modelId
stringlengths 5
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| author
stringlengths 2
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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-08-28 18:27:53
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 525
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
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| card
stringlengths 11
1.01M
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|---|---|---|---|---|---|---|---|---|---|
rkla/minetester-treechop_shaped-v0-dqn_fastversion4_seed1-seed1
|
rkla
| 2023-09-29T23:07:50Z | 0 | 0 |
minetest-baselines
|
[
"minetest-baselines",
"tensorboard",
"minetester-treechop_shaped-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-09-29T23:07:33Z |
---
tags:
- minetester-treechop_shaped-v0
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: minetest-baselines
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: minetester-treechop_shaped-v0
type: minetester-treechop_shaped-v0
metrics:
- type: mean_reward
value: -1.91 +/- 0.62
name: mean_reward
verified: false
---
# **DQN** Agent Playing **minetester-treechop_shaped-v0**
This is a trained model of a DQN agent playing minetester-treechop_shaped-v0.
The model was trained by using
[minetest-baselines](https://github.com/EleutherAI/minetest-baselines).
## Command to reproduce the training
```bash
python -m minetest_baselines.train --algo dqn --exp-name dqn_fastversion4_seed1 --seed 1 --capture-video --video-frequency 100 --track --wandb-entity rkla --save-model --upload-model --hf-entity rkla --total-timesteps 2000000 --num-envs 1 --buffer-size 50000 --learning-starts 5000 --learning-rate 0.0003
```
# Hyperparameters
```python
{'batch_size': 128,
'buffer_size': 50000,
'capture_video': True,
'end_e': 0.01,
'env_id': 'minetester-treechop_shaped-v0',
'exp_name': 'dqn_fastversion4_seed1',
'exploration_fraction': 0.9,
'gamma': 0.99,
'hf_entity': 'rkla',
'learning_rate': 0.0003,
'learning_starts': 5000,
'num_envs': 1,
'save_model': True,
'seed': 1,
'start_e': 1,
'target_network_frequency': 10000,
'tau': 1.0,
'total_timesteps': 2000000,
'track': True,
'train_frequency': 10,
'upload_model': True,
'video_frequency': 100,
'wandb_entity': 'rkla',
'wandb_project_name': 'minetest-baselines'}
```
|
HazemHM/ppo-SnowballTarget2
|
HazemHM
| 2023-09-29T22:55:21Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
] |
reinforcement-learning
| 2023-09-29T22:42:49Z |
---
library_name: ml-agents
tags:
- SnowballTarget
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: HazemHM/ppo-SnowballTarget2
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
actionpace/CalliopeDS-v2-L2-13B
|
actionpace
| 2023-09-29T22:41:03Z | 0 | 0 | null |
[
"gguf",
"en",
"license:other",
"endpoints_compatible",
"region:us"
] | null | 2023-09-29T22:33:16Z |
---
license: other
language:
- en
---
**Some of my own quants:**
* CalliopeDS-v2-L2-13B_Q5_K_M.gguf
**Source:** [Doctor-Shotgun](https://huggingface.co/Doctor-Shotgun)
**Source Model:** [CalliopeDS-v2-L2-13B](https://huggingface.co/Doctor-Shotgun/CalliopeDS-v2-L2-13B)
**Source models for Doctor-Shotgun/CalliopeDS-v2-L2-13B (Merge)**
- [PygmalionAI/pygmalion-2-13b](https://huggingface.co/PygmalionAI/pygmalion-2-13b) ([Ref](https://huggingface.co/actionpace/pygmalion-2-13b))
- [NousResearch/Nous-Hermes-Llama2-13b](https://huggingface.co/NousResearch/Nous-Hermes-Llama2-13b) ([Ref](https://huggingface.co/actionpace/Nous-Hermes-Llama2-13b))
