modelId
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42
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| library_name
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cociweb/whisper-tiny-hu
|
cociweb
| 2023-12-17T14:16:09Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:openai/whisper-tiny",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-12-17T14:04:13Z |
---
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: whisper-tiny-hu
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. -->
# whisper-tiny-hu
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the Common Voice 15.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4814
- Wer: 73.6804
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.6641 | 0.35 | 1000 | 0.6586 | 74.1481 |
| 0.5371 | 0.7 | 2000 | 0.5432 | 76.1248 |
| 0.4045 | 1.05 | 3000 | 0.4955 | 73.3178 |
| 0.4205 | 1.4 | 4000 | 0.4814 | 73.6804 |
### Framework versions
- Transformers 4.37.0.dev0
- Pytorch 2.1.2+cpu
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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gdf2h1gfh2/Napa-County-Board-of-Supervisors-races-set-with-some-familiar-faces-11-updated
|
gdf2h1gfh2
| 2023-12-17T14:05:47Z | 0 | 0 | null |
[
"en",
"region:us"
] | null | 2023-12-17T14:04:34Z |
---
language:
- en
---
[]()
read the full article here : https://pasteio.com/x6Lb4zSgf9q4
Source : https://notes.io/wrRXu
Flash News : https://paste.ee/p/eDuXo
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Russian Ukrain Breaking News : https://searchtech.fogbugz.com/default.asp
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https://jsbin.com/zerezozuzo/edit?html,output
The Philippines is contingency planning for an escalation of hostilities in the South China Sea, according to a senior military official, including a scenario where crew repel Chinese forces attempting to board Philippine vessels.
Ties between the two countries have deteriorated this year after several collisions and repeated standoffs near disputed features of the South China Sea, with the Philippines accusing China of aggressive, deliberate and dangerous manoeuvres.
The Philippines has taken a tougher line with China this year, coinciding with its boosting of military ties with defence treaty ally the United States and increased security engagement with other Western powers.
"Expect more coercive actions from China, short of armed attack," Alberto Carlos, chief of the Philippines' Western Command told CNN Philippines late on Wednesday.
"Next after the water cannon is probably ramming and also they will attempt to board our vessel, which is something that we will not allow them to do."
That scenario, Mr. Carlos said, was part of Philippine war games exercises and academic discussions on what other actions China might take. The Philippines on Tuesday summoned China's ambassador to protest "back-to-back harassments" at the weekend in different locations, including collisions and use of water cannon.
Beijing has repeatedly accused Philippine vessels operating in Manila's exclusive economic zone (EEZ) of trespassing in Chinese waters.
The Philippines has grown increasingly wary of China's coastguard and the presence of hundreds of Chinese fishing boats that it considers to be militia forces.
"We're brainstorming this, we are wargaming this and we are prepared for any contingency that will happen," said Mr. Carlos, whose remit includes defence of the Philippines' EEZ.
China claims sovereignty over almost the entire South China Sea, a conduit for more than $3 trillion of annual ship-borne commerce. Those claims, which an arbitral tribunal has declared baseless, extend to the exclusive economic zones of the Philippines, Vietnam, Indonesia, Malaysia and Brunei. -- Reuters....
|
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] |
alexandrualexandru/fine_tuned_pretrained_bert_qa_squad_4
|
alexandrualexandru
| 2023-12-17T14:05:18Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"question-answering",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-12-17T14:04:57Z |
Entry not found
|
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] |
fqbx/Advanced_AI-6b_base
|
fqbx
| 2023-12-17T14:45:49Z | 0 | 0 | null |
[
"pytorch",
"license:other",
"region:us"
] | null | 2023-12-17T14:06:47Z |
---
license: other
license_name: chatglm3
license_link: LICENSE
---
|
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gdf2h1gfh2/Judge-Andrew-Napolitano-In-defense-of-free-speech-and-free-silence-ag-updated
|
gdf2h1gfh2
| 2023-12-17T14:08:24Z | 0 | 0 | null |
[
"en",
"region:us"
] | null | 2023-12-17T14:07:10Z |
---
language:
- en
---
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After the High Court questioned whether the privileges given to rape convict Gurmeet Ram Rahim Singh, such as parole and furlough, were also being offered to other convicts, Haryana Chief Minister Manohar Lal Khattar said the right to parole or furlough is granted to every convict with good conduct in jail.
Khattar on Thursday said, "Parole is given based on conduct in jail. Every convict has the right to get parole and furlough. We are now creating an open jail. Inmates showing good conduct will be placed in an open jail."
Dera Chief Gurmeet Ram Rahim Singh received 184 days of parole and furlough in the past three years.
In response to a PIL filed by the Shiromani Gurdwara Prabandhak Committee (SGPC), the Punjab and Haryana High Court directed the Haryana government to explain if similar provisions of furlough and parole were extended to other convicts.
The High Court also instructed the state government to provide a status report.
The SGPC, concerned about Gurmeet Ram Rahim's frequent paroles, argued that the PIL aimed to protect national sovereignty, integrity, public harmony, peace, and social fabric.
In a petition filed in February, the SGPC highlighted the potential disastrous consequences of granting parole to Gurmeet Ram Rahim, who claims to be a 'messenger of God' with titles like Baba and 'maharaja.'
On Wednesday, Gurmeet Ram Rahim surrendered to Sunaria jail authorities after completing a 21-day furlough leave, spending the time at Barnava ashram in Uttar Pradesh without holding any discourse with disciples, as per court orders.
Gurmeet Ram Rahim has been granted three temporary releases in 2023, totalling 184 days out of jail.
He was sentenced to a 20-year jail term in 2017 for raping two disciples and later received life imprisonment in two murder cases, including the killings of journalist Ram Chander Chhatrapati and Dera Manager Ranjit Singh in 2002.....
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] |
kejolong/kaela
|
kejolong
| 2023-12-17T14:14:26Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-12-17T14:07:18Z |
---
license: creativeml-openrail-m
---
|
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] |
yjs616/my-midm
|
yjs616
| 2023-12-17T14:40:18Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:KT-AI/midm-bitext-S-7B-inst-v1",
"region:us"
] | null | 2023-12-17T14:07:43Z |
---
library_name: peft
base_model: KT-AI/midm-bitext-S-7B-inst-v1
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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## Training Details
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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## Model Card Contact
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### Framework versions
- PEFT 0.7.1
|
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gdf2h1gfh2/The-Immoral-Gamble-of-Shopping-for-Health-Insurance-FlaglerLive-2h-updated
|
gdf2h1gfh2
| 2023-12-17T14:12:07Z | 0 | 0 | null |
[
"en",
"region:us"
] | null | 2023-12-17T14:10:54Z |
---
language:
- en
---
[]()
read the full article here : https://paste.ee/p/bE9iZ
Source : https://jsitor.com/IarVvGFaXK
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https://jsbin.com/silawaliya/edit?html,output
MANILA, The Philippines: In a worrisome turn of events, the Chinese coast guard once again targeted Philippine vessels in the South China Sea.
The incident unfolded near the Second Thomas Shoal, where Philippine navy-operated supply boats and coast guard escort ships were delivering crucial supplies to Filipino forces stationed on a marooned navy ship serving as a territorial outpost.
During the altercation, the Chinese coast guard employed high-pressure water cannon blasts, severely damaging the engines of the Philippine supply boat M\/L Kalayaan and causing it to be towed back to the Philippines. The incident placed the lives of the crew in peril.
Additionally, the BRP Cabra, one of two Philippine coast guard escort ships, suffered mast damage from the water cannon blast. Another supply boat faced a collision with a Chinese coast guard ship but still managed to deliver supplies to Filipino marines at Second Thomas Shoal. Drone footage and photographs depict two Chinese coast guard ships targeting BRP Cabra and a smaller supply boat with close-range water cannon blasts.
"The systematic and consistent manner in which the People's Republic of China carries out these illegal and irresponsible actions puts into question and significant doubt the sincerity of its calls for peaceful dialogue," a Philippine government task force that deals with the territorial disputes said in a statement. "We demand that China demonstrate that it is a responsible and trustworthy member of the international community."
China's coast guard responded by asserting that it had acted in accordance with laws and regulations. While it did not provide specifics about its actions, China accused the Philippines of ignoring warnings and intentionally colliding with a Chinese coast guard ship, shifting the blame entirely to the Philippine side.
Foreign diplomats in the Philippines, including those from the United States, the European Union, and Japan, strongly condemned China's actions. The U.S. Ambassador to the Philippines, MaryKay L. Carlson, expressed solidarity with the Philippines in denouncing China's repeated "illegal and dangerous actions against vessels."
China has long been involved in territorial disputes in the South China Sea, and its actions have drawn international criticism. It has rejected legal rulings that invalidate its claims to certain areas in the region and insists on its right to defend its sovereignty. Tensions continue to simmer, with the Philippines reaffirming its commitment to exercising its legal rights in its maritime zones.....
|
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abdouaziiz/test_small100
|
abdouaziiz
| 2023-12-17T14:11:26Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-17T14:11:26Z |
Entry not found
|
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] |
BramDelisse/9_16-k-Murdoch_data
|
BramDelisse
| 2023-12-17T14:17:07Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-17T14:11:47Z |
Entry not found
|
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gdf2h1gfh2/Highland-Heights-Fire-Department-station-gets-updates-df-updated
|
gdf2h1gfh2
| 2023-12-17T14:13:28Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-17T14:13:28Z |
Entry not found
|
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] |
rippertnt/animatediff-motion-adapter-v3
|
rippertnt
| 2023-12-17T14:15:54Z | 0 | 0 |
transformers
|
[
"transformers",
"license:gpl-3.0",
"endpoints_compatible",
"region:us"
] | null | 2023-12-17T14:15:04Z |
---
license: gpl-3.0
---
|
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] |
m1ch79/Reinforce-Pixelcopter-PLE-v0
|
m1ch79
| 2023-12-17T14:17:50Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-17T14:17:46Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter-PLE-v0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 43.70 +/- 37.84
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
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yjs616/midm
|
yjs616
| 2023-12-17T14:19:26Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-17T14:19:26Z |
Entry not found
|
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Ashif18/LLM
|
Ashif18
| 2023-12-17T14:19:53Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-17T14:19:53Z |
Entry not found
|
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AmmarAl/Taxi-v3
|
AmmarAl
| 2023-12-17T14:21:29Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-17T14:20:55Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="AmmarAl/Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
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] |
hkivancoral/smids_5x_deit_base_sgd_0001_fold5
|
hkivancoral
| 2023-12-17T15:36:42Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:facebook/deit-base-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-17T14:22:06Z |
---
license: apache-2.0
base_model: facebook/deit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_5x_deit_base_sgd_0001_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: 0.8133333333333334
---
<!-- 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_base_sgd_0001_fold5
This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5006
- Accuracy: 0.8133
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.0891 | 1.0 | 375 | 1.0922 | 0.36 |
| 1.0632 | 2.0 | 750 | 1.0728 | 0.4133 |
| 1.0234 | 3.0 | 1125 | 1.0519 | 0.4667 |
| 1.0095 | 4.0 | 1500 | 1.0279 | 0.505 |
| 0.9691 | 5.0 | 1875 | 1.0014 | 0.54 |
| 0.9521 | 6.0 | 2250 | 0.9722 | 0.5683 |
| 0.9099 | 7.0 | 2625 | 0.9405 | 0.6033 |
| 0.8832 | 8.0 | 3000 | 0.9085 | 0.6267 |
| 0.8563 | 9.0 | 3375 | 0.8771 | 0.6533 |
| 0.8097 | 10.0 | 3750 | 0.8470 | 0.685 |
| 0.7629 | 11.0 | 4125 | 0.8186 | 0.705 |
| 0.7531 | 12.0 | 4500 | 0.7923 | 0.715 |
| 0.7082 | 13.0 | 4875 | 0.7677 | 0.7333 |
| 0.7318 | 14.0 | 5250 | 0.7449 | 0.7433 |
| 0.7243 | 15.0 | 5625 | 0.7237 | 0.7533 |
| 0.6668 | 16.0 | 6000 | 0.7041 | 0.7567 |
| 0.6939 | 17.0 | 6375 | 0.6860 | 0.76 |
| 0.6736 | 18.0 | 6750 | 0.6692 | 0.77 |
| 0.6795 | 19.0 | 7125 | 0.6538 | 0.78 |
| 0.6094 | 20.0 | 7500 | 0.6398 | 0.7833 |
| 0.5982 | 21.0 | 7875 | 0.6269 | 0.7817 |
| 0.5784 | 22.0 | 8250 | 0.6150 | 0.7867 |
| 0.6034 | 23.0 | 8625 | 0.6042 | 0.7933 |
| 0.6235 | 24.0 | 9000 | 0.5942 | 0.7967 |
| 0.5888 | 25.0 | 9375 | 0.5851 | 0.7933 |
| 0.5892 | 26.0 | 9750 | 0.5766 | 0.7933 |
| 0.5908 | 27.0 | 10125 | 0.5688 | 0.7983 |
| 0.5781 | 28.0 | 10500 | 0.5616 | 0.7983 |
| 0.5631 | 29.0 | 10875 | 0.5551 | 0.8 |
| 0.5055 | 30.0 | 11250 | 0.5492 | 0.8017 |
| 0.5168 | 31.0 | 11625 | 0.5436 | 0.805 |
| 0.5659 | 32.0 | 12000 | 0.5386 | 0.81 |
| 0.568 | 33.0 | 12375 | 0.5339 | 0.8083 |
| 0.5472 | 34.0 | 12750 | 0.5295 | 0.8117 |
| 0.5227 | 35.0 | 13125 | 0.5256 | 0.81 |
| 0.4679 | 36.0 | 13500 | 0.5220 | 0.81 |
| 0.5236 | 37.0 | 13875 | 0.5188 | 0.8117 |
| 0.5206 | 38.0 | 14250 | 0.5158 | 0.8117 |
| 0.5047 | 39.0 | 14625 | 0.5132 | 0.8133 |
| 0.5461 | 40.0 | 15000 | 0.5108 | 0.8133 |
| 0.495 | 41.0 | 15375 | 0.5087 | 0.8133 |
| 0.508 | 42.0 | 15750 | 0.5069 | 0.8133 |
| 0.5153 | 43.0 | 16125 | 0.5053 | 0.8133 |
| 0.4846 | 44.0 | 16500 | 0.5040 | 0.8133 |
| 0.5055 | 45.0 | 16875 | 0.5029 | 0.8133 |
| 0.5156 | 46.0 | 17250 | 0.5020 | 0.8133 |
| 0.525 | 47.0 | 17625 | 0.5013 | 0.8133 |
| 0.4795 | 48.0 | 18000 | 0.5009 | 0.8133 |
| 0.4888 | 49.0 | 18375 | 0.5006 | 0.8133 |
| 0.4989 | 50.0 | 18750 | 0.5006 | 0.8133 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
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hkivancoral/smids_5x_deit_base_sgd_00001_fold5
|
hkivancoral
| 2023-12-17T15:36:37Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:facebook/deit-base-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-17T14:22:10Z |
