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hkivancoral/smids_5x_deit_tiny_adamax_00001_fold5
|
hkivancoral
| 2023-12-14T19:07:14Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:facebook/deit-tiny-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-14T18:03:57Z |
---
license: apache-2.0
base_model: facebook/deit-tiny-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_5x_deit_tiny_adamax_00001_fold5
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8933333333333333
---
<!-- 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. -->
# smids_5x_deit_tiny_adamax_00001_fold5
This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8918
- Accuracy: 0.8933
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3356 | 1.0 | 375 | 0.3637 | 0.8583 |
| 0.2941 | 2.0 | 750 | 0.2859 | 0.8917 |
| 0.2173 | 3.0 | 1125 | 0.2723 | 0.8933 |
| 0.2107 | 4.0 | 1500 | 0.2619 | 0.9017 |
| 0.1256 | 5.0 | 1875 | 0.3096 | 0.8767 |
| 0.0844 | 6.0 | 2250 | 0.2740 | 0.8983 |
| 0.0863 | 7.0 | 2625 | 0.3155 | 0.895 |
| 0.0472 | 8.0 | 3000 | 0.3497 | 0.895 |
| 0.0763 | 9.0 | 3375 | 0.3686 | 0.895 |
| 0.0335 | 10.0 | 3750 | 0.4149 | 0.8967 |
| 0.0338 | 11.0 | 4125 | 0.4756 | 0.8933 |
| 0.0184 | 12.0 | 4500 | 0.5125 | 0.89 |
| 0.0027 | 13.0 | 4875 | 0.6023 | 0.8767 |
| 0.0397 | 14.0 | 5250 | 0.6231 | 0.885 |
| 0.0014 | 15.0 | 5625 | 0.7069 | 0.8733 |
| 0.0003 | 16.0 | 6000 | 0.6770 | 0.8983 |
| 0.0258 | 17.0 | 6375 | 0.7038 | 0.895 |
| 0.0256 | 18.0 | 6750 | 0.7293 | 0.89 |
| 0.0002 | 19.0 | 7125 | 0.7746 | 0.8833 |
| 0.0001 | 20.0 | 7500 | 0.7738 | 0.8917 |
| 0.0001 | 21.0 | 7875 | 0.8059 | 0.8833 |
| 0.026 | 22.0 | 8250 | 0.8287 | 0.8933 |
| 0.0138 | 23.0 | 8625 | 0.8293 | 0.8867 |
| 0.0 | 24.0 | 9000 | 0.8289 | 0.88 |
| 0.0 | 25.0 | 9375 | 0.8428 | 0.8933 |
| 0.0001 | 26.0 | 9750 | 0.8343 | 0.8917 |
| 0.0016 | 27.0 | 10125 | 0.8455 | 0.89 |
| 0.0 | 28.0 | 10500 | 0.8478 | 0.89 |
| 0.0001 | 29.0 | 10875 | 0.8508 | 0.8917 |
| 0.0173 | 30.0 | 11250 | 0.8741 | 0.8917 |
| 0.0 | 31.0 | 11625 | 0.8677 | 0.8933 |
| 0.0 | 32.0 | 12000 | 0.8682 | 0.8933 |
| 0.0 | 33.0 | 12375 | 0.8819 | 0.8917 |
| 0.0 | 34.0 | 12750 | 0.8684 | 0.8917 |
| 0.0 | 35.0 | 13125 | 0.8910 | 0.8933 |
| 0.0 | 36.0 | 13500 | 0.8845 | 0.8933 |
| 0.0 | 37.0 | 13875 | 0.8700 | 0.8917 |
| 0.0072 | 38.0 | 14250 | 0.8781 | 0.8917 |
| 0.0 | 39.0 | 14625 | 0.8840 | 0.8933 |
| 0.0 | 40.0 | 15000 | 0.8993 | 0.895 |
| 0.0 | 41.0 | 15375 | 0.8767 | 0.8883 |
| 0.0 | 42.0 | 15750 | 0.8820 | 0.8967 |
| 0.0 | 43.0 | 16125 | 0.8803 | 0.8983 |
| 0.0 | 44.0 | 16500 | 0.8853 | 0.895 |
| 0.0 | 45.0 | 16875 | 0.8969 | 0.8917 |
| 0.0 | 46.0 | 17250 | 0.8999 | 0.8917 |
| 0.0073 | 47.0 | 17625 | 0.8921 | 0.895 |
| 0.0 | 48.0 | 18000 | 0.8943 | 0.8917 |
| 0.0 | 49.0 | 18375 | 0.8940 | 0.8933 |
| 0.0065 | 50.0 | 18750 | 0.8918 | 0.8933 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
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jan-hq/pandora-v1-13b-GGUF
|
jan-hq
| 2023-12-15T06:57:21Z | 0 | 1 | null |
[
"gguf",
"en",
"base_model:janhq/Pandora-v1-13B",
"license:apache-2.0",
"region:us"
] | null | 2023-12-14T18:04:33Z |
---
license: apache-2.0
language:
- en
base_model: janhq/Pandora-v1-13B
model_creator: janhq
model_name: Pandora-v1-13B
quantized_by: JanHQ
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://github.com/janhq/jan/assets/89722390/35daac7d-b895-487c-a6ac-6663daaad78e" alt="Jan banner" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<p align="center">
<a href="https://jan.ai/">Jan</a>
- <a href="https://discord.gg/AsJ8krTT3N">Discord</a>
</p>
<!-- header end -->
# Model Description
This is a GGUF version of [janhq/Pandora-v1-13B](https://huggingface.co/janhq/Pandora-v1-13B)
- Model creator: [janhq](https://huggingface.co/janhq)
- Original model: [Pandora-v1-13B](https://huggingface.co/janhq/Pandora-v1-13B)
- Model description: [Readme](https://huggingface.co/janhq/Pandora-v1-13B/blob/main/README.md)
# Run this model
You can run this model using [Jan Desktop](https://jan.ai/) on Mac, Windows, or Linux.
Jan is an open source, ChatGPT alternative that is:
- 💻 **100% offline on your machine**: Your conversations remain confidential, and visible only to you.
- 🗂️ **An Open File Format**: Conversations and model settings stay on your computer and can be exported or deleted at any time.
- 🌐 **OpenAI Compatible**: Local server on port `1337` with OpenAI compatible endpoints
- 🌍 **Open Source & Free**: We build in public; check out our [Github](https://github.com/janhq)
# About Jan
Jan believes in the need for an open-source AI ecosystem and is building the infra and tooling to allow open-source AIs to compete on a level playing field with proprietary ones.
Jan's long-term vision is to build a cognitive framework for future robots, who are practical, useful assistants for humans and businesses in everyday life.
# Jan Model Converter
This is a repository for the [open-source converter](https://github.com/janhq/model-converter). We would be grateful if the community could contribute and strengthen this repository. We are aiming to expand the repo that can convert into various types of format
|
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arxyzan/perceptron
|
arxyzan
| 2023-12-14T18:06:02Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T18:04:37Z |
Entry not found
|
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] |
stvhuang/rcr-run-g6knz4zq-v2-61961-master-0_20231212T101450-ep09
|
stvhuang
| 2023-12-14T18:07:40Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"xlm-roberta",
"feature-extraction",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2023-12-14T18:04:51Z |
Entry not found
|
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] |
Vinceorz/codellama_py_34b_12_14_checkpoint_148
|
Vinceorz
| 2023-12-14T18:06:21Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"region:us"
] | null | 2023-12-14T18:06:17Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.4.0
|
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owanr/SChem5Labels-roberta-base-inter-data-frequency-model_annots
|
owanr
| 2023-12-14T18:06:45Z | 0 | 0 | null |
[
"safetensors",
"generated_from_trainer",
"base_model:roberta-base",
"license:mit",
"region:us"
] | null | 2023-12-14T18:06:23Z |
---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
model-index:
- name: SChem5Labels-roberta-base-inter-data-frequency-model_annots
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. -->
# SChem5Labels-roberta-base-inter-data-frequency-model_annots
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 138.7986
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 158.4938 | 1.0 | 99 | 139.7526 |
| 110.3932 | 2.0 | 198 | 138.4107 |
| 105.2326 | 3.0 | 297 | 138.6402 |
| 184.8952 | 4.0 | 396 | 138.3319 |
| 118.8304 | 5.0 | 495 | 138.7986 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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] |
Vinceorz/codellama_py_34b_12_14_checkpoint_153
|
Vinceorz
| 2023-12-14T19:14:11Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"region:us"
] | null | 2023-12-14T18:06:37Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.4.0
|
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p0k3m0n/mmds-project2
|
p0k3m0n
| 2023-12-14T18:08:12Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T18:08:12Z |
Entry not found
|
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] |
balzanilo/dialogs-emotions-classifier-bert-base-seed4096
|
balzanilo
| 2023-12-14T18:35:00Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-14T18:09:08Z |
Entry not found
|
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owanr/SBIC-roberta-base-inter-data-frequency-human_annots
|
owanr
| 2023-12-14T18:10:04Z | 0 | 0 | null |
[
"safetensors",
"generated_from_trainer",
"base_model:roberta-base",
"license:mit",
"region:us"
] | null | 2023-12-14T18:09:44Z |
---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
model-index:
- name: SBIC-roberta-base-inter-data-frequency-human_annots
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. -->
# SBIC-roberta-base-inter-data-frequency-human_annots
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 16.0618
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 6.6006 | 1.0 | 392 | 37.3281 |
| 2.9653 | 2.0 | 784 | 22.5487 |
| 4.3072 | 3.0 | 1176 | 17.5771 |
| 1.0223 | 4.0 | 1568 | 16.0618 |
| 3.2083 | 5.0 | 1960 | 16.0618 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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] |
pariakashani/en-multinerd-masked-ner-more-training
|
pariakashani
| 2023-12-14T19:35:12Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"token-classification",
"generated_from_trainer",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-12-14T18:09:59Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: en-multinerd-masked-ner-more-training
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. -->
# en-multinerd-masked-ner-more-training
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0371
- Precision: 0.9386
- Recall: 0.9456
- F1: 0.9421
- Accuracy: 0.9920
- Per-precision: 0.9960
- Per-recall: 0.9961
- Per-f1: 0.9961
- Org-precision: 0.9364
- Org-recall: 0.9419
- Org-f1: 0.9392
- Loc-precision: 0.9719
- Loc-recall: 0.9752
- Loc-f1: 0.9735
- Dis-precision: 0.7059
- Dis-recall: 0.7363
- Dis-f1: 0.7208
- Anim-precision: 0.7033
- Anim-recall: 0.7275
- Anim-f1: 0.7152
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | Per-precision | Per-recall | Per-f1 | Org-precision | Org-recall | Org-f1 | Loc-precision | Loc-recall | Loc-f1 | Dis-precision | Dis-recall | Dis-f1 | Anim-precision | Anim-recall | Anim-f1 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|:-------------:|:----------:|:------:|:-------------:|:----------:|:------:|:-------------:|:----------:|:------:|:-------------:|:----------:|:------:|:--------------:|:-----------:|:-------:|
| 0.0243 | 1.0 | 8205 | 0.0271 | 0.9184 | 0.9458 | 0.9319 | 0.9906 | 0.9927 | 0.9963 | 0.9945 | 0.8838 | 0.9499 | 0.9157 | 0.9586 | 0.9707 | 0.9646 | 0.6719 | 0.7274 | 0.6986 | 0.6659 | 0.7557 | 0.7080 |
| 0.0159 | 2.0 | 16410 | 0.0263 | 0.9324 | 0.9448 | 0.9386 | 0.9917 | 0.9913 | 0.9965 | 0.9939 | 0.9237 | 0.9507 | 0.9370 | 0.9613 | 0.9739 | 0.9675 | 0.7128 | 0.7233 | 0.7180 | 0.6990 | 0.7187 | 0.7087 |
| 0.0101 | 3.0 | 24615 | 0.0295 | 0.9396 | 0.9426 | 0.9411 | 0.9919 | 0.9935 | 0.9963 | 0.9949 | 0.9352 | 0.9386 | 0.9369 | 0.9704 | 0.9732 | 0.9718 | 0.7168 | 0.7286 | 0.7226 | 0.7143 | 0.7055 | 0.7098 |
| 0.0057 | 4.0 | 32820 | 0.0335 | 0.9375 | 0.9465 | 0.9420 | 0.9919 | 0.9967 | 0.9955 | 0.9961 | 0.9341 | 0.9470 | 0.9405 | 0.9694 | 0.9739 | 0.9717 | 0.7065 | 0.7499 | 0.7275 | 0.7052 | 0.7257 | 0.7153 |
| 0.004 | 5.0 | 41025 | 0.0371 | 0.9386 | 0.9456 | 0.9421 | 0.9920 | 0.9960 | 0.9961 | 0.9961 | 0.9364 | 0.9419 | 0.9392 | 0.9719 | 0.9752 | 0.9735 | 0.7059 | 0.7363 | 0.7208 | 0.7033 | 0.7275 | 0.7152 |
### Framework versions
- Transformers 4.36.1
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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owanr/SChem5Labels-roberta-base-intra-data-frequency-model_annots
|
owanr
| 2023-12-14T18:12:56Z | 0 | 0 | null |
[
"safetensors",
"generated_from_trainer",
"base_model:roberta-base",
"license:mit",
"region:us"
] | null | 2023-12-14T18:12:35Z |
---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
model-index:
- name: SChem5Labels-roberta-base-intra-data-frequency-model_annots
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. -->
# SChem5Labels-roberta-base-intra-data-frequency-model_annots
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 109.8733
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 104.9582 | 1.0 | 99 | 115.8823 |
| 96.7881 | 2.0 | 198 | 114.0660 |
| 101.1136 | 3.0 | 297 | 110.8983 |
| 88.253 | 4.0 | 396 | 109.5061 |
| 99.5406 | 5.0 | 495 | 109.8733 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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MaryamMMMMMMM/TRY
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MaryamMMMMMMM
| 2023-12-14T18:12:44Z | 0 | 0 | null |
[
"license:unknown",
"region:us"
] | null | 2023-12-14T18:12:44Z |
---
license: unknown
---
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owanr/ghc-roberta-base-inter-data-frequency-human_annots
|
owanr
| 2023-12-14T18:13:07Z | 0 | 0 | null |
[
"safetensors",
"generated_from_trainer",
"base_model:roberta-base",
"license:mit",
"region:us"
] | null | 2023-12-14T18:12:47Z |
---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
model-index:
- name: ghc-roberta-base-inter-data-frequency-human_annots
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. -->
# ghc-roberta-base-inter-data-frequency-human_annots
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4742
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 5.0257 | 1.0 | 345 | 12.6515 |
| 1.911 | 2.0 | 690 | 5.2590 |
| 0.6202 | 3.0 | 1035 | 2.6192 |
| 1.7033 | 4.0 | 1380 | 2.4742 |
| 1.4799 | 5.0 | 1725 | 2.4742 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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] |
jcparrad/unal_llms
|
jcparrad
| 2023-12-14T18:14:22Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2023-12-14T18:14:22Z |
---
license: apache-2.0
---
|
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gkallergis/pretrained
|
gkallergis
| 2023-12-14T18:27:20Z | 0 | 0 | null |
[
"license:cc-by-nc-nd-4.0",
