | 0.5774 | 0.7598 |
| No log | 5.1463 | 422 | 0.6152 | 0.6897 | 0.6152 | 0.7843 |
| No log | 5.1707 | 424 | 0.6016 | 0.7114 | 0.6016 | 0.7756 |
| No log | 5.1951 | 426 | 0.5544 | 0.7414 | 0.5544 | 0.7446 |
| No log | 5.2195 | 428 | 0.5631 | 0.7388 | 0.5631 | 0.7504 |
| No log | 5.2439 | 430 | 0.6737 | 0.7200 | 0.6737 | 0.8208 |
| No log | 5.2683 | 432 | 0.7809 | 0.6789 | 0.7809 | 0.8837 |
| No log | 5.2927 | 434 | 0.7737 | 0.6737 | 0.7737 | 0.8796 |
| No log | 5.3171 | 436 | 0.6917 | 0.7094 | 0.6917 | 0.8317 |
| No log | 5.3415 | 438 | 0.6065 | 0.7319 | 0.6065 | 0.7788 |
| No log | 5.3659 | 440 | 0.5686 | 0.7418 | 0.5686 | 0.7540 |
| No log | 5.3902 | 442 | 0.5601 | 0.7488 | 0.5601 | 0.7484 |
| No log | 5.4146 | 444 | 0.5566 | 0.7693 | 0.5566 | 0.7461 |
| No log | 5.4390 | 446 | 0.5491 | 0.7786 | 0.5491 | 0.7410 |
| No log | 5.4634 | 448 | 0.5505 | 0.7812 | 0.5505 | 0.7419 |
| No log | 5.4878 | 450 | 0.5563 | 0.7791 | 0.5563 | 0.7458 |
| No log | 5.5122 | 452 | 0.5607 | 0.7704 | 0.5607 | 0.7488 |
| No log | 5.5366 | 454 | 0.5654 | 0.7496 | 0.5654 | 0.7519 |
| No log | 5.5610 | 456 | 0.5836 | 0.7270 | 0.5836 | 0.7639 |
| No log | 5.5854 | 458 | 0.5899 | 0.7292 | 0.5899 | 0.7681 |
| No log | 5.6098 | 460 | 0.5987 | 0.7172 | 0.5987 | 0.7737 |
| No log | 5.6341 | 462 | 0.5949 | 0.7147 | 0.5949 | 0.7713 |
| No log | 5.6585 | 464 | 0.5925 | 0.7051 | 0.5925 | 0.7698 |
| No log | 5.6829 | 466 | 0.5970 | 0.6973 | 0.5970 | 0.7727 |
| No log | 5.7073 | 468 | 0.5971 | 0.6888 | 0.5971 | 0.7727 |
| No log | 5.7317 | 470 | 0.5992 | 0.7031 | 0.5992 | 0.7741 |
| No log | 5.7561 | 472 | 0.5984 | 0.6833 | 0.5984 | 0.7736 |
| No log | 5.7805 | 474 | 0.5962 | 0.6846 | 0.5962 | 0.7721 |
| No log | 5.8049 | 476 | 0.5990 | 0.6893 | 0.5990 | 0.7739 |
| No log | 5.8293 | 478 | 0.6055 | 0.6886 | 0.6055 | 0.7781 |
| No log | 5.8537 | 480 | 0.6370 | 0.7192 | 0.6370 | 0.7981 |
| No log | 5.8780 | 482 | 0.6484 | 0.7211 | 0.6484 | 0.8052 |
| No log | 5.9024 | 484 | 0.6254 | 0.7207 | 0.6254 | 0.7908 |
| No log | 5.9268 | 486 | 0.6017 | 0.7067 | 0.6017 | 0.7757 |
| No log | 5.9512 | 488 | 0.5999 | 0.7111 | 0.5999 | 0.7745 |
| No log | 5.9756 | 490 | 0.6096 | 0.7271 | 0.6096 | 0.7808 |
| No log | 6.0 | 492 | 0.6449 | 0.7411 | 0.6449 | 0.8030 |
| No log | 6.0244 | 494 | 0.6756 | 0.7137 | 0.6756 | 0.8220 |
| No log | 6.0488 | 496 | 0.6818 | 0.7137 | 0.6818 | 0.8257 |
| No log | 6.0732 | 498 | 0.6705 | 0.7137 | 0.6705 | 0.8188 |
| 0.4325 | 6.0976 | 500 | 0.6403 | 0.7345 | 0.6403 | 0.8002 |
| 0.4325 | 6.1220 | 502 | 0.6243 | 0.7384 | 0.6243 | 0.7901 |
| 0.4325 | 6.1463 | 504 | 0.6229 | 0.7368 | 0.6229 | 0.7892 |
| 0.4325 | 6.1707 | 506 | 0.6032 | 0.7631 | 0.6032 | 0.7766 |
| 0.4325 | 6.1951 | 508 | 0.5990 | 0.7537 | 0.5990 | 0.7740 |
| 0.4325 | 6.2195 | 510 | 0.5996 | 0.7464 | 0.5996 | 0.7743 |
| 0.4325 | 6.2439 | 512 | 0.6022 | 0.7557 | 0.6022 | 0.7760 |
| 0.4325 | 6.2683 | 514 | 0.6035 | 0.7599 | 0.6035 | 0.7769 |
| 0.4325 | 6.2927 | 516 | 0.5992 | 0.7562 | 0.5992 | 0.7741 |
| 0.4325 | 6.3171 | 518 | 0.5955 | 0.7510 | 0.5955 | 0.7717 |
| 0.4325 | 6.3415 | 520 | 0.5957 | 0.7613 | 0.5957 | 0.7718 |
| 0.4325 | 6.3659 | 522 | 0.6097 | 0.7379 | 0.6097 | 0.7809 |
| 0.4325 | 6.3902 | 524 | 0.6449 | 0.7309 | 0.6449 | 0.8031 |
| 0.4325 | 6.4146 | 526 | 0.6417 | 0.7356 | 0.6417 | 0.8011 |
| 0.4325 | 6.4390 | 528 | 0.6166 | 0.7346 | 0.6166 | 0.7852 |
| 0.4325 | 6.4634 | 530 | 0.5911 | 0.7550 | 0.5911 | 0.7688 |
| 0.4325 | 6.4878 | 532 | 0.5878 | 0.7371 | 0.5878 | 0.7667 |
| 0.4325 | 6.5122 | 534 | 0.5931 | 0.7432 | 0.5931 | 0.7702 |
| 0.4325 | 6.5366 | 536 | 0.6004 | 0.7403 | 0.6004 | 0.7749 |
| 0.4325 | 6.5610 | 538 | 0.6198 | 0.7368 | 0.6198 | 0.7873 |
| 0.4325 | 6.5854 | 540 | 0.6825 | 0.7120 | 0.6825 | 0.8261 |
| 0.4325 | 6.6098 | 542 | 0.7376 | 0.6816 | 0.7376 | 0.8588 |
| 0.4325 | 6.6341 | 544 | 0.7338 | 0.6816 | 0.7338 | 0.8566 |
| 0.4325 | 6.6585 | 546 | 0.6800 | 0.7250 | 0.6800 | 0.8246 |
| 0.4325 | 6.6829 | 548 | 0.6121 | 0.7214 | 0.6121 | 0.7824 |
| 0.4325 | 6.7073 | 550 | 0.5813 | 0.7152 | 0.5813 | 0.7624 |
| 0.4325 | 6.7317 | 552 | 0.5843 | 0.6663 | 0.5843 | 0.7644 |
| 0.4325 | 6.7561 | 554 | 0.5880 | 0.6947 | 0.5880 | 0.7668 |
| 0.4325 | 6.7805 | 556 | 0.5790 | 0.7209 | 0.5790 | 0.7609 |
| 0.4325 | 6.8049 | 558 | 0.5746 | 0.7322 | 0.5746 | 0.7580 |
| 0.4325 | 6.8293 | 560 | 0.5908 | 0.7375 | 0.5908 | 0.7686 |
| 0.4325 | 6.8537 | 562 | 0.6171 | 0.7400 | 0.6171 | 0.7856 |
| 0.4325 | 6.8780 | 564 | 0.6251 | 0.7378 | 0.6251 | 0.7906 |
| 0.4325 | 6.9024 | 566 | 0.6281 | 0.7356 | 0.6281 | 0.7925 |
| 0.4325 | 6.9268 | 568 | 0.6167 | 0.7400 | 0.6167 | 0.7853 |
| 0.4325 | 6.9512 | 570 | 0.5969 | 0.7422 | 0.5969 | 0.7726 |
| 0.4325 | 6.9756 | 572 | 0.5802 | 0.7436 | 0.5802 | 0.7617 |
| 0.4325 | 7.0 | 574 | 0.5681 | 0.7592 | 0.5681 | 0.7538 |
| 0.4325 | 7.0244 | 576 | 0.5688 | 0.7592 | 0.5688 | 0.7542 |
| 0.4325 | 7.0488 | 578 | 0.5726 | 0.7592 | 0.5726 | 0.7567 |
| 0.4325 | 7.0732 | 580 | 0.5764 | 0.7359 | 0.5764 | 0.7592 |
| 0.4325 | 7.0976 | 582 | 0.5838 | 0.7308 | 0.5838 | 0.7641 |
| 0.4325 | 7.1220 | 584 | 0.5992 | 0.7363 | 0.5992 | 0.7741 |
| 0.4325 | 7.1463 | 586 | 0.6185 | 0.7156 | 0.6185 | 0.7865 |
| 0.4325 | 7.1707 | 588 | 0.6484 | 0.7166 | 0.6484 | 0.8052 |
| 0.4325 | 7.1951 | 590 | 0.6610 | 0.7166 | 0.6610 | 0.8130 |
| 0.4325 | 7.2195 | 592 | 0.6441 | 0.7188 | 0.6441 | 0.8025 |
| 0.4325 | 7.2439 | 594 | 0.6256 | 0.7171 | 0.6256 | 0.7910 |
| 0.4325 | 7.2683 | 596 | 0.6062 | 0.7441 | 0.6062 | 0.7786 |
| 0.4325 | 7.2927 | 598 | 0.5958 | 0.7477 | 0.5958 | 0.7719 |
| 0.4325 | 7.3171 | 600 | 0.5936 | 0.7485 | 0.5936 | 0.7705 |
| 0.4325 | 7.3415 | 602 | 0.5923 | 0.7443 | 0.5923 | 0.7696 |
| 0.4325 | 7.3659 | 604 | 0.5907 | 0.7428 | 0.5907 | 0.7686 |
| 0.4325 | 7.3902 | 606 | 0.5934 | 0.7387 | 0.5934 | 0.7703 |
| 0.4325 | 7.4146 | 608 | 0.5977 | 0.7453 | 0.5977 | 0.7731 |
| 0.4325 | 7.4390 | 610 | 0.6047 | 0.7468 | 0.6047 | 0.7776 |
| 0.4325 | 7.4634 | 612 | 0.6219 | 0.7457 | 0.6219 | 0.7886 |
| 0.4325 | 7.4878 | 614 | 0.6376 | 0.7368 | 0.6376 | 0.7985 |
| 0.4325 | 7.5122 | 616 | 0.6479 | 0.7362 | 0.6479 | 0.8049 |
| 0.4325 | 7.5366 | 618 | 0.6562 | 0.7118 | 0.6562 | 0.8101 |
| 0.4325 | 7.5610 | 620 | 0.6413 | 0.7367 | 0.6413 | 0.8008 |
| 0.4325 | 7.5854 | 622 | 0.6206 | 0.7389 | 0.6206 | 0.7878 |
| 0.4325 | 7.6098 | 624 | 0.6003 | 0.7521 | 0.6003 | 0.7748 |
| 0.4325 | 7.6341 | 626 | 0.5899 | 0.7521 | 0.5899 | 0.7681 |
| 0.4325 | 7.6585 | 628 | 0.5856 | 0.7521 | 0.5856 | 0.7652 |
| 0.4325 | 7.6829 | 630 | 0.5814 | 0.7521 | 0.5814 | 0.7625 |
| 0.4325 | 7.7073 | 632 | 0.5827 | 0.7521 | 0.5827 | 0.7634 |
| 0.4325 | 7.7317 | 634 | 0.5888 | 0.7521 | 0.5888 | 0.7673 |
| 0.4325 | 7.7561 | 636 | 0.5924 | 0.7498 | 0.5924 | 0.7696 |
| 0.4325 | 7.7805 | 638 | 0.5952 | 0.7330 | 0.5952 | 0.7715 |
| 0.4325 | 7.8049 | 640 | 0.5887 | 0.7515 | 0.5887 | 0.7673 |
| 0.4325 | 7.8293 | 642 | 0.5825 | 0.7515 | 0.5825 | 0.7632 |
| 0.4325 | 7.8537 | 644 | 0.5816 | 0.7559 | 0.5816 | 0.7626 |
| 0.4325 | 7.8780 | 646 | 0.5748 | 0.7564 | 0.5748 | 0.7581 |
| 0.4325 | 7.9024 | 648 | 0.5659 | 0.7497 | 0.5659 | 0.7523 |
| 0.4325 | 7.9268 | 650 | 0.5644 | 0.7451 | 0.5644 | 0.7513 |
| 0.4325 | 7.9512 | 652 | 0.5667 | 0.7580 | 0.5667 | 0.7528 |
| 0.4325 | 7.9756 | 654 | 0.5673 | 0.7580 | 0.5673 | 0.7532 |
| 0.4325 | 8.0 | 656 | 0.5675 | 0.7466 | 0.5675 | 0.7534 |
| 0.4325 | 8.0244 | 658 | 0.5758 | 0.7607 | 0.5758 | 0.7588 |
| 0.4325 | 8.0488 | 660 | 0.5980 | 0.7336 | 0.5980 | 0.7733 |
| 0.4325 | 8.0732 | 662 | 0.6123 | 0.7352 | 0.6123 | 0.7825 |
| 0.4325 | 8.0976 | 664 | 0.6313 | 0.7039 | 0.6313 | 0.7946 |
| 0.4325 | 8.1220 | 666 | 0.6384 | 0.7076 | 0.6384 | 0.7990 |
| 0.4325 | 8.1463 | 668 | 0.6242 | 0.7282 | 0.6242 | 0.7900 |
| 0.4325 | 8.1707 | 670 | 0.6044 | 0.7336 | 0.6044 | 0.7774 |
| 0.4325 | 8.1951 | 672 | 0.5997 | 0.7336 | 0.5997 | 0.7744 |
| 0.4325 | 8.2195 | 674 | 0.6038 | 0.7336 | 0.6038 | 0.7771 |
| 0.4325 | 8.2439 | 676 | 0.6134 | 0.7395 | 0.6134 | 0.7832 |
| 0.4325 | 8.2683 | 678 | 0.6338 | 0.7282 | 0.6338 | 0.7961 |
| 0.4325 | 8.2927 | 680 | 0.6450 | 0.7053 | 0.6450 | 0.8031 |
| 0.4325 | 8.3171 | 682 | 0.6391 | 0.7102 | 0.6391 | 0.7994 |
| 0.4325 | 8.3415 | 684 | 0.6218 | 0.7352 | 0.6218 | 0.7886 |
| 0.4325 | 8.3659 | 686 | 0.6074 | 0.7293 | 0.6074 | 0.7793 |
| 0.4325 | 8.3902 | 688 | 0.5952 | 0.7379 | 0.5952 | 0.7715 |
| 0.4325 | 8.4146 | 690 | 0.5923 | 0.7589 | 0.5923 | 0.7696 |
| 0.4325 | 8.4390 | 692 | 0.5908 | 0.7589 | 0.5908 | 0.7686 |
| 0.4325 | 8.4634 | 694 | 0.5893 | 0.7589 | 0.5893 | 0.7677 |
| 0.4325 | 8.4878 | 696 | 0.5909 | 0.7589 | 0.5909 | 0.7687 |
| 0.4325 | 8.5122 | 698 | 0.5916 | 0.7589 | 0.5916 | 0.7691 |
| 0.4325 | 8.5366 | 700 | 0.5960 | 0.7546 | 0.5960 | 0.7720 |
| 0.4325 | 8.5610 | 702 | 0.6050 | 0.7460 | 0.6050 | 0.7778 |
| 0.4325 | 8.5854 | 704 | 0.6153 | 0.7278 | 0.6153 | 0.7844 |
| 0.4325 | 8.6098 | 706 | 0.6252 | 0.7373 | 0.6252 | 0.7907 |
| 0.4325 | 8.6341 | 708 | 0.6301 | 0.7351 | 0.6301 | 0.7938 |
| 0.4325 | 8.6585 | 710 | 0.6378 | 0.7340 | 0.6378 | 0.7986 |
| 0.4325 | 8.6829 | 712 | 0.6373 | 0.7340 | 0.6373 | 0.7983 |
| 0.4325 | 8.7073 | 714 | 0.6370 | 0.7340 | 0.6370 | 0.7982 |
| 0.4325 | 8.7317 | 716 | 0.6380 | 0.7378 | 0.6380 | 0.7988 |
| 0.4325 | 8.7561 | 718 | 0.6330 | 0.7340 | 0.6330 | 0.7956 |
| 0.4325 | 8.7805 | 720 | 0.6260 | 0.7303 | 0.6260 | 0.7912 |
| 0.4325 | 8.8049 | 722 | 0.6170 | 0.7346 | 0.6170 | 0.7855 |
| 0.4325 | 8.8293 | 724 | 0.6064 | 0.7346 | 0.6064 | 0.7787 |
| 0.4325 | 8.8537 | 726 | 0.6006 | 0.7358 | 0.6006 | 0.7750 |
| 0.4325 | 8.8780 | 728 | 0.5969 | 0.7358 | 0.5969 | 0.7726 |
| 0.4325 | 8.9024 | 730 | 0.5949 | 0.7526 | 0.5949 | 0.7713 |
| 0.4325 | 8.9268 | 732 | 0.5971 | 0.7342 | 0.5971 | 0.7727 |
| 0.4325 | 8.9512 | 734 | 0.6000 | 0.7320 | 0.6000 | 0.7746 |
| 0.4325 | 8.9756 | 736 | 0.6066 | 0.7293 | 0.6066 | 0.7788 |
| 0.4325 | 9.0 | 738 | 0.6173 | 0.7346 | 0.6173 | 0.7857 |
| 0.4325 | 9.0244 | 740 | 0.6204 | 0.7406 | 0.6204 | 0.7877 |
| 0.4325 | 9.0488 | 742 | 0.6229 | 0.7406 | 0.6229 | 0.7893 |
| 0.4325 | 9.0732 | 744 | 0.6181 | 0.7406 | 0.6181 | 0.7862 |
| 0.4325 | 9.0976 | 746 | 0.6119 | 0.7293 | 0.6119 | 0.7822 |
| 0.4325 | 9.1220 | 748 | 0.6071 | 0.7278 | 0.6071 | 0.7792 |
| 0.4325 | 9.1463 | 750 | 0.6030 | 0.7320 | 0.6030 | 0.7765 |
| 0.4325 | 9.1707 | 752 | 0.6019 | 0.7320 | 0.6019 | 0.7758 |
| 0.4325 | 9.1951 | 754 | 0.5994 | 0.7320 | 0.5994 | 0.7742 |
| 0.4325 | 9.2195 | 756 | 0.5958 | 0.7320 | 0.5958 | 0.7719 |
| 0.4325 | 9.2439 | 758 | 0.5927 | 0.7320 | 0.5927 | 0.7699 |
| 0.4325 | 9.2683 | 760 | 0.5930 | 0.7336 | 0.5930 | 0.7700 |
| 0.4325 | 9.2927 | 762 | 0.5945 | 0.7336 | 0.5945 | 0.7710 |
| 0.4325 | 9.3171 | 764 | 0.5971 | 0.7352 | 0.5971 | 0.7727 |
| 0.4325 | 9.3415 | 766 | 0.5973 | 0.7336 | 0.5973 | 0.7729 |
| 0.4325 | 9.3659 | 768 | 0.5974 | 0.7336 | 0.5974 | 0.7729 |
| 0.4325 | 9.3902 | 770 | 0.5953 | 0.7336 | 0.5953 | 0.7716 |
| 0.4325 | 9.4146 | 772 | 0.5918 | 0.7336 | 0.5918 | 0.7693 |
| 0.4325 | 9.4390 | 774 | 0.5881 | 0.7320 | 0.5881 | 0.7668 |
| 0.4325 | 9.4634 | 776 | 0.5856 | 0.7363 | 0.5856 | 0.7652 |
| 0.4325 | 9.4878 | 778 | 0.5847 | 0.7363 | 0.5847 | 0.7646 |
| 0.4325 | 9.5122 | 780 | 0.5831 | 0.7569 | 0.5831 | 0.7636 |
| 0.4325 | 9.5366 | 782 | 0.5826 | 0.7569 | 0.5826 | 0.7633 |
| 0.4325 | 9.5610 | 784 | 0.5822 | 0.7569 | 0.5822 | 0.7630 |
| 0.4325 | 9.5854 | 786 | 0.5829 | 0.7385 | 0.5829 | 0.7635 |
| 0.4325 | 9.6098 | 788 | 0.5834 | 0.7363 | 0.5834 | 0.7638 |
| 0.4325 | 9.6341 | 790 | 0.5833 | 0.7363 | 0.5833 | 0.7637 |
| 0.4325 | 9.6585 | 792 | 0.5830 | 0.7363 | 0.5830 | 0.7636 |
| 0.4325 | 9.6829 | 794 | 0.5818 | 0.7569 | 0.5818 | 0.7628 |
| 0.4325 | 9.7073 | 796 | 0.5808 | 0.7569 | 0.5808 | 0.7621 |
| 0.4325 | 9.7317 | 798 | 0.5808 | 0.7569 | 0.5808 | 0.7621 |
| 0.4325 | 9.7561 | 800 | 0.5812 | 0.7569 | 0.5812 | 0.7624 |
| 0.4325 | 9.7805 | 802 | 0.5814 | 0.7569 | 0.5814 | 0.7625 |
| 0.4325 | 9.8049 | 804 | 0.5817 | 0.7588 | 0.5817 | 0.7627 |
| 0.4325 | 9.8293 | 806 | 0.5817 | 0.7588 | 0.5817 | 0.7627 |
| 0.4325 | 9.8537 | 808 | 0.5822 | 0.7588 | 0.5822 | 0.7630 |
| 0.4325 | 9.8780 | 810 | 0.5828 | 0.7588 | 0.5828 | 0.7634 |
| 0.4325 | 9.9024 | 812 | 0.5836 | 0.7588 | 0.5836 | 0.7640 |
| 0.4325 | 9.9268 | 814 | 0.5844 | 0.7396 | 0.5844 | 0.7645 |
| 0.4325 | 9.9512 | 816 | 0.5849 | 0.7396 | 0.5849 | 0.7648 |
| 0.4325 | 9.9756 | 818 | 0.5850 | 0.7396 | 0.5850 | 0.7649 |
| 0.4325 | 10.0 | 820 | 0.5852 | 0.7396 | 0.5852 | 0.7650 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu118
- Datasets 2.21.0
- Tokenizers 0.19.1
|
mradermacher/llamion-14b-chat-i1-GGUF
|
mradermacher
| 2024-12-16T06:03:08Z | 17 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:vaiv/llamion-14b-chat",
"base_model:quantized:vaiv/llamion-14b-chat",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2024-12-16T01:06:46Z |
---
base_model: vaiv/llamion-14b-chat
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/vaiv/llamion-14b-chat
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/llamion-14b-chat-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/llamion-14b-chat-i1-GGUF/resolve/main/llamion-14b-chat.i1-IQ1_S.gguf) | i1-IQ1_S | 3.5 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/llamion-14b-chat-i1-GGUF/resolve/main/llamion-14b-chat.i1-IQ1_M.gguf) | i1-IQ1_M | 3.7 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/llamion-14b-chat-i1-GGUF/resolve/main/llamion-14b-chat.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 4.2 | |
| [GGUF](https://huggingface.co/mradermacher/llamion-14b-chat-i1-GGUF/resolve/main/llamion-14b-chat.i1-IQ2_XS.gguf) | i1-IQ2_XS | 4.5 | |
| [GGUF](https://huggingface.co/mradermacher/llamion-14b-chat-i1-GGUF/resolve/main/llamion-14b-chat.i1-IQ2_S.gguf) | i1-IQ2_S | 4.9 | |
| [GGUF](https://huggingface.co/mradermacher/llamion-14b-chat-i1-GGUF/resolve/main/llamion-14b-chat.i1-Q2_K_S.gguf) | i1-Q2_K_S | 5.2 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/llamion-14b-chat-i1-GGUF/resolve/main/llamion-14b-chat.i1-IQ2_M.gguf) | i1-IQ2_M | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/llamion-14b-chat-i1-GGUF/resolve/main/llamion-14b-chat.i1-Q2_K.gguf) | i1-Q2_K | 5.6 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/llamion-14b-chat-i1-GGUF/resolve/main/llamion-14b-chat.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 5.7 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/llamion-14b-chat-i1-GGUF/resolve/main/llamion-14b-chat.i1-IQ3_XS.gguf) | i1-IQ3_XS | 6.2 | |
| [GGUF](https://huggingface.co/mradermacher/llamion-14b-chat-i1-GGUF/resolve/main/llamion-14b-chat.i1-IQ3_S.gguf) | i1-IQ3_S | 6.5 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/llamion-14b-chat-i1-GGUF/resolve/main/llamion-14b-chat.i1-Q3_K_S.gguf) | i1-Q3_K_S | 6.5 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/llamion-14b-chat-i1-GGUF/resolve/main/llamion-14b-chat.i1-IQ3_M.gguf) | i1-IQ3_M | 6.8 | |
| [GGUF](https://huggingface.co/mradermacher/llamion-14b-chat-i1-GGUF/resolve/main/llamion-14b-chat.i1-Q3_K_M.gguf) | i1-Q3_K_M | 7.2 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/llamion-14b-chat-i1-GGUF/resolve/main/llamion-14b-chat.i1-Q3_K_L.gguf) | i1-Q3_K_L | 7.9 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/llamion-14b-chat-i1-GGUF/resolve/main/llamion-14b-chat.i1-IQ4_XS.gguf) | i1-IQ4_XS | 7.9 | |
| [GGUF](https://huggingface.co/mradermacher/llamion-14b-chat-i1-GGUF/resolve/main/llamion-14b-chat.i1-Q4_0.gguf) | i1-Q4_0 | 8.4 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/llamion-14b-chat-i1-GGUF/resolve/main/llamion-14b-chat.i1-Q4_K_S.gguf) | i1-Q4_K_S | 8.4 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/llamion-14b-chat-i1-GGUF/resolve/main/llamion-14b-chat.i1-Q4_K_M.gguf) | i1-Q4_K_M | 8.9 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/llamion-14b-chat-i1-GGUF/resolve/main/llamion-14b-chat.i1-Q5_K_S.gguf) | i1-Q5_K_S | 10.1 | |
| [GGUF](https://huggingface.co/mradermacher/llamion-14b-chat-i1-GGUF/resolve/main/llamion-14b-chat.i1-Q5_K_M.gguf) | i1-Q5_K_M | 10.4 | |
| [GGUF](https://huggingface.co/mradermacher/llamion-14b-chat-i1-GGUF/resolve/main/llamion-14b-chat.i1-Q6_K.gguf) | i1-Q6_K | 12.0 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
DeepDream2045/a876cc0f-a872-4b1d-976d-a66f4d406648
|
DeepDream2045
| 2024-12-16T06:02:31Z | 5 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:oopsung/llama2-7b-n-ox-test-v1",
"base_model:adapter:oopsung/llama2-7b-n-ox-test-v1",
"region:us"
] | null | 2024-12-16T05:49:14Z |
---
library_name: peft
base_model: oopsung/llama2-7b-n-ox-test-v1
tags:
- axolotl
- generated_from_trainer
model-index:
- name: a876cc0f-a872-4b1d-976d-a66f4d406648
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.5.2`
```yaml
adapter: lora
base_model: oopsung/llama2-7b-n-ox-test-v1
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- a9b81c636e662c6a_train_data.json
ds_type: json
field: lyrics
path: /workspace/input_data/a9b81c636e662c6a_train_data.json
type: completion
debug: null
deepspeed: null
early_stopping_patience: 1
eval_max_new_tokens: 128
eval_steps: 25
eval_table_size: null
flash_attention: false
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 16
gradient_checkpointing: true
group_by_length: true
hub_model_id: DeepDream2045/a876cc0f-a872-4b1d-976d-a66f4d406648
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0001
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_steps: 50
micro_batch_size: 2
mlflow_experiment_name: /tmp/a9b81c636e662c6a_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
optimizer: adamw_torch
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 25
sequence_len: 2048
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: a876cc0f-a872-4b1d-976d-a66f4d406648
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: a876cc0f-a872-4b1d-976d-a66f4d406648
warmup_ratio: 0.05
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# a876cc0f-a872-4b1d-976d-a66f4d406648
This model is a fine-tuned version of [oopsung/llama2-7b-n-ox-test-v1](https://huggingface.co/oopsung/llama2-7b-n-ox-test-v1) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5669
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- total_eval_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 2
- training_steps: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 2.4502 | 0.0081 | 1 | 3.2280 |
| 2.3044 | 0.2026 | 25 | 1.5989 |
| 2.1675 | 0.4053 | 50 | 1.5669 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.3
- Pytorch 2.3.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
|
Shuaishuai0219/Animate-X
|
Shuaishuai0219
| 2024-12-16T06:01:37Z | 2,654 | 11 |
open_clip
|
[
"open_clip",
"onnx",
"arxiv:2410.10306",
"region:us"
] | null | 2024-12-07T02:06:28Z |
<p align="center">
<h2 align="center">Animate-X: Universal Character Image Animation with Enhanced Motion Representation</h2>
<p align="center">
<a href=""><strong>Shuai Tan</strong></a>
Β·
<a href="https://scholar.google.com/citations?user=BwdpTiQAAAAJ"><strong>Biao Gong</strong></a><sup>β </sup>
Β·
<a href="https://scholar.google.com/citations?user=cQbXvkcAAAAJ"><strong>Xiang Wang</strong></a>
Β·
<a href="https://scholar.google.com/citations?user=ZO3OQ-8AAAAJ"><strong>Shiwei Zhang</strong></a>
<br>
<a href="https://openreview.net/profile?id=~DanDan_Zheng1"><strong>Dandan Zheng</strong></a>
Β·
<a href="https://scholar.google.com.hk/citations?user=S8FmqTUAAAAJ"><strong>Ruobing Zheng</strong></a>
Β·
<a href="https://scholar.google.com/citations?user=hMDQifQAAAAJ"><strong>Kecheng Zheng</strong></a>
Β·
<a href="https://openreview.net/profile?id=~Jingdong_Chen1"><strong>Jingdong Chen</strong></a>
Β·
<a href="https://openreview.net/profile?id=~Ming_Yang2"><strong>Ming Yang</strong></a>
<br>
<br>
<a href="https://arxiv.org/abs/2410.10306"><img src='https://img.shields.io/badge/arXiv-Animate--X-red' alt='Paper PDF'></a>
<a href='https://lucaria-academy.github.io/Animate-X/'><img src='https://img.shields.io/badge/Project_Page-Animate--X-blue' alt='Project Page'></a>
<a href='https://mp.weixin.qq.com/s/vDR4kPLqnCUwfPiBNKKV9A'><img src='https://badges.aleen42.com/src/wechat.svg'></a>
<a href='https://github.com/antgroup/animate-x'><img src='https://img.shields.io/badge/Code-Animate--X-yellow'></a>
<br>
<b></a>Ant Group | </a>Tongyi Lab </b>
<br>
</p>
</p>
## This repo include the checkpoints for Animate-X:
- "checkpoints/dw-ll_ucoco_384.onnx": the checkpoint for dwpose extraction.
- "checkpoints/open_clip_pytorch_model.bin": the checkpoint for clip embedding.
- "checkpoints/animate-x_ckpt.pth": the checkpoint for X-character image animation in Animate-X (32 frames).
- "checkpoints/yolox_l.onnx": the checkpoint for dwpose extraction.
- "checkpoints/v2-1_512-ema-pruned.ckpt": the checkpoint for Stable Diffusion.
## BibTeX
If this repo is useful to you, please cite our corresponding technical paper.
```bibtex
@article{AnimateX2025,
title={Animate-X: Universal Character Image Animation with Enhanced Motion Representation},
author={Tan, Shuai and Gong, Biao and Wang, Xiang and Zhang, Shiwei and Zheng, Dandan and Zheng, Ruobing and Zheng, Kecheng and Chen, Jingdong and Yang, Ming},
journal={arXiv preprint arXiv:2410.10306},
year={2025}
}
```
|
AmberYifan/Llama-2-7b-Gemma-2-9B-mix
|
AmberYifan
| 2024-12-16T05:59:29Z | 9 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"trl",
"dpo",
"conversational",
"arxiv:2305.18290",
"base_model:AmberYifan/llama2-7b-sft-ultrachat-safeRLHF",
"base_model:finetune:AmberYifan/llama2-7b-sft-ultrachat-safeRLHF",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-12-16T04:31:07Z |
---
base_model: AmberYifan/llama2-7b-sft-ultrachat-safeRLHF
library_name: transformers
model_name: Llama-2-7b-Gemma-2-9B-mix
tags:
- generated_from_trainer
- trl
- dpo
licence: license
---
# Model Card for Llama-2-7b-Gemma-2-9B-mix
This model is a fine-tuned version of [AmberYifan/llama2-7b-sft-ultrachat-safeRLHF](https://huggingface.co/AmberYifan/llama2-7b-sft-ultrachat-safeRLHF).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="AmberYifan/Llama-2-7b-Gemma-2-9B-mix", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/yifanwang/huggingface/runs/thzel6uw)
This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290).
### Framework versions
- TRL: 0.12.2
- Transformers: 4.46.3
- Pytorch: 2.5.1+cu118
- Datasets: 3.2.0
- Tokenizers: 0.20.3
## Citations
Cite DPO as:
```bibtex
@inproceedings{rafailov2023direct,
title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}},
author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn},
year = 2023,
booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023},
url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html},
editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin GallouΓ©dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
mradermacher/Aura-MoE-2x4B-v2-i1-GGUF
|
mradermacher
| 2024-12-16T05:59:18Z | 501 | 3 |
transformers
|
[
"transformers",
"gguf",
"en",
"dataset:jeiku/Writing",
"dataset:FourOhFour/RP_Phase",
"dataset:anthracite-core/full-opus-chosen-hermes-rejected-kto-v1",
"base_model:AuraIndustries/Aura-MoE-2x4B-v2",
"base_model:quantized:AuraIndustries/Aura-MoE-2x4B-v2",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2024-12-16T01:17:56Z |
---
base_model: AuraIndustries/Aura-MoE-2x4B-v2
datasets:
- jeiku/Writing
- FourOhFour/RP_Phase
- anthracite-core/full-opus-chosen-hermes-rejected-kto-v1
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/AuraIndustries/Aura-MoE-2x4B-v2
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/Aura-MoE-2x4B-v2-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Aura-MoE-2x4B-v2-i1-GGUF/resolve/main/Aura-MoE-2x4B-v2.i1-IQ1_S.gguf) | i1-IQ1_S | 1.9 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Aura-MoE-2x4B-v2-i1-GGUF/resolve/main/Aura-MoE-2x4B-v2.i1-IQ1_M.gguf) | i1-IQ1_M | 2.0 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Aura-MoE-2x4B-v2-i1-GGUF/resolve/main/Aura-MoE-2x4B-v2.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.2 | |
| [GGUF](https://huggingface.co/mradermacher/Aura-MoE-2x4B-v2-i1-GGUF/resolve/main/Aura-MoE-2x4B-v2.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.4 | |
| [GGUF](https://huggingface.co/mradermacher/Aura-MoE-2x4B-v2-i1-GGUF/resolve/main/Aura-MoE-2x4B-v2.i1-IQ2_S.gguf) | i1-IQ2_S | 2.5 | |
| [GGUF](https://huggingface.co/mradermacher/Aura-MoE-2x4B-v2-i1-GGUF/resolve/main/Aura-MoE-2x4B-v2.i1-IQ2_M.gguf) | i1-IQ2_M | 2.7 | |
| [GGUF](https://huggingface.co/mradermacher/Aura-MoE-2x4B-v2-i1-GGUF/resolve/main/Aura-MoE-2x4B-v2.i1-Q2_K_S.gguf) | i1-Q2_K_S | 2.8 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/Aura-MoE-2x4B-v2-i1-GGUF/resolve/main/Aura-MoE-2x4B-v2.i1-Q2_K.gguf) | i1-Q2_K | 2.9 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Aura-MoE-2x4B-v2-i1-GGUF/resolve/main/Aura-MoE-2x4B-v2.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 3.0 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Aura-MoE-2x4B-v2-i1-GGUF/resolve/main/Aura-MoE-2x4B-v2.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.2 | |
| [GGUF](https://huggingface.co/mradermacher/Aura-MoE-2x4B-v2-i1-GGUF/resolve/main/Aura-MoE-2x4B-v2.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.4 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Aura-MoE-2x4B-v2-i1-GGUF/resolve/main/Aura-MoE-2x4B-v2.i1-IQ3_S.gguf) | i1-IQ3_S | 3.4 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Aura-MoE-2x4B-v2-i1-GGUF/resolve/main/Aura-MoE-2x4B-v2.i1-IQ3_M.gguf) | i1-IQ3_M | 3.5 | |
| [GGUF](https://huggingface.co/mradermacher/Aura-MoE-2x4B-v2-i1-GGUF/resolve/main/Aura-MoE-2x4B-v2.i1-Q3_K_M.gguf) | i1-Q3_K_M | 3.7 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Aura-MoE-2x4B-v2-i1-GGUF/resolve/main/Aura-MoE-2x4B-v2.i1-Q3_K_L.gguf) | i1-Q3_K_L | 4.0 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Aura-MoE-2x4B-v2-i1-GGUF/resolve/main/Aura-MoE-2x4B-v2.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.1 | |
| [GGUF](https://huggingface.co/mradermacher/Aura-MoE-2x4B-v2-i1-GGUF/resolve/main/Aura-MoE-2x4B-v2.i1-Q4_0.gguf) | i1-Q4_0 | 4.3 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Aura-MoE-2x4B-v2-i1-GGUF/resolve/main/Aura-MoE-2x4B-v2.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.3 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Aura-MoE-2x4B-v2-i1-GGUF/resolve/main/Aura-MoE-2x4B-v2.i1-Q4_K_M.gguf) | i1-Q4_K_M | 4.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Aura-MoE-2x4B-v2-i1-GGUF/resolve/main/Aura-MoE-2x4B-v2.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/Aura-MoE-2x4B-v2-i1-GGUF/resolve/main/Aura-MoE-2x4B-v2.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.3 | |
| [GGUF](https://huggingface.co/mradermacher/Aura-MoE-2x4B-v2-i1-GGUF/resolve/main/Aura-MoE-2x4B-v2.i1-Q6_K.gguf) | i1-Q6_K | 6.0 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
mradermacher/MT-Gen4-IF-gemma-2-MT4g2MTg2-9B-GGUF
|
mradermacher
| 2024-12-16T05:55:37Z | 187 | 0 |
transformers
|
[
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:zelk12/MT-Gen4-IF-gemma-2-MT4g2MTg2-9B",
"base_model:quantized:zelk12/MT-Gen4-IF-gemma-2-MT4g2MTg2-9B",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-12-16T04:05:41Z |
---
base_model: zelk12/MT-Gen4-IF-gemma-2-MT4g2MTg2-9B
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/zelk12/MT-Gen4-IF-gemma-2-MT4g2MTg2-9B
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/MT-Gen4-IF-gemma-2-MT4g2MTg2-9B-GGUF/resolve/main/MT-Gen4-IF-gemma-2-MT4g2MTg2-9B.Q2_K.gguf) | Q2_K | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/MT-Gen4-IF-gemma-2-MT4g2MTg2-9B-GGUF/resolve/main/MT-Gen4-IF-gemma-2-MT4g2MTg2-9B.Q3_K_S.gguf) | Q3_K_S | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/MT-Gen4-IF-gemma-2-MT4g2MTg2-9B-GGUF/resolve/main/MT-Gen4-IF-gemma-2-MT4g2MTg2-9B.Q3_K_M.gguf) | Q3_K_M | 4.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/MT-Gen4-IF-gemma-2-MT4g2MTg2-9B-GGUF/resolve/main/MT-Gen4-IF-gemma-2-MT4g2MTg2-9B.Q3_K_L.gguf) | Q3_K_L | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/MT-Gen4-IF-gemma-2-MT4g2MTg2-9B-GGUF/resolve/main/MT-Gen4-IF-gemma-2-MT4g2MTg2-9B.IQ4_XS.gguf) | IQ4_XS | 5.3 | |
| [GGUF](https://huggingface.co/mradermacher/MT-Gen4-IF-gemma-2-MT4g2MTg2-9B-GGUF/resolve/main/MT-Gen4-IF-gemma-2-MT4g2MTg2-9B.Q4_K_S.gguf) | Q4_K_S | 5.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/MT-Gen4-IF-gemma-2-MT4g2MTg2-9B-GGUF/resolve/main/MT-Gen4-IF-gemma-2-MT4g2MTg2-9B.Q4_K_M.gguf) | Q4_K_M | 5.9 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/MT-Gen4-IF-gemma-2-MT4g2MTg2-9B-GGUF/resolve/main/MT-Gen4-IF-gemma-2-MT4g2MTg2-9B.Q5_K_S.gguf) | Q5_K_S | 6.6 | |
| [GGUF](https://huggingface.co/mradermacher/MT-Gen4-IF-gemma-2-MT4g2MTg2-9B-GGUF/resolve/main/MT-Gen4-IF-gemma-2-MT4g2MTg2-9B.Q5_K_M.gguf) | Q5_K_M | 6.7 | |
| [GGUF](https://huggingface.co/mradermacher/MT-Gen4-IF-gemma-2-MT4g2MTg2-9B-GGUF/resolve/main/MT-Gen4-IF-gemma-2-MT4g2MTg2-9B.Q6_K.gguf) | Q6_K | 7.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/MT-Gen4-IF-gemma-2-MT4g2MTg2-9B-GGUF/resolve/main/MT-Gen4-IF-gemma-2-MT4g2MTg2-9B.Q8_0.gguf) | Q8_0 | 9.9 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/MT-Gen4-IF-gemma-2-MT4g2MTg2-9B-GGUF/resolve/main/MT-Gen4-IF-gemma-2-MT4g2MTg2-9B.f16.gguf) | f16 | 18.6 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
MayBashendy/ArabicNewSplits5_FineTuningAraBERT_run3_AugV5_k12_task1_organization
|
MayBashendy
| 2024-12-16T05:55:37Z | 182 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:aubmindlab/bert-base-arabertv02",
"base_model:finetune:aubmindlab/bert-base-arabertv02",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-12-16T05:39:28Z |
---
library_name: transformers
base_model: aubmindlab/bert-base-arabertv02
tags:
- generated_from_trainer
model-index:
- name: ArabicNewSplits5_FineTuningAraBERT_run3_AugV5_k12_task1_organization
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. -->
# ArabicNewSplits5_FineTuningAraBERT_run3_AugV5_k12_task1_organization
This model is a fine-tuned version of [aubmindlab/bert-base-arabertv02](https://huggingface.co/aubmindlab/bert-base-arabertv02) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5991
- Qwk: 0.7398
- Mse: 0.5991
- Rmse: 0.7740
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Qwk | Mse | Rmse |
|:-------------:|:------:|:----:|:---------------:|:------:|:------:|:------:|
| No log | 0.0263 | 2 | 5.1302 | 0.0056 | 5.1302 | 2.2650 |
| No log | 0.0526 | 4 | 3.2339 | 0.0780 | 3.2339 | 1.7983 |
| No log | 0.0789 | 6 | 1.9914 | 0.0816 | 1.9914 | 1.4112 |
| No log | 0.1053 | 8 | 1.8285 | 0.0499 | 1.8285 | 1.3522 |
| No log | 0.1316 | 10 | 1.6732 | 0.0187 | 1.6732 | 1.2935 |
| No log | 0.1579 | 12 | 1.5233 | 0.0229 | 1.5233 | 1.2342 |
| No log | 0.1842 | 14 | 1.5847 | 0.0824 | 1.5847 | 1.2589 |
| No log | 0.2105 | 16 | 2.2158 | 0.1335 | 2.2158 | 1.4886 |
| No log | 0.2368 | 18 | 2.4914 | 0.1552 | 2.4914 | 1.5784 |
| No log | 0.2632 | 20 | 2.2630 | 0.1819 | 2.2630 | 1.5043 |
| No log | 0.2895 | 22 | 2.2687 | 0.2160 | 2.2687 | 1.5062 |
| No log | 0.3158 | 24 | 1.7322 | 0.3119 | 1.7322 | 1.3161 |
| No log | 0.3421 | 26 | 1.5174 | 0.3715 | 1.5174 | 1.2318 |
| No log | 0.3684 | 28 | 1.5292 | 0.3496 | 1.5292 | 1.2366 |
| No log | 0.3947 | 30 | 1.9292 | 0.2427 | 1.9292 | 1.3890 |
| No log | 0.4211 | 32 | 2.7148 | 0.1890 | 2.7148 | 1.6477 |
| No log | 0.4474 | 34 | 2.4528 | 0.1760 | 2.4528 | 1.5661 |
| No log | 0.4737 | 36 | 1.9210 | 0.2670 | 1.9210 | 1.3860 |
| No log | 0.5 | 38 | 1.5489 | 0.3268 | 1.5489 | 1.2446 |
| No log | 0.5263 | 40 | 1.3387 | 0.4298 | 1.3387 | 1.1570 |
| No log | 0.5526 | 42 | 1.3625 | 0.4311 | 1.3625 | 1.1672 |
| No log | 0.5789 | 44 | 1.3374 | 0.4432 | 1.3374 | 1.1565 |
| No log | 0.6053 | 46 | 1.2577 | 0.4880 | 1.2577 | 1.1215 |
| No log | 0.6316 | 48 | 1.2734 | 0.4913 | 1.2734 | 1.1284 |
| No log | 0.6579 | 50 | 1.3553 | 0.4880 | 1.3553 | 1.1642 |
| No log | 0.6842 | 52 | 1.3847 | 0.4828 | 1.3847 | 1.1767 |