- [Doctor-Shotgun/llama-2-supercot-lora](https://huggingface.co/Doctor-Shotgun/llama-2-supercot-lora)
- [lemonilia/LimaRP-Llama2-13B-v3-EXPERIMENT](https://huggingface.co/lemonilia/LimaRP-Llama2-13B-v3-EXPERIMENT)
- [Undi95/Storytelling-v2-13B-lora](https://huggingface.co/Undi95/Storytelling-v2-13B-lora)
|
Powidl43/glow_n_flow
|
Powidl43
| 2023-09-29T22:36:03Z | 0 | 0 | null |
[
"stable-diffusion",
"text-to-image",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2023-09-29T21:34:16Z |
---
license: creativeml-openrail-m
tags:
- stable-diffusion
- text-to-image
---
---
# Glow n Flow (P4A)
trained with kohya_ss (edg settings)
dana_ulama dataset deviantart.com/dana-ulama/gallery
kelvinsf dataset deviantart.com/kelvinsf/gallery/86790128/fluid-sculpture
trigger "p4a psychedelic" + EasyNegative
huggingface.co/LibreSD/Various/resolve/main/EasyNegative.safetensors
huggingface.co/LibreSD/Various/resolve/main/EasyNegativeV2.safetensors
samples civitai.com/models/154028/glow-n-flow-p4a
---
# Merge Info
GnF64
- step1 = dana_ulama 0.6 + kelvinsf 0.4
- step2_a = step1-camelliamix_v3 0.6 + step1-greymix_v2 0.4
- step2_b = step1-counterfeit_v3 0.6 + step1-nabimix_v2 0.4
- gnf64_v1_a = step2_a 0.6 + step2_b 0.4
- gnf64_v1_b = step2_b 0.6 + step2_a 0.4
GnF82
- step1 = dana_ulama 0.8 + kelvinsf 0.2
- step2_a = step1-camelliamix_v3 0.6 + step1-greymix_v2 0.4
- step2_b = step1-counterfeit_v3 0.6 + step1-nabimix_v2 0.4
- gnf82_v1_a = step2_a 0.6 + step2_b 0.4
- gnf82_v1_b = step2_b 0.6 + step2_a 0.4
P4A Glow
- step1 = [p4a-step2](https://huggingface.co/Powidl43/psychedelic/tree/main/step2-merge) 0.6 + dana_ulama 0.4
- step2_a = step1-camelliamix_v3 0.6 + step1-greymix_v2 0.4
- step2_b = step1-counterfeit_v3 0.6 + step1-nabimix_v2 0.4
- p4a_glow_v1_a = step2_a 0.6 + step2_b 0.4
- p4a_glow_v1_b = step2_b 0.6 + step2_a 0.4
P4A Flow
- step1 = [p4a-step2](https://huggingface.co/Powidl43/psychedelic/tree/main/step2-merge) 0.6 + kelvinsf 0.4
- step2_a = step1-camelliamix_v3 0.6 + step1-greymix_v2 0.4
- step2_b = step1-counterfeit_v3 0.6 + step1-nabimix_v2 0.4
- p4a_flow_v1_a = step2_a 0.6 + step2_b 0.4
- p4a_flow_v1_b = step2_b 0.6 + step2_a 0.4
---
base models and other essentials huggingface.co/LibreSD
|
AmelieSchreiber/esm2_t12_35M_qlora_binding_sites_v0
|
AmelieSchreiber
| 2023-09-29T22:26:36Z | 2 | 0 |
peft
|
[
"peft",
"ESM-2",
"Proteins",
"Binding Sites",
"QLoRA",
"biology",
"en",
"license:mit",
"region:us"
] | null | 2023-09-29T04:01:02Z |
---
license: mit
language:
- en
library_name: peft
tags:
- ESM-2
- Proteins
- Binding Sites
- QLoRA
- biology
---
# ESM-2 QLoRA for Predicting Binding Sites
## QLoRA Info
```
trainable params: 208322 || all params: 17382365 || trainable%: 1.198467527289871
```
## Testing for Overfitting
```python
Train metrics:
{'eval_loss': 0.09572703391313553,
'eval_accuracy': 0.9670769479865963,
'eval_precision': 0.3970221190232079,
'eval_recall': 0.9411011487595375,
'eval_f1': 0.5584507515735834,
'eval_auc': 0.9543828770020467,
'eval_mcc': 0.5996252550053665}
Test metrics:
{'eval_loss': 0.1680256575345993,
'eval_accuracy': 0.943313091525589,
'eval_precision': 0.2342637814982173,
'eval_recall': 0.7618306193745306,
'eval_f1': 0.35833816875074714,
'eval_auc': 0.8544971814140561,
'eval_mcc': 0.40290081143832884}
```
The metrics on the PDB datasets from [this paper](https://github.com/hamzagamouh/pt-lm-gnn) can be
[found here](https://huggingface.co/AmelieSchreiber/esmt12_35M_qlora_binding_sites_v0/blob/main/pdb_structure_metrics.txt).