---
license: apache-2.0
base_model: facebook/deit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_5x_deit_base_sgd_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: 0.42833333333333334
---
<!-- 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_base_sgd_00001_fold5
This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0685
- Accuracy: 0.4283
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.1186 | 1.0 | 375 | 1.1098 | 0.3483 |
| 1.1102 | 2.0 | 750 | 1.1079 | 0.3417 |
| 1.1136 | 3.0 | 1125 | 1.1060 | 0.345 |
| 1.1131 | 4.0 | 1500 | 1.1043 | 0.35 |
| 1.0975 | 5.0 | 1875 | 1.1026 | 0.3433 |
| 1.1051 | 6.0 | 2250 | 1.1010 | 0.345 |
| 1.0892 | 7.0 | 2625 | 1.0994 | 0.3417 |
| 1.0802 | 8.0 | 3000 | 1.0978 | 0.3533 |
| 1.0951 | 9.0 | 3375 | 1.0964 | 0.3517 |
| 1.0929 | 10.0 | 3750 | 1.0949 | 0.3517 |
| 1.0628 | 11.0 | 4125 | 1.0935 | 0.3533 |
| 1.0809 | 12.0 | 4500 | 1.0922 | 0.36 |
| 1.0566 | 13.0 | 4875 | 1.0909 | 0.375 |
| 1.0849 | 14.0 | 5250 | 1.0897 | 0.38 |
| 1.0684 | 15.0 | 5625 | 1.0884 | 0.3817 |
| 1.0868 | 16.0 | 6000 | 1.0873 | 0.3817 |
| 1.0653 | 17.0 | 6375 | 1.0861 | 0.3817 |
| 1.0768 | 18.0 | 6750 | 1.0850 | 0.385 |
| 1.0758 | 19.0 | 7125 | 1.0839 | 0.385 |
| 1.0932 | 20.0 | 7500 | 1.0829 | 0.3883 |
| 1.072 | 21.0 | 7875 | 1.0819 | 0.39 |
| 1.06 | 22.0 | 8250 | 1.0809 | 0.3917 |
| 1.0521 | 23.0 | 8625 | 1.0800 | 0.3967 |
| 1.0558 | 24.0 | 9000 | 1.0792 | 0.3983 |
| 1.0773 | 25.0 | 9375 | 1.0783 | 0.4 |
| 1.0609 | 26.0 | 9750 | 1.0775 | 0.4 |
| 1.0495 | 27.0 | 10125 | 1.0767 | 0.4017 |
| 1.0658 | 28.0 | 10500 | 1.0760 | 0.4 |
| 1.0475 | 29.0 | 10875 | 1.0753 | 0.4083 |
| 1.0538 | 30.0 | 11250 | 1.0746 | 0.415 |
| 1.0455 | 31.0 | 11625 | 1.0740 | 0.4133 |
| 1.0741 | 32.0 | 12000 | 1.0734 | 0.415 |
| 1.0518 | 33.0 | 12375 | 1.0728 | 0.4167 |
| 1.04 | 34.0 | 12750 | 1.0723 | 0.4183 |
| 1.0566 | 35.0 | 13125 | 1.0718 | 0.4167 |
| 1.0416 | 36.0 | 13500 | 1.0714 | 0.4167 |
| 1.0546 | 37.0 | 13875 | 1.0709 | 0.4217 |
| 1.0514 | 38.0 | 14250 | 1.0706 | 0.4233 |
| 1.0481 | 39.0 | 14625 | 1.0702 | 0.425 |
| 1.0581 | 40.0 | 15000 | 1.0699 | 0.4267 |
| 1.0484 | 41.0 | 15375 | 1.0696 | 0.4267 |
| 1.0544 | 42.0 | 15750 | 1.0694 | 0.4267 |
| 1.0499 | 43.0 | 16125 | 1.0691 | 0.4267 |
| 1.0424 | 44.0 | 16500 | 1.0690 | 0.4267 |
| 1.0515 | 45.0 | 16875 | 1.0688 | 0.4283 |
| 1.0389 | 46.0 | 17250 | 1.0687 | 0.4283 |
| 1.0556 | 47.0 | 17625 | 1.0686 | 0.4283 |
| 1.0595 | 48.0 | 18000 | 1.0685 | 0.4283 |
| 1.0528 | 49.0 | 18375 | 1.0685 | 0.4283 |
| 1.0533 | 50.0 | 18750 | 1.0685 | 0.4283 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
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JKuang96/rl_taxi_v3-fix
|
JKuang96
| 2023-12-17T14:24:17Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-17T14:24:14Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: rl_taxi_v3-fix
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="JKuang96/rl_taxi_v3-fix", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
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chrisgru/mis-7b-airoboros
|
chrisgru
| 2023-12-17T14:27:38Z | 0 | 0 | null |
[
"gguf",
"region:us"
] | null | 2023-12-17T14:24:50Z |
Entry not found
|
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] |
ahmed-ai/med-phi-2-adapter
|
ahmed-ai
| 2023-12-17T14:31:32Z | 0 | 0 |
peft
|
[
"peft",
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:microsoft/phi-2",
"license:other",
"region:us"
] | null | 2023-12-17T14:24:57Z |
---
license: other
library_name: peft
tags:
- generated_from_trainer
base_model: microsoft/phi-2
model-index:
- name: results
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
<!-- # results
This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- training_steps: 1500
### Training results
### Framework versions
- PEFT 0.7.2.dev0
- Transformers 4.36.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.15.0 -->
|
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] |
BramDelisse/1_8-k-Murdoch_data
|
BramDelisse
| 2023-12-17T14:31:38Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-17T14:26:37Z |
Entry not found
|
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behzadnet/Llama-2-7b-chat-hf-sharded-bf16-fine-tuned-adapters_SystemError0.4_Seed103
|
behzadnet
| 2023-12-17T14:27:04Z | 0 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:Trelis/Llama-2-7b-chat-hf-sharded-bf16",
"region:us"
] | null | 2023-12-17T14:26:57Z |
---
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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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]
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#### 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
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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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behzadnet/Llama-2-7b-chat-hf-sharded-bf16-fine-tuned_SystemError0.4_Seed103
|
behzadnet
| 2023-12-17T14:27:12Z | 0 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:Trelis/Llama-2-7b-chat-hf-sharded-bf16",
"region:us"
] | null | 2023-12-17T14:27:10Z |
---
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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### 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.
[More Information Needed]
## 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]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
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#### Testing Data
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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
|
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yi-ching/distil-whisper-medium-hi-test
|
yi-ching
| 2023-12-17T14:27:29Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-17T14:27:29Z |
Entry not found
|
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abdouaziiz/SMALL100_418M
|
abdouaziiz
| 2023-12-17T14:29:38Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-17T14:29:38Z |
Entry not found
|
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sogeeking/vq_burgers_16_v3
|
sogeeking
| 2023-12-18T01:23:05Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"pdetokenizer",
"endpoints_compatible",
"region:us"
] | null | 2023-12-17T14:31:35Z |
Entry not found
|
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asharib12/ai_assintant
|
asharib12
| 2023-12-17T14:31:39Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-17T14:31:39Z |
Entry not found
|
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tsunemoto/Mistral-7B-Instruct-v0.2-GGUF
|
tsunemoto
| 2023-12-17T14:47:40Z | 0 | 0 | null |
[
"gguf",
"GGUF",
"en",
"arxiv:2310.06825",
"region:us"
] | null | 2023-12-17T14:32:31Z |
---
title: "Mistral-7B-Instruct-v0.2 Quantized in GGUF"
tags:
- GGUF
language: en
---
# GGUF's of Mistral-7B-Instruct-v0.2
This is a GGUF quantization of Mistral-7B-Instruct-v0.2.
## Original Model Card:
---
license: apache-2.0
pipeline_tag: text-generation
tags:
- finetuned
inference: false
---
# Model Card for Mistral-7B-Instruct-v0.2
The Mistral-7B-Instruct-v0.2 Large Language Model (LLM) is an improved instruct fine-tuned version of [Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1).
For full details of this model please read our [paper](https://arxiv.org/abs/2310.06825) and [release blog post](https://mistral.ai/news/la-plateforme/).
## Instruction format
In order to leverage instruction fine-tuning, your prompt should be surrounded by `[INST]` and `[/INST]` tokens. The very first instruction should begin with a begin of sentence id. The next instructions should not. The assistant generation will be ended by the end-of-sentence token id.
E.g.
```
text = "<s>[INST] What is your favourite condiment? [/INST]"
"Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!</s> "
"[INST] Do you have mayonnaise recipes? [/INST]"
```
This format is available as a [chat template](https://huggingface.co/docs/transformers/main/chat_templating) via the `apply_chat_template()` method:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2")
tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2")
messages = [
{"role": "user", "content": "What is your favourite condiment?"},
{"role": "assistant", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"},
{"role": "user", "content": "Do you have mayonnaise recipes?"}
]
encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt")
model_inputs = encodeds.to(device)
model.to(device)
generated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True)
decoded = tokenizer.batch_decode(generated_ids)
print(decoded[0])
```
## Model Architecture
This instruction model is based on Mistral-7B-v0.1, a transformer model with the following architecture choices:
- Grouped-Query Attention
- Sliding-Window Attention
- Byte-fallback BPE tokenizer
## Troubleshooting
- If you see the following error:
```
Traceback (most recent call last):
File "", line 1, in
File "/transformers/models/auto/auto_factory.py", line 482, in from_pretrained
config, kwargs = AutoConfig.from_pretrained(
File "/transformers/models/auto/configuration_auto.py", line 1022, in from_pretrained
config_class = CONFIG_MAPPING[config_dict["model_type"]]
File "/transformers/models/auto/configuration_auto.py", line 723, in getitem
raise KeyError(key)
KeyError: 'mistral'
```
Installing transformers from source should solve the issue
pip install git+https://github.com/huggingface/transformers
This should not be required after transformers-v4.33.4.
## Limitations
The Mistral 7B Instruct model is a quick demonstration that the base model can be easily fine-tuned to achieve compelling performance.
It does not have any moderation mechanisms. We're looking forward to engaging with the community on ways to
make the model finely respect guardrails, allowing for deployment in environments requiring moderated outputs.
## The Mistral AI Team
Albert Jiang, Alexandre Sablayrolles, Arthur Mensch, Blanche Savary, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Emma Bou Hanna, Florian Bressand, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Lélio Renard Lavaud, Louis Ternon, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Théophile Gervet, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed.
|
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yjs616/test-midm
|
yjs616
| 2023-12-17T14:33:44Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-17T14:33:44Z |
Entry not found
|
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] |
DuLo/BillyIdol
|
DuLo
| 2023-12-17T14:46:45Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-17T14:38:42Z |
---
license: openrail
---
|
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] |
qualis2006/flan-t5-base-samsum
|
qualis2006
| 2023-12-17T14:39:48Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:samsum",
"base_model:google/flan-t5-base",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text2text-generation
| 2023-12-17T14:38:56Z |
---
license: apache-2.0
base_model: google/flan-t5-base
tags:
- generated_from_trainer
datasets:
- samsum
metrics:
- rouge
model-index:
- name: flan-t5-base-samsum
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: samsum
type: samsum
config: samsum
split: test
args: samsum
metrics:
- name: Rouge1
type: rouge
value: 47.5194
---
<!-- 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. -->
# flan-t5-base-samsum
This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on the samsum dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3707
- Rouge1: 47.5194
- Rouge2: 23.7737
- Rougel: 39.9592
- Rougelsum: 43.6505
- Gen Len: 17.2027
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Gen Len | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:-------:|:---------------:|:-------:|:-------:|:-------:|:---------:|
| 1.4543 | 1.0 | 1842 | 17.5531 | 1.3866 | 46.7793 | 22.9078 | 39.1288 | 42.9793 |
| 1.3399 | 2.0 | 3684 | 17.3297 | 1.3731 | 47.3308 | 24.0129 | 39.9801 | 43.7026 |
| 1.2682 | 3.0 | 5526 | 17.3309 | 1.3744 | 47.3898 | 23.7923 | 39.7349 | 43.5254 |
| 1.2302 | 4.0 | 7368 | 17.2027 | 1.3707 | 47.5194 | 23.7737 | 39.9592 | 43.6505 |
| 1.2082 | 5.0 | 9210 | 1.3751 | 47.6088 | 23.8305 | 40.0216 | 43.6941 | 17.2295 |
### Framework versions
- Transformers 4.36.1
- Pytorch 2.0.1+cu117
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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] |
micdestefano/Reinforce-Pixelcopter-PLE-v0
|
micdestefano
| 2023-12-17T22:54:51Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-17T14:39:02Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter-PLE-v0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 220.80 +/- 155.05
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
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skrishna/chess_puzzles
|
skrishna
| 2023-12-17T14:40:22Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-17T14:40:22Z |
Entry not found
|
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] |
Sheng08/babi_gpt2_med_tw
|
Sheng08
| 2023-12-17T14:42:14Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-17T14:41:15Z |
Entry not found
|
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] |
Aradwy/Stas_Sheminov_aradwy_500Epoch
|
Aradwy
| 2023-12-17T14:44:18Z | 0 | 0 | null |
[
"license:unknown",
"region:us"
] | null | 2023-12-17T14:41:39Z |
---
license: unknown
---
|
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] |
liewchooichin/hb_fpn
|
liewchooichin
| 2023-12-17T14:43:26Z | 0 | 0 |
keras
|
[
"keras",
"license:apache-2.0",
"region:us"
] | null | 2023-12-17T14:41:44Z |
---
license: apache-2.0
---
|
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henriquehenry/SeuMadruga
|
henriquehenry
| 2023-12-17T23:13:13Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-17T14:45:32Z |
Entry not found
|
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] |
HelgeKn/Testing-blub
|
HelgeKn
| 2023-12-17T15:55:56Z | 0 | 0 |
setfit
|
[
"setfit",
"safetensors",
"mpnet",
"sentence-transformers",
"text-classification",
"generated_from_setfit_trainer",
"dataset:HelgeKn/SATHAME-generator-train",
"arxiv:2209.11055",
"base_model:sentence-transformers/paraphrase-mpnet-base-v2",
"region:us"
] |
text-classification
| 2023-12-17T14:47:21Z |
---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
datasets:
- HelgeKn/SATHAME-generator-train
metrics:
- accuracy
widget:
- text: '`` So crunch , crunch , crunch , bang , bang , bang -- here come the ringers
from above , making a very obvious exit while the congregation is at prayer ,
`` he says . '
- text: 'The others here today live elsewhere . '
- text: 'Then , at a signal , the ringers begin varying the order in which the bells
sound without altering the steady rhythm of the striking . '
- text: 'Mr. Hammond worries that old age and the flightiness of youth will diminish
the ranks of the East Anglian group that keeps the Aslacton bells pealing . '
pipeline_tag: text-classification
inference: true
base_model: sentence-transformers/paraphrase-mpnet-base-v2
---
# SetFit with sentence-transformers/paraphrase-mpnet-base-v2
This is a [SetFit](https://github.com/huggingface/setfit) model trained on the [HelgeKn/SATHAME-generator-train](https://huggingface.co/datasets/HelgeKn/SATHAME-generator-train) dataset that can be used for Text Classification. This SetFit model uses [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) as the Sentence Transformer embedding model. A [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) instance is used for 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.