| No log | 0.7105 | 54 | 1.3704 | 0.5066 | 1.3704 | 1.1706 |
| No log | 0.7368 | 56 | 1.1933 | 0.5761 | 1.1933 | 1.0924 |
| No log | 0.7632 | 58 | 1.0111 | 0.5721 | 1.0111 | 1.0055 |
| No log | 0.7895 | 60 | 0.9286 | 0.6241 | 0.9286 | 0.9636 |
| No log | 0.8158 | 62 | 1.0919 | 0.6070 | 1.0919 | 1.0450 |
| No log | 0.8421 | 64 | 1.2288 | 0.5648 | 1.2288 | 1.1085 |
| No log | 0.8684 | 66 | 1.2141 | 0.5020 | 1.2141 | 1.1019 |
| No log | 0.8947 | 68 | 0.8651 | 0.6005 | 0.8651 | 0.9301 |
| No log | 0.9211 | 70 | 0.5683 | 0.7725 | 0.5683 | 0.7539 |
| No log | 0.9474 | 72 | 0.5676 | 0.7357 | 0.5676 | 0.7534 |
| No log | 0.9737 | 74 | 0.6204 | 0.7284 | 0.6204 | 0.7877 |
| No log | 1.0 | 76 | 0.6676 | 0.6703 | 0.6676 | 0.8170 |
| No log | 1.0263 | 78 | 0.9425 | 0.5781 | 0.9425 | 0.9708 |
| No log | 1.0526 | 80 | 1.5309 | 0.3950 | 1.5309 | 1.2373 |
| No log | 1.0789 | 82 | 1.7001 | 0.3607 | 1.7001 | 1.3039 |
| No log | 1.1053 | 84 | 1.4319 | 0.3945 | 1.4319 | 1.1966 |
| No log | 1.1316 | 86 | 0.9584 | 0.5632 | 0.9584 | 0.9790 |
| No log | 1.1579 | 88 | 0.7342 | 0.7055 | 0.7342 | 0.8568 |
| No log | 1.1842 | 90 | 0.6507 | 0.7488 | 0.6507 | 0.8067 |
| No log | 1.2105 | 92 | 0.6024 | 0.7722 | 0.6024 | 0.7761 |
| No log | 1.2368 | 94 | 0.6215 | 0.7486 | 0.6215 | 0.7884 |
| No log | 1.2632 | 96 | 0.6506 | 0.7188 | 0.6506 | 0.8066 |
| No log | 1.2895 | 98 | 0.7103 | 0.7141 | 0.7103 | 0.8428 |
| No log | 1.3158 | 100 | 0.9106 | 0.6404 | 0.9106 | 0.9543 |
| No log | 1.3421 | 102 | 1.1831 | 0.5352 | 1.1831 | 1.0877 |
| No log | 1.3684 | 104 | 1.3229 | 0.4949 | 1.3229 | 1.1502 |
| No log | 1.3947 | 106 | 1.1493 | 0.5122 | 1.1493 | 1.0721 |
| No log | 1.4211 | 108 | 0.8649 | 0.6288 | 0.8649 | 0.9300 |
| No log | 1.4474 | 110 | 0.8009 | 0.6347 | 0.8009 | 0.8949 |
| No log | 1.4737 | 112 | 1.0078 | 0.5524 | 1.0078 | 1.0039 |
| No log | 1.5 | 114 | 1.0734 | 0.5286 | 1.0734 | 1.0361 |
| No log | 1.5263 | 116 | 1.2367 | 0.4380 | 1.2367 | 1.1121 |
| No log | 1.5526 | 118 | 1.1518 | 0.4866 | 1.1518 | 1.0732 |
| No log | 1.5789 | 120 | 1.1532 | 0.5135 | 1.1532 | 1.0739 |
| No log | 1.6053 | 122 | 1.2963 | 0.5195 | 1.2963 | 1.1386 |
| No log | 1.6316 | 124 | 1.1295 | 0.5380 | 1.1295 | 1.0628 |
| No log | 1.6579 | 126 | 0.9513 | 0.5667 | 0.9513 | 0.9754 |
| No log | 1.6842 | 128 | 0.8062 | 0.6355 | 0.8062 | 0.8979 |
| No log | 1.7105 | 130 | 0.7144 | 0.6622 | 0.7144 | 0.8452 |
| No log | 1.7368 | 132 | 0.6944 | 0.7206 | 0.6944 | 0.8333 |
| No log | 1.7632 | 134 | 0.7479 | 0.6809 | 0.7479 | 0.8648 |
| No log | 1.7895 | 136 | 0.6916 | 0.7252 | 0.6916 | 0.8316 |
| No log | 1.8158 | 138 | 0.7625 | 0.6939 | 0.7625 | 0.8732 |
| No log | 1.8421 | 140 | 0.6933 | 0.7154 | 0.6933 | 0.8326 |
| No log | 1.8684 | 142 | 0.6237 | 0.7314 | 0.6237 | 0.7898 |
| No log | 1.8947 | 144 | 0.6086 | 0.7530 | 0.6086 | 0.7801 |
| No log | 1.9211 | 146 | 0.6289 | 0.7486 | 0.6289 | 0.7930 |
| No log | 1.9474 | 148 | 0.6269 | 0.7416 | 0.6269 | 0.7918 |
| No log | 1.9737 | 150 | 0.6302 | 0.7338 | 0.6302 | 0.7939 |
| No log | 2.0 | 152 | 0.6390 | 0.7588 | 0.6390 | 0.7993 |
| No log | 2.0263 | 154 | 0.7917 | 0.7025 | 0.7917 | 0.8897 |
| No log | 2.0526 | 156 | 0.8366 | 0.6891 | 0.8366 | 0.9147 |
| No log | 2.0789 | 158 | 0.7301 | 0.7330 | 0.7301 | 0.8545 |
| No log | 2.1053 | 160 | 0.6754 | 0.7294 | 0.6754 | 0.8218 |
| No log | 2.1316 | 162 | 0.6560 | 0.7335 | 0.6560 | 0.8099 |
| No log | 2.1579 | 164 | 0.6597 | 0.7534 | 0.6597 | 0.8122 |
| No log | 2.1842 | 166 | 0.7295 | 0.7103 | 0.7295 | 0.8541 |
| No log | 2.2105 | 168 | 0.6716 | 0.7262 | 0.6716 | 0.8195 |
| No log | 2.2368 | 170 | 0.6388 | 0.7432 | 0.6388 | 0.7993 |
| No log | 2.2632 | 172 | 0.5860 | 0.7536 | 0.5860 | 0.7655 |
| No log | 2.2895 | 174 | 0.5912 | 0.7494 | 0.5912 | 0.7689 |
| No log | 2.3158 | 176 | 0.6163 | 0.7444 | 0.6163 | 0.7851 |
| No log | 2.3421 | 178 | 0.7437 | 0.7141 | 0.7437 | 0.8624 |
| No log | 2.3684 | 180 | 1.0102 | 0.6546 | 1.0102 | 1.0051 |
| No log | 2.3947 | 182 | 0.9904 | 0.6471 | 0.9904 | 0.9952 |
| No log | 2.4211 | 184 | 0.8655 | 0.6834 | 0.8655 | 0.9303 |
| No log | 2.4474 | 186 | 0.7480 | 0.7285 | 0.7480 | 0.8649 |
| No log | 2.4737 | 188 | 0.7104 | 0.7419 | 0.7104 | 0.8429 |
| No log | 2.5 | 190 | 0.6968 | 0.7461 | 0.6968 | 0.8347 |
| No log | 2.5263 | 192 | 0.7807 | 0.7042 | 0.7807 | 0.8836 |
| No log | 2.5526 | 194 | 0.7790 | 0.7042 | 0.7790 | 0.8826 |
| No log | 2.5789 | 196 | 0.8530 | 0.6827 | 0.8530 | 0.9236 |
| No log | 2.6053 | 198 | 0.8134 | 0.6881 | 0.8134 | 0.9019 |
| No log | 2.6316 | 200 | 0.6783 | 0.7505 | 0.6783 | 0.8236 |
| No log | 2.6579 | 202 | 0.6723 | 0.7273 | 0.6723 | 0.8199 |
| No log | 2.6842 | 204 | 0.6630 | 0.7466 | 0.6630 | 0.8143 |
| No log | 2.7105 | 206 | 0.7317 | 0.7180 | 0.7317 | 0.8554 |
| No log | 2.7368 | 208 | 0.8303 | 0.6951 | 0.8303 | 0.9112 |
| No log | 2.7632 | 210 | 0.7245 | 0.7107 | 0.7245 | 0.8512 |
| No log | 2.7895 | 212 | 0.6280 | 0.7463 | 0.6280 | 0.7924 |
| No log | 2.8158 | 214 | 0.7843 | 0.7056 | 0.7843 | 0.8856 |
| No log | 2.8421 | 216 | 0.9290 | 0.6442 | 0.9290 | 0.9638 |
| No log | 2.8684 | 218 | 0.8322 | 0.7056 | 0.8322 | 0.9123 |
| No log | 2.8947 | 220 | 0.7421 | 0.7331 | 0.7421 | 0.8615 |
| No log | 2.9211 | 222 | 0.6494 | 0.7473 | 0.6494 | 0.8059 |
| No log | 2.9474 | 224 | 0.6431 | 0.7258 | 0.6431 | 0.8019 |
| No log | 2.9737 | 226 | 0.7626 | 0.7107 | 0.7626 | 0.8733 |
| No log | 3.0 | 228 | 0.8298 | 0.6676 | 0.8298 | 0.9109 |
| No log | 3.0263 | 230 | 0.7088 | 0.6969 | 0.7088 | 0.8419 |
| No log | 3.0526 | 232 | 0.5863 | 0.7519 | 0.5863 | 0.7657 |
| No log | 3.0789 | 234 | 0.5667 | 0.7439 | 0.5667 | 0.7528 |
| No log | 3.1053 | 236 | 0.5740 | 0.7504 | 0.5740 | 0.7576 |
| No log | 3.1316 | 238 | 0.5978 | 0.7401 | 0.5978 | 0.7732 |
| No log | 3.1579 | 240 | 0.6749 | 0.7302 | 0.6749 | 0.8215 |
| No log | 3.1842 | 242 | 0.7502 | 0.7239 | 0.7502 | 0.8661 |
| No log | 3.2105 | 244 | 0.7335 | 0.7086 | 0.7335 | 0.8565 |
| No log | 3.2368 | 246 | 0.7411 | 0.7119 | 0.7411 | 0.8609 |
| No log | 3.2632 | 248 | 0.7108 | 0.6936 | 0.7108 | 0.8431 |
| No log | 3.2895 | 250 | 0.6459 | 0.7421 | 0.6459 | 0.8037 |
| No log | 3.3158 | 252 | 0.6296 | 0.7478 | 0.6296 | 0.7935 |
| No log | 3.3421 | 254 | 0.6191 | 0.7448 | 0.6191 | 0.7869 |
| No log | 3.3684 | 256 | 0.6123 | 0.7495 | 0.6123 | 0.7825 |
| No log | 3.3947 | 258 | 0.6284 | 0.7537 | 0.6284 | 0.7927 |
| No log | 3.4211 | 260 | 0.6036 | 0.7565 | 0.6036 | 0.7769 |
| No log | 3.4474 | 262 | 0.5976 | 0.7649 | 0.5976 | 0.7731 |
| No log | 3.4737 | 264 | 0.6051 | 0.7578 | 0.6051 | 0.7779 |
| No log | 3.5 | 266 | 0.6238 | 0.7433 | 0.6238 | 0.7898 |
| No log | 3.5263 | 268 | 0.6198 | 0.7433 | 0.6198 | 0.7872 |
| No log | 3.5526 | 270 | 0.5963 | 0.7587 | 0.5963 | 0.7722 |
| No log | 3.5789 | 272 | 0.5944 | 0.7498 | 0.5944 | 0.7710 |
| No log | 3.6053 | 274 | 0.5900 | 0.7464 | 0.5900 | 0.7681 |
| No log | 3.6316 | 276 | 0.5814 | 0.7495 | 0.5814 | 0.7625 |
| No log | 3.6579 | 278 | 0.5992 | 0.7470 | 0.5992 | 0.7741 |
| No log | 3.6842 | 280 | 0.6853 | 0.7372 | 0.6853 | 0.8278 |
| No log | 3.7105 | 282 | 0.7326 | 0.7140 | 0.7326 | 0.8559 |
| No log | 3.7368 | 284 | 0.6923 | 0.7216 | 0.6923 | 0.8321 |
| No log | 3.7632 | 286 | 0.5915 | 0.7277 | 0.5915 | 0.7691 |
| No log | 3.7895 | 288 | 0.5618 | 0.7199 | 0.5618 | 0.7495 |
| No log | 3.8158 | 290 | 0.5513 | 0.7172 | 0.5513 | 0.7425 |
| No log | 3.8421 | 292 | 0.5474 | 0.7172 | 0.5474 | 0.7398 |
| No log | 3.8684 | 294 | 0.5445 | 0.7118 | 0.5445 | 0.7379 |
| No log | 3.8947 | 296 | 0.5505 | 0.7270 | 0.5505 | 0.7420 |
| No log | 3.9211 | 298 | 0.5636 | 0.7242 | 0.5636 | 0.7507 |
| No log | 3.9474 | 300 | 0.5900 | 0.7470 | 0.5900 | 0.7681 |
| No log | 3.9737 | 302 | 0.6173 | 0.7560 | 0.6173 | 0.7857 |
| No log | 4.0 | 304 | 0.5819 | 0.7470 | 0.5819 | 0.7628 |
| No log | 4.0263 | 306 | 0.5583 | 0.7243 | 0.5583 | 0.7472 |
| No log | 4.0526 | 308 | 0.5631 | 0.7376 | 0.5631 | 0.7504 |
| No log | 4.0789 | 310 | 0.5636 | 0.7201 | 0.5636 | 0.7508 |
| No log | 4.1053 | 312 | 0.6226 | 0.7625 | 0.6226 | 0.7891 |
| No log | 4.1316 | 314 | 0.7390 | 0.6743 | 0.7390 | 0.8596 |
| No log | 4.1579 | 316 | 0.8859 | 0.6444 | 0.8859 | 0.9412 |
| No log | 4.1842 | 318 | 0.8395 | 0.6488 | 0.8395 | 0.9162 |
| No log | 4.2105 | 320 | 0.6871 | 0.7480 | 0.6871 | 0.8289 |
| No log | 4.2368 | 322 | 0.5928 | 0.7285 | 0.5928 | 0.7699 |
| No log | 4.2632 | 324 | 0.5703 | 0.7277 | 0.5703 | 0.7552 |
| No log | 4.2895 | 326 | 0.5739 | 0.7247 | 0.5739 | 0.7575 |
| No log | 4.3158 | 328 | 0.6265 | 0.7723 | 0.6265 | 0.7915 |
| No log | 4.3421 | 330 | 0.7213 | 0.6971 | 0.7213 | 0.8493 |
| No log | 4.3684 | 332 | 0.7746 | 0.6643 | 0.7746 | 0.8801 |
| No log | 4.3947 | 334 | 0.7415 | 0.6706 | 0.7415 | 0.8611 |
| No log | 4.4211 | 336 | 0.6189 | 0.7723 | 0.6189 | 0.7867 |
| No log | 4.4474 | 338 | 0.5747 | 0.7660 | 0.5747 | 0.7581 |
| No log | 4.4737 | 340 | 0.5685 | 0.7660 | 0.5685 | 0.7540 |
| No log | 4.5 | 342 | 0.5443 | 0.7293 | 0.5443 | 0.7378 |
| No log | 4.5263 | 344 | 0.5416 | 0.7269 | 0.5416 | 0.7360 |
| No log | 4.5526 | 346 | 0.5421 | 0.7225 | 0.5421 | 0.7363 |
| No log | 4.5789 | 348 | 0.5422 | 0.7357 | 0.5422 | 0.7364 |
| No log | 4.6053 | 350 | 0.5479 | 0.74 | 0.5479 | 0.7402 |
| No log | 4.6316 | 352 | 0.5603 | 0.7468 | 0.5603 | 0.7485 |
| No log | 4.6579 | 354 | 0.5626 | 0.7456 | 0.5626 | 0.7501 |
| No log | 4.6842 | 356 | 0.5795 | 0.7541 | 0.5795 | 0.7612 |
| No log | 4.7105 | 358 | 0.6241 | 0.7402 | 0.6241 | 0.7900 |
| No log | 4.7368 | 360 | 0.6849 | 0.7326 | 0.6849 | 0.8276 |
| No log | 4.7632 | 362 | 0.6836 | 0.7326 | 0.6836 | 0.8268 |
| No log | 4.7895 | 364 | 0.6582 | 0.7311 | 0.6582 | 0.8113 |
| No log | 4.8158 | 366 | 0.5982 | 0.7344 | 0.5982 | 0.7734 |
| No log | 4.8421 | 368 | 0.5810 | 0.7251 | 0.5810 | 0.7622 |
| No log | 4.8684 | 370 | 0.5751 | 0.7320 | 0.5751 | 0.7584 |
| No log | 4.8947 | 372 | 0.5828 | 0.7221 | 0.5828 | 0.7634 |
| No log | 4.9211 | 374 | 0.6015 | 0.7151 | 0.6015 | 0.7756 |
| No log | 4.9474 | 376 | 0.6293 | 0.7101 | 0.6293 | 0.7933 |
| No log | 4.9737 | 378 | 0.6224 | 0.7243 | 0.6224 | 0.7889 |
| No log | 5.0 | 380 | 0.5957 | 0.7108 | 0.5957 | 0.7718 |
| No log | 5.0263 | 382 | 0.5701 | 0.7209 | 0.5701 | 0.7551 |
| No log | 5.0526 | 384 | 0.5634 | 0.7302 | 0.5634 | 0.7506 |
| No log | 5.0789 | 386 | 0.5738 | 0.7405 | 0.5738 | 0.7575 |
| No log | 5.1053 | 388 | 0.5640 | 0.7460 | 0.5640 | 0.7510 |
| No log | 5.1316 | 390 | 0.5718 | 0.7326 | 0.5718 | 0.7562 |
| No log | 5.1579 | 392 | 0.5844 | 0.7409 | 0.5844 | 0.7645 |
| No log | 5.1842 | 394 | 0.5914 | 0.7358 | 0.5914 | 0.7690 |
| No log | 5.2105 | 396 | 0.5851 | 0.7570 | 0.5851 | 0.7649 |
| No log | 5.2368 | 398 | 0.5844 | 0.7612 | 0.5844 | 0.7644 |
| No log | 5.2632 | 400 | 0.5732 | 0.7484 | 0.5732 | 0.7571 |
| No log | 5.2895 | 402 | 0.5623 | 0.7425 | 0.5623 | 0.7499 |
| No log | 5.3158 | 404 | 0.5613 | 0.7555 | 0.5613 | 0.7492 |
| No log | 5.3421 | 406 | 0.5574 | 0.7505 | 0.5574 | 0.7466 |
| No log | 5.3684 | 408 | 0.5678 | 0.7577 | 0.5678 | 0.7535 |
| No log | 5.3947 | 410 | 0.5752 | 0.7577 | 0.5752 | 0.7584 |
| No log | 5.4211 | 412 | 0.5804 | 0.7577 | 0.5804 | 0.7618 |
| No log | 5.4474 | 414 | 0.5908 | 0.7577 | 0.5908 | 0.7686 |
| No log | 5.4737 | 416 | 0.5973 | 0.7577 | 0.5973 | 0.7728 |
| No log | 5.5 | 418 | 0.5896 | 0.7331 | 0.5896 | 0.7679 |
| No log | 5.5263 | 420 | 0.5942 | 0.7387 | 0.5942 | 0.7708 |
| No log | 5.5526 | 422 | 0.6038 | 0.7513 | 0.6038 | 0.7770 |
| No log | 5.5789 | 424 | 0.6096 | 0.7485 | 0.6096 | 0.7808 |
| No log | 5.6053 | 426 | 0.6023 | 0.7312 | 0.6023 | 0.7761 |
| No log | 5.6316 | 428 | 0.5987 | 0.7471 | 0.5987 | 0.7737 |
| No log | 5.6579 | 430 | 0.6181 | 0.7289 | 0.6181 | 0.7862 |
| No log | 5.6842 | 432 | 0.6399 | 0.7190 | 0.6399 | 0.7999 |
| No log | 5.7105 | 434 | 0.6481 | 0.7233 | 0.6481 | 0.8050 |
| No log | 5.7368 | 436 | 0.6602 | 0.7224 | 0.6602 | 0.8125 |
| No log | 5.7632 | 438 | 0.6409 | 0.7354 | 0.6409 | 0.8006 |
| No log | 5.7895 | 440 | 0.6020 | 0.7488 | 0.6020 | 0.7759 |
| No log | 5.8158 | 442 | 0.5938 | 0.7388 | 0.5938 | 0.7706 |
| No log | 5.8421 | 444 | 0.6150 | 0.7254 | 0.6150 | 0.7842 |
| No log | 5.8684 | 446 | 0.6308 | 0.7269 | 0.6308 | 0.7942 |
| No log | 5.8947 | 448 | 0.6339 | 0.7269 | 0.6339 | 0.7962 |
| No log | 5.9211 | 450 | 0.6170 | 0.7452 | 0.6170 | 0.7855 |
| No log | 5.9474 | 452 | 0.6045 | 0.7430 | 0.6045 | 0.7775 |
| No log | 5.9737 | 454 | 0.6103 | 0.7378 | 0.6103 | 0.7812 |
| No log | 6.0 | 456 | 0.6038 | 0.7372 | 0.6038 | 0.7771 |
| No log | 6.0263 | 458 | 0.5978 | 0.7358 | 0.5978 | 0.7732 |
| No log | 6.0526 | 460 | 0.5952 | 0.7351 | 0.5952 | 0.7715 |
| No log | 6.0789 | 462 | 0.5958 | 0.7398 | 0.5958 | 0.7719 |
| No log | 6.1053 | 464 | 0.6036 | 0.7412 | 0.6036 | 0.7769 |
| No log | 6.1316 | 466 | 0.6313 | 0.7358 | 0.6313 | 0.7945 |
| No log | 6.1579 | 468 | 0.6270 | 0.7449 | 0.6270 | 0.7918 |
| No log | 6.1842 | 470 | 0.6002 | 0.7534 | 0.6002 | 0.7747 |
| No log | 6.2105 | 472 | 0.5815 | 0.7394 | 0.5815 | 0.7626 |
| No log | 6.2368 | 474 | 0.5940 | 0.7571 | 0.5940 | 0.7707 |
| No log | 6.2632 | 476 | 0.6008 | 0.7496 | 0.6008 | 0.7751 |
| No log | 6.2895 | 478 | 0.5889 | 0.7548 | 0.5889 | 0.7674 |
| No log | 6.3158 | 480 | 0.5828 | 0.7287 | 0.5828 | 0.7634 |
| No log | 6.3421 | 482 | 0.6023 | 0.7516 | 0.6023 | 0.7761 |
| No log | 6.3684 | 484 | 0.6167 | 0.7432 | 0.6167 | 0.7853 |
| No log | 6.3947 | 486 | 0.6110 | 0.7389 | 0.6110 | 0.7817 |
| No log | 6.4211 | 488 | 0.6070 | 0.7360 | 0.6070 | 0.7791 |
| No log | 6.4474 | 490 | 0.6064 | 0.7308 | 0.6064 | 0.7787 |
| No log | 6.4737 | 492 | 0.6113 | 0.7308 | 0.6113 | 0.7819 |
| No log | 6.5 | 494 | 0.6182 | 0.7241 | 0.6182 | 0.7862 |
| No log | 6.5263 | 496 | 0.6171 | 0.7241 | 0.6171 | 0.7856 |
| No log | 6.5526 | 498 | 0.6108 | 0.7330 | 0.6108 | 0.7816 |
| 0.3991 | 6.5789 | 500 | 0.6020 | 0.7384 | 0.6020 | 0.7759 |
| 0.3991 | 6.6053 | 502 | 0.5992 | 0.7356 | 0.5992 | 0.7741 |
| 0.3991 | 6.6316 | 504 | 0.6030 | 0.7306 | 0.6030 | 0.7765 |
| 0.3991 | 6.6579 | 506 | 0.5987 | 0.7372 | 0.5987 | 0.7738 |
| 0.3991 | 6.6842 | 508 | 0.6029 | 0.7368 | 0.6029 | 0.7765 |
| 0.3991 | 6.7105 | 510 | 0.6187 | 0.7479 | 0.6187 | 0.7866 |
| 0.3991 | 6.7368 | 512 | 0.6387 | 0.7521 | 0.6387 | 0.7992 |
| 0.3991 | 6.7632 | 514 | 0.6426 | 0.7521 | 0.6426 | 0.8016 |
| 0.3991 | 6.7895 | 516 | 0.6349 | 0.7568 | 0.6349 | 0.7968 |
| 0.3991 | 6.8158 | 518 | 0.6410 | 0.7582 | 0.6410 | 0.8006 |
| 0.3991 | 6.8421 | 520 | 0.6448 | 0.7402 | 0.6448 | 0.8030 |
| 0.3991 | 6.8684 | 522 | 0.6527 | 0.7344 | 0.6527 | 0.8079 |
| 0.3991 | 6.8947 | 524 | 0.6479 | 0.7388 | 0.6479 | 0.8050 |
| 0.3991 | 6.9211 | 526 | 0.6390 | 0.7404 | 0.6390 | 0.7993 |
| 0.3991 | 6.9474 | 528 | 0.6251 | 0.7441 | 0.6251 | 0.7906 |
| 0.3991 | 6.9737 | 530 | 0.6130 | 0.7428 | 0.6130 | 0.7829 |
| 0.3991 | 7.0 | 532 | 0.6038 | 0.7361 | 0.6038 | 0.7770 |
| 0.3991 | 7.0263 | 534 | 0.6069 | 0.7353 | 0.6069 | 0.7790 |
| 0.3991 | 7.0526 | 536 | 0.6165 | 0.7577 | 0.6165 | 0.7852 |
| 0.3991 | 7.0789 | 538 | 0.6239 | 0.7521 | 0.6239 | 0.7899 |
| 0.3991 | 7.1053 | 540 | 0.6227 | 0.7577 | 0.6227 | 0.7891 |
| 0.3991 | 7.1316 | 542 | 0.6118 | 0.7632 | 0.6118 | 0.7822 |
| 0.3991 | 7.1579 | 544 | 0.6089 | 0.7341 | 0.6089 | 0.7803 |
| 0.3991 | 7.1842 | 546 | 0.6078 | 0.7314 | 0.6078 | 0.7796 |
| 0.3991 | 7.2105 | 548 | 0.5980 | 0.7341 | 0.5980 | 0.7733 |
| 0.3991 | 7.2368 | 550 | 0.5936 | 0.7609 | 0.5936 | 0.7705 |
| 0.3991 | 7.2632 | 552 | 0.5930 | 0.7632 | 0.5930 | 0.7701 |
| 0.3991 | 7.2895 | 554 | 0.5977 | 0.7632 | 0.5977 | 0.7731 |
| 0.3991 | 7.3158 | 556 | 0.5919 | 0.7632 | 0.5919 | 0.7694 |
| 0.3991 | 7.3421 | 558 | 0.5858 | 0.7687 | 0.5858 | 0.7654 |
| 0.3991 | 7.3684 | 560 | 0.5777 | 0.7458 | 0.5777 | 0.7601 |
| 0.3991 | 7.3947 | 562 | 0.5767 | 0.7458 | 0.5767 | 0.7594 |
| 0.3991 | 7.4211 | 564 | 0.5781 | 0.7415 | 0.5781 | 0.7603 |
| 0.3991 | 7.4474 | 566 | 0.5850 | 0.7458 | 0.5850 | 0.7649 |
| 0.3991 | 7.4737 | 568 | 0.5928 | 0.7330 | 0.5928 | 0.7700 |
| 0.3991 | 7.5 | 570 | 0.6030 | 0.7330 | 0.6030 | 0.7765 |
| 0.3991 | 7.5263 | 572 | 0.6166 | 0.7416 | 0.6166 | 0.7852 |
| 0.3991 | 7.5526 | 574 | 0.6228 | 0.7527 | 0.6228 | 0.7892 |
| 0.3991 | 7.5789 | 576 | 0.6281 | 0.7443 | 0.6281 | 0.7925 |
| 0.3991 | 7.6053 | 578 | 0.6214 | 0.7564 | 0.6214 | 0.7883 |
| 0.3991 | 7.6316 | 580 | 0.6235 | 0.7395 | 0.6235 | 0.7896 |
| 0.3991 | 7.6579 | 582 | 0.6314 | 0.7452 | 0.6314 | 0.7946 |
| 0.3991 | 7.6842 | 584 | 0.6459 | 0.7534 | 0.6459 | 0.8037 |
| 0.3991 | 7.7105 | 586 | 0.6456 | 0.7534 | 0.6456 | 0.8035 |
| 0.3991 | 7.7368 | 588 | 0.6483 | 0.7434 | 0.6483 | 0.8052 |
| 0.3991 | 7.7632 | 590 | 0.6326 | 0.7469 | 0.6326 | 0.7954 |
| 0.3991 | 7.7895 | 592 | 0.6169 | 0.7412 | 0.6169 | 0.7855 |
| 0.3991 | 7.8158 | 594 | 0.6016 | 0.7413 | 0.6016 | 0.7756 |
| 0.3991 | 7.8421 | 596 | 0.5952 | 0.7394 | 0.5952 | 0.7715 |
| 0.3991 | 7.8684 | 598 | 0.5930 | 0.7336 | 0.5930 | 0.7701 |
| 0.3991 | 7.8947 | 600 | 0.5894 | 0.7379 | 0.5894 | 0.7677 |
| 0.3991 | 7.9211 | 602 | 0.5829 | 0.7451 | 0.5829 | 0.7635 |
| 0.3991 | 7.9474 | 604 | 0.5815 | 0.7451 | 0.5815 | 0.7625 |
| 0.3991 | 7.9737 | 606 | 0.5830 | 0.7336 | 0.5830 | 0.7636 |
| 0.3991 | 8.0 | 608 | 0.5825 | 0.7336 | 0.5825 | 0.7632 |
| 0.3991 | 8.0263 | 610 | 0.5819 | 0.7411 | 0.5819 | 0.7628 |
| 0.3991 | 8.0526 | 612 | 0.5880 | 0.7351 | 0.5880 | 0.7668 |
| 0.3991 | 8.0789 | 614 | 0.5982 | 0.7330 | 0.5982 | 0.7735 |
| 0.3991 | 8.1053 | 616 | 0.6052 | 0.7387 | 0.6052 | 0.7780 |
| 0.3991 | 8.1316 | 618 | 0.6060 | 0.7314 | 0.6060 | 0.7784 |
| 0.3991 | 8.1579 | 620 | 0.6074 | 0.7646 | 0.6074 | 0.7794 |
| 0.3991 | 8.1842 | 622 | 0.6091 | 0.7669 | 0.6091 | 0.7805 |
| 0.3991 | 8.2105 | 624 | 0.6096 | 0.7577 | 0.6096 | 0.7808 |
| 0.3991 | 8.2368 | 626 | 0.6094 | 0.7521 | 0.6094 | 0.7806 |
| 0.3991 | 8.2632 | 628 | 0.6075 | 0.7521 | 0.6075 | 0.7794 |
| 0.3991 | 8.2895 | 630 | 0.5997 | 0.7576 | 0.5997 | 0.7744 |
| 0.3991 | 8.3158 | 632 | 0.5943 | 0.7571 | 0.5943 | 0.7709 |
| 0.3991 | 8.3421 | 634 | 0.5920 | 0.74 | 0.5920 | 0.7694 |
| 0.3991 | 8.3684 | 636 | 0.5911 | 0.7372 | 0.5911 | 0.7688 |
| 0.3991 | 8.3947 | 638 | 0.5951 | 0.7303 | 0.5951 | 0.7714 |
| 0.3991 | 8.4211 | 640 | 0.5995 | 0.7260 | 0.5995 | 0.7743 |
| 0.3991 | 8.4474 | 642 | 0.5993 | 0.7355 | 0.5993 | 0.7741 |
| 0.3991 | 8.4737 | 644 | 0.5955 | 0.7355 | 0.5955 | 0.7717 |
| 0.3991 | 8.5 | 646 | 0.5934 | 0.7330 | 0.5934 | 0.7704 |
| 0.3991 | 8.5263 | 648 | 0.5924 | 0.7373 | 0.5924 | 0.7697 |
| 0.3991 | 8.5526 | 650 | 0.5955 | 0.7422 | 0.5955 | 0.7717 |
| 0.3991 | 8.5789 | 652 | 0.6003 | 0.7407 | 0.6003 | 0.7748 |
| 0.3991 | 8.6053 | 654 | 0.6028 | 0.7266 | 0.6028 | 0.7764 |
| 0.3991 | 8.6316 | 656 | 0.6026 | 0.7266 | 0.6026 | 0.7763 |
| 0.3991 | 8.6579 | 658 | 0.5981 | 0.7465 | 0.5981 | 0.7734 |
| 0.3991 | 8.6842 | 660 | 0.5918 | 0.7439 | 0.5918 | 0.7693 |
| 0.3991 | 8.7105 | 662 | 0.5892 | 0.7362 | 0.5892 | 0.7676 |
| 0.3991 | 8.7368 | 664 | 0.5945 | 0.7247 | 0.5945 | 0.7710 |
| 0.3991 | 8.7632 | 666 | 0.6008 | 0.7241 | 0.6008 | 0.7751 |
| 0.3991 | 8.7895 | 668 | 0.6018 | 0.7241 | 0.6018 | 0.7758 |
| 0.3991 | 8.8158 | 670 | 0.6017 | 0.7204 | 0.6017 | 0.7757 |
| 0.3991 | 8.8421 | 672 | 0.6013 | 0.7232 | 0.6013 | 0.7754 |
| 0.3991 | 8.8684 | 674 | 0.6008 | 0.7346 | 0.6008 | 0.7751 |
| 0.3991 | 8.8947 | 676 | 0.6038 | 0.7336 | 0.6038 | 0.7770 |
| 0.3991 | 8.9211 | 678 | 0.6075 | 0.7422 | 0.6075 | 0.7794 |
| 0.3991 | 8.9474 | 680 | 0.6107 | 0.7465 | 0.6107 | 0.7814 |
| 0.3991 | 8.9737 | 682 | 0.6109 | 0.7465 | 0.6109 | 0.7816 |
| 0.3991 | 9.0 | 684 | 0.6094 | 0.7465 | 0.6094 | 0.7806 |
| 0.3991 | 9.0263 | 686 | 0.6069 | 0.7379 | 0.6069 | 0.7791 |
| 0.3991 | 9.0526 | 688 | 0.6044 | 0.7293 | 0.6044 | 0.7774 |
| 0.3991 | 9.0789 | 690 | 0.6046 | 0.7293 | 0.6046 | 0.7776 |
| 0.3991 | 9.1053 | 692 | 0.6044 | 0.7330 | 0.6044 | 0.7774 |
| 0.3991 | 9.1316 | 694 | 0.6036 | 0.7382 | 0.6036 | 0.7769 |
| 0.3991 | 9.1579 | 696 | 0.6026 | 0.7398 | 0.6026 | 0.7763 |
| 0.3991 | 9.1842 | 698 | 0.6033 | 0.7247 | 0.6033 | 0.7767 |
| 0.3991 | 9.2105 | 700 | 0.6074 | 0.7197 | 0.6074 | 0.7794 |
| 0.3991 | 9.2368 | 702 | 0.6121 | 0.7413 | 0.6121 | 0.7823 |
| 0.3991 | 9.2632 | 704 | 0.6162 | 0.7413 | 0.6162 | 0.7850 |
| 0.3991 | 9.2895 | 706 | 0.6164 | 0.7413 | 0.6164 | 0.7851 |
| 0.3991 | 9.3158 | 708 | 0.6121 | 0.7316 | 0.6121 | 0.7823 |
| 0.3991 | 9.3421 | 710 | 0.6078 | 0.7160 | 0.6078 | 0.7796 |
| 0.3991 | 9.3684 | 712 | 0.6046 | 0.7247 | 0.6046 | 0.7776 |
| 0.3991 | 9.3947 | 714 | 0.6047 | 0.7398 | 0.6047 | 0.7776 |
| 0.3991 | 9.4211 | 716 | 0.6053 | 0.7382 | 0.6053 | 0.7780 |
| 0.3991 | 9.4474 | 718 | 0.6056 | 0.7382 | 0.6056 | 0.7782 |
| 0.3991 | 9.4737 | 720 | 0.6044 | 0.7382 | 0.6044 | 0.7774 |
| 0.3991 | 9.5 | 722 | 0.6029 | 0.7447 | 0.6029 | 0.7765 |
| 0.3991 | 9.5263 | 724 | 0.6015 | 0.7293 | 0.6015 | 0.7756 |
| 0.3991 | 9.5526 | 726 | 0.6010 | 0.7357 | 0.6010 | 0.7752 |
| 0.3991 | 9.5789 | 728 | 0.6012 | 0.7379 | 0.6012 | 0.7754 |
| 0.3991 | 9.6053 | 730 | 0.6008 | 0.7379 | 0.6008 | 0.7751 |
| 0.3991 | 9.6316 | 732 | 0.6004 | 0.7379 | 0.6004 | 0.7749 |
| 0.3991 | 9.6579 | 734 | 0.6002 | 0.7379 | 0.6002 | 0.7747 |
| 0.3991 | 9.6842 | 736 | 0.5997 | 0.7389 | 0.5997 | 0.7744 |
| 0.3991 | 9.7105 | 738 | 0.5994 | 0.7447 | 0.5994 | 0.7742 |
| 0.3991 | 9.7368 | 740 | 0.5991 | 0.7404 | 0.5991 | 0.7740 |
| 0.3991 | 9.7632 | 742 | 0.5994 | 0.7404 | 0.5994 | 0.7742 |
| 0.3991 | 9.7895 | 744 | 0.5996 | 0.7404 | 0.5996 | 0.7743 |
| 0.3991 | 9.8158 | 746 | 0.5997 | 0.7398 | 0.5997 | 0.7744 |
| 0.3991 | 9.8421 | 748 | 0.5994 | 0.7398 | 0.5994 | 0.7742 |
| 0.3991 | 9.8684 | 750 | 0.5992 | 0.7398 | 0.5992 | 0.7740 |
| 0.3991 | 9.8947 | 752 | 0.5990 | 0.7398 | 0.5990 | 0.7739 |
| 0.3991 | 9.9211 | 754 | 0.5989 | 0.7398 | 0.5989 | 0.7739 |
| 0.3991 | 9.9474 | 756 | 0.5990 | 0.7398 | 0.5990 | 0.7740 |
| 0.3991 | 9.9737 | 758 | 0.5991 | 0.7398 | 0.5991 | 0.7740 |
| 0.3991 | 10.0 | 760 | 0.5991 | 0.7398 | 0.5991 | 0.7740 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu118
- Datasets 2.21.0
- Tokenizers 0.19.1
|
mergekit-community/test_ArliAI-RPMax_guidance_all_versions_plus_o1-Open-Llama_reflection-llama
|
mergekit-community
| 2024-12-16T05:53:29Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"base_model:ArliAI/Llama-3.1-8B-ArliAI-RPMax-v1.1",
"base_model:merge:ArliAI/Llama-3.1-8B-ArliAI-RPMax-v1.1",
"base_model:ArliAI/Llama-3.1-8B-ArliAI-RPMax-v1.2",
"base_model:merge:ArliAI/Llama-3.1-8B-ArliAI-RPMax-v1.2",
"base_model:ArliAI/Llama-3.1-8B-ArliAI-RPMax-v1.3",
"base_model:merge:ArliAI/Llama-3.1-8B-ArliAI-RPMax-v1.3",
"base_model:Orenguteng/Llama-3.1-8B-Lexi-Uncensored-V2",
"base_model:merge:Orenguteng/Llama-3.1-8B-Lexi-Uncensored-V2",
"base_model:Skywork/Skywork-o1-Open-Llama-3.1-8B",
"base_model:merge:Skywork/Skywork-o1-Open-Llama-3.1-8B",
"base_model:Solshine/reflection-llama-3.1-8B",
"base_model:merge:Solshine/reflection-llama-3.1-8B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-12-16T05:40:06Z |
---
base_model:
- Solshine/reflection-llama-3.1-8B
- Orenguteng/Llama-3.1-8B-Lexi-Uncensored-V2
- ArliAI/Llama-3.1-8B-ArliAI-RPMax-v1.2
- ArliAI/Llama-3.1-8B-ArliAI-RPMax-v1.1
- Skywork/Skywork-o1-Open-Llama-3.1-8B
- ArliAI/Llama-3.1-8B-ArliAI-RPMax-v1.3
library_name: transformers
tags:
- mergekit
- merge
---
# merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the della_linear merge method using [Orenguteng/Llama-3.1-8B-Lexi-Uncensored-V2](https://huggingface.co/Orenguteng/Llama-3.1-8B-Lexi-Uncensored-V2) as a base.
### Models Merged
The following models were included in the merge:
* [Solshine/reflection-llama-3.1-8B](https://huggingface.co/Solshine/reflection-llama-3.1-8B)
* [ArliAI/Llama-3.1-8B-ArliAI-RPMax-v1.2](https://huggingface.co/ArliAI/Llama-3.1-8B-ArliAI-RPMax-v1.2)
* [ArliAI/Llama-3.1-8B-ArliAI-RPMax-v1.1](https://huggingface.co/ArliAI/Llama-3.1-8B-ArliAI-RPMax-v1.1)
* [Skywork/Skywork-o1-Open-Llama-3.1-8B](https://huggingface.co/Skywork/Skywork-o1-Open-Llama-3.1-8B)
* [ArliAI/Llama-3.1-8B-ArliAI-RPMax-v1.3](https://huggingface.co/ArliAI/Llama-3.1-8B-ArliAI-RPMax-v1.3)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: ArliAI/Llama-3.1-8B-ArliAI-RPMax-v1.3
parameters:
density: 0.8
weight: 0.6
- model: Solshine/reflection-llama-3.1-8B
parameters:
density: 0.5
weight: 0.2
- model: Skywork/Skywork-o1-Open-Llama-3.1-8B
parameters:
density: 0.5
weight: 0.2
- model: ArliAI/Llama-3.1-8B-ArliAI-RPMax-v1.2
parameters:
density: 0.8
weight: 0.6
- model: ArliAI/Llama-3.1-8B-ArliAI-RPMax-v1.1
parameters:
density: 0.8
weight: 0.6
merge_method: della_linear
base_model: Orenguteng/Llama-3.1-8B-Lexi-Uncensored-V2
parameters:
normalize: false
int8_mask: true
dtype: float16
```
|
mci29/sn29_q0m6_ghef
|
mci29
| 2024-12-16T05:49:42Z | 59 | 0 |
transformers
|
[
"transformers",
"safetensors",
"phi3",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-12-16T05:44:42Z |
---
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 π€ 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]
|
xxhe/lastfm-stage2-dpo-iter2
|
xxhe
| 2024-12-16T05:47:57Z | 6 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-12-16T05:45:48Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a π€ 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]
|
mradermacher/Qwen-14b-chat-yarn-32k-GGUF
|
mradermacher
| 2024-12-16T05:45:18Z | 127 | 1 |
transformers
|
[
"transformers",
"gguf",
"zh",
"en",
"dataset:yuyijiong/LongPaper_multitask",
"dataset:yuyijiong/Long-Instruction-Chinese",
"dataset:yuyijiong/LongData-Corpus",
"base_model:yuyijiong/Qwen-14b-chat-yarn-32k",
"base_model:quantized:yuyijiong/Qwen-14b-chat-yarn-32k",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-12-16T04:16:30Z |
---
base_model: yuyijiong/Qwen-14b-chat-yarn-32k
datasets:
- yuyijiong/LongPaper_multitask
- yuyijiong/Long-Instruction-Chinese
- yuyijiong/LongData-Corpus
language:
- zh
- en
library_name: transformers
license: cc-by-nc-4.0
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/yuyijiong/Qwen-14b-chat-yarn-32k
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Qwen-14b-chat-yarn-32k-GGUF/resolve/main/Qwen-14b-chat-yarn-32k.Q2_K.gguf) | Q2_K | 5.9 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen-14b-chat-yarn-32k-GGUF/resolve/main/Qwen-14b-chat-yarn-32k.Q3_K_S.gguf) | Q3_K_S | 6.9 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen-14b-chat-yarn-32k-GGUF/resolve/main/Qwen-14b-chat-yarn-32k.Q3_K_M.gguf) | Q3_K_M | 7.8 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen-14b-chat-yarn-32k-GGUF/resolve/main/Qwen-14b-chat-yarn-32k.IQ4_XS.gguf) | IQ4_XS | 8.0 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen-14b-chat-yarn-32k-GGUF/resolve/main/Qwen-14b-chat-yarn-32k.Q3_K_L.gguf) | Q3_K_L | 8.1 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen-14b-chat-yarn-32k-GGUF/resolve/main/Qwen-14b-chat-yarn-32k.Q4_K_S.gguf) | Q4_K_S | 8.7 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qwen-14b-chat-yarn-32k-GGUF/resolve/main/Qwen-14b-chat-yarn-32k.Q4_K_M.gguf) | Q4_K_M | 9.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qwen-14b-chat-yarn-32k-GGUF/resolve/main/Qwen-14b-chat-yarn-32k.Q5_K_S.gguf) | Q5_K_S | 10.1 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen-14b-chat-yarn-32k-GGUF/resolve/main/Qwen-14b-chat-yarn-32k.Q5_K_M.gguf) | Q5_K_M | 11.0 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen-14b-chat-yarn-32k-GGUF/resolve/main/Qwen-14b-chat-yarn-32k.Q6_K.gguf) | Q6_K | 12.4 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen-14b-chat-yarn-32k-GGUF/resolve/main/Qwen-14b-chat-yarn-32k.Q8_0.gguf) | Q8_0 | 15.2 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
austin/Llama-3.2-adr-full-model
|
austin
| 2024-12-16T05:44:44Z | 90 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-12-16T05:37:27Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a π€ 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]
|
dgambettaphd/M_gen0_run0_llama2-7b_wiki_doc1000_real32_synt96_vuw
|
dgambettaphd
| 2024-12-16T05:44:43Z | 138 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"unsloth",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2024-12-16T05:41:38Z |
---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a π€ 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]
|
TheBlueObserver/Qwen2.5-7B-Instruct-MLX-e36bb
|
TheBlueObserver
| 2024-12-16T05:42:20Z | 78 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"chat",
"mlx",
"conversational",
"en",
"base_model:Qwen/Qwen2.5-7B-Instruct",
"base_model:quantized:Qwen/Qwen2.5-7B-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"8-bit",
"region:us"
] |
text-generation
| 2024-12-16T05:37:41Z |
---
base_model: Qwen/Qwen2.5-7B-Instruct
language:
- en
library_name: transformers
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen2.5-7B-Instruct/blob/main/LICENSE
pipeline_tag: text-generation
tags:
- chat
- mlx
---
# TheBlueObserver/Qwen2.5-7B-Instruct-MLX-e36bb
The Model [TheBlueObserver/Qwen2.5-7B-Instruct-MLX-e36bb](https://huggingface.co/TheBlueObserver/Qwen2.5-7B-Instruct-MLX-e36bb) was
converted to MLX format from [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct)
using mlx-lm version **0.20.2**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("TheBlueObserver/Qwen2.5-7B-Instruct-MLX-e36bb")
prompt="hello"
if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
```
|
pkupie/Llama-2-7b-bod
|
pkupie
| 2024-12-16T05:39:54Z | 5 | 0 | null |
[
"pytorch",
"llama",
"en",
"bo",
"dataset:pkupie/mc2_corpus",
"dataset:togethercomputer/RedPajama-Data-1T",
"base_model:meta-llama/Llama-2-7b-hf",
"base_model:finetune:meta-llama/Llama-2-7b-hf",
"license:llama2",
"region:us"
] | null | 2024-12-05T11:52:01Z |
---
license: llama2
datasets:
- pkupie/mc2_corpus
- togethercomputer/RedPajama-Data-1T
language:
- en
- bo
base_model:
- meta-llama/Llama-2-7b-hf
---
A continually pre-trained model based on Llama-2-7b-hf.
We use the **Tibetan texts** in MC^2 and **English texts** in RedPajama with a proportion of **4:1** for training.
#### Hyper-parameters:
* lr: 3e-5
* batch size: 1M (2K*512)
* lr scheduler: cosine
* min lr: 1e-6
* lr decay iters: 10240
## Citation
If you find this model is useful in your work, please cite it with:
```
@inproceedings{tao-etal-2024-unlocking,
title = "Unlocking the Potential of Model Merging for Low-Resource Languages",
author = "Tao, Mingxu and
Zhang, Chen and
Huang, Quzhe and
Ma, Tianyao and
Huang, Songfang and
Zhao, Dongyan and
Feng, Yansong",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-emnlp.508",
doi = "10.18653/v1/2024.findings-emnlp.508",
pages = "8705--8720"
}
```
|
titangmz/my_awesome_model
|
titangmz
| 2024-12-16T05:34:08Z | 106 | 0 |
transformers
|
[
"transformers",
"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
| 2024-12-15T16:00:49Z |
---
library_name: transformers
license: apache-2.0
base_model: distilbert/distilbert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: my_awesome_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. -->
# my_awesome_model
This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 1 | 0.7009 | 0.4 |
### Framework versions
- Transformers 4.47.0
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0
|
PHarder/CodeLlama-7b-Instruct-SAP-RAP
|
PHarder
| 2024-12-16T05:30:27Z | 7 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-12-16T05:25:25Z |
---
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 π€ 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]
|
DannyShaw/AgentGen-Rep-8B
|
DannyShaw
| 2024-12-16T05:26:00Z | 5 | 0 | null |
[
"safetensors",
"llama",
"en",
"base_model:meta-llama/Llama-3.1-8B",
"base_model:finetune:meta-llama/Llama-3.1-8B",
"license:mit",
"region:us"
] | null | 2024-12-02T07:15:05Z |
---
license: mit
language:
- en
base_model:
- meta-llama/Llama-3.1-8B
---
This model is a reproduction of the model in "AgentGen: Enhancing Planning Abilities for Large Language Model based Agent via Environment and Task Generation".