"region:us"
] | null | 2023-12-14T18:16:40Z |
---
license: cc-by-nc-nd-4.0
---
|
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gkallergis/tokenizer
|
gkallergis
| 2023-12-14T18:18:23Z | 0 | 0 | null |
[
"license:cc-by-nc-nd-4.0",
"region:us"
] | null | 2023-12-14T18:18:09Z |
---
license: cc-by-nc-nd-4.0
---
|
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] |
Frixi/Nengo_Flow
|
Frixi
| 2023-12-14T18:24:23Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-14T18:24:07Z |
---
license: openrail
---
|
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themanas021/llama2-docsum-adapter
|
themanas021
| 2023-12-14T18:32:33Z | 0 | 1 | null |
[
"tensorboard",
"safetensors",
"generated_from_trainer",
"math",
"question-answering",
"en",
"dataset:camel-ai/math",
"base_model:NousResearch/Llama-2-7b-hf",
"license:mit",
"region:us"
] |
question-answering
| 2023-12-14T18:25:32Z |
---
base_model: NousResearch/Llama-2-7b-hf
tags:
- generated_from_trainer
- math
model-index:
- name: llama2-docsum-adapter
results: []
license: mit
datasets:
- camel-ai/math
language:
- en
metrics:
- bleurt
- bleu
- bertscore
pipeline_tag: question-answering
---
<!-- 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. -->
# llama2-docsum-adapter
This model is a fine-tuned version of [NousResearch/Llama-2-7b-hf](https://huggingface.co/NousResearch/Llama-2-7b-hf) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7568
## 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: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.0125 | 0.42 | 13 | 0.9909 |
| 0.8517 | 0.83 | 26 | 0.8135 |
| 0.7423 | 1.25 | 39 | 0.7766 |
| 0.5581 | 1.66 | 52 | 0.7568 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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] |
willhath/e5-base-v2-deu_Latn-400000-0.0001-0.4
|
willhath
| 2023-12-14T18:31:18Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"feature-extraction",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2023-12-14T18:27:13Z |
Entry not found
|
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] |
sumitj39/openhathi-7b-base-q4_0.ggml
|
sumitj39
| 2023-12-14T18:38:42Z | 0 | 0 | null |
[
"ggml",
"license:llama2",
"region:us"
] | null | 2023-12-14T18:29:19Z |
---
license: llama2
tags:
- ggml
---
This dataset contains the ggml version of OpenHathi model released by Sarvam AI. [Link to original model](https://huggingface.co/sarvamai/OpenHathi-7B-Hi-v0.1-Base).
The ggml file provided is 4 bit quantized version, it can be run on local devices such as an M1 MacBook or other hardware.
### How to use?
1. Download llama.cpp from [here](https://github.com/ggerganov/llama.cpp)
```bash
git clone https://github.com/ggerganov/llama.cpp
```
3. Note: The ggml support has been deprecated, new file format is gguf. But since this repository contains ggml file, we have to switch back to an older commit of llama.cpp that worked with ggml files.
Execute this command to switch to the commit that worked with ggml files:
```
bash git checkout dadbed9
```
3. Read the instructions mentioned [here](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#build) to create an executable file in the llama.cpp directory.
4. Run the model:
```bash
./main -t 4 -m ~/ggml-models/openhathi-7b-base-q4_0.ggml -p "tell me about india in hindi: - भारत" --ctx-size 1024 -ngl 1 2>/dev/null
```
5. The model prints output:
>भारत दुनिया के सबसे पुराने देशों में से एक है और दुनिया की 7वीं सबसे बड़ी आबादी वाला देश है। The nation has a rich and diverse history, dating back to ancient times when it was ruled by various empires and kingdoms. भारत में दो मुख्य भौगोलिक क्षेत्र शामिल हैंः एक द्वीपसमूह जिसमें कई बड़े द्वीपों के साथ-साथ छोटे द्वीप भी शामिल हैं और दूसरा समतल क्षेत्रों से घिरा हुआ है। भारत की अनूठी सांस्कृतिक विरासत, विविध धर्मों और भाषाओं को बढ़ावा देता है जो देश की समृद्ध विविधता का प्रमाण हैं। भारत में सबसे अधिक बोली जाने वाली भाषाएँ हिंदी, बंगाली, तमिल, मराठी, कन्नड़, उड़िया और मलयालम हैं। 40 प्रतिशत आबादी हिंदू है, जबकि अन्य प्रमुख धर्म इस्लाम, बौद्ध धर्म, ईसाई धर्म और सिख धर्म हैं। भारत अपनी समृद्ध कृषि अर्थव्यवस्था के लिए जाना जाता है और यह अपने विविध व्यंजनों, समृद्ध इतिहास और जीवंत सांस्कृतिक विरासत के लिए भी मनाया जाता है। The country has made remarkable strides in areas such as information technology and manufacturing, which have contributed to its global economic position. एक बड़े देश होने के बावजूद, भारत में सभी को एक स्थान से दूसरे स्थान पर जाने की आवश्यकता नहीं है। India's transport infrastructure is extensive, with a well-developed road network that connects most major cities and towns. इसके अलावा, मुंबई और दिल्ली जैसे प्रमुख शहरों में अंतर्राष्ट्रीय हवाई अड्डे हैं जो दुनिया भर के गंतव्यों के लिए उड़ान भरते हैं। India has also gained prominence as a popular tourist destination in recent years. देश अपने अनूठे अनुभवों, आश्चर्यजनक प्राकृतिक परिदृश्यों, विविध संस्कृतियों और समृद्ध इतिहास की पेशकश करता है। From world-famous sites such as the Taj Mahal to lesser- .....
> Note: This is a base model, to use it in your applications you need to fine tune it.
|
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] |
Vinceorz/codellama_py_34b_12_14_checkpoint_98
|
Vinceorz
| 2023-12-14T18:30:10Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"region:us"
] | null | 2023-12-14T18:30:06Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.4.0
|
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dzakwan/my_autotrain_llm
|
dzakwan
| 2023-12-14T18:30:26Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T18:30:26Z |
Entry not found
|
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] |
Vinceorz/codellama_py_34b_12_14_checkpoint_93
|
Vinceorz
| 2023-12-14T18:30:31Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"region:us"
] | null | 2023-12-14T18:30:27Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.4.0
|
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kanishka/smolm-autoreg-bpe-counterfactual-babylm-aann-prototypical_only-1e-3
|
kanishka
| 2023-12-15T07:41:25Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"opt",
"text-generation",
"generated_from_trainer",
"dataset:kanishka/counterfactual_babylm_prototypical_only",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-14T18:30:36Z |
---
tags:
- generated_from_trainer
datasets:
- kanishka/counterfactual_babylm_prototypical_only
metrics:
- accuracy
model-index:
- name: smolm-autoreg-bpe-counterfactual-babylm-aann-prototypical_only-1e-3
results:
- task:
name: Causal Language Modeling
type: text-generation
dataset:
name: kanishka/counterfactual_babylm_prototypical_only
type: kanishka/counterfactual_babylm_prototypical_only
metrics:
- name: Accuracy
type: accuracy
value: 0.4126066894735785
---
<!-- 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. -->
# smolm-autoreg-bpe-counterfactual-babylm-aann-prototypical_only-1e-3
This model was trained from scratch on the kanishka/counterfactual_babylm_prototypical_only dataset.
It achieves the following results on the evaluation set:
- Loss: 3.3998
- Accuracy: 0.4126
## 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: 0.001
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 32000
- num_epochs: 20.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 3.6032 | 1.0 | 18593 | 3.7810 | 0.3584 |
| 3.3845 | 2.0 | 37186 | 3.5963 | 0.3798 |
| 3.2554 | 3.0 | 55779 | 3.4491 | 0.3922 |
| 3.1823 | 4.0 | 74372 | 3.4299 | 0.3975 |
| 3.126 | 5.0 | 92965 | 3.3983 | 0.4004 |
| 3.0782 | 6.0 | 111558 | 3.4061 | 0.4035 |
| 3.0476 | 7.0 | 130151 | 3.3689 | 0.4055 |
| 3.0126 | 8.0 | 148744 | 3.3660 | 0.4087 |
| 2.9875 | 9.0 | 167337 | 3.3452 | 0.4100 |
| 2.9573 | 10.0 | 185930 | 3.3508 | 0.4099 |
| 2.9399 | 11.0 | 204523 | 3.3627 | 0.4095 |
| 2.9164 | 12.0 | 223116 | 3.3622 | 0.4103 |
| 2.8939 | 13.0 | 241709 | 3.3502 | 0.4119 |
| 2.872 | 14.0 | 260302 | 3.3635 | 0.4119 |
| 2.8506 | 15.0 | 278895 | 3.3691 | 0.4118 |
| 2.8368 | 16.0 | 297488 | 3.3674 | 0.4131 |
| 2.8123 | 17.0 | 316081 | 3.3751 | 0.4130 |
| 2.7955 | 18.0 | 334674 | 3.3827 | 0.4134 |
| 2.7767 | 19.0 | 353267 | 3.3969 | 0.4124 |
| 2.7592 | 20.0 | 371860 | 3.3998 | 0.4126 |
### Framework versions
- Transformers 4.36.0
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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-0.0646161139011383,
-0.3263164758682251,
0.060955021530389786,
0.05317677929997444,
-0.5665509700775146
] |
Vinceorz/codellama_py_34b_12_14_checkpoint_87
|
Vinceorz
| 2023-12-14T18:30:49Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"region:us"
] | null | 2023-12-14T18:30:45Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.4.0
|
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humbertonc/mistral_test1_lora
|
humbertonc
| 2023-12-14T19:22:07Z | 0 | 0 | null |
[
"tensorboard",
"safetensors",
"region:us"
] | null | 2023-12-14T18:31:09Z |
Entry not found
|
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] |
Vinceorz/codellama_py_34b_12_14_checkpoint_82
|
Vinceorz
| 2023-12-14T18:31:16Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"region:us"
] | null | 2023-12-14T18:31:12Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.4.0
|
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] |
humbertonc/mistral_test1
|
humbertonc
| 2023-12-14T19:30:15Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"8-bit",
"region:us"
] |
text-generation
| 2023-12-14T18:31:18Z |
Entry not found
|
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] |
Vinceorz/codellama_py_34b_12_14_checkpoint_76
|
Vinceorz
| 2023-12-14T18:31:33Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"region:us"
] | null | 2023-12-14T18:31:29Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.4.0
|
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0.8133335709571838,
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-0.22831028699874878,
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-0.5138282775878906,
-0.044981881976127625,
0.033184610307216644,
-0.921461284160614
] |
IndicRAGware/mistral-hindi-RAG-7b
|
IndicRAGware
| 2023-12-14T18:35:18Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-14T18:31:51Z |
---
license: apache-2.0
---
|
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] |
Vinceorz/codellama_py_34b_12_14_checkpoint_71
|
Vinceorz
| 2023-12-14T18:32:30Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"region:us"
] | null | 2023-12-14T18:32:26Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.4.0
|
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Xerxz/mistral_b_finance_finetuned_test
|
Xerxz
| 2023-12-14T18:33:06Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T18:33:06Z |
Entry not found
|
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] |
abhishek/wf85-h28o-tffz-0
|
abhishek
| 2023-12-14T18:33:51Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"vit",
"image-classification",
"autotrain",
"dataset:abhishek/autotrain-data-wf85-h28o-tffz",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-14T18:33:46Z |
---
tags:
- autotrain
- image-classification
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
example_title: Palace
datasets:
- abhishek/autotrain-data-wf85-h28o-tffz
---
# Model Trained Using AutoTrain
- Problem type: Image Classification
## Validation Metricsg
loss: nan
f1_macro: 0.06153846153846154
f1_micro: 0.18181818181818182
f1_weighted: 0.055944055944055944
precision_macro: 0.03636363636363636
precision_micro: 0.18181818181818182
precision_weighted: 0.03305785123966942
recall_macro: 0.2
recall_micro: 0.18181818181818182
recall_weighted: 0.18181818181818182
accuracy: 0.18181818181818182
|
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] |
salomaosnff/Eliana-rvc-v2
|
salomaosnff
| 2023-12-14T18:46:56Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2023-12-14T18:35:31Z |
---
license: mit
---
|
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] |
stvhuang/rcr-run-v57whqm7-61968-master-0_20231212T103333-ep06
|
stvhuang
| 2023-12-14T18:38:59Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"deberta-v2",
"feature-extraction",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2023-12-14T18:37:50Z |
Entry not found
|
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] |
TheBloke/Pandora-v1-10.7B-AWQ
|
TheBloke
| 2023-12-14T19:00:20Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"en",
"base_model:janhq/Pandora-v1-10.7B",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] |
text-generation
| 2023-12-14T18:38:02Z |
---
base_model: janhq/Pandora-v1-10.7B
inference: false
language:
- en
license: apache-2.0
model_creator: Jan
model_name: Pandora V1 10.7B
model_type: mistral
prompt_template: '<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
'
quantized_by: TheBloke
---
<!-- markdownlint-disable MD041 -->
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
<div style="display: flex; flex-direction: column; align-items: flex-start;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p>
</div>
<div style="display: flex; flex-direction: column; align-items: flex-end;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
</div>
</div>
<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# Pandora V1 10.7B - AWQ
- Model creator: [Jan](https://huggingface.co/janhq)
- Original model: [Pandora V1 10.7B](https://huggingface.co/janhq/Pandora-v1-10.7B)
<!-- description start -->
## Description
This repo contains AWQ model files for [Jan's Pandora V1 10.7B](https://huggingface.co/janhq/Pandora-v1-10.7B).