## Model Details
### Model Description
- **Model Type:** SetFit
- **Sentence Transformer body:** [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2)
- **Classification head:** a [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) instance
- **Maximum Sequence Length:** 512 tokens
- **Number of Classes:** 4 classes
- **Training Dataset:** [HelgeKn/SATHAME-generator-train](https://huggingface.co/datasets/HelgeKn/SATHAME-generator-train)
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
### Model Labels
| Label | Examples |
|:------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 3 | <ul><li>'The art of change-ringing is peculiar to the English , and , like most English peculiarities , unintelligible to the rest of the world . '</li><li>'Of all scenes that evoke rural England , this is one of the loveliest : An ancient stone church stands amid the fields , the sound of bells cascading from its tower , calling the faithful to evensong . '</li><li>'In the tower , five men and women pull rhythmically on ropes attached to the same five bells that first sounded here in 1614 . '</li></ul> |
| 1 | <ul><li>'The parishioners of St. Michael and All Angels stop to chat at the church door , as members here always have . '</li><li>'History , after all , is not on his side . '</li><li>"According to a nationwide survey taken a year ago , nearly a third of England 's church bells are no longer rung on Sundays because there is no one to ring them . "</li></ul> |
| 2 | <ul><li>'Now , only one local ringer remains : 64-year-old Derek Hammond . '</li><li>'The others here today live elsewhere . '</li><li>'No one speaks , and the snaking of the ropes seems to make as much sound as the bells themselves , muffled by the ceiling . '</li></ul> |
| 0 | <ul><li>'`` To ring for even one service at this tower , we have to scrape , `` says Mr. Hammond , a retired water-authority worker . `` '</li><li>'When their changes are completed , and after they have worked up a sweat , ringers often skip off to the local pub , leaving worship for others below . '</li><li>"Two years ago , the Rev. Jeremy Hummerstone , vicar of Great Torrington , Devon , got so fed up with ringers who did n't attend service he sacked the entire band ; the ringers promptly set up a picket line in protest . "</li></ul> |
## Uses
### Direct Use for Inference
First install the SetFit library:
```bash
pip install setfit
```
Then you can load this model and run inference.
```python
from setfit import SetFitModel
# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("HelgeKn/Testing-blub")
# Run inference
preds = model("The others here today live elsewhere . ")
```
<!--
### Downstream Use
*List how someone could finetune this model on their own dataset.*
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
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### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count | 8 | 27.275 | 45 |
| Label | Training Sample Count |
|:------|:----------------------|
| 0 | 10 |
| 1 | 10 |
| 2 | 10 |
| 3 | 10 |
### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (2, 2)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 20
- body_learning_rate: (2e-05, 2e-05)
- head_learning_rate: 2e-05
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: False
- warmup_proportion: 0.1
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:-----:|:----:|:-------------:|:---------------:|
| 0.01 | 1 | 0.2799 | - |
| 0.5 | 50 | 0.1155 | - |
| 1.0 | 100 | 0.0023 | - |
| 1.5 | 150 | 0.0008 | - |
| 2.0 | 200 | 0.0017 | - |
### Framework Versions
- Python: 3.9.13
- SetFit: 1.0.1
- Sentence Transformers: 2.2.2
- Transformers: 4.36.0
- PyTorch: 2.1.1+cpu
- Datasets: 2.15.0
- Tokenizers: 0.15.0
## Citation
### BibTeX
```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}
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
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*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
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|
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Boss9xy/llama-2-7b-custom
|
Boss9xy
| 2023-12-17T14:47:32Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-17T14:47:32Z |
Entry not found
|
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] |
anhtunguyen98/llama2-squad
|
anhtunguyen98
| 2023-12-17T22:45:09Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-12-17T14:48:25Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: QuantizationMethod.BITS_AND_BYTES
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.5.0
|
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dmedhi/generate-news
|
dmedhi
| 2023-12-17T15:16:15Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2023-12-17T14:49:48Z |
---
license: apache-2.0
---
|
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kt220/review_classification_bert_base_jp_v3_ratio1_10_2label_add_dropout-epoch30_v4
|
kt220
| 2023-12-17T16:02:26Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:cl-tohoku/bert-base-japanese-v3",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-17T14:50:14Z |
---
license: apache-2.0
base_model: cl-tohoku/bert-base-japanese-v3
tags:
- generated_from_trainer
model-index:
- name: review_classification_bert_base_jp_v3_ratio1_10_2label_add_dropout-epoch30_v4
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. -->
# review_classification_bert_base_jp_v3_ratio1_10_2label_add_dropout-epoch30_v4
This model is a fine-tuned version of [cl-tohoku/bert-base-japanese-v3](https://huggingface.co/cl-tohoku/bert-base-japanese-v3) on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.4092
- eval_accuracy: 0.8931
- eval_runtime: 31.1064
- eval_samples_per_second: 62.881
- eval_steps_per_second: 3.954
- epoch: 4.0
- step: 1144
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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] |
TheBloke/go-bruins-v2.1.1-GPTQ
|
TheBloke
| 2023-12-17T15:20:42Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"base_model:rwitz2/go-bruins-v2.1.1",
"license:cc",
"autotrain_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] |
text-generation
| 2023-12-17T14:51:51Z |
---
base_model: rwitz2/go-bruins-v2.1.1
inference: false
license: cc
model_creator: ryan witzman
model_name: Go Bruins v2.1.1
model_type: mistral
prompt_template: '{prompt}
'
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 -->
# Go Bruins v2.1.1 - GPTQ
- Model creator: [ryan witzman](https://huggingface.co/rwitz2)
- Original model: [Go Bruins v2.1.1](https://huggingface.co/rwitz2/go-bruins-v2.1.1)
<!-- description start -->
# Description
This repo contains GPTQ model files for [ryan witzman's Go Bruins v2.1.1](https://huggingface.co/rwitz2/go-bruins-v2.1.1).
Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them.
These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).
<!-- description end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/go-bruins-v2.1.1-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/go-bruins-v2.1.1-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/go-bruins-v2.1.1-GGUF)
* [ryan witzman's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/rwitz2/go-bruins-v2.1.1)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: Unknown
```
{prompt}
```
<!-- prompt-template end -->
<!-- README_GPTQ.md-compatible clients start -->
## Known compatible clients / servers
GPTQ models are currently supported on Linux (NVidia/AMD) and Windows (NVidia only). macOS users: please use GGUF models.
These GPTQ models are known to work in the following inference servers/webuis.
- [text-generation-webui](https://github.com/oobabooga/text-generation-webui)
- [KoboldAI United](https://github.com/henk717/koboldai)
- [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui)
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference)
This may not be a complete list; if you know of others, please let me know!
<!-- README_GPTQ.md-compatible clients end -->
<!-- README_GPTQ.md-provided-files start -->
## Provided files, and GPTQ parameters
Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements.
Each separate quant is in a different branch. See below for instructions on fetching from different branches.
Most GPTQ files are made with AutoGPTQ. Mistral models are currently made with Transformers.
<details>
<summary>Explanation of GPTQ parameters</summary>
- Bits: The bit size of the quantised model.
- GS: GPTQ group size. Higher numbers use less VRAM, but have lower quantisation accuracy. "None" is the lowest possible value.
- Act Order: True or False. Also known as `desc_act`. True results in better quantisation accuracy. Some GPTQ clients have had issues with models that use Act Order plus Group Size, but this is generally resolved now.
- Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 0.01 is default, but 0.1 results in slightly better accuracy.
- GPTQ dataset: The calibration dataset used during quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ calibration dataset is not the same as the dataset used to train the model - please refer to the original model repo for details of the training dataset(s).
- Sequence Length: The length of the dataset sequences used for quantisation. Ideally this is the same as the model sequence length. For some very long sequence models (16+K), a lower sequence length may have to be used. Note that a lower sequence length does not limit the sequence length of the quantised model. It only impacts the quantisation accuracy on longer inference sequences.
- ExLlama Compatibility: Whether this file can be loaded with ExLlama, which currently only supports Llama and Mistral models in 4-bit.
</details>
| Branch | Bits | GS | Act Order | Damp % | GPTQ Dataset | Seq Len | Size | ExLlama | Desc |
| ------ | ---- | -- | --------- | ------ | ------------ | ------- | ---- | ------- | ---- |
| [main](https://huggingface.co/TheBloke/go-bruins-v2.1.1-GPTQ/tree/main) | 4 | 128 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 4.16 GB | Yes | 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. |
| [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/go-bruins-v2.1.1-GPTQ/tree/gptq-4bit-32g-actorder_True) | 4 | 32 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 4.57 GB | Yes | 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. |
| [gptq-8bit--1g-actorder_True](https://huggingface.co/TheBloke/go-bruins-v2.1.1-GPTQ/tree/gptq-8bit--1g-actorder_True) | 8 | None | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 7.52 GB | No | 8-bit, with Act Order. No group size, to lower VRAM requirements. |
| [gptq-8bit-128g-actorder_True](https://huggingface.co/TheBloke/go-bruins-v2.1.1-GPTQ/tree/gptq-8bit-128g-actorder_True) | 8 | 128 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 7.68 GB | No | 8-bit, with group size 128g for higher inference quality and with Act Order for even higher accuracy. |
| [gptq-8bit-32g-actorder_True](https://huggingface.co/TheBloke/go-bruins-v2.1.1-GPTQ/tree/gptq-8bit-32g-actorder_True) | 8 | 32 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 8.17 GB | No | 8-bit, with group size 32g and Act Order for maximum inference quality. |
| [gptq-4bit-64g-actorder_True](https://huggingface.co/TheBloke/go-bruins-v2.1.1-GPTQ/tree/gptq-4bit-64g-actorder_True) | 4 | 64 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 4.29 GB | Yes | 4-bit, with Act Order and group size 64g. Uses less VRAM than 32g, but with slightly lower accuracy. |
<!-- README_GPTQ.md-provided-files end -->
<!-- README_GPTQ.md-download-from-branches start -->
## How to download, including from branches
### In text-generation-webui
To download from the `main` branch, enter `TheBloke/go-bruins-v2.1.1-GPTQ` in the "Download model" box.
To download from another branch, add `:branchname` to the end of the download name, eg `TheBloke/go-bruins-v2.1.1-GPTQ:gptq-4bit-32g-actorder_True`
### From the command line
I recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub
```
To download the `main` branch to a folder called `go-bruins-v2.1.1-GPTQ`:
```shell
mkdir go-bruins-v2.1.1-GPTQ
huggingface-cli download TheBloke/go-bruins-v2.1.1-GPTQ --local-dir go-bruins-v2.1.1-GPTQ --local-dir-use-symlinks False
```
To download from a different branch, add the `--revision` parameter:
```shell
mkdir go-bruins-v2.1.1-GPTQ
huggingface-cli download TheBloke/go-bruins-v2.1.1-GPTQ --revision gptq-4bit-32g-actorder_True --local-dir go-bruins-v2.1.1-GPTQ --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage</summary>
If you remove the `--local-dir-use-symlinks False` parameter, the files will instead be stored in the central Hugging Face cache directory (default location on Linux is: `~/.cache/huggingface`), and symlinks will be added to the specified `--local-dir`, pointing to their real location in the cache. This allows for interrupted downloads to be resumed, and allows you to quickly clone the repo to multiple places on disk without triggering a download again. The downside, and the reason why I don't list that as the default option, is that the files are then hidden away in a cache folder and it's harder to know where your disk space is being used, and to clear it up if/when you want to remove a download model.
The cache location can be changed with the `HF_HOME` environment variable, and/or the `--cache-dir` parameter to `huggingface-cli`.