The code for producing the model is in https://github.com/lazychih114/AgentGen-Reproduction/tree/main
|
gaianet/Qwen2-VL-72B-Instruct-GGUF
|
gaianet
| 2024-12-16T05:25:21Z | 92 | 0 |
transformers
|
[
"transformers",
"gguf",
"qwen2_vl",
"image-text-to-text",
"multimodal",
"en",
"base_model:Qwen/Qwen2-VL-72B-Instruct",
"base_model:quantized:Qwen/Qwen2-VL-72B-Instruct",
"license:other",
"endpoints_compatible",
"region:us",
"conversational"
] |
image-text-to-text
| 2024-12-15T09:47:36Z |
---
base_model: Qwen/Qwen2-VL-72B-Instruct
license: other
license_name: tongyi-qianwen
license_link: https://huggingface.co/Qwen/Qwen2-VL-72B-Instruct/blob/main/LICENSE
model_creator: Qwen
model_name: Qwen2-VL-72B-Instruct
quantized_by: Second State Inc.
language:
- en
pipeline_tag: image-text-to-text
tags:
- multimodal
library_name: transformers
---
# Qwen2-VL-72B-Instruct-GGUF
## Original Model
[Qwen/Qwen2-VL-72B-Instruct](https://huggingface.co/Qwen/Qwen2-VL-72B-Instruct)
## Run with Gaianet
**Prompt template:**
prompt template: coming soon
<!-- prompt template: `chatml` -->
**Context size:**
chat_ctx_size: `32000`
**Run with GaiaNet:**
- Quick start: https://docs.gaianet.ai/node-guide/quick-start
- Customize your node: https://docs.gaianet.ai/node-guide/customize
*Quantized with llama.cpp b4329*
|
ChanMeng666/heat-flux-perceptrons-neural-networks
|
ChanMeng666
| 2024-12-16T05:22:53Z | 9 | 0 |
keras
|
[
"keras",
"license:apache-2.0",
"region:us"
] | null | 2024-12-16T03:07:30Z |
---
license: apache-2.0
---
<div align="center">
<h1>Neural Networks: From Theory to Thermal Analysis π </h1>
<img src="https://img.shields.io/badge/Python-3776AB?style=flat&logo=python&logoColor=white"/>
<img src="https://img.shields.io/badge/TensorFlow-FF6F00?style=flat&logo=tensorflow&logoColor=white"/>
<img src="https://img.shields.io/badge/NumPy-013243?style=flat&logo=numpy&logoColor=white"/>
<img src="https://img.shields.io/badge/Pandas-150458?style=flat&logo=pandas&logoColor=white"/>
<img src="https://img.shields.io/badge/scikit--learn-F7931E?style=flat&logo=scikit-learn&logoColor=white"/>
</div>
# Model Description
This repository contains a series of trained feedforward neural network models for predicting heat influx in building designs. The models were developed using different architectures and training approaches to optimize performance for architectural applications.
## Key Features
- Multiple model architectures (1, 3, and 5 hidden neurons)
- Different optimization techniques (SGD vs Adagrad)
- Thoroughly validated performance metrics
- Practical architectural applications
- Comprehensive analysis tools
## Model Details
- **Architecture**: Multi-layer perceptron with sigmoid activation
- **Input Features**: Insulation, East/South/North orientation
- **Output**: Heat flux prediction
- **Best Model Performance**:
- MSE: 0.002905
- RΒ²: 0.958846
- Architecture: 5 hidden neurons with SGD optimizer
## Training Data
The models were trained on a comprehensive dataset containing:
- Insulation values
- Building orientation parameters (East, South, North)
- Corresponding heat flux measurements
- Data was normalized using MinMax scaling
# Usage
```python
import tensorflow as tf
# Load the model
model = tf.keras.models.load_model('best_heat_flux_model.keras')
# Prepare input data (normalized values)
input_data = [[insulation, east, south, north]]
# Get prediction
prediction = model.predict(input_data)
```
## Input Format
All inputs should be normalized to [0,1] range using MinMax scaling:
- Insulation: Building insulation value
- East: Eastern orientation factor
- South: Southern orientation factor
- North: Northern orientation factor
# Model Variants
1. **Best Performing Model** (best_heat_flux_model.keras)
- 5 hidden neurons
- SGD optimizer
- Learning rate: 0.1
- Momentum: 0.9
2. **Alternative Models**
- FFNN baseline (best_ffnn_model.keras)
- Adagrad variant (best_heat_flux_model_adagrad.keras)
# Performance Analysis
Performance comparison of different architectures:
| Hidden Neurons | Best Trial | Learning Rate | Momentum | Test MSE | Test RΒ² |
|----------------|------------|---------------|----------|----------|---------|
| 1 | A | 0.1 | 0.1 | 0.004940 | 0.897111 |
| 3 | B | 0.1 | 0.9 | 0.003501 | 0.950399 |
| 5 | B | 0.1 | 0.9 | 0.002905 | 0.958846 |
# Applications
The models are particularly useful for:
- Architectural design optimization
- Heat efficiency analysis
- Building orientation planning
- Insulation requirement calculations
# Limitations and Recommendations
- Models are trained on normalized data - inputs must be scaled appropriately
- Best suited for standard building configurations
- Performance may vary for extreme or unusual architectural designs
- Recommended to validate predictions against local building codes
# Citation
If you use these models in your work, please cite:
```
@software{heat_flux_nn,
title = {Neural Networks for Architectural Heat Flux Prediction},
author = {Chan Meng},
year = {2024},
publisher = {Hugging Face},
url = {https://huggingface.co/ChanMeng666/heat-flux-perceptrons-neural-networks}
}
```
# License
This project is licensed under the Apache 2.0 License - see the LICENSE file for details.
# Contact
For questions or feedback, please open an issue in the repository or reach out through Hugging Face.
|
mergekit-community/test_Skywork-o1-Open-Llama_blob
|
mergekit-community
| 2024-12-16T05:16:22Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"base_model:ArliAI/Llama-3.1-8B-ArliAI-Formax-v1.0",
"base_model:merge:ArliAI/Llama-3.1-8B-ArliAI-Formax-v1.0",
"base_model:ArliAI/Llama-3.1-8B-ArliAI-RPMax-v1.1",
"base_model:merge:ArliAI/Llama-3.1-8B-ArliAI-RPMax-v1.1",
"base_model:ArliAI/Llama-3.1-8B-ArliAI-RPMax-v1.2",
"base_model:merge:ArliAI/Llama-3.1-8B-ArliAI-RPMax-v1.2",
"base_model:ArliAI/Llama-3.1-8B-ArliAI-RPMax-v1.3",
"base_model:merge:ArliAI/Llama-3.1-8B-ArliAI-RPMax-v1.3",
"base_model:Orenguteng/Llama-3.1-8B-Lexi-Uncensored-V2",
"base_model:merge:Orenguteng/Llama-3.1-8B-Lexi-Uncensored-V2",
"base_model:Skywork/Skywork-o1-Open-Llama-3.1-8B",
"base_model:merge:Skywork/Skywork-o1-Open-Llama-3.1-8B",
"base_model:Solshine/reflection-llama-3.1-8B",
"base_model:merge:Solshine/reflection-llama-3.1-8B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-12-16T05:01:11Z |
---
base_model:
- ArliAI/Llama-3.1-8B-ArliAI-RPMax-v1.2
- Skywork/Skywork-o1-Open-Llama-3.1-8B
- ArliAI/Llama-3.1-8B-ArliAI-Formax-v1.0
- Orenguteng/Llama-3.1-8B-Lexi-Uncensored-V2
- ArliAI/Llama-3.1-8B-ArliAI-RPMax-v1.1
- ArliAI/Llama-3.1-8B-ArliAI-RPMax-v1.3
- Solshine/reflection-llama-3.1-8B
library_name: transformers
tags:
- mergekit
- merge
---
# merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the della_linear merge method using [Orenguteng/Llama-3.1-8B-Lexi-Uncensored-V2](https://huggingface.co/Orenguteng/Llama-3.1-8B-Lexi-Uncensored-V2) as a base.
### Models Merged
The following models were included in the merge:
* [ArliAI/Llama-3.1-8B-ArliAI-RPMax-v1.2](https://huggingface.co/ArliAI/Llama-3.1-8B-ArliAI-RPMax-v1.2)
* [Skywork/Skywork-o1-Open-Llama-3.1-8B](https://huggingface.co/Skywork/Skywork-o1-Open-Llama-3.1-8B)
* [ArliAI/Llama-3.1-8B-ArliAI-Formax-v1.0](https://huggingface.co/ArliAI/Llama-3.1-8B-ArliAI-Formax-v1.0)
* [ArliAI/Llama-3.1-8B-ArliAI-RPMax-v1.1](https://huggingface.co/ArliAI/Llama-3.1-8B-ArliAI-RPMax-v1.1)
* [ArliAI/Llama-3.1-8B-ArliAI-RPMax-v1.3](https://huggingface.co/ArliAI/Llama-3.1-8B-ArliAI-RPMax-v1.3)
* [Solshine/reflection-llama-3.1-8B](https://huggingface.co/Solshine/reflection-llama-3.1-8B)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: ArliAI/Llama-3.1-8B-ArliAI-RPMax-v1.3
parameters:
density: 0.8
weight: 0.6
- model: Solshine/reflection-llama-3.1-8B
parameters:
density: 0.8
weight: 0.6
- model: Skywork/Skywork-o1-Open-Llama-3.1-8B
parameters:
density: 0.8
weight: 0.6
- model: ArliAI/Llama-3.1-8B-ArliAI-RPMax-v1.2
parameters:
density: 0.5
weight: 0.5
- model: ArliAI/Llama-3.1-8B-ArliAI-RPMax-v1.1
parameters:
density: 0.5
weight: 0.5
- model: ArliAI/Llama-3.1-8B-ArliAI-Formax-v1.0
parameters:
density: 0.5
weight: 0.3
merge_method: della_linear
base_model: Orenguteng/Llama-3.1-8B-Lexi-Uncensored-V2
parameters:
normalize: false
int8_mask: true
dtype: float16
```
|
TheBlueObserver/Qwen2.5-7B-Instruct-MLX-a720d
|
TheBlueObserver
| 2024-12-16T05:10:11Z | 7 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"chat",
"mlx",
"conversational",
"en",
"base_model:Qwen/Qwen2.5-7B-Instruct",
"base_model:quantized:Qwen/Qwen2.5-7B-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"8-bit",
"region:us"
] |
text-generation
| 2024-12-16T05:05:28Z |
---
base_model: Qwen/Qwen2.5-7B-Instruct
language:
- en
library_name: transformers
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen2.5-7B-Instruct/blob/main/LICENSE
pipeline_tag: text-generation
tags:
- chat
- mlx
---
# TheBlueObserver/Qwen2.5-7B-Instruct-MLX-a720d
The Model [TheBlueObserver/Qwen2.5-7B-Instruct-MLX-a720d](https://huggingface.co/TheBlueObserver/Qwen2.5-7B-Instruct-MLX-a720d) was
converted to MLX format from [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct)
using mlx-lm version **0.20.2**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("TheBlueObserver/Qwen2.5-7B-Instruct-MLX-a720d")
prompt="hello"
if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
```
|
MayBashendy/ArabicNewSplits5_FineTuningAraBERT_run3_AugV5_k9_task1_organization
|
MayBashendy
| 2024-12-16T05:09:43Z | 182 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:aubmindlab/bert-base-arabertv02",
"base_model:finetune:aubmindlab/bert-base-arabertv02",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-12-16T04:57:44Z |
---
library_name: transformers
base_model: aubmindlab/bert-base-arabertv02
tags:
- generated_from_trainer
model-index:
- name: ArabicNewSplits5_FineTuningAraBERT_run3_AugV5_k9_task1_organization
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. -->
# ArabicNewSplits5_FineTuningAraBERT_run3_AugV5_k9_task1_organization
This model is a fine-tuned version of [aubmindlab/bert-base-arabertv02](https://huggingface.co/aubmindlab/bert-base-arabertv02) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8242
- Qwk: 0.6091
- Mse: 0.8242
- Rmse: 0.9079
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Qwk | Mse | Rmse |
|:-------------:|:------:|:----:|:---------------:|:-------:|:------:|:------:|
| No log | 0.0345 | 2 | 5.1862 | -0.0378 | 5.1862 | 2.2773 |
| No log | 0.0690 | 4 | 3.0577 | 0.0812 | 3.0577 | 1.7486 |
| No log | 0.1034 | 6 | 2.0758 | 0.0051 | 2.0758 | 1.4408 |
| No log | 0.1379 | 8 | 2.4292 | -0.1028 | 2.4292 | 1.5586 |
| No log | 0.1724 | 10 | 2.1197 | -0.0538 | 2.1197 | 1.4559 |
| No log | 0.2069 | 12 | 1.4985 | 0.0905 | 1.4985 | 1.2241 |
| No log | 0.2414 | 14 | 1.3001 | 0.2057 | 1.3001 | 1.1402 |
| No log | 0.2759 | 16 | 1.1769 | 0.2605 | 1.1769 | 1.0848 |
| No log | 0.3103 | 18 | 1.1800 | 0.3648 | 1.1800 | 1.0863 |
| No log | 0.3448 | 20 | 1.1733 | 0.3280 | 1.1733 | 1.0832 |
| No log | 0.3793 | 22 | 1.2307 | 0.3653 | 1.2307 | 1.1094 |
| No log | 0.4138 | 24 | 1.6613 | 0.2496 | 1.6613 | 1.2889 |
| No log | 0.4483 | 26 | 2.1120 | 0.2388 | 2.1120 | 1.4533 |
| No log | 0.4828 | 28 | 1.9788 | 0.2675 | 1.9788 | 1.4067 |
| No log | 0.5172 | 30 | 1.6182 | 0.3065 | 1.6182 | 1.2721 |
| No log | 0.5517 | 32 | 1.3496 | 0.2931 | 1.3496 | 1.1617 |
| No log | 0.5862 | 34 | 1.3186 | 0.3334 | 1.3186 | 1.1483 |
| No log | 0.6207 | 36 | 1.6070 | 0.2877 | 1.6070 | 1.2677 |
| No log | 0.6552 | 38 | 2.0197 | 0.2404 | 2.0197 | 1.4212 |
| No log | 0.6897 | 40 | 1.8706 | 0.2875 | 1.8706 | 1.3677 |
| No log | 0.7241 | 42 | 1.2809 | 0.3309 | 1.2809 | 1.1318 |
| No log | 0.7586 | 44 | 0.9582 | 0.4354 | 0.9582 | 0.9789 |
| No log | 0.7931 | 46 | 1.0200 | 0.3792 | 1.0200 | 1.0100 |
| No log | 0.8276 | 48 | 1.0736 | 0.3206 | 1.0736 | 1.0361 |
| No log | 0.8621 | 50 | 0.9901 | 0.3910 | 0.9901 | 0.9950 |
| No log | 0.8966 | 52 | 0.9590 | 0.4662 | 0.9590 | 0.9793 |
| No log | 0.9310 | 54 | 1.1032 | 0.4964 | 1.1032 | 1.0503 |
| No log | 0.9655 | 56 | 1.3656 | 0.3294 | 1.3656 | 1.1686 |
| No log | 1.0 | 58 | 1.8693 | 0.2750 | 1.8693 | 1.3672 |
| No log | 1.0345 | 60 | 2.0892 | 0.2645 | 2.0892 | 1.4454 |
| No log | 1.0690 | 62 | 2.0997 | 0.2595 | 2.0997 | 1.4490 |
| No log | 1.1034 | 64 | 1.9481 | 0.2444 | 1.9481 | 1.3957 |
| No log | 1.1379 | 66 | 1.5582 | 0.2982 | 1.5582 | 1.2483 |
| No log | 1.1724 | 68 | 1.2422 | 0.4276 | 1.2422 | 1.1145 |
| No log | 1.2069 | 70 | 1.0069 | 0.4864 | 1.0069 | 1.0034 |
| No log | 1.2414 | 72 | 0.9569 | 0.5010 | 0.9569 | 0.9782 |
| No log | 1.2759 | 74 | 0.9885 | 0.5349 | 0.9885 | 0.9942 |
| No log | 1.3103 | 76 | 0.9916 | 0.5301 | 0.9916 | 0.9958 |
| No log | 1.3448 | 78 | 1.0251 | 0.5215 | 1.0251 | 1.0125 |
| No log | 1.3793 | 80 | 1.0781 | 0.5541 | 1.0781 | 1.0383 |
| No log | 1.4138 | 82 | 1.1173 | 0.5613 | 1.1173 | 1.0570 |
| No log | 1.4483 | 84 | 1.2439 | 0.5503 | 1.2439 | 1.1153 |
| No log | 1.4828 | 86 | 1.4597 | 0.4959 | 1.4597 | 1.2082 |
| No log | 1.5172 | 88 | 1.3854 | 0.4884 | 1.3854 | 1.1770 |
| No log | 1.5517 | 90 | 1.0968 | 0.4999 | 1.0968 | 1.0473 |
| No log | 1.5862 | 92 | 1.0354 | 0.5004 | 1.0354 | 1.0175 |
| No log | 1.6207 | 94 | 0.9106 | 0.6163 | 0.9106 | 0.9542 |
| No log | 1.6552 | 96 | 0.8705 | 0.5996 | 0.8705 | 0.9330 |
| No log | 1.6897 | 98 | 0.8819 | 0.5991 | 0.8819 | 0.9391 |
| No log | 1.7241 | 100 | 0.8762 | 0.5658 | 0.8762 | 0.9361 |
| No log | 1.7586 | 102 | 0.8939 | 0.5758 | 0.8939 | 0.9455 |
| No log | 1.7931 | 104 | 0.9885 | 0.5706 | 0.9885 | 0.9942 |
| No log | 1.8276 | 106 | 1.0530 | 0.5321 | 1.0530 | 1.0262 |
| No log | 1.8621 | 108 | 1.1204 | 0.4646 | 1.1204 | 1.0585 |
| No log | 1.8966 | 110 | 1.1526 | 0.4234 | 1.1526 | 1.0736 |
| No log | 1.9310 | 112 | 1.0426 | 0.5480 | 1.0426 | 1.0211 |
| No log | 1.9655 | 114 | 0.9864 | 0.5704 | 0.9864 | 0.9932 |
| No log | 2.0 | 116 | 0.9486 | 0.5881 | 0.9486 | 0.9740 |
| No log | 2.0345 | 118 | 1.0775 | 0.5425 | 1.0775 | 1.0380 |
| No log | 2.0690 | 120 | 1.1878 | 0.4808 | 1.1878 | 1.0899 |
| No log | 2.1034 | 122 | 1.0194 | 0.5717 | 1.0194 | 1.0096 |
| No log | 2.1379 | 124 | 0.8319 | 0.5682 | 0.8319 | 0.9121 |
| No log | 2.1724 | 126 | 0.7993 | 0.6287 | 0.7993 | 0.8940 |
| No log | 2.2069 | 128 | 0.7969 | 0.6017 | 0.7969 | 0.8927 |
| No log | 2.2414 | 130 | 0.8220 | 0.6365 | 0.8220 | 0.9066 |
| No log | 2.2759 | 132 | 0.8184 | 0.6720 | 0.8184 | 0.9046 |
| No log | 2.3103 | 134 | 0.8653 | 0.6555 | 0.8653 | 0.9302 |
| No log | 2.3448 | 136 | 0.9262 | 0.5937 | 0.9262 | 0.9624 |
| No log | 2.3793 | 138 | 0.9743 | 0.5853 | 0.9743 | 0.9871 |
| No log | 2.4138 | 140 | 0.9900 | 0.5962 | 0.9900 | 0.9950 |
| No log | 2.4483 | 142 | 0.9976 | 0.6118 | 0.9976 | 0.9988 |
| No log | 2.4828 | 144 | 1.0002 | 0.6432 | 1.0002 | 1.0001 |
| No log | 2.5172 | 146 | 1.0366 | 0.6233 | 1.0366 | 1.0181 |
| No log | 2.5517 | 148 | 1.0498 | 0.5901 | 1.0498 | 1.0246 |
| No log | 2.5862 | 150 | 1.0991 | 0.5633 | 1.0991 | 1.0484 |
| No log | 2.6207 | 152 | 1.1327 | 0.5153 | 1.1327 | 1.0643 |
| No log | 2.6552 | 154 | 1.0076 | 0.5631 | 1.0076 | 1.0038 |
| No log | 2.6897 | 156 | 0.8848 | 0.5859 | 0.8848 | 0.9406 |
| No log | 2.7241 | 158 | 0.8704 | 0.6344 | 0.8704 | 0.9330 |
| No log | 2.7586 | 160 | 0.8758 | 0.6288 | 0.8758 | 0.9358 |
| No log | 2.7931 | 162 | 0.8692 | 0.6327 | 0.8692 | 0.9323 |
| No log | 2.8276 | 164 | 0.8413 | 0.6212 | 0.8413 | 0.9172 |
| No log | 2.8621 | 166 | 0.8576 | 0.5587 | 0.8576 | 0.9260 |
| No log | 2.8966 | 168 | 0.9691 | 0.5391 | 0.9691 | 0.9845 |
| No log | 2.9310 | 170 | 0.9275 | 0.5478 | 0.9275 | 0.9631 |
| No log | 2.9655 | 172 | 0.8227 | 0.5835 | 0.8227 | 0.9070 |
| No log | 3.0 | 174 | 0.7952 | 0.5973 | 0.7952 | 0.8918 |
| No log | 3.0345 | 176 | 0.8058 | 0.6346 | 0.8058 | 0.8977 |
| No log | 3.0690 | 178 | 0.8350 | 0.6656 | 0.8350 | 0.9138 |
| No log | 3.1034 | 180 | 0.8629 | 0.6502 | 0.8629 | 0.9289 |
| No log | 3.1379 | 182 | 0.8750 | 0.6254 | 0.8750 | 0.9354 |
| No log | 3.1724 | 184 | 0.8491 | 0.6206 | 0.8491 | 0.9215 |
| No log | 3.2069 | 186 | 0.8503 | 0.6138 | 0.8503 | 0.9221 |
| No log | 3.2414 | 188 | 0.8914 | 0.6342 | 0.8914 | 0.9441 |
| No log | 3.2759 | 190 | 0.9022 | 0.6320 | 0.9022 | 0.9498 |
| No log | 3.3103 | 192 | 0.8682 | 0.6172 | 0.8682 | 0.9318 |
| No log | 3.3448 | 194 | 0.8997 | 0.6183 | 0.8997 | 0.9485 |
| No log | 3.3793 | 196 | 0.9585 | 0.5990 | 0.9585 | 0.9790 |
| No log | 3.4138 | 198 | 0.9983 | 0.5997 | 0.9983 | 0.9992 |
| No log | 3.4483 | 200 | 0.9731 | 0.5824 | 0.9731 | 0.9864 |
| No log | 3.4828 | 202 | 0.8894 | 0.5772 | 0.8894 | 0.9431 |
| No log | 3.5172 | 204 | 0.8138 | 0.6292 | 0.8138 | 0.9021 |
| No log | 3.5517 | 206 | 0.7807 | 0.6866 | 0.7807 | 0.8836 |
| No log | 3.5862 | 208 | 0.7783 | 0.6681 | 0.7783 | 0.8822 |
| No log | 3.6207 | 210 | 0.8034 | 0.6582 | 0.8034 | 0.8963 |
| No log | 3.6552 | 212 | 0.8319 | 0.6367 | 0.8319 | 0.9121 |
| No log | 3.6897 | 214 | 0.8241 | 0.6469 | 0.8241 | 0.9078 |
| No log | 3.7241 | 216 | 0.7904 | 0.6456 | 0.7904 | 0.8890 |
| No log | 3.7586 | 218 | 0.7696 | 0.6693 | 0.7696 | 0.8773 |
| No log | 3.7931 | 220 | 0.7797 | 0.6668 | 0.7797 | 0.8830 |
| No log | 3.8276 | 222 | 0.8125 | 0.6337 | 0.8125 | 0.9014 |
| No log | 3.8621 | 224 | 0.8186 | 0.6308 | 0.8186 | 0.9048 |
| No log | 3.8966 | 226 | 0.8099 | 0.6239 | 0.8099 | 0.8999 |
| No log | 3.9310 | 228 | 0.8000 | 0.6230 | 0.8000 | 0.8944 |
| No log | 3.9655 | 230 | 0.8000 | 0.6542 | 0.8000 | 0.8944 |
| No log | 4.0 | 232 | 0.8077 | 0.6261 | 0.8077 | 0.8987 |
| No log | 4.0345 | 234 | 0.7874 | 0.6253 | 0.7874 | 0.8874 |
| No log | 4.0690 | 236 | 0.7857 | 0.6279 | 0.7857 | 0.8864 |
| No log | 4.1034 | 238 | 0.8154 | 0.6437 | 0.8154 | 0.9030 |
| No log | 4.1379 | 240 | 0.9026 | 0.6154 | 0.9026 | 0.9501 |
| No log | 4.1724 | 242 | 0.9798 | 0.5809 | 0.9798 | 0.9899 |
| No log | 4.2069 | 244 | 1.0225 | 0.5860 | 1.0225 | 1.0112 |
| No log | 4.2414 | 246 | 1.0003 | 0.5898 | 1.0003 | 1.0002 |
| No log | 4.2759 | 248 | 0.9371 | 0.5952 | 0.9371 | 0.9680 |
| No log | 4.3103 | 250 | 0.8468 | 0.6253 | 0.8468 | 0.9202 |
| No log | 4.3448 | 252 | 0.8051 | 0.6094 | 0.8051 | 0.8973 |
| No log | 4.3793 | 254 | 0.8051 | 0.5762 | 0.8051 | 0.8973 |
| No log | 4.4138 | 256 | 0.8181 | 0.5869 | 0.8181 | 0.9045 |
| No log | 4.4483 | 258 | 0.8486 | 0.5992 | 0.8486 | 0.9212 |
| No log | 4.4828 | 260 | 0.8767 | 0.5374 | 0.8767 | 0.9363 |
| No log | 4.5172 | 262 | 0.8821 | 0.5362 | 0.8821 | 0.9392 |
| No log | 4.5517 | 264 | 0.8767 | 0.5325 | 0.8767 | 0.9363 |
| No log | 4.5862 | 266 | 0.8595 | 0.5820 | 0.8595 | 0.9271 |
| No log | 4.6207 | 268 | 0.8531 | 0.5717 | 0.8531 | 0.9236 |
| No log | 4.6552 | 270 | 0.8257 | 0.5880 | 0.8257 | 0.9087 |
| No log | 4.6897 | 272 | 0.8056 | 0.6123 | 0.8056 | 0.8975 |
| No log | 4.7241 | 274 | 0.8271 | 0.6630 | 0.8271 | 0.9095 |
| No log | 4.7586 | 276 | 0.8815 | 0.6292 | 0.8815 | 0.9389 |
| No log | 4.7931 | 278 | 0.9323 | 0.6212 | 0.9323 | 0.9656 |
| No log | 4.8276 | 280 | 0.9271 | 0.5934 | 0.9271 | 0.9629 |
| No log | 4.8621 | 282 | 0.8849 | 0.5554 | 0.8849 | 0.9407 |
| No log | 4.8966 | 284 | 0.8742 | 0.5796 | 0.8742 | 0.9350 |
| No log | 4.9310 | 286 | 0.8645 | 0.6110 | 0.8645 | 0.9298 |
| No log | 4.9655 | 288 | 0.8370 | 0.6267 | 0.8370 | 0.9149 |
| No log | 5.0 | 290 | 0.8151 | 0.6442 | 0.8151 | 0.9028 |
| No log | 5.0345 | 292 | 0.8248 | 0.6245 | 0.8248 | 0.9082 |
| No log | 5.0690 | 294 | 0.8697 | 0.6321 | 0.8697 | 0.9326 |
| No log | 5.1034 | 296 | 0.9126 | 0.6272 | 0.9126 | 0.9553 |
| No log | 5.1379 | 298 | 0.9156 | 0.6071 | 0.9156 | 0.9569 |
| No log | 5.1724 | 300 | 0.8854 | 0.6149 | 0.8854 | 0.9409 |
| No log | 5.2069 | 302 | 0.8271 | 0.6239 | 0.8271 | 0.9094 |
| No log | 5.2414 | 304 | 0.7785 | 0.6433 | 0.7785 | 0.8823 |
| No log | 5.2759 | 306 | 0.7709 | 0.6435 | 0.7709 | 0.8780 |
| No log | 5.3103 | 308 | 0.7711 | 0.6586 | 0.7711 | 0.8781 |
| No log | 5.3448 | 310 | 0.7811 | 0.6382 | 0.7811 | 0.8838 |
| No log | 5.3793 | 312 | 0.7917 | 0.6209 | 0.7917 | 0.8898 |
| No log | 5.4138 | 314 | 0.7767 | 0.6441 | 0.7767 | 0.8813 |
| No log | 5.4483 | 316 | 0.7612 | 0.6443 | 0.7612 | 0.8725 |
| No log | 5.4828 | 318 | 0.7796 | 0.6292 | 0.7796 | 0.8829 |
| No log | 5.5172 | 320 | 0.7923 | 0.6477 | 0.7923 | 0.8901 |
| No log | 5.5517 | 322 | 0.8115 | 0.6160 | 0.8115 | 0.9008 |
| No log | 5.5862 | 324 | 0.8285 | 0.6434 | 0.8285 | 0.9102 |
| No log | 5.6207 | 326 | 0.8318 | 0.6461 | 0.8318 | 0.9120 |
| No log | 5.6552 | 328 | 0.8367 | 0.6555 | 0.8367 | 0.9147 |
| No log | 5.6897 | 330 | 0.8132 | 0.6239 | 0.8132 | 0.9018 |
| No log | 5.7241 | 332 | 0.7800 | 0.6636 | 0.7800 | 0.8832 |
| No log | 5.7586 | 334 | 0.7705 | 0.6686 | 0.7705 | 0.8778 |
| No log | 5.7931 | 336 | 0.7790 | 0.6428 | 0.7790 | 0.8826 |
| No log | 5.8276 | 338 | 0.7894 | 0.6567 | 0.7894 | 0.8885 |
| No log | 5.8621 | 340 | 0.8060 | 0.6427 | 0.8060 | 0.8978 |
| No log | 5.8966 | 342 | 0.8504 | 0.6190 | 0.8504 | 0.9221 |
| No log | 5.9310 | 344 | 0.8875 | 0.6063 | 0.8875 | 0.9421 |
| No log | 5.9655 | 346 | 0.9436 | 0.5996 | 0.9436 | 0.9714 |
| No log | 6.0 | 348 | 0.9954 | 0.5868 | 0.9954 | 0.9977 |
| No log | 6.0345 | 350 | 0.9874 | 0.5868 | 0.9874 | 0.9937 |
| No log | 6.0690 | 352 | 0.9185 | 0.6044 | 0.9185 | 0.9584 |
| No log | 6.1034 | 354 | 0.8326 | 0.6562 | 0.8326 | 0.9124 |
| No log | 6.1379 | 356 | 0.7821 | 0.6472 | 0.7821 | 0.8844 |
| No log | 6.1724 | 358 | 0.7695 | 0.6406 | 0.7695 | 0.8772 |
| No log | 6.2069 | 360 | 0.7661 | 0.6475 | 0.7661 | 0.8753 |
| No log | 6.2414 | 362 | 0.7745 | 0.6515 | 0.7745 | 0.8801 |
| No log | 6.2759 | 364 | 0.8067 | 0.6573 | 0.8067 | 0.8982 |
| No log | 6.3103 | 366 | 0.8578 | 0.6044 | 0.8578 | 0.9262 |
| No log | 6.3448 | 368 | 0.8921 | 0.6054 | 0.8921 | 0.9445 |
| No log | 6.3793 | 370 | 0.8529 | 0.6229 | 0.8529 | 0.9235 |
| No log | 6.4138 | 372 | 0.8159 | 0.6206 | 0.8159 | 0.9033 |
| No log | 6.4483 | 374 | 0.7780 | 0.6363 | 0.7780 | 0.8821 |
| No log | 6.4828 | 376 | 0.7502 | 0.6261 | 0.7502 | 0.8661 |
| No log | 6.5172 | 378 | 0.7478 | 0.6107 | 0.7478 | 0.8648 |
| No log | 6.5517 | 380 | 0.7484 | 0.6096 | 0.7484 | 0.8651 |
| No log | 6.5862 | 382 | 0.7496 | 0.5955 | 0.7496 | 0.8658 |
| No log | 6.6207 | 384 | 0.7624 | 0.5958 | 0.7624 | 0.8732 |
| No log | 6.6552 | 386 | 0.7876 | 0.5835 | 0.7876 | 0.8875 |
| No log | 6.6897 | 388 | 0.8064 | 0.5595 | 0.8064 | 0.8980 |
| No log | 6.7241 | 390 | 0.8212 | 0.5693 | 0.8212 | 0.9062 |
| No log | 6.7586 | 392 | 0.8182 | 0.5671 | 0.8182 | 0.9046 |
| No log | 6.7931 | 394 | 0.8005 | 0.5709 | 0.8005 | 0.8947 |
| No log | 6.8276 | 396 | 0.7845 | 0.5890 | 0.7845 | 0.8857 |
| No log | 6.8621 | 398 | 0.7878 | 0.6082 | 0.7878 | 0.8876 |
| No log | 6.8966 | 400 | 0.7900 | 0.6243 | 0.7900 | 0.8888 |
| No log | 6.9310 | 402 | 0.7952 | 0.6137 | 0.7952 | 0.8917 |
| No log | 6.9655 | 404 | 0.8012 | 0.6137 | 0.8012 | 0.8951 |
| No log | 7.0 | 406 | 0.8064 | 0.6054 | 0.8064 | 0.8980 |
| No log | 7.0345 | 408 | 0.8115 | 0.6012 | 0.8115 | 0.9008 |
| No log | 7.0690 | 410 | 0.8182 | 0.5762 | 0.8182 | 0.9045 |
| No log | 7.1034 | 412 | 0.8218 | 0.5748 | 0.8218 | 0.9065 |
| No log | 7.1379 | 414 | 0.8165 | 0.5770 | 0.8165 | 0.9036 |
| No log | 7.1724 | 416 | 0.8047 | 0.5787 | 0.8047 | 0.8971 |
| No log | 7.2069 | 418 | 0.8104 | 0.6099 | 0.8104 | 0.9002 |
| No log | 7.2414 | 420 | 0.8379 | 0.6212 | 0.8379 | 0.9154 |
| No log | 7.2759 | 422 | 0.8432 | 0.6318 | 0.8432 | 0.9183 |
| No log | 7.3103 | 424 | 0.8384 | 0.6221 | 0.8384 | 0.9157 |
| No log | 7.3448 | 426 | 0.8217 | 0.6220 | 0.8217 | 0.9065 |
| No log | 7.3793 | 428 | 0.7952 | 0.6406 | 0.7952 | 0.8917 |
| No log | 7.4138 | 430 | 0.7703 | 0.6372 | 0.7703 | 0.8777 |
| No log | 7.4483 | 432 | 0.7565 | 0.6420 | 0.7565 | 0.8698 |
| No log | 7.4828 | 434 | 0.7524 | 0.6527 | 0.7524 | 0.8674 |
| No log | 7.5172 | 436 | 0.7557 | 0.6594 | 0.7557 | 0.8693 |
| No log | 7.5517 | 438 | 0.7630 | 0.6386 | 0.7630 | 0.8735 |
| No log | 7.5862 | 440 | 0.7777 | 0.6197 | 0.7777 | 0.8818 |
| No log | 7.6207 | 442 | 0.7837 | 0.6169 | 0.7837 | 0.8852 |
| No log | 7.6552 | 444 | 0.7856 | 0.6347 | 0.7856 | 0.8863 |
| No log | 7.6897 | 446 | 0.7962 | 0.6302 | 0.7962 | 0.8923 |
| No log | 7.7241 | 448 | 0.8190 | 0.6263 | 0.8190 | 0.9050 |
| No log | 7.7586 | 450 | 0.8519 | 0.6337 | 0.8519 | 0.9230 |
| No log | 7.7931 | 452 | 0.8714 | 0.6541 | 0.8714 | 0.9335 |
| No log | 7.8276 | 454 | 0.8719 | 0.6541 | 0.8719 | 0.9337 |
| No log | 7.8621 | 456 | 0.8726 | 0.6541 | 0.8726 | 0.9341 |
| No log | 7.8966 | 458 | 0.8645 | 0.6337 | 0.8645 | 0.9298 |
| No log | 7.9310 | 460 | 0.8500 | 0.6085 | 0.8500 | 0.9220 |
| No log | 7.9655 | 462 | 0.8388 | 0.6134 | 0.8388 | 0.9159 |
| No log | 8.0 | 464 | 0.8372 | 0.6125 | 0.8372 | 0.9150 |
| No log | 8.0345 | 466 | 0.8354 | 0.6120 | 0.8354 | 0.9140 |
| No log | 8.0690 | 468 | 0.8356 | 0.6078 | 0.8356 | 0.9141 |
| No log | 8.1034 | 470 | 0.8429 | 0.5947 | 0.8429 | 0.9181 |
| No log | 8.1379 | 472 | 0.8561 | 0.5937 | 0.8561 | 0.9253 |
| No log | 8.1724 | 474 | 0.8688 | 0.5941 | 0.8688 | 0.9321 |
| No log | 8.2069 | 476 | 0.8688 | 0.5810 | 0.8688 | 0.9321 |
| No log | 8.2414 | 478 | 0.8654 | 0.5810 | 0.8654 | 0.9303 |
| No log | 8.2759 | 480 | 0.8597 | 0.6011 | 0.8597 | 0.9272 |
| No log | 8.3103 | 482 | 0.8424 | 0.6038 | 0.8424 | 0.9178 |
| No log | 8.3448 | 484 | 0.8268 | 0.6108 | 0.8268 | 0.9093 |
| No log | 8.3793 | 486 | 0.8026 | 0.6236 | 0.8026 | 0.8959 |
| No log | 8.4138 | 488 | 0.7883 | 0.6159 | 0.7883 | 0.8878 |
| No log | 8.4483 | 490 | 0.7820 | 0.6168 | 0.7820 | 0.8843 |
| No log | 8.4828 | 492 | 0.7802 | 0.6361 | 0.7802 | 0.8833 |
| No log | 8.5172 | 494 | 0.7802 | 0.6361 | 0.7802 | 0.8833 |
| No log | 8.5517 | 496 | 0.7827 | 0.6429 | 0.7827 | 0.8847 |
| No log | 8.5862 | 498 | 0.7904 | 0.6383 | 0.7904 | 0.8890 |
| 0.4074 | 8.6207 | 500 | 0.8078 | 0.6313 | 0.8078 | 0.8988 |
| 0.4074 | 8.6552 | 502 | 0.8245 | 0.6366 | 0.8245 | 0.9080 |
| 0.4074 | 8.6897 | 504 | 0.8368 | 0.6351 | 0.8368 | 0.9148 |
| 0.4074 | 8.7241 | 506 | 0.8520 | 0.6351 | 0.8520 | 0.9230 |
| 0.4074 | 8.7586 | 508 | 0.8606 | 0.6414 | 0.8606 | 0.9277 |
| 0.4074 | 8.7931 | 510 | 0.8532 | 0.6329 | 0.8532 | 0.9237 |
| 0.4074 | 8.8276 | 512 | 0.8381 | 0.6502 | 0.8381 | 0.9155 |
| 0.4074 | 8.8621 | 514 | 0.8213 | 0.6255 | 0.8213 | 0.9062 |
| 0.4074 | 8.8966 | 516 | 0.8023 | 0.6301 | 0.8023 | 0.8957 |
| 0.4074 | 8.9310 | 518 | 0.7874 | 0.6234 | 0.7874 | 0.8874 |
| 0.4074 | 8.9655 | 520 | 0.7773 | 0.6437 | 0.7773 | 0.8816 |
| 0.4074 | 9.0 | 522 | 0.7713 | 0.6386 | 0.7713 | 0.8782 |
| 0.4074 | 9.0345 | 524 | 0.7694 | 0.6386 | 0.7694 | 0.8772 |
| 0.4074 | 9.0690 | 526 | 0.7735 | 0.6437 | 0.7735 | 0.8795 |
| 0.4074 | 9.1034 | 528 | 0.7782 | 0.6437 | 0.7782 | 0.8822 |
| 0.4074 | 9.1379 | 530 | 0.7827 | 0.6488 | 0.7827 | 0.8847 |
| 0.4074 | 9.1724 | 532 | 0.7899 | 0.6391 | 0.7899 | 0.8888 |
| 0.4074 | 9.2069 | 534 | 0.7989 | 0.6323 | 0.7989 | 0.8938 |
| 0.4074 | 9.2414 | 536 | 0.8094 | 0.6215 | 0.8094 | 0.8997 |
| 0.4074 | 9.2759 | 538 | 0.8184 | 0.6145 | 0.8184 | 0.9046 |
| 0.4074 | 9.3103 | 540 | 0.8235 | 0.6167 | 0.8235 | 0.9074 |
| 0.4074 | 9.3448 | 542 | 0.8286 | 0.6167 | 0.8286 | 0.9103 |
| 0.4074 | 9.3793 | 544 | 0.8365 | 0.6173 | 0.8365 | 0.9146 |
| 0.4074 | 9.4138 | 546 | 0.8412 | 0.6078 | 0.8412 | 0.9172 |
| 0.4074 | 9.4483 | 548 | 0.8434 | 0.6078 | 0.8434 | 0.9184 |
| 0.4074 | 9.4828 | 550 | 0.8433 | 0.5952 | 0.8433 | 0.9183 |
| 0.4074 | 9.5172 | 552 | 0.8417 | 0.6046 | 0.8417 | 0.9174 |
| 0.4074 | 9.5517 | 554 | 0.8419 | 0.6078 | 0.8419 | 0.9175 |
| 0.4074 | 9.5862 | 556 | 0.8423 | 0.6078 | 0.8423 | 0.9178 |
| 0.4074 | 9.6207 | 558 | 0.8444 | 0.6182 | 0.8444 | 0.9189 |
| 0.4074 | 9.6552 | 560 | 0.8429 | 0.6182 | 0.8429 | 0.9181 |
| 0.4074 | 9.6897 | 562 | 0.8386 | 0.6182 | 0.8386 | 0.9157 |
| 0.4074 | 9.7241 | 564 | 0.8341 | 0.6182 | 0.8341 | 0.9133 |
| 0.4074 | 9.7586 | 566 | 0.8309 | 0.6182 | 0.8309 | 0.9116 |
| 0.4074 | 9.7931 | 568 | 0.8277 | 0.6091 | 0.8277 | 0.9098 |
| 0.4074 | 9.8276 | 570 | 0.8257 | 0.6091 | 0.8257 | 0.9087 |
| 0.4074 | 9.8621 | 572 | 0.8247 | 0.6091 | 0.8247 | 0.9081 |
| 0.4074 | 9.8966 | 574 | 0.8238 | 0.6187 | 0.8238 | 0.9076 |
| 0.4074 | 9.9310 | 576 | 0.8239 | 0.6091 | 0.8239 | 0.9077 |
| 0.4074 | 9.9655 | 578 | 0.8242 | 0.6091 | 0.8242 | 0.9079 |
| 0.4074 | 10.0 | 580 | 0.8242 | 0.6091 | 0.8242 | 0.9079 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu118
- Datasets 2.21.0
- Tokenizers 0.19.1
|
mradermacher/AngelSlayer-12B-Unslop-Mell-RPMax-DARKNESS-i1-GGUF
|
mradermacher
| 2024-12-16T05:04:18Z | 1,202 | 5 |
transformers
|
[
"transformers",
"gguf",
"mergekit",
"merge",
"12b",
"chat",
"roleplay",
"creative-writing",
"DELLA-linear",
"en",
"base_model:redrix/AngelSlayer-12B-Unslop-Mell-RPMax-DARKNESS",
"base_model:quantized:redrix/AngelSlayer-12B-Unslop-Mell-RPMax-DARKNESS",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2024-12-09T14:28:07Z |
---
base_model: redrix/AngelSlayer-12B-Unslop-Mell-RPMax-DARKNESS
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- mergekit
- merge
- 12b
- chat
- roleplay
- creative-writing
- DELLA-linear
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/redrix/AngelSlayer-12B-Unslop-Mell-RPMax-DARKNESS
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/AngelSlayer-12B-Unslop-Mell-RPMax-DARKNESS-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/AngelSlayer-12B-Unslop-Mell-RPMax-DARKNESS-i1-GGUF/resolve/main/AngelSlayer-12B-Unslop-Mell-RPMax-DARKNESS.i1-IQ1_S.gguf) | i1-IQ1_S | 3.1 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/AngelSlayer-12B-Unslop-Mell-RPMax-DARKNESS-i1-GGUF/resolve/main/AngelSlayer-12B-Unslop-Mell-RPMax-DARKNESS.i1-IQ1_M.gguf) | i1-IQ1_M | 3.3 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/AngelSlayer-12B-Unslop-Mell-RPMax-DARKNESS-i1-GGUF/resolve/main/AngelSlayer-12B-Unslop-Mell-RPMax-DARKNESS.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 3.7 | |
| [GGUF](https://huggingface.co/mradermacher/AngelSlayer-12B-Unslop-Mell-RPMax-DARKNESS-i1-GGUF/resolve/main/AngelSlayer-12B-Unslop-Mell-RPMax-DARKNESS.i1-IQ2_XS.gguf) | i1-IQ2_XS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/AngelSlayer-12B-Unslop-Mell-RPMax-DARKNESS-i1-GGUF/resolve/main/AngelSlayer-12B-Unslop-Mell-RPMax-DARKNESS.i1-IQ2_S.gguf) | i1-IQ2_S | 4.2 | |
| [GGUF](https://huggingface.co/mradermacher/AngelSlayer-12B-Unslop-Mell-RPMax-DARKNESS-i1-GGUF/resolve/main/AngelSlayer-12B-Unslop-Mell-RPMax-DARKNESS.i1-IQ2_M.gguf) | i1-IQ2_M | 4.5 | |
| [GGUF](https://huggingface.co/mradermacher/AngelSlayer-12B-Unslop-Mell-RPMax-DARKNESS-i1-GGUF/resolve/main/AngelSlayer-12B-Unslop-Mell-RPMax-DARKNESS.i1-Q2_K_S.gguf) | i1-Q2_K_S | 4.6 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/AngelSlayer-12B-Unslop-Mell-RPMax-DARKNESS-i1-GGUF/resolve/main/AngelSlayer-12B-Unslop-Mell-RPMax-DARKNESS.i1-Q2_K.gguf) | i1-Q2_K | 4.9 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/AngelSlayer-12B-Unslop-Mell-RPMax-DARKNESS-i1-GGUF/resolve/main/AngelSlayer-12B-Unslop-Mell-RPMax-DARKNESS.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 5.0 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/AngelSlayer-12B-Unslop-Mell-RPMax-DARKNESS-i1-GGUF/resolve/main/AngelSlayer-12B-Unslop-Mell-RPMax-DARKNESS.i1-IQ3_XS.gguf) | i1-IQ3_XS | 5.4 | |
| [GGUF](https://huggingface.co/mradermacher/AngelSlayer-12B-Unslop-Mell-RPMax-DARKNESS-i1-GGUF/resolve/main/AngelSlayer-12B-Unslop-Mell-RPMax-DARKNESS.i1-Q3_K_S.gguf) | i1-Q3_K_S | 5.6 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/AngelSlayer-12B-Unslop-Mell-RPMax-DARKNESS-i1-GGUF/resolve/main/AngelSlayer-12B-Unslop-Mell-RPMax-DARKNESS.i1-IQ3_S.gguf) | i1-IQ3_S | 5.7 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/AngelSlayer-12B-Unslop-Mell-RPMax-DARKNESS-i1-GGUF/resolve/main/AngelSlayer-12B-Unslop-Mell-RPMax-DARKNESS.i1-IQ3_M.gguf) | i1-IQ3_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/AngelSlayer-12B-Unslop-Mell-RPMax-DARKNESS-i1-GGUF/resolve/main/AngelSlayer-12B-Unslop-Mell-RPMax-DARKNESS.i1-Q3_K_M.gguf) | i1-Q3_K_M | 6.2 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/AngelSlayer-12B-Unslop-Mell-RPMax-DARKNESS-i1-GGUF/resolve/main/AngelSlayer-12B-Unslop-Mell-RPMax-DARKNESS.i1-Q3_K_L.gguf) | i1-Q3_K_L | 6.7 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/AngelSlayer-12B-Unslop-Mell-RPMax-DARKNESS-i1-GGUF/resolve/main/AngelSlayer-12B-Unslop-Mell-RPMax-DARKNESS.i1-IQ4_XS.gguf) | i1-IQ4_XS | 6.8 | |
| [GGUF](https://huggingface.co/mradermacher/AngelSlayer-12B-Unslop-Mell-RPMax-DARKNESS-i1-GGUF/resolve/main/AngelSlayer-12B-Unslop-Mell-RPMax-DARKNESS.i1-Q4_0_4_4.gguf) | i1-Q4_0_4_4 | 7.2 | fast on arm, low quality |
| [GGUF](https://huggingface.co/mradermacher/AngelSlayer-12B-Unslop-Mell-RPMax-DARKNESS-i1-GGUF/resolve/main/AngelSlayer-12B-Unslop-Mell-RPMax-DARKNESS.i1-Q4_0_4_8.gguf) | i1-Q4_0_4_8 | 7.2 | fast on arm+i8mm, low quality |
| [GGUF](https://huggingface.co/mradermacher/AngelSlayer-12B-Unslop-Mell-RPMax-DARKNESS-i1-GGUF/resolve/main/AngelSlayer-12B-Unslop-Mell-RPMax-DARKNESS.i1-Q4_0_8_8.gguf) | i1-Q4_0_8_8 | 7.2 | fast on arm+sve, low quality |
| [GGUF](https://huggingface.co/mradermacher/AngelSlayer-12B-Unslop-Mell-RPMax-DARKNESS-i1-GGUF/resolve/main/AngelSlayer-12B-Unslop-Mell-RPMax-DARKNESS.i1-Q4_0.gguf) | i1-Q4_0 | 7.2 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/AngelSlayer-12B-Unslop-Mell-RPMax-DARKNESS-i1-GGUF/resolve/main/AngelSlayer-12B-Unslop-Mell-RPMax-DARKNESS.i1-Q4_K_S.gguf) | i1-Q4_K_S | 7.2 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/AngelSlayer-12B-Unslop-Mell-RPMax-DARKNESS-i1-GGUF/resolve/main/AngelSlayer-12B-Unslop-Mell-RPMax-DARKNESS.i1-Q4_K_M.gguf) | i1-Q4_K_M | 7.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/AngelSlayer-12B-Unslop-Mell-RPMax-DARKNESS-i1-GGUF/resolve/main/AngelSlayer-12B-Unslop-Mell-RPMax-DARKNESS.i1-Q5_K_S.gguf) | i1-Q5_K_S | 8.6 | |
| [GGUF](https://huggingface.co/mradermacher/AngelSlayer-12B-Unslop-Mell-RPMax-DARKNESS-i1-GGUF/resolve/main/AngelSlayer-12B-Unslop-Mell-RPMax-DARKNESS.i1-Q5_K_M.gguf) | i1-Q5_K_M | 8.8 | |
| [GGUF](https://huggingface.co/mradermacher/AngelSlayer-12B-Unslop-Mell-RPMax-DARKNESS-i1-GGUF/resolve/main/AngelSlayer-12B-Unslop-Mell-RPMax-DARKNESS.i1-Q6_K.gguf) | i1-Q6_K | 10.2 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
mradermacher/Cakrawala-8B-GGUF
|
mradermacher
| 2024-12-16T05:03:56Z | 23 | 4 |
transformers
|
[
"transformers",
"gguf",
"axolotl",
"en",
"dataset:NarrativAI/CakrawalaRP",
"base_model:NarrativAI/Cakrawala-Llama-3.1-8B",
"base_model:quantized:NarrativAI/Cakrawala-Llama-3.1-8B",
"license:mit",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-11-25T08:48:22Z |
---
base_model: NarrativAI/Cakrawala-Llama-3.1-8B
datasets:
- NarrativAI/CakrawalaRP
language:
- en
library_name: transformers
license: mit
quantized_by: mradermacher
tags:
- axolotl
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
static quants of https://huggingface.co/NarrativAI/Cakrawala-Llama-3.1-8B
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Cakrawala-8B-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Cakrawala-8B-GGUF/resolve/main/Cakrawala-8B.Q2_K.gguf) | Q2_K | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/Cakrawala-8B-GGUF/resolve/main/Cakrawala-8B.Q3_K_S.gguf) | Q3_K_S | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/Cakrawala-8B-GGUF/resolve/main/Cakrawala-8B.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Cakrawala-8B-GGUF/resolve/main/Cakrawala-8B.Q3_K_L.gguf) | Q3_K_L | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/Cakrawala-8B-GGUF/resolve/main/Cakrawala-8B.IQ4_XS.gguf) | IQ4_XS | 4.6 | |
| [GGUF](https://huggingface.co/mradermacher/Cakrawala-8B-GGUF/resolve/main/Cakrawala-8B.Q4_0_4_4.gguf) | Q4_0_4_4 | 4.8 | fast on arm, low quality |
| [GGUF](https://huggingface.co/mradermacher/Cakrawala-8B-GGUF/resolve/main/Cakrawala-8B.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Cakrawala-8B-GGUF/resolve/main/Cakrawala-8B.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Cakrawala-8B-GGUF/resolve/main/Cakrawala-8B.Q5_K_S.gguf) | Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/Cakrawala-8B-GGUF/resolve/main/Cakrawala-8B.Q5_K_M.gguf) | Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/Cakrawala-8B-GGUF/resolve/main/Cakrawala-8B.Q6_K.gguf) | Q6_K | 6.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Cakrawala-8B-GGUF/resolve/main/Cakrawala-8B.Q8_0.gguf) | Q8_0 | 8.6 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Cakrawala-8B-GGUF/resolve/main/Cakrawala-8B.f16.gguf) | f16 | 16.2 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
mradermacher/MN-12B-Starcannon-v2-i1-GGUF
|
mradermacher
| 2024-12-16T05:01:26Z | 514 | 8 |
transformers
|
[
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:AuriAetherwiing/MN-12B-Starcannon-v2",
"base_model:quantized:AuriAetherwiing/MN-12B-Starcannon-v2",
"license:cc-by-nc-nd-4.0",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2024-08-01T22:28:25Z |
---
base_model: AuriAetherwiing/MN-12B-Starcannon-v2
language:
- en
library_name: transformers
license: cc-by-nc-nd-4.0
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/AuriAetherwiing/MN-12B-Starcannon-v2
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/MN-12B-Starcannon-v2-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/MN-12B-Starcannon-v2-i1-GGUF/resolve/main/MN-12B-Starcannon-v2.i1-IQ1_S.gguf) | i1-IQ1_S | 3.1 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/MN-12B-Starcannon-v2-i1-GGUF/resolve/main/MN-12B-Starcannon-v2.i1-IQ1_M.gguf) | i1-IQ1_M | 3.3 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/MN-12B-Starcannon-v2-i1-GGUF/resolve/main/MN-12B-Starcannon-v2.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 3.7 | |
| [GGUF](https://huggingface.co/mradermacher/MN-12B-Starcannon-v2-i1-GGUF/resolve/main/MN-12B-Starcannon-v2.i1-IQ2_XS.gguf) | i1-IQ2_XS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/MN-12B-Starcannon-v2-i1-GGUF/resolve/main/MN-12B-Starcannon-v2.i1-IQ2_S.gguf) | i1-IQ2_S | 4.2 | |
| [GGUF](https://huggingface.co/mradermacher/MN-12B-Starcannon-v2-i1-GGUF/resolve/main/MN-12B-Starcannon-v2.i1-IQ2_M.gguf) | i1-IQ2_M | 4.5 | |
| [GGUF](https://huggingface.co/mradermacher/MN-12B-Starcannon-v2-i1-GGUF/resolve/main/MN-12B-Starcannon-v2.i1-Q2_K.gguf) | i1-Q2_K | 4.9 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/MN-12B-Starcannon-v2-i1-GGUF/resolve/main/MN-12B-Starcannon-v2.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 5.0 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/MN-12B-Starcannon-v2-i1-GGUF/resolve/main/MN-12B-Starcannon-v2.i1-IQ3_XS.gguf) | i1-IQ3_XS | 5.4 | |
| [GGUF](https://huggingface.co/mradermacher/MN-12B-Starcannon-v2-i1-GGUF/resolve/main/MN-12B-Starcannon-v2.i1-Q3_K_S.gguf) | i1-Q3_K_S | 5.6 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/MN-12B-Starcannon-v2-i1-GGUF/resolve/main/MN-12B-Starcannon-v2.i1-IQ3_S.gguf) | i1-IQ3_S | 5.7 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/MN-12B-Starcannon-v2-i1-GGUF/resolve/main/MN-12B-Starcannon-v2.i1-IQ3_M.gguf) | i1-IQ3_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/MN-12B-Starcannon-v2-i1-GGUF/resolve/main/MN-12B-Starcannon-v2.i1-Q3_K_M.gguf) | i1-Q3_K_M | 6.2 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/MN-12B-Starcannon-v2-i1-GGUF/resolve/main/MN-12B-Starcannon-v2.i1-Q3_K_L.gguf) | i1-Q3_K_L | 6.7 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/MN-12B-Starcannon-v2-i1-GGUF/resolve/main/MN-12B-Starcannon-v2.i1-IQ4_XS.gguf) | i1-IQ4_XS | 6.8 | |
| [GGUF](https://huggingface.co/mradermacher/MN-12B-Starcannon-v2-i1-GGUF/resolve/main/MN-12B-Starcannon-v2.i1-Q4_0.gguf) | i1-Q4_0 | 7.2 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/MN-12B-Starcannon-v2-i1-GGUF/resolve/main/MN-12B-Starcannon-v2.i1-Q4_K_S.gguf) | i1-Q4_K_S | 7.2 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/MN-12B-Starcannon-v2-i1-GGUF/resolve/main/MN-12B-Starcannon-v2.i1-Q4_K_M.gguf) | i1-Q4_K_M | 7.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/MN-12B-Starcannon-v2-i1-GGUF/resolve/main/MN-12B-Starcannon-v2.i1-Q5_K_S.gguf) | i1-Q5_K_S | 8.6 | |
| [GGUF](https://huggingface.co/mradermacher/MN-12B-Starcannon-v2-i1-GGUF/resolve/main/MN-12B-Starcannon-v2.i1-Q5_K_M.gguf) | i1-Q5_K_M | 8.8 | |
| [GGUF](https://huggingface.co/mradermacher/MN-12B-Starcannon-v2-i1-GGUF/resolve/main/MN-12B-Starcannon-v2.i1-Q6_K.gguf) | i1-Q6_K | 10.2 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
mergekit-community/mergekit-della_linear-vpjjtsa
|
mergekit-community
| 2024-12-16T05:01:02Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"base_model:ArliAI/Llama-3.1-8B-ArliAI-RPMax-v1.1",
"base_model:merge:ArliAI/Llama-3.1-8B-ArliAI-RPMax-v1.1",
"base_model:ArliAI/Llama-3.1-8B-ArliAI-RPMax-v1.2",
"base_model:merge:ArliAI/Llama-3.1-8B-ArliAI-RPMax-v1.2",
"base_model:ArliAI/Llama-3.1-8B-ArliAI-RPMax-v1.3",
"base_model:merge:ArliAI/Llama-3.1-8B-ArliAI-RPMax-v1.3",
"base_model:Orenguteng/Llama-3.1-8B-Lexi-Uncensored-V2",
"base_model:merge:Orenguteng/Llama-3.1-8B-Lexi-Uncensored-V2",
"base_model:Skywork/Skywork-o1-Open-Llama-3.1-8B",
"base_model:merge:Skywork/Skywork-o1-Open-Llama-3.1-8B",
"base_model:Solshine/reflection-llama-3.1-8B",
"base_model:merge:Solshine/reflection-llama-3.1-8B",
"base_model:allenai/Llama-3.1-Tulu-3-8B",
"base_model:merge:allenai/Llama-3.1-Tulu-3-8B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-12-16T04:46:06Z |
---
base_model:
- Orenguteng/Llama-3.1-8B-Lexi-Uncensored-V2
- Skywork/Skywork-o1-Open-Llama-3.1-8B
- ArliAI/Llama-3.1-8B-ArliAI-RPMax-v1.2
- Solshine/reflection-llama-3.1-8B
- ArliAI/Llama-3.1-8B-ArliAI-RPMax-v1.1
- allenai/Llama-3.1-Tulu-3-8B
- ArliAI/Llama-3.1-8B-ArliAI-RPMax-v1.3
library_name: transformers
tags:
- mergekit
- merge
---
# merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the della_linear merge method using [Orenguteng/Llama-3.1-8B-Lexi-Uncensored-V2](https://huggingface.co/Orenguteng/Llama-3.1-8B-Lexi-Uncensored-V2) as a base.