### About AWQ
AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings.
AWQ models are currently supported on Linux and Windows, with NVidia GPUs only. macOS users: please use GGUF models instead.
It is supported by:
- [Text Generation Webui](https://github.com/oobabooga/text-generation-webui) - using Loader: AutoAWQ
- [vLLM](https://github.com/vllm-project/vllm) - version 0.2.2 or later for support for all model types.
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference)
- [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later, from any code or client that supports Transformers
- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) - for use from Python code
<!-- description end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Pandora-v1-10.7B-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Pandora-v1-10.7B-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Pandora-v1-10.7B-GGUF)
* [Jan's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/janhq/Pandora-v1-10.7B)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: ChatML
```
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
<!-- prompt-template end -->
<!-- README_AWQ.md-provided-files start -->
## Provided files, and AWQ parameters
I currently release 128g GEMM models only. The addition of group_size 32 models, and GEMV kernel models, is being actively considered.
Models are released as sharded safetensors files.
| Branch | Bits | GS | AWQ Dataset | Seq Len | Size |
| ------ | ---- | -- | ----------- | ------- | ---- |
| [main](https://huggingface.co/TheBloke/Pandora-v1-10.7B-AWQ/tree/main) | 4 | 128 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 5.96 GB
<!-- README_AWQ.md-provided-files end -->
<!-- README_AWQ.md-text-generation-webui start -->
## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui)
Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui).
It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install.
1. Click the **Model tab**.
2. Under **Download custom model or LoRA**, enter `TheBloke/Pandora-v1-10.7B-AWQ`.
3. Click **Download**.
4. The model will start downloading. Once it's finished it will say "Done".
5. In the top left, click the refresh icon next to **Model**.
6. In the **Model** dropdown, choose the model you just downloaded: `Pandora-v1-10.7B-AWQ`
7. Select **Loader: AutoAWQ**.
8. Click Load, and the model will load and is now ready for use.
9. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right.
10. Once you're ready, click the **Text Generation** tab and enter a prompt to get started!
<!-- README_AWQ.md-text-generation-webui end -->
<!-- README_AWQ.md-use-from-vllm start -->
## Multi-user inference server: vLLM
Documentation on installing and using vLLM [can be found here](https://vllm.readthedocs.io/en/latest/).
- Please ensure you are using vLLM version 0.2 or later.
- When using vLLM as a server, pass the `--quantization awq` parameter.
For example:
```shell
python3 -m vllm.entrypoints.api_server --model TheBloke/Pandora-v1-10.7B-AWQ --quantization awq --dtype auto
```
- When using vLLM from Python code, again set `quantization=awq`.
For example:
```python
from vllm import LLM, SamplingParams
prompts = [
"Tell me about AI",
"Write a story about llamas",
"What is 291 - 150?",
"How much wood would a woodchuck chuck if a woodchuck could chuck wood?",
]
prompt_template=f'''<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
'''
prompts = [prompt_template.format(prompt=prompt) for prompt in prompts]
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
llm = LLM(model="TheBloke/Pandora-v1-10.7B-AWQ", quantization="awq", dtype="auto")
outputs = llm.generate(prompts, sampling_params)
# Print the outputs.
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
```
<!-- README_AWQ.md-use-from-vllm start -->
<!-- README_AWQ.md-use-from-tgi start -->
## Multi-user inference server: Hugging Face Text Generation Inference (TGI)
Use TGI version 1.1.0 or later. The official Docker container is: `ghcr.io/huggingface/text-generation-inference:1.1.0`
Example Docker parameters:
```shell
--model-id TheBloke/Pandora-v1-10.7B-AWQ --port 3000 --quantize awq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096
```
Example Python code for interfacing with TGI (requires [huggingface-hub](https://github.com/huggingface/huggingface_hub) 0.17.0 or later):
```shell
pip3 install huggingface-hub
```
```python
from huggingface_hub import InferenceClient
endpoint_url = "https://your-endpoint-url-here"
prompt = "Tell me about AI"
prompt_template=f'''<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
'''
client = InferenceClient(endpoint_url)
response = client.text_generation(prompt,
max_new_tokens=128,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
repetition_penalty=1.1)
print(f"Model output: ", response)
```
<!-- README_AWQ.md-use-from-tgi end -->
<!-- README_AWQ.md-use-from-python start -->
## Inference from Python code using Transformers
### Install the necessary packages
- Requires: [Transformers](https://huggingface.co/docs/transformers) 4.35.0 or later.
- Requires: [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) 0.1.6 or later.
```shell
pip3 install --upgrade "autoawq>=0.1.6" "transformers>=4.35.0"
```
Note that if you are using PyTorch 2.0.1, the above AutoAWQ command will automatically upgrade you to PyTorch 2.1.0.
If you are using CUDA 11.8 and wish to continue using PyTorch 2.0.1, instead run this command:
```shell
pip3 install https://github.com/casper-hansen/AutoAWQ/releases/download/v0.1.6/autoawq-0.1.6+cu118-cp310-cp310-linux_x86_64.whl
```
If you have problems installing [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) using the pre-built wheels, install it from source instead:
```shell
pip3 uninstall -y autoawq
git clone https://github.com/casper-hansen/AutoAWQ
cd AutoAWQ
pip3 install .
```
### Transformers example code (requires Transformers 4.35.0 and later)
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
model_name_or_path = "TheBloke/Pandora-v1-10.7B-AWQ"
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
model = AutoModelForCausalLM.from_pretrained(
model_name_or_path,
low_cpu_mem_usage=True,
device_map="cuda:0"
)
# Using the text streamer to stream output one token at a time
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
prompt = "Tell me about AI"
prompt_template=f'''<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
'''
# Convert prompt to tokens
tokens = tokenizer(
prompt_template,
return_tensors='pt'
).input_ids.cuda()
generation_params = {
"do_sample": True,
"temperature": 0.7,
"top_p": 0.95,
"top_k": 40,
"max_new_tokens": 512,
"repetition_penalty": 1.1
}
# Generate streamed output, visible one token at a time
generation_output = model.generate(
tokens,
streamer=streamer,
**generation_params
)
# Generation without a streamer, which will include the prompt in the output
generation_output = model.generate(
tokens,
**generation_params
)
# Get the tokens from the output, decode them, print them
token_output = generation_output[0]
text_output = tokenizer.decode(token_output)
print("model.generate output: ", text_output)
# Inference is also possible via Transformers' pipeline
from transformers import pipeline
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
**generation_params
)
pipe_output = pipe(prompt_template)[0]['generated_text']
print("pipeline output: ", pipe_output)
```
<!-- README_AWQ.md-use-from-python end -->
<!-- README_AWQ.md-compatibility start -->
## Compatibility
The files provided are tested to work with:
- [text-generation-webui](https://github.com/oobabooga/text-generation-webui) using `Loader: AutoAWQ`.
- [vLLM](https://github.com/vllm-project/vllm) version 0.2.0 and later.
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) version 1.1.0 and later.
- [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later.
- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) version 0.1.1 and later.
<!-- README_AWQ.md-compatibility end -->
<!-- footer start -->
<!-- 200823 -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute
Thanks to the [chirper.ai](https://chirper.ai) team!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Aemon Algiz.
**Patreon special mentions**: Michael Levine, 阿明, Trailburnt, Nikolai Manek, John Detwiler, Randy H, Will Dee, Sebastain Graf, NimbleBox.ai, Eugene Pentland, Emad Mostaque, Ai Maven, Jim Angel, Jeff Scroggin, Michael Davis, Manuel Alberto Morcote, Stephen Murray, Robert, Justin Joy, Luke @flexchar, Brandon Frisco, Elijah Stavena, S_X, Dan Guido, Undi ., Komninos Chatzipapas, Shadi, theTransient, Lone Striker, Raven Klaugh, jjj, Cap'n Zoog, Michel-Marie MAUDET (LINAGORA), Matthew Berman, David, Fen Risland, Omer Bin Jawed, Luke Pendergrass, Kalila, OG, Erik Bjäreholt, Rooh Singh, Joseph William Delisle, Dan Lewis, TL, John Villwock, AzureBlack, Brad, Pedro Madruga, Caitlyn Gatomon, K, jinyuan sun, Mano Prime, Alex, Jeffrey Morgan, Alicia Loh, Illia Dulskyi, Chadd, transmissions 11, fincy, Rainer Wilmers, ReadyPlayerEmma, knownsqashed, Mandus, biorpg, Deo Leter, Brandon Phillips, SuperWojo, Sean Connelly, Iucharbius, Jack West, Harry Royden McLaughlin, Nicholas, terasurfer, Vitor Caleffi, Duane Dunston, Johann-Peter Hartmann, David Ziegler, Olakabola, Ken Nordquist, Trenton Dambrowitz, Tom X Nguyen, Vadim, Ajan Kanaga, Leonard Tan, Clay Pascal, Alexandros Triantafyllidis, JM33133, Xule, vamX, ya boyyy, subjectnull, Talal Aujan, Alps Aficionado, wassieverse, Ari Malik, James Bentley, Woland, Spencer Kim, Michael Dempsey, Fred von Graf, Elle, zynix, William Richards, Stanislav Ovsiannikov, Edmond Seymore, Jonathan Leane, Martin Kemka, usrbinkat, Enrico Ros
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
# Original model card: Jan's Pandora V1 10.7B
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://github.com/janhq/jan/assets/89722390/35daac7d-b895-487c-a6ac-6663daaad78e" alt="Jan banner" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<p align="center">
<a href="https://jan.ai/">Jan</a
>
- <a href="https://discord.gg/AsJ8krTT3N">Discord</a>
</p>
<!-- header end -->
# Model Description
This model uses the `passthrough` merge method from the best 7B models on the [OpenLLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard):
1. [viethq188/LeoScorpius-7B-Chat-DPO](https://huggingface.co/viethq188/LeoScorpius-7B-Chat-DPO)
2. [GreenNode/GreenNodeLM-7B-v1olet](https://huggingface.co/GreenNode/GreenNodeLM-7B-v1olet)
The yaml config file for this model is here:
```yaml
slices:
- sources:
- model: "viethq188/LeoScorpius-7B-Chat-DPO"
layer_range: [0, 24]
- sources:
- model: "GreenNode/GreenNodeLM-7B-v1olet"
layer_range: [8, 32]
merge_method: passthrough
dtype: bfloat16
```
# Prompt template
- **ChatML**
```
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
# Run this model
You can run this model using [Jan](https://jan.ai/) on Mac, Windows, or Linux.
**Jan is an open source, ChatGPT alternative that is:**
💻 **100% offline on your machine**: Your conversations remain confidential, and visible only to you.
🗂️ **An Open File Format**: Conversations and model settings stay on your computer and can be exported or deleted at any time.