|
pn51/unit2_taxi
|
pn51
| 2023-09-29T22:26:04Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-09-29T22:26:01Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: unit2_taxi
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.54 +/- 2.73
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="pn51/unit2_taxi", 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"])
```
|
pn51/q-FrozenLake-v1-4x4-noSlippery
|
pn51
| 2023-09-29T22:23:44Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-09-29T22:23:41Z |
---
tags:
- FrozenLake-v1-4x4
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4
type: FrozenLake-v1-4x4
metrics:
- type: mean_reward
value: 0.67 +/- 0.47
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="pn51/q-FrozenLake-v1-4x4-noSlippery", 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"])
```
|
afaranda/my_awesome_geopolitical_model
|
afaranda
| 2023-09-29T22:18:46Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:geopolitica",
"base_model:gsarti/it5-small",
"base_model:finetune:gsarti/it5-small",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-09-29T15:13:08Z |
---
license: apache-2.0
base_model: gsarti/it5-small
tags:
- generated_from_trainer
datasets:
- geopolitica
metrics:
- rouge
model-index:
- name: my_awesome_geopolitical_model
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: geopolitica
type: geopolitica
config: default
split: train
args: default
metrics:
- name: Rouge1
type: rouge
value: 0.1409
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_geopolitical_model
This model is a fine-tuned version of [gsarti/it5-small](https://huggingface.co/gsarti/it5-small) on the geopolitica dataset.
It achieves the following results on the evaluation set:
- Loss: nan
- Rouge1: 0.1409
- Rouge2: 0.0203
- Rougel: 0.1247
- Rougelsum: 0.125
- Gen Len: 18.781
## 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: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| No log | 1.0 | 53 | nan | 0.1409 | 0.0203 | 0.1247 | 0.125 | 18.781 |
| No log | 2.0 | 106 | nan | 0.1409 | 0.0203 | 0.1247 | 0.125 | 18.781 |
| No log | 3.0 | 159 | nan | 0.1409 | 0.0203 | 0.1247 | 0.125 | 18.781 |
| No log | 4.0 | 212 | nan | 0.1409 | 0.0203 | 0.1247 | 0.125 | 18.781 |
### Framework versions
- Transformers 4.33.3
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|
LarryAIDraw/tsurugi_bluearchive
|
LarryAIDraw
| 2023-09-29T21:44:56Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-09-29T19:44:57Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/130791/tsurugi-blue-archive
|
roa7n/gpt2-human_nontata_promoters-randomized_10_layers_0.003_lr_8_e
|
roa7n
| 2023-09-29T21:43:00Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-09-29T21:42:58Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.4.0.dev0
|
Samuael/wav2vec2-base-alffaamharic-google-colab
|
Samuael
| 2023-09-29T21:10:52Z | 108 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:Samuael/wav2vec2-base-alffaamharic-google-colab",
"base_model:finetune:Samuael/wav2vec2-base-alffaamharic-google-colab",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-09-20T18:33:29Z |
---
license: apache-2.0
base_model: Samuael/wav2vec2-base-alffaamharic-google-colab
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: wav2vec2-base-alffaamharic-google-colab
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. -->
# wav2vec2-base-alffaamharic-google-colab
This model is a fine-tuned version of [Samuael/wav2vec2-base-alffaamharic-google-colab](https://huggingface.co/Samuael/wav2vec2-base-alffaamharic-google-colab) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6124
- Wer: 0.3502
## 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: 0.0005
- train_batch_size: 32
- 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: 1000
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.1622 | 1.44 | 200 | 0.4605 | 0.3455 |
| 0.1619 | 2.88 | 400 | 0.5327 | 0.3658 |
| 0.1566 | 4.32 | 600 | 0.5235 | 0.3834 |
| 0.246 | 5.76 | 800 | 0.5781 | 0.4329 |
| 0.3211 | 7.19 | 1000 | 0.6142 | 0.4535 |
| 0.2705 | 8.63 | 1200 | 0.6172 | 0.4519 |
| 0.2113 | 10.07 | 1400 | 0.6140 | 0.4111 |
| 0.2642 | 11.51 | 1600 | 0.6020 | 0.4527 |
| 0.2881 | 12.95 | 1800 | 0.5931 | 0.4393 |