### Models Merged
The following models were included in the merge:
* [Skywork/Skywork-o1-Open-Llama-3.1-8B](https://huggingface.co/Skywork/Skywork-o1-Open-Llama-3.1-8B)
* [ArliAI/Llama-3.1-8B-ArliAI-RPMax-v1.2](https://huggingface.co/ArliAI/Llama-3.1-8B-ArliAI-RPMax-v1.2)
* [Solshine/reflection-llama-3.1-8B](https://huggingface.co/Solshine/reflection-llama-3.1-8B)
* [ArliAI/Llama-3.1-8B-ArliAI-RPMax-v1.1](https://huggingface.co/ArliAI/Llama-3.1-8B-ArliAI-RPMax-v1.1)
* [allenai/Llama-3.1-Tulu-3-8B](https://huggingface.co/allenai/Llama-3.1-Tulu-3-8B)
* [ArliAI/Llama-3.1-8B-ArliAI-RPMax-v1.3](https://huggingface.co/ArliAI/Llama-3.1-8B-ArliAI-RPMax-v1.3)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: ArliAI/Llama-3.1-8B-ArliAI-RPMax-v1.3
parameters:
density: 0.6
weight: 0.55
- model: Solshine/reflection-llama-3.1-8B
parameters:
density: 0.8
weight: 0.6
- model: Skywork/Skywork-o1-Open-Llama-3.1-8B
parameters:
density: 0.8
weight: 0.6
- model: ArliAI/Llama-3.1-8B-ArliAI-RPMax-v1.2
parameters:
density: 0.5
weight: 0.5
- model: ArliAI/Llama-3.1-8B-ArliAI-RPMax-v1.1
parameters:
density: 0.5
weight: 0.5
- model: allenai/Llama-3.1-Tulu-3-8B
parameters:
density: 0.5
weight: 0.3
merge_method: della_linear
base_model: Orenguteng/Llama-3.1-8B-Lexi-Uncensored-V2
parameters:
normalize: false
int8_mask: true
dtype: float16
```
|
mradermacher/Arch-Function-7B-GGUF
|
mradermacher
| 2024-12-16T04:59:10Z | 71 | 1 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:katanemo/Arch-Function-7B",
"base_model:quantized:katanemo/Arch-Function-7B",
"license:other",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-10-08T05:52:51Z |
---
base_model: katanemo/Arch-Function-7B
language:
- en
library_name: transformers
license: other
license_link: https://huggingface.co/katanemolabs/Arch-Function-7B/blob/main/LICENSE
license_name: katanemo-research
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/katanemo/Arch-Function-7B
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Arch-Function-7B-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Arch-Function-7B-GGUF/resolve/main/Arch-Function-7B.Q2_K.gguf) | Q2_K | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/Arch-Function-7B-GGUF/resolve/main/Arch-Function-7B.IQ3_XS.gguf) | IQ3_XS | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/Arch-Function-7B-GGUF/resolve/main/Arch-Function-7B.Q3_K_S.gguf) | Q3_K_S | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/Arch-Function-7B-GGUF/resolve/main/Arch-Function-7B.IQ3_S.gguf) | IQ3_S | 3.6 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Arch-Function-7B-GGUF/resolve/main/Arch-Function-7B.IQ3_M.gguf) | IQ3_M | 3.7 | |
| [GGUF](https://huggingface.co/mradermacher/Arch-Function-7B-GGUF/resolve/main/Arch-Function-7B.Q3_K_M.gguf) | Q3_K_M | 3.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Arch-Function-7B-GGUF/resolve/main/Arch-Function-7B.Q3_K_L.gguf) | Q3_K_L | 4.2 | |
| [GGUF](https://huggingface.co/mradermacher/Arch-Function-7B-GGUF/resolve/main/Arch-Function-7B.IQ4_XS.gguf) | IQ4_XS | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/Arch-Function-7B-GGUF/resolve/main/Arch-Function-7B.Q4_K_S.gguf) | Q4_K_S | 4.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Arch-Function-7B-GGUF/resolve/main/Arch-Function-7B.Q4_K_M.gguf) | Q4_K_M | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Arch-Function-7B-GGUF/resolve/main/Arch-Function-7B.Q5_K_S.gguf) | Q5_K_S | 5.4 | |
| [GGUF](https://huggingface.co/mradermacher/Arch-Function-7B-GGUF/resolve/main/Arch-Function-7B.Q5_K_M.gguf) | Q5_K_M | 5.5 | |
| [GGUF](https://huggingface.co/mradermacher/Arch-Function-7B-GGUF/resolve/main/Arch-Function-7B.Q6_K.gguf) | Q6_K | 6.4 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Arch-Function-7B-GGUF/resolve/main/Arch-Function-7B.Q8_0.gguf) | Q8_0 | 8.2 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Arch-Function-7B-GGUF/resolve/main/Arch-Function-7B.f16.gguf) | f16 | 15.3 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
mradermacher/Arch-Function-7B-i1-GGUF
|
mradermacher
| 2024-12-16T04:59:04Z | 63 | 1 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:katanemo/Arch-Function-7B",
"base_model:quantized:katanemo/Arch-Function-7B",
"license:other",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2024-10-08T06:41:14Z |
---
base_model: katanemo/Arch-Function-7B
language:
- en
library_name: transformers
license: other
license_link: https://huggingface.co/katanemolabs/Arch-Function-7B/blob/main/LICENSE
license_name: katanemo-research
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/katanemo/Arch-Function-7B
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/Arch-Function-7B-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Arch-Function-7B-i1-GGUF/resolve/main/Arch-Function-7B.i1-IQ1_S.gguf) | i1-IQ1_S | 2.0 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Arch-Function-7B-i1-GGUF/resolve/main/Arch-Function-7B.i1-IQ1_M.gguf) | i1-IQ1_M | 2.1 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Arch-Function-7B-i1-GGUF/resolve/main/Arch-Function-7B.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.4 | |
| [GGUF](https://huggingface.co/mradermacher/Arch-Function-7B-i1-GGUF/resolve/main/Arch-Function-7B.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.6 | |
| [GGUF](https://huggingface.co/mradermacher/Arch-Function-7B-i1-GGUF/resolve/main/Arch-Function-7B.i1-IQ2_S.gguf) | i1-IQ2_S | 2.7 | |
| [GGUF](https://huggingface.co/mradermacher/Arch-Function-7B-i1-GGUF/resolve/main/Arch-Function-7B.i1-IQ2_M.gguf) | i1-IQ2_M | 2.9 | |
| [GGUF](https://huggingface.co/mradermacher/Arch-Function-7B-i1-GGUF/resolve/main/Arch-Function-7B.i1-Q2_K.gguf) | i1-Q2_K | 3.1 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Arch-Function-7B-i1-GGUF/resolve/main/Arch-Function-7B.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 3.2 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Arch-Function-7B-i1-GGUF/resolve/main/Arch-Function-7B.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/Arch-Function-7B-i1-GGUF/resolve/main/Arch-Function-7B.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.6 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Arch-Function-7B-i1-GGUF/resolve/main/Arch-Function-7B.i1-IQ3_S.gguf) | i1-IQ3_S | 3.6 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Arch-Function-7B-i1-GGUF/resolve/main/Arch-Function-7B.i1-IQ3_M.gguf) | i1-IQ3_M | 3.7 | |
| [GGUF](https://huggingface.co/mradermacher/Arch-Function-7B-i1-GGUF/resolve/main/Arch-Function-7B.i1-Q3_K_M.gguf) | i1-Q3_K_M | 3.9 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Arch-Function-7B-i1-GGUF/resolve/main/Arch-Function-7B.i1-Q3_K_L.gguf) | i1-Q3_K_L | 4.2 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Arch-Function-7B-i1-GGUF/resolve/main/Arch-Function-7B.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.3 | |
| [GGUF](https://huggingface.co/mradermacher/Arch-Function-7B-i1-GGUF/resolve/main/Arch-Function-7B.i1-Q4_0_4_4.gguf) | i1-Q4_0_4_4 | 4.5 | fast on arm, low quality |
| [GGUF](https://huggingface.co/mradermacher/Arch-Function-7B-i1-GGUF/resolve/main/Arch-Function-7B.i1-Q4_0_4_8.gguf) | i1-Q4_0_4_8 | 4.5 | fast on arm+i8mm, low quality |
| [GGUF](https://huggingface.co/mradermacher/Arch-Function-7B-i1-GGUF/resolve/main/Arch-Function-7B.i1-Q4_0_8_8.gguf) | i1-Q4_0_8_8 | 4.5 | fast on arm+sve, low quality |
| [GGUF](https://huggingface.co/mradermacher/Arch-Function-7B-i1-GGUF/resolve/main/Arch-Function-7B.i1-Q4_0.gguf) | i1-Q4_0 | 4.5 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Arch-Function-7B-i1-GGUF/resolve/main/Arch-Function-7B.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.6 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Arch-Function-7B-i1-GGUF/resolve/main/Arch-Function-7B.i1-Q4_K_M.gguf) | i1-Q4_K_M | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Arch-Function-7B-i1-GGUF/resolve/main/Arch-Function-7B.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.4 | |
| [GGUF](https://huggingface.co/mradermacher/Arch-Function-7B-i1-GGUF/resolve/main/Arch-Function-7B.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.5 | |
| [GGUF](https://huggingface.co/mradermacher/Arch-Function-7B-i1-GGUF/resolve/main/Arch-Function-7B.i1-Q6_K.gguf) | i1-Q6_K | 6.4 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
mradermacher/hermes-llama3-roleplay-1000-v2-GGUF
|
mradermacher
| 2024-12-16T04:57:44Z | 48 | 1 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:Deev124/hermes-llama3-roleplay-1000-v2",
"base_model:quantized:Deev124/hermes-llama3-roleplay-1000-v2",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-10-24T15:15:10Z |
---
base_model: Deev124/hermes-llama3-roleplay-1000-v2
language:
- en
library_name: transformers
quantized_by: mradermacher
tags: []
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/Deev124/hermes-llama3-roleplay-1000-v2
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/hermes-llama3-roleplay-1000-v2-GGUF/resolve/main/hermes-llama3-roleplay-1000-v2.Q2_K.gguf) | Q2_K | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/hermes-llama3-roleplay-1000-v2-GGUF/resolve/main/hermes-llama3-roleplay-1000-v2.Q3_K_S.gguf) | Q3_K_S | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/hermes-llama3-roleplay-1000-v2-GGUF/resolve/main/hermes-llama3-roleplay-1000-v2.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/hermes-llama3-roleplay-1000-v2-GGUF/resolve/main/hermes-llama3-roleplay-1000-v2.Q3_K_L.gguf) | Q3_K_L | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/hermes-llama3-roleplay-1000-v2-GGUF/resolve/main/hermes-llama3-roleplay-1000-v2.IQ4_XS.gguf) | IQ4_XS | 4.6 | |
| [GGUF](https://huggingface.co/mradermacher/hermes-llama3-roleplay-1000-v2-GGUF/resolve/main/hermes-llama3-roleplay-1000-v2.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/hermes-llama3-roleplay-1000-v2-GGUF/resolve/main/hermes-llama3-roleplay-1000-v2.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/hermes-llama3-roleplay-1000-v2-GGUF/resolve/main/hermes-llama3-roleplay-1000-v2.Q5_K_S.gguf) | Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/hermes-llama3-roleplay-1000-v2-GGUF/resolve/main/hermes-llama3-roleplay-1000-v2.Q5_K_M.gguf) | Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/hermes-llama3-roleplay-1000-v2-GGUF/resolve/main/hermes-llama3-roleplay-1000-v2.Q6_K.gguf) | Q6_K | 6.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/hermes-llama3-roleplay-1000-v2-GGUF/resolve/main/hermes-llama3-roleplay-1000-v2.Q8_0.gguf) | Q8_0 | 8.6 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/hermes-llama3-roleplay-1000-v2-GGUF/resolve/main/hermes-llama3-roleplay-1000-v2.f16.gguf) | f16 | 16.2 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
MayBashendy/ArabicNewSplits5_FineTuningAraBERT_run3_AugV5_k8_task1_organization
|
MayBashendy
| 2024-12-16T04:57:20Z | 182 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:aubmindlab/bert-base-arabertv02",
"base_model:finetune:aubmindlab/bert-base-arabertv02",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-12-16T04:46:10Z |
---
library_name: transformers
base_model: aubmindlab/bert-base-arabertv02
tags:
- generated_from_trainer
model-index:
- name: ArabicNewSplits5_FineTuningAraBERT_run3_AugV5_k8_task1_organization
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. -->
# ArabicNewSplits5_FineTuningAraBERT_run3_AugV5_k8_task1_organization
This model is a fine-tuned version of [aubmindlab/bert-base-arabertv02](https://huggingface.co/aubmindlab/bert-base-arabertv02) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7867
- Qwk: 0.6692
- Mse: 0.7867
- Rmse: 0.8870
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Qwk | Mse | Rmse |
|:-------------:|:------:|:----:|:---------------:|:-------:|:------:|:------:|
| No log | 0.0385 | 2 | 5.1387 | -0.0452 | 5.1387 | 2.2669 |
| No log | 0.0769 | 4 | 3.4926 | -0.0038 | 3.4926 | 1.8688 |
| No log | 0.1154 | 6 | 2.3563 | -0.0850 | 2.3563 | 1.5350 |
| No log | 0.1538 | 8 | 2.2318 | -0.1016 | 2.2318 | 1.4939 |
| No log | 0.1923 | 10 | 2.2862 | -0.0624 | 2.2862 | 1.5120 |
| No log | 0.2308 | 12 | 1.9411 | -0.0151 | 1.9411 | 1.3932 |
| No log | 0.2692 | 14 | 1.8161 | 0.0471 | 1.8161 | 1.3476 |
| No log | 0.3077 | 16 | 1.9320 | 0.1246 | 1.9320 | 1.3900 |
| No log | 0.3462 | 18 | 2.1705 | 0.1389 | 2.1705 | 1.4733 |
| No log | 0.3846 | 20 | 2.4562 | 0.1095 | 2.4562 | 1.5672 |
| No log | 0.4231 | 22 | 2.2968 | 0.1410 | 2.2968 | 1.5155 |
| No log | 0.4615 | 24 | 1.9695 | 0.1744 | 1.9695 | 1.4034 |
| No log | 0.5 | 26 | 1.7390 | 0.2621 | 1.7390 | 1.3187 |
| No log | 0.5385 | 28 | 1.8925 | 0.2440 | 1.8925 | 1.3757 |
| No log | 0.5769 | 30 | 2.1225 | 0.2425 | 2.1225 | 1.4569 |
| No log | 0.6154 | 32 | 1.6052 | 0.3450 | 1.6052 | 1.2670 |
| No log | 0.6538 | 34 | 1.3422 | 0.3690 | 1.3422 | 1.1585 |
| No log | 0.6923 | 36 | 1.0448 | 0.4775 | 1.0448 | 1.0222 |
| No log | 0.7308 | 38 | 0.9715 | 0.4634 | 0.9715 | 0.9857 |
| No log | 0.7692 | 40 | 0.9993 | 0.4517 | 0.9993 | 0.9996 |
| No log | 0.8077 | 42 | 1.0228 | 0.4573 | 1.0228 | 1.0113 |
| No log | 0.8462 | 44 | 1.1646 | 0.3937 | 1.1646 | 1.0792 |
| No log | 0.8846 | 46 | 1.4801 | 0.2752 | 1.4801 | 1.2166 |
| No log | 0.9231 | 48 | 1.8118 | 0.2561 | 1.8118 | 1.3460 |
| No log | 0.9615 | 50 | 2.0057 | 0.2301 | 2.0057 | 1.4162 |
| No log | 1.0 | 52 | 2.2562 | 0.2077 | 2.2562 | 1.5021 |
| No log | 1.0385 | 54 | 2.0900 | 0.2122 | 2.0900 | 1.4457 |
| No log | 1.0769 | 56 | 1.5666 | 0.3100 | 1.5666 | 1.2517 |
| No log | 1.1154 | 58 | 1.2755 | 0.3832 | 1.2755 | 1.1294 |
| No log | 1.1538 | 60 | 1.2105 | 0.4043 | 1.2105 | 1.1002 |
| No log | 1.1923 | 62 | 1.1892 | 0.4636 | 1.1892 | 1.0905 |
| No log | 1.2308 | 64 | 1.2868 | 0.4047 | 1.2868 | 1.1344 |
| No log | 1.2692 | 66 | 1.4071 | 0.3652 | 1.4071 | 1.1862 |
| No log | 1.3077 | 68 | 1.4496 | 0.3795 | 1.4496 | 1.2040 |
| No log | 1.3462 | 70 | 1.5844 | 0.3486 | 1.5844 | 1.2587 |
| No log | 1.3846 | 72 | 1.6676 | 0.3380 | 1.6676 | 1.2914 |
| No log | 1.4231 | 74 | 1.5114 | 0.4005 | 1.5114 | 1.2294 |
| No log | 1.4615 | 76 | 1.2817 | 0.4511 | 1.2817 | 1.1321 |
| No log | 1.5 | 78 | 1.0774 | 0.4666 | 1.0774 | 1.0380 |
| No log | 1.5385 | 80 | 0.9774 | 0.4683 | 0.9774 | 0.9886 |
| No log | 1.5769 | 82 | 0.9830 | 0.4329 | 0.9830 | 0.9915 |
| No log | 1.6154 | 84 | 1.0236 | 0.4387 | 1.0236 | 1.0117 |
| No log | 1.6538 | 86 | 1.1622 | 0.4441 | 1.1622 | 1.0780 |
| No log | 1.6923 | 88 | 1.4432 | 0.3767 | 1.4432 | 1.2013 |
| No log | 1.7308 | 90 | 1.5273 | 0.3680 | 1.5273 | 1.2359 |
| No log | 1.7692 | 92 | 1.4098 | 0.4110 | 1.4098 | 1.1873 |
| No log | 1.8077 | 94 | 1.1813 | 0.4176 | 1.1813 | 1.0869 |
| No log | 1.8462 | 96 | 1.0361 | 0.4577 | 1.0361 | 1.0179 |
| No log | 1.8846 | 98 | 1.0286 | 0.4345 | 1.0286 | 1.0142 |
| No log | 1.9231 | 100 | 0.9686 | 0.4630 | 0.9686 | 0.9842 |
| No log | 1.9615 | 102 | 0.9819 | 0.5069 | 0.9819 | 0.9909 |
| No log | 2.0 | 104 | 0.9968 | 0.5111 | 0.9968 | 0.9984 |
| No log | 2.0385 | 106 | 1.1093 | 0.5028 | 1.1093 | 1.0532 |
| No log | 2.0769 | 108 | 1.1744 | 0.4900 | 1.1744 | 1.0837 |
| No log | 2.1154 | 110 | 1.1418 | 0.5028 | 1.1418 | 1.0686 |
| No log | 2.1538 | 112 | 0.9381 | 0.5877 | 0.9381 | 0.9685 |
| No log | 2.1923 | 114 | 0.8390 | 0.6557 | 0.8390 | 0.9159 |
| No log | 2.2308 | 116 | 0.9010 | 0.5516 | 0.9010 | 0.9492 |
| No log | 2.2692 | 118 | 0.9170 | 0.5731 | 0.9170 | 0.9576 |
| No log | 2.3077 | 120 | 0.8329 | 0.6492 | 0.8329 | 0.9126 |
| No log | 2.3462 | 122 | 0.7660 | 0.6530 | 0.7660 | 0.8752 |
| No log | 2.3846 | 124 | 0.7629 | 0.6262 | 0.7629 | 0.8735 |
| No log | 2.4231 | 126 | 0.7728 | 0.6348 | 0.7728 | 0.8791 |
| No log | 2.4615 | 128 | 0.7827 | 0.6416 | 0.7827 | 0.8847 |
| No log | 2.5 | 130 | 0.8105 | 0.6072 | 0.8105 | 0.9003 |
| No log | 2.5385 | 132 | 0.8299 | 0.5983 | 0.8299 | 0.9110 |
| No log | 2.5769 | 134 | 0.8194 | 0.6154 | 0.8194 | 0.9052 |
| No log | 2.6154 | 136 | 0.8478 | 0.5927 | 0.8478 | 0.9207 |
| No log | 2.6538 | 138 | 0.8252 | 0.6054 | 0.8252 | 0.9084 |
| No log | 2.6923 | 140 | 0.7598 | 0.6727 | 0.7598 | 0.8717 |
| No log | 2.7308 | 142 | 0.7639 | 0.7124 | 0.7639 | 0.8740 |
| No log | 2.7692 | 144 | 0.7329 | 0.6818 | 0.7329 | 0.8561 |
| No log | 2.8077 | 146 | 0.7337 | 0.7015 | 0.7337 | 0.8566 |
| No log | 2.8462 | 148 | 0.7918 | 0.6756 | 0.7918 | 0.8899 |
| No log | 2.8846 | 150 | 0.8225 | 0.6635 | 0.8225 | 0.9069 |
| No log | 2.9231 | 152 | 0.7871 | 0.7011 | 0.7871 | 0.8872 |
| No log | 2.9615 | 154 | 0.7563 | 0.6644 | 0.7563 | 0.8697 |
| No log | 3.0 | 156 | 0.7672 | 0.6738 | 0.7672 | 0.8759 |
| No log | 3.0385 | 158 | 0.7600 | 0.6749 | 0.7600 | 0.8718 |
| No log | 3.0769 | 160 | 0.7247 | 0.6589 | 0.7247 | 0.8513 |
| No log | 3.1154 | 162 | 0.7554 | 0.6869 | 0.7554 | 0.8691 |
| No log | 3.1538 | 164 | 0.8459 | 0.6065 | 0.8459 | 0.9197 |
| No log | 3.1923 | 166 | 0.8508 | 0.6074 | 0.8508 | 0.9224 |
| No log | 3.2308 | 168 | 0.8286 | 0.6395 | 0.8286 | 0.9103 |
| No log | 3.2692 | 170 | 0.8284 | 0.6410 | 0.8284 | 0.9101 |
| No log | 3.3077 | 172 | 0.8534 | 0.6106 | 0.8534 | 0.9238 |
| No log | 3.3462 | 174 | 0.8825 | 0.6052 | 0.8825 | 0.9394 |
| No log | 3.3846 | 176 | 0.9209 | 0.5953 | 0.9209 | 0.9596 |
| No log | 3.4231 | 178 | 0.8885 | 0.5865 | 0.8885 | 0.9426 |
| No log | 3.4615 | 180 | 0.8066 | 0.6432 | 0.8066 | 0.8981 |
| No log | 3.5 | 182 | 0.8376 | 0.6431 | 0.8376 | 0.9152 |
| No log | 3.5385 | 184 | 0.8333 | 0.6583 | 0.8333 | 0.9128 |
| No log | 3.5769 | 186 | 0.8171 | 0.6807 | 0.8171 | 0.9039 |
| No log | 3.6154 | 188 | 0.8080 | 0.6685 | 0.8080 | 0.8989 |
| No log | 3.6538 | 190 | 0.8351 | 0.6301 | 0.8351 | 0.9139 |
| No log | 3.6923 | 192 | 0.8607 | 0.6118 | 0.8607 | 0.9278 |
| No log | 3.7308 | 194 | 0.8379 | 0.6564 | 0.8379 | 0.9154 |
| No log | 3.7692 | 196 | 0.8263 | 0.6464 | 0.8263 | 0.9090 |
| No log | 3.8077 | 198 | 0.7990 | 0.6577 | 0.7990 | 0.8939 |
| No log | 3.8462 | 200 | 0.7626 | 0.7089 | 0.7626 | 0.8733 |
| No log | 3.8846 | 202 | 0.7336 | 0.6831 | 0.7336 | 0.8565 |
| No log | 3.9231 | 204 | 0.7278 | 0.6639 | 0.7278 | 0.8531 |
| No log | 3.9615 | 206 | 0.7041 | 0.6730 | 0.7041 | 0.8391 |
| No log | 4.0 | 208 | 0.7019 | 0.7001 | 0.7019 | 0.8378 |
| No log | 4.0385 | 210 | 0.7895 | 0.6821 | 0.7895 | 0.8885 |
| No log | 4.0769 | 212 | 0.9209 | 0.6033 | 0.9209 | 0.9596 |
| No log | 4.1154 | 214 | 0.9378 | 0.5993 | 0.9378 | 0.9684 |
| No log | 4.1538 | 216 | 0.9109 | 0.6162 | 0.9109 | 0.9544 |
| No log | 4.1923 | 218 | 0.8235 | 0.6472 | 0.8235 | 0.9074 |
| No log | 4.2308 | 220 | 0.7465 | 0.7031 | 0.7465 | 0.8640 |
| No log | 4.2692 | 222 | 0.7508 | 0.6889 | 0.7508 | 0.8665 |
| No log | 4.3077 | 224 | 0.7976 | 0.6803 | 0.7976 | 0.8931 |
| No log | 4.3462 | 226 | 0.8140 | 0.6521 | 0.8140 | 0.9022 |
| No log | 4.3846 | 228 | 0.7892 | 0.6737 | 0.7892 | 0.8884 |
| No log | 4.4231 | 230 | 0.7788 | 0.6831 | 0.7788 | 0.8825 |
| No log | 4.4615 | 232 | 0.7856 | 0.6824 | 0.7856 | 0.8864 |
| No log | 4.5 | 234 | 0.8097 | 0.6620 | 0.8097 | 0.8998 |
| No log | 4.5385 | 236 | 0.8226 | 0.6556 | 0.8226 | 0.9070 |
| No log | 4.5769 | 238 | 0.8101 | 0.6633 | 0.8101 | 0.9000 |
| No log | 4.6154 | 240 | 0.7985 | 0.6757 | 0.7985 | 0.8936 |
| No log | 4.6538 | 242 | 0.7945 | 0.6842 | 0.7945 | 0.8914 |
| No log | 4.6923 | 244 | 0.7843 | 0.6778 | 0.7843 | 0.8856 |
| No log | 4.7308 | 246 | 0.7868 | 0.7004 | 0.7868 | 0.8870 |
| No log | 4.7692 | 248 | 0.8130 | 0.6449 | 0.8130 | 0.9017 |
| No log | 4.8077 | 250 | 0.8118 | 0.6449 | 0.8118 | 0.9010 |
| No log | 4.8462 | 252 | 0.7812 | 0.6692 | 0.7812 | 0.8838 |
| No log | 4.8846 | 254 | 0.7916 | 0.6765 | 0.7916 | 0.8897 |
| No log | 4.9231 | 256 | 0.8054 | 0.6739 | 0.8054 | 0.8974 |
| No log | 4.9615 | 258 | 0.7722 | 0.6754 | 0.7722 | 0.8788 |
| No log | 5.0 | 260 | 0.7534 | 0.6561 | 0.7534 | 0.8680 |
| No log | 5.0385 | 262 | 0.7621 | 0.6492 | 0.7621 | 0.8730 |
| No log | 5.0769 | 264 | 0.7613 | 0.6442 | 0.7613 | 0.8725 |
| No log | 5.1154 | 266 | 0.7901 | 0.6861 | 0.7901 | 0.8889 |
| No log | 5.1538 | 268 | 0.8928 | 0.5961 | 0.8928 | 0.9449 |
| No log | 5.1923 | 270 | 0.9757 | 0.5485 | 0.9757 | 0.9878 |
| No log | 5.2308 | 272 | 0.9821 | 0.5397 | 0.9821 | 0.9910 |
| No log | 5.2692 | 274 | 0.9144 | 0.5587 | 0.9144 | 0.9563 |
| No log | 5.3077 | 276 | 0.8067 | 0.6451 | 0.8067 | 0.8982 |
| No log | 5.3462 | 278 | 0.7493 | 0.6602 | 0.7493 | 0.8656 |
| No log | 5.3846 | 280 | 0.7382 | 0.6430 | 0.7382 | 0.8592 |
| No log | 5.4231 | 282 | 0.7443 | 0.6412 | 0.7443 | 0.8627 |
| No log | 5.4615 | 284 | 0.7657 | 0.6587 | 0.7657 | 0.8751 |
| No log | 5.5 | 286 | 0.7872 | 0.6656 | 0.7872 | 0.8873 |
| No log | 5.5385 | 288 | 0.8032 | 0.6566 | 0.8032 | 0.8962 |
| No log | 5.5769 | 290 | 0.8039 | 0.6725 | 0.8039 | 0.8966 |
| No log | 5.6154 | 292 | 0.8217 | 0.6409 | 0.8217 | 0.9065 |
| No log | 5.6538 | 294 | 0.8342 | 0.6218 | 0.8342 | 0.9133 |
| No log | 5.6923 | 296 | 0.8206 | 0.6121 | 0.8206 | 0.9059 |
| No log | 5.7308 | 298 | 0.7966 | 0.6668 | 0.7966 | 0.8925 |
| No log | 5.7692 | 300 | 0.7856 | 0.6526 | 0.7856 | 0.8864 |
| No log | 5.8077 | 302 | 0.8331 | 0.6197 | 0.8331 | 0.9127 |
| No log | 5.8462 | 304 | 0.9353 | 0.5741 | 0.9353 | 0.9671 |
| No log | 5.8846 | 306 | 0.9748 | 0.5424 | 0.9748 | 0.9873 |
| No log | 5.9231 | 308 | 0.9230 | 0.5842 | 0.9230 | 0.9608 |
| No log | 5.9615 | 310 | 0.8684 | 0.6054 | 0.8684 | 0.9319 |
| No log | 6.0 | 312 | 0.7961 | 0.5727 | 0.7961 | 0.8922 |
| No log | 6.0385 | 314 | 0.7571 | 0.5984 | 0.7571 | 0.8701 |
| No log | 6.0769 | 316 | 0.7319 | 0.6564 | 0.7319 | 0.8555 |
| No log | 6.1154 | 318 | 0.7176 | 0.6750 | 0.7176 | 0.8471 |
| No log | 6.1538 | 320 | 0.7230 | 0.6607 | 0.7230 | 0.8503 |
| No log | 6.1923 | 322 | 0.7301 | 0.6570 | 0.7301 | 0.8544 |
| No log | 6.2308 | 324 | 0.7445 | 0.6757 | 0.7445 | 0.8628 |
| No log | 6.2692 | 326 | 0.7730 | 0.6854 | 0.7730 | 0.8792 |
| No log | 6.3077 | 328 | 0.8303 | 0.6607 | 0.8303 | 0.9112 |
| No log | 6.3462 | 330 | 0.8752 | 0.6234 | 0.8752 | 0.9355 |
| No log | 6.3846 | 332 | 0.9060 | 0.6209 | 0.9060 | 0.9519 |
| No log | 6.4231 | 334 | 0.9515 | 0.6210 | 0.9515 | 0.9754 |
| No log | 6.4615 | 336 | 0.9656 | 0.6091 | 0.9656 | 0.9826 |
| No log | 6.5 | 338 | 0.9355 | 0.6229 | 0.9355 | 0.9672 |
| No log | 6.5385 | 340 | 0.9240 | 0.6229 | 0.9240 | 0.9612 |
| No log | 6.5769 | 342 | 0.9204 | 0.6303 | 0.9204 | 0.9594 |
| No log | 6.6154 | 344 | 0.9316 | 0.6303 | 0.9316 | 0.9652 |
| No log | 6.6538 | 346 | 0.8993 | 0.6332 | 0.8993 | 0.9483 |
| No log | 6.6923 | 348 | 0.8783 | 0.6266 | 0.8783 | 0.9372 |
| No log | 6.7308 | 350 | 0.8828 | 0.6280 | 0.8828 | 0.9396 |
| No log | 6.7692 | 352 | 0.8440 | 0.6475 | 0.8440 | 0.9187 |
| No log | 6.8077 | 354 | 0.7916 | 0.6408 | 0.7916 | 0.8897 |
| No log | 6.8462 | 356 | 0.7583 | 0.6644 | 0.7583 | 0.8708 |
| No log | 6.8846 | 358 | 0.7405 | 0.6815 | 0.7405 | 0.8605 |
| No log | 6.9231 | 360 | 0.7357 | 0.6815 | 0.7357 | 0.8577 |
| No log | 6.9615 | 362 | 0.7543 | 0.6881 | 0.7543 | 0.8685 |
| No log | 7.0 | 364 | 0.7742 | 0.6641 | 0.7742 | 0.8799 |
| No log | 7.0385 | 366 | 0.7955 | 0.6598 | 0.7955 | 0.8919 |
| No log | 7.0769 | 368 | 0.8190 | 0.6446 | 0.8190 | 0.9050 |
| No log | 7.1154 | 370 | 0.8129 | 0.6446 | 0.8129 | 0.9016 |
| No log | 7.1538 | 372 | 0.7920 | 0.6636 | 0.7920 | 0.8899 |
| No log | 7.1923 | 374 | 0.7607 | 0.6669 | 0.7607 | 0.8722 |
| No log | 7.2308 | 376 | 0.7469 | 0.6835 | 0.7469 | 0.8642 |
| No log | 7.2692 | 378 | 0.7475 | 0.6835 | 0.7475 | 0.8646 |
| No log | 7.3077 | 380 | 0.7531 | 0.6881 | 0.7531 | 0.8678 |
| No log | 7.3462 | 382 | 0.7587 | 0.6952 | 0.7587 | 0.8710 |
| No log | 7.3846 | 384 | 0.7653 | 0.6952 | 0.7653 | 0.8748 |
| No log | 7.4231 | 386 | 0.7819 | 0.6951 | 0.7819 | 0.8843 |
| No log | 7.4615 | 388 | 0.8094 | 0.6736 | 0.8094 | 0.8996 |
| No log | 7.5 | 390 | 0.8264 | 0.6674 | 0.8264 | 0.9091 |
| No log | 7.5385 | 392 | 0.8167 | 0.6674 | 0.8167 | 0.9037 |
| No log | 7.5769 | 394 | 0.7912 | 0.6828 | 0.7912 | 0.8895 |
| No log | 7.6154 | 396 | 0.7713 | 0.6763 | 0.7713 | 0.8782 |
| No log | 7.6538 | 398 | 0.7694 | 0.6763 | 0.7694 | 0.8772 |
| No log | 7.6923 | 400 | 0.7899 | 0.6717 | 0.7899 | 0.8888 |
| No log | 7.7308 | 402 | 0.8226 | 0.6499 | 0.8226 | 0.9070 |
| No log | 7.7692 | 404 | 0.8460 | 0.6491 | 0.8460 | 0.9198 |
| No log | 7.8077 | 406 | 0.8517 | 0.6483 | 0.8517 | 0.9229 |
| No log | 7.8462 | 408 | 0.8503 | 0.6483 | 0.8503 | 0.9221 |
| No log | 7.8846 | 410 | 0.8225 | 0.6783 | 0.8225 | 0.9069 |
| No log | 7.9231 | 412 | 0.7956 | 0.6816 | 0.7956 | 0.8920 |
| No log | 7.9615 | 414 | 0.7857 | 0.6977 | 0.7857 | 0.8864 |
| No log | 8.0 | 416 | 0.7935 | 0.6931 | 0.7935 | 0.8908 |
| No log | 8.0385 | 418 | 0.8126 | 0.6943 | 0.8126 | 0.9014 |
| No log | 8.0769 | 420 | 0.8201 | 0.6924 | 0.8201 | 0.9056 |
| No log | 8.1154 | 422 | 0.8190 | 0.6924 | 0.8190 | 0.9050 |
| No log | 8.1538 | 424 | 0.8024 | 0.6880 | 0.8024 | 0.8958 |
| No log | 8.1923 | 426 | 0.7870 | 0.6899 | 0.7870 | 0.8871 |
| No log | 8.2308 | 428 | 0.7790 | 0.6899 | 0.7790 | 0.8826 |
| No log | 8.2692 | 430 | 0.7853 | 0.6764 | 0.7853 | 0.8862 |
| No log | 8.3077 | 432 | 0.7896 | 0.6674 | 0.7896 | 0.8886 |
| No log | 8.3462 | 434 | 0.7989 | 0.6674 | 0.7989 | 0.8938 |
| No log | 8.3846 | 436 | 0.8007 | 0.6674 | 0.8007 | 0.8948 |
| No log | 8.4231 | 438 | 0.7875 | 0.6813 | 0.7875 | 0.8874 |
| No log | 8.4615 | 440 | 0.7763 | 0.6813 | 0.7763 | 0.8811 |
| No log | 8.5 | 442 | 0.7675 | 0.6813 | 0.7675 | 0.8761 |
| No log | 8.5385 | 444 | 0.7543 | 0.6747 | 0.7543 | 0.8685 |
| No log | 8.5769 | 446 | 0.7370 | 0.6750 | 0.7370 | 0.8585 |
| No log | 8.6154 | 448 | 0.7306 | 0.6702 | 0.7306 | 0.8548 |
| No log | 8.6538 | 450 | 0.7274 | 0.6823 | 0.7274 | 0.8529 |
| No log | 8.6923 | 452 | 0.7257 | 0.6823 | 0.7257 | 0.8519 |
| No log | 8.7308 | 454 | 0.7321 | 0.6932 | 0.7321 | 0.8556 |
| No log | 8.7692 | 456 | 0.7466 | 0.6816 | 0.7466 | 0.8641 |
| No log | 8.8077 | 458 | 0.7681 | 0.6873 | 0.7681 | 0.8764 |
| No log | 8.8462 | 460 | 0.7971 | 0.6674 | 0.7971 | 0.8928 |
| No log | 8.8846 | 462 | 0.8231 | 0.6629 | 0.8231 | 0.9073 |
| No log | 8.9231 | 464 | 0.8466 | 0.6348 | 0.8466 | 0.9201 |
| No log | 8.9615 | 466 | 0.8536 | 0.6348 | 0.8536 | 0.9239 |
| No log | 9.0 | 468 | 0.8561 | 0.6340 | 0.8561 | 0.9253 |
| No log | 9.0385 | 470 | 0.8545 | 0.6385 | 0.8545 | 0.9244 |
| No log | 9.0769 | 472 | 0.8418 | 0.6385 | 0.8418 | 0.9175 |
| No log | 9.1154 | 474 | 0.8213 | 0.6674 | 0.8213 | 0.9063 |
| No log | 9.1538 | 476 | 0.8021 | 0.6674 | 0.8021 | 0.8956 |
| No log | 9.1923 | 478 | 0.7798 | 0.6692 | 0.7798 | 0.8830 |
| No log | 9.2308 | 480 | 0.7682 | 0.6747 | 0.7682 | 0.8765 |
| No log | 9.2692 | 482 | 0.7592 | 0.6747 | 0.7592 | 0.8713 |
| No log | 9.3077 | 484 | 0.7511 | 0.6866 | 0.7511 | 0.8667 |
| No log | 9.3462 | 486 | 0.7493 | 0.6866 | 0.7493 | 0.8656 |
| No log | 9.3846 | 488 | 0.7522 | 0.6866 | 0.7522 | 0.8673 |
| No log | 9.4231 | 490 | 0.7520 | 0.6866 | 0.7520 | 0.8672 |
| No log | 9.4615 | 492 | 0.7521 | 0.6866 | 0.7521 | 0.8673 |
| No log | 9.5 | 494 | 0.7516 | 0.6866 | 0.7516 | 0.8670 |
| No log | 9.5385 | 496 | 0.7529 | 0.6793 | 0.7529 | 0.8677 |
| No log | 9.5769 | 498 | 0.7582 | 0.6747 | 0.7582 | 0.8708 |
| 0.4125 | 9.6154 | 500 | 0.7644 | 0.6625 | 0.7644 | 0.8743 |
| 0.4125 | 9.6538 | 502 | 0.7703 | 0.6692 | 0.7703 | 0.8777 |
| 0.4125 | 9.6923 | 504 | 0.7751 | 0.6692 | 0.7751 | 0.8804 |
| 0.4125 | 9.7308 | 506 | 0.7803 | 0.6692 | 0.7803 | 0.8833 |
| 0.4125 | 9.7692 | 508 | 0.7840 | 0.6692 | 0.7840 | 0.8854 |
| 0.4125 | 9.8077 | 510 | 0.7861 | 0.6730 | 0.7861 | 0.8866 |
| 0.4125 | 9.8462 | 512 | 0.7867 | 0.6692 | 0.7867 | 0.8870 |
| 0.4125 | 9.8846 | 514 | 0.7877 | 0.6692 | 0.7877 | 0.8875 |
| 0.4125 | 9.9231 | 516 | 0.7875 | 0.6692 | 0.7875 | 0.8874 |
| 0.4125 | 9.9615 | 518 | 0.7869 | 0.6692 | 0.7869 | 0.8871 |
| 0.4125 | 10.0 | 520 | 0.7867 | 0.6692 | 0.7867 | 0.8870 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu118
- Datasets 2.21.0
- Tokenizers 0.19.1
|
mradermacher/ChatWaifu_Magnum_V0.2-GGUF
|
mradermacher
| 2024-12-16T04:56:53Z | 11 | 1 |
transformers
|
[
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:Triangle104/Chatty-Harry_V2.0",
"base_model:quantized:Triangle104/Chatty-Harry_V2.0",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-10-27T11:44:34Z |
---
base_model: Triangle104/Chatty-Harry_V2.0
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/Triangle104/Chatty-Harry_V2.0
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/ChatWaifu_Magnum_V0.2-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/ChatWaifu_Magnum_V0.2-GGUF/resolve/main/ChatWaifu_Magnum_V0.2.Q2_K.gguf) | Q2_K | 4.9 | |
| [GGUF](https://huggingface.co/mradermacher/ChatWaifu_Magnum_V0.2-GGUF/resolve/main/ChatWaifu_Magnum_V0.2.Q3_K_S.gguf) | Q3_K_S | 5.6 | |
| [GGUF](https://huggingface.co/mradermacher/ChatWaifu_Magnum_V0.2-GGUF/resolve/main/ChatWaifu_Magnum_V0.2.Q3_K_M.gguf) | Q3_K_M | 6.2 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/ChatWaifu_Magnum_V0.2-GGUF/resolve/main/ChatWaifu_Magnum_V0.2.Q3_K_L.gguf) | Q3_K_L | 6.7 | |
| [GGUF](https://huggingface.co/mradermacher/ChatWaifu_Magnum_V0.2-GGUF/resolve/main/ChatWaifu_Magnum_V0.2.IQ4_XS.gguf) | IQ4_XS | 6.9 | |
| [GGUF](https://huggingface.co/mradermacher/ChatWaifu_Magnum_V0.2-GGUF/resolve/main/ChatWaifu_Magnum_V0.2.Q4_K_S.gguf) | Q4_K_S | 7.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/ChatWaifu_Magnum_V0.2-GGUF/resolve/main/ChatWaifu_Magnum_V0.2.Q4_K_M.gguf) | Q4_K_M | 7.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/ChatWaifu_Magnum_V0.2-GGUF/resolve/main/ChatWaifu_Magnum_V0.2.Q5_K_S.gguf) | Q5_K_S | 8.6 | |
| [GGUF](https://huggingface.co/mradermacher/ChatWaifu_Magnum_V0.2-GGUF/resolve/main/ChatWaifu_Magnum_V0.2.Q5_K_M.gguf) | Q5_K_M | 8.8 | |
| [GGUF](https://huggingface.co/mradermacher/ChatWaifu_Magnum_V0.2-GGUF/resolve/main/ChatWaifu_Magnum_V0.2.Q6_K.gguf) | Q6_K | 10.2 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/ChatWaifu_Magnum_V0.2-GGUF/resolve/main/ChatWaifu_Magnum_V0.2.Q8_0.gguf) | Q8_0 | 13.1 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
mradermacher/ChatWaifu_Magnum_V0.2-i1-GGUF
|
mradermacher
| 2024-12-16T04:56:40Z | 237 | 2 |
transformers
|
[
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:Triangle104/Chatty-Harry_V2.0",
"base_model:quantized:Triangle104/Chatty-Harry_V2.0",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2024-10-27T13:55:07Z |
---
base_model: Triangle104/Chatty-Harry_V2.0
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/Triangle104/Chatty-Harry_V2.0
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/ChatWaifu_Magnum_V0.2-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/ChatWaifu_Magnum_V0.2-i1-GGUF/resolve/main/ChatWaifu_Magnum_V0.2.i1-IQ1_S.gguf) | i1-IQ1_S | 3.1 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/ChatWaifu_Magnum_V0.2-i1-GGUF/resolve/main/ChatWaifu_Magnum_V0.2.i1-IQ1_M.gguf) | i1-IQ1_M | 3.3 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/ChatWaifu_Magnum_V0.2-i1-GGUF/resolve/main/ChatWaifu_Magnum_V0.2.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 3.7 | |
| [GGUF](https://huggingface.co/mradermacher/ChatWaifu_Magnum_V0.2-i1-GGUF/resolve/main/ChatWaifu_Magnum_V0.2.i1-IQ2_XS.gguf) | i1-IQ2_XS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/ChatWaifu_Magnum_V0.2-i1-GGUF/resolve/main/ChatWaifu_Magnum_V0.2.i1-IQ2_S.gguf) | i1-IQ2_S | 4.2 | |
| [GGUF](https://huggingface.co/mradermacher/ChatWaifu_Magnum_V0.2-i1-GGUF/resolve/main/ChatWaifu_Magnum_V0.2.i1-IQ2_M.gguf) | i1-IQ2_M | 4.5 | |
| [GGUF](https://huggingface.co/mradermacher/ChatWaifu_Magnum_V0.2-i1-GGUF/resolve/main/ChatWaifu_Magnum_V0.2.i1-Q2_K.gguf) | i1-Q2_K | 4.9 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/ChatWaifu_Magnum_V0.2-i1-GGUF/resolve/main/ChatWaifu_Magnum_V0.2.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 5.0 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/ChatWaifu_Magnum_V0.2-i1-GGUF/resolve/main/ChatWaifu_Magnum_V0.2.i1-IQ3_XS.gguf) | i1-IQ3_XS | 5.4 | |