🌐 **OpenAI Compatible**: Local server on port `
1337` with OpenAI compatible endpoints
🌍 **Open Source & Free**: We build in public; check out our [Github](https://github.com/janhq)
- Please use the [Pandora-v1-10.7B-GGUF](https://huggingface.co/janhq/Pandora-v1-10.7B-GGUF) when using on Jan.
# About Jan
Jan believes in the need for an open-source AI ecosystem and is building the infra and tooling to allow open-source AIs to compete on a level playing field with proprietary ones.
Jan's long-term vision is to build a cognitive framework for future robots, who are practical, useful assistants for humans and businesses in everyday life.
# Jan Model Merger
This is a test project for merging models.
# Open LLM Leaderboard Evaluation Results
Detailed results can be found here.
| Metric | Value |
|-----------------------|---------------------------|
| Avg. | ?|
| ARC (25-shot) | ? |
| HellaSwag (10-shot) | ? |
| MMLU (5-shot) | ?|
| TruthfulQA (0-shot) | ? |
| Winogrande (5-shot) | ? |
| GSM8K (5-shot) | ? |
# Acknowlegement
- [mergekit](https://github.com/cg123/mergekit)
- [DARE](https://github.com/yule-BUAA/MergeLM/blob/main/README.md)
-
[SLERP](https://github.com/Digitous/LLM-SLERP-Merge)
- [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness)
|
[
-0.5796108245849609,
-0.9158378839492798,
0.43768733739852905,
0.20339414477348328,
-0.14588244259357452,
-0.23609352111816406,
0.06366344541311264,
-0.4743320643901825,
0.09478218108415604,
0.5034976005554199,
-0.8299790620803833,
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-0.3379412293434143,
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] |
TheBloke/Pandora-v1-10.7B-GGUF
|
TheBloke
| 2023-12-14T18:46:26Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"mistral",
"en",
"base_model:janhq/Pandora-v1-10.7B",
"license:apache-2.0",
"text-generation-inference",
"region:us"
] | null | 2023-12-14T18:38:02Z |
---
base_model: janhq/Pandora-v1-10.7B
inference: false
language:
- en
license: apache-2.0
model_creator: Jan
model_name: Pandora V1 10.7B
model_type: mistral
prompt_template: '<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
'
quantized_by: TheBloke
---
<!-- markdownlint-disable MD041 -->
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
<div style="display: flex; flex-direction: column; align-items: flex-start;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p>
</div>
<div style="display: flex; flex-direction: column; align-items: flex-end;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
</div>
</div>
<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# Pandora V1 10.7B - GGUF
- Model creator: [Jan](https://huggingface.co/janhq)
- Original model: [Pandora V1 10.7B](https://huggingface.co/janhq/Pandora-v1-10.7B)
<!-- description start -->
## Description
This repo contains GGUF format model files for [Jan's Pandora V1 10.7B](https://huggingface.co/janhq/Pandora-v1-10.7B).
<!-- description end -->
<!-- README_GGUF.md-about-gguf start -->
### About GGUF
GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.
Here is an incomplete list of clients and libraries that are known to support GGUF:
* [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option.
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
* [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.
* [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.
* [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.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
* [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.
* [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.
<!-- README_GGUF.md-about-gguf end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Pandora-v1-10.7B-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Pandora-v1-10.7B-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Pandora-v1-10.7B-GGUF)
* [Jan's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/janhq/Pandora-v1-10.7B)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: ChatML
```
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
<!-- prompt-template end -->
<!-- compatibility_gguf start -->
## Compatibility
These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221)
They are also compatible with many third party UIs and libraries - please see the list at the top of this README.
## Explanation of quantisation methods
<details>
<summary>Click to see details</summary>
The new methods available are:
* GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
* GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
* GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
* GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
* GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw
Refer to the Provided Files table below to see what files use which methods, and how.
</details>
<!-- compatibility_gguf end -->
<!-- README_GGUF.md-provided-files start -->
## Provided files
| Name | Quant method | Bits | Size | Max RAM required | Use case |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [pandora-v1-10.7b.Q2_K.gguf](https://huggingface.co/TheBloke/Pandora-v1-10.7B-GGUF/blob/main/pandora-v1-10.7b.Q2_K.gguf) | Q2_K | 2 | 4.55 GB| 7.05 GB | smallest, significant quality loss - not recommended for most purposes |
| [pandora-v1-10.7b.Q3_K_S.gguf](https://huggingface.co/TheBloke/Pandora-v1-10.7B-GGUF/blob/main/pandora-v1-10.7b.Q3_K_S.gguf) | Q3_K_S | 3 | 4.67 GB| 7.17 GB | very small, high quality loss |
| [pandora-v1-10.7b.Q3_K_M.gguf](https://huggingface.co/TheBloke/Pandora-v1-10.7B-GGUF/blob/main/pandora-v1-10.7b.Q3_K_M.gguf) | Q3_K_M | 3 | 5.19 GB| 7.69 GB | very small, high quality loss |
| [pandora-v1-10.7b.Q3_K_L.gguf](https://huggingface.co/TheBloke/Pandora-v1-10.7B-GGUF/blob/main/pandora-v1-10.7b.Q3_K_L.gguf) | Q3_K_L | 3 | 5.65 GB| 8.15 GB | small, substantial quality loss |
| [pandora-v1-10.7b.Q4_0.gguf](https://huggingface.co/TheBloke/Pandora-v1-10.7B-GGUF/blob/main/pandora-v1-10.7b.Q4_0.gguf) | Q4_0 | 4 | 6.07 GB| 8.57 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [pandora-v1-10.7b.Q4_K_S.gguf](https://huggingface.co/TheBloke/Pandora-v1-10.7B-GGUF/blob/main/pandora-v1-10.7b.Q4_K_S.gguf) | Q4_K_S | 4 | 6.10 GB| 8.60 GB | small, greater quality loss |
| [pandora-v1-10.7b.Q4_K_M.gguf](https://huggingface.co/TheBloke/Pandora-v1-10.7B-GGUF/blob/main/pandora-v1-10.7b.Q4_K_M.gguf) | Q4_K_M | 4 | 6.46 GB| 8.96 GB | medium, balanced quality - recommended |
| [pandora-v1-10.7b.Q5_0.gguf](https://huggingface.co/TheBloke/Pandora-v1-10.7B-GGUF/blob/main/pandora-v1-10.7b.Q5_0.gguf) | Q5_0 | 5 | 7.40 GB| 9.90 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [pandora-v1-10.7b.Q5_K_S.gguf](https://huggingface.co/TheBloke/Pandora-v1-10.7B-GGUF/blob/main/pandora-v1-10.7b.Q5_K_S.gguf) | Q5_K_S | 5 | 7.40 GB| 9.90 GB | large, low quality loss - recommended |
| [pandora-v1-10.7b.Q5_K_M.gguf](https://huggingface.co/TheBloke/Pandora-v1-10.7B-GGUF/blob/main/pandora-v1-10.7b.Q5_K_M.gguf) | Q5_K_M | 5 | 7.60 GB| 10.10 GB | large, very low quality loss - recommended |
| [pandora-v1-10.7b.Q6_K.gguf](https://huggingface.co/TheBloke/Pandora-v1-10.7B-GGUF/blob/main/pandora-v1-10.7b.Q6_K.gguf) | Q6_K | 6 | 8.81 GB| 11.31 GB | very large, extremely low quality loss |
| [pandora-v1-10.7b.Q8_0.gguf](https://huggingface.co/TheBloke/Pandora-v1-10.7B-GGUF/blob/main/pandora-v1-10.7b.Q8_0.gguf) | Q8_0 | 8 | 11.41 GB| 13.91 GB | very large, extremely low quality loss - not recommended |
**Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.
<!-- README_GGUF.md-provided-files end -->
<!-- README_GGUF.md-how-to-download start -->
## How to download GGUF files
**Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.
The following clients/libraries will automatically download models for you, providing a list of available models to choose from:
* LM Studio
* LoLLMS Web UI
* Faraday.dev
### In `text-generation-webui`
Under Download Model, you can enter the model repo: TheBloke/Pandora-v1-10.7B-GGUF and below it, a specific filename to download, such as: pandora-v1-10.7b.Q4_K_M.gguf.
Then click Download.
### On the command line, including multiple files at once
I recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub
```
Then you can download any individual model file to the current directory, at high speed, with a command like this:
```shell
huggingface-cli download TheBloke/Pandora-v1-10.7B-GGUF pandora-v1-10.7b.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage (click to read)</summary>
You can also download multiple files at once with a pattern:
```shell
huggingface-cli download TheBloke/Pandora-v1-10.7B-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:
```shell
pip3 install hf_transfer
```
And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
```shell
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/Pandora-v1-10.7B-GGUF pandora-v1-10.7b.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.
</details>
<!-- README_GGUF.md-how-to-download end -->
<!-- README_GGUF.md-how-to-run start -->
## Example `llama.cpp` command
Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later.
```shell
./main -ngl 35 -m pandora-v1-10.7b.Q4_K_M.gguf --color -c 32768 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<|im_start|>system\n{system_message}<|im_end|>\n<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 32768` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value.
If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`
For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md)
## How to run in `text-generation-webui`
Further instructions can be found in the text-generation-webui documentation, here: [text-generation-webui/docs/04 ‐ Model Tab.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/04%20%E2%80%90%20Model%20Tab.md#llamacpp).
## How to run from Python code
You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python.
### How to load this model in Python code, using llama-cpp-python
For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/).
#### First install the package
Run one of the following commands, according to your system:
```shell
# Base ctransformers with no GPU acceleration
pip install llama-cpp-python
# With NVidia CUDA acceleration
CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python
# Or with OpenBLAS acceleration
CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python
# Or with CLBLast acceleration
CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python
# Or with AMD ROCm GPU acceleration (Linux only)
CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python
# Or with Metal GPU acceleration for macOS systems only
CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python
# In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA:
$env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on"
pip install llama-cpp-python
```
#### Simple llama-cpp-python example code
```python
from llama_cpp import Llama
# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
llm = Llama(
model_path="./pandora-v1-10.7b.Q4_K_M.gguf", # Download the model file first
n_ctx=32768, # The max sequence length to use - note that longer sequence lengths require much more resources
n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance
n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available
)
# Simple inference example
output = llm(
"<|im_start|>system\n{system_message}<|im_end|>\n<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant", # Prompt
max_tokens=512, # Generate up to 512 tokens
stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using.
echo=True # Whether to echo the prompt
)
# Chat Completion API
llm = Llama(model_path="./pandora-v1-10.7b.Q4_K_M.gguf", chat_format="llama-2") # Set chat_format according to the model you are using
llm.create_chat_completion(
messages = [
{"role": "system", "content": "You are a story writing assistant."},
{
"role": "user",
"content": "Write a story about llamas."
}
]
)
```
## How to use with LangChain
Here are guides on using llama-cpp-python and ctransformers with LangChain:
* [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp)
* [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers)
<!-- README_GGUF.md-how-to-run end -->
<!-- footer start -->
<!-- 200823 -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute
Thanks to the [chirper.ai](https://chirper.ai) team!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Aemon Algiz.
**Patreon special mentions**: Michael Levine, 阿明, Trailburnt, Nikolai Manek, John Detwiler, Randy H, Will Dee, Sebastain Graf, NimbleBox.ai, Eugene Pentland, Emad Mostaque, Ai Maven, Jim Angel, Jeff Scroggin, Michael Davis, Manuel Alberto Morcote, Stephen Murray, Robert, Justin Joy, Luke @flexchar, Brandon Frisco, Elijah Stavena, S_X, Dan Guido, Undi ., Komninos Chatzipapas, Shadi, theTransient, Lone Striker, Raven Klaugh, jjj, Cap'n Zoog, Michel-Marie MAUDET (LINAGORA), Matthew Berman, David, Fen Risland, Omer Bin Jawed, Luke Pendergrass, Kalila, OG, Erik Bjäreholt, Rooh Singh, Joseph William Delisle, Dan Lewis, TL, John Villwock, AzureBlack, Brad, Pedro Madruga, Caitlyn Gatomon, K, jinyuan sun, Mano Prime, Alex, Jeffrey Morgan, Alicia Loh, Illia Dulskyi, Chadd, transmissions 11, fincy, Rainer Wilmers, ReadyPlayerEmma, knownsqashed, Mandus, biorpg, Deo Leter, Brandon Phillips, SuperWojo, Sean Connelly, Iucharbius, Jack West, Harry Royden McLaughlin, Nicholas, terasurfer, Vitor Caleffi, Duane Dunston, Johann-Peter Hartmann, David Ziegler, Olakabola, Ken Nordquist, Trenton Dambrowitz, Tom X Nguyen, Vadim, Ajan Kanaga, Leonard Tan, Clay Pascal, Alexandros Triantafyllidis, JM33133, Xule, vamX, ya boyyy, subjectnull, Talal Aujan, Alps Aficionado, wassieverse, Ari Malik, James Bentley, Woland, Spencer Kim, Michael Dempsey, Fred von Graf, Elle, zynix, William Richards, Stanislav Ovsiannikov, Edmond Seymore, Jonathan Leane, Martin Kemka, usrbinkat, Enrico Ros
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
<!-- original-model-card start -->
# Original model card: Jan's Pandora V1 10.7B
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://github.com/janhq/jan/assets/89722390/35daac7d-b895-487c-a6ac-6663daaad78e" alt="Jan banner" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<p align="center">
<a href="https://jan.ai/">Jan</a
>
- <a href="https://discord.gg/AsJ8krTT3N">Discord</a>
</p>
<!-- header end -->
# Model Description
This model uses the `passthrough` merge method from the best 7B models on the [OpenLLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard):
1. [viethq188/LeoScorpius-7B-Chat-DPO](https://huggingface.co/viethq188/LeoScorpius-7B-Chat-DPO)
2. [GreenNode/GreenNodeLM-7B-v1olet](https://huggingface.co/GreenNode/GreenNodeLM-7B-v1olet)
The yaml config file for this model is here:
```yaml
slices:
- sources:
- model: "viethq188/LeoScorpius-7B-Chat-DPO"
layer_range: [0, 24]
- sources:
- model: "GreenNode/GreenNodeLM-7B-v1olet"
layer_range: [8, 32]
merge_method: passthrough
dtype: bfloat16
```
# Prompt template
- **ChatML**
```
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
# Run this model
You can run this model using [Jan](https://jan.ai/) on Mac, Windows, or Linux.