| 0.1801 | 14.39 | 2000 | 0.6565 | 0.4262 |
| 0.1494 | 15.83 | 2200 | 0.5732 | 0.4003 |
| 0.1684 | 17.27 | 2400 | 0.6419 | 0.4071 |
| 0.1805 | 18.71 | 2600 | 0.5973 | 0.4005 |
| 0.1518 | 20.14 | 2800 | 0.5846 | 0.3821 |
| 0.0821 | 21.58 | 3000 | 0.6149 | 0.3764 |
| 0.1049 | 23.02 | 3200 | 0.5965 | 0.3724 |
| 0.1229 | 24.46 | 3400 | 0.6032 | 0.3671 |
| 0.0848 | 25.9 | 3600 | 0.6005 | 0.3605 |
| 0.0724 | 27.34 | 3800 | 0.6258 | 0.3634 |
| 0.1149 | 28.78 | 4000 | 0.6124 | 0.3502 |
### Framework versions
- Transformers 4.33.3
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|
tylerkiser/ppo-LunarLander-v4
|
tylerkiser
| 2023-09-29T21:10:01Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-09-29T21:09:47Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 289.04 +/- 14.31
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Samy09/test_sd
|
Samy09
| 2023-09-29T20:55:38Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-09-27T16:58:35Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: test_sd
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. -->
# test_sd
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) 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: 1e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 1
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|
actionpace/Athena-v3
|
actionpace
| 2023-09-29T20:50:58Z | 1 | 0 | null |
[
"gguf",
"en",
"license:other",
"endpoints_compatible",
"region:us"
] | null | 2023-09-29T19:38:11Z |
---
license: other
language:
- en
---
**Some of my own quants:**
* Athena-v3_Q4_K_M.gguf
* Athena-v3_Q5_K_M.gguf
**Source:** [IkariDev](https://huggingface.co/IkariDev)
**Source Model:** [Athena-v3](https://huggingface.co/IkariDev/Athena-v3)
**Source models for IkariDev/Athena-v3 (Merge)**
- [IkariDev/Athena-v2](https://huggingface.co/IkariDev/Athena-v2)
- [migtissera/Synthia-13B-v1.2](https://huggingface.co/migtissera/Synthia-13B-v1.2)
- [The-Face-Of-Goonery/Huginn-13b-FP16](https://huggingface.co/The-Face-Of-Goonery/Huginn-13b-FP16) ([Ref](https://huggingface.co/actionpace/Huginn-13b-FP16))
- [PygmalionAI/pygmalion-2-13b](https://huggingface.co/PygmalionAI/pygmalion-2-13b) ([Ref](https://huggingface.co/actionpace/pygmalion-2-13b))
- [The-Face-Of-Goonery/LegerDemain-FP16](https://huggingface.co/The-Face-Of-Goonery/LegerDemain-FP16)
- [chargoddard/storytime-13b](https://huggingface.co/chargoddard/storytime-13b)
- [lemonilia/LimaRP-Llama2-13B-v3-EXPERIMENT](https://huggingface.co/lemonilia/LimaRP-Llama2-13B-v3-EXPERIMENT)
- [zattio770/120-Days-of-LORA-v2-13B](https://huggingface.co/zattio770/120-Days-of-LORA-v2-13B)
|
schengal1/SAM-Med2D_model
|
schengal1
| 2023-09-29T20:49:42Z | 0 | 1 | null |
[
"arxiv:2308.16184",
"license:apache-2.0",
"region:us"
] | null | 2023-09-29T20:27:28Z |
---
license: apache-2.0
---
This model is from the paper SAM Med-2D: https://arxiv.org/abs/2308.16184. The authors are Junlong Cheng, Jin Ye, Zhongying Deng, Jianpin Chen, Tianbin Li, Haoyu Wang, Yanzhou Su, Ziyan Huang, Jilong Chen, Lei Jiang, Hui Sun, Junjun He, Shaoting Zhang, Min Zhu, Yu Qiao. The Github repo link associated with the paper is https://github.com/OpenGVLab/SAM-Med2D .
|
TheBloke/Mistral-7B-v0.1-GPTQ
|
TheBloke
| 2023-09-29T20:49:41Z | 1,483 | 36 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"pretrained",
"base_model:mistralai/Mistral-7B-v0.1",
"base_model:quantized:mistralai/Mistral-7B-v0.1",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"4-bit",
"gptq",
"region:us"
] |
text-generation
| 2023-09-28T22:35:40Z |
---
base_model: mistralai/Mistral-7B-v0.1
inference: false
license: apache-2.0
model_creator: Mistral AI
model_name: Mistral 7B v0.1
model_type: mistral
pipeline_tag: text-generation
prompt_template: '{prompt}'
quantized_by: TheBloke
tags:
- pretrained
---
<!-- 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 -->
# Mistral 7B v0.1 - GPTQ
- Model creator: [Mistral AI](https://huggingface.co/mistralai)
- Original model: [Mistral 7B v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)
<!-- description start -->
## Description
This repo contains GPTQ model files for [Mistral AI's Mistral 7B v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1).