| [GGUF](https://huggingface.co/mradermacher/ChatWaifu_Magnum_V0.2-i1-GGUF/resolve/main/ChatWaifu_Magnum_V0.2.i1-Q3_K_S.gguf) | i1-Q3_K_S | 5.6 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/ChatWaifu_Magnum_V0.2-i1-GGUF/resolve/main/ChatWaifu_Magnum_V0.2.i1-IQ3_S.gguf) | i1-IQ3_S | 5.7 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/ChatWaifu_Magnum_V0.2-i1-GGUF/resolve/main/ChatWaifu_Magnum_V0.2.i1-IQ3_M.gguf) | i1-IQ3_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/ChatWaifu_Magnum_V0.2-i1-GGUF/resolve/main/ChatWaifu_Magnum_V0.2.i1-Q3_K_M.gguf) | i1-Q3_K_M | 6.2 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/ChatWaifu_Magnum_V0.2-i1-GGUF/resolve/main/ChatWaifu_Magnum_V0.2.i1-Q3_K_L.gguf) | i1-Q3_K_L | 6.7 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/ChatWaifu_Magnum_V0.2-i1-GGUF/resolve/main/ChatWaifu_Magnum_V0.2.i1-IQ4_XS.gguf) | i1-IQ4_XS | 6.8 | |
| [GGUF](https://huggingface.co/mradermacher/ChatWaifu_Magnum_V0.2-i1-GGUF/resolve/main/ChatWaifu_Magnum_V0.2.i1-Q4_0_4_4.gguf) | i1-Q4_0_4_4 | 7.2 | fast on arm, low quality |
| [GGUF](https://huggingface.co/mradermacher/ChatWaifu_Magnum_V0.2-i1-GGUF/resolve/main/ChatWaifu_Magnum_V0.2.i1-Q4_0_4_8.gguf) | i1-Q4_0_4_8 | 7.2 | fast on arm+i8mm, low quality |
| [GGUF](https://huggingface.co/mradermacher/ChatWaifu_Magnum_V0.2-i1-GGUF/resolve/main/ChatWaifu_Magnum_V0.2.i1-Q4_0_8_8.gguf) | i1-Q4_0_8_8 | 7.2 | fast on arm+sve, low quality |
| [GGUF](https://huggingface.co/mradermacher/ChatWaifu_Magnum_V0.2-i1-GGUF/resolve/main/ChatWaifu_Magnum_V0.2.i1-Q4_0.gguf) | i1-Q4_0 | 7.2 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/ChatWaifu_Magnum_V0.2-i1-GGUF/resolve/main/ChatWaifu_Magnum_V0.2.i1-Q4_K_S.gguf) | i1-Q4_K_S | 7.2 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/ChatWaifu_Magnum_V0.2-i1-GGUF/resolve/main/ChatWaifu_Magnum_V0.2.i1-Q4_K_M.gguf) | i1-Q4_K_M | 7.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/ChatWaifu_Magnum_V0.2-i1-GGUF/resolve/main/ChatWaifu_Magnum_V0.2.i1-Q5_K_S.gguf) | i1-Q5_K_S | 8.6 | |
| [GGUF](https://huggingface.co/mradermacher/ChatWaifu_Magnum_V0.2-i1-GGUF/resolve/main/ChatWaifu_Magnum_V0.2.i1-Q5_K_M.gguf) | i1-Q5_K_M | 8.8 | |
| [GGUF](https://huggingface.co/mradermacher/ChatWaifu_Magnum_V0.2-i1-GGUF/resolve/main/ChatWaifu_Magnum_V0.2.i1-Q6_K.gguf) | i1-Q6_K | 10.2 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
ariW/fine_tuned_t5_small_model-naive-approach
|
ariW
| 2024-12-16T04:52:00Z | 115 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:google-t5/t5-small",
"base_model:finetune:google-t5/t5-small",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-12-02T14:05:42Z |
---
library_name: transformers
license: apache-2.0
base_model: t5-small
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: fine_tuned_t5_small_model-naive-approach
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. -->
# fine_tuned_t5_small_model-naive-approach
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.3579
- Rouge1: 0.3553
- Rouge2: 0.1154
- Rougel: 0.2155
- Rougelsum: 0.2154
- Gen Len: 130.1211
- Bert F1: 0.8401
## 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: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | Bert F1 |
|:-------------:|:-------:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:--------:|:-------:|
| 4.3358 | 2.1053 | 200 | 3.5813 | 0.3207 | 0.1049 | 0.1965 | 0.1964 | 112.5737 | 0.8379 |
| 3.6728 | 4.2105 | 400 | 3.4776 | 0.3307 | 0.1098 | 0.2012 | 0.2007 | 120.2947 | 0.8382 |
| 3.5819 | 6.3158 | 600 | 3.4250 | 0.3422 | 0.114 | 0.2086 | 0.2084 | 122.5947 | 0.8399 |
| 3.5355 | 8.4211 | 800 | 3.3926 | 0.345 | 0.1142 | 0.2106 | 0.2106 | 125.2474 | 0.8398 |
| 3.5078 | 10.5263 | 1000 | 3.3709 | 0.3475 | 0.113 | 0.2118 | 0.2117 | 128.4211 | 0.8386 |
| 3.4899 | 12.6316 | 1200 | 3.3615 | 0.3538 | 0.1145 | 0.2157 | 0.2155 | 130.8632 | 0.8396 |
| 3.4672 | 14.7368 | 1400 | 3.3579 | 0.3553 | 0.1154 | 0.2155 | 0.2154 | 130.1211 | 0.8401 |
### Framework versions
- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0
|
mradermacher/MN-12B-Inferor-v0.0-i1-GGUF
|
mradermacher
| 2024-12-16T04:51:42Z | 92 | 1 |
transformers
|
[
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:Infermatic/MN-12B-Inferor-v0.0",
"base_model:quantized:Infermatic/MN-12B-Inferor-v0.0",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2024-11-08T05:45:15Z |
---
base_model: Infermatic/MN-12B-Inferor-v0.0
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/Infermatic/MN-12B-Inferor-v0.0
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/MN-12B-Inferor-v0.0-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/MN-12B-Inferor-v0.0-i1-GGUF/resolve/main/MN-12B-Inferor-v0.0.i1-IQ1_S.gguf) | i1-IQ1_S | 3.1 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/MN-12B-Inferor-v0.0-i1-GGUF/resolve/main/MN-12B-Inferor-v0.0.i1-IQ1_M.gguf) | i1-IQ1_M | 3.3 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/MN-12B-Inferor-v0.0-i1-GGUF/resolve/main/MN-12B-Inferor-v0.0.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 3.7 | |
| [GGUF](https://huggingface.co/mradermacher/MN-12B-Inferor-v0.0-i1-GGUF/resolve/main/MN-12B-Inferor-v0.0.i1-IQ2_XS.gguf) | i1-IQ2_XS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/MN-12B-Inferor-v0.0-i1-GGUF/resolve/main/MN-12B-Inferor-v0.0.i1-IQ2_S.gguf) | i1-IQ2_S | 4.2 | |
| [GGUF](https://huggingface.co/mradermacher/MN-12B-Inferor-v0.0-i1-GGUF/resolve/main/MN-12B-Inferor-v0.0.i1-IQ2_M.gguf) | i1-IQ2_M | 4.5 | |
| [GGUF](https://huggingface.co/mradermacher/MN-12B-Inferor-v0.0-i1-GGUF/resolve/main/MN-12B-Inferor-v0.0.i1-Q2_K.gguf) | i1-Q2_K | 4.9 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/MN-12B-Inferor-v0.0-i1-GGUF/resolve/main/MN-12B-Inferor-v0.0.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 5.0 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/MN-12B-Inferor-v0.0-i1-GGUF/resolve/main/MN-12B-Inferor-v0.0.i1-IQ3_XS.gguf) | i1-IQ3_XS | 5.4 | |
| [GGUF](https://huggingface.co/mradermacher/MN-12B-Inferor-v0.0-i1-GGUF/resolve/main/MN-12B-Inferor-v0.0.i1-Q3_K_S.gguf) | i1-Q3_K_S | 5.6 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/MN-12B-Inferor-v0.0-i1-GGUF/resolve/main/MN-12B-Inferor-v0.0.i1-IQ3_S.gguf) | i1-IQ3_S | 5.7 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/MN-12B-Inferor-v0.0-i1-GGUF/resolve/main/MN-12B-Inferor-v0.0.i1-IQ3_M.gguf) | i1-IQ3_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/MN-12B-Inferor-v0.0-i1-GGUF/resolve/main/MN-12B-Inferor-v0.0.i1-Q3_K_M.gguf) | i1-Q3_K_M | 6.2 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/MN-12B-Inferor-v0.0-i1-GGUF/resolve/main/MN-12B-Inferor-v0.0.i1-Q3_K_L.gguf) | i1-Q3_K_L | 6.7 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/MN-12B-Inferor-v0.0-i1-GGUF/resolve/main/MN-12B-Inferor-v0.0.i1-IQ4_XS.gguf) | i1-IQ4_XS | 6.8 | |
| [GGUF](https://huggingface.co/mradermacher/MN-12B-Inferor-v0.0-i1-GGUF/resolve/main/MN-12B-Inferor-v0.0.i1-Q4_0_4_4.gguf) | i1-Q4_0_4_4 | 7.2 | fast on arm, low quality |
| [GGUF](https://huggingface.co/mradermacher/MN-12B-Inferor-v0.0-i1-GGUF/resolve/main/MN-12B-Inferor-v0.0.i1-Q4_0_4_8.gguf) | i1-Q4_0_4_8 | 7.2 | fast on arm+i8mm, low quality |
| [GGUF](https://huggingface.co/mradermacher/MN-12B-Inferor-v0.0-i1-GGUF/resolve/main/MN-12B-Inferor-v0.0.i1-Q4_0_8_8.gguf) | i1-Q4_0_8_8 | 7.2 | fast on arm+sve, low quality |
| [GGUF](https://huggingface.co/mradermacher/MN-12B-Inferor-v0.0-i1-GGUF/resolve/main/MN-12B-Inferor-v0.0.i1-Q4_0.gguf) | i1-Q4_0 | 7.2 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/MN-12B-Inferor-v0.0-i1-GGUF/resolve/main/MN-12B-Inferor-v0.0.i1-Q4_K_S.gguf) | i1-Q4_K_S | 7.2 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/MN-12B-Inferor-v0.0-i1-GGUF/resolve/main/MN-12B-Inferor-v0.0.i1-Q4_K_M.gguf) | i1-Q4_K_M | 7.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/MN-12B-Inferor-v0.0-i1-GGUF/resolve/main/MN-12B-Inferor-v0.0.i1-Q5_K_S.gguf) | i1-Q5_K_S | 8.6 | |
| [GGUF](https://huggingface.co/mradermacher/MN-12B-Inferor-v0.0-i1-GGUF/resolve/main/MN-12B-Inferor-v0.0.i1-Q5_K_M.gguf) | i1-Q5_K_M | 8.8 | |
| [GGUF](https://huggingface.co/mradermacher/MN-12B-Inferor-v0.0-i1-GGUF/resolve/main/MN-12B-Inferor-v0.0.i1-Q6_K.gguf) | i1-Q6_K | 10.2 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
kuleshov-group/udlm-qm9
|
kuleshov-group
| 2024-12-16T04:48:32Z | 145 | 0 |
transformers
|
[
"transformers",
"safetensors",
"udlm",
"fill-mask",
"custom_code",
"dataset:yairschiff/qm9",
"arxiv:2412.10193",
"arxiv:2212.09748",
"license:apache-2.0",
"autotrain_compatible",
"region:us"
] |
fill-mask
| 2024-12-15T05:05:07Z |
---
library_name: transformers
license: apache-2.0
datasets:
- yairschiff/qm9
---
## Quick Start Guide
To use this pre-trained model with the HuggingFace APIs, use the following snippet:
```python
from transformers import AutoModelForMaskedLM, AutoTokenizer
# See the `UDLM` collection page on the hub for list of available models.
tokenizer = transformers.AutoTokenizer.from_pretrained('yairschiff/qm9-tokenizer')
model_name = 'kuleshov-group/udlm-qm9'
model = AutoModelForMaskedLM.from_pretrained(model_name)
```
## Model Details
UDLM stands for **U**niform **D**iffusion **L**anguage **M**odels.
This model was trained using the refined uniform noise discrete diffusion continuous-time ELBO introduced [here](https://arxiv.org/abs/2412.10193).
### Architecture
The model has a context size of 32 tokens. The model has 92M parameters.
The model architecture is based off of the [Diffusion Transformer architecture](https://arxiv.org/abs/2212.09748) and consists of:
- 12 multi-head attention blocks (with 12 attention heads),
- hidden dimension of 768,
- `adaLN` for conditioning on time-step (i.e., during diffusion training / generation).
### Training Details
The model was trained using the `yairschiff/qm9-tokenizer` tokenizer, a custom tokenizer for parsing SMILES strings.
We trained for 25k gradient update steps using a batch size of 2,048.
We used linear warm-up with 1,000 steps until we reach a learning rate of 3e-4 and the applied cosine-decay until reaching a minimum learning rate of 3e-6.
For more details, please refer to our work: [Simple Guidance Mechanisms for Discrete Diffusion Models](https://arxiv.org/abs/2412.10193).
## Citation
Please cite our work using the bibtex below:
### BibTeX:
```
@article{schiff2024discreteguidance,
title={Simple Guidance Mechanisms for Discrete Diffusion Models},
author={Schiff, Yair and Sahoo, Subham Sekhar and Phung, Hao and Wang, Guanghan and Boshar, Sam and Dalla-torre, Hugo and de Almeida, Bernardo P and Rush, Alexander and Pierrot, Thomas and Kuleshov, Volodymyr},
journal={arXiv preprint arXiv:2412.10193},
year={2024}
}
```
|
mradermacher/distilabeled-Hermes-2.5-Mistral-7B-i1-GGUF
|
mradermacher
| 2024-12-16T04:47:49Z | 363 | 1 |
transformers
|
[
"transformers",
"gguf",
"distilabel",
"dpo",
"rlaif",
"rlhf",
"en",
"dataset:argilla/distilabel-intel-orca-dpo-pairs",
"base_model:argilla/distilabeled-OpenHermes-2.5-Mistral-7B",
"base_model:quantized:argilla/distilabeled-OpenHermes-2.5-Mistral-7B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2024-11-15T02:45:42Z |
---
base_model: argilla/distilabeled-OpenHermes-2.5-Mistral-7B
datasets:
- argilla/distilabel-intel-orca-dpo-pairs
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- distilabel
- dpo
- rlaif
- rlhf
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/argilla/distilabeled-OpenHermes-2.5-Mistral-7B
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/distilabeled-Hermes-2.5-Mistral-7B-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/distilabeled-Hermes-2.5-Mistral-7B-i1-GGUF/resolve/main/distilabeled-Hermes-2.5-Mistral-7B.i1-IQ1_S.gguf) | i1-IQ1_S | 1.7 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/distilabeled-Hermes-2.5-Mistral-7B-i1-GGUF/resolve/main/distilabeled-Hermes-2.5-Mistral-7B.i1-IQ1_M.gguf) | i1-IQ1_M | 1.9 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/distilabeled-Hermes-2.5-Mistral-7B-i1-GGUF/resolve/main/distilabeled-Hermes-2.5-Mistral-7B.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.1 | |
| [GGUF](https://huggingface.co/mradermacher/distilabeled-Hermes-2.5-Mistral-7B-i1-GGUF/resolve/main/distilabeled-Hermes-2.5-Mistral-7B.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.3 | |
| [GGUF](https://huggingface.co/mradermacher/distilabeled-Hermes-2.5-Mistral-7B-i1-GGUF/resolve/main/distilabeled-Hermes-2.5-Mistral-7B.i1-IQ2_S.gguf) | i1-IQ2_S | 2.4 | |
| [GGUF](https://huggingface.co/mradermacher/distilabeled-Hermes-2.5-Mistral-7B-i1-GGUF/resolve/main/distilabeled-Hermes-2.5-Mistral-7B.i1-IQ2_M.gguf) | i1-IQ2_M | 2.6 | |
| [GGUF](https://huggingface.co/mradermacher/distilabeled-Hermes-2.5-Mistral-7B-i1-GGUF/resolve/main/distilabeled-Hermes-2.5-Mistral-7B.i1-Q2_K.gguf) | i1-Q2_K | 2.8 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/distilabeled-Hermes-2.5-Mistral-7B-i1-GGUF/resolve/main/distilabeled-Hermes-2.5-Mistral-7B.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 2.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/distilabeled-Hermes-2.5-Mistral-7B-i1-GGUF/resolve/main/distilabeled-Hermes-2.5-Mistral-7B.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/distilabeled-Hermes-2.5-Mistral-7B-i1-GGUF/resolve/main/distilabeled-Hermes-2.5-Mistral-7B.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.3 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/distilabeled-Hermes-2.5-Mistral-7B-i1-GGUF/resolve/main/distilabeled-Hermes-2.5-Mistral-7B.i1-IQ3_S.gguf) | i1-IQ3_S | 3.3 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/distilabeled-Hermes-2.5-Mistral-7B-i1-GGUF/resolve/main/distilabeled-Hermes-2.5-Mistral-7B.i1-IQ3_M.gguf) | i1-IQ3_M | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/distilabeled-Hermes-2.5-Mistral-7B-i1-GGUF/resolve/main/distilabeled-Hermes-2.5-Mistral-7B.i1-Q3_K_M.gguf) | i1-Q3_K_M | 3.6 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/distilabeled-Hermes-2.5-Mistral-7B-i1-GGUF/resolve/main/distilabeled-Hermes-2.5-Mistral-7B.i1-Q3_K_L.gguf) | i1-Q3_K_L | 3.9 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/distilabeled-Hermes-2.5-Mistral-7B-i1-GGUF/resolve/main/distilabeled-Hermes-2.5-Mistral-7B.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/distilabeled-Hermes-2.5-Mistral-7B-i1-GGUF/resolve/main/distilabeled-Hermes-2.5-Mistral-7B.i1-Q4_0_4_4.gguf) | i1-Q4_0_4_4 | 4.2 | fast on arm, low quality |
| [GGUF](https://huggingface.co/mradermacher/distilabeled-Hermes-2.5-Mistral-7B-i1-GGUF/resolve/main/distilabeled-Hermes-2.5-Mistral-7B.i1-Q4_0_4_8.gguf) | i1-Q4_0_4_8 | 4.2 | fast on arm+i8mm, low quality |
| [GGUF](https://huggingface.co/mradermacher/distilabeled-Hermes-2.5-Mistral-7B-i1-GGUF/resolve/main/distilabeled-Hermes-2.5-Mistral-7B.i1-Q4_0_8_8.gguf) | i1-Q4_0_8_8 | 4.2 | fast on arm+sve, low quality |
| [GGUF](https://huggingface.co/mradermacher/distilabeled-Hermes-2.5-Mistral-7B-i1-GGUF/resolve/main/distilabeled-Hermes-2.5-Mistral-7B.i1-Q4_0.gguf) | i1-Q4_0 | 4.2 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/distilabeled-Hermes-2.5-Mistral-7B-i1-GGUF/resolve/main/distilabeled-Hermes-2.5-Mistral-7B.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.2 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/distilabeled-Hermes-2.5-Mistral-7B-i1-GGUF/resolve/main/distilabeled-Hermes-2.5-Mistral-7B.i1-Q4_K_M.gguf) | i1-Q4_K_M | 4.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/distilabeled-Hermes-2.5-Mistral-7B-i1-GGUF/resolve/main/distilabeled-Hermes-2.5-Mistral-7B.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/distilabeled-Hermes-2.5-Mistral-7B-i1-GGUF/resolve/main/distilabeled-Hermes-2.5-Mistral-7B.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/distilabeled-Hermes-2.5-Mistral-7B-i1-GGUF/resolve/main/distilabeled-Hermes-2.5-Mistral-7B.i1-Q6_K.gguf) | i1-Q6_K | 6.0 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
mradermacher/Awanllm-Llama-3-8B-Instruct-ORPO-v0.1-GGUF
|
mradermacher
| 2024-12-16T04:47:21Z | 8 | 1 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:OwenArli/ArliAI-Llama-3-8B-Instruct-ORPO-v0.1",
"base_model:quantized:OwenArli/ArliAI-Llama-3-8B-Instruct-ORPO-v0.1",
"license:llama3",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-11-15T12:36:16Z |
---
base_model: OwenArli/ArliAI-Llama-3-8B-Instruct-ORPO-v0.1
language:
- en
library_name: transformers
license: llama3
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/OwenArli/ArliAI-Llama-3-8B-Instruct-ORPO-v0.1
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Awanllm-Llama-3-8B-Instruct-ORPO-v0.1-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Awanllm-Llama-3-8B-Instruct-ORPO-v0.1-GGUF/resolve/main/Awanllm-Llama-3-8B-Instruct-ORPO-v0.1.Q2_K.gguf) | Q2_K | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/Awanllm-Llama-3-8B-Instruct-ORPO-v0.1-GGUF/resolve/main/Awanllm-Llama-3-8B-Instruct-ORPO-v0.1.Q3_K_S.gguf) | Q3_K_S | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/Awanllm-Llama-3-8B-Instruct-ORPO-v0.1-GGUF/resolve/main/Awanllm-Llama-3-8B-Instruct-ORPO-v0.1.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Awanllm-Llama-3-8B-Instruct-ORPO-v0.1-GGUF/resolve/main/Awanllm-Llama-3-8B-Instruct-ORPO-v0.1.Q3_K_L.gguf) | Q3_K_L | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/Awanllm-Llama-3-8B-Instruct-ORPO-v0.1-GGUF/resolve/main/Awanllm-Llama-3-8B-Instruct-ORPO-v0.1.IQ4_XS.gguf) | IQ4_XS | 4.6 | |
| [GGUF](https://huggingface.co/mradermacher/Awanllm-Llama-3-8B-Instruct-ORPO-v0.1-GGUF/resolve/main/Awanllm-Llama-3-8B-Instruct-ORPO-v0.1.Q4_0_4_4.gguf) | Q4_0_4_4 | 4.8 | fast on arm, low quality |
| [GGUF](https://huggingface.co/mradermacher/Awanllm-Llama-3-8B-Instruct-ORPO-v0.1-GGUF/resolve/main/Awanllm-Llama-3-8B-Instruct-ORPO-v0.1.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Awanllm-Llama-3-8B-Instruct-ORPO-v0.1-GGUF/resolve/main/Awanllm-Llama-3-8B-Instruct-ORPO-v0.1.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Awanllm-Llama-3-8B-Instruct-ORPO-v0.1-GGUF/resolve/main/Awanllm-Llama-3-8B-Instruct-ORPO-v0.1.Q5_K_S.gguf) | Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/Awanllm-Llama-3-8B-Instruct-ORPO-v0.1-GGUF/resolve/main/Awanllm-Llama-3-8B-Instruct-ORPO-v0.1.Q5_K_M.gguf) | Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/Awanllm-Llama-3-8B-Instruct-ORPO-v0.1-GGUF/resolve/main/Awanllm-Llama-3-8B-Instruct-ORPO-v0.1.Q6_K.gguf) | Q6_K | 6.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Awanllm-Llama-3-8B-Instruct-ORPO-v0.1-GGUF/resolve/main/Awanllm-Llama-3-8B-Instruct-ORPO-v0.1.Q8_0.gguf) | Q8_0 | 8.6 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Awanllm-Llama-3-8B-Instruct-ORPO-v0.1-GGUF/resolve/main/Awanllm-Llama-3-8B-Instruct-ORPO-v0.1.f16.gguf) | f16 | 16.2 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
mradermacher/Awanllm-Llama-3-8B-Instruct-ORPO-v0.1-i1-GGUF
|
mradermacher
| 2024-12-16T04:47:11Z | 67 | 1 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:OwenArli/ArliAI-Llama-3-8B-Instruct-ORPO-v0.1",
"base_model:quantized:OwenArli/ArliAI-Llama-3-8B-Instruct-ORPO-v0.1",
"license:llama3",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2024-11-15T13:30:02Z |
---
base_model: OwenArli/ArliAI-Llama-3-8B-Instruct-ORPO-v0.1
language:
- en
library_name: transformers
license: llama3
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/OwenArli/ArliAI-Llama-3-8B-Instruct-ORPO-v0.1
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/Awanllm-Llama-3-8B-Instruct-ORPO-v0.1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Awanllm-Llama-3-8B-Instruct-ORPO-v0.1-i1-GGUF/resolve/main/Awanllm-Llama-3-8B-Instruct-ORPO-v0.1.i1-IQ1_S.gguf) | i1-IQ1_S | 2.1 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Awanllm-Llama-3-8B-Instruct-ORPO-v0.1-i1-GGUF/resolve/main/Awanllm-Llama-3-8B-Instruct-ORPO-v0.1.i1-IQ1_M.gguf) | i1-IQ1_M | 2.3 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Awanllm-Llama-3-8B-Instruct-ORPO-v0.1-i1-GGUF/resolve/main/Awanllm-Llama-3-8B-Instruct-ORPO-v0.1.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.5 | |
| [GGUF](https://huggingface.co/mradermacher/Awanllm-Llama-3-8B-Instruct-ORPO-v0.1-i1-GGUF/resolve/main/Awanllm-Llama-3-8B-Instruct-ORPO-v0.1.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.7 | |
| [GGUF](https://huggingface.co/mradermacher/Awanllm-Llama-3-8B-Instruct-ORPO-v0.1-i1-GGUF/resolve/main/Awanllm-Llama-3-8B-Instruct-ORPO-v0.1.i1-IQ2_S.gguf) | i1-IQ2_S | 2.9 | |
| [GGUF](https://huggingface.co/mradermacher/Awanllm-Llama-3-8B-Instruct-ORPO-v0.1-i1-GGUF/resolve/main/Awanllm-Llama-3-8B-Instruct-ORPO-v0.1.i1-IQ2_M.gguf) | i1-IQ2_M | 3.0 | |
| [GGUF](https://huggingface.co/mradermacher/Awanllm-Llama-3-8B-Instruct-ORPO-v0.1-i1-GGUF/resolve/main/Awanllm-Llama-3-8B-Instruct-ORPO-v0.1.i1-Q2_K.gguf) | i1-Q2_K | 3.3 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Awanllm-Llama-3-8B-Instruct-ORPO-v0.1-i1-GGUF/resolve/main/Awanllm-Llama-3-8B-Instruct-ORPO-v0.1.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 3.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Awanllm-Llama-3-8B-Instruct-ORPO-v0.1-i1-GGUF/resolve/main/Awanllm-Llama-3-8B-Instruct-ORPO-v0.1.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/Awanllm-Llama-3-8B-Instruct-ORPO-v0.1-i1-GGUF/resolve/main/Awanllm-Llama-3-8B-Instruct-ORPO-v0.1.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.8 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Awanllm-Llama-3-8B-Instruct-ORPO-v0.1-i1-GGUF/resolve/main/Awanllm-Llama-3-8B-Instruct-ORPO-v0.1.i1-IQ3_S.gguf) | i1-IQ3_S | 3.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Awanllm-Llama-3-8B-Instruct-ORPO-v0.1-i1-GGUF/resolve/main/Awanllm-Llama-3-8B-Instruct-ORPO-v0.1.i1-IQ3_M.gguf) | i1-IQ3_M | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/Awanllm-Llama-3-8B-Instruct-ORPO-v0.1-i1-GGUF/resolve/main/Awanllm-Llama-3-8B-Instruct-ORPO-v0.1.i1-Q3_K_M.gguf) | i1-Q3_K_M | 4.1 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Awanllm-Llama-3-8B-Instruct-ORPO-v0.1-i1-GGUF/resolve/main/Awanllm-Llama-3-8B-Instruct-ORPO-v0.1.i1-Q3_K_L.gguf) | i1-Q3_K_L | 4.4 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Awanllm-Llama-3-8B-Instruct-ORPO-v0.1-i1-GGUF/resolve/main/Awanllm-Llama-3-8B-Instruct-ORPO-v0.1.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.5 | |
| [GGUF](https://huggingface.co/mradermacher/Awanllm-Llama-3-8B-Instruct-ORPO-v0.1-i1-GGUF/resolve/main/Awanllm-Llama-3-8B-Instruct-ORPO-v0.1.i1-Q4_0_4_4.gguf) | i1-Q4_0_4_4 | 4.8 | fast on arm, low quality |
| [GGUF](https://huggingface.co/mradermacher/Awanllm-Llama-3-8B-Instruct-ORPO-v0.1-i1-GGUF/resolve/main/Awanllm-Llama-3-8B-Instruct-ORPO-v0.1.i1-Q4_0_4_8.gguf) | i1-Q4_0_4_8 | 4.8 | fast on arm+i8mm, low quality |
| [GGUF](https://huggingface.co/mradermacher/Awanllm-Llama-3-8B-Instruct-ORPO-v0.1-i1-GGUF/resolve/main/Awanllm-Llama-3-8B-Instruct-ORPO-v0.1.i1-Q4_0_8_8.gguf) | i1-Q4_0_8_8 | 4.8 | fast on arm+sve, low quality |
| [GGUF](https://huggingface.co/mradermacher/Awanllm-Llama-3-8B-Instruct-ORPO-v0.1-i1-GGUF/resolve/main/Awanllm-Llama-3-8B-Instruct-ORPO-v0.1.i1-Q4_0.gguf) | i1-Q4_0 | 4.8 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Awanllm-Llama-3-8B-Instruct-ORPO-v0.1-i1-GGUF/resolve/main/Awanllm-Llama-3-8B-Instruct-ORPO-v0.1.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.8 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Awanllm-Llama-3-8B-Instruct-ORPO-v0.1-i1-GGUF/resolve/main/Awanllm-Llama-3-8B-Instruct-ORPO-v0.1.i1-Q4_K_M.gguf) | i1-Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Awanllm-Llama-3-8B-Instruct-ORPO-v0.1-i1-GGUF/resolve/main/Awanllm-Llama-3-8B-Instruct-ORPO-v0.1.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/Awanllm-Llama-3-8B-Instruct-ORPO-v0.1-i1-GGUF/resolve/main/Awanllm-Llama-3-8B-Instruct-ORPO-v0.1.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/Awanllm-Llama-3-8B-Instruct-ORPO-v0.1-i1-GGUF/resolve/main/Awanllm-Llama-3-8B-Instruct-ORPO-v0.1.i1-Q6_K.gguf) | i1-Q6_K | 6.7 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
kainatq/KPT-7b-v0.3-Q4_K_M-GGUF
|
kainatq
| 2024-12-16T04:46:28Z | 6 | 1 |
transformers
|
[
"transformers",
"gguf",
"mergekit",
"merge",
"llama-cpp",
"gguf-my-repo",
"base_model:kainatq/KPT-7b-v0.3",
"base_model:quantized:kainatq/KPT-7b-v0.3",
"endpoints_compatible",
"region:us"
] | null | 2024-12-16T04:46:08Z |
---
base_model: kainatq/KPT-7b-v0.3
library_name: transformers
tags:
- mergekit
- merge
- llama-cpp
- gguf-my-repo
---
# kainatq/KPT-7b-v0.3-Q4_K_M-GGUF
This model was converted to GGUF format from [`kainatq/KPT-7b-v0.3`](https://huggingface.co/kainatq/KPT-7b-v0.3) 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/kainatq/KPT-7b-v0.3) 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 kainatq/KPT-7b-v0.3-Q4_K_M-GGUF --hf-file kpt-7b-v0.3-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo kainatq/KPT-7b-v0.3-Q4_K_M-GGUF --hf-file kpt-7b-v0.3-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 kainatq/KPT-7b-v0.3-Q4_K_M-GGUF --hf-file kpt-7b-v0.3-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo kainatq/KPT-7b-v0.3-Q4_K_M-GGUF --hf-file kpt-7b-v0.3-q4_k_m.gguf -c 2048
```
|
mradermacher/llama-3-cat-8b-instruct-v1-GGUF
|
mradermacher
| 2024-12-16T04:46:27Z | 66 | 1 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:SteelStorage/llama-3-cat-8b-instruct-v1",
"base_model:quantized:SteelStorage/llama-3-cat-8b-instruct-v1",
"license:llama3",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-11-17T00:23:34Z |
---
base_model: SteelStorage/llama-3-cat-8b-instruct-v1
language:
- en
library_name: transformers
license: llama3
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/SteelStorage/llama-3-cat-8b-instruct-v1
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/llama-3-cat-8b-instruct-v1-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/llama-3-cat-8b-instruct-v1-GGUF/resolve/main/llama-3-cat-8b-instruct-v1.Q2_K.gguf) | Q2_K | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/llama-3-cat-8b-instruct-v1-GGUF/resolve/main/llama-3-cat-8b-instruct-v1.Q3_K_S.gguf) | Q3_K_S | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/llama-3-cat-8b-instruct-v1-GGUF/resolve/main/llama-3-cat-8b-instruct-v1.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/llama-3-cat-8b-instruct-v1-GGUF/resolve/main/llama-3-cat-8b-instruct-v1.Q3_K_L.gguf) | Q3_K_L | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/llama-3-cat-8b-instruct-v1-GGUF/resolve/main/llama-3-cat-8b-instruct-v1.IQ4_XS.gguf) | IQ4_XS | 4.6 | |
| [GGUF](https://huggingface.co/mradermacher/llama-3-cat-8b-instruct-v1-GGUF/resolve/main/llama-3-cat-8b-instruct-v1.Q4_0_4_4.gguf) | Q4_0_4_4 | 4.8 | fast on arm, low quality |
| [GGUF](https://huggingface.co/mradermacher/llama-3-cat-8b-instruct-v1-GGUF/resolve/main/llama-3-cat-8b-instruct-v1.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/llama-3-cat-8b-instruct-v1-GGUF/resolve/main/llama-3-cat-8b-instruct-v1.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/llama-3-cat-8b-instruct-v1-GGUF/resolve/main/llama-3-cat-8b-instruct-v1.Q5_K_S.gguf) | Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/llama-3-cat-8b-instruct-v1-GGUF/resolve/main/llama-3-cat-8b-instruct-v1.Q5_K_M.gguf) | Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/llama-3-cat-8b-instruct-v1-GGUF/resolve/main/llama-3-cat-8b-instruct-v1.Q6_K.gguf) | Q6_K | 6.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/llama-3-cat-8b-instruct-v1-GGUF/resolve/main/llama-3-cat-8b-instruct-v1.Q8_0.gguf) | Q8_0 | 8.6 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/llama-3-cat-8b-instruct-v1-GGUF/resolve/main/llama-3-cat-8b-instruct-v1.f16.gguf) | f16 | 16.2 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
mradermacher/Hathor_Tahsin-L3-8B-v0.9-GGUF
|
mradermacher
| 2024-12-16T04:46:17Z | 16 | 1 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:ChaoticNeutrals/Hathor_Tahsin-L3-8B-v0.9",
"base_model:quantized:ChaoticNeutrals/Hathor_Tahsin-L3-8B-v0.9",
"license:other",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-11-17T04:44:40Z |
---
base_model: ChaoticNeutrals/Hathor_Tahsin-L3-8B-v0.9
language:
- en
library_name: transformers
license: other
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/ChaoticNeutrals/Hathor_Tahsin-L3-8B-v0.9
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Hathor_Tahsin-L3-8B-v0.9-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Hathor_Tahsin-L3-8B-v0.9-GGUF/resolve/main/Hathor_Tahsin-L3-8B-v0.9.Q2_K.gguf) | Q2_K | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/Hathor_Tahsin-L3-8B-v0.9-GGUF/resolve/main/Hathor_Tahsin-L3-8B-v0.9.Q3_K_S.gguf) | Q3_K_S | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/Hathor_Tahsin-L3-8B-v0.9-GGUF/resolve/main/Hathor_Tahsin-L3-8B-v0.9.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Hathor_Tahsin-L3-8B-v0.9-GGUF/resolve/main/Hathor_Tahsin-L3-8B-v0.9.Q3_K_L.gguf) | Q3_K_L | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/Hathor_Tahsin-L3-8B-v0.9-GGUF/resolve/main/Hathor_Tahsin-L3-8B-v0.9.IQ4_XS.gguf) | IQ4_XS | 4.6 | |
| [GGUF](https://huggingface.co/mradermacher/Hathor_Tahsin-L3-8B-v0.9-GGUF/resolve/main/Hathor_Tahsin-L3-8B-v0.9.Q4_0_4_4.gguf) | Q4_0_4_4 | 4.8 | fast on arm, low quality |
| [GGUF](https://huggingface.co/mradermacher/Hathor_Tahsin-L3-8B-v0.9-GGUF/resolve/main/Hathor_Tahsin-L3-8B-v0.9.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Hathor_Tahsin-L3-8B-v0.9-GGUF/resolve/main/Hathor_Tahsin-L3-8B-v0.9.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Hathor_Tahsin-L3-8B-v0.9-GGUF/resolve/main/Hathor_Tahsin-L3-8B-v0.9.Q5_K_S.gguf) | Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/Hathor_Tahsin-L3-8B-v0.9-GGUF/resolve/main/Hathor_Tahsin-L3-8B-v0.9.Q5_K_M.gguf) | Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/Hathor_Tahsin-L3-8B-v0.9-GGUF/resolve/main/Hathor_Tahsin-L3-8B-v0.9.Q6_K.gguf) | Q6_K | 6.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Hathor_Tahsin-L3-8B-v0.9-GGUF/resolve/main/Hathor_Tahsin-L3-8B-v0.9.Q8_0.gguf) | Q8_0 | 8.6 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Hathor_Tahsin-L3-8B-v0.9-GGUF/resolve/main/Hathor_Tahsin-L3-8B-v0.9.f16.gguf) | f16 | 16.2 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
mradermacher/Mozaic-7B-i1-GGUF
|
mradermacher
| 2024-12-16T04:45:50Z | 102 | 1 |
transformers
|
[
"transformers",
"gguf",
"en",
"dataset:argilla/distilabel-intel-orca-dpo-pairs",
"base_model:VitalContribution/Evangelion-7B",
"base_model:quantized:VitalContribution/Evangelion-7B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2024-11-17T14:38:59Z |
---
base_model: VitalContribution/Evangelion-7B
datasets:
- argilla/distilabel-intel-orca-dpo-pairs
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/VitalContribution/Evangelion-7B
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/Mozaic-7B-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Mozaic-7B-i1-GGUF/resolve/main/Mozaic-7B.i1-IQ1_S.gguf) | i1-IQ1_S | 1.7 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Mozaic-7B-i1-GGUF/resolve/main/Mozaic-7B.i1-IQ1_M.gguf) | i1-IQ1_M | 1.9 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Mozaic-7B-i1-GGUF/resolve/main/Mozaic-7B.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.1 | |
| [GGUF](https://huggingface.co/mradermacher/Mozaic-7B-i1-GGUF/resolve/main/Mozaic-7B.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.3 | |
| [GGUF](https://huggingface.co/mradermacher/Mozaic-7B-i1-GGUF/resolve/main/Mozaic-7B.i1-IQ2_S.gguf) | i1-IQ2_S | 2.4 | |
| [GGUF](https://huggingface.co/mradermacher/Mozaic-7B-i1-GGUF/resolve/main/Mozaic-7B.i1-IQ2_M.gguf) | i1-IQ2_M | 2.6 | |
| [GGUF](https://huggingface.co/mradermacher/Mozaic-7B-i1-GGUF/resolve/main/Mozaic-7B.i1-Q2_K.gguf) | i1-Q2_K | 2.8 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Mozaic-7B-i1-GGUF/resolve/main/Mozaic-7B.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 2.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Mozaic-7B-i1-GGUF/resolve/main/Mozaic-7B.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/Mozaic-7B-i1-GGUF/resolve/main/Mozaic-7B.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.3 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Mozaic-7B-i1-GGUF/resolve/main/Mozaic-7B.i1-IQ3_S.gguf) | i1-IQ3_S | 3.3 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Mozaic-7B-i1-GGUF/resolve/main/Mozaic-7B.i1-IQ3_M.gguf) | i1-IQ3_M | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/Mozaic-7B-i1-GGUF/resolve/main/Mozaic-7B.i1-Q3_K_M.gguf) | i1-Q3_K_M | 3.6 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Mozaic-7B-i1-GGUF/resolve/main/Mozaic-7B.i1-Q3_K_L.gguf) | i1-Q3_K_L | 3.9 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Mozaic-7B-i1-GGUF/resolve/main/Mozaic-7B.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/Mozaic-7B-i1-GGUF/resolve/main/Mozaic-7B.i1-Q4_0_4_4.gguf) | i1-Q4_0_4_4 | 4.2 | fast on arm, low quality |