**Jan is an open source, ChatGPT alternative that is:**
💻 **100% offline on your machine**: Your conversations remain confidential, and visible only to you.
🗂️ **An Open File Format**: Conversations and model settings stay on your computer and can be exported or deleted at any time.
🌐 **OpenAI Compatible**: Local server on port `
1337` with OpenAI compatible endpoints
🌍 **Open Source & Free**: We build in public; check out our [Github](https://github.com/janhq)
- Please use the [Pandora-v1-10.7B-GGUF](https://huggingface.co/janhq/Pandora-v1-10.7B-GGUF) when using on Jan.
# About Jan
Jan believes in the need for an open-source AI ecosystem and is building the infra and tooling to allow open-source AIs to compete on a level playing field with proprietary ones.
Jan's long-term vision is to build a cognitive framework for future robots, who are practical, useful assistants for humans and businesses in everyday life.
# Jan Model Merger
This is a test project for merging models.
# Open LLM Leaderboard Evaluation Results
Detailed results can be found here.
| Metric | Value |
|-----------------------|---------------------------|
| Avg. | ?|
| ARC (25-shot) | ? |
| HellaSwag (10-shot) | ? |
| MMLU (5-shot) | ?|
| TruthfulQA (0-shot) | ? |
| Winogrande (5-shot) | ? |
| GSM8K (5-shot) | ? |
# Acknowlegement
- [mergekit](https://github.com/cg123/mergekit)
- [DARE](https://github.com/yule-BUAA/MergeLM/blob/main/README.md)
-
[SLERP](https://github.com/Digitous/LLM-SLERP-Merge)
- [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness)
<!-- original-model-card end -->
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] |
abhishek/wf85-h28o-tffz1-0
|
abhishek
| 2023-12-14T18:38:31Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"vit",
"image-classification",
"autotrain",
"dataset:abhishek/autotrain-data-wf85-h28o-tffz1",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-14T18:38:26Z |
---
tags:
- autotrain
- image-classification
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
example_title: Palace
datasets:
- abhishek/autotrain-data-wf85-h28o-tffz1
---
# Model Trained Using AutoTrain
- Problem type: Image Classification
## Validation Metricsg
loss: nan
f1_macro: 0.06153846153846154
f1_micro: 0.18181818181818182
f1_weighted: 0.055944055944055944
precision_macro: 0.03636363636363636
precision_micro: 0.18181818181818182
precision_weighted: 0.03305785123966942
recall_macro: 0.2
recall_micro: 0.18181818181818182
recall_weighted: 0.18181818181818182
accuracy: 0.18181818181818182
|
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] |
abhishek/wf85-h28o-tffz2-0
|
abhishek
| 2023-12-14T18:40:18Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"vit",
"image-classification",
"autotrain",
"dataset:abhishek/autotrain-data-wf85-h28o-tffz2",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-14T18:40:12Z |
---
tags:
- autotrain
- image-classification
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
example_title: Palace
datasets:
- abhishek/autotrain-data-wf85-h28o-tffz2
---
# Model Trained Using AutoTrain
- Problem type: Image Classification
## Validation Metricsg
loss: nan
f1_macro: 0.06153846153846154
f1_micro: 0.18181818181818182
f1_weighted: 0.055944055944055944
precision_macro: 0.03636363636363636
precision_micro: 0.18181818181818182
precision_weighted: 0.03305785123966942
recall_macro: 0.2
recall_micro: 0.18181818181818182
recall_weighted: 0.18181818181818182
accuracy: 0.18181818181818182
|
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] |
arnavgrg/phi-1_5-test-adapter
|
arnavgrg
| 2023-12-14T18:40:14Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:susnato/phi-1_5_dev",
"region:us"
] | null | 2023-12-14T18:40:13Z |
---
library_name: peft
base_model: susnato/phi-1_5_dev
---
# Model Card for Model ID
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### Framework versions
- PEFT 0.7.1
|
[
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0.0016281773569062352,
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0.3766721785068512,
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0.3593457341194153,
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1.099904179573059,
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0.02380078285932541,
-0.028382565826177597,
-0.44001150131225586
] |
TheBloke/ShiningValiantXS-1.1-GGUF
|
TheBloke
| 2023-12-14T18:47:59Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"llama",
"shining-valiant",
"valiant",
"valiant-labs",
"llama-2",
"llama-2-chat",
"13b",
"text-generation",
"en",
"base_model:ValiantLabs/ShiningValiantXS",
"license:llama2",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-14T18:40:52Z |
---
base_model: ValiantLabs/ShiningValiantXS
inference: false
language:
- en
license: llama2
model_creator: Valiant Labs
model_name: ShiningValiantXS 1.1 13B
model_type: llama
pipeline_tag: text-generation
prompt_template: '[INST] <<SYS>>
{system_message}
<</SYS>>
{prompt} [/INST]
'
quantized_by: TheBloke
tags:
- shining-valiant
- valiant
- valiant-labs
- llama
- llama-2
- llama-2-chat
- 13b
---
<!-- markdownlint-disable MD041 -->
<!-- header start -->
<!-- 200823 -->
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<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# ShiningValiantXS 1.1 13B - GGUF
- Model creator: [Valiant Labs](https://huggingface.co/ValiantLabs)
- Original model: [ShiningValiantXS 1.1 13B](https://huggingface.co/ValiantLabs/ShiningValiantXS)
<!-- description start -->
## Description
This repo contains GGUF format model files for [Valiant Labs's ShiningValiantXS 1.1 13B](https://huggingface.co/ValiantLabs/ShiningValiantXS).
These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).
<!-- description end -->
<!-- README_GGUF.md-about-gguf start -->
### About GGUF
GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.
Here is an incomplete list of clients and libraries that are known to support GGUF:
* [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option.
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
* [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.
* [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.
* [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.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
* [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.
* [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.
<!-- README_GGUF.md-about-gguf end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/ShiningValiantXS-1.1-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/ShiningValiantXS-1.1-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/ShiningValiantXS-1.1-GGUF)
* [Valiant Labs's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/ValiantLabs/ShiningValiantXS)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: Llama-2-Chat
```
[INST] <<SYS>>
{system_message}
<</SYS>>
{prompt} [/INST]
```
<!-- prompt-template end -->
<!-- compatibility_gguf start -->
## Compatibility
These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221)
They are also compatible with many third party UIs and libraries - please see the list at the top of this README.
## Explanation of quantisation methods
<details>
<summary>Click to see details</summary>
The new methods available are:
* GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
* GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
* GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
* GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
* GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw
Refer to the Provided Files table below to see what files use which methods, and how.
</details>
<!-- compatibility_gguf end -->
<!-- README_GGUF.md-provided-files start -->
## Provided files
| Name | Quant method | Bits | Size | Max RAM required | Use case |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [shiningvaliantxs.Q2_K.gguf](https://huggingface.co/TheBloke/ShiningValiantXS-1.1-GGUF/blob/main/shiningvaliantxs.Q2_K.gguf) | Q2_K | 2 | 5.43 GB| 7.93 GB | smallest, significant quality loss - not recommended for most purposes |
| [shiningvaliantxs.Q3_K_S.gguf](https://huggingface.co/TheBloke/ShiningValiantXS-1.1-GGUF/blob/main/shiningvaliantxs.Q3_K_S.gguf) | Q3_K_S | 3 | 5.66 GB| 8.16 GB | very small, high quality loss |
| [shiningvaliantxs.Q3_K_M.gguf](https://huggingface.co/TheBloke/ShiningValiantXS-1.1-GGUF/blob/main/shiningvaliantxs.Q3_K_M.gguf) | Q3_K_M | 3 | 6.34 GB| 8.84 GB | very small, high quality loss |
| [shiningvaliantxs.Q3_K_L.gguf](https://huggingface.co/TheBloke/ShiningValiantXS-1.1-GGUF/blob/main/shiningvaliantxs.Q3_K_L.gguf) | Q3_K_L | 3 | 6.93 GB| 9.43 GB | small, substantial quality loss |
| [shiningvaliantxs.Q4_0.gguf](https://huggingface.co/TheBloke/ShiningValiantXS-1.1-GGUF/blob/main/shiningvaliantxs.Q4_0.gguf) | Q4_0 | 4 | 7.37 GB| 9.87 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [shiningvaliantxs.Q4_K_S.gguf](https://huggingface.co/TheBloke/ShiningValiantXS-1.1-GGUF/blob/main/shiningvaliantxs.Q4_K_S.gguf) | Q4_K_S | 4 | 7.42 GB| 9.92 GB | small, greater quality loss |
| [shiningvaliantxs.Q4_K_M.gguf](https://huggingface.co/TheBloke/ShiningValiantXS-1.1-GGUF/blob/main/shiningvaliantxs.Q4_K_M.gguf) | Q4_K_M | 4 | 7.87 GB| 10.37 GB | medium, balanced quality - recommended |
| [shiningvaliantxs.Q5_0.gguf](https://huggingface.co/TheBloke/ShiningValiantXS-1.1-GGUF/blob/main/shiningvaliantxs.Q5_0.gguf) | Q5_0 | 5 | 8.97 GB| 11.47 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [shiningvaliantxs.Q5_K_S.gguf](https://huggingface.co/TheBloke/ShiningValiantXS-1.1-GGUF/blob/main/shiningvaliantxs.Q5_K_S.gguf) | Q5_K_S | 5 | 8.97 GB| 11.47 GB | large, low quality loss - recommended |
| [shiningvaliantxs.Q5_K_M.gguf](https://huggingface.co/TheBloke/ShiningValiantXS-1.1-GGUF/blob/main/shiningvaliantxs.Q5_K_M.gguf) | Q5_K_M | 5 | 9.23 GB| 11.73 GB | large, very low quality loss - recommended |
| [shiningvaliantxs.Q6_K.gguf](https://huggingface.co/TheBloke/ShiningValiantXS-1.1-GGUF/blob/main/shiningvaliantxs.Q6_K.gguf) | Q6_K | 6 | 10.68 GB| 13.18 GB | very large, extremely low quality loss |
| [shiningvaliantxs.Q8_0.gguf](https://huggingface.co/TheBloke/ShiningValiantXS-1.1-GGUF/blob/main/shiningvaliantxs.Q8_0.gguf) | Q8_0 | 8 | 13.83 GB| 16.33 GB | very large, extremely low quality loss - not recommended |
**Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.
<!-- README_GGUF.md-provided-files end -->
<!-- README_GGUF.md-how-to-download start -->
## How to download GGUF files
**Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.
The following clients/libraries will automatically download models for you, providing a list of available models to choose from:
* LM Studio
* LoLLMS Web UI
* Faraday.dev
### In `text-generation-webui`
Under Download Model, you can enter the model repo: TheBloke/ShiningValiantXS-1.1-GGUF and below it, a specific filename to download, such as: shiningvaliantxs.Q4_K_M.gguf.
Then click Download.
### On the command line, including multiple files at once
I recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub
```
Then you can download any individual model file to the current directory, at high speed, with a command like this:
```shell
huggingface-cli download TheBloke/ShiningValiantXS-1.1-GGUF shiningvaliantxs.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage (click to read)</summary>
You can also download multiple files at once with a pattern:
```shell
huggingface-cli download TheBloke/ShiningValiantXS-1.1-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:
```shell
pip3 install hf_transfer
```
And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
```shell
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/ShiningValiantXS-1.1-GGUF shiningvaliantxs.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.
</details>
<!-- README_GGUF.md-how-to-download end -->
<!-- README_GGUF.md-how-to-run start -->
## Example `llama.cpp` command
Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later.
```shell
./main -ngl 35 -m shiningvaliantxs.Q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "[INST] <<SYS>>\n{system_message}\n<</SYS>>\n{prompt} [/INST]"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 4096` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value.
If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`
For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md)
## How to run in `text-generation-webui`
Further instructions can be found in the text-generation-webui documentation, here: [text-generation-webui/docs/04 ‐ Model Tab.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/04%20%E2%80%90%20Model%20Tab.md#llamacpp).