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.
### GPTQs will work in ExLlama, or via Transformers (requiring Transformers from Github)
These models are confirmed to work with ExLlama v1.
At the time of writing (September 28th), AutoGPTQ has not yet added support for the new Mistral models.
These GPTQs were made directly from Transformers, and so can be loaded via the Transformers interface. They can't be loaded directly from AutoGPTQ.
To load them via Transformers, you will need to install Transformers from Github, with:
```
pip3 install git+https://github.com/huggingface/transformers.git@72958fcd3c98a7afdc61f953aa58c544ebda2f79
```
<!-- description end -->
<!-- description end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Mistral-7B-v0.1-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Mistral-7B-v0.1-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Mistral-7B-v0.1-GGUF)
* [Mistral AI's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/mistralai/Mistral-7B-v0.1)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: None
```
{prompt}
```
<!-- prompt-template 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.
These files were made with Transformers 4.34.0.dev0, from commit 72958fcd3c98a7afdc61f953aa58c544ebda2f79.
<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 models in 4-bit.
</details>
| Branch | Bits | GS | Act Order | Damp % | GPTQ Dataset | Seq Len | Size | ExLlama | Desc |
| ------ | ---- | -- | --------- | ------ | ------------ | ------- | ---- | ------- | ---- |
| [main](https://huggingface.co/TheBloke/Mistral-7B-v0.1-GPTQ/tree/main) | 4 | 128 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 32768 | 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/Mistral-7B-v0.1-GPTQ/tree/gptq-4bit-32g-actorder_True) | 4 | 32 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 32768 | 4.57 GB | Yes | 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. |
| [gptq-8bit-128g-actorder_True](https://huggingface.co/TheBloke/Mistral-7B-v0.1-GPTQ/tree/gptq-8bit-128g-actorder_True) | 8 | 128 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 32768 | 4.16 GB | Yes | 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/Mistral-7B-v0.1-GPTQ/tree/gptq-8bit-32g-actorder_True) | 8 | 32 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 32768 | 4.57 GB | Yes | 8-bit, with group size 32g and Act Order for maximum inference quality. |
<!-- 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/Mistral-7B-v0.1-GPTQ` in the "Download model" box.
To download from another branch, add `:branchname` to the end of the download name, eg `TheBloke/Mistral-7B-v0.1-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 `Mistral-7B-v0.1-GPTQ`:
```shell
mkdir Mistral-7B-v0.1-GPTQ
huggingface-cli download TheBloke/Mistral-7B-v0.1-GPTQ --local-dir Mistral-7B-v0.1-GPTQ --local-dir-use-symlinks False
```
To download from a different branch, add the `--revision` parameter:
```shell
mkdir Mistral-7B-v0.1-GPTQ
huggingface-cli download TheBloke/Mistral-7B-v0.1-GPTQ --revision gptq-4bit-32g-actorder_True --local-dir Mistral-7B-v0.1-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 Huggingface 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 Mistral-7B-v0.1-GPTQ
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/Mistral-7B-v0.1-GPTQ --local-dir Mistral-7B-v0.1-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/Mistral-7B-v0.1-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).
These models are confirmed to work via the ExLlama Loader in text-generation-webui.
Use **Loader: ExLlama** - or Transformers may work too. AutoGPTQ will not work.
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/Mistral-7B-v0.1-GPTQ`.
- To download from a specific branch, enter for example `TheBloke/Mistral-7B-v0.1-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: `Mistral-7B-v0.1-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-python start -->
## How to use this GPTQ model from Python code
### Install the necessary packages
Requires: Transformers 4.34.0.dev0 from Github source, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later.