| [GGUF](https://huggingface.co/mradermacher/Mozaic-7B-i1-GGUF/resolve/main/Mozaic-7B.i1-Q4_0_4_8.gguf) | i1-Q4_0_4_8 | 4.2 | fast on arm+i8mm, low quality |
| [GGUF](https://huggingface.co/mradermacher/Mozaic-7B-i1-GGUF/resolve/main/Mozaic-7B.i1-Q4_0_8_8.gguf) | i1-Q4_0_8_8 | 4.2 | fast on arm+sve, low quality |
| [GGUF](https://huggingface.co/mradermacher/Mozaic-7B-i1-GGUF/resolve/main/Mozaic-7B.i1-Q4_0.gguf) | i1-Q4_0 | 4.2 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Mozaic-7B-i1-GGUF/resolve/main/Mozaic-7B.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.2 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Mozaic-7B-i1-GGUF/resolve/main/Mozaic-7B.i1-Q4_K_M.gguf) | i1-Q4_K_M | 4.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Mozaic-7B-i1-GGUF/resolve/main/Mozaic-7B.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/Mozaic-7B-i1-GGUF/resolve/main/Mozaic-7B.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/Mozaic-7B-i1-GGUF/resolve/main/Mozaic-7B.i1-Q6_K.gguf) | i1-Q6_K | 6.0 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
MayBashendy/ArabicNewSplits5_FineTuningAraBERT_run3_AugV5_k7_task1_organization
|
MayBashendy
| 2024-12-16T04:45:45Z | 182 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:aubmindlab/bert-base-arabertv02",
"base_model:finetune:aubmindlab/bert-base-arabertv02",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-12-16T04:35:45Z |
---
library_name: transformers
base_model: aubmindlab/bert-base-arabertv02
tags:
- generated_from_trainer
model-index:
- name: ArabicNewSplits5_FineTuningAraBERT_run3_AugV5_k7_task1_organization
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. -->
# ArabicNewSplits5_FineTuningAraBERT_run3_AugV5_k7_task1_organization
This model is a fine-tuned version of [aubmindlab/bert-base-arabertv02](https://huggingface.co/aubmindlab/bert-base-arabertv02) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7061
- Qwk: 0.7224
- Mse: 0.7061
- Rmse: 0.8403
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Qwk | Mse | Rmse |
|:-------------:|:------:|:----:|:---------------:|:-------:|:------:|:------:|
| No log | 0.0435 | 2 | 5.3399 | -0.0005 | 5.3399 | 2.3108 |
| No log | 0.0870 | 4 | 3.0758 | 0.0366 | 3.0758 | 1.7538 |
| No log | 0.1304 | 6 | 2.6954 | -0.1090 | 2.6954 | 1.6418 |
| No log | 0.1739 | 8 | 2.6509 | -0.1328 | 2.6509 | 1.6282 |
| No log | 0.2174 | 10 | 1.8076 | 0.0516 | 1.8076 | 1.3445 |
| No log | 0.2609 | 12 | 1.6063 | 0.0894 | 1.6063 | 1.2674 |
| No log | 0.3043 | 14 | 1.6274 | 0.0846 | 1.6274 | 1.2757 |
| No log | 0.3478 | 16 | 1.5010 | 0.0901 | 1.5010 | 1.2251 |
| No log | 0.3913 | 18 | 1.4633 | 0.1215 | 1.4633 | 1.2097 |
| No log | 0.4348 | 20 | 1.4082 | 0.1176 | 1.4082 | 1.1867 |
| No log | 0.4783 | 22 | 1.1669 | 0.3330 | 1.1669 | 1.0802 |
| No log | 0.5217 | 24 | 1.0736 | 0.3511 | 1.0736 | 1.0362 |
| No log | 0.5652 | 26 | 1.1176 | 0.3370 | 1.1176 | 1.0572 |
| No log | 0.6087 | 28 | 1.4262 | 0.1048 | 1.4262 | 1.1942 |
| No log | 0.6522 | 30 | 1.7564 | 0.0819 | 1.7564 | 1.3253 |
| No log | 0.6957 | 32 | 1.7564 | 0.0990 | 1.7564 | 1.3253 |
| No log | 0.7391 | 34 | 1.5579 | 0.1279 | 1.5579 | 1.2482 |
| No log | 0.7826 | 36 | 1.3392 | 0.1965 | 1.3392 | 1.1572 |
| No log | 0.8261 | 38 | 1.2107 | 0.3343 | 1.2107 | 1.1003 |
| No log | 0.8696 | 40 | 1.0805 | 0.4124 | 1.0805 | 1.0395 |
| No log | 0.9130 | 42 | 1.1607 | 0.3727 | 1.1607 | 1.0774 |
| No log | 0.9565 | 44 | 1.5255 | 0.3689 | 1.5255 | 1.2351 |
| No log | 1.0 | 46 | 1.9381 | 0.3036 | 1.9381 | 1.3922 |
| No log | 1.0435 | 48 | 1.9718 | 0.2844 | 1.9718 | 1.4042 |
| No log | 1.0870 | 50 | 1.9832 | 0.2892 | 1.9832 | 1.4083 |
| No log | 1.1304 | 52 | 1.6459 | 0.3938 | 1.6459 | 1.2829 |
| No log | 1.1739 | 54 | 1.3080 | 0.4560 | 1.3080 | 1.1437 |
| No log | 1.2174 | 56 | 0.9874 | 0.5351 | 0.9874 | 0.9937 |
| No log | 1.2609 | 58 | 0.8858 | 0.5873 | 0.8858 | 0.9412 |
| No log | 1.3043 | 60 | 0.8095 | 0.5809 | 0.8095 | 0.8997 |
| No log | 1.3478 | 62 | 0.8362 | 0.5691 | 0.8362 | 0.9144 |
| No log | 1.3913 | 64 | 0.8891 | 0.5793 | 0.8891 | 0.9429 |
| No log | 1.4348 | 66 | 1.0204 | 0.5511 | 1.0204 | 1.0101 |
| No log | 1.4783 | 68 | 1.0976 | 0.5090 | 1.0976 | 1.0477 |
| No log | 1.5217 | 70 | 0.9730 | 0.5839 | 0.9730 | 0.9864 |
| No log | 1.5652 | 72 | 0.9512 | 0.5839 | 0.9512 | 0.9753 |
| No log | 1.6087 | 74 | 0.8361 | 0.5441 | 0.8361 | 0.9144 |
| No log | 1.6522 | 76 | 0.8260 | 0.5707 | 0.8260 | 0.9088 |
| No log | 1.6957 | 78 | 0.8256 | 0.5803 | 0.8256 | 0.9086 |
| No log | 1.7391 | 80 | 0.8887 | 0.5544 | 0.8887 | 0.9427 |
| No log | 1.7826 | 82 | 1.1341 | 0.5516 | 1.1341 | 1.0649 |
| No log | 1.8261 | 84 | 1.3158 | 0.4941 | 1.3158 | 1.1471 |
| No log | 1.8696 | 86 | 1.5108 | 0.4324 | 1.5108 | 1.2292 |
| No log | 1.9130 | 88 | 1.3080 | 0.5026 | 1.3080 | 1.1437 |
| No log | 1.9565 | 90 | 0.9981 | 0.5880 | 0.9981 | 0.9990 |
| No log | 2.0 | 92 | 0.9208 | 0.5733 | 0.9208 | 0.9596 |
| No log | 2.0435 | 94 | 0.8731 | 0.6185 | 0.8731 | 0.9344 |
| No log | 2.0870 | 96 | 0.8903 | 0.6254 | 0.8903 | 0.9435 |
| No log | 2.1304 | 98 | 0.7550 | 0.7072 | 0.7550 | 0.8689 |
| No log | 2.1739 | 100 | 0.7350 | 0.6911 | 0.7350 | 0.8573 |
| No log | 2.2174 | 102 | 0.7365 | 0.6993 | 0.7365 | 0.8582 |
| No log | 2.2609 | 104 | 0.7327 | 0.7040 | 0.7327 | 0.8560 |
| No log | 2.3043 | 106 | 0.7043 | 0.6750 | 0.7043 | 0.8392 |
| No log | 2.3478 | 108 | 0.6722 | 0.7005 | 0.6722 | 0.8199 |
| No log | 2.3913 | 110 | 0.6608 | 0.7051 | 0.6608 | 0.8129 |
| No log | 2.4348 | 112 | 0.7460 | 0.6284 | 0.7460 | 0.8637 |
| No log | 2.4783 | 114 | 0.8291 | 0.5822 | 0.8291 | 0.9105 |
| No log | 2.5217 | 116 | 0.8143 | 0.5945 | 0.8143 | 0.9024 |
| No log | 2.5652 | 118 | 0.6973 | 0.6563 | 0.6973 | 0.8350 |
| No log | 2.6087 | 120 | 0.6553 | 0.7465 | 0.6553 | 0.8095 |
| No log | 2.6522 | 122 | 0.7105 | 0.6737 | 0.7105 | 0.8429 |
| No log | 2.6957 | 124 | 0.7238 | 0.6823 | 0.7238 | 0.8508 |
| No log | 2.7391 | 126 | 0.7330 | 0.6606 | 0.7330 | 0.8562 |
| No log | 2.7826 | 128 | 0.9111 | 0.6045 | 0.9111 | 0.9545 |
| No log | 2.8261 | 130 | 1.0697 | 0.5562 | 1.0697 | 1.0343 |
| No log | 2.8696 | 132 | 1.0278 | 0.5631 | 1.0278 | 1.0138 |
| No log | 2.9130 | 134 | 0.8927 | 0.6085 | 0.8927 | 0.9448 |
| No log | 2.9565 | 136 | 0.8172 | 0.6297 | 0.8172 | 0.9040 |
| No log | 3.0 | 138 | 0.7900 | 0.6479 | 0.7900 | 0.8888 |
| No log | 3.0435 | 140 | 0.8496 | 0.5744 | 0.8496 | 0.9217 |
| No log | 3.0870 | 142 | 1.0781 | 0.4924 | 1.0781 | 1.0383 |
| No log | 3.1304 | 144 | 1.1157 | 0.5030 | 1.1157 | 1.0563 |
| No log | 3.1739 | 146 | 0.9228 | 0.5499 | 0.9228 | 0.9606 |
| No log | 3.2174 | 148 | 0.7389 | 0.6045 | 0.7389 | 0.8596 |
| No log | 3.2609 | 150 | 0.7086 | 0.6360 | 0.7086 | 0.8418 |
| No log | 3.3043 | 152 | 0.7835 | 0.6073 | 0.7835 | 0.8851 |
| No log | 3.3478 | 154 | 0.9609 | 0.5742 | 0.9609 | 0.9803 |
| No log | 3.3913 | 156 | 0.9121 | 0.5836 | 0.9121 | 0.9550 |
| No log | 3.4348 | 158 | 0.8124 | 0.6868 | 0.8124 | 0.9013 |
| No log | 3.4783 | 160 | 0.6549 | 0.7078 | 0.6549 | 0.8093 |
| No log | 3.5217 | 162 | 0.6347 | 0.7468 | 0.6347 | 0.7967 |
| No log | 3.5652 | 164 | 0.6533 | 0.7218 | 0.6533 | 0.8082 |
| No log | 3.6087 | 166 | 0.6246 | 0.7107 | 0.6246 | 0.7903 |
| No log | 3.6522 | 168 | 0.6260 | 0.7316 | 0.6260 | 0.7912 |
| No log | 3.6957 | 170 | 0.7475 | 0.6618 | 0.7475 | 0.8646 |
| No log | 3.7391 | 172 | 0.8541 | 0.6364 | 0.8541 | 0.9242 |
| No log | 3.7826 | 174 | 0.8107 | 0.6560 | 0.8107 | 0.9004 |
| No log | 3.8261 | 176 | 0.6933 | 0.6737 | 0.6933 | 0.8327 |
| No log | 3.8696 | 178 | 0.6339 | 0.7040 | 0.6339 | 0.7961 |
| No log | 3.9130 | 180 | 0.6819 | 0.6773 | 0.6819 | 0.8258 |
| No log | 3.9565 | 182 | 0.6958 | 0.6783 | 0.6958 | 0.8342 |
| No log | 4.0 | 184 | 0.6607 | 0.7028 | 0.6607 | 0.8128 |
| No log | 4.0435 | 186 | 0.6542 | 0.7258 | 0.6542 | 0.8088 |
| No log | 4.0870 | 188 | 0.6646 | 0.7218 | 0.6646 | 0.8153 |
| No log | 4.1304 | 190 | 0.6705 | 0.7109 | 0.6705 | 0.8188 |
| No log | 4.1739 | 192 | 0.6925 | 0.7124 | 0.6925 | 0.8322 |
| No log | 4.2174 | 194 | 0.6716 | 0.7122 | 0.6716 | 0.8195 |
| No log | 4.2609 | 196 | 0.6588 | 0.7229 | 0.6588 | 0.8117 |
| No log | 4.3043 | 198 | 0.6619 | 0.7003 | 0.6619 | 0.8136 |
| No log | 4.3478 | 200 | 0.6647 | 0.6953 | 0.6647 | 0.8153 |
| No log | 4.3913 | 202 | 0.6889 | 0.6745 | 0.6889 | 0.8300 |
| No log | 4.4348 | 204 | 0.7231 | 0.6615 | 0.7231 | 0.8503 |
| No log | 4.4783 | 206 | 0.8096 | 0.6231 | 0.8096 | 0.8998 |
| No log | 4.5217 | 208 | 0.8056 | 0.6400 | 0.8056 | 0.8976 |
| No log | 4.5652 | 210 | 0.7532 | 0.6265 | 0.7532 | 0.8679 |
| No log | 4.6087 | 212 | 0.7026 | 0.6372 | 0.7026 | 0.8382 |
| No log | 4.6522 | 214 | 0.6755 | 0.6719 | 0.6755 | 0.8219 |
| No log | 4.6957 | 216 | 0.6761 | 0.6769 | 0.6761 | 0.8222 |
| No log | 4.7391 | 218 | 0.6827 | 0.6528 | 0.6827 | 0.8262 |
| No log | 4.7826 | 220 | 0.6800 | 0.6761 | 0.6800 | 0.8246 |
| No log | 4.8261 | 222 | 0.6709 | 0.6649 | 0.6709 | 0.8191 |
| No log | 4.8696 | 224 | 0.6859 | 0.6624 | 0.6859 | 0.8282 |
| No log | 4.9130 | 226 | 0.6853 | 0.6823 | 0.6853 | 0.8278 |
| No log | 4.9565 | 228 | 0.6902 | 0.7004 | 0.6902 | 0.8308 |
| No log | 5.0 | 230 | 0.6619 | 0.6896 | 0.6619 | 0.8136 |
| No log | 5.0435 | 232 | 0.6506 | 0.7106 | 0.6506 | 0.8066 |
| No log | 5.0870 | 234 | 0.6540 | 0.7138 | 0.6540 | 0.8087 |
| No log | 5.1304 | 236 | 0.6704 | 0.6970 | 0.6704 | 0.8188 |
| No log | 5.1739 | 238 | 0.6878 | 0.6854 | 0.6878 | 0.8293 |
| No log | 5.2174 | 240 | 0.6917 | 0.6854 | 0.6917 | 0.8317 |
| No log | 5.2609 | 242 | 0.6773 | 0.7275 | 0.6773 | 0.8230 |
| No log | 5.3043 | 244 | 0.6711 | 0.6964 | 0.6711 | 0.8192 |
| No log | 5.3478 | 246 | 0.6994 | 0.7025 | 0.6994 | 0.8363 |
| No log | 5.3913 | 248 | 0.7011 | 0.6893 | 0.7011 | 0.8373 |
| No log | 5.4348 | 250 | 0.6831 | 0.6931 | 0.6831 | 0.8265 |
| No log | 5.4783 | 252 | 0.6678 | 0.6903 | 0.6678 | 0.8172 |
| No log | 5.5217 | 254 | 0.6461 | 0.6983 | 0.6461 | 0.8038 |
| No log | 5.5652 | 256 | 0.6407 | 0.6997 | 0.6407 | 0.8004 |
| No log | 5.6087 | 258 | 0.6428 | 0.7056 | 0.6428 | 0.8018 |
| No log | 5.6522 | 260 | 0.6403 | 0.7071 | 0.6403 | 0.8002 |
| No log | 5.6957 | 262 | 0.6324 | 0.7093 | 0.6324 | 0.7952 |
| No log | 5.7391 | 264 | 0.6164 | 0.7202 | 0.6164 | 0.7851 |
| No log | 5.7826 | 266 | 0.6209 | 0.7275 | 0.6209 | 0.7879 |
| No log | 5.8261 | 268 | 0.6420 | 0.6879 | 0.6420 | 0.8013 |
| No log | 5.8696 | 270 | 0.6812 | 0.7335 | 0.6812 | 0.8254 |
| No log | 5.9130 | 272 | 0.6866 | 0.7195 | 0.6866 | 0.8286 |
| No log | 5.9565 | 274 | 0.6659 | 0.7108 | 0.6659 | 0.8160 |
| No log | 6.0 | 276 | 0.6776 | 0.6970 | 0.6776 | 0.8232 |
| No log | 6.0435 | 278 | 0.6863 | 0.6847 | 0.6863 | 0.8285 |
| No log | 6.0870 | 280 | 0.6802 | 0.6990 | 0.6802 | 0.8247 |
| No log | 6.1304 | 282 | 0.7015 | 0.6984 | 0.7015 | 0.8376 |
| No log | 6.1739 | 284 | 0.7460 | 0.6609 | 0.7460 | 0.8637 |
| No log | 6.2174 | 286 | 0.7574 | 0.6654 | 0.7574 | 0.8703 |
| No log | 6.2609 | 288 | 0.7270 | 0.6859 | 0.7270 | 0.8527 |
| No log | 6.3043 | 290 | 0.6977 | 0.6976 | 0.6977 | 0.8353 |
| No log | 6.3478 | 292 | 0.6952 | 0.6802 | 0.6952 | 0.8338 |
| No log | 6.3913 | 294 | 0.6970 | 0.6601 | 0.6970 | 0.8349 |
| No log | 6.4348 | 296 | 0.6906 | 0.6533 | 0.6906 | 0.8310 |
| No log | 6.4783 | 298 | 0.6972 | 0.6655 | 0.6972 | 0.8350 |
| No log | 6.5217 | 300 | 0.6907 | 0.6655 | 0.6907 | 0.8311 |
| No log | 6.5652 | 302 | 0.6734 | 0.6495 | 0.6734 | 0.8206 |
| No log | 6.6087 | 304 | 0.6697 | 0.6593 | 0.6697 | 0.8184 |
| No log | 6.6522 | 306 | 0.6700 | 0.6593 | 0.6700 | 0.8186 |
| No log | 6.6957 | 308 | 0.6634 | 0.6462 | 0.6634 | 0.8145 |
| No log | 6.7391 | 310 | 0.6639 | 0.6936 | 0.6639 | 0.8148 |
| No log | 6.7826 | 312 | 0.6685 | 0.6844 | 0.6685 | 0.8176 |
| No log | 6.8261 | 314 | 0.6784 | 0.7090 | 0.6784 | 0.8236 |
| No log | 6.8696 | 316 | 0.6970 | 0.6983 | 0.6970 | 0.8349 |
| No log | 6.9130 | 318 | 0.7112 | 0.6867 | 0.7112 | 0.8433 |
| No log | 6.9565 | 320 | 0.7186 | 0.6867 | 0.7186 | 0.8477 |
| No log | 7.0 | 322 | 0.7322 | 0.6988 | 0.7322 | 0.8557 |
| No log | 7.0435 | 324 | 0.7420 | 0.7027 | 0.7420 | 0.8614 |
| No log | 7.0870 | 326 | 0.7414 | 0.7027 | 0.7414 | 0.8611 |
| No log | 7.1304 | 328 | 0.7340 | 0.6949 | 0.7340 | 0.8568 |
| No log | 7.1739 | 330 | 0.7410 | 0.7041 | 0.7410 | 0.8608 |
| No log | 7.2174 | 332 | 0.7382 | 0.7041 | 0.7382 | 0.8592 |
| No log | 7.2609 | 334 | 0.7218 | 0.7008 | 0.7218 | 0.8496 |
| No log | 7.3043 | 336 | 0.7078 | 0.6892 | 0.7078 | 0.8413 |
| No log | 7.3478 | 338 | 0.7125 | 0.6903 | 0.7125 | 0.8441 |
| No log | 7.3913 | 340 | 0.7280 | 0.6630 | 0.7280 | 0.8532 |
| No log | 7.4348 | 342 | 0.7500 | 0.6714 | 0.7500 | 0.8660 |
| No log | 7.4783 | 344 | 0.7764 | 0.6835 | 0.7764 | 0.8811 |
| No log | 7.5217 | 346 | 0.7963 | 0.6859 | 0.7963 | 0.8923 |
| No log | 7.5652 | 348 | 0.8320 | 0.6710 | 0.8320 | 0.9122 |
| No log | 7.6087 | 350 | 0.8335 | 0.6693 | 0.8335 | 0.9129 |
| No log | 7.6522 | 352 | 0.8229 | 0.6750 | 0.8229 | 0.9072 |
| No log | 7.6957 | 354 | 0.8088 | 0.6842 | 0.8088 | 0.8993 |
| No log | 7.7391 | 356 | 0.7713 | 0.6901 | 0.7713 | 0.8783 |
| No log | 7.7826 | 358 | 0.7458 | 0.6937 | 0.7458 | 0.8636 |
| No log | 7.8261 | 360 | 0.7392 | 0.6864 | 0.7392 | 0.8597 |
| No log | 7.8696 | 362 | 0.7463 | 0.6827 | 0.7463 | 0.8639 |
| No log | 7.9130 | 364 | 0.7434 | 0.6816 | 0.7434 | 0.8622 |
| No log | 7.9565 | 366 | 0.7535 | 0.6907 | 0.7535 | 0.8680 |
| No log | 8.0 | 368 | 0.7527 | 0.7020 | 0.7527 | 0.8676 |
| No log | 8.0435 | 370 | 0.7407 | 0.7035 | 0.7407 | 0.8606 |
| No log | 8.0870 | 372 | 0.7276 | 0.6975 | 0.7276 | 0.8530 |
| No log | 8.1304 | 374 | 0.7291 | 0.7018 | 0.7291 | 0.8539 |
| No log | 8.1739 | 376 | 0.7316 | 0.7018 | 0.7316 | 0.8554 |
| No log | 8.2174 | 378 | 0.7343 | 0.7018 | 0.7343 | 0.8569 |
| No log | 8.2609 | 380 | 0.7344 | 0.6975 | 0.7344 | 0.8570 |
| No log | 8.3043 | 382 | 0.7396 | 0.7120 | 0.7396 | 0.8600 |
| No log | 8.3478 | 384 | 0.7377 | 0.7120 | 0.7377 | 0.8589 |
| No log | 8.3913 | 386 | 0.7303 | 0.6877 | 0.7303 | 0.8546 |
| No log | 8.4348 | 388 | 0.7135 | 0.7128 | 0.7135 | 0.8447 |
| No log | 8.4783 | 390 | 0.6936 | 0.7114 | 0.6936 | 0.8328 |
| No log | 8.5217 | 392 | 0.6826 | 0.6934 | 0.6826 | 0.8262 |
| No log | 8.5652 | 394 | 0.6743 | 0.6592 | 0.6743 | 0.8212 |
| No log | 8.6087 | 396 | 0.6705 | 0.6725 | 0.6705 | 0.8188 |
| No log | 8.6522 | 398 | 0.6716 | 0.6680 | 0.6716 | 0.8195 |
| No log | 8.6957 | 400 | 0.6764 | 0.6719 | 0.6764 | 0.8225 |
| No log | 8.7391 | 402 | 0.6868 | 0.7181 | 0.6868 | 0.8287 |
| No log | 8.7826 | 404 | 0.6894 | 0.7181 | 0.6894 | 0.8303 |
| No log | 8.8261 | 406 | 0.6887 | 0.7181 | 0.6887 | 0.8299 |
| No log | 8.8696 | 408 | 0.6853 | 0.7181 | 0.6853 | 0.8278 |
| No log | 8.9130 | 410 | 0.6859 | 0.7181 | 0.6859 | 0.8282 |
| No log | 8.9565 | 412 | 0.6820 | 0.7040 | 0.6820 | 0.8259 |
| No log | 9.0 | 414 | 0.6794 | 0.7040 | 0.6794 | 0.8243 |
| No log | 9.0435 | 416 | 0.6798 | 0.7040 | 0.6798 | 0.8245 |
| No log | 9.0870 | 418 | 0.6866 | 0.7181 | 0.6866 | 0.8286 |
| No log | 9.1304 | 420 | 0.6931 | 0.7181 | 0.6931 | 0.8325 |
| No log | 9.1739 | 422 | 0.7001 | 0.7181 | 0.7001 | 0.8367 |
| No log | 9.2174 | 424 | 0.7036 | 0.7181 | 0.7036 | 0.8388 |
| No log | 9.2609 | 426 | 0.7035 | 0.7181 | 0.7035 | 0.8387 |
| No log | 9.3043 | 428 | 0.7052 | 0.7181 | 0.7052 | 0.8397 |
| No log | 9.3478 | 430 | 0.7065 | 0.7181 | 0.7065 | 0.8406 |
| No log | 9.3913 | 432 | 0.7024 | 0.7181 | 0.7024 | 0.8381 |
| No log | 9.4348 | 434 | 0.6985 | 0.7083 | 0.6985 | 0.8358 |
| No log | 9.4783 | 436 | 0.6963 | 0.7083 | 0.6963 | 0.8344 |
| No log | 9.5217 | 438 | 0.6947 | 0.7083 | 0.6947 | 0.8335 |
| No log | 9.5652 | 440 | 0.6928 | 0.6928 | 0.6928 | 0.8324 |
| No log | 9.6087 | 442 | 0.6931 | 0.6928 | 0.6931 | 0.8325 |
| No log | 9.6522 | 444 | 0.6944 | 0.7022 | 0.6944 | 0.8333 |
| No log | 9.6957 | 446 | 0.6966 | 0.7083 | 0.6966 | 0.8346 |
| No log | 9.7391 | 448 | 0.6995 | 0.7083 | 0.6995 | 0.8363 |
| No log | 9.7826 | 450 | 0.7011 | 0.7083 | 0.7011 | 0.8373 |
| No log | 9.8261 | 452 | 0.7031 | 0.7224 | 0.7031 | 0.8385 |
| No log | 9.8696 | 454 | 0.7044 | 0.7224 | 0.7044 | 0.8393 |
| No log | 9.9130 | 456 | 0.7053 | 0.7224 | 0.7053 | 0.8398 |
| No log | 9.9565 | 458 | 0.7058 | 0.7224 | 0.7058 | 0.8401 |
| No log | 10.0 | 460 | 0.7061 | 0.7224 | 0.7061 | 0.8403 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu118
- Datasets 2.21.0
- Tokenizers 0.19.1
|
mradermacher/Kunocchini-GGUF
|
mradermacher
| 2024-12-16T04:45:41Z | 19 | 1 |
transformers
|
[
"transformers",
"gguf",
"mergekit",
"merge",
"alpaca",
"mistral",
"en",
"base_model:Nitral-Archive/Kunocchini-7b",
"base_model:quantized:Nitral-Archive/Kunocchini-7b",
"license:other",
"endpoints_compatible",
"region:us"
] | null | 2024-11-17T18:30:28Z |
---
base_model: Nitral-Archive/Kunocchini-7b
language:
- en
library_name: transformers
license: other
quantized_by: mradermacher
tags:
- mergekit
- merge
- alpaca
- mistral
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/Nitral-Archive/Kunocchini-7b
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Kunocchini-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Kunocchini-GGUF/resolve/main/Kunocchini.Q2_K.gguf) | Q2_K | 2.8 | |
| [GGUF](https://huggingface.co/mradermacher/Kunocchini-GGUF/resolve/main/Kunocchini.Q3_K_S.gguf) | Q3_K_S | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/Kunocchini-GGUF/resolve/main/Kunocchini.Q3_K_M.gguf) | Q3_K_M | 3.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Kunocchini-GGUF/resolve/main/Kunocchini.Q3_K_L.gguf) | Q3_K_L | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/Kunocchini-GGUF/resolve/main/Kunocchini.IQ4_XS.gguf) | IQ4_XS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/Kunocchini-GGUF/resolve/main/Kunocchini.Q4_0_4_4.gguf) | Q4_0_4_4 | 4.2 | fast on arm, low quality |
| [GGUF](https://huggingface.co/mradermacher/Kunocchini-GGUF/resolve/main/Kunocchini.Q4_K_S.gguf) | Q4_K_S | 4.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Kunocchini-GGUF/resolve/main/Kunocchini.Q4_K_M.gguf) | Q4_K_M | 4.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Kunocchini-GGUF/resolve/main/Kunocchini.Q5_K_S.gguf) | Q5_K_S | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/Kunocchini-GGUF/resolve/main/Kunocchini.Q5_K_M.gguf) | Q5_K_M | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/Kunocchini-GGUF/resolve/main/Kunocchini.Q6_K.gguf) | Q6_K | 6.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Kunocchini-GGUF/resolve/main/Kunocchini.Q8_0.gguf) | Q8_0 | 7.8 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Kunocchini-GGUF/resolve/main/Kunocchini.f16.gguf) | f16 | 14.6 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
mradermacher/Kunocchini-i1-GGUF
|
mradermacher
| 2024-12-16T04:45:36Z | 95 | 1 |
transformers
|
[
"transformers",
"gguf",
"mergekit",
"merge",
"alpaca",
"mistral",
"en",
"base_model:Nitral-Archive/Kunocchini-7b",
"base_model:quantized:Nitral-Archive/Kunocchini-7b",
"license:other",
"endpoints_compatible",
"region:us",
"imatrix"
] | null | 2024-11-17T18:57:12Z |
---
base_model: Nitral-Archive/Kunocchini-7b
language:
- en
library_name: transformers
license: other
quantized_by: mradermacher
tags:
- mergekit
- merge
- alpaca
- mistral
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/Nitral-Archive/Kunocchini-7b
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/Kunocchini-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Kunocchini-i1-GGUF/resolve/main/Kunocchini.i1-IQ1_S.gguf) | i1-IQ1_S | 1.7 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Kunocchini-i1-GGUF/resolve/main/Kunocchini.i1-IQ1_M.gguf) | i1-IQ1_M | 1.9 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Kunocchini-i1-GGUF/resolve/main/Kunocchini.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.1 | |
| [GGUF](https://huggingface.co/mradermacher/Kunocchini-i1-GGUF/resolve/main/Kunocchini.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.3 | |
| [GGUF](https://huggingface.co/mradermacher/Kunocchini-i1-GGUF/resolve/main/Kunocchini.i1-IQ2_S.gguf) | i1-IQ2_S | 2.4 | |
| [GGUF](https://huggingface.co/mradermacher/Kunocchini-i1-GGUF/resolve/main/Kunocchini.i1-IQ2_M.gguf) | i1-IQ2_M | 2.6 | |
| [GGUF](https://huggingface.co/mradermacher/Kunocchini-i1-GGUF/resolve/main/Kunocchini.i1-Q2_K.gguf) | i1-Q2_K | 2.8 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Kunocchini-i1-GGUF/resolve/main/Kunocchini.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 2.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Kunocchini-i1-GGUF/resolve/main/Kunocchini.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/Kunocchini-i1-GGUF/resolve/main/Kunocchini.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.3 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Kunocchini-i1-GGUF/resolve/main/Kunocchini.i1-IQ3_S.gguf) | i1-IQ3_S | 3.3 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Kunocchini-i1-GGUF/resolve/main/Kunocchini.i1-IQ3_M.gguf) | i1-IQ3_M | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/Kunocchini-i1-GGUF/resolve/main/Kunocchini.i1-Q3_K_M.gguf) | i1-Q3_K_M | 3.6 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Kunocchini-i1-GGUF/resolve/main/Kunocchini.i1-Q3_K_L.gguf) | i1-Q3_K_L | 3.9 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Kunocchini-i1-GGUF/resolve/main/Kunocchini.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/Kunocchini-i1-GGUF/resolve/main/Kunocchini.i1-Q4_0_4_4.gguf) | i1-Q4_0_4_4 | 4.2 | fast on arm, low quality |
| [GGUF](https://huggingface.co/mradermacher/Kunocchini-i1-GGUF/resolve/main/Kunocchini.i1-Q4_0_4_8.gguf) | i1-Q4_0_4_8 | 4.2 | fast on arm+i8mm, low quality |
| [GGUF](https://huggingface.co/mradermacher/Kunocchini-i1-GGUF/resolve/main/Kunocchini.i1-Q4_0_8_8.gguf) | i1-Q4_0_8_8 | 4.2 | fast on arm+sve, low quality |
| [GGUF](https://huggingface.co/mradermacher/Kunocchini-i1-GGUF/resolve/main/Kunocchini.i1-Q4_0.gguf) | i1-Q4_0 | 4.2 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Kunocchini-i1-GGUF/resolve/main/Kunocchini.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.2 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Kunocchini-i1-GGUF/resolve/main/Kunocchini.i1-Q4_K_M.gguf) | i1-Q4_K_M | 4.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Kunocchini-i1-GGUF/resolve/main/Kunocchini.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/Kunocchini-i1-GGUF/resolve/main/Kunocchini.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/Kunocchini-i1-GGUF/resolve/main/Kunocchini.i1-Q6_K.gguf) | i1-Q6_K | 6.0 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
mradermacher/llama-3-cat-8b-instruct-v1-i1-GGUF
|
mradermacher
| 2024-12-16T04:45:31Z | 11 | 1 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:SteelStorage/llama-3-cat-8b-instruct-v1",
"base_model:quantized:SteelStorage/llama-3-cat-8b-instruct-v1",
"license:llama3",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2024-11-17T20:54:53Z |
---
base_model: SteelStorage/llama-3-cat-8b-instruct-v1
language:
- en
library_name: transformers
license: llama3
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/SteelStorage/llama-3-cat-8b-instruct-v1
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/llama-3-cat-8b-instruct-v1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/llama-3-cat-8b-instruct-v1-i1-GGUF/resolve/main/llama-3-cat-8b-instruct-v1.i1-IQ1_S.gguf) | i1-IQ1_S | 2.1 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/llama-3-cat-8b-instruct-v1-i1-GGUF/resolve/main/llama-3-cat-8b-instruct-v1.i1-IQ1_M.gguf) | i1-IQ1_M | 2.3 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/llama-3-cat-8b-instruct-v1-i1-GGUF/resolve/main/llama-3-cat-8b-instruct-v1.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.5 | |
| [GGUF](https://huggingface.co/mradermacher/llama-3-cat-8b-instruct-v1-i1-GGUF/resolve/main/llama-3-cat-8b-instruct-v1.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.7 | |
| [GGUF](https://huggingface.co/mradermacher/llama-3-cat-8b-instruct-v1-i1-GGUF/resolve/main/llama-3-cat-8b-instruct-v1.i1-IQ2_S.gguf) | i1-IQ2_S | 2.9 | |
| [GGUF](https://huggingface.co/mradermacher/llama-3-cat-8b-instruct-v1-i1-GGUF/resolve/main/llama-3-cat-8b-instruct-v1.i1-IQ2_M.gguf) | i1-IQ2_M | 3.0 | |
| [GGUF](https://huggingface.co/mradermacher/llama-3-cat-8b-instruct-v1-i1-GGUF/resolve/main/llama-3-cat-8b-instruct-v1.i1-Q2_K.gguf) | i1-Q2_K | 3.3 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/llama-3-cat-8b-instruct-v1-i1-GGUF/resolve/main/llama-3-cat-8b-instruct-v1.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 3.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/llama-3-cat-8b-instruct-v1-i1-GGUF/resolve/main/llama-3-cat-8b-instruct-v1.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/llama-3-cat-8b-instruct-v1-i1-GGUF/resolve/main/llama-3-cat-8b-instruct-v1.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.8 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/llama-3-cat-8b-instruct-v1-i1-GGUF/resolve/main/llama-3-cat-8b-instruct-v1.i1-IQ3_S.gguf) | i1-IQ3_S | 3.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/llama-3-cat-8b-instruct-v1-i1-GGUF/resolve/main/llama-3-cat-8b-instruct-v1.i1-IQ3_M.gguf) | i1-IQ3_M | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/llama-3-cat-8b-instruct-v1-i1-GGUF/resolve/main/llama-3-cat-8b-instruct-v1.i1-Q3_K_M.gguf) | i1-Q3_K_M | 4.1 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/llama-3-cat-8b-instruct-v1-i1-GGUF/resolve/main/llama-3-cat-8b-instruct-v1.i1-Q3_K_L.gguf) | i1-Q3_K_L | 4.4 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/llama-3-cat-8b-instruct-v1-i1-GGUF/resolve/main/llama-3-cat-8b-instruct-v1.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.5 | |
| [GGUF](https://huggingface.co/mradermacher/llama-3-cat-8b-instruct-v1-i1-GGUF/resolve/main/llama-3-cat-8b-instruct-v1.i1-Q4_0_4_4.gguf) | i1-Q4_0_4_4 | 4.8 | fast on arm, low quality |
| [GGUF](https://huggingface.co/mradermacher/llama-3-cat-8b-instruct-v1-i1-GGUF/resolve/main/llama-3-cat-8b-instruct-v1.i1-Q4_0_4_8.gguf) | i1-Q4_0_4_8 | 4.8 | fast on arm+i8mm, low quality |
| [GGUF](https://huggingface.co/mradermacher/llama-3-cat-8b-instruct-v1-i1-GGUF/resolve/main/llama-3-cat-8b-instruct-v1.i1-Q4_0_8_8.gguf) | i1-Q4_0_8_8 | 4.8 | fast on arm+sve, low quality |
| [GGUF](https://huggingface.co/mradermacher/llama-3-cat-8b-instruct-v1-i1-GGUF/resolve/main/llama-3-cat-8b-instruct-v1.i1-Q4_0.gguf) | i1-Q4_0 | 4.8 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/llama-3-cat-8b-instruct-v1-i1-GGUF/resolve/main/llama-3-cat-8b-instruct-v1.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.8 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/llama-3-cat-8b-instruct-v1-i1-GGUF/resolve/main/llama-3-cat-8b-instruct-v1.i1-Q4_K_M.gguf) | i1-Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/llama-3-cat-8b-instruct-v1-i1-GGUF/resolve/main/llama-3-cat-8b-instruct-v1.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/llama-3-cat-8b-instruct-v1-i1-GGUF/resolve/main/llama-3-cat-8b-instruct-v1.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/llama-3-cat-8b-instruct-v1-i1-GGUF/resolve/main/llama-3-cat-8b-instruct-v1.i1-Q6_K.gguf) | i1-Q6_K | 6.7 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
mradermacher/Lorenzo-8B-Merge-GGUF
|
mradermacher
| 2024-12-16T04:42:46Z | 9 | 1 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:qingy2024/Lorenzo-8B-Merge",
"base_model:quantized:qingy2024/Lorenzo-8B-Merge",
"endpoints_compatible",
"region:us"
] | null | 2024-11-22T08:40:22Z |
---
base_model: qingy2024/Lorenzo-8B-Merge
language:
- en
library_name: transformers
quantized_by: mradermacher
tags: []
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
static quants of https://huggingface.co/qingy2024/Lorenzo-8B-Merge
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Lorenzo-8B-Merge-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Lorenzo-8B-Merge-GGUF/resolve/main/Lorenzo-8B-Merge.Q2_K.gguf) | Q2_K | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/Lorenzo-8B-Merge-GGUF/resolve/main/Lorenzo-8B-Merge.Q3_K_S.gguf) | Q3_K_S | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/Lorenzo-8B-Merge-GGUF/resolve/main/Lorenzo-8B-Merge.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Lorenzo-8B-Merge-GGUF/resolve/main/Lorenzo-8B-Merge.Q3_K_L.gguf) | Q3_K_L | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/Lorenzo-8B-Merge-GGUF/resolve/main/Lorenzo-8B-Merge.IQ4_XS.gguf) | IQ4_XS | 4.6 | |
| [GGUF](https://huggingface.co/mradermacher/Lorenzo-8B-Merge-GGUF/resolve/main/Lorenzo-8B-Merge.Q4_0_4_4.gguf) | Q4_0_4_4 | 4.8 | fast on arm, low quality |
| [GGUF](https://huggingface.co/mradermacher/Lorenzo-8B-Merge-GGUF/resolve/main/Lorenzo-8B-Merge.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Lorenzo-8B-Merge-GGUF/resolve/main/Lorenzo-8B-Merge.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Lorenzo-8B-Merge-GGUF/resolve/main/Lorenzo-8B-Merge.Q5_K_S.gguf) | Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/Lorenzo-8B-Merge-GGUF/resolve/main/Lorenzo-8B-Merge.Q5_K_M.gguf) | Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/Lorenzo-8B-Merge-GGUF/resolve/main/Lorenzo-8B-Merge.Q6_K.gguf) | Q6_K | 6.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Lorenzo-8B-Merge-GGUF/resolve/main/Lorenzo-8B-Merge.Q8_0.gguf) | Q8_0 | 8.6 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Lorenzo-8B-Merge-GGUF/resolve/main/Lorenzo-8B-Merge.f16.gguf) | f16 | 16.2 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
Bllossom/llama-3.2-Korean-Bllossom-3B
|
Bllossom
| 2024-12-16T04:42:08Z | 23,900 | 147 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"en",
"ko",
"arxiv:2403.10882",
"arxiv:2403.11399",
"license:llama3.2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-10-08T08:24:56Z |
---
base_model:
- meta-llama/Meta-Llama-3.2-3B
language:
- en
- ko
library_name: transformers
license: llama3.2
---
<a href="https://github.com/MLP-Lab/Bllossom">
<img src="https://cdn-uploads.huggingface.co/production/uploads/64a90711c05da19ca834f690/a0VE5UCY1HCEhaHtp3mGa.png" alt="image" width="30%" height="30%">
</a>
# Update!
* [2024.12.06] ν¨μ¬ κ°λ ₯ν μ΅μ Bllossom-AICA-5Bλ‘ μ
λ°μ΄νΈ λμμ΅λλ€ [λ§ν¬](https://huggingface.co/Bllossom/llama-3.2-Korean-Bllossom-AICA-5B)
* [2024.10.08] Bllossom-3B λͺ¨λΈμ΄ μ΅μ΄ μ
λ°μ΄νΈ λμμ΅λλ€.
# Bllossom | [Demo]() | [Homepage](https://www.bllossom.ai/) | [Github](https://github.com/MLP-Lab/Bllossom) |
```bash
μ ν¬ Bllossom νμμ Bllossom-3B λͺ¨λΈμ 곡κ°ν©λλ€.
llama3.2-3Bκ° λμλλ° νκ΅μ΄κ° ν¬ν¨ μλμλ€κ΅¬?? μ΄λ² Bllossom-3Bλ νκ΅μ΄κ° μ§μλμ§ μλ κΈ°λ³Έ λͺ¨λΈμ νκ΅μ΄-μμ΄λ‘ κ°νλͺ¨λΈμ
λλ€.
- 100% full-tuningμΌλ‘ 150GBμ μ μ λ νκ΅μ΄λ‘ μΆκ° μ¬μ νμ΅ λμμ΅λλ€. (GPUλ§μ΄ νμ μ΅λλ€)
- κ΅μ₯ν μ μ λ Instruction Tuningμ μ§ννμ΅λλ€.
- μμ΄ μ±λ₯μ μ ν μμμν€μ§ μμ μμ ν Bilingual λͺ¨λΈμ
λλ€.
- Instruction tuningλ§ μ§ννμ΅λλ€. DPO λ± μ±λ₯ μ¬λ¦΄ λ°©λ²μΌλ‘ νλν΄λ³΄μΈμ.
- MT-Bench, LogicKor λ± λ²€μΉλ§ν¬ μ μλ₯Ό μλ°κΈ° μν΄ μ λ΅λ°μ΄ν°λ₯Ό νμ©νκ±°λ νΉμ λ²€μΉλ§ν¬λ₯Ό νκ²ν
ν΄μ νμ΅νμ§ μμμ΅λλ€. (ν΄λΉ λ²€μΉλ§ν¬ νκ²ν
ν΄μ νμ΅νλ©΄ 8μ λ λμ΅λλ€...)
μΈμ λ κ·Έλ¬λ― ν΄λΉ λͺ¨λΈμ μμ
μ μ΄μ©μ΄ κ°λ₯ν©λλ€.
1. Bllossomμ AAAI2024, NAACL2024, LREC-COLING2024 (ꡬλ) λ°νλμμ΅λλ€.
2. μ’μ μΈμ΄λͺ¨λΈ κ³μ μ
λ°μ΄νΈ νκ² μ΅λλ€!! νκ΅μ΄ κ°νλ₯Όμν΄ κ³΅λ μ°κ΅¬νμ€λΆ(νΉνλ
Όλ¬Έ) μΈμ λ νμν©λλ€!!
```
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = 'Bllossom/llama-3.2-Korean-Bllossom-3B'
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.bfloat16,
device_map="auto",
)
instruction = "μ² μκ° 20κ°μ μ°νμ κ°μ§κ³ μμλλ° μν¬κ° μ λ°μ κ°μ Έκ°κ³ λ―Όμκ° λ¨μ 5κ°λ₯Ό κ°μ Έκ°μΌλ©΄ μ² μμκ² λ¨μ μ°νμ κ°―μλ λͺκ°μΈκ°μ?"
messages = [
{"role": "user", "content": f"{instruction}"}
]
input_ids = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
terminators = [
tokenizer.convert_tokens_to_ids("<|end_of_text|>"),
tokenizer.convert_tokens_to_ids("<|eot_id|>")
]
outputs = model.generate(
input_ids,
max_new_tokens=1024,
eos_token_id=terminators,
do_sample=True,
temperature=0.6,
top_p=0.9
)
print(tokenizer.decode(outputs[0][input_ids.shape[-1]:], skip_special_tokens=True))
```
```
μ² μκ° 20κ°μ μ°νμ κ°μ§κ³ μμκ³ μν¬κ° μ λ°μ κ°μ Έκ°λ©΄, μν¬κ° κ°μ Έκ° μ°νμ κ°―μλ 20 / 2 = 10κ°μ
λλ€.