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
mkdir go-bruins-v2.1.1-GPTQ
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/go-bruins-v2.1.1-GPTQ --local-dir go-bruins-v2.1.1-GPTQ --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>
### With `git` (**not** recommended)
To clone a specific branch with `git`, use a command like this:
```shell
git clone --single-branch --branch gptq-4bit-32g-actorder_True https://huggingface.co/TheBloke/go-bruins-v2.1.1-GPTQ
```
Note that using Git with HF repos is strongly discouraged. It will be much slower than using `huggingface-hub`, and will use twice as much disk space as it has to store the model files twice (it stores every byte both in the intended target folder, and again in the `.git` folder as a blob.)
<!-- README_GPTQ.md-download-from-branches end -->
<!-- README_GPTQ.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/go-bruins-v2.1.1-GPTQ`.
- To download from a specific branch, enter for example `TheBloke/go-bruins-v2.1.1-GPTQ:gptq-4bit-32g-actorder_True`
- see Provided Files above for the list of branches for each option.
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: `go-bruins-v2.1.1-GPTQ`
7. The model will automatically load, and is now ready for use!
8. 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.
- Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file `quantize_config.json`.
9. Once you're ready, click the **Text Generation** tab and enter a prompt to get started!
<!-- README_GPTQ.md-text-generation-webui end -->
<!-- README_GPTQ.md-use-from-tgi start -->
## Serving this model from Text Generation Inference (TGI)
It's recommended to 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/go-bruins-v2.1.1-GPTQ --port 3000 --quantize gptq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096
```
Example Python code for interfacing with TGI (requires 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'''{prompt}
'''
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_GPTQ.md-use-from-tgi end -->
<!-- README_GPTQ.md-use-from-python start -->
## Python code example: inference from this GPTQ model
### Install the necessary packages
Requires: Transformers 4.33.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later.
```shell
pip3 install --upgrade transformers optimum
# If using PyTorch 2.1 + CUDA 12.x:
pip3 install --upgrade auto-gptq
# or, if using PyTorch 2.1 + CUDA 11.x:
pip3 install --upgrade auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/
```
If you are using PyTorch 2.0, you will need to install AutoGPTQ from source. Likewise if you have problems with the pre-built wheels, you should try building from source:
```shell
pip3 uninstall -y auto-gptq
git clone https://github.com/PanQiWei/AutoGPTQ
cd AutoGPTQ
git checkout v0.5.1
pip3 install .
```
### Example Python code
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
model_name_or_path = "TheBloke/go-bruins-v2.1.1-GPTQ"
# To use a different branch, change revision
# For example: revision="gptq-4bit-32g-actorder_True"
model = AutoModelForCausalLM.from_pretrained(model_name_or_path,
device_map="auto",
trust_remote_code=False,
revision="main")
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)
prompt = "Write a story about llamas"
system_message = "You are a story writing assistant"
prompt_template=f'''{prompt}
'''
print("\n\n*** Generate:")
input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512)
print(tokenizer.decode(output[0]))
# Inference can also be done using transformers' pipeline
print("*** Pipeline:")
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
repetition_penalty=1.1
)
print(pipe(prompt_template)[0]['generated_text'])
```
<!-- README_GPTQ.md-use-from-python end -->
<!-- README_GPTQ.md-compatibility start -->
## Compatibility
The files provided are tested to work with Transformers. For non-Mistral models, AutoGPTQ can also be used directly.
[ExLlama](https://github.com/turboderp/exllama) is compatible with Llama architecture models (including Mistral, Yi, DeepSeek, SOLAR, etc) in 4-bit. Please see the Provided Files table above for per-file compatibility.
For a list of clients/servers, please see "Known compatible clients / servers", above.
<!-- README_GPTQ.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: ryan witzman's Go Bruins v2.1.1
jan-hq/trinity-v1 DPO-trained on Intel/orca_dpo_pairs
#1 Model on the Leaderboard of ANY SIZE 12/16/2023
|
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TheBloke/go-bruins-v2.1.1-GGUF
|
TheBloke
| 2023-12-17T14:56:23Z | 0 | 3 |
transformers
|
[
"transformers",
"gguf",
"mistral",
"base_model:rwitz2/go-bruins-v2.1.1",
"license:cc",
"text-generation-inference",
"region:us"
] | null | 2023-12-17T14:51:51Z |
---
base_model: rwitz2/go-bruins-v2.1.1
inference: false
license: cc
model_creator: ryan witzman
model_name: Go Bruins v2.1.1
model_type: mistral
prompt_template: '{prompt}
'
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 -->
# Go Bruins v2.1.1 - GGUF
- Model creator: [ryan witzman](https://huggingface.co/rwitz2)
- Original model: [Go Bruins v2.1.1](https://huggingface.co/rwitz2/go-bruins-v2.1.1)
<!-- description start -->
## Description
This repo contains GGUF format model files for [ryan witzman's Go Bruins v2.1.1](https://huggingface.co/rwitz2/go-bruins-v2.1.1).
These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).
<!-- description end -->
<!-- README_GGUF.md-about-gguf start -->
### About GGUF
GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.
Here is an incomplete list of clients and libraries that are known to support GGUF:
* [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option.
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
* [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/go-bruins-v2.1.1-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/go-bruins-v2.1.1-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/go-bruins-v2.1.1-GGUF)
* [ryan witzman's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/rwitz2/go-bruins-v2.1.1)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: Unknown
```
{prompt}
```
<!-- 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 |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [go-bruins-v2.1.1.Q2_K.gguf](https://huggingface.co/TheBloke/go-bruins-v2.1.1-GGUF/blob/main/go-bruins-v2.1.1.Q2_K.gguf) | Q2_K | 2 | 3.08 GB| 5.58 GB | smallest, significant quality loss - not recommended for most purposes |
| [go-bruins-v2.1.1.Q3_K_S.gguf](https://huggingface.co/TheBloke/go-bruins-v2.1.1-GGUF/blob/main/go-bruins-v2.1.1.Q3_K_S.gguf) | Q3_K_S | 3 | 3.17 GB| 5.67 GB | very small, high quality loss |
| [go-bruins-v2.1.1.Q3_K_M.gguf](https://huggingface.co/TheBloke/go-bruins-v2.1.1-GGUF/blob/main/go-bruins-v2.1.1.Q3_K_M.gguf) | Q3_K_M | 3 | 3.52 GB| 6.02 GB | very small, high quality loss |
| [go-bruins-v2.1.1.Q3_K_L.gguf](https://huggingface.co/TheBloke/go-bruins-v2.1.1-GGUF/blob/main/go-bruins-v2.1.1.Q3_K_L.gguf) | Q3_K_L | 3 | 3.82 GB| 6.32 GB | small, substantial quality loss |
| [go-bruins-v2.1.1.Q4_0.gguf](https://huggingface.co/TheBloke/go-bruins-v2.1.1-GGUF/blob/main/go-bruins-v2.1.1.Q4_0.gguf) | Q4_0 | 4 | 4.11 GB| 6.61 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [go-bruins-v2.1.1.Q4_K_S.gguf](https://huggingface.co/TheBloke/go-bruins-v2.1.1-GGUF/blob/main/go-bruins-v2.1.1.Q4_K_S.gguf) | Q4_K_S | 4 | 4.14 GB| 6.64 GB | small, greater quality loss |
| [go-bruins-v2.1.1.Q4_K_M.gguf](https://huggingface.co/TheBloke/go-bruins-v2.1.1-GGUF/blob/main/go-bruins-v2.1.1.Q4_K_M.gguf) | Q4_K_M | 4 | 4.37 GB| 6.87 GB | medium, balanced quality - recommended |
| [go-bruins-v2.1.1.Q5_0.gguf](https://huggingface.co/TheBloke/go-bruins-v2.1.1-GGUF/blob/main/go-bruins-v2.1.1.Q5_0.gguf) | Q5_0 | 5 | 5.00 GB| 7.50 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [go-bruins-v2.1.1.Q5_K_S.gguf](https://huggingface.co/TheBloke/go-bruins-v2.1.1-GGUF/blob/main/go-bruins-v2.1.1.Q5_K_S.gguf) | Q5_K_S | 5 | 5.00 GB| 7.50 GB | large, low quality loss - recommended |
| [go-bruins-v2.1.1.Q5_K_M.gguf](https://huggingface.co/TheBloke/go-bruins-v2.1.1-GGUF/blob/main/go-bruins-v2.1.1.Q5_K_M.gguf) | Q5_K_M | 5 | 5.13 GB| 7.63 GB | large, very low quality loss - recommended |
| [go-bruins-v2.1.1.Q6_K.gguf](https://huggingface.co/TheBloke/go-bruins-v2.1.1-GGUF/blob/main/go-bruins-v2.1.1.Q6_K.gguf) | Q6_K | 6 | 5.94 GB| 8.44 GB | very large, extremely low quality loss |
| [go-bruins-v2.1.1.Q8_0.gguf](https://huggingface.co/TheBloke/go-bruins-v2.1.1-GGUF/blob/main/go-bruins-v2.1.1.Q8_0.gguf) | Q8_0 | 8 | 7.70 GB| 10.20 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/go-bruins-v2.1.1-GGUF and below it, a specific filename to download, such as: go-bruins-v2.1.1.Q4_K_M.gguf.
Then click Download.
### On the command line, including multiple files at once
I recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub
```
Then you can download any individual model file to the current directory, at high speed, with a command like this:
```shell
huggingface-cli download TheBloke/go-bruins-v2.1.1-GGUF go-bruins-v2.1.1.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/go-bruins-v2.1.1-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/go-bruins-v2.1.1-GGUF go-bruins-v2.1.1.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.
</details>
<!-- README_GGUF.md-how-to-download end -->
<!-- README_GGUF.md-how-to-run start -->
## Example `llama.cpp` command
Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later.
```shell
./main -ngl 35 -m go-bruins-v2.1.1.Q4_K_M.gguf --color -c 32768 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "{prompt}"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 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="./go-bruins-v2.1.1.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(
"{prompt}", # 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="./go-bruins-v2.1.1.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: ryan witzman's Go Bruins v2.1.1
jan-hq/trinity-v1 DPO-trained on Intel/orca_dpo_pairs
#1 Model on the Leaderboard of ANY SIZE 12/16/2023
<!-- original-model-card end -->
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parjisu/Wow
|
parjisu
| 2023-12-17T14:54:06Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-17T14:52:16Z |
Entry not found
|
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gdf2h1gfh2/Reuters-US-Domestic-News-Summary-Politics-af-updated
|
gdf2h1gfh2
| 2023-12-17T14:55:13Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-17T14:55:13Z |
Entry not found
|
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TheBloke/Mixtral-Fusion-4x7B-Instruct-v0.1-GGUF
|
TheBloke
| 2023-12-17T15:03:31Z | 0 | 1 |
transformers
|
[
"transformers",
"gguf",
"mixtral",
"fr",
"it",
"de",
"es",
"en",
"base_model:mmnga/Mixtral-Fusion-4x7B-Instruct-v0.1",
"license:apache-2.0",
"text-generation-inference",
"region:us"
] | null | 2023-12-17T14:55:44Z |
---
base_model: mmnga/Mixtral-Fusion-4x7B-Instruct-v0.1
inference: false
language:
- fr
- it
- de
- es
- en
license: apache-2.0
model_creator: momonga
model_name: Mixtral Fusion 4X7B Instruct v0.1
model_type: mixtral
prompt_template: '[INST] {prompt} [/INST]
'
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;">
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</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 -->
# Mixtral Fusion 4X7B Instruct v0.1 - GGUF
- Model creator: [momonga](https://huggingface.co/mmnga)
- Original model: [Mixtral Fusion 4X7B Instruct v0.1](https://huggingface.co/mmnga/Mixtral-Fusion-4x7B-Instruct-v0.1)
<!-- description start -->
## Description
This repo contains GGUF format model files for [momonga's Mixtral Fusion 4X7B Instruct v0.1](https://huggingface.co/mmnga/Mixtral-Fusion-4x7B-Instruct-v0.1).
<!-- 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.
### Mixtral GGUF
Support for Mixtral was merged into Llama.cpp on December 13th.
These Mixtral GGUFs are known to work in:
* llama.cpp as of December 13th
* KoboldCpp 1.52 as later
* LM Studio 0.2.9 and later
* llama-cpp-python 0.2.23 and later
Other clients/libraries, not listed above, may not yet work.
<!-- README_GGUF.md-about-gguf end -->
<!-- repositories-available start -->
## Repositories available
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Mixtral-Fusion-4x7B-Instruct-v0.1-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Mixtral-Fusion-4x7B-Instruct-v0.1-GGUF)
* [momonga's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/mmnga/Mixtral-Fusion-4x7B-Instruct-v0.1)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: Mistral
```
[INST] {prompt} [/INST]
```
<!-- prompt-template end -->
<!-- compatibility_gguf start -->
## Compatibility
These Mixtral GGUFs are compatible with llama.cpp from December 13th onwards. Other clients/libraries may not work yet.