```shell
pip3 install optimum
pip3 install git+https://github.com/huggingface/transformers.git@72958fcd3c98a7afdc61f953aa58c544ebda2f79
pip3 install auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/ # Use cu117 if on CUDA 11.7
```
If you have problems installing AutoGPTQ using the pre-built wheels, install it from source instead:
```shell
pip3 uninstall -y auto-gptq
git clone https://github.com/PanQiWei/AutoGPTQ
cd AutoGPTQ
git checkout v0.4.2
pip3 install .
```
### You can then use the following code
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
model_name_or_path = "TheBloke/Mistral-7B-v0.1-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 = "Tell me about AI"
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 only tested to work with Transformers 4.34.0.dev0 as of commit 72958fcd3c98a7afdc61f953aa58c544ebda2f79.
<!-- 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**: Pierre Kircher, Stanislav Ovsiannikov, Michael Levine, Eugene Pentland, Andrey, 준교 김, Randy H, Fred von Graf, Artur Olbinski, Caitlyn Gatomon, terasurfer, Jeff Scroggin, James Bentley, Vadim, Gabriel Puliatti, Harry Royden McLaughlin, Sean Connelly, Dan Guido, Edmond Seymore, Alicia Loh, subjectnull, AzureBlack, Manuel Alberto Morcote, Thomas Belote, Lone Striker, Chris Smitley, Vitor Caleffi, Johann-Peter Hartmann, Clay Pascal, biorpg, Brandon Frisco, sidney chen, transmissions 11, Pedro Madruga, jinyuan sun, Ajan Kanaga, Emad Mostaque, Trenton Dambrowitz, Jonathan Leane, Iucharbius, usrbinkat, vamX, George Stoitzev, Luke Pendergrass, theTransient, Olakabola, Swaroop Kallakuri, Cap'n Zoog, Brandon Phillips, Michael Dempsey, Nikolai Manek, danny, Matthew Berman, Gabriel Tamborski, alfie_i, Raymond Fosdick, Tom X Nguyen, Raven Klaugh, LangChain4j, Magnesian, Illia Dulskyi, David Ziegler, Mano Prime, Luis Javier Navarrete Lozano, Erik Bjäreholt, 阿明, Nathan Dryer, Alex, Rainer Wilmers, zynix, TL, Joseph William Delisle, John Villwock, Nathan LeClaire, Willem Michiel, Joguhyik, GodLy, OG, Alps Aficionado, Jeffrey Morgan, ReadyPlayerEmma, Tiffany J. Kim, Sebastain Graf, Spencer Kim, Michael Davis, webtim, Talal Aujan, knownsqashed, John Detwiler, Imad Khwaja, Deo Leter, Jerry Meng, Elijah Stavena, Rooh Singh, Pieter, SuperWojo, Alexandros Triantafyllidis, Stephen Murray, Ai Maven, ya boyyy, Enrico Ros, Ken Nordquist, Deep Realms, Nicholas, Spiking Neurons AB, Elle, Will Dee, Jack West, RoA, Luke @flexchar, Viktor Bowallius, Derek Yates, Subspace Studios, jjj, Toran Billups, Asp the Wyvern, Fen Risland, Ilya, NimbleBox.ai, Chadd, Nitin Borwankar, Emre, Mandus, Leonard Tan, Kalila, K, Trailburnt, S_X, Cory Kujawski
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
# Original model card: Mistral AI's Mistral 7B v0.1
# Model Card for Mistral-7B-v0.1
The Mistral-7B-v0.1 Large Language Model (LLM) is a pretrained generative text model with 7 billion parameters.
Mistral-7B-v0.1 outperforms Llama 2 13B on all benchmarks we tested.
For full details of this model please read our [Release blog post](https://mistral.ai/news/announcing-mistral-7b/)
## Model Architecture
Mistral-7B-v0.1 is 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.
## Notice
Mistral 7B is a pretrained base model and therefore does not have any moderation mechanisms.
## The Mistral AI Team
Albert Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lélio Renard Lavaud, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed.
|
TheBloke/Mistral-7B-Instruct-v0.1-GPTQ
|
TheBloke
| 2023-09-29T20:48:48Z | 4,510 | 79 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"finetuned",
"conversational",
"base_model:mistralai/Mistral-7B-Instruct-v0.1",
"base_model:quantized:mistralai/Mistral-7B-Instruct-v0.1",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"4-bit",
"gptq",
"region:us"
] |
text-generation
| 2023-09-28T22:34:03Z |
---
base_model: mistralai/Mistral-7B-Instruct-v0.1
inference: false
license: apache-2.0
model_creator: Mistral AI
model_name: Mistral 7B Instruct v0.1
model_type: mistral
pipeline_tag: text-generation
prompt_template: '<s>[INST] {prompt} [/INST]'
quantized_by: TheBloke
tags:
- finetuned
---
<!-- 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 -->
# Mistral 7B Instruct v0.1 - GPTQ
- Model creator: [Mistral AI](https://huggingface.co/mistralai)
- Original model: [Mistral 7B Instruct v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1)
<!-- description start -->
## Description
This repo contains GPTQ model files for [Mistral AI's Mistral 7B Instruct v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1).