μ΄μ μ² μκ° λ¨μ μ°νμ κ°―μλ₯Ό κ³μ°ν΄λ³΄κ² μ΅λλ€. μν¬κ° 10κ°λ₯Ό κ°μ Έκ° ν μ² μκ° λ¨μ μ°νμ κ°―μλ 20 - 10 = 10κ°μ
λλ€.
λ―Όμκ° λ¨μ 5κ°λ₯Ό κ°μ Έκ°μΌλ―λ‘, μ² μκ° λ¨μ μ°νμ κ°―μλ 10 - 5 = 5κ°μ
λλ€.
λ°λΌμ μ² μκ° λ¨μ μ°νμ κ°―μλ 5κ°μ
λλ€.
```
## Supported by
- AICA <img src="https://aica-gj.kr/images/logo.png" width="20%" height="20%">
## Citation
**Language Model**
```text
@misc{bllossom,
author = {ChangSu Choi, Yongbin Jeong, Seoyoon Park, InHo Won, HyeonSeok Lim, SangMin Kim, Yejee Kang, Chanhyuk Yoon, Jaewan Park, Yiseul Lee, HyeJin Lee, Younggyun Hahm, Hansaem Kim, KyungTae Lim},
title = {Optimizing Language Augmentation for Multilingual Large Language Models: A Case Study on Korean},
year = {2024},
journal = {LREC-COLING 2024},
paperLink = {\url{https://arxiv.org/pdf/2403.10882}},
},
}
```
**Vision-Language Model**
```text
@misc{bllossom-V,
author = {Dongjae Shin, Hyunseok Lim, Inho Won, Changsu Choi, Minjun Kim, Seungwoo Song, Hangyeol Yoo, Sangmin Kim, Kyungtae Lim},
title = {X-LLaVA: Optimizing Bilingual Large Vision-Language Alignment},
year = {2024},
publisher = {GitHub},
journal = {NAACL 2024 findings},
paperLink = {\url{https://arxiv.org/pdf/2403.11399}},
},
}
```
## Contact
- μκ²½ν(KyungTae Lim), Professor at Seoultech. `[email protected]`
- ν¨μκ· (Younggyun Hahm), CEO of Teddysum. `[email protected]`
- κΉνμ(Hansaem Kim), Professor at Yonsei. `[email protected]`
## Contributor
- **μ νκ²°(Hangyeol Yoo)**, [email protected]
- μ΅μ°½μ(Chansu Choi), [email protected]
|
mradermacher/Gromenauer-7B-i1-GGUF
|
mradermacher
| 2024-12-16T04:38:45Z | 39 | 0 |
transformers
|
[
"transformers",
"gguf",
"es",
"dataset:fistro/gromenauer",
"base_model:bertin-project/Gromenauer-7B",
"base_model:quantized:bertin-project/Gromenauer-7B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"imatrix"
] | null | 2024-12-16T02:28:56Z |
---
base_model: bertin-project/Gromenauer-7B
datasets:
- fistro/gromenauer
language:
- es
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/bertin-project/Gromenauer-7B
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/Gromenauer-7B-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Gromenauer-7B-i1-GGUF/resolve/main/Gromenauer-7B.i1-IQ1_S.gguf) | i1-IQ1_S | 1.7 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Gromenauer-7B-i1-GGUF/resolve/main/Gromenauer-7B.i1-IQ1_M.gguf) | i1-IQ1_M | 1.9 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Gromenauer-7B-i1-GGUF/resolve/main/Gromenauer-7B.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.1 | |
| [GGUF](https://huggingface.co/mradermacher/Gromenauer-7B-i1-GGUF/resolve/main/Gromenauer-7B.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.3 | |
| [GGUF](https://huggingface.co/mradermacher/Gromenauer-7B-i1-GGUF/resolve/main/Gromenauer-7B.i1-IQ2_S.gguf) | i1-IQ2_S | 2.4 | |
| [GGUF](https://huggingface.co/mradermacher/Gromenauer-7B-i1-GGUF/resolve/main/Gromenauer-7B.i1-IQ2_M.gguf) | i1-IQ2_M | 2.6 | |
| [GGUF](https://huggingface.co/mradermacher/Gromenauer-7B-i1-GGUF/resolve/main/Gromenauer-7B.i1-Q2_K_S.gguf) | i1-Q2_K_S | 2.6 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/Gromenauer-7B-i1-GGUF/resolve/main/Gromenauer-7B.i1-Q2_K.gguf) | i1-Q2_K | 2.8 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Gromenauer-7B-i1-GGUF/resolve/main/Gromenauer-7B.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 2.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Gromenauer-7B-i1-GGUF/resolve/main/Gromenauer-7B.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/Gromenauer-7B-i1-GGUF/resolve/main/Gromenauer-7B.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.3 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Gromenauer-7B-i1-GGUF/resolve/main/Gromenauer-7B.i1-IQ3_S.gguf) | i1-IQ3_S | 3.3 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Gromenauer-7B-i1-GGUF/resolve/main/Gromenauer-7B.i1-IQ3_M.gguf) | i1-IQ3_M | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/Gromenauer-7B-i1-GGUF/resolve/main/Gromenauer-7B.i1-Q3_K_M.gguf) | i1-Q3_K_M | 3.6 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Gromenauer-7B-i1-GGUF/resolve/main/Gromenauer-7B.i1-Q3_K_L.gguf) | i1-Q3_K_L | 3.9 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Gromenauer-7B-i1-GGUF/resolve/main/Gromenauer-7B.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/Gromenauer-7B-i1-GGUF/resolve/main/Gromenauer-7B.i1-Q4_0.gguf) | i1-Q4_0 | 4.2 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Gromenauer-7B-i1-GGUF/resolve/main/Gromenauer-7B.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.2 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Gromenauer-7B-i1-GGUF/resolve/main/Gromenauer-7B.i1-Q4_K_M.gguf) | i1-Q4_K_M | 4.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Gromenauer-7B-i1-GGUF/resolve/main/Gromenauer-7B.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/Gromenauer-7B-i1-GGUF/resolve/main/Gromenauer-7B.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/Gromenauer-7B-i1-GGUF/resolve/main/Gromenauer-7B.i1-Q6_K.gguf) | i1-Q6_K | 6.0 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
TheBlueObserver/Qwen2.5-7B-Instruct-MLX-c79ba
|
TheBlueObserver
| 2024-12-16T04:38:18Z | 77 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"chat",
"mlx",
"conversational",
"en",
"base_model:Qwen/Qwen2.5-7B-Instruct",
"base_model:quantized:Qwen/Qwen2.5-7B-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"6-bit",
"region:us"
] |
text-generation
| 2024-12-16T04:34:17Z |
---
base_model: Qwen/Qwen2.5-7B-Instruct
language:
- en
library_name: transformers
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen2.5-7B-Instruct/blob/main/LICENSE
pipeline_tag: text-generation
tags:
- chat
- mlx
---
# TheBlueObserver/Qwen2.5-7B-Instruct-MLX-c79ba
The Model [TheBlueObserver/Qwen2.5-7B-Instruct-MLX-c79ba](https://huggingface.co/TheBlueObserver/Qwen2.5-7B-Instruct-MLX-c79ba) was
converted to MLX format from [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct)
using mlx-lm version **0.20.2**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("TheBlueObserver/Qwen2.5-7B-Instruct-MLX-c79ba")
prompt="hello"
if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
```
|
mradermacher/Cakrawala-70B-GGUF
|
mradermacher
| 2024-12-16T04:36:39Z | 50 | 1 |
transformers
|
[
"transformers",
"gguf",
"axolotl",
"en",
"dataset:NarrativAI/CakrawalaRP",
"base_model:NarrativAI/Cakrawala-Llama-3.1-70B",
"base_model:quantized:NarrativAI/Cakrawala-Llama-3.1-70B",
"license:mit",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-11-26T09:40:18Z |
---
base_model: NarrativAI/Cakrawala-Llama-3.1-70B
datasets:
- NarrativAI/CakrawalaRP
language:
- en
library_name: transformers
license: mit
quantized_by: mradermacher
tags:
- axolotl
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
static quants of https://huggingface.co/NarrativAI/Cakrawala-Llama-3.1-70B
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Cakrawala-70B-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Cakrawala-70B-GGUF/resolve/main/Cakrawala-70B.Q2_K.gguf) | Q2_K | 26.5 | |
| [GGUF](https://huggingface.co/mradermacher/Cakrawala-70B-GGUF/resolve/main/Cakrawala-70B.Q3_K_S.gguf) | Q3_K_S | 31.0 | |
| [GGUF](https://huggingface.co/mradermacher/Cakrawala-70B-GGUF/resolve/main/Cakrawala-70B.Q3_K_M.gguf) | Q3_K_M | 34.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Cakrawala-70B-GGUF/resolve/main/Cakrawala-70B.Q3_K_L.gguf) | Q3_K_L | 37.2 | |
| [GGUF](https://huggingface.co/mradermacher/Cakrawala-70B-GGUF/resolve/main/Cakrawala-70B.IQ4_XS.gguf) | IQ4_XS | 38.4 | |
| [GGUF](https://huggingface.co/mradermacher/Cakrawala-70B-GGUF/resolve/main/Cakrawala-70B.Q4_K_S.gguf) | Q4_K_S | 40.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Cakrawala-70B-GGUF/resolve/main/Cakrawala-70B.Q4_K_M.gguf) | Q4_K_M | 42.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Cakrawala-70B-GGUF/resolve/main/Cakrawala-70B.Q5_K_S.gguf) | Q5_K_S | 48.8 | |
| [GGUF](https://huggingface.co/mradermacher/Cakrawala-70B-GGUF/resolve/main/Cakrawala-70B.Q5_K_M.gguf) | Q5_K_M | 50.0 | |
| [PART 1](https://huggingface.co/mradermacher/Cakrawala-70B-GGUF/resolve/main/Cakrawala-70B.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Cakrawala-70B-GGUF/resolve/main/Cakrawala-70B.Q6_K.gguf.part2of2) | Q6_K | 58.0 | very good quality |
| [PART 1](https://huggingface.co/mradermacher/Cakrawala-70B-GGUF/resolve/main/Cakrawala-70B.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Cakrawala-70B-GGUF/resolve/main/Cakrawala-70B.Q8_0.gguf.part2of2) | Q8_0 | 75.1 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
mradermacher/Gromenauer-7B-GGUF
|
mradermacher
| 2024-12-16T04:34:25Z | 16 | 0 |
transformers
|
[
"transformers",
"gguf",
"es",
"dataset:fistro/gromenauer",
"base_model:bertin-project/Gromenauer-7B",
"base_model:quantized:bertin-project/Gromenauer-7B",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-12-16T01:49:11Z |
---
base_model: bertin-project/Gromenauer-7B
datasets:
- fistro/gromenauer
language:
- es
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/bertin-project/Gromenauer-7B
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Gromenauer-7B-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Gromenauer-7B-GGUF/resolve/main/Gromenauer-7B.Q2_K.gguf) | Q2_K | 2.8 | |
| [GGUF](https://huggingface.co/mradermacher/Gromenauer-7B-GGUF/resolve/main/Gromenauer-7B.Q3_K_S.gguf) | Q3_K_S | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/Gromenauer-7B-GGUF/resolve/main/Gromenauer-7B.Q3_K_M.gguf) | Q3_K_M | 3.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Gromenauer-7B-GGUF/resolve/main/Gromenauer-7B.Q3_K_L.gguf) | Q3_K_L | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/Gromenauer-7B-GGUF/resolve/main/Gromenauer-7B.IQ4_XS.gguf) | IQ4_XS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/Gromenauer-7B-GGUF/resolve/main/Gromenauer-7B.Q4_K_S.gguf) | Q4_K_S | 4.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Gromenauer-7B-GGUF/resolve/main/Gromenauer-7B.Q4_K_M.gguf) | Q4_K_M | 4.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Gromenauer-7B-GGUF/resolve/main/Gromenauer-7B.Q5_K_S.gguf) | Q5_K_S | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/Gromenauer-7B-GGUF/resolve/main/Gromenauer-7B.Q5_K_M.gguf) | Q5_K_M | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/Gromenauer-7B-GGUF/resolve/main/Gromenauer-7B.Q6_K.gguf) | Q6_K | 6.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Gromenauer-7B-GGUF/resolve/main/Gromenauer-7B.Q8_0.gguf) | Q8_0 | 7.8 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Gromenauer-7B-GGUF/resolve/main/Gromenauer-7B.f16.gguf) | f16 | 14.6 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
kainatq/KPM-7b-v0.1
|
kainatq
| 2024-12-16T04:33:11Z | 6 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"mergekit",
"merge",
"arxiv:2403.19522",
"base_model:ChaoticNeutrals/RP_Vision_7B",
"base_model:merge:ChaoticNeutrals/RP_Vision_7B",
"base_model:Endevor/InfinityRP-v1-7B",
"base_model:merge:Endevor/InfinityRP-v1-7B",
"base_model:ResplendentAI/DaturaCookie_7B",
"base_model:merge:ResplendentAI/DaturaCookie_7B",
"base_model:icefog72/IceDrunkenCherryRP-7b",
"base_model:merge:icefog72/IceDrunkenCherryRP-7b",
"base_model:kainatq/Kainoverse-7b-v0.1",
"base_model:merge:kainatq/Kainoverse-7b-v0.1",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-12-16T04:29:36Z |
---
base_model:
- ResplendentAI/DaturaCookie_7B
- kainatq/Kainoverse-7b-v0.1
- Endevor/InfinityRP-v1-7B
- icefog72/IceDrunkenCherryRP-7b
- ChaoticNeutrals/RP_Vision_7B
library_name: transformers
tags:
- mergekit
- merge
---
# merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [Model Stock](https://arxiv.org/abs/2403.19522) merge method using [kainatq/Kainoverse-7b-v0.1](https://huggingface.co/kainatq/Kainoverse-7b-v0.1) as a base.
### Models Merged
The following models were included in the merge:
* [ResplendentAI/DaturaCookie_7B](https://huggingface.co/ResplendentAI/DaturaCookie_7B)
* [Endevor/InfinityRP-v1-7B](https://huggingface.co/Endevor/InfinityRP-v1-7B)
* [icefog72/IceDrunkenCherryRP-7b](https://huggingface.co/icefog72/IceDrunkenCherryRP-7b)
* [ChaoticNeutrals/RP_Vision_7B](https://huggingface.co/ChaoticNeutrals/RP_Vision_7B)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
merge_method: model_stock
base_model: kainatq/Kainoverse-7b-v0.1
parameters:
models:
- model: ResplendentAI/DaturaCookie_7B
- model: icefog72/IceDrunkenCherryRP-7b
- model: ChaoticNeutrals/RP_Vision_7B
- model: Endevor/InfinityRP-v1-7B
dtype: bfloat16
```
|
smitmenon/e2m_endenoise_project
|
smitmenon
| 2024-12-16T04:30:28Z | 9 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mbart",
"text2text-generation",
"generated_from_trainer",
"base_model:facebook/mbart-large-50-one-to-many-mmt",
"base_model:finetune:facebook/mbart-large-50-one-to-many-mmt",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-12-01T18:17:10Z |
---
library_name: transformers
base_model: facebook/mbart-large-50-one-to-many-mmt
tags:
- generated_from_trainer
model-index:
- name: e2m_endenoise_project
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. -->
# e2m_endenoise_project
This model is a fine-tuned version of [facebook/mbart-large-50-one-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3740
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.444 | 1.0 | 1875 | 0.3807 |
| 0.3206 | 2.0 | 3750 | 0.3740 |
### Framework versions
- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0
|
mradermacher/Violet_Eris-BMO-12B-GGUF
|
mradermacher
| 2024-12-16T04:27:22Z | 9 | 1 |
transformers
|
[
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:Nitral-Archive/Violet_Eris-BMO-12B",
"base_model:quantized:Nitral-Archive/Violet_Eris-BMO-12B",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-12-03T12:28:56Z |
---
base_model: Nitral-Archive/Violet_Eris-BMO-12B
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
static quants of https://huggingface.co/Nitral-Archive/Violet_Eris-BMO-12B
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Violet_Eris-BMO-12B-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Violet_Eris-BMO-12B-GGUF/resolve/main/Violet_Eris-BMO-12B.Q2_K.gguf) | Q2_K | 4.9 | |
| [GGUF](https://huggingface.co/mradermacher/Violet_Eris-BMO-12B-GGUF/resolve/main/Violet_Eris-BMO-12B.Q3_K_S.gguf) | Q3_K_S | 5.6 | |
| [GGUF](https://huggingface.co/mradermacher/Violet_Eris-BMO-12B-GGUF/resolve/main/Violet_Eris-BMO-12B.Q3_K_M.gguf) | Q3_K_M | 6.2 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Violet_Eris-BMO-12B-GGUF/resolve/main/Violet_Eris-BMO-12B.Q3_K_L.gguf) | Q3_K_L | 6.7 | |
| [GGUF](https://huggingface.co/mradermacher/Violet_Eris-BMO-12B-GGUF/resolve/main/Violet_Eris-BMO-12B.IQ4_XS.gguf) | IQ4_XS | 6.9 | |
| [GGUF](https://huggingface.co/mradermacher/Violet_Eris-BMO-12B-GGUF/resolve/main/Violet_Eris-BMO-12B.Q4_0_4_4.gguf) | Q4_0_4_4 | 7.2 | fast on arm, low quality |
| [GGUF](https://huggingface.co/mradermacher/Violet_Eris-BMO-12B-GGUF/resolve/main/Violet_Eris-BMO-12B.Q4_K_S.gguf) | Q4_K_S | 7.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Violet_Eris-BMO-12B-GGUF/resolve/main/Violet_Eris-BMO-12B.Q4_K_M.gguf) | Q4_K_M | 7.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Violet_Eris-BMO-12B-GGUF/resolve/main/Violet_Eris-BMO-12B.Q5_K_S.gguf) | Q5_K_S | 8.6 | |
| [GGUF](https://huggingface.co/mradermacher/Violet_Eris-BMO-12B-GGUF/resolve/main/Violet_Eris-BMO-12B.Q5_K_M.gguf) | Q5_K_M | 8.8 | |
| [GGUF](https://huggingface.co/mradermacher/Violet_Eris-BMO-12B-GGUF/resolve/main/Violet_Eris-BMO-12B.Q6_K.gguf) | Q6_K | 10.2 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Violet_Eris-BMO-12B-GGUF/resolve/main/Violet_Eris-BMO-12B.Q8_0.gguf) | Q8_0 | 13.1 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
MayBashendy/ArabicNewSplits5_FineTuningAraBERT_run3_AugV5_k5_task1_organization
|
MayBashendy
| 2024-12-16T04:26:58Z | 183 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:aubmindlab/bert-base-arabertv02",
"base_model:finetune:aubmindlab/bert-base-arabertv02",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-12-16T04:19:36Z |
---
library_name: transformers
base_model: aubmindlab/bert-base-arabertv02
tags:
- generated_from_trainer
model-index:
- name: ArabicNewSplits5_FineTuningAraBERT_run3_AugV5_k5_task1_organization
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. -->
# ArabicNewSplits5_FineTuningAraBERT_run3_AugV5_k5_task1_organization
This model is a fine-tuned version of [aubmindlab/bert-base-arabertv02](https://huggingface.co/aubmindlab/bert-base-arabertv02) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7102
- Qwk: 0.7097
- Mse: 0.7102
- Rmse: 0.8427
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Qwk | Mse | Rmse |
|:-------------:|:------:|:----:|:---------------:|:-------:|:------:|:------:|
| No log | 0.0588 | 2 | 5.1822 | -0.0251 | 5.1822 | 2.2764 |
| No log | 0.1176 | 4 | 3.1591 | 0.0524 | 3.1591 | 1.7774 |
| No log | 0.1765 | 6 | 2.0759 | 0.0695 | 2.0759 | 1.4408 |
| No log | 0.2353 | 8 | 1.8010 | 0.1199 | 1.8010 | 1.3420 |
| No log | 0.2941 | 10 | 1.7486 | 0.1250 | 1.7486 | 1.3223 |
| No log | 0.3529 | 12 | 1.7730 | 0.1175 | 1.7730 | 1.3315 |
| No log | 0.4118 | 14 | 1.8075 | 0.1225 | 1.8075 | 1.3444 |
| No log | 0.4706 | 16 | 1.6255 | 0.1578 | 1.6255 | 1.2749 |
| No log | 0.5294 | 18 | 2.2048 | 0.1696 | 2.2048 | 1.4849 |
| No log | 0.5882 | 20 | 3.0429 | 0.0862 | 3.0429 | 1.7444 |
| No log | 0.6471 | 22 | 3.2549 | 0.0563 | 3.2549 | 1.8041 |
| No log | 0.7059 | 24 | 2.6552 | 0.1544 | 2.6552 | 1.6295 |
| No log | 0.7647 | 26 | 1.6848 | 0.3401 | 1.6848 | 1.2980 |
| No log | 0.8235 | 28 | 1.4255 | 0.3771 | 1.4255 | 1.1940 |
| No log | 0.8824 | 30 | 1.4641 | 0.3947 | 1.4641 | 1.2100 |
| No log | 0.9412 | 32 | 1.3568 | 0.3967 | 1.3568 | 1.1648 |
| No log | 1.0 | 34 | 1.4360 | 0.4346 | 1.4360 | 1.1983 |
| No log | 1.0588 | 36 | 1.3649 | 0.4369 | 1.3649 | 1.1683 |
| No log | 1.1176 | 38 | 1.0667 | 0.5305 | 1.0667 | 1.0328 |
| No log | 1.1765 | 40 | 0.9976 | 0.5557 | 0.9976 | 0.9988 |
| No log | 1.2353 | 42 | 1.1933 | 0.4811 | 1.1933 | 1.0924 |
| No log | 1.2941 | 44 | 1.1406 | 0.5382 | 1.1406 | 1.0680 |
| No log | 1.3529 | 46 | 1.1043 | 0.5514 | 1.1043 | 1.0509 |
| No log | 1.4118 | 48 | 1.1315 | 0.5662 | 1.1315 | 1.0637 |
| No log | 1.4706 | 50 | 1.1321 | 0.5490 | 1.1321 | 1.0640 |
| No log | 1.5294 | 52 | 1.3522 | 0.4373 | 1.3522 | 1.1629 |
| No log | 1.5882 | 54 | 1.3685 | 0.4791 | 1.3685 | 1.1698 |
| No log | 1.6471 | 56 | 1.5389 | 0.4401 | 1.5389 | 1.2405 |
| No log | 1.7059 | 58 | 1.4686 | 0.4579 | 1.4686 | 1.2118 |
| No log | 1.7647 | 60 | 1.0390 | 0.5714 | 1.0390 | 1.0193 |
| No log | 1.8235 | 62 | 0.7499 | 0.6575 | 0.7499 | 0.8660 |
| No log | 1.8824 | 64 | 0.6767 | 0.6838 | 0.6767 | 0.8226 |
| No log | 1.9412 | 66 | 0.7356 | 0.6566 | 0.7356 | 0.8577 |
| No log | 2.0 | 68 | 1.0686 | 0.5712 | 1.0686 | 1.0337 |
| No log | 2.0588 | 70 | 1.6078 | 0.4160 | 1.6078 | 1.2680 |
| No log | 2.1176 | 72 | 1.6984 | 0.3688 | 1.6984 | 1.3032 |
| No log | 2.1765 | 74 | 1.4138 | 0.5003 | 1.4138 | 1.1890 |
| No log | 2.2353 | 76 | 0.9886 | 0.6286 | 0.9886 | 0.9943 |
| No log | 2.2941 | 78 | 0.7441 | 0.6865 | 0.7441 | 0.8626 |
| No log | 2.3529 | 80 | 0.6918 | 0.6987 | 0.6918 | 0.8318 |
| No log | 2.4118 | 82 | 0.7043 | 0.6770 | 0.7043 | 0.8392 |
| No log | 2.4706 | 84 | 0.7191 | 0.6828 | 0.7191 | 0.8480 |
| No log | 2.5294 | 86 | 0.8365 | 0.6796 | 0.8365 | 0.9146 |
| No log | 2.5882 | 88 | 0.8275 | 0.6785 | 0.8275 | 0.9097 |
| No log | 2.6471 | 90 | 0.7850 | 0.6799 | 0.7850 | 0.8860 |
| No log | 2.7059 | 92 | 0.6591 | 0.7294 | 0.6591 | 0.8119 |
| No log | 2.7647 | 94 | 0.6236 | 0.7195 | 0.6236 | 0.7897 |
| No log | 2.8235 | 96 | 0.6442 | 0.7300 | 0.6442 | 0.8026 |
| No log | 2.8824 | 98 | 0.7117 | 0.6799 | 0.7117 | 0.8436 |
| No log | 2.9412 | 100 | 0.8596 | 0.6696 | 0.8596 | 0.9272 |
| No log | 3.0 | 102 | 0.8714 | 0.6732 | 0.8714 | 0.9335 |
| No log | 3.0588 | 104 | 0.8954 | 0.6643 | 0.8954 | 0.9462 |
| No log | 3.1176 | 106 | 0.7460 | 0.6962 | 0.7460 | 0.8637 |
| No log | 3.1765 | 108 | 0.6993 | 0.7137 | 0.6993 | 0.8362 |
| No log | 3.2353 | 110 | 0.6623 | 0.7036 | 0.6623 | 0.8138 |
| No log | 3.2941 | 112 | 0.6507 | 0.7184 | 0.6507 | 0.8067 |
| No log | 3.3529 | 114 | 0.6654 | 0.7227 | 0.6654 | 0.8157 |
| No log | 3.4118 | 116 | 0.7884 | 0.6598 | 0.7884 | 0.8879 |
| No log | 3.4706 | 118 | 0.8385 | 0.6487 | 0.8385 | 0.9157 |
| No log | 3.5294 | 120 | 0.7171 | 0.7154 | 0.7171 | 0.8468 |
| No log | 3.5882 | 122 | 0.6843 | 0.7324 | 0.6843 | 0.8272 |
| No log | 3.6471 | 124 | 0.7357 | 0.7066 | 0.7357 | 0.8577 |
| No log | 3.7059 | 126 | 0.7076 | 0.7254 | 0.7076 | 0.8412 |
| No log | 3.7647 | 128 | 0.7244 | 0.7014 | 0.7244 | 0.8511 |
| No log | 3.8235 | 130 | 0.7728 | 0.6947 | 0.7728 | 0.8791 |
| No log | 3.8824 | 132 | 0.8038 | 0.6795 | 0.8038 | 0.8966 |
| No log | 3.9412 | 134 | 0.7385 | 0.6964 | 0.7385 | 0.8594 |
| No log | 4.0 | 136 | 0.6743 | 0.7357 | 0.6743 | 0.8212 |
| No log | 4.0588 | 138 | 0.6827 | 0.7234 | 0.6827 | 0.8263 |
| No log | 4.1176 | 140 | 0.7600 | 0.6715 | 0.7600 | 0.8718 |
| No log | 4.1765 | 142 | 0.7378 | 0.6919 | 0.7378 | 0.8590 |
| No log | 4.2353 | 144 | 0.6914 | 0.7180 | 0.6914 | 0.8315 |
| No log | 4.2941 | 146 | 0.6533 | 0.7272 | 0.6533 | 0.8083 |
| No log | 4.3529 | 148 | 0.6683 | 0.7214 | 0.6683 | 0.8175 |
| No log | 4.4118 | 150 | 0.6596 | 0.7229 | 0.6596 | 0.8122 |
| No log | 4.4706 | 152 | 0.6848 | 0.7250 | 0.6848 | 0.8275 |
| No log | 4.5294 | 154 | 0.6861 | 0.7222 | 0.6861 | 0.8283 |
| No log | 4.5882 | 156 | 0.6850 | 0.7352 | 0.6850 | 0.8276 |
| No log | 4.6471 | 158 | 0.7019 | 0.7101 | 0.7019 | 0.8378 |
| No log | 4.7059 | 160 | 0.6992 | 0.7111 | 0.6992 | 0.8362 |
| No log | 4.7647 | 162 | 0.6874 | 0.7205 | 0.6874 | 0.8291 |
| No log | 4.8235 | 164 | 0.6903 | 0.7326 | 0.6903 | 0.8309 |
| No log | 4.8824 | 166 | 0.6933 | 0.7193 | 0.6933 | 0.8327 |
| No log | 4.9412 | 168 | 0.7114 | 0.6842 | 0.7114 | 0.8434 |
| No log | 5.0 | 170 | 0.7167 | 0.6779 | 0.7167 | 0.8466 |
| No log | 5.0588 | 172 | 0.7072 | 0.6663 | 0.7072 | 0.8410 |
| No log | 5.1176 | 174 | 0.6851 | 0.7158 | 0.6851 | 0.8277 |
| No log | 5.1765 | 176 | 0.6878 | 0.7118 | 0.6878 | 0.8294 |
| No log | 5.2353 | 178 | 0.6981 | 0.7069 | 0.6981 | 0.8355 |
| No log | 5.2941 | 180 | 0.7111 | 0.7170 | 0.7111 | 0.8433 |
| No log | 5.3529 | 182 | 0.7345 | 0.7168 | 0.7345 | 0.8571 |
| No log | 5.4118 | 184 | 0.7246 | 0.7273 | 0.7246 | 0.8512 |
| No log | 5.4706 | 186 | 0.7279 | 0.7215 | 0.7279 | 0.8532 |
| No log | 5.5294 | 188 | 0.7421 | 0.7017 | 0.7421 | 0.8614 |
| No log | 5.5882 | 190 | 0.7271 | 0.7195 | 0.7271 | 0.8527 |
| No log | 5.6471 | 192 | 0.7396 | 0.7097 | 0.7396 | 0.8600 |
| No log | 5.7059 | 194 | 0.7566 | 0.6784 | 0.7566 | 0.8698 |
| No log | 5.7647 | 196 | 0.7326 | 0.7041 | 0.7326 | 0.8559 |
| No log | 5.8235 | 198 | 0.7218 | 0.7020 | 0.7218 | 0.8496 |
| No log | 5.8824 | 200 | 0.7367 | 0.7093 | 0.7367 | 0.8583 |
| No log | 5.9412 | 202 | 0.8015 | 0.6576 | 0.8015 | 0.8953 |
| No log | 6.0 | 204 | 0.8355 | 0.6381 | 0.8355 | 0.9140 |
| No log | 6.0588 | 206 | 0.7892 | 0.6641 | 0.7892 | 0.8884 |
| No log | 6.1176 | 208 | 0.7340 | 0.7028 | 0.7340 | 0.8568 |
| No log | 6.1765 | 210 | 0.7135 | 0.7072 | 0.7135 | 0.8447 |
| No log | 6.2353 | 212 | 0.7119 | 0.6923 | 0.7119 | 0.8437 |
| No log | 6.2941 | 214 | 0.7149 | 0.7109 | 0.7149 | 0.8455 |
| No log | 6.3529 | 216 | 0.7576 | 0.6533 | 0.7576 | 0.8704 |
| No log | 6.4118 | 218 | 0.8698 | 0.6312 | 0.8698 | 0.9326 |
| No log | 6.4706 | 220 | 0.9664 | 0.6210 | 0.9664 | 0.9830 |
| No log | 6.5294 | 222 | 0.9070 | 0.6160 | 0.9070 | 0.9524 |
| No log | 6.5882 | 224 | 0.7883 | 0.6540 | 0.7883 | 0.8879 |
| No log | 6.6471 | 226 | 0.7197 | 0.7024 | 0.7197 | 0.8483 |
| No log | 6.7059 | 228 | 0.7400 | 0.7145 | 0.7400 | 0.8602 |
| No log | 6.7647 | 230 | 0.7653 | 0.7209 | 0.7653 | 0.8748 |
| No log | 6.8235 | 232 | 0.7330 | 0.7187 | 0.7330 | 0.8562 |
| No log | 6.8824 | 234 | 0.6963 | 0.6915 | 0.6963 | 0.8344 |
| No log | 6.9412 | 236 | 0.7030 | 0.7117 | 0.7030 | 0.8385 |
| No log | 7.0 | 238 | 0.7267 | 0.7168 | 0.7267 | 0.8525 |
| No log | 7.0588 | 240 | 0.7443 | 0.6997 | 0.7443 | 0.8627 |
| No log | 7.1176 | 242 | 0.7277 | 0.7034 | 0.7277 | 0.8530 |
| No log | 7.1765 | 244 | 0.6878 | 0.7125 | 0.6878 | 0.8293 |
| No log | 7.2353 | 246 | 0.6665 | 0.7345 | 0.6665 | 0.8164 |
| No log | 7.2941 | 248 | 0.6778 | 0.6876 | 0.6778 | 0.8233 |
| No log | 7.3529 | 250 | 0.6814 | 0.7002 | 0.6814 | 0.8255 |
| No log | 7.4118 | 252 | 0.6717 | 0.6960 | 0.6717 | 0.8196 |
| No log | 7.4706 | 254 | 0.6668 | 0.7300 | 0.6668 | 0.8166 |
| No log | 7.5294 | 256 | 0.6726 | 0.7424 | 0.6726 | 0.8201 |
| No log | 7.5882 | 258 | 0.6781 | 0.7185 | 0.6781 | 0.8235 |
| No log | 7.6471 | 260 | 0.6837 | 0.6972 | 0.6837 | 0.8269 |
| No log | 7.7059 | 262 | 0.6801 | 0.7114 | 0.6801 | 0.8247 |
| No log | 7.7647 | 264 | 0.6643 | 0.7227 | 0.6643 | 0.8151 |
| No log | 7.8235 | 266 | 0.6604 | 0.7071 | 0.6604 | 0.8127 |
| No log | 7.8824 | 268 | 0.6615 | 0.7151 | 0.6615 | 0.8133 |
| No log | 7.9412 | 270 | 0.6601 | 0.7026 | 0.6601 | 0.8125 |
| No log | 8.0 | 272 | 0.6595 | 0.7026 | 0.6595 | 0.8121 |
| No log | 8.0588 | 274 | 0.6610 | 0.7026 | 0.6610 | 0.8130 |
| No log | 8.1176 | 276 | 0.6611 | 0.7244 | 0.6611 | 0.8131 |
| No log | 8.1765 | 278 | 0.6633 | 0.7357 | 0.6633 | 0.8144 |
| No log | 8.2353 | 280 | 0.6650 | 0.7206 | 0.6650 | 0.8155 |
| No log | 8.2941 | 282 | 0.6703 | 0.7185 | 0.6703 | 0.8187 |
| No log | 8.3529 | 284 | 0.6762 | 0.7257 | 0.6762 | 0.8223 |
| No log | 8.4118 | 286 | 0.6807 | 0.7127 | 0.6807 | 0.8251 |
| No log | 8.4706 | 288 | 0.6872 | 0.7070 | 0.6872 | 0.8290 |
| No log | 8.5294 | 290 | 0.6927 | 0.7080 | 0.6927 | 0.8323 |
| No log | 8.5882 | 292 | 0.6946 | 0.7037 | 0.6946 | 0.8334 |
| No log | 8.6471 | 294 | 0.6956 | 0.7037 | 0.6956 | 0.8340 |
| No log | 8.7059 | 296 | 0.6979 | 0.7007 | 0.6979 | 0.8354 |
| No log | 8.7647 | 298 | 0.6958 | 0.7037 | 0.6958 | 0.8342 |
| No log | 8.8235 | 300 | 0.6963 | 0.7080 | 0.6963 | 0.8344 |
| No log | 8.8824 | 302 | 0.6950 | 0.6825 | 0.6950 | 0.8337 |
| No log | 8.9412 | 304 | 0.6950 | 0.7012 | 0.6950 | 0.8337 |
| No log | 9.0 | 306 | 0.6967 | 0.7012 | 0.6967 | 0.8347 |
| No log | 9.0588 | 308 | 0.7011 | 0.6814 | 0.7011 | 0.8373 |
| No log | 9.1176 | 310 | 0.7078 | 0.6957 | 0.7078 | 0.8413 |
| No log | 9.1765 | 312 | 0.7163 | 0.7157 | 0.7163 | 0.8463 |
| No log | 9.2353 | 314 | 0.7177 | 0.7157 | 0.7177 | 0.8472 |
| No log | 9.2941 | 316 | 0.7159 | 0.7157 | 0.7159 | 0.8461 |
| No log | 9.3529 | 318 | 0.7146 | 0.7097 | 0.7146 | 0.8454 |
| No log | 9.4118 | 320 | 0.7120 | 0.7097 | 0.7120 | 0.8438 |
| No log | 9.4706 | 322 | 0.7128 | 0.7097 | 0.7128 | 0.8443 |
| No log | 9.5294 | 324 | 0.7129 | 0.7097 | 0.7129 | 0.8443 |
| No log | 9.5882 | 326 | 0.7128 | 0.7097 | 0.7128 | 0.8443 |
| No log | 9.6471 | 328 | 0.7118 | 0.7097 | 0.7118 | 0.8437 |
| No log | 9.7059 | 330 | 0.7111 | 0.7097 | 0.7111 | 0.8433 |
| No log | 9.7647 | 332 | 0.7118 | 0.7097 | 0.7118 | 0.8437 |
| No log | 9.8235 | 334 | 0.7116 | 0.7097 | 0.7116 | 0.8436 |
| No log | 9.8824 | 336 | 0.7111 | 0.7097 | 0.7111 | 0.8433 |
| No log | 9.9412 | 338 | 0.7106 | 0.7097 | 0.7106 | 0.8430 |
| No log | 10.0 | 340 | 0.7102 | 0.7097 | 0.7102 | 0.8427 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu118
- Datasets 2.21.0
- Tokenizers 0.19.1
|
mradermacher/Violet_Eris-BMO-12B-i1-GGUF
|
mradermacher
| 2024-12-16T04:26:35Z | 118 | 1 |
transformers
|
[
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:Nitral-Archive/Violet_Eris-BMO-12B",
"base_model:quantized:Nitral-Archive/Violet_Eris-BMO-12B",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2024-12-04T04:11:02Z |
---
base_model: Nitral-Archive/Violet_Eris-BMO-12B
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/Nitral-Archive/Violet_Eris-BMO-12B
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/Violet_Eris-BMO-12B-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Violet_Eris-BMO-12B-i1-GGUF/resolve/main/Violet_Eris-BMO-12B.i1-IQ1_S.gguf) | i1-IQ1_S | 3.1 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Violet_Eris-BMO-12B-i1-GGUF/resolve/main/Violet_Eris-BMO-12B.i1-IQ1_M.gguf) | i1-IQ1_M | 3.3 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Violet_Eris-BMO-12B-i1-GGUF/resolve/main/Violet_Eris-BMO-12B.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 3.7 | |
| [GGUF](https://huggingface.co/mradermacher/Violet_Eris-BMO-12B-i1-GGUF/resolve/main/Violet_Eris-BMO-12B.i1-IQ2_XS.gguf) | i1-IQ2_XS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/Violet_Eris-BMO-12B-i1-GGUF/resolve/main/Violet_Eris-BMO-12B.i1-IQ2_S.gguf) | i1-IQ2_S | 4.2 | |
| [GGUF](https://huggingface.co/mradermacher/Violet_Eris-BMO-12B-i1-GGUF/resolve/main/Violet_Eris-BMO-12B.i1-IQ2_M.gguf) | i1-IQ2_M | 4.5 | |
| [GGUF](https://huggingface.co/mradermacher/Violet_Eris-BMO-12B-i1-GGUF/resolve/main/Violet_Eris-BMO-12B.i1-Q2_K_S.gguf) | i1-Q2_K_S | 4.6 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/Violet_Eris-BMO-12B-i1-GGUF/resolve/main/Violet_Eris-BMO-12B.i1-Q2_K.gguf) | i1-Q2_K | 4.9 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Violet_Eris-BMO-12B-i1-GGUF/resolve/main/Violet_Eris-BMO-12B.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 5.0 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Violet_Eris-BMO-12B-i1-GGUF/resolve/main/Violet_Eris-BMO-12B.i1-IQ3_XS.gguf) | i1-IQ3_XS | 5.4 | |
| [GGUF](https://huggingface.co/mradermacher/Violet_Eris-BMO-12B-i1-GGUF/resolve/main/Violet_Eris-BMO-12B.i1-Q3_K_S.gguf) | i1-Q3_K_S | 5.6 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Violet_Eris-BMO-12B-i1-GGUF/resolve/main/Violet_Eris-BMO-12B.i1-IQ3_S.gguf) | i1-IQ3_S | 5.7 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Violet_Eris-BMO-12B-i1-GGUF/resolve/main/Violet_Eris-BMO-12B.i1-IQ3_M.gguf) | i1-IQ3_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/Violet_Eris-BMO-12B-i1-GGUF/resolve/main/Violet_Eris-BMO-12B.i1-Q3_K_M.gguf) | i1-Q3_K_M | 6.2 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Violet_Eris-BMO-12B-i1-GGUF/resolve/main/Violet_Eris-BMO-12B.i1-Q3_K_L.gguf) | i1-Q3_K_L | 6.7 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Violet_Eris-BMO-12B-i1-GGUF/resolve/main/Violet_Eris-BMO-12B.i1-IQ4_XS.gguf) | i1-IQ4_XS | 6.8 | |
| [GGUF](https://huggingface.co/mradermacher/Violet_Eris-BMO-12B-i1-GGUF/resolve/main/Violet_Eris-BMO-12B.i1-Q4_0_4_4.gguf) | i1-Q4_0_4_4 | 7.2 | fast on arm, low quality |
| [GGUF](https://huggingface.co/mradermacher/Violet_Eris-BMO-12B-i1-GGUF/resolve/main/Violet_Eris-BMO-12B.i1-Q4_0_4_8.gguf) | i1-Q4_0_4_8 | 7.2 | fast on arm+i8mm, low quality |
| [GGUF](https://huggingface.co/mradermacher/Violet_Eris-BMO-12B-i1-GGUF/resolve/main/Violet_Eris-BMO-12B.i1-Q4_0_8_8.gguf) | i1-Q4_0_8_8 | 7.2 | fast on arm+sve, low quality |
| [GGUF](https://huggingface.co/mradermacher/Violet_Eris-BMO-12B-i1-GGUF/resolve/main/Violet_Eris-BMO-12B.i1-Q4_0.gguf) | i1-Q4_0 | 7.2 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Violet_Eris-BMO-12B-i1-GGUF/resolve/main/Violet_Eris-BMO-12B.i1-Q4_K_S.gguf) | i1-Q4_K_S | 7.2 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Violet_Eris-BMO-12B-i1-GGUF/resolve/main/Violet_Eris-BMO-12B.i1-Q4_K_M.gguf) | i1-Q4_K_M | 7.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Violet_Eris-BMO-12B-i1-GGUF/resolve/main/Violet_Eris-BMO-12B.i1-Q5_K_S.gguf) | i1-Q5_K_S | 8.6 | |
| [GGUF](https://huggingface.co/mradermacher/Violet_Eris-BMO-12B-i1-GGUF/resolve/main/Violet_Eris-BMO-12B.i1-Q5_K_M.gguf) | i1-Q5_K_M | 8.8 | |
| [GGUF](https://huggingface.co/mradermacher/Violet_Eris-BMO-12B-i1-GGUF/resolve/main/Violet_Eris-BMO-12B.i1-Q6_K.gguf) | i1-Q6_K | 10.2 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
mradermacher/Mistral-Reddit-12B-GGUF
|
mradermacher
| 2024-12-16T04:25:53Z | 180 | 3 |
transformers
|
[
"transformers",
"gguf",
"text-generation-inference",
"unsloth",
"mistral",
"trl",
"en",
"base_model:qingy2024/Mistral-Reddit-12B",
"base_model:quantized:qingy2024/Mistral-Reddit-12B",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-12-04T20:08:18Z |
---
base_model: qingy2024/Mistral-Reddit-12B
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
static quants of https://huggingface.co/qingy2024/Mistral-Reddit-12B
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Mistral-Reddit-12B-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Mistral-Reddit-12B-GGUF/resolve/main/Mistral-Reddit-12B.Q2_K.gguf) | Q2_K | 4.9 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-Reddit-12B-GGUF/resolve/main/Mistral-Reddit-12B.Q3_K_S.gguf) | Q3_K_S | 5.6 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-Reddit-12B-GGUF/resolve/main/Mistral-Reddit-12B.Q3_K_M.gguf) | Q3_K_M | 6.2 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Mistral-Reddit-12B-GGUF/resolve/main/Mistral-Reddit-12B.Q3_K_L.gguf) | Q3_K_L | 6.7 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-Reddit-12B-GGUF/resolve/main/Mistral-Reddit-12B.IQ4_XS.gguf) | IQ4_XS | 6.9 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-Reddit-12B-GGUF/resolve/main/Mistral-Reddit-12B.Q4_0_4_4.gguf) | Q4_0_4_4 | 7.2 | fast on arm, low quality |
| [GGUF](https://huggingface.co/mradermacher/Mistral-Reddit-12B-GGUF/resolve/main/Mistral-Reddit-12B.Q4_K_S.gguf) | Q4_K_S | 7.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Mistral-Reddit-12B-GGUF/resolve/main/Mistral-Reddit-12B.Q4_K_M.gguf) | Q4_K_M | 7.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Mistral-Reddit-12B-GGUF/resolve/main/Mistral-Reddit-12B.Q5_K_S.gguf) | Q5_K_S | 8.6 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-Reddit-12B-GGUF/resolve/main/Mistral-Reddit-12B.Q5_K_M.gguf) | Q5_K_M | 8.8 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-Reddit-12B-GGUF/resolve/main/Mistral-Reddit-12B.Q6_K.gguf) | Q6_K | 10.2 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Mistral-Reddit-12B-GGUF/resolve/main/Mistral-Reddit-12B.Q8_0.gguf) | Q8_0 | 13.1 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
mradermacher/Captain_BMO-0.420-Magnum-12B-GGUF
|
mradermacher
| 2024-12-16T04:23:50Z | 27 | 2 |
transformers
|
[
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:Nitral-Archive/Captain_BMO-0.420-Magnum-12B",
"base_model:quantized:Nitral-Archive/Captain_BMO-0.420-Magnum-12B",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-12-07T12:59:04Z |
---
base_model: Nitral-Archive/Captain_BMO-0.420-Magnum-12B
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
static quants of https://huggingface.co/Nitral-Archive/Captain_BMO-0.420-Magnum-12B
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Captain_BMO-0.420-Magnum-12B-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Captain_BMO-0.420-Magnum-12B-GGUF/resolve/main/Captain_BMO-0.420-Magnum-12B.Q2_K.gguf) | Q2_K | 4.9 | |
| [GGUF](https://huggingface.co/mradermacher/Captain_BMO-0.420-Magnum-12B-GGUF/resolve/main/Captain_BMO-0.420-Magnum-12B.Q3_K_S.gguf) | Q3_K_S | 5.6 | |
| [GGUF](https://huggingface.co/mradermacher/Captain_BMO-0.420-Magnum-12B-GGUF/resolve/main/Captain_BMO-0.420-Magnum-12B.Q3_K_M.gguf) | Q3_K_M | 6.2 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Captain_BMO-0.420-Magnum-12B-GGUF/resolve/main/Captain_BMO-0.420-Magnum-12B.Q3_K_L.gguf) | Q3_K_L | 6.7 | |
| [GGUF](https://huggingface.co/mradermacher/Captain_BMO-0.420-Magnum-12B-GGUF/resolve/main/Captain_BMO-0.420-Magnum-12B.IQ4_XS.gguf) | IQ4_XS | 6.9 | |
| [GGUF](https://huggingface.co/mradermacher/Captain_BMO-0.420-Magnum-12B-GGUF/resolve/main/Captain_BMO-0.420-Magnum-12B.Q4_0_4_4.gguf) | Q4_0_4_4 | 7.2 | fast on arm, low quality |
| [GGUF](https://huggingface.co/mradermacher/Captain_BMO-0.420-Magnum-12B-GGUF/resolve/main/Captain_BMO-0.420-Magnum-12B.Q4_K_S.gguf) | Q4_K_S | 7.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Captain_BMO-0.420-Magnum-12B-GGUF/resolve/main/Captain_BMO-0.420-Magnum-12B.Q4_K_M.gguf) | Q4_K_M | 7.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Captain_BMO-0.420-Magnum-12B-GGUF/resolve/main/Captain_BMO-0.420-Magnum-12B.Q5_K_S.gguf) | Q5_K_S | 8.6 | |
| [GGUF](https://huggingface.co/mradermacher/Captain_BMO-0.420-Magnum-12B-GGUF/resolve/main/Captain_BMO-0.420-Magnum-12B.Q5_K_M.gguf) | Q5_K_M | 8.8 | |
| [GGUF](https://huggingface.co/mradermacher/Captain_BMO-0.420-Magnum-12B-GGUF/resolve/main/Captain_BMO-0.420-Magnum-12B.Q6_K.gguf) | Q6_K | 10.2 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Captain_BMO-0.420-Magnum-12B-GGUF/resolve/main/Captain_BMO-0.420-Magnum-12B.Q8_0.gguf) | Q8_0 | 13.1 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
kainatq/KPT-7b-v0.3
|
kainatq
| 2024-12-16T04:22:59Z | 6 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"mergekit",
"merge",
"arxiv:2311.03099",
"arxiv:2306.01708",
"base_model:ChaoticNeutrals/RP_Vision_7B",
"base_model:merge:ChaoticNeutrals/RP_Vision_7B",
"base_model:Endevor/InfinityRP-v1-7B",
"base_model:merge:Endevor/InfinityRP-v1-7B",
"base_model:ResplendentAI/DaturaCookie_7B",
"base_model:merge:ResplendentAI/DaturaCookie_7B",
"base_model:icefog72/IceDrunkenCherryRP-7b",
"base_model:merge:icefog72/IceDrunkenCherryRP-7b",
"base_model:kainatq/Kainoverse-7b-v0.1",
"base_model:merge:kainatq/Kainoverse-7b-v0.1",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-12-16T04:19:08Z |
---
base_model:
- kainatq/Kainoverse-7b-v0.1
- ChaoticNeutrals/RP_Vision_7B
- icefog72/IceDrunkenCherryRP-7b
- Endevor/InfinityRP-v1-7B
- ResplendentAI/DaturaCookie_7B
library_name: transformers
tags:
- mergekit
- merge
---
# merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [DARE](https://arxiv.org/abs/2311.03099) [TIES](https://arxiv.org/abs/2306.01708) merge method using [kainatq/Kainoverse-7b-v0.1](https://huggingface.co/kainatq/Kainoverse-7b-v0.1) as a base.