## 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 |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [mixtral-fusion-4x7b-instruct-v0.1.Q2_K.gguf](https://huggingface.co/TheBloke/Mixtral-Fusion-4x7B-Instruct-v0.1-GGUF/blob/main/mixtral-fusion-4x7b-instruct-v0.1.Q2_K.gguf) | Q2_K | 2 | 8.06 GB| 10.56 GB | smallest, significant quality loss - not recommended for most purposes |
| [mixtral-fusion-4x7b-instruct-v0.1.Q3_K_M.gguf](https://huggingface.co/TheBloke/Mixtral-Fusion-4x7B-Instruct-v0.1-GGUF/blob/main/mixtral-fusion-4x7b-instruct-v0.1.Q3_K_M.gguf) | Q3_K_M | 3 | 10.52 GB| 13.02 GB | very small, high quality loss |
| [mixtral-fusion-4x7b-instruct-v0.1.Q4_0.gguf](https://huggingface.co/TheBloke/Mixtral-Fusion-4x7B-Instruct-v0.1-GGUF/blob/main/mixtral-fusion-4x7b-instruct-v0.1.Q4_0.gguf) | Q4_0 | 4 | 13.62 GB| 16.12 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [mixtral-fusion-4x7b-instruct-v0.1.Q4_K_M.gguf](https://huggingface.co/TheBloke/Mixtral-Fusion-4x7B-Instruct-v0.1-GGUF/blob/main/mixtral-fusion-4x7b-instruct-v0.1.Q4_K_M.gguf) | Q4_K_M | 4 | 13.64 GB| 16.14 GB | medium, balanced quality - recommended |
| [mixtral-fusion-4x7b-instruct-v0.1.Q5_0.gguf](https://huggingface.co/TheBloke/Mixtral-Fusion-4x7B-Instruct-v0.1-GGUF/blob/main/mixtral-fusion-4x7b-instruct-v0.1.Q5_0.gguf) | Q5_0 | 5 | 16.63 GB| 19.13 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [mixtral-fusion-4x7b-instruct-v0.1.Q5_K_M.gguf](https://huggingface.co/TheBloke/Mixtral-Fusion-4x7B-Instruct-v0.1-GGUF/blob/main/mixtral-fusion-4x7b-instruct-v0.1.Q5_K_M.gguf) | Q5_K_M | 5 | 16.64 GB| 19.14 GB | large, very low quality loss - recommended |
| [mixtral-fusion-4x7b-instruct-v0.1.Q6_K.gguf](https://huggingface.co/TheBloke/Mixtral-Fusion-4x7B-Instruct-v0.1-GGUF/blob/main/mixtral-fusion-4x7b-instruct-v0.1.Q6_K.gguf) | Q6_K | 6 | 19.82 GB| 22.32 GB | very large, extremely low quality loss |
| [mixtral-fusion-4x7b-instruct-v0.1.Q8_0.gguf](https://huggingface.co/TheBloke/Mixtral-Fusion-4x7B-Instruct-v0.1-GGUF/blob/main/mixtral-fusion-4x7b-instruct-v0.1.Q8_0.gguf) | Q8_0 | 8 | 25.67 GB| 28.17 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/Mixtral-Fusion-4x7B-Instruct-v0.1-GGUF and below it, a specific filename to download, such as: mixtral-fusion-4x7b-instruct-v0.1.Q4_K_M.gguf.
Then click Download.
### On the command line, including multiple files at once
I recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub
```
Then you can download any individual model file to the current directory, at high speed, with a command like this:
```shell
huggingface-cli download TheBloke/Mixtral-Fusion-4x7B-Instruct-v0.1-GGUF mixtral-fusion-4x7b-instruct-v0.1.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/Mixtral-Fusion-4x7B-Instruct-v0.1-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/Mixtral-Fusion-4x7B-Instruct-v0.1-GGUF mixtral-fusion-4x7b-instruct-v0.1.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.
</details>
<!-- README_GGUF.md-how-to-download end -->
<!-- README_GGUF.md-how-to-run start -->
## Example `llama.cpp` command
Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later.
```shell
./main -ngl 35 -m mixtral-fusion-4x7b-instruct-v0.1.Q4_K_M.gguf --color -c 32768 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "[INST] {prompt} [/INST]"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 32768` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value.
If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`
For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md)
## How to run in `text-generation-webui`
Note that text-generation-webui may not yet be compatible with Mixtral GGUFs. Please check compatibility first.
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) version 0.2.23 and later.
### 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="./mixtral-fusion-4x7b-instruct-v0.1.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(
"[INST] {prompt} [/INST]", # Prompt
max_tokens=512, # Generate up to 512 tokens
stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using.
echo=True # Whether to echo the prompt
)
# Chat Completion API
llm = Llama(model_path="./mixtral-fusion-4x7b-instruct-v0.1.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)
<!-- 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: momonga's Mixtral Fusion 4X7B Instruct v0.1
# Model Card for Mixtral-Fusion-4x7B-Instruct-v0.1
This model is an experimental model created by merging [mistralai/Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1) experts.
# How we merged experts
We simply take the average of every two experts.weight.
The same goes for gate.weight.
# How To Convert
use colab cpu-high-memory.
[convert_mixtral_8x7b_to_4x7b.ipynb](https://huggingface.co/mmnga/Mixtral-Fusion-4x7B-Instruct-v0.1/blob/main/notebook/convert_mixtral_8x7b_to_4x7b.ipynb)
# Usage
~~~python
pip install git+https://github.com/huggingface/transformers --upgrade
pip install torch accelerate bitsandbytes flash_attn
~~~
~~~python
from transformers import AutoTokenizer, AutoModelForCausalLM, MixtralForCausalLM
import torch
model_name_or_path = "mmnga/Mixtral-Fusion-4x7B-Instruct-v0.1"
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
model = MixtralForCausalLM.from_pretrained(model_name_or_path, load_in_8bit=True)
# set num_experts_per_tok 1 or 2 ?
model.config.num_experts_per_tok = 2
# message
messages = [
{"role": "user", "content": "Tell me what's for dinner tonight."},
]
with torch.no_grad():
token_ids = tokenizer.apply_chat_template(messages, return_tensors="pt")
output_ids = model.generate(
token_ids.to(model.device),
temperature=0.5,
do_sample=True,
top_p=0.95,
top_k=40,
max_new_tokens=128,
repetition_penalty=1.5
)
output = tokenizer.decode(output_ids[0][token_ids.size(1) :])
print(output)
~~~
<!-- original-model-card end -->
|
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] |
anhtunguyen98/llama-commonsense
|
anhtunguyen98
| 2023-12-17T22:46:06Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-12-17T14:56:46Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: QuantizationMethod.BITS_AND_BYTES
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.5.0
|
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] |
BramDelisse/25_32-k-Murdoch_data
|
BramDelisse
| 2023-12-17T15:06:51Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-17T14:56:59Z |
Entry not found
|
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] |
vbanda/mistral-finetuned-samsum
|
vbanda
| 2023-12-17T15:40:19Z | 0 | 0 |
peft
|
[
"peft",
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:TheBloke/Mistral-7B-Instruct-v0.1-GPTQ",
"license:apache-2.0",
"region:us"
] | null | 2023-12-17T14:57:08Z |
---
license: apache-2.0
library_name: peft
tags:
- generated_from_trainer
base_model: TheBloke/Mistral-7B-Instruct-v0.1-GPTQ
model-index:
- name: mistral-finetuned-samsum
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mistral-finetuned-samsum
This model is a fine-tuned version of [TheBloke/Mistral-7B-Instruct-v0.1-GPTQ](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GPTQ) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- training_steps: 250
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.7.1
- Transformers 4.37.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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alokproc/my-new-shiny-tokenizer
|
alokproc
| 2023-12-17T14:57:47Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-17T14:57:47Z |
Entry not found
|
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] |
Abdulla2010/Hamad
|
Abdulla2010
| 2023-12-17T14:58:32Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2023-12-17T14:58:32Z |
---
license: apache-2.0
---
|
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] |
Azulejo/Nicc2
|
Azulejo
| 2023-12-17T15:02:59Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-17T14:59:35Z |
---
license: openrail
---
|
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] |
Mik99/llama2_7b_10_samples_per_feature_class
|
Mik99
| 2023-12-17T15:01:00Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-2-7b-chat-hf",
"region:us"
] | null | 2023-12-17T15:00:34Z |
---
library_name: peft
base_model: meta-llama/Llama-2-7b-chat-hf
---
# Model Card for Model ID
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## Training Details
### Training Data
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[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
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#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
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## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
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**APA:**
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## More Information [optional]
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## Model Card Authors [optional]
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## Model Card Contact
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### Framework versions
- PEFT 0.7.1
|
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shenshaowei/gpt2-xl-sentiment
|
shenshaowei
| 2023-12-17T15:02:57Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-17T15:02:17Z |
该研究的主要目标是深入研究CriticControl在引导文本生成中的潜力，特别是在情感控制任务中，通过情感提示引导模型生成积极的电影评论，数据集选择了IMDB电影评论数据集，包含2.5K条用于测试的极性评论，涵盖了积极和消极的情感倾向。通过积极性、流畅性、多样性和人为评价指标评估各基准系统的性能和指标：
|
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chathuru/CuATR-distilbert-v1.1
|
chathuru
| 2023-12-17T15:16:07Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-17T15:03:43Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: CuATR-distilbert-v1.1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# CuATR-distilbert-v1.1
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: 0.0075
- Accuracy: 1.0
## 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: 14
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 7 | 0.5010 | 1.0 |
| No log | 2.0 | 14 | 0.3674 | 0.95 |
| No log | 3.0 | 21 | 0.1793 | 1.0 |
| No log | 4.0 | 28 | 0.0852 | 1.0 |
| No log | 5.0 | 35 | 0.0432 | 1.0 |
| No log | 6.0 | 42 | 0.0256 | 1.0 |
| No log | 7.0 | 49 | 0.0165 | 1.0 |
| No log | 8.0 | 56 | 0.0125 | 1.0 |
| No log | 9.0 | 63 | 0.0103 | 1.0 |
| No log | 10.0 | 70 | 0.0091 | 1.0 |
| No log | 11.0 | 77 | 0.0083 | 1.0 |
| No log | 12.0 | 84 | 0.0078 | 1.0 |
| No log | 13.0 | 91 | 0.0076 | 1.0 |
| No log | 14.0 | 98 | 0.0075 | 1.0 |
### Framework versions
- Transformers 4.36.1
- Pytorch 2.1.2+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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Aradwy/Shastoon_aradwy_500Epoch
|
Aradwy
| 2023-12-17T15:07:49Z | 0 | 0 | null |
[
"license:unknown",
"region:us"
] | null | 2023-12-17T15:04:20Z |
---
license: unknown
---
|
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] |
LoneStriker/Mixtral-8x7B-v0.1-2.4bpw-h6-exl2-2
|
LoneStriker
| 2023-12-17T16:06:39Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mixtral",
"text-generation",
"fr",
"it",
"de",
"es",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-17T15:05:21Z |
---
license: apache-2.0
language:
- fr
- it
- de
- es
- en
---
# Model Card for Mixtral-8x7B
The Mixtral-8x7B Large Language Model (LLM) is a pretrained generative Sparse Mixture of Experts. The Mistral-8x7B outperforms Llama 2 70B on most benchmarks we tested.
For full details of this model please read our [release blog post](https://mistral.ai/news/mixtral-of-experts/).
## Warning
This repo contains weights that are compatible with [vLLM](https://github.com/vllm-project/vllm) serving of the model as well as Hugging Face [transformers](https://github.com/huggingface/transformers) library. It is based on the original Mixtral [torrent release](magnet:?xt=urn:btih:5546272da9065eddeb6fcd7ffddeef5b75be79a7&dn=mixtral-8x7b-32kseqlen&tr=udp%3A%2F%http://2Fopentracker.i2p.rocks%3A6969%2Fannounce&tr=http%3A%2F%http://2Ftracker.openbittorrent.com%3A80%2Fannounce), but the file format and parameter names are different. Please note that model cannot (yet) be instantiated with HF.
## Run the model
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "mistralai/Mixtral-8x7B-v0.1"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
text = "Hello my name is"
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=20)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
By default, transformers will load the model in full precision. Therefore you might be interested to further reduce down the memory requirements to run the model through the optimizations we offer in HF ecosystem:
### In half-precision
Note `float16` precision only works on GPU devices
<details>
<summary> Click to expand </summary>
```diff
+ import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "mistralai/Mixtral-8x7B-v0.1"
tokenizer = AutoTokenizer.from_pretrained(model_id)
+ model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16).to(0)
text = "Hello my name is"
+ inputs = tokenizer(text, return_tensors="pt").to(0)
outputs = model.generate(**inputs, max_new_tokens=20)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
</details>
### Lower precision using (8-bit & 4-bit) using `bitsandbytes`
<details>
<summary> Click to expand </summary>
```diff
+ import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "mistralai/Mixtral-8x7B-v0.1"
tokenizer = AutoTokenizer.from_pretrained(model_id)
+ model = AutoModelForCausalLM.from_pretrained(model_id, load_in_4bit=True)
text = "Hello my name is"
+ inputs = tokenizer(text, return_tensors="pt").to(0)
outputs = model.generate(**inputs, max_new_tokens=20)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
</details>
### Load the model with Flash Attention 2
<details>
<summary> Click to expand </summary>
```diff
+ import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "mistralai/Mixtral-8x7B-v0.1"
tokenizer = AutoTokenizer.from_pretrained(model_id)
+ model = AutoModelForCausalLM.from_pretrained(model_id, use_flash_attention_2=True)
text = "Hello my name is"
+ inputs = tokenizer(text, return_tensors="pt").to(0)
outputs = model.generate(**inputs, max_new_tokens=20)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
</details>
## Notice
Mixtral-8x7B is a pretrained base model and therefore does not have any moderation mechanisms.
# The Mistral AI Team
Albert Jiang, Alexandre Sablayrolles, Arthur Mensch, Blanche Savary, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Emma Bou Hanna, Florian Bressand, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Lélio Renard Lavaud, Louis Ternon, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Théophile Gervet, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed.
|
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] |
AICODER009/distilbert-base-uncased-finetuned-emotion
|
AICODER009
| 2023-12-17T15:25:04Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-17T15:05:43Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: split
split: validation
args: split
metrics:
- name: Accuracy
type: accuracy
value: 0.937
- name: F1
type: f1
value: 0.9369696658764198
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1395
- Accuracy: 0.937
- F1: 0.9370
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.1356 | 1.0 | 250 | 0.1475 | 0.9355 | 0.9356 |
| 0.0968 | 2.0 | 500 | 0.1395 | 0.937 | 0.9370 |
### Framework versions
- Transformers 4.36.1
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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] |
bachngo/111
|
bachngo
| 2023-12-17T15:15:27Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-17T15:06:47Z |
Entry not found
|
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] |
FaridTA/ddpm-butterflies-128
|
FaridTA
| 2023-12-17T22:06:32Z | 0 | 0 | null |
[
"tensorboard",
"license:mit",
"region:us"
] | null | 2023-12-17T15:07:03Z |
---
license: mit
---
|
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] |
Kooten/Noromaid-13b-v0.2-8bpw-exl2
|
Kooten
| 2023-12-17T15:42:16Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-17T15:09:18Z |
---
license: cc-by-nc-4.0
---
# This is a 8BPW EXL2 quant of Noromaid-13b-v0.2
Exllama 2 quant of [NeverSleep/Noromaid-13b-v0.2](https://huggingface.co/NeverSleep/Noromaid-13b-v0.2)
## Prompt template: Custom format, or Alpaca
### Custom format:
SillyTavern config files: [Context](https://files.catbox.moe/ifmhai.json), [Instruct](https://files.catbox.moe/ttw1l9.json).