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.
### GPTQs will work in ExLlama, or via Transformers (requiring Transformers from Github)
These models are confirmed to work with ExLlama v1.
At the time of writing (September 28th), AutoGPTQ has not yet added support for the new Mistral models.
These GPTQs were made directly from Transformers, and so can be loaded via the Transformers interface. They can't be loaded directly from AutoGPTQ.
To load them via Transformers, you will need to install Transformers from Github, with:
```
pip3 install git+https://github.com/huggingface/transformers.git@72958fcd3c98a7afdc61f953aa58c544ebda2f79
```
<!-- description end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GGUF)
* [Mistral AI's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: Mistral
```
<s>[INST] {prompt} [/INST]
```
<!-- prompt-template 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.
These files were made with Transformers 4.34.0.dev0, from commit 72958fcd3c98a7afdc61f953aa58c544ebda2f79.
<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 models in 4-bit.
</details>
| Branch | Bits | GS | Act Order | Damp % | GPTQ Dataset | Seq Len | Size | ExLlama | Desc |
| ------ | ---- | -- | --------- | ------ | ------------ | ------- | ---- | ------- | ---- |
| [main](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GPTQ/tree/main) | 4 | 128 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 32768 | 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/Mistral-7B-Instruct-v0.1-GPTQ/tree/gptq-4bit-32g-actorder_True) | 4 | 32 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 32768 | 4.57 GB | Yes | 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. |
| [gptq-8bit-128g-actorder_True](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GPTQ/tree/gptq-8bit-128g-actorder_True) | 8 | 128 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 32768 | 7.68 GB | Yes | 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/Mistral-7B-Instruct-v0.1-GPTQ/tree/gptq-8bit-32g-actorder_True) | 8 | 32 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 32768 | 8.17 GB | Yes | 8-bit, with group size 32g and Act Order for maximum inference quality. |
<!-- 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/Mistral-7B-Instruct-v0.1-GPTQ` in the "Download model" box.
To download from another branch, add `:branchname` to the end of the download name, eg `TheBloke/Mistral-7B-Instruct-v0.1-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 `Mistral-7B-Instruct-v0.1-GPTQ`:
```shell
mkdir Mistral-7B-Instruct-v0.1-GPTQ
huggingface-cli download TheBloke/Mistral-7B-Instruct-v0.1-GPTQ --local-dir Mistral-7B-Instruct-v0.1-GPTQ --local-dir-use-symlinks False
```
To download from a different branch, add the `--revision` parameter:
```shell
mkdir Mistral-7B-Instruct-v0.1-GPTQ
huggingface-cli download TheBloke/Mistral-7B-Instruct-v0.1-GPTQ --revision gptq-4bit-32g-actorder_True --local-dir Mistral-7B-Instruct-v0.1-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 Huggingface 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 Mistral-7B-Instruct-v0.1-GPTQ
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/Mistral-7B-Instruct-v0.1-GPTQ --local-dir Mistral-7B-Instruct-v0.1-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/Mistral-7B-Instruct-v0.1-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).
These models are confirmed to work via the ExLlama Loader in text-generation-webui.
Use **Loader: ExLlama** - or Transformers may work too. AutoGPTQ will not work.
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/Mistral-7B-Instruct-v0.1-GPTQ`.
- To download from a specific branch, enter for example `TheBloke/Mistral-7B-Instruct-v0.1-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: `Mistral-7B-Instruct-v0.1-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-python start -->
## How to use this GPTQ model from Python code
### Install the necessary packages
Requires: Transformers 4.34.0.dev0 from Github source, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later.