## How to run from Python code
You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python.
### How to load this model in Python code, using llama-cpp-python
For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/).
#### First install the package
Run one of the following commands, according to your system:
```shell
# Base ctransformers with no GPU acceleration
pip install llama-cpp-python
# With NVidia CUDA acceleration
CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python
# Or with OpenBLAS acceleration
CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python
# Or with CLBLast acceleration
CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python
# Or with AMD ROCm GPU acceleration (Linux only)
CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python
# Or with Metal GPU acceleration for macOS systems only
CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python
# In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA:
$env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on"
pip install llama-cpp-python
```
#### Simple llama-cpp-python example code
```python
from llama_cpp import Llama
# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
llm = Llama(
model_path="./shiningvaliantxs.Q4_K_M.gguf", # Download the model file first
n_ctx=4096, # The max sequence length to use - note that longer sequence lengths require much more resources
n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance
n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available
)
# Simple inference example
output = llm(
"[INST] <<SYS>>\n{system_message}\n<</SYS>>\n{prompt} [/INST]", # Prompt
max_tokens=512, # Generate up to 512 tokens
stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using.
echo=True # Whether to echo the prompt
)
# Chat Completion API
llm = Llama(model_path="./shiningvaliantxs.Q4_K_M.gguf", chat_format="llama-2") # Set chat_format according to the model you are using
llm.create_chat_completion(
messages = [
{"role": "system", "content": "You are a story writing assistant."},
{
"role": "user",
"content": "Write a story about llamas."
}
]
)
```
## How to use with LangChain
Here are guides on using llama-cpp-python and ctransformers with LangChain:
* [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp)
* [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers)
<!-- README_GGUF.md-how-to-run end -->
<!-- footer start -->
<!-- 200823 -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute
Thanks to the [chirper.ai](https://chirper.ai) team!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Aemon Algiz.
**Patreon special mentions**: Michael Levine, 阿明, Trailburnt, Nikolai Manek, John Detwiler, Randy H, Will Dee, Sebastain Graf, NimbleBox.ai, Eugene Pentland, Emad Mostaque, Ai Maven, Jim Angel, Jeff Scroggin, Michael Davis, Manuel Alberto Morcote, Stephen Murray, Robert, Justin Joy, Luke @flexchar, Brandon Frisco, Elijah Stavena, S_X, Dan Guido, Undi ., Komninos Chatzipapas, Shadi, theTransient, Lone Striker, Raven Klaugh, jjj, Cap'n Zoog, Michel-Marie MAUDET (LINAGORA), Matthew Berman, David, Fen Risland, Omer Bin Jawed, Luke Pendergrass, Kalila, OG, Erik Bjäreholt, Rooh Singh, Joseph William Delisle, Dan Lewis, TL, John Villwock, AzureBlack, Brad, Pedro Madruga, Caitlyn Gatomon, K, jinyuan sun, Mano Prime, Alex, Jeffrey Morgan, Alicia Loh, Illia Dulskyi, Chadd, transmissions 11, fincy, Rainer Wilmers, ReadyPlayerEmma, knownsqashed, Mandus, biorpg, Deo Leter, Brandon Phillips, SuperWojo, Sean Connelly, Iucharbius, Jack West, Harry Royden McLaughlin, Nicholas, terasurfer, Vitor Caleffi, Duane Dunston, Johann-Peter Hartmann, David Ziegler, Olakabola, Ken Nordquist, Trenton Dambrowitz, Tom X Nguyen, Vadim, Ajan Kanaga, Leonard Tan, Clay Pascal, Alexandros Triantafyllidis, JM33133, Xule, vamX, ya boyyy, subjectnull, Talal Aujan, Alps Aficionado, wassieverse, Ari Malik, James Bentley, Woland, Spencer Kim, Michael Dempsey, Fred von Graf, Elle, zynix, William Richards, Stanislav Ovsiannikov, Edmond Seymore, Jonathan Leane, Martin Kemka, usrbinkat, Enrico Ros
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
<!-- original-model-card start -->
# Original model card: Valiant Labs's ShiningValiantXS 1.1 13B
Shining Valiant XS is a chat model built on the Llama 2 architecture, finetuned on our data for insight, creativity, passion, and friendliness.
- Uses the llama-2-13b-chat model, with safetensors
- Trained through multiple finetuning runs on public and private data
- the personality of our 70b [Shining Valiant](https://huggingface.co/ValiantLabs/ShiningValiant) model, now at 13b!
## Version
This is Version **1.1** of Shining Valiant XS.
Congrats to the team on the new release!
New models are released for everyone once our team's training and validation process is complete.
## Evaluation
| Model | Avg | ARC | HS | MMLU | TQA | WG | GSM |
|-----------------------|--------|-------|-------|--------|-------|-------|-------|
| **Shining Valiant XS 1.1** | 62.48 | 64.42 | 83.58 | 60.37 | 55.00 | 76.80 | 34.72 |
## Prompting Guide
Shining Valiant XS uses the same prompt format as Llama 2 Chat - feel free to use your existing prompts and scripts!
A few examples of different formats:
1. [INST] Good morning! Can you let me know how to parse a text file and turn the semicolons into commas? [/INST]
2. [INST] (You are an intelligent, helpful AI assistant.) Hello, can you write me a thank you letter? [/INST]
3. [INST] << SYS >> You are an intelligent, helpful AI assistant. << /SYS >> Deep dive about a country with interesting history: [/INST]
## The Model
Shining Valiant XS is built on top of Dynamic Factor, which uses Llama 2's 13b parameter architecture and features upgraded general capability.
From there, we've created Shining Valiant XS through multiple finetuning runs on different compositions of our private dataset, the same one we use for our [Shining Valiant](https://huggingface.co/ValiantLabs/ShiningValiant) model.
Our private data focuses primarily on applying Shining Valiant's personality: she's friendly, enthusiastic, insightful, knowledgeable, and loves to learn!
We are actively working on expanding and improving the Shining Valiant dataset for use in future releases of the Shining Valiant series of models.
Shining Valiant XS is created by [Valiant Labs.](http://valiantlabs.ca/)
[Follow us on X for updates on our models!](https://twitter.com/valiant_labs)
We care about open source.
For everyone to use.
We encourage others to finetune further from our models.
<!-- original-model-card end -->
|
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G-ML-Hyly/stg-cli13b-t6.mt.him.cln.inter-b4s1e1-20231212-0801-merged
|
G-ML-Hyly
| 2023-12-14T18:42:02Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T18:42:02Z |
Entry not found
|
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] |
alpcaferoglu/distilbert-lstm-stance-detection
|
alpcaferoglu
| 2023-12-14T18:42:16Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2023-12-14T18:42:16Z |
---
license: apache-2.0
---
|
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Kana31/biel
|
Kana31
| 2023-12-14T18:46:12Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T18:45:41Z |
Entry not found
|
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luismorelli/testing
|
luismorelli
| 2023-12-14T18:51:07Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T18:47:06Z |
Entry not found
|
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] |
abhishek/wf85-h28o-tffz3-0
|
abhishek
| 2023-12-14T18:50:25Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"vit",
"image-classification",
"autotrain",
"dataset:abhishek/autotrain-data-wf85-h28o-tffz3",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-14T18:50:17Z |
---
tags:
- autotrain
- image-classification
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
example_title: Palace
datasets:
- abhishek/autotrain-data-wf85-h28o-tffz3
---
# Model Trained Using AutoTrain
- Problem type: Image Classification
## Validation Metricsg
loss: nan
f1_macro: 0.06153846153846154
f1_micro: 0.18181818181818182
f1_weighted: 0.055944055944055944
precision_macro: 0.03636363636363636
precision_micro: 0.18181818181818182
precision_weighted: 0.03305785123966942
recall_macro: 0.2
recall_micro: 0.18181818181818182
recall_weighted: 0.18181818181818182
accuracy: 0.18181818181818182
|
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] |
jpablomch/llama7b-nas-min-unmerged-80
|
jpablomch
| 2023-12-15T00:44:15Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-14T18:51:25Z |
---
license: mit
---
|
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] |
seyabde/29wf-jx7v-jye2-0
|
seyabde
| 2023-12-14T19:21:48Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"mistral",
"text-generation",
"autotrain",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-14T18:51:48Z |
---
tags:
- autotrain
- text-generation
widget:
- text: "I love AutoTrain because "
license: other
---
# Model Trained Using AutoTrain
This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain).
# Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = "PATH_TO_THIS_REPO"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
model_path,
device_map="auto",
torch_dtype='auto'
).eval()
# Prompt content: "hi"
messages = [
{"role": "user", "content": "hi"}
]
input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt')
output_ids = model.generate(input_ids.to('cuda'))
response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)
# Model response: "Hello! How can I assist you today?"
print(response)
```
|
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bourdoiscatie/setfit_review_ko
|
bourdoiscatie
| 2023-12-14T18:52:50Z | 0 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"flaubert",
"setfit",
"text-classification",
"arxiv:2209.11055",
"license:apache-2.0",
"region:us"
] |
text-classification
| 2023-12-14T18:52:27Z |
---
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
pipeline_tag: text-classification
---
# bourdoiscatie/setfit_review_ko
This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("bourdoiscatie/setfit_review_ko")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
```
## BibTeX entry and citation info
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
|
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multimodalart/aoa90a00a-v0
|
multimodalart
| 2023-12-14T18:53:00Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T18:53:00Z |
Entry not found
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multimodalart/aoa90a00a
|
multimodalart
| 2023-12-14T18:53:07Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T18:53:07Z |
Entry not found
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] |
Zong-Yan/esm2_t12_35M_UR50D-finetuned-localization
|
Zong-Yan
| 2023-12-14T22:01:55Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"esm",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-14T18:53:26Z |
Entry not found
|
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] |
marlex7/straightforwardKD-Medium-from-Base
|
marlex7
| 2023-12-14T19:15:21Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-14T18:54:16Z |
Entry not found
|
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] |
gianx/pruebas
|
gianx
| 2023-12-14T18:59:10Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2023-12-14T18:59:10Z |
---
license: apache-2.0
---
|
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] |
abhishek/wf85-h28o-tffz5-0
|
abhishek
| 2023-12-14T18:59:29Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"vit",
"image-classification",
"autotrain",
"dataset:abhishek/autotrain-data-wf85-h28o-tffz5",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-14T18:59:19Z |
---
tags:
- autotrain
- image-classification
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
example_title: Palace
datasets:
- abhishek/autotrain-data-wf85-h28o-tffz5
---
# Model Trained Using AutoTrain
- Problem type: Image Classification
## Validation Metricsg
loss: nan
f1_macro: 0.06153846153846154
f1_micro: 0.18181818181818182
f1_weighted: 0.055944055944055944
precision_macro: 0.03636363636363636
precision_micro: 0.18181818181818182
precision_weighted: 0.03305785123966942
recall_macro: 0.2
recall_micro: 0.18181818181818182
recall_weighted: 0.18181818181818182
accuracy: 0.18181818181818182
|
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] |
abhishek/wf85-h28o-tffz6-0
|
abhishek
| 2023-12-14T18:59:38Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"vit",
"image-classification",
"autotrain",
"dataset:abhishek/autotrain-data-wf85-h28o-tffz6",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-14T18:59:31Z |
---
tags:
- autotrain
- image-classification
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
example_title: Palace
datasets:
- abhishek/autotrain-data-wf85-h28o-tffz6
---
# Model Trained Using AutoTrain
- Problem type: Image Classification
## Validation Metricsg
loss: nan
f1_macro: 0.06153846153846154
f1_micro: 0.18181818181818182
f1_weighted: 0.055944055944055944
precision_macro: 0.03636363636363636
precision_micro: 0.18181818181818182
precision_weighted: 0.03305785123966942
recall_macro: 0.2
recall_micro: 0.18181818181818182
recall_weighted: 0.18181818181818182
accuracy: 0.18181818181818182
|
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kwwww/bert-base-uncased-test_8_1039
|
kwwww
| 2023-12-14T22:38:19Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:bert-base-uncased",
"region:us"
] | null | 2023-12-14T18:59:49Z |
---
library_name: peft
base_model: bert-base-uncased
---
# Model Card for Model ID
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### Framework versions
- PEFT 0.7.1.dev0
|
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] |
abhishek/wf85-h28o-tffz4-0
|
abhishek
| 2023-12-14T19:00:04Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"vit",
"image-classification",
"autotrain",
"dataset:abhishek/autotrain-data-wf85-h28o-tffz4",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-14T18:59:54Z |
---
tags:
- autotrain
- image-classification
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
example_title: Palace
datasets:
- abhishek/autotrain-data-wf85-h28o-tffz4
---
# Model Trained Using AutoTrain
- Problem type: Image Classification
## Validation Metricsg
loss: nan
f1_macro: 0.06153846153846154
f1_micro: 0.18181818181818182
f1_weighted: 0.055944055944055944
precision_macro: 0.03636363636363636
precision_micro: 0.18181818181818182
precision_weighted: 0.03305785123966942
recall_macro: 0.2
recall_micro: 0.18181818181818182
recall_weighted: 0.18181818181818182
accuracy: 0.18181818181818182
|
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Vvifsdf/123
|
Vvifsdf
| 2023-12-14T19:00:50Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T19:00:50Z |
Entry not found
|
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yannatorres/wav2vec2-xls-r-1b-portuguese-casa-civil
|
yannatorres
| 2023-12-14T19:09:25Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T19:01:12Z |
Entry not found
|
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yannatorres/result
|
yannatorres
| 2023-12-14T19:01:47Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T19:01:47Z |
Entry not found
|
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] |
Batheri/Dulquer_Salman
|
Batheri
| 2023-12-14T19:02:30Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2023-12-14T19:02:30Z |
---
license: apache-2.0
---
|
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] |
sho3er/mistral-finetuned-alpaca
|
sho3er
| 2023-12-14T19:25:27Z | 0 | 0 | null |
[
"tensorboard",
"safetensors",
"region:us"
] | null | 2023-12-14T19:02:38Z |
Entry not found
|
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] |
frankmurray/CaptainSalarzar
|
frankmurray
| 2023-12-14T19:05:44Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-14T19:04:13Z |
---
license: openrail
---
|
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teilomillet/TinyMerlin-1.1B-v0.1
|
teilomillet
| 2023-12-14T19:21:18Z | 0 | 0 | null |
[
"safetensors",
"region:us"
] | null | 2023-12-14T19:08:57Z |
Entry not found
|
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ADISH007/Aws_donut_10k_incremental_1_Epoch_21
|
ADISH007
| 2023-12-14T19:10:33Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"vision-encoder-decoder",
"endpoints_compatible",
"region:us"
] | null | 2023-12-14T19:10:12Z |
Entry not found
|
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jan-hq/mistral-7b-instruct-v0.2-dare-GGUF
|
jan-hq
| 2023-12-15T07:00:12Z | 0 | 0 | null |
[
"gguf",
"en",
"base_model:janhq/Mistral-7B-Instruct-v0.2-DARE",
"license:apache-2.0",
"region:us"
] | null | 2023-12-14T19:10:15Z |
---
license: apache-2.0
language:
- en
base_model: janhq/Mistral-7B-Instruct-v0.2-DARE
model_creator: janhq
model_name: Mistral-7B-Instruct-v0.2-DARE
quantized_by: JanHQ
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://github.com/janhq/jan/assets/89722390/35daac7d-b895-487c-a6ac-6663daaad78e" alt="Jan banner" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<p align="center">
<a href="https://jan.ai/">Jan</a>
- <a href="https://discord.gg/AsJ8krTT3N">Discord</a>
</p>
<!-- header end -->
# Model Description
This is a GGUF version of [janhq/Mistral-7B-Instruct-v0.2-DARE](https://huggingface.co/janhq/Mistral-7B-Instruct-v0.2-DARE)
- Model creator: [janhq](https://huggingface.co/janhq)
- Original model: [Mistral-7B-Instruct-v0.2-DARE](https://huggingface.co/janhq/Mistral-7B-Instruct-v0.2-DARE)
- Model description: [Readme](https://huggingface.co/janhq/Mistral-7B-Instruct-v0.2-DARE/blob/main/README.md)
# Run this model
You can run this model using [Jan Desktop](https://jan.ai/) on Mac, Windows, or Linux.