### Alpaca:
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
```
|
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] |
rinormaloku/test
|
rinormaloku
| 2023-12-17T23:14:02Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"phi-msft",
"text-generation",
"custom_code",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-12-17T15:09:31Z |
Entry not found
|
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] |
Kooten/Noromaid-13b-v0.2-4bpw-exl2
|
Kooten
| 2023-12-17T15:21:54Z | 0 | 1 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-17T15:11:21Z |
---
license: cc-by-nc-4.0
---
# This is a 4BPW EXL2 quant of Noromaid-13b-v0.2
Exllama 2 quant of [NeverSleep/Noromaid-13b-v0.2](https://huggingface.co/NeverSleep/Noromaid-13b-v0.2)
## Prompt template: Custom format, or Alpaca
### Custom format:
SillyTavern config files: [Context](https://files.catbox.moe/ifmhai.json), [Instruct](https://files.catbox.moe/ttw1l9.json).
### Alpaca:
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
```
|
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parjisu/llama2
|
parjisu
| 2023-12-17T15:12:36Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-17T15:11:43Z |
Entry not found
|
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CircleRadon/Osprey-7b
|
CircleRadon
| 2023-12-17T15:12:54Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-17T15:12:54Z |
Entry not found
|
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TMK04/sheep-duck-llama-2-70b-v1.1-6.0bpw-h6-exl2
|
TMK04
| 2023-12-17T15:15:45Z | 0 | 0 | null |
[
"license:llama2",
"region:us"
] | null | 2023-12-17T15:15:45Z |
---
license: llama2
---
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] |
fatihay/a8gq-jrt6-zuvu-0
|
fatihay
| 2023-12-17T15:16:00Z | 0 | 0 | null |
[
"safetensors",
"autotrain",
"text-generation",
"license:other",
"region:us"
] |
text-generation
| 2023-12-17T15:15:58Z |
---
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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Jaym1207/character_model_v1
|
Jaym1207
| 2023-12-17T15:43:32Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-17T15:18:48Z |
Entry not found
|
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chathuru/CuATR-distilbert-v1.2
|
chathuru
| 2023-12-17T15:19:16Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-17T15:19:01Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: CuATR-distilbert-v1.2
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. -->
# CuATR-distilbert-v1.2
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: 0.0004
- Accuracy: 1.0
- F1: 1.0
## 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: 14
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---:|
| No log | 1.0 | 7 | 0.0033 | 1.0 | 1.0 |
| No log | 2.0 | 14 | 0.0020 | 1.0 | 1.0 |
| No log | 3.0 | 21 | 0.0013 | 1.0 | 1.0 |
| No log | 4.0 | 28 | 0.0010 | 1.0 | 1.0 |
| No log | 5.0 | 35 | 0.0008 | 1.0 | 1.0 |
| No log | 6.0 | 42 | 0.0007 | 1.0 | 1.0 |
| No log | 7.0 | 49 | 0.0006 | 1.0 | 1.0 |
| No log | 8.0 | 56 | 0.0005 | 1.0 | 1.0 |
| No log | 9.0 | 63 | 0.0005 | 1.0 | 1.0 |
| No log | 10.0 | 70 | 0.0004 | 1.0 | 1.0 |
| No log | 11.0 | 77 | 0.0004 | 1.0 | 1.0 |
| No log | 12.0 | 84 | 0.0004 | 1.0 | 1.0 |
| No log | 13.0 | 91 | 0.0004 | 1.0 | 1.0 |
| No log | 14.0 | 98 | 0.0004 | 1.0 | 1.0 |
### Framework versions
- Transformers 4.36.1
- Pytorch 2.1.2+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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] |
paateus/Eunha_GFRIEND
|
paateus
| 2023-12-17T15:26:03Z | 0 | 1 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2023-12-17T15:19:31Z |
---
license: apache-2.0
---
|
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] |
TheBloke/PiVoT-10.7B-Mistral-v0.2-RP-AWQ
|
TheBloke
| 2023-12-17T15:48:49Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"en",
"base_model:maywell/PiVoT-10.7B-Mistral-v0.2-RP",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] |
text-generation
| 2023-12-17T15:22:05Z |
---
base_model: maywell/PiVoT-10.7B-Mistral-v0.2-RP
inference: false
language:
- en
license: cc-by-sa-4.0
model_creator: Jeonghwan Park
model_name: Pivot 10.7B Mistral V0.2 RP
model_type: mistral
pipeline_tag: text-generation
prompt_template: '[INST] {prompt} [/INST]
'
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 -->
# Pivot 10.7B Mistral V0.2 RP - AWQ
- Model creator: [Jeonghwan Park](https://huggingface.co/maywell)
- Original model: [Pivot 10.7B Mistral V0.2 RP](https://huggingface.co/maywell/PiVoT-10.7B-Mistral-v0.2-RP)
<!-- description start -->
## Description
This repo contains AWQ model files for [Jeonghwan Park's Pivot 10.7B Mistral V0.2 RP](https://huggingface.co/maywell/PiVoT-10.7B-Mistral-v0.2-RP).
These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).
### 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/PiVoT-10.7B-Mistral-v0.2-RP-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/PiVoT-10.7B-Mistral-v0.2-RP-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/PiVoT-10.7B-Mistral-v0.2-RP-GGUF)
* [Jeonghwan Park's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/maywell/PiVoT-10.7B-Mistral-v0.2-RP)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: Mistral
```
[INST] {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/PiVoT-10.7B-Mistral-v0.2-RP-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/PiVoT-10.7B-Mistral-v0.2-RP-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: `PiVoT-10.7B-Mistral-v0.2-RP-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/PiVoT-10.7B-Mistral-v0.2-RP-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] {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/PiVoT-10.7B-Mistral-v0.2-RP-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/PiVoT-10.7B-Mistral-v0.2-RP-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] {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/PiVoT-10.7B-Mistral-v0.2-RP-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] {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: Jeonghwan Park's Pivot 10.7B Mistral V0.2 RP
# PiVoT-10.7B-Mistral-v0.2-RP
# **Model Details**
### Description
PivoT is RP Finetuned model based on PiVoT-10.7B-Mistral-v0.2. Private RP dataset collected from ArcaLive Ai Chat Chan used.
This model has sequence length(Sliding Window) of **8192**
Follow me on twitter: https://twitter.com/stablefluffy
Consider Support me making these model alone: https://www.buymeacoffee.com/mwell or with Runpod Credit Gift 💕
Contact me on Telegram: https://t.me/AlzarTakkarsen
|
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TheBloke/PiVoT-10.7B-Mistral-v0.2-RP-GGUF
|
TheBloke
| 2023-12-17T15:28:20Z | 0 | 2 |
transformers
|
[
"transformers",
"gguf",
"mistral",
"text-generation",
"en",
"base_model:maywell/PiVoT-10.7B-Mistral-v0.2-RP",
"license:cc-by-sa-4.0",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-17T15:22:05Z |
---
base_model: maywell/PiVoT-10.7B-Mistral-v0.2-RP
inference: false
language:
- en
license: cc-by-sa-4.0
model_creator: Jeonghwan Park
model_name: Pivot 10.7B Mistral V0.2 RP
model_type: mistral
pipeline_tag: text-generation
prompt_template: '[INST] {prompt} [/INST]
'
quantized_by: TheBloke
---
<!-- markdownlint-disable MD041 -->
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<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>
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<!-- header end -->
# Pivot 10.7B Mistral V0.2 RP - GGUF
- Model creator: [Jeonghwan Park](https://huggingface.co/maywell)
- Original model: [Pivot 10.7B Mistral V0.2 RP](https://huggingface.co/maywell/PiVoT-10.7B-Mistral-v0.2-RP)
<!-- description start -->
## Description
This repo contains GGUF format model files for [Jeonghwan Park's Pivot 10.7B Mistral V0.2 RP](https://huggingface.co/maywell/PiVoT-10.7B-Mistral-v0.2-RP).
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/PiVoT-10.7B-Mistral-v0.2-RP-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/PiVoT-10.7B-Mistral-v0.2-RP-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/PiVoT-10.7B-Mistral-v0.2-RP-GGUF)
* [Jeonghwan Park's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/maywell/PiVoT-10.7B-Mistral-v0.2-RP)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: Mistral
```
[INST] {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 |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [pivot-10.7b-mistral-v0.2-rp.Q2_K.gguf](https://huggingface.co/TheBloke/PiVoT-10.7B-Mistral-v0.2-RP-GGUF/blob/main/pivot-10.7b-mistral-v0.2-rp.Q2_K.gguf) | Q2_K | 2 | 4.55 GB| 7.05 GB | smallest, significant quality loss - not recommended for most purposes |
| [pivot-10.7b-mistral-v0.2-rp.Q3_K_S.gguf](https://huggingface.co/TheBloke/PiVoT-10.7B-Mistral-v0.2-RP-GGUF/blob/main/pivot-10.7b-mistral-v0.2-rp.Q3_K_S.gguf) | Q3_K_S | 3 | 4.67 GB| 7.17 GB | very small, high quality loss |
| [pivot-10.7b-mistral-v0.2-rp.Q3_K_M.gguf](https://huggingface.co/TheBloke/PiVoT-10.7B-Mistral-v0.2-RP-GGUF/blob/main/pivot-10.7b-mistral-v0.2-rp.Q3_K_M.gguf) | Q3_K_M | 3 | 5.19 GB| 7.69 GB | very small, high quality loss |
| [pivot-10.7b-mistral-v0.2-rp.Q3_K_L.gguf](https://huggingface.co/TheBloke/PiVoT-10.7B-Mistral-v0.2-RP-GGUF/blob/main/pivot-10.7b-mistral-v0.2-rp.Q3_K_L.gguf) | Q3_K_L | 3 | 5.65 GB| 8.15 GB | small, substantial quality loss |
| [pivot-10.7b-mistral-v0.2-rp.Q4_0.gguf](https://huggingface.co/TheBloke/PiVoT-10.7B-Mistral-v0.2-RP-GGUF/blob/main/pivot-10.7b-mistral-v0.2-rp.Q4_0.gguf) | Q4_0 | 4 | 6.07 GB| 8.57 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [pivot-10.7b-mistral-v0.2-rp.Q4_K_S.gguf](https://huggingface.co/TheBloke/PiVoT-10.7B-Mistral-v0.2-RP-GGUF/blob/main/pivot-10.7b-mistral-v0.2-rp.Q4_K_S.gguf) | Q4_K_S | 4 | 6.10 GB| 8.60 GB | small, greater quality loss |
| [pivot-10.7b-mistral-v0.2-rp.Q4_K_M.gguf](https://huggingface.co/TheBloke/PiVoT-10.7B-Mistral-v0.2-RP-GGUF/blob/main/pivot-10.7b-mistral-v0.2-rp.Q4_K_M.gguf) | Q4_K_M | 4 | 6.46 GB| 8.96 GB | medium, balanced quality - recommended |
| [pivot-10.7b-mistral-v0.2-rp.Q5_0.gguf](https://huggingface.co/TheBloke/PiVoT-10.7B-Mistral-v0.2-RP-GGUF/blob/main/pivot-10.7b-mistral-v0.2-rp.Q5_0.gguf) | Q5_0 | 5 | 7.40 GB| 9.90 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [pivot-10.7b-mistral-v0.2-rp.Q5_K_S.gguf](https://huggingface.co/TheBloke/PiVoT-10.7B-Mistral-v0.2-RP-GGUF/blob/main/pivot-10.7b-mistral-v0.2-rp.Q5_K_S.gguf) | Q5_K_S | 5 | 7.40 GB| 9.90 GB | large, low quality loss - recommended |
| [pivot-10.7b-mistral-v0.2-rp.Q5_K_M.gguf](https://huggingface.co/TheBloke/PiVoT-10.7B-Mistral-v0.2-RP-GGUF/blob/main/pivot-10.7b-mistral-v0.2-rp.Q5_K_M.gguf) | Q5_K_M | 5 | 7.60 GB| 10.10 GB | large, very low quality loss - recommended |
| [pivot-10.7b-mistral-v0.2-rp.Q6_K.gguf](https://huggingface.co/TheBloke/PiVoT-10.7B-Mistral-v0.2-RP-GGUF/blob/main/pivot-10.7b-mistral-v0.2-rp.Q6_K.gguf) | Q6_K | 6 | 8.81 GB| 11.31 GB | very large, extremely low quality loss |
| [pivot-10.7b-mistral-v0.2-rp.Q8_0.gguf](https://huggingface.co/TheBloke/PiVoT-10.7B-Mistral-v0.2-RP-GGUF/blob/main/pivot-10.7b-mistral-v0.2-rp.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/PiVoT-10.7B-Mistral-v0.2-RP-GGUF and below it, a specific filename to download, such as: pivot-10.7b-mistral-v0.2-rp.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/PiVoT-10.7B-Mistral-v0.2-RP-GGUF pivot-10.7b-mistral-v0.2-rp.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/PiVoT-10.7B-Mistral-v0.2-RP-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/PiVoT-10.7B-Mistral-v0.2-RP-GGUF pivot-10.7b-mistral-v0.2-rp.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 pivot-10.7b-mistral-v0.2-rp.Q4_K_M.gguf --color -c 32768 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "[INST] {prompt} [/INST]"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 32768` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value.