```shell
pip3 install optimum
pip3 install git+https://github.com/huggingface/transformers.git@72958fcd3c98a7afdc61f953aa58c544ebda2f79
pip3 install auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/ # Use cu117 if on CUDA 11.7
```
If you have problems installing AutoGPTQ using the pre-built wheels, install it from source instead:
```shell
pip3 uninstall -y auto-gptq
git clone https://github.com/PanQiWei/AutoGPTQ
cd AutoGPTQ
git checkout v0.4.2
pip3 install .
```
### You can then use the following code
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
model_name_or_path = "TheBloke/Mistral-7B-Instruct-v0.1-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 = "Tell me about AI"
prompt_template=f'''<s>[INST] {prompt} [/INST]
'''
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 only tested to work with ExLlama v1, and Transformers 4.34.0.dev0 as of commit 72958fcd3c98a7afdc61f953aa58c544ebda2f79.
<!-- 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**: Pierre Kircher, Stanislav Ovsiannikov, Michael Levine, Eugene Pentland, Andrey, 준교 김, Randy H, Fred von Graf, Artur Olbinski, Caitlyn Gatomon, terasurfer, Jeff Scroggin, James Bentley, Vadim, Gabriel Puliatti, Harry Royden McLaughlin, Sean Connelly, Dan Guido, Edmond Seymore, Alicia Loh, subjectnull, AzureBlack, Manuel Alberto Morcote, Thomas Belote, Lone Striker, Chris Smitley, Vitor Caleffi, Johann-Peter Hartmann, Clay Pascal, biorpg, Brandon Frisco, sidney chen, transmissions 11, Pedro Madruga, jinyuan sun, Ajan Kanaga, Emad Mostaque, Trenton Dambrowitz, Jonathan Leane, Iucharbius, usrbinkat, vamX, George Stoitzev, Luke Pendergrass, theTransient, Olakabola, Swaroop Kallakuri, Cap'n Zoog, Brandon Phillips, Michael Dempsey, Nikolai Manek, danny, Matthew Berman, Gabriel Tamborski, alfie_i, Raymond Fosdick, Tom X Nguyen, Raven Klaugh, LangChain4j, Magnesian, Illia Dulskyi, David Ziegler, Mano Prime, Luis Javier Navarrete Lozano, Erik Bjäreholt, 阿明, Nathan Dryer, Alex, Rainer Wilmers, zynix, TL, Joseph William Delisle, John Villwock, Nathan LeClaire, Willem Michiel, Joguhyik, GodLy, OG, Alps Aficionado, Jeffrey Morgan, ReadyPlayerEmma, Tiffany J. Kim, Sebastain Graf, Spencer Kim, Michael Davis, webtim, Talal Aujan, knownsqashed, John Detwiler, Imad Khwaja, Deo Leter, Jerry Meng, Elijah Stavena, Rooh Singh, Pieter, SuperWojo, Alexandros Triantafyllidis, Stephen Murray, Ai Maven, ya boyyy, Enrico Ros, Ken Nordquist, Deep Realms, Nicholas, Spiking Neurons AB, Elle, Will Dee, Jack West, RoA, Luke @flexchar, Viktor Bowallius, Derek Yates, Subspace Studios, jjj, Toran Billups, Asp the Wyvern, Fen Risland, Ilya, NimbleBox.ai, Chadd, Nitin Borwankar, Emre, Mandus, Leonard Tan, Kalila, K, Trailburnt, S_X, Cory Kujawski
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
# Original model card: Mistral AI's Mistral 7B Instruct v0.1
# Model Card for Mistral-7B-Instruct-v0.1
The Mistral-7B-Instruct-v0.1 Large Language Model (LLM) is a instruct fine-tuned version of the [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) generative text model using a variety of publicly available conversation datasets.
For full details of this model please read our [release blog post](https://mistral.ai/news/announcing-mistral-7b/)
## 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-v0.1")
tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1")
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, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lélio Renard Lavaud, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed.
|
bartmiller/q-FrozenLake-v1-4x4-noSlippery
|
bartmiller
| 2023-09-29T20:40:36Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-09-29T20:40:34Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="bartmiller/q-FrozenLake-v1-4x4-noSlippery", 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"])
```
|
Schadom/Reinforce-CartPole-v1
|
Schadom
| 2023-09-29T20:29:44Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-09-29T20:29:41Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartPole-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 133.60 +/- 38.68
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
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
|
navradio/swin-tiny-patch4-window7-224-finetuned-eurosat
|
navradio
| 2023-09-29T20:11:12Z | 43 | 0 |
transformers