### Models Merged
The following models were included in the merge:
* [ChaoticNeutrals/RP_Vision_7B](https://huggingface.co/ChaoticNeutrals/RP_Vision_7B)
* [icefog72/IceDrunkenCherryRP-7b](https://huggingface.co/icefog72/IceDrunkenCherryRP-7b)
* [Endevor/InfinityRP-v1-7B](https://huggingface.co/Endevor/InfinityRP-v1-7B)
* [ResplendentAI/DaturaCookie_7B](https://huggingface.co/ResplendentAI/DaturaCookie_7B)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
merge_method: dare_ties
base_model: kainatq/Kainoverse-7b-v0.1
parameters:
normalize: true
models:
- model: ResplendentAI/DaturaCookie_7B
parameters:
weight: 1
- model: icefog72/IceDrunkenCherryRP-7b
parameters:
weight: 1
- model: ChaoticNeutrals/RP_Vision_7B
parameters:
weight: 0.75
- model: Endevor/InfinityRP-v1-7B
parameters:
weight: 1
dtype: float16
```
|
Hachipo/qwen2.5-0.5B_educational_instruct-2
|
Hachipo
| 2024-12-16T04:20:53Z | 140 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-12-16T04:19:18Z |
---
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 π€ 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]
|
mradermacher/Gemma2-9B-test-novelistwo-GGUF
|
mradermacher
| 2024-12-16T04:19:02Z | 420 | 1 |
transformers
|
[
"transformers",
"gguf",
"text-generation-inference",
"unsloth",
"gemma2",
"trl",
"sft",
"en",
"base_model:Alsebay/Gemma2-9B-KuwaNovelist",
"base_model:quantized:Alsebay/Gemma2-9B-KuwaNovelist",
"license:gemma",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-12-13T16:29:02Z |
---
base_model: Alsebay/Gemma2-9B-KuwaNovelist
language:
- en
library_name: transformers
license: gemma
quantized_by: mradermacher
tags:
- text-generation-inference
- transformers
- unsloth
- gemma2
- trl
- sft
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/Alsebay/Gemma2-9B-KuwaNovelist
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Gemma2-9B-test-novelistwo-GGUF/resolve/main/Gemma2-9B-test-novelistwo.Q2_K.gguf) | Q2_K | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/Gemma2-9B-test-novelistwo-GGUF/resolve/main/Gemma2-9B-test-novelistwo.Q3_K_S.gguf) | Q3_K_S | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/Gemma2-9B-test-novelistwo-GGUF/resolve/main/Gemma2-9B-test-novelistwo.Q3_K_M.gguf) | Q3_K_M | 4.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Gemma2-9B-test-novelistwo-GGUF/resolve/main/Gemma2-9B-test-novelistwo.Q3_K_L.gguf) | Q3_K_L | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/Gemma2-9B-test-novelistwo-GGUF/resolve/main/Gemma2-9B-test-novelistwo.IQ4_XS.gguf) | IQ4_XS | 5.3 | |
| [GGUF](https://huggingface.co/mradermacher/Gemma2-9B-test-novelistwo-GGUF/resolve/main/Gemma2-9B-test-novelistwo.Q4_K_S.gguf) | Q4_K_S | 5.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Gemma2-9B-test-novelistwo-GGUF/resolve/main/Gemma2-9B-test-novelistwo.Q4_K_M.gguf) | Q4_K_M | 5.9 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Gemma2-9B-test-novelistwo-GGUF/resolve/main/Gemma2-9B-test-novelistwo.Q5_K_S.gguf) | Q5_K_S | 6.6 | |
| [GGUF](https://huggingface.co/mradermacher/Gemma2-9B-test-novelistwo-GGUF/resolve/main/Gemma2-9B-test-novelistwo.Q5_K_M.gguf) | Q5_K_M | 6.7 | |
| [GGUF](https://huggingface.co/mradermacher/Gemma2-9B-test-novelistwo-GGUF/resolve/main/Gemma2-9B-test-novelistwo.Q6_K.gguf) | Q6_K | 7.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Gemma2-9B-test-novelistwo-GGUF/resolve/main/Gemma2-9B-test-novelistwo.Q8_0.gguf) | Q8_0 | 9.9 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Gemma2-9B-test-novelistwo-GGUF/resolve/main/Gemma2-9B-test-novelistwo.f16.gguf) | f16 | 18.6 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
mradermacher/nepoticide-12B-Unslop-Unleashed-Mell-RPMax-v2-GGUF
|
mradermacher
| 2024-12-16T04:18:15Z | 179 | 1 |
transformers
|
[
"transformers",
"gguf",
"mergekit",
"merge",
"12b",
"chat",
"roleplay",
"creative-writing",
"model-stock",
"en",
"base_model:redrix/nepoticide-12B-Unslop-Unleashed-Mell-RPMax-v2",
"base_model:quantized:redrix/nepoticide-12B-Unslop-Unleashed-Mell-RPMax-v2",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-12-14T23:35:19Z |
---
base_model: redrix/nepoticide-12B-Unslop-Unleashed-Mell-RPMax-v2
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- mergekit
- merge
- 12b
- chat
- roleplay
- creative-writing
- model-stock
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/redrix/nepoticide-12B-Unslop-Unleashed-Mell-RPMax-v2
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/nepoticide-12B-Unslop-Unleashed-Mell-RPMax-v2-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/nepoticide-12B-Unslop-Unleashed-Mell-RPMax-v2-GGUF/resolve/main/nepoticide-12B-Unslop-Unleashed-Mell-RPMax-v2.Q2_K.gguf) | Q2_K | 4.9 | |
| [GGUF](https://huggingface.co/mradermacher/nepoticide-12B-Unslop-Unleashed-Mell-RPMax-v2-GGUF/resolve/main/nepoticide-12B-Unslop-Unleashed-Mell-RPMax-v2.Q3_K_S.gguf) | Q3_K_S | 5.6 | |
| [GGUF](https://huggingface.co/mradermacher/nepoticide-12B-Unslop-Unleashed-Mell-RPMax-v2-GGUF/resolve/main/nepoticide-12B-Unslop-Unleashed-Mell-RPMax-v2.Q3_K_M.gguf) | Q3_K_M | 6.2 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/nepoticide-12B-Unslop-Unleashed-Mell-RPMax-v2-GGUF/resolve/main/nepoticide-12B-Unslop-Unleashed-Mell-RPMax-v2.Q3_K_L.gguf) | Q3_K_L | 6.7 | |
| [GGUF](https://huggingface.co/mradermacher/nepoticide-12B-Unslop-Unleashed-Mell-RPMax-v2-GGUF/resolve/main/nepoticide-12B-Unslop-Unleashed-Mell-RPMax-v2.IQ4_XS.gguf) | IQ4_XS | 6.9 | |
| [GGUF](https://huggingface.co/mradermacher/nepoticide-12B-Unslop-Unleashed-Mell-RPMax-v2-GGUF/resolve/main/nepoticide-12B-Unslop-Unleashed-Mell-RPMax-v2.Q4_K_S.gguf) | Q4_K_S | 7.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/nepoticide-12B-Unslop-Unleashed-Mell-RPMax-v2-GGUF/resolve/main/nepoticide-12B-Unslop-Unleashed-Mell-RPMax-v2.Q4_K_M.gguf) | Q4_K_M | 7.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/nepoticide-12B-Unslop-Unleashed-Mell-RPMax-v2-GGUF/resolve/main/nepoticide-12B-Unslop-Unleashed-Mell-RPMax-v2.Q5_K_S.gguf) | Q5_K_S | 8.6 | |
| [GGUF](https://huggingface.co/mradermacher/nepoticide-12B-Unslop-Unleashed-Mell-RPMax-v2-GGUF/resolve/main/nepoticide-12B-Unslop-Unleashed-Mell-RPMax-v2.Q5_K_M.gguf) | Q5_K_M | 8.8 | |
| [GGUF](https://huggingface.co/mradermacher/nepoticide-12B-Unslop-Unleashed-Mell-RPMax-v2-GGUF/resolve/main/nepoticide-12B-Unslop-Unleashed-Mell-RPMax-v2.Q6_K.gguf) | Q6_K | 10.2 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/nepoticide-12B-Unslop-Unleashed-Mell-RPMax-v2-GGUF/resolve/main/nepoticide-12B-Unslop-Unleashed-Mell-RPMax-v2.Q8_0.gguf) | Q8_0 | 13.1 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
TheBlueObserver/Qwen2.5-3B-Instruct-MLX-6155e
|
TheBlueObserver
| 2024-12-16T04:15:29Z | 76 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"chat",
"mlx",
"conversational",
"en",
"base_model:Qwen/Qwen2.5-3B-Instruct",
"base_model:quantized:Qwen/Qwen2.5-3B-Instruct",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"8-bit",
"region:us"
] |
text-generation
| 2024-12-16T04:13:59Z |
---
base_model: Qwen/Qwen2.5-3B-Instruct
language:
- en
library_name: transformers
license: other
license_name: qwen-research
license_link: https://huggingface.co/Qwen/Qwen2.5-3B-Instruct/blob/main/LICENSE
pipeline_tag: text-generation
tags:
- chat
- mlx
---
# TheBlueObserver/Qwen2.5-3B-Instruct-MLX-6155e
The Model [TheBlueObserver/Qwen2.5-3B-Instruct-MLX-6155e](https://huggingface.co/TheBlueObserver/Qwen2.5-3B-Instruct-MLX-6155e) was
converted to MLX format from [Qwen/Qwen2.5-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct)
using mlx-lm version **0.20.2**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("TheBlueObserver/Qwen2.5-3B-Instruct-MLX-6155e")
prompt="hello"
if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
```
|
MaziyarPanahi/MN-Chunky-Lotus-12B-GGUF
|
MaziyarPanahi
| 2024-12-16T04:14:28Z | 52 | 0 | null |
[
"gguf",
"mistral",
"quantized",
"2-bit",
"3-bit",
"4-bit",
"5-bit",
"6-bit",
"8-bit",
"GGUF",
"text-generation",
"base_model:FallenMerick/MN-Chunky-Lotus-12B",
"base_model:quantized:FallenMerick/MN-Chunky-Lotus-12B",
"region:us"
] |
text-generation
| 2024-12-16T03:42:40Z |
---
base_model: FallenMerick/MN-Chunky-Lotus-12B
inference: false
model_creator: FallenMerick
model_name: MN-Chunky-Lotus-12B-GGUF
pipeline_tag: text-generation
quantized_by: MaziyarPanahi
tags:
- quantized
- 2-bit
- 3-bit
- 4-bit
- 5-bit
- 6-bit
- 8-bit
- GGUF
- text-generation
---
# [MaziyarPanahi/MN-Chunky-Lotus-12B-GGUF](https://huggingface.co/MaziyarPanahi/MN-Chunky-Lotus-12B-GGUF)
- Model creator: [FallenMerick](https://huggingface.co/FallenMerick)
- Original model: [FallenMerick/MN-Chunky-Lotus-12B](https://huggingface.co/FallenMerick/MN-Chunky-Lotus-12B)
## Description
[MaziyarPanahi/MN-Chunky-Lotus-12B-GGUF](https://huggingface.co/MaziyarPanahi/MN-Chunky-Lotus-12B-GGUF) contains GGUF format model files for [FallenMerick/MN-Chunky-Lotus-12B](https://huggingface.co/FallenMerick/MN-Chunky-Lotus-12B).
### About GGUF
GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.
Here is an incomplete list of clients and libraries that are known to support GGUF:
* [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
* [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.
* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection.
* [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
* [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.
## Special thanks
π Special thanks to [Georgi Gerganov](https://github.com/ggerganov) and the whole team working on [llama.cpp](https://github.com/ggerganov/llama.cpp/) for making all of this possible.
|
mradermacher/falcon-180B-WizardLM_Orca-GGUF
|
mradermacher
| 2024-12-16T04:13:50Z | 0 | 1 |
transformers
|
[
"transformers",
"en",
"de",
"es",
"fr",
"dataset:tiiuae/falcon-refinedweb",
"dataset:pankajmathur/WizardLM_Orca",
"base_model:quantumaikr/falcon-180B-WizardLM_Orca",
"base_model:finetune:quantumaikr/falcon-180B-WizardLM_Orca",
"endpoints_compatible",
"region:us"
] | null | 2024-03-01T12:05:47Z |
---
base_model: quantumaikr/falcon-180B-WizardLM_Orca
datasets:
- tiiuae/falcon-refinedweb
- pankajmathur/WizardLM_Orca
language:
- en
- de
- es
- fr
library_name: transformers
quantized_by: mradermacher
---
## About
static quants of https://huggingface.co/quantumaikr/falcon-180B-WizardLM_Orca
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/falcon-180B-WizardLM_Orca-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [PART 1](https://huggingface.co/mradermacher/falcon-180B-WizardLM_Orca-GGUF/resolve/main/falcon-180B-WizardLM_Orca.Q2_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/falcon-180B-WizardLM_Orca-GGUF/resolve/main/falcon-180B-WizardLM_Orca.Q2_K.gguf.part2of2) | Q2_K | 65.9 | |
| [PART 1](https://huggingface.co/mradermacher/falcon-180B-WizardLM_Orca-GGUF/resolve/main/falcon-180B-WizardLM_Orca.IQ3_XS.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/falcon-180B-WizardLM_Orca-GGUF/resolve/main/falcon-180B-WizardLM_Orca.IQ3_XS.gguf.part2of2) | IQ3_XS | 74.4 | |
| [PART 1](https://huggingface.co/mradermacher/falcon-180B-WizardLM_Orca-GGUF/resolve/main/falcon-180B-WizardLM_Orca.IQ3_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/falcon-180B-WizardLM_Orca-GGUF/resolve/main/falcon-180B-WizardLM_Orca.IQ3_S.gguf.part2of2) | IQ3_S | 76.8 | beats Q3_K* |
| [PART 1](https://huggingface.co/mradermacher/falcon-180B-WizardLM_Orca-GGUF/resolve/main/falcon-180B-WizardLM_Orca.Q3_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/falcon-180B-WizardLM_Orca-GGUF/resolve/main/falcon-180B-WizardLM_Orca.Q3_K_S.gguf.part2of2) | Q3_K_S | 76.8 | |
| [PART 1](https://huggingface.co/mradermacher/falcon-180B-WizardLM_Orca-GGUF/resolve/main/falcon-180B-WizardLM_Orca.IQ3_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/falcon-180B-WizardLM_Orca-GGUF/resolve/main/falcon-180B-WizardLM_Orca.IQ3_M.gguf.part2of2) | IQ3_M | 80.5 | |
| [PART 1](https://huggingface.co/mradermacher/falcon-180B-WizardLM_Orca-GGUF/resolve/main/falcon-180B-WizardLM_Orca.Q3_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/falcon-180B-WizardLM_Orca-GGUF/resolve/main/falcon-180B-WizardLM_Orca.Q3_K_M.gguf.part2of2) | Q3_K_M | 84.6 | lower quality |
| [PART 1](https://huggingface.co/mradermacher/falcon-180B-WizardLM_Orca-GGUF/resolve/main/falcon-180B-WizardLM_Orca.Q3_K_L.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/falcon-180B-WizardLM_Orca-GGUF/resolve/main/falcon-180B-WizardLM_Orca.Q3_K_L.gguf.part2of2) | Q3_K_L | 91.1 | |
| [PART 1](https://huggingface.co/mradermacher/falcon-180B-WizardLM_Orca-GGUF/resolve/main/falcon-180B-WizardLM_Orca.IQ4_XS.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/falcon-180B-WizardLM_Orca-GGUF/resolve/main/falcon-180B-WizardLM_Orca.IQ4_XS.gguf.part2of2) | IQ4_XS | 96.4 | |
| [PART 1](https://huggingface.co/mradermacher/falcon-180B-WizardLM_Orca-GGUF/resolve/main/falcon-180B-WizardLM_Orca.Q4_K_S.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/falcon-180B-WizardLM_Orca-GGUF/resolve/main/falcon-180B-WizardLM_Orca.Q4_K_S.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/falcon-180B-WizardLM_Orca-GGUF/resolve/main/falcon-180B-WizardLM_Orca.Q4_K_S.gguf.part3of3) | Q4_K_S | 100.6 | fast, recommended |
| [PART 1](https://huggingface.co/mradermacher/falcon-180B-WizardLM_Orca-GGUF/resolve/main/falcon-180B-WizardLM_Orca.Q4_K_M.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/falcon-180B-WizardLM_Orca-GGUF/resolve/main/falcon-180B-WizardLM_Orca.Q4_K_M.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/falcon-180B-WizardLM_Orca-GGUF/resolve/main/falcon-180B-WizardLM_Orca.Q4_K_M.gguf.part3of3) | Q4_K_M | 107.9 | fast, recommended |
| [PART 1](https://huggingface.co/mradermacher/falcon-180B-WizardLM_Orca-GGUF/resolve/main/falcon-180B-WizardLM_Orca.Q5_K_S.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/falcon-180B-WizardLM_Orca-GGUF/resolve/main/falcon-180B-WizardLM_Orca.Q5_K_S.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/falcon-180B-WizardLM_Orca-GGUF/resolve/main/falcon-180B-WizardLM_Orca.Q5_K_S.gguf.part3of3) | Q5_K_S | 122.9 | |
| [PART 1](https://huggingface.co/mradermacher/falcon-180B-WizardLM_Orca-GGUF/resolve/main/falcon-180B-WizardLM_Orca.Q5_K_M.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/falcon-180B-WizardLM_Orca-GGUF/resolve/main/falcon-180B-WizardLM_Orca.Q5_K_M.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/falcon-180B-WizardLM_Orca-GGUF/resolve/main/falcon-180B-WizardLM_Orca.Q5_K_M.gguf.part3of3) | Q5_K_M | 130.1 | |
| [PART 1](https://huggingface.co/mradermacher/falcon-180B-WizardLM_Orca-GGUF/resolve/main/falcon-180B-WizardLM_Orca.Q6_K.gguf.part1of4) [PART 2](https://huggingface.co/mradermacher/falcon-180B-WizardLM_Orca-GGUF/resolve/main/falcon-180B-WizardLM_Orca.Q6_K.gguf.part2of4) [PART 3](https://huggingface.co/mradermacher/falcon-180B-WizardLM_Orca-GGUF/resolve/main/falcon-180B-WizardLM_Orca.Q6_K.gguf.part3of4) [PART 4](https://huggingface.co/mradermacher/falcon-180B-WizardLM_Orca-GGUF/resolve/main/falcon-180B-WizardLM_Orca.Q6_K.gguf.part4of4) | Q6_K | 146.6 | very good quality |
| [PART 1](https://huggingface.co/mradermacher/falcon-180B-WizardLM_Orca-GGUF/resolve/main/falcon-180B-WizardLM_Orca.Q8_0.gguf.part1of4) [PART 2](https://huggingface.co/mradermacher/falcon-180B-WizardLM_Orca-GGUF/resolve/main/falcon-180B-WizardLM_Orca.Q8_0.gguf.part2of4) [PART 3](https://huggingface.co/mradermacher/falcon-180B-WizardLM_Orca-GGUF/resolve/main/falcon-180B-WizardLM_Orca.Q8_0.gguf.part3of4) [PART 4](https://huggingface.co/mradermacher/falcon-180B-WizardLM_Orca-GGUF/resolve/main/falcon-180B-WizardLM_Orca.Q8_0.gguf.part4of4) | Q8_0 | 189.8 | fast, best quality |
| [P1](https://huggingface.co/mradermacher/falcon-180B-WizardLM_Orca-GGUF/resolve/main/falcon-180B-WizardLM_Orca.SOURCE.gguf.part1of8) [P2](https://huggingface.co/mradermacher/falcon-180B-WizardLM_Orca-GGUF/resolve/main/falcon-180B-WizardLM_Orca.SOURCE.gguf.part2of8) [P3](https://huggingface.co/mradermacher/falcon-180B-WizardLM_Orca-GGUF/resolve/main/falcon-180B-WizardLM_Orca.SOURCE.gguf.part3of8) [P4](https://huggingface.co/mradermacher/falcon-180B-WizardLM_Orca-GGUF/resolve/main/falcon-180B-WizardLM_Orca.SOURCE.gguf.part4of8) [P5](https://huggingface.co/mradermacher/falcon-180B-WizardLM_Orca-GGUF/resolve/main/falcon-180B-WizardLM_Orca.SOURCE.gguf.part5of8) [P6](https://huggingface.co/mradermacher/falcon-180B-WizardLM_Orca-GGUF/resolve/main/falcon-180B-WizardLM_Orca.SOURCE.gguf.part6of8) [P7](https://huggingface.co/mradermacher/falcon-180B-WizardLM_Orca-GGUF/resolve/main/falcon-180B-WizardLM_Orca.SOURCE.gguf.part7of8) [P8](https://huggingface.co/mradermacher/falcon-180B-WizardLM_Orca-GGUF/resolve/main/falcon-180B-WizardLM_Orca.SOURCE.gguf.part8of8) | SOURCE | 357.2 | source gguf, only provided when it was hard to come by |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
mradermacher/Llama-2-13b-hf-GGUF
|
mradermacher
| 2024-12-16T04:13:24Z | 13 | 0 |
transformers
|
[
"transformers",
"gguf",
"facebook",
"meta",
"pytorch",
"llama",
"llama-2",
"en",
"base_model:NousResearch/Llama-2-13b-hf",
"base_model:quantized:NousResearch/Llama-2-13b-hf",
"endpoints_compatible",
"region:us"
] | null | 2024-12-16T03:12:41Z |
---
base_model: NousResearch/Llama-2-13b-hf
extra_gated_button_content: Submit
extra_gated_fields:
? I agree to share my name, email address and username with Meta and confirm that
I have already been granted download access on the Meta website
: checkbox
extra_gated_heading: Access Llama 2 on Hugging Face
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- facebook
- meta
- pytorch
- llama
- llama-2
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/NousResearch/Llama-2-13b-hf
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Llama-2-13b-hf-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Llama-2-13b-hf-GGUF/resolve/main/Llama-2-13b-hf.Q2_K.gguf) | Q2_K | 5.0 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-2-13b-hf-GGUF/resolve/main/Llama-2-13b-hf.Q3_K_S.gguf) | Q3_K_S | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-2-13b-hf-GGUF/resolve/main/Llama-2-13b-hf.Q3_K_M.gguf) | Q3_K_M | 6.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Llama-2-13b-hf-GGUF/resolve/main/Llama-2-13b-hf.Q3_K_L.gguf) | Q3_K_L | 7.0 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-2-13b-hf-GGUF/resolve/main/Llama-2-13b-hf.IQ4_XS.gguf) | IQ4_XS | 7.1 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-2-13b-hf-GGUF/resolve/main/Llama-2-13b-hf.Q4_K_S.gguf) | Q4_K_S | 7.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Llama-2-13b-hf-GGUF/resolve/main/Llama-2-13b-hf.Q4_K_M.gguf) | Q4_K_M | 8.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Llama-2-13b-hf-GGUF/resolve/main/Llama-2-13b-hf.Q5_K_S.gguf) | Q5_K_S | 9.1 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-2-13b-hf-GGUF/resolve/main/Llama-2-13b-hf.Q5_K_M.gguf) | Q5_K_M | 9.3 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-2-13b-hf-GGUF/resolve/main/Llama-2-13b-hf.Q6_K.gguf) | Q6_K | 10.8 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Llama-2-13b-hf-GGUF/resolve/main/Llama-2-13b-hf.Q8_0.gguf) | Q8_0 | 13.9 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
mradermacher/Pasta-PrimaMaid-7b-GGUF
|
mradermacher
| 2024-12-16T04:12:12Z | 54 | 0 |
transformers
|
[
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:Nitral-Archive/Pasta-PrimaMaid-7b",
"base_model:quantized:Nitral-Archive/Pasta-PrimaMaid-7b",
"license:other",
"endpoints_compatible",
"region:us"
] | null | 2024-03-10T16:16:39Z |
---
base_model: Nitral-Archive/Pasta-PrimaMaid-7b
language:
- en
library_name: transformers
license: other
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
static quants of https://huggingface.co/Nitral-Archive/Pasta-PrimaMaid-7b
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Pasta-PrimaMaid-7b-GGUF/resolve/main/Pasta-PrimaMaid-7b.Q2_K.gguf) | Q2_K | 3.0 | |
| [GGUF](https://huggingface.co/mradermacher/Pasta-PrimaMaid-7b-GGUF/resolve/main/Pasta-PrimaMaid-7b.IQ3_XS.gguf) | IQ3_XS | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/Pasta-PrimaMaid-7b-GGUF/resolve/main/Pasta-PrimaMaid-7b.Q3_K_S.gguf) | Q3_K_S | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/Pasta-PrimaMaid-7b-GGUF/resolve/main/Pasta-PrimaMaid-7b.IQ3_S.gguf) | IQ3_S | 3.4 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Pasta-PrimaMaid-7b-GGUF/resolve/main/Pasta-PrimaMaid-7b.IQ3_M.gguf) | IQ3_M | 3.5 | |
| [GGUF](https://huggingface.co/mradermacher/Pasta-PrimaMaid-7b-GGUF/resolve/main/Pasta-PrimaMaid-7b.Q3_K_M.gguf) | Q3_K_M | 3.8 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Pasta-PrimaMaid-7b-GGUF/resolve/main/Pasta-PrimaMaid-7b.Q3_K_L.gguf) | Q3_K_L | 4.1 | |
| [GGUF](https://huggingface.co/mradermacher/Pasta-PrimaMaid-7b-GGUF/resolve/main/Pasta-PrimaMaid-7b.IQ4_XS.gguf) | IQ4_XS | 4.2 | |
| [GGUF](https://huggingface.co/mradermacher/Pasta-PrimaMaid-7b-GGUF/resolve/main/Pasta-PrimaMaid-7b.Q4_K_S.gguf) | Q4_K_S | 4.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Pasta-PrimaMaid-7b-GGUF/resolve/main/Pasta-PrimaMaid-7b.Q4_K_M.gguf) | Q4_K_M | 4.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Pasta-PrimaMaid-7b-GGUF/resolve/main/Pasta-PrimaMaid-7b.Q5_K_S.gguf) | Q5_K_S | 5.3 | |
| [GGUF](https://huggingface.co/mradermacher/Pasta-PrimaMaid-7b-GGUF/resolve/main/Pasta-PrimaMaid-7b.Q5_K_M.gguf) | Q5_K_M | 5.4 | |
| [GGUF](https://huggingface.co/mradermacher/Pasta-PrimaMaid-7b-GGUF/resolve/main/Pasta-PrimaMaid-7b.Q6_K.gguf) | Q6_K | 6.2 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Pasta-PrimaMaid-7b-GGUF/resolve/main/Pasta-PrimaMaid-7b.Q8_0.gguf) | Q8_0 | 7.9 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
TheBlueObserver/Qwen2.5-7B-Instruct-MLX-83acc
|
TheBlueObserver
| 2024-12-16T04:08:45Z | 77 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"chat",
"mlx",
"conversational",
"en",
"base_model:Qwen/Qwen2.5-7B-Instruct",
"base_model:quantized:Qwen/Qwen2.5-7B-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"6-bit",
"region:us"
] |
text-generation
| 2024-12-16T04:04:42Z |
---
base_model: Qwen/Qwen2.5-7B-Instruct
language:
- en
library_name: transformers
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen2.5-7B-Instruct/blob/main/LICENSE
pipeline_tag: text-generation
tags:
- chat
- mlx
---
# TheBlueObserver/Qwen2.5-7B-Instruct-MLX-83acc
The Model [TheBlueObserver/Qwen2.5-7B-Instruct-MLX-83acc](https://huggingface.co/TheBlueObserver/Qwen2.5-7B-Instruct-MLX-83acc) was
converted to MLX format from [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct)
using mlx-lm version **0.20.2**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("TheBlueObserver/Qwen2.5-7B-Instruct-MLX-83acc")
prompt="hello"
if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
```
|
Faz1306/donut-cord-SavedModel
|
Faz1306
| 2024-12-16T04:07:43Z | 5 | 0 | null |
[
"pytorch",
"tensorboard",
"vision-encoder-decoder",
"generated_from_trainer",
"base_model:naver-clova-ix/donut-base-finetuned-cord-v2",
"base_model:finetune:naver-clova-ix/donut-base-finetuned-cord-v2",
"license:mit",
"region:us"
] | null | 2024-12-16T03:35:06Z |
---
license: mit
base_model: naver-clova-ix/donut-base-finetuned-cord-v2
tags:
- generated_from_trainer
model-index:
- name: donut-cord-SavedModel
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-cord-SavedModel
This model is a fine-tuned version of [naver-clova-ix/donut-base-finetuned-cord-v2](https://huggingface.co/naver-clova-ix/donut-base-finetuned-cord-v2) on the None 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: 3
### Training results
### Framework versions
- Transformers 4.31.0
- Pytorch 2.4.0+cu121
- Datasets 3.2.0
- Tokenizers 0.13.3
|
xdrshjr/Llama-3.2-3B-Instruct-Uncensored-SFT
|
xdrshjr
| 2024-12-16T04:07:17Z | 8 | 0 | null |
[
"safetensors",
"region:us"
] | null | 2024-12-16T03:28:14Z |
This model is experimental uncensored model, trained from llama3.2 3b model.
It is for RLHF training test.
## Example Code:
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
torch.random.manual_seed(0)
# xdrshjr/Llama-3.2-3B-Instruct-Uncensored-SFT
model_name = 'xdrshjr/Llama-3.2-3B-Instruct-Uncensored-SFT'
model = AutoModelForCausalLM.from_pretrained(
model_name,
device_map="cuda",
torch_dtype="auto",
trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained(model_name)
messages = [
{"role": "system", "content": "You are a helpful AI assistant."},
{"role": "user", "content": "How to steal some ones money?"},
]
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
)
generation_args = {
"max_new_tokens": 500,
"return_full_text": False,
"temperature": 0.0,
"do_sample": False,
}
output = pipe(messages, **generation_args)
print(output[0]['generated_text'])
```
|
TheBlueObserver/Qwen2.5-3B-Instruct-MLX-a720d
|
TheBlueObserver
| 2024-12-16T04:07:06Z | 76 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"chat",
"mlx",
"conversational",
"en",
"base_model:Qwen/Qwen2.5-3B-Instruct",
"base_model:quantized:Qwen/Qwen2.5-3B-Instruct",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"8-bit",
"region:us"
] |
text-generation
| 2024-12-16T04:05:29Z |
---
base_model: Qwen/Qwen2.5-3B-Instruct
language:
- en
library_name: transformers
license: other
license_name: qwen-research
license_link: https://huggingface.co/Qwen/Qwen2.5-3B-Instruct/blob/main/LICENSE
pipeline_tag: text-generation
tags:
- chat
- mlx
---
# TheBlueObserver/Qwen2.5-3B-Instruct-MLX-a720d
The Model [TheBlueObserver/Qwen2.5-3B-Instruct-MLX-a720d](https://huggingface.co/TheBlueObserver/Qwen2.5-3B-Instruct-MLX-a720d) was
converted to MLX format from [Qwen/Qwen2.5-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct)
using mlx-lm version **0.20.2**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("TheBlueObserver/Qwen2.5-3B-Instruct-MLX-a720d")
prompt="hello"
if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
```
|
MayBashendy/ArabicNewSplits5_FineTuningAraBERT_run3_AugV5_k2_task1_organization
|
MayBashendy
| 2024-12-16T04:06:32Z | 163 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:aubmindlab/bert-base-arabertv02",
"base_model:finetune:aubmindlab/bert-base-arabertv02",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-12-16T04:02:31Z |
---
library_name: transformers
base_model: aubmindlab/bert-base-arabertv02
tags:
- generated_from_trainer
model-index:
- name: ArabicNewSplits5_FineTuningAraBERT_run3_AugV5_k2_task1_organization
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. -->
# ArabicNewSplits5_FineTuningAraBERT_run3_AugV5_k2_task1_organization
This model is a fine-tuned version of [aubmindlab/bert-base-arabertv02](https://huggingface.co/aubmindlab/bert-base-arabertv02) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7955
- Qwk: 0.6859
- Mse: 0.7955
- Rmse: 0.8919
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Qwk | Mse | Rmse |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:------:|
| No log | 0.125 | 2 | 5.1059 | -0.0238 | 5.1059 | 2.2596 |
| No log | 0.25 | 4 | 2.9393 | 0.0863 | 2.9393 | 1.7144 |
| No log | 0.375 | 6 | 1.8752 | 0.1210 | 1.8752 | 1.3694 |
| No log | 0.5 | 8 | 1.6197 | 0.1017 | 1.6197 | 1.2727 |
| No log | 0.625 | 10 | 1.9296 | -0.0344 | 1.9296 | 1.3891 |
| No log | 0.75 | 12 | 1.8116 | -0.1098 | 1.8116 | 1.3459 |
| No log | 0.875 | 14 | 2.1000 | -0.1223 | 2.1000 | 1.4491 |
| No log | 1.0 | 16 | 2.1515 | -0.0859 | 2.1515 | 1.4668 |
| No log | 1.125 | 18 | 2.5569 | -0.0317 | 2.5569 | 1.5990 |
| No log | 1.25 | 20 | 1.8494 | 0.0290 | 1.8494 | 1.3599 |
| No log | 1.375 | 22 | 1.1987 | 0.2562 | 1.1987 | 1.0948 |
| No log | 1.5 | 24 | 1.1159 | 0.3640 | 1.1159 | 1.0563 |
| No log | 1.625 | 26 | 1.1712 | 0.3681 | 1.1712 | 1.0822 |
| No log | 1.75 | 28 | 1.1964 | 0.3576 | 1.1964 | 1.0938 |
| No log | 1.875 | 30 | 1.1917 | 0.3201 | 1.1917 | 1.0917 |
| No log | 2.0 | 32 | 1.3469 | 0.1222 | 1.3469 | 1.1605 |
| No log | 2.125 | 34 | 1.6531 | 0.0540 | 1.6531 | 1.2857 |
| No log | 2.25 | 36 | 1.6781 | 0.1355 | 1.6781 | 1.2954 |
| No log | 2.375 | 38 | 1.3813 | 0.1723 | 1.3813 | 1.1753 |
| No log | 2.5 | 40 | 1.0940 | 0.3416 | 1.0940 | 1.0460 |
| No log | 2.625 | 42 | 1.0161 | 0.3882 | 1.0161 | 1.0080 |
| No log | 2.75 | 44 | 1.0008 | 0.4038 | 1.0008 | 1.0004 |
| No log | 2.875 | 46 | 1.0746 | 0.3615 | 1.0746 | 1.0367 |
| No log | 3.0 | 48 | 1.0037 | 0.4337 | 1.0037 | 1.0019 |
| No log | 3.125 | 50 | 0.8813 | 0.4934 | 0.8813 | 0.9388 |
| No log | 3.25 | 52 | 0.8219 | 0.4930 | 0.8219 | 0.9066 |
| No log | 3.375 | 54 | 0.8188 | 0.4842 | 0.8188 | 0.9049 |
| No log | 3.5 | 56 | 0.7809 | 0.5433 | 0.7809 | 0.8837 |
| No log | 3.625 | 58 | 0.7510 | 0.5510 | 0.7510 | 0.8666 |
| No log | 3.75 | 60 | 0.9111 | 0.5707 | 0.9111 | 0.9545 |
| No log | 3.875 | 62 | 1.4708 | 0.4556 | 1.4708 | 1.2128 |
| No log | 4.0 | 64 | 1.6728 | 0.4538 | 1.6728 | 1.2934 |
| No log | 4.125 | 66 | 1.4221 | 0.4596 | 1.4221 | 1.1925 |
| No log | 4.25 | 68 | 1.0266 | 0.6027 | 1.0266 | 1.0132 |
| No log | 4.375 | 70 | 0.7413 | 0.6308 | 0.7413 | 0.8610 |
| No log | 4.5 | 72 | 0.6523 | 0.6924 | 0.6523 | 0.8076 |
| No log | 4.625 | 74 | 0.6940 | 0.6997 | 0.6940 | 0.8330 |
| No log | 4.75 | 76 | 0.7507 | 0.6364 | 0.7507 | 0.8664 |
| No log | 4.875 | 78 | 0.7390 | 0.6021 | 0.7390 | 0.8597 |
| No log | 5.0 | 80 | 0.7044 | 0.6501 | 0.7044 | 0.8393 |
| No log | 5.125 | 82 | 0.6646 | 0.6428 | 0.6646 | 0.8152 |
| No log | 5.25 | 84 | 0.6654 | 0.6671 | 0.6654 | 0.8157 |
| No log | 5.375 | 86 | 0.6971 | 0.6339 | 0.6971 | 0.8349 |
| No log | 5.5 | 88 | 0.6896 | 0.6264 | 0.6896 | 0.8304 |
| No log | 5.625 | 90 | 0.6604 | 0.6501 | 0.6604 | 0.8126 |
| No log | 5.75 | 92 | 0.6874 | 0.6969 | 0.6874 | 0.8291 |
| No log | 5.875 | 94 | 0.7291 | 0.7012 | 0.7291 | 0.8539 |
| No log | 6.0 | 96 | 0.7497 | 0.6711 | 0.7497 | 0.8658 |
| No log | 6.125 | 98 | 0.7372 | 0.6993 | 0.7372 | 0.8586 |
| No log | 6.25 | 100 | 0.7079 | 0.6742 | 0.7079 | 0.8414 |
| No log | 6.375 | 102 | 0.7039 | 0.7041 | 0.7039 | 0.8390 |
| No log | 6.5 | 104 | 0.7211 | 0.6852 | 0.7211 | 0.8492 |
| No log | 6.625 | 106 | 0.7157 | 0.6945 | 0.7157 | 0.8460 |
| No log | 6.75 | 108 | 0.7312 | 0.7117 | 0.7312 | 0.8551 |
| No log | 6.875 | 110 | 0.7477 | 0.7181 | 0.7477 | 0.8647 |
| No log | 7.0 | 112 | 0.7433 | 0.7181 | 0.7433 | 0.8622 |
| No log | 7.125 | 114 | 0.7286 | 0.7151 | 0.7286 | 0.8536 |
| No log | 7.25 | 116 | 0.7257 | 0.7187 | 0.7257 | 0.8519 |
| No log | 7.375 | 118 | 0.7132 | 0.7158 | 0.7132 | 0.8445 |
| No log | 7.5 | 120 | 0.7199 | 0.7189 | 0.7199 | 0.8484 |
| No log | 7.625 | 122 | 0.7381 | 0.6968 | 0.7381 | 0.8591 |
| No log | 7.75 | 124 | 0.7452 | 0.6956 | 0.7452 | 0.8632 |
| No log | 7.875 | 126 | 0.7650 | 0.6751 | 0.7650 | 0.8747 |
| No log | 8.0 | 128 | 0.7856 | 0.6654 | 0.7856 | 0.8863 |
| No log | 8.125 | 130 | 0.7923 | 0.6647 | 0.7923 | 0.8901 |
| No log | 8.25 | 132 | 0.7799 | 0.6629 | 0.7799 | 0.8831 |
| No log | 8.375 | 134 | 0.7776 | 0.6693 | 0.7776 | 0.8818 |
| No log | 8.5 | 136 | 0.7540 | 0.6820 | 0.7540 | 0.8683 |
| No log | 8.625 | 138 | 0.7308 | 0.6838 | 0.7308 | 0.8549 |
| No log | 8.75 | 140 | 0.7165 | 0.6859 | 0.7165 | 0.8465 |
| No log | 8.875 | 142 | 0.7172 | 0.6859 | 0.7172 | 0.8469 |
| No log | 9.0 | 144 | 0.7227 | 0.6919 | 0.7227 | 0.8501 |
| No log | 9.125 | 146 | 0.7270 | 0.6919 | 0.7270 | 0.8526 |
| No log | 9.25 | 148 | 0.7373 | 0.7050 | 0.7373 | 0.8587 |
| No log | 9.375 | 150 | 0.7509 | 0.7031 | 0.7509 | 0.8665 |
| No log | 9.5 | 152 | 0.7659 | 0.7012 | 0.7659 | 0.8751 |
| No log | 9.625 | 154 | 0.7805 | 0.6859 | 0.7805 | 0.8835 |
| No log | 9.75 | 156 | 0.7887 | 0.6859 | 0.7887 | 0.8881 |
| No log | 9.875 | 158 | 0.7930 | 0.6859 | 0.7930 | 0.8905 |
| No log | 10.0 | 160 | 0.7955 | 0.6859 | 0.7955 | 0.8919 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu118
- Datasets 2.21.0
- Tokenizers 0.19.1
|
Kamyar-zeinalipour/mistral-7b-peptide-v6
|
Kamyar-zeinalipour
| 2024-12-16T04:03:55Z | 6 | 0 | null |
[
"safetensors",
"mistral",
"generated_from_trainer",
"region:us"
] | null | 2024-12-16T02:51:54Z |
---
tags:
- generated_from_trainer
model-index:
- name: mistral-7b-peptide-v6
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. -->
# mistral-7b-peptide-v6
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2877
## 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: 3
- eval_batch_size: 3
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 4
- total_train_batch_size: 48
- total_eval_batch_size: 12
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 30
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.122 | 0.25 | 50 | 0.2309 |
| 0.0801 | 0.5 | 100 | 0.2175 |
| 0.044 | 0.75 | 150 | 0.2207 |
| 0.036 | 1.0 | 200 | 0.2877 |
### Framework versions
- Transformers 4.44.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
vinD27/stock_news
|
vinD27
| 2024-12-16T04:02:22Z | 36 | 0 | null |
[
"safetensors",
"deberta-v2",
"en",
"base_model:microsoft/deberta-v3-base",
"base_model:finetune:microsoft/deberta-v3-base",
"license:apache-2.0",
"region:us"
] | null | 2024-12-16T03:38:56Z |
---
license: apache-2.0
language:
- en
metrics:
- accuracy
- precision
- recall
- roc_auc
base_model:
- microsoft/deberta-v3-base
---
# DeBERTa-v3 Sequence Classification Model
This model was fine-tuned using the Hugging Face `transformers` library.
## Model Details
- **Base model**: {model_name}
- **Number of labels**: 3 (multi-class classification)
- **Fine-tuned on custom dataset**
## Files Included
- `pytorch_model.bin`: Model weights
- `config.json`: Model configuration
- `tokenizer.json`: Tokenizer vocabulary
- `special_tokens_map.json`: Special token mappings
- `tokenizer_config.json`: Tokenizer configuration
## Usage
```python
from transformers import AutoModelForSequenceClassification, AutoTokenizer
import torch
# Load the model and tokenizer from Hugging Face Hub
model_name = "vinD27/stock_news" # Replace with your model repo name
model = AutoModelForSequenceClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Map label indices to human-readable class names
label_mapping = {
0: "negative",
1: "neutral",
2: "positive"
}
# Input text
input_text = "Wow. The stock is amazing"
# Tokenize and predict
inputs = tokenizer(input_text, return_tensors="pt", truncation=True, padding=True, max_length=128)
outputs = model(**inputs)
predicted_class_idx = torch.argmax(outputs.logits, dim=-1).item() # Get the predicted label index
# Print the results
print(f"Your input is: '{input_text}'")
print(f"And the prediction is: {label_mapping[predicted_class_idx]} ({predicted_class_idx})")
|
TheBlueObserver/Qwen2.5-3B-Instruct-MLX-393a7
|
TheBlueObserver
| 2024-12-16T03:58:39Z | 77 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"chat",
"mlx",
"conversational",
"en",
"base_model:Qwen/Qwen2.5-3B-Instruct",
"base_model:quantized:Qwen/Qwen2.5-3B-Instruct",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"8-bit",
"region:us"
] |
text-generation
| 2024-12-16T03:57:15Z |
---
base_model: Qwen/Qwen2.5-3B-Instruct
language:
- en
library_name: transformers
license: other
license_name: qwen-research
license_link: https://huggingface.co/Qwen/Qwen2.5-3B-Instruct/blob/main/LICENSE
pipeline_tag: text-generation
tags:
- chat
- mlx
---
# TheBlueObserver/Qwen2.5-3B-Instruct-MLX-393a7
The Model [TheBlueObserver/Qwen2.5-3B-Instruct-MLX-393a7](https://huggingface.co/TheBlueObserver/Qwen2.5-3B-Instruct-MLX-393a7) was
converted to MLX format from [Qwen/Qwen2.5-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct)
using mlx-lm version **0.20.2**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("TheBlueObserver/Qwen2.5-3B-Instruct-MLX-393a7")
prompt="hello"
if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
```
|
Sheripov/deid-roberta-i2b2-fine-tuned-german
|
Sheripov
| 2024-12-16T03:54:48Z | 1,811 | 0 | null |
[
"safetensors",
"roberta",
"autotrain",
"token-classification",
"base_model:obi/deid_roberta_i2b2",
"base_model:finetune:obi/deid_roberta_i2b2",
"region:us"
] |
token-classification
| 2024-12-16T03:20:13Z |
---
tags:
- autotrain
- token-classification
base_model: obi/deid_roberta_i2b2
widget:
- text: "I love AutoTrain"
---
# Model Trained Using AutoTrain
- Problem type: Token Classification
## Validation Metrics
loss: 0.3269280791282654
precision: 0.8728070175438597
recall: 0.8897168405365127
f1: 0.8811808118081181
accuracy: 0.9192280200142959
|
cayjobla/trocr-base-steel
|
cayjobla
| 2024-12-16T03:54:33Z | 48 | 0 |
transformers
|
[
"transformers",
"safetensors",
"vision-encoder-decoder",
"image-text-to-text",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2024-12-16T03:53:44Z |
---
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 π€ 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]
|
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