Jan is an open source, ChatGPT alternative that is:
- 💻 **100% offline on your machine**: Your conversations remain confidential, and visible only to you.
- 🗂️ **An Open File Format**: Conversations and model settings stay on your computer and can be exported or deleted at any time.
- 🌐 **OpenAI Compatible**: Local server on port `1337` with OpenAI compatible endpoints
- 🌍 **Open Source & Free**: We build in public; check out our [Github](https://github.com/janhq)
# About Jan
Jan believes in the need for an open-source AI ecosystem and is building the infra and tooling to allow open-source AIs to compete on a level playing field with proprietary ones.
Jan's long-term vision is to build a cognitive framework for future robots, who are practical, useful assistants for humans and businesses in everyday life.
# Jan Model Converter
This is a repository for the [open-source converter](https://github.com/janhq/model-converter). We would be grateful if the community could contribute and strengthen this repository. We are aiming to expand the repo that can convert into various types of format
|
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Ciros/Academy
|
Ciros
| 2023-12-14T20:30:47Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T19:10:19Z |
Entry not found
|
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s-gladkykh/sky-diffusion-256-bs4
|
s-gladkykh
| 2023-12-14T21:58:28Z | 0 | 0 | null |
[
"tensorboard",
"region:us"
] | null | 2023-12-14T19:16:20Z |
Entry not found
|
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] |
Tsuinzues/allmight
|
Tsuinzues
| 2023-12-14T19:18:36Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-14T19:18:19Z |
---
license: openrail
---
|
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avi22nash/aisha
|
avi22nash
| 2023-12-14T19:20:21Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T19:20:21Z |
Entry not found
|
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] |
Lucy1616/Prueba_1
|
Lucy1616
| 2023-12-14T19:20:33Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2023-12-14T19:20:33Z |
---
license: apache-2.0
---
|
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henriquehenry/Chaves
|
henriquehenry
| 2023-12-14T21:56:50Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T19:22:23Z |
Entry not found
|
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] |
Lucy1616/asdfg
|
Lucy1616
| 2023-12-14T19:23:24Z | 0 | 0 | null |
[
"license:wtfpl",
"region:us"
] | null | 2023-12-14T19:23:24Z |
---
license: wtfpl
---
|
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] |
marlex7/straightforwardKD-Small-from-Base
|
marlex7
| 2023-12-14T19:45:49Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-14T19:23:37Z |
Entry not found
|
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Dirus007/CheckTrafficImageSegmentation
|
Dirus007
| 2023-12-14T19:32:56Z | 0 | 0 | null |
[
"license:unknown",
"region:us"
] | null | 2023-12-14T19:25:06Z |
---
license: unknown
---
|
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] |
EthanRhys/Bowser-Jr
|
EthanRhys
| 2023-12-14T19:29:50Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-14T19:28:34Z |
---
license: openrail
---
|
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] |
codys12/MAmmoTH-phi-2
|
codys12
| 2023-12-14T20:34:35Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mixformer-sequential",
"text-generation",
"custom_code",
"autotrain_compatible",
"region:us"
] |
text-generation
| 2023-12-14T19:30:48Z |
Entry not found
|
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imspidey/LoAbstractDreamwave
|
imspidey
| 2023-12-14T19:31:20Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T19:30:48Z |
Entry not found
|
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shreydan/vit-base-oxford-iiit-pets
|
shreydan
| 2023-12-14T19:49:55Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:pcuenq/oxford-pets",
"base_model:google/vit-base-patch16-224",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-14T19:33:54Z |
---
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- image-classification
- generated_from_trainer
datasets:
- pcuenq/oxford-pets
metrics:
- accuracy
model-index:
- name: vit-base-oxford-iiit-pets
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-base-oxford-iiit-pets
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the pcuenq/oxford-pets dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2048
- Accuracy: 0.9432
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.7025 | 1.0 | 185 | 0.3849 | 0.9242 |
| 0.2944 | 2.0 | 370 | 0.2704 | 0.9337 |
| 0.2129 | 3.0 | 555 | 0.2417 | 0.9378 |
| 0.1761 | 4.0 | 740 | 0.2305 | 0.9350 |
| 0.157 | 5.0 | 925 | 0.2281 | 0.9378 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.14.1
|
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ZachSun/Sophon
|
ZachSun
| 2023-12-14T19:34:32Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T19:34:32Z |
Entry not found
|
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abhishek/bdju-zlei-d8ng1-0
|
abhishek
| 2023-12-14T19:38:39Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"vit",
"image-classification",
"autotrain",
"dataset:abhishek/autotrain-data-bdju-zlei-d8ng1",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-14T19:38:32Z |
---
tags:
- autotrain
- image-classification
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
example_title: Palace
datasets:
- abhishek/autotrain-data-bdju-zlei-d8ng1
---
# Model Trained Using AutoTrain
- Problem type: Image Classification
## Validation Metricsg
loss: nan
f1_macro: 0.06153846153846154
f1_micro: 0.18181818181818182
f1_weighted: 0.055944055944055944
precision_macro: 0.03636363636363636
precision_micro: 0.18181818181818182
precision_weighted: 0.03305785123966942
recall_macro: 0.2
recall_micro: 0.18181818181818182
recall_weighted: 0.18181818181818182
accuracy: 0.18181818181818182
|
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] |
abhishek/bdju-zlei-d8ng2-0
|
abhishek
| 2023-12-14T19:39:11Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"vit",
"image-classification",
"autotrain",
"dataset:abhishek/autotrain-data-bdju-zlei-d8ng2",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-14T19:39:06Z |
---
tags:
- autotrain
- image-classification
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
example_title: Palace
datasets:
- abhishek/autotrain-data-bdju-zlei-d8ng2
---
# Model Trained Using AutoTrain
- Problem type: Image Classification
## Validation Metricsg
loss: nan
f1_macro: 0.06153846153846154
f1_micro: 0.18181818181818182
f1_weighted: 0.055944055944055944
precision_macro: 0.03636363636363636
precision_micro: 0.18181818181818182
precision_weighted: 0.03305785123966942
recall_macro: 0.2
recall_micro: 0.18181818181818182
recall_weighted: 0.18181818181818182
accuracy: 0.18181818181818182
|
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Aishidas/PersonalChatbotwithLlama
|
Aishidas
| 2023-12-14T19:49:47Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2023-12-14T19:39:38Z |
---
license: mit
---
## Personal Chatbot with Llama-2-7B
### Description
This chatbot uses the powerful Llama-2-7B language model to provide assistance and answer questions in markdown format.
### Usage
1. Input your question in the text box.
2. Receive helpful responses generated by the Llama-2-7B model.
### Tags
- Chatbot
- Llama-2-7B
- AI
- Language Model
|
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TheBloke/ShiningValiant-1.3-GPTQ
|
TheBloke
| 2023-12-14T19:41:42Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T19:41:42Z |
Entry not found
|
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TheBloke/ShiningValiant-1.3-AWQ
|
TheBloke
| 2023-12-14T21:47:06Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"shining-valiant",
"valiant",
"valiant-labs",
"llama-2",
"llama-2-chat",
"70b",
"en",
"base_model:ValiantLabs/ShiningValiant",
"license:llama2",
"autotrain_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] |
text-generation
| 2023-12-14T19:41:42Z |
---
base_model: ValiantLabs/ShiningValiant
inference: false
language:
- en
license: llama2
model_creator: Valiant Labs
model_name: ShiningValiant 1.3
model_type: llama
pipeline_tag: text-generation
prompt_template: '[INST] <<SYS>>
{system_message}
<</SYS>>
{prompt} [/INST]
'
quantized_by: TheBloke
tags:
- shining-valiant
- valiant
- valiant-labs
- llama
- llama-2
- llama-2-chat
- 70b
---
<!-- markdownlint-disable MD041 -->
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
<div style="display: flex; flex-direction: column; align-items: flex-start;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p>
</div>
<div style="display: flex; flex-direction: column; align-items: flex-end;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
</div>
</div>
<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# ShiningValiant 1.3 - AWQ
- Model creator: [Valiant Labs](https://huggingface.co/ValiantLabs)
- Original model: [ShiningValiant 1.3](https://huggingface.co/ValiantLabs/ShiningValiant)
<!-- description start -->
## Description
This repo contains AWQ model files for [Valiant Labs's ShiningValiant 1.3](https://huggingface.co/ValiantLabs/ShiningValiant).
### About AWQ
AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings.
AWQ models are currently supported on Linux and Windows, with NVidia GPUs only. macOS users: please use GGUF models instead.
It is supported by:
- [Text Generation Webui](https://github.com/oobabooga/text-generation-webui) - using Loader: AutoAWQ
- [vLLM](https://github.com/vllm-project/vllm) - version 0.2.2 or later for support for all model types.
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference)
- [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later, from any code or client that supports Transformers
- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) - for use from Python code
<!-- description end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/ShiningValiant-1.3-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/ShiningValiant-1.3-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/ShiningValiant-1.3-GGUF)
* [Valiant Labs's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/ValiantLabs/ShiningValiant)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: Llama-2-Chat
```
[INST] <<SYS>>
{system_message}
<</SYS>>
{prompt} [/INST]
```
<!-- prompt-template end -->
<!-- README_AWQ.md-provided-files start -->
## Provided files, and AWQ parameters
I currently release 128g GEMM models only. The addition of group_size 32 models, and GEMV kernel models, is being actively considered.
Models are released as sharded safetensors files.
| Branch | Bits | GS | AWQ Dataset | Seq Len | Size |
| ------ | ---- | -- | ----------- | ------- | ---- |
| [main](https://huggingface.co/TheBloke/ShiningValiant-1.3-AWQ/tree/main) | 4 | 128 | [Wikitext2 Full](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-raw-v1) | 4096 | 36.61 GB
<!-- README_AWQ.md-provided-files end -->
<!-- README_AWQ.md-text-generation-webui start -->
## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui)
Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui).
It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install.