If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`
For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md)
## 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="./pivot-10.7b-mistral-v0.2-rp.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(
"[INST] {prompt} [/INST]", # Prompt
max_tokens=512, # Generate up to 512 tokens
stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using.
echo=True # Whether to echo the prompt
)
# Chat Completion API
llm = Llama(model_path="./pivot-10.7b-mistral-v0.2-rp.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: Jeonghwan Park's Pivot 10.7B Mistral V0.2 RP
# PiVoT-10.7B-Mistral-v0.2-RP
# **Model Details**
### Description
PivoT is RP Finetuned model based on PiVoT-10.7B-Mistral-v0.2. Private RP dataset collected from ArcaLive Ai Chat Chan used.
This model has sequence length(Sliding Window) of **8192**
Follow me on twitter: https://twitter.com/stablefluffy
Consider Support me making these model alone: https://www.buymeacoffee.com/mwell or with Runpod Credit Gift 💕
Contact me on Telegram: https://t.me/AlzarTakkarsen
<!-- original-model-card end -->
|
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Ansu/qa_model
|
Ansu
| 2023-12-17T15:25:11Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-17T15:25:11Z |
Entry not found
|
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] |
sunshine-lwt/Osprey-7b
|
sunshine-lwt
| 2023-12-17T16:08:49Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"osprey",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-12-17T15:25:32Z |
Entry not found
|
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] |
TheBloke/BigPlap-8x20B-GGUF
|
TheBloke
| 2023-12-17T18:30:52Z | 0 | 1 |
transformers
|
[
"transformers",
"gguf",
"mixtral",
"not-for-all-audiences",
"nsfw",
"base_model:Undi95/BigPlap-8x20B",
"license:cc-by-nc-4.0",
"text-generation-inference",
"region:us"
] | null | 2023-12-17T15:26:15Z |
---
base_model: Undi95/BigPlap-8x20B
inference: false
license: cc-by-nc-4.0
model_creator: Undi
model_name: Bigplap 8X20B
model_type: mixtral
prompt_template: '{prompt}
'
quantized_by: TheBloke
tags:
- not-for-all-audiences
- nsfw
---
<!-- markdownlint-disable MD041 -->
<!-- header start -->
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<div style="width: auto; margin-left: auto; margin-right: auto">
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<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 -->
# Bigplap 8X20B - GGUF
- Model creator: [Undi](https://huggingface.co/Undi95)
- Original model: [Bigplap 8X20B](https://huggingface.co/Undi95/BigPlap-8x20B)
<!-- description start -->
## Description
This repo contains GGUF format model files for [Undi's Bigplap 8X20B](https://huggingface.co/Undi95/BigPlap-8x20B).
<!-- 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.
### Mixtral GGUF
Support for Mixtral was merged into Llama.cpp on December 13th.
These Mixtral GGUFs are known to work in:
* llama.cpp as of December 13th
* KoboldCpp 1.52 as later
* LM Studio 0.2.9 and later
* llama-cpp-python 0.2.23 and later
Other clients/libraries, not listed above, may not yet work.
<!-- README_GGUF.md-about-gguf end -->
<!-- repositories-available start -->
## Repositories available
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/BigPlap-8x20B-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/BigPlap-8x20B-GGUF)
* [Undi's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/Undi95/BigPlap-8x20B)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: Unknown
```
{prompt}
```
<!-- prompt-template end -->
<!-- compatibility_gguf start -->
## Compatibility
These Mixtral GGUFs are compatible with llama.cpp from December 13th onwards. Other clients/libraries may not work yet.
## 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 |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [bigplap-8x20b.Q2_K.gguf](https://huggingface.co/TheBloke/BigPlap-8x20B-GGUF/blob/main/bigplap-8x20b.Q2_K.gguf) | Q2_K | 2 | 39.44 GB| 41.94 GB | smallest, significant quality loss - not recommended for most purposes |
| bigplap-8x20b.Q3_K_M.gguf | Q3_K_M | 3 | 50.53 GB| 53.03 GB | very small, high quality loss |
| bigplap-8x20b.Q4_0.gguf | Q4_0 | 4 | 64.76 GB| 67.26 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| bigplap-8x20b.Q4_K_M.gguf | Q4_K_M | 4 | 64.76 GB| 67.26 GB | medium, balanced quality - recommended |
| bigplap-8x20b.Q5_0.gguf | Q5_0 | 5 | 78.35 GB| 80.85 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| bigplap-8x20b.Q5_K_M.gguf | Q5_K_M | 5 | 78.35 GB| 80.85 GB | large, very low quality loss - recommended |
| bigplap-8x20b.Q6_K.gguf | Q6_K | 6 | 92.79 GB| 95.29 GB | very large, extremely low quality loss |
| bigplap-8x20b.Q8_0.gguf | Q8_0 | 8 | 119.17 GB| 121.67 GB | very large, extremely low quality loss - not recommended |
**Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.
### Q6_K and Q8_0 files are split and require joining
**Note:** HF does not support uploading files larger than 50GB. Therefore I have uploaded the Q6_K and Q8_0 files as split files.
<details>
<summary>Click for instructions regarding Q6_K and Q8_0 files</summary>
### q6_K
Please download:
* `bigplap-8x20b.Q6_K.gguf-split-a`
* `bigplap-8x20b.Q6_K.gguf-split-b`
### q8_0
Please download:
* `bigplap-8x20b.Q8_0.gguf-split-a`
* `bigplap-8x20b.Q8_0.gguf-split-b`
To join the files, do the following:
Linux and macOS:
```
cat bigplap-8x20b.Q6_K.gguf-split-* > bigplap-8x20b.Q6_K.gguf && rm bigplap-8x20b.Q6_K.gguf-split-*
cat bigplap-8x20b.Q8_0.gguf-split-* > bigplap-8x20b.Q8_0.gguf && rm bigplap-8x20b.Q8_0.gguf-split-*
```
Windows command line:
```
COPY /B bigplap-8x20b.Q6_K.gguf-split-a + bigplap-8x20b.Q6_K.gguf-split-b bigplap-8x20b.Q6_K.gguf
del bigplap-8x20b.Q6_K.gguf-split-a bigplap-8x20b.Q6_K.gguf-split-b
COPY /B bigplap-8x20b.Q8_0.gguf-split-a + bigplap-8x20b.Q8_0.gguf-split-b bigplap-8x20b.Q8_0.gguf
del bigplap-8x20b.Q8_0.gguf-split-a bigplap-8x20b.Q8_0.gguf-split-b
```
</details>
<!-- README_GGUF.md-provided-files end -->
<!-- README_GGUF.md-how-to-download start -->
## How to download GGUF files
**Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.
The following clients/libraries will automatically download models for you, providing a list of available models to choose from:
* LM Studio
* LoLLMS Web UI
* Faraday.dev
### In `text-generation-webui`
Under Download Model, you can enter the model repo: TheBloke/BigPlap-8x20B-GGUF and below it, a specific filename to download, such as: bigplap-8x20b.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/BigPlap-8x20B-GGUF bigplap-8x20b.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/BigPlap-8x20B-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/BigPlap-8x20B-GGUF bigplap-8x20b.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 bigplap-8x20b.Q4_K_M.gguf --color -c 32768 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "{prompt}"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 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`
Note that text-generation-webui may not yet be compatible with Mixtral GGUFs. Please check compatibility first.
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) version 0.2.23 and later.
### 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="./bigplap-8x20b.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(
"{prompt}", # 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="./bigplap-8x20b.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)
<!-- 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: Undi's Bigplap 8X20B
Here is Plap.
Personalized Love Assistant Program.
Plap is made for Plapping.
An attempt to make 20B MoE, can be broken.
<!-- original-model-card end -->
|
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] |
kasrahabib/falcon-7b-ReqBrain
|
kasrahabib
| 2023-12-17T15:30:16Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"falcon",
"text-generation",
"custom_code",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-17T15:26:43Z |
Entry not found
|
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Lwooster/ProfessorOwl
|
Lwooster
| 2023-12-17T15:32:02Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-17T15:27:28Z |
Entry not found
|
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] |
LemTenku/hub
|
LemTenku
| 2023-12-17T15:32:30Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-17T15:27:57Z |
Entry not found
|
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WaltterGM/JorgeIggor
|
WaltterGM
| 2023-12-17T15:28:28Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-17T15:28:01Z |
Entry not found
|
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-0.05796251446008682,
0.31015992164611816,
-1.0262157917022705
] |
Lauragira8/Pucca
|
Lauragira8
| 2023-12-17T15:32:25Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-17T15:29:25Z |
---
license: openrail
---
|
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LoneStriker/Mixtral-8x7B-v0.1-3.0bpw-h6-exl2-2
|
LoneStriker
| 2023-12-17T16:18:03Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mixtral",
"text-generation",
"fr",
"it",
"de",
"es",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-17T15:30:41Z |
---
license: apache-2.0
language:
- fr
- it
- de
- es
- en
---
# Model Card for Mixtral-8x7B
The Mixtral-8x7B Large Language Model (LLM) is a pretrained generative Sparse Mixture of Experts. The Mistral-8x7B outperforms Llama 2 70B on most benchmarks we tested.
For full details of this model please read our [release blog post](https://mistral.ai/news/mixtral-of-experts/).
## Warning
This repo contains weights that are compatible with [vLLM](https://github.com/vllm-project/vllm) serving of the model as well as Hugging Face [transformers](https://github.com/huggingface/transformers) library. It is based on the original Mixtral [torrent release](magnet:?xt=urn:btih:5546272da9065eddeb6fcd7ffddeef5b75be79a7&dn=mixtral-8x7b-32kseqlen&tr=udp%3A%2F%http://2Fopentracker.i2p.rocks%3A6969%2Fannounce&tr=http%3A%2F%http://2Ftracker.openbittorrent.com%3A80%2Fannounce), but the file format and parameter names are different. Please note that model cannot (yet) be instantiated with HF.
## Run the model
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "mistralai/Mixtral-8x7B-v0.1"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
text = "Hello my name is"
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=20)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
By default, transformers will load the model in full precision. Therefore you might be interested to further reduce down the memory requirements to run the model through the optimizations we offer in HF ecosystem:
### In half-precision
Note `float16` precision only works on GPU devices
<details>
<summary> Click to expand </summary>
```diff
+ import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "mistralai/Mixtral-8x7B-v0.1"
tokenizer = AutoTokenizer.from_pretrained(model_id)
+ model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16).to(0)
text = "Hello my name is"
+ inputs = tokenizer(text, return_tensors="pt").to(0)
outputs = model.generate(**inputs, max_new_tokens=20)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
</details>
### Lower precision using (8-bit & 4-bit) using `bitsandbytes`
<details>
<summary> Click to expand </summary>
```diff
+ import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "mistralai/Mixtral-8x7B-v0.1"
tokenizer = AutoTokenizer.from_pretrained(model_id)
+ model = AutoModelForCausalLM.from_pretrained(model_id, load_in_4bit=True)
text = "Hello my name is"
+ inputs = tokenizer(text, return_tensors="pt").to(0)
outputs = model.generate(**inputs, max_new_tokens=20)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
</details>
### Load the model with Flash Attention 2
<details>
<summary> Click to expand </summary>
```diff
+ import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "mistralai/Mixtral-8x7B-v0.1"
tokenizer = AutoTokenizer.from_pretrained(model_id)
+ model = AutoModelForCausalLM.from_pretrained(model_id, use_flash_attention_2=True)
text = "Hello my name is"
+ inputs = tokenizer(text, return_tensors="pt").to(0)
outputs = model.generate(**inputs, max_new_tokens=20)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
</details>
## Notice
Mixtral-8x7B is a pretrained base model and therefore does not have any moderation mechanisms.
# The Mistral AI Team
Albert Jiang, Alexandre Sablayrolles, Arthur Mensch, Blanche Savary, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Emma Bou Hanna, Florian Bressand, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Lélio Renard Lavaud, Louis Ternon, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Théophile Gervet, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed.
|
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] |
PeterV09/mistral-7b-300k-6k-a100-6e-valid-7
|
PeterV09
| 2023-12-17T15:39:12Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mistral",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-17T15:30:58Z |
---
tags:
- generated_from_trainer
model-index:
- name: mistral-7b-300k-6k-a100-6e-valid-7
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mistral-7b-300k-6k-a100-6e-valid-7
This model was trained from scratch on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 128
- total_train_batch_size: 512
- total_eval_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 6.0
### Training results
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1
|
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] |
khhuang/chart-to-table
|
khhuang
| 2023-12-17T15:40:07Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"vision-encoder-decoder",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2023-12-17T15:31:54Z |
---
license: apache-2.0
language: en
---
# The Chart-To-Table Model
The Chart-To-Table model was introduced in the paper "Do LVLMs Understand Charts?
Analyzing and Correcting Factual Errors in Chart Captioning" for converting a chart into a structured table. The generated tables use `&&&` to delimit rows and `|` to delimit columns. The underlying architecture of this model is UniChart.
### How to use
```python
from transformers import DonutProcessor, VisionEncoderDecoderModel
from PIL import Image
model_name = "khhuang/chart-to-table"
model = VisionEncoderDecoderModel.from_pretrained(model_name).cuda()
processor = DonutProcessor.from_pretrained(model_name)
image_path = "PATH_TO_IMAGE"
def format_query(sentence):
return f"Does the image entails this statement: \"{sentence}\"?"