1. Click the **Model tab**.
2. Under **Download custom model or LoRA**, enter `TheBloke/ShiningValiant-1.3-AWQ`.
3. Click **Download**.
4. The model will start downloading. Once it's finished it will say "Done".
5. In the top left, click the refresh icon next to **Model**.
6. In the **Model** dropdown, choose the model you just downloaded: `ShiningValiant-1.3-AWQ`
7. Select **Loader: AutoAWQ**.
8. Click Load, and the model will load and is now ready for use.
9. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right.
10. Once you're ready, click the **Text Generation** tab and enter a prompt to get started!
<!-- README_AWQ.md-text-generation-webui end -->
<!-- README_AWQ.md-use-from-vllm start -->
## Multi-user inference server: vLLM
Documentation on installing and using vLLM [can be found here](https://vllm.readthedocs.io/en/latest/).
- Please ensure you are using vLLM version 0.2 or later.
- When using vLLM as a server, pass the `--quantization awq` parameter.
For example:
```shell
python3 -m vllm.entrypoints.api_server --model TheBloke/ShiningValiant-1.3-AWQ --quantization awq --dtype auto
```
- When using vLLM from Python code, again set `quantization=awq`.
For example:
```python
from vllm import LLM, SamplingParams
prompts = [
"Tell me about AI",
"Write a story about llamas",
"What is 291 - 150?",
"How much wood would a woodchuck chuck if a woodchuck could chuck wood?",
]
prompt_template=f'''[INST] <<SYS>>
{system_message}
<</SYS>>
{prompt} [/INST]
'''
prompts = [prompt_template.format(prompt=prompt) for prompt in prompts]
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
llm = LLM(model="TheBloke/ShiningValiant-1.3-AWQ", quantization="awq", dtype="auto")
outputs = llm.generate(prompts, sampling_params)
# Print the outputs.
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
```
<!-- README_AWQ.md-use-from-vllm start -->
<!-- README_AWQ.md-use-from-tgi start -->
## Multi-user inference server: Hugging Face Text Generation Inference (TGI)
Use TGI version 1.1.0 or later. The official Docker container is: `ghcr.io/huggingface/text-generation-inference:1.1.0`
Example Docker parameters:
```shell
--model-id TheBloke/ShiningValiant-1.3-AWQ --port 3000 --quantize awq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096
```
Example Python code for interfacing with TGI (requires [huggingface-hub](https://github.com/huggingface/huggingface_hub) 0.17.0 or later):
```shell
pip3 install huggingface-hub
```
```python
from huggingface_hub import InferenceClient
endpoint_url = "https://your-endpoint-url-here"
prompt = "Tell me about AI"
prompt_template=f'''[INST] <<SYS>>
{system_message}
<</SYS>>
{prompt} [/INST]
'''
client = InferenceClient(endpoint_url)
response = client.text_generation(prompt,
max_new_tokens=128,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
repetition_penalty=1.1)
print(f"Model output: ", response)
```
<!-- README_AWQ.md-use-from-tgi end -->
<!-- README_AWQ.md-use-from-python start -->
## Inference from Python code using Transformers
### Install the necessary packages
- Requires: [Transformers](https://huggingface.co/docs/transformers) 4.35.0 or later.
- Requires: [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) 0.1.6 or later.
```shell
pip3 install --upgrade "autoawq>=0.1.6" "transformers>=4.35.0"
```
Note that if you are using PyTorch 2.0.1, the above AutoAWQ command will automatically upgrade you to PyTorch 2.1.0.
If you are using CUDA 11.8 and wish to continue using PyTorch 2.0.1, instead run this command:
```shell
pip3 install https://github.com/casper-hansen/AutoAWQ/releases/download/v0.1.6/autoawq-0.1.6+cu118-cp310-cp310-linux_x86_64.whl
```
If you have problems installing [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) using the pre-built wheels, install it from source instead:
```shell
pip3 uninstall -y autoawq
git clone https://github.com/casper-hansen/AutoAWQ
cd AutoAWQ
pip3 install .
```
### Transformers example code (requires Transformers 4.35.0 and later)
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
model_name_or_path = "TheBloke/ShiningValiant-1.3-AWQ"
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
model = AutoModelForCausalLM.from_pretrained(
model_name_or_path,
low_cpu_mem_usage=True,
device_map="cuda:0"
)
# Using the text streamer to stream output one token at a time
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
prompt = "Tell me about AI"
prompt_template=f'''[INST] <<SYS>>
{system_message}
<</SYS>>
{prompt} [/INST]
'''
# Convert prompt to tokens
tokens = tokenizer(
prompt_template,
return_tensors='pt'
).input_ids.cuda()
generation_params = {
"do_sample": True,
"temperature": 0.7,
"top_p": 0.95,
"top_k": 40,
"max_new_tokens": 512,
"repetition_penalty": 1.1
}
# Generate streamed output, visible one token at a time
generation_output = model.generate(
tokens,
streamer=streamer,
**generation_params
)
# Generation without a streamer, which will include the prompt in the output
generation_output = model.generate(
tokens,
**generation_params
)
# Get the tokens from the output, decode them, print them
token_output = generation_output[0]
text_output = tokenizer.decode(token_output)
print("model.generate output: ", text_output)
# Inference is also possible via Transformers' pipeline
from transformers import pipeline
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
**generation_params
)
pipe_output = pipe(prompt_template)[0]['generated_text']
print("pipeline output: ", pipe_output)
```
<!-- README_AWQ.md-use-from-python end -->
<!-- README_AWQ.md-compatibility start -->
## Compatibility
The files provided are tested to work with:
- [text-generation-webui](https://github.com/oobabooga/text-generation-webui) using `Loader: AutoAWQ`.
- [vLLM](https://github.com/vllm-project/vllm) version 0.2.0 and later.
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) version 1.1.0 and later.
- [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later.
- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) version 0.1.1 and later.
<!-- README_AWQ.md-compatibility end -->
<!-- footer start -->
<!-- 200823 -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute
Thanks to the [chirper.ai](https://chirper.ai) team!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Aemon Algiz.
**Patreon special mentions**: Michael Levine, 阿明, Trailburnt, Nikolai Manek, John Detwiler, Randy H, Will Dee, Sebastain Graf, NimbleBox.ai, Eugene Pentland, Emad Mostaque, Ai Maven, Jim Angel, Jeff Scroggin, Michael Davis, Manuel Alberto Morcote, Stephen Murray, Robert, Justin Joy, Luke @flexchar, Brandon Frisco, Elijah Stavena, S_X, Dan Guido, Undi ., Komninos Chatzipapas, Shadi, theTransient, Lone Striker, Raven Klaugh, jjj, Cap'n Zoog, Michel-Marie MAUDET (LINAGORA), Matthew Berman, David, Fen Risland, Omer Bin Jawed, Luke Pendergrass, Kalila, OG, Erik Bjäreholt, Rooh Singh, Joseph William Delisle, Dan Lewis, TL, John Villwock, AzureBlack, Brad, Pedro Madruga, Caitlyn Gatomon, K, jinyuan sun, Mano Prime, Alex, Jeffrey Morgan, Alicia Loh, Illia Dulskyi, Chadd, transmissions 11, fincy, Rainer Wilmers, ReadyPlayerEmma, knownsqashed, Mandus, biorpg, Deo Leter, Brandon Phillips, SuperWojo, Sean Connelly, Iucharbius, Jack West, Harry Royden McLaughlin, Nicholas, terasurfer, Vitor Caleffi, Duane Dunston, Johann-Peter Hartmann, David Ziegler, Olakabola, Ken Nordquist, Trenton Dambrowitz, Tom X Nguyen, Vadim, Ajan Kanaga, Leonard Tan, Clay Pascal, Alexandros Triantafyllidis, JM33133, Xule, vamX, ya boyyy, subjectnull, Talal Aujan, Alps Aficionado, wassieverse, Ari Malik, James Bentley, Woland, Spencer Kim, Michael Dempsey, Fred von Graf, Elle, zynix, William Richards, Stanislav Ovsiannikov, Edmond Seymore, Jonathan Leane, Martin Kemka, usrbinkat, Enrico Ros
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
# Original model card: Valiant Labs's ShiningValiant 1.3
Shining Valiant is a chat model built on the Llama 2 architecture, finetuned on our data for insight, creativity, passion, and friendliness.
- Uses the llama-2-70b-chat model, with safetensors
- Finetuned on multiple runs across private and public data
- Data focused on knowledge, enthusiasm, and structured reasoning
## Version
The current version is **1.3!**
We're thrilled to bring you our newest release!
Previous versions remain available in the repository. New models will be released for everyone once our team's training and validation process is complete.
## Evaluation
| Model | Avg | ARC | HS | MMLU | TQA | WG | GSM |
|-----------------------|--------|-------|-------|--------|-------|-------|-------|
| **Shining Valiant 1.3** | 73.78 | 71.33 | 90.96 | 71.21 | 70.29 | 84.21 | 54.66 |
## Prompting Guide
Shining Valiant uses the same prompt format as Llama 2 Chat - feel free to use your existing prompts and scripts!
A few examples of different formats:
1. [INST] Good morning! Can you let me know how to parse a text file and turn the semicolons into commas? [/INST]
2. [INST] (You are an intelligent, helpful AI assistant.) Hello, can you write me a thank you letter? [/INST]
3. [INST] << SYS >>You are an intelligent, helpful AI assistant.<< /SYS >>Deep dive about a country with interesting history: [/INST]
## The Model
Shining Valiant is built on top of Sunset Boulevard, which uses Llama 2's 70b parameter architecture and features upgraded general capability.
From there, we've created Shining Valiant through multiple finetuning runs on different compositions of our private dataset.
Our private data focuses primarily on applying Shining Valiant's personality: she's friendly, enthusiastic, insightful, knowledgeable, and loves to learn!
We are actively working on expanding and improving the Shining Valiant dataset for use in future releases of this model and others.
Shining Valiant is created by [Valiant Labs.](http://valiantlabs.ca/)
[Follow us on X for updates on our models!](https://twitter.com/valiant_labs)
We care about open source.
For everyone to use.
We encourage others to finetune further from our models.
|
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] |
abhishek/bdju-zlei-d8ng-0
|
abhishek
| 2023-12-14T19:42:21Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"vit",
"image-classification",
"autotrain",
"dataset:abhishek/autotrain-data-bdju-zlei-d8ng",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-14T19:42:14Z |
---
tags:
- autotrain
- image-classification
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
example_title: Palace
datasets:
- abhishek/autotrain-data-bdju-zlei-d8ng
---
# Model Trained Using AutoTrain
- Problem type: Image Classification
## Validation Metricsg
loss: nan
f1_macro: 0.06153846153846154
f1_micro: 0.18181818181818182
f1_weighted: 0.055944055944055944
precision_macro: 0.03636363636363636
precision_micro: 0.18181818181818182
precision_weighted: 0.03305785123966942
recall_macro: 0.2
recall_micro: 0.18181818181818182
recall_weighted: 0.18181818181818182
accuracy: 0.18181818181818182
|
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devlocalhost/phi-2-finetuned-lr
|
devlocalhost
| 2023-12-14T19:43:40Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T19:43:40Z |
Entry not found
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] |
marlex7/straightforwardKD-Mini-from-Base
|
marlex7
| 2023-12-14T20:09:49Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-14T19:49:26Z |
Entry not found
|
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camenduru/genhowto
|
camenduru
| 2023-12-14T19:53:06Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T19:50:52Z |
Entry not found
|
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Benjimo/mistral-gptq-finetuned-medmcqa
|
Benjimo
| 2023-12-14T19:51:53Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T19:51:53Z |
Entry not found
|
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madplue/Alpha
|
madplue
| 2023-12-14T20:20:41Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T19:53:52Z |
Entry not found
|
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djbirdsall/my_awesome_food_model
|
djbirdsall
| 2023-12-14T19:57:50Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T19:57:50Z |
Entry not found
|
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Waflon/my_awesome_qa_model
|
Waflon
| 2023-12-14T20:00:02Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T20:00:02Z |
Entry not found
|
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BouArt/comicfr
|
BouArt
| 2023-12-14T20:03:13Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T20:03:13Z |
Entry not found
|
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] |
Alvin-Nahabwe/Whisper_Synthesis_LG
|
Alvin-Nahabwe
| 2023-12-14T20:09:37Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T20:09:37Z |
Entry not found
|
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DopeorNope/SOLAR-D-Ko-10.7B
|
DopeorNope
| 2023-12-14T20:12:21Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T20:12:21Z |
Entry not found
|
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] |
marlex7/successiveKD-Small
|
marlex7
| 2023-12-14T20:23:03Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-14T20:13:13Z |
Entry not found
|
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behzadnet/Llama-2-7b-chat-hf-sharded-bf16-fine-tuned-adapters_chatGPT_Seed103
|
behzadnet
| 2023-12-14T20:14:52Z | 0 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:Trelis/Llama-2-7b-chat-hf-sharded-bf16",
"region:us"
] | null | 2023-12-14T20:14:46Z |
---
library_name: peft
base_model: Trelis/Llama-2-7b-chat-hf-sharded-bf16
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
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- **Developed by:** [More Information Needed]
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### Model Sources [optional]
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## Uses
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### Direct Use
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### Recommendations
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
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## Training Details
### Training Data
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### 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]
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## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
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#### Metrics
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### Results
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#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
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## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.7.0.dev0
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.7.0.dev0
|
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