# Format text inputs
input_prompt = "<data_table_generation> <s_answer>"
# Encode chart figure and tokenize text
img = Image.open(IMAGE_PATH)
pixel_values = processor(img.convert("RGB"), random_padding=False, return_tensors="pt").pixel_values
pixel_values = pixel_values.cuda()
decoder_input_ids = processor.tokenizer(input_prompt, add_special_tokens=False, return_tensors="pt", max_length=510).input_ids.cuda()#.squeeze(0)
outputs = model.generate(
pixel_values.to(device),
decoder_input_ids=decoder_input_ids.to(device),
max_length=model.decoder.config.max_position_embeddings,
early_stopping=True,
pad_token_id=processor.tokenizer.pad_token_id,
eos_token_id=processor.tokenizer.eos_token_id,
use_cache=True,
num_beams=4,
bad_words_ids=[[processor.tokenizer.unk_token_id]],
return_dict_in_generate=True,
)
sequence = processor.batch_decode(outputs.sequences)[0]
sequence = sequence.replace(processor.tokenizer.eos_token, "").replace(processor.tokenizer.pad_token, "")
extracted_table = sequence.split("<s_answer>")[1].strip()
```
### Citation
```
@misc{huang-etal-2023-do,
title = "Do LVLMs Understand Charts? Analyzing and Correcting Factual Errors in Chart Captioning",
author = "Huang, Kung-Hsiang and
Zhou, Mingyang and
Chan, Hou Pong and
Fung, Yi R. and
Wang, Zhenhailong and
Zhang, Lingyu and
Chang, Shih-Fu and
Ji, Heng",
year={2023},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
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] |
LoneStriker/Toppy-Mix-4x7B-6.0bpw-h6-exl2-2
|
LoneStriker
| 2023-12-17T15:44:00Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mixtral",
"text-generation",
"not-for-all-audiences",
"nsfw",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-17T15:32:59Z |
---
license: cc-by-nc-4.0
tags:
- not-for-all-audiences
- nsfw
---
<!-- description start -->
## Description
This repo contains fp16 files of Toppy-Mix-4x7B.
This project was originaly a request from [BlueNipples](https://huggingface.co/BlueNipples) : [link](https://huggingface.co/Undi95/Utopia-13B/discussions/1)
The difference with the OG Toppy-M is the addition of Noromaid with the 3 models used to do Toppy-M, to have all the model as Expert in this MoE model, and not just merged one into one.
WARNING: ALL THE "K" GGUF QUANT OF MIXTRAL MODELS SEEMS TO BE [BROKEN](https://cdn-uploads.huggingface.co/production/uploads/63ab1241ad514ca8d1430003/TvjEP14ps7ZUgJ-0-mhIX.png), PREFER Q4_0, Q5_0 or Q8_0!
<!-- description end -->
<!-- description start -->
## Models and loras used
- [openchat/openchat_3.5](https://huggingface.co/openchat/openchat_3.5)
- [NousResearch/Nous-Capybara-7B-V1.9](https://huggingface.co/NousResearch/Nous-Capybara-7B-V1.9)
- [HuggingFaceH4/zephyr-7b-beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta)
- [NeverSleep/Noromaid-13B-v0.1.1](https://huggingface.co/NeverSleep/Noromaid-13B-v0.1.1)
- [lemonilia/AshhLimaRP-Mistral-7B](lemonilia/AshhLimaRP-Mistral-7B)
- [Vulkane/120-Days-of-Sodom-LoRA-Mistral-7b](https://huggingface.co/Vulkane/120-Days-of-Sodom-LoRA-Mistral-7b)
- [Undi95/Mistral-pippa-sharegpt-7b-qlora](Undi95/Mistral-pippa-sharegpt-7b-qlora)
<!-- description end -->
<!-- prompt-template start -->
## Prompt template: Alpaca
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
```
If you want to support me, you can [here](https://ko-fi.com/undiai).
|
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ivanmartinezmurillo/eacl_model
|
ivanmartinezmurillo
| 2023-12-17T15:35:34Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-17T15:35:34Z |
Entry not found
|
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danangharis/falcon-7b-python_code_instructions_18k_v2
|
danangharis
| 2023-12-17T15:51:28Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-17T15:37:35Z |
Entry not found
|
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] |
DzmitryShchamialiou/whisper-large-v2-int4
|
DzmitryShchamialiou
| 2023-12-17T15:46:16Z | 0 | 0 | null |
[
"onnx",
"license:apache-2.0",
"region:us"
] | null | 2023-12-17T15:38:17Z |
---
license: apache-2.0
---
|
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] |
margati/my_awesome_model
|
margati
| 2023-12-17T16:52:32Z | 0 | 0 |
transformers
|
[
"transformers",
"tf",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-17T15:41:38Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_keras_callback
model-index:
- name: margati/my_awesome_model
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# margati/my_awesome_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.6910
- Validation Loss: 0.6989
- Train Accuracy: 0.5051
- Epoch: 1
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 440, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 0.6947 | 0.6931 | 0.5051 | 0 |
| 0.6910 | 0.6989 | 0.5051 | 1 |
### Framework versions
- Transformers 4.35.2
- TensorFlow 2.15.0
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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] |
imZoe/abd_0.1
|
imZoe
| 2023-12-17T16:17:27Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-17T15:42:00Z |
Entry not found
|
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khalidalt/bloom_7B_8bit_qlora_opus
|
khalidalt
| 2023-12-17T15:42:02Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-17T15:42:02Z |
Entry not found
|
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tsunemoto/MiniChat-1.5-3B-GGUF
|
tsunemoto
| 2023-12-17T17:16:40Z | 0 | 0 | null |
[
"gguf",
"GGUF",
"en",
"arxiv:2311.07052",
"arxiv:2310.05914",
"arxiv:2305.18290",
"region:us"
] | null | 2023-12-17T15:44:00Z |
---
title: "MiniChat-1.5-3B Quantized in GGUF"
tags:
- GGUF
language: en
---
# GGUF's of MiniChat-1.5-3B
This is a GGUF quantization of MiniChat-1.5-3B.
## Original Model Card:
---
## MiniChat-1.5-3B
📑 [arXiv](https://arxiv.org/abs/2311.07052) | 👻 [GitHub](https://github.com/GeneZC/MiniMA) | 🤗 [HuggingFace-MiniMA](https://huggingface.co/GeneZC/MiniMA-3B) | 🤗 [HuggingFace-MiniChat](https://huggingface.co/GeneZC/MiniChat-3B) | 🤗 [HuggingFace-MiniChat-1.5](https://huggingface.co/GeneZC/MiniChat-1.5-3B) | 🤖 [ModelScope-MiniMA](https://modelscope.cn/models/GeneZC/MiniMA-3B) | 🤖 [ModelScope-MiniChat](https://modelscope.cn/models/GeneZC/MiniChat-3B)
🆕 **Updates from MiniChat-3B**:
- better data mixture;
- use of [NEFTune](https://arxiv.org/abs/2310.05914);
- use of [DPO](https://arxiv.org/abs/2305.18290).
❗ Must comply with LICENSE of LLaMA2 since it is derived from LLaMA2.
A language model distilled and finetuned from an adapted version of LLaMA2-7B following "Towards the Law of Capacity Gap in Distilling Language Models".
Outperforming a wide range of 3B competitors in GPT4 evaluation and even competing with several 7B chat models.
<img src="./teaser_b.jpg" alt="teaser_b" width="687" />
The following is an example code snippet to use MiniChat-3B:
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from conversation import get_default_conv_template
# MiniChat
tokenizer = AutoTokenizer.from_pretrained("GeneZC/MiniChat-3B", use_fast=False)
# GPU.
model = AutoModelForCausalLM.from_pretrained("GeneZC/MiniChat-3B", use_cache=True, device_map="auto", torch_dtype=torch.float16).eval()
# CPU.
# model = AutoModelForCausalLM.from_pretrained("GeneZC/MiniChat-3B", use_cache=True, device_map="cpu", torch_dtype=torch.float16).eval()
conv = get_default_conv_template("minichat")
question = "Implement a program to find the common elements in two arrays without using any extra data structures."
conv.append_message(conv.roles[0], question)
conv.append_message(conv.roles[1], None)
prompt = conv.get_prompt()
input_ids = tokenizer([prompt]).input_ids
output_ids = model.generate(
torch.as_tensor(input_ids).cuda(),
do_sample=True,
temperature=0.7,
max_new_tokens=1024,
)
output_ids = output_ids[0][len(input_ids[0]):]
output = tokenizer.decode(output_ids, skip_special_tokens=True).strip()
# output: "def common_elements(arr1, arr2):\n if len(arr1) == 0:\n return []\n if len(arr2) == 0:\n return arr1\n\n common_elements = []\n for element in arr1:\n if element in arr2:\n common_elements.append(element)\n\n return common_elements"
# Multiturn conversation could be realized by continuously appending questions to `conv`.
```
## Bibtex
```bibtex
@article{zhang2023law,
title={Towards the Law of Capacity Gap in Distilling Language Models},
author={Zhang, Chen and Song, Dawei and Ye, Zheyu and Gao, Yan},
year={2023},
url={https://arxiv.org/abs/2311.07052}
}
```
|
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] |
Kooten/Noromaid-13b-v0.2-6bpw-exl2
|
Kooten
| 2023-12-17T16:40:47Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-17T15:45:40Z |
---
license: cc-by-nc-4.0
---
# This is a 6BPW EXL2 quant of Noromaid-13b-v0.2
Exllama 2 quant of [NeverSleep/Noromaid-13b-v0.2](https://huggingface.co/NeverSleep/Noromaid-13b-v0.2)
## Prompt template: Custom format, or Alpaca
### Custom format:
SillyTavern config files: [Context](https://files.catbox.moe/ifmhai.json), [Instruct](https://files.catbox.moe/ttw1l9.json).
### Alpaca:
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
```
|
[
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] |
spellingdragon/whisper-tiny-en-ft
|
spellingdragon
| 2023-12-17T16:50:31Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-12-17T15:45:52Z |
Entry not found
|
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Sam135/Cross-Encoder-Con-MiniLM-L-6-v2-epo10-batch8-lr1e-05-weidec0.01
|
Sam135
| 2023-12-17T15:46:02Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-17T15:45:58Z |
Entry not found
|
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ModelsForAll/First_Clean_Experiment
|
ModelsForAll
| 2023-12-17T15:57:46Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-17T15:47:40Z |
“Oh, the cleverness of me!” P.Pen
|
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] |
YAGO1818/LilBiza12
|
YAGO1818
| 2023-12-17T15:49:02Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2023-12-17T15:47:41Z |
---
license: apache-2.0
---
|
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] |
tndklab/hubert-rinna-jdrt_RTSplit_Model-1218-1
|
tndklab
| 2023-12-18T01:28:57Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"hubert",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:rinna/japanese-hubert-base",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-12-17T15:47:42Z |
---
license: apache-2.0
base_model: rinna/japanese-hubert-base
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: hubert-rinna-jdrt_RTSplit_Model-1218-1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# hubert-rinna-jdrt_RTSplit_Model-1218-1
This model is a fine-tuned version of [rinna/japanese-hubert-base](https://huggingface.co/rinna/japanese-hubert-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0244
- Wer: 0.1941
- Cer: 0.0603
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 32
- eval_batch_size: 32
- seed: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 7.9926 | 1.0 | 120 | 6.9523 | 0.9963 | 0.9989 |
| 4.564 | 2.0 | 240 | 4.1715 | 0.9963 | 0.9989 |
| 2.9405 | 3.0 | 360 | 2.9020 | 0.9963 | 0.9989 |
| 1.4781 | 4.0 | 480 | 1.3915 | 1.0 | 0.6034 |
| 0.954 | 5.0 | 600 | 0.8600 | 0.8184 | 0.4850 |
| 0.9579 | 6.0 | 720 | 0.7503 | 0.8163 | 0.4729 |
| 0.7011 | 7.0 | 840 | 0.6898 | 0.8165 | 0.4755 |
| 0.6852 | 8.0 | 960 | 0.6270 | 0.8121 | 0.4940 |
| 0.6305 | 9.0 | 1080 | 0.5556 | 0.7929 | 0.4821 |
| 0.5272 | 10.0 | 1200 | 0.4809 | 0.7525 | 0.4121 |
| 0.471 | 11.0 | 1320 | 0.4482 | 0.7092 | 0.3170 |
| 0.4651 | 12.0 | 1440 | 0.4320 | 0.6883 | 0.35 |
| 0.4357 | 13.0 | 1560 | 0.4043 | 0.6563 | 0.3318 |
| 0.3903 | 14.0 | 1680 | 0.3263 | 0.5800 | 0.2480 |
| 0.317 | 15.0 | 1800 | 0.2928 | 0.5032 | 0.2099 |
| 0.2914 | 16.0 | 1920 | 0.2445 | 0.4792 | 0.2114 |
| 0.267 | 17.0 | 2040 | 0.1837 | 0.3830 | 0.1268 |
| 0.2975 | 18.0 | 2160 | 0.1596 | 0.3539 | 0.1230 |
| 0.1784 | 19.0 | 2280 | 0.1314 | 0.3186 | 0.1079 |
| 0.1545 | 20.0 | 2400 | 0.1000 | 0.2914 | 0.0930 |
| 0.1278 | 21.0 | 2520 | 0.0846 | 0.2644 | 0.0850 |
| 0.1631 | 22.0 | 2640 | 0.0765 | 0.2549 | 0.0814 |
| 0.1297 | 23.0 | 2760 | 0.0627 | 0.2456 | 0.0763 |
| 0.1063 | 24.0 | 2880 | 0.0485 | 0.2255 | 0.0708 |
| 0.0735 | 25.0 | 3000 | 0.0423 | 0.2172 | 0.0688 |
| 0.0718 | 26.0 | 3120 | 0.0370 | 0.2090 | 0.0650 |
| 0.0762 | 27.0 | 3240 | 0.0300 | 0.2008 | 0.0630 |
| 0.0589 | 28.0 | 3360 | 0.0301 | 0.2012 | 0.0632 |
| 0.0761 | 29.0 | 3480 | 0.0256 | 0.1956 | 0.0608 |
| 0.0648 | 30.0 | 3600 | 0.0244 | 0.1941 | 0.0603 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.14.6
- Tokenizers 0.15.0
|
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] |
muktadiur/Llama-2-7b-chat-hf-fine-tuned
|
muktadiur
| 2023-12-17T16:16:06Z | 0 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"pytorch",
"code",
"llama2",
"meta",
"text-generation",
"en",
"dataset:Abirate/english_quotes",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2023-12-17T15:48:37Z |
---
license: apache-2.0
datasets:
- Abirate/english_quotes
language:
- en
library_name: adapter-transformers
pipeline_tag: text-generation
tags:
- code
- llama2
- meta
---
|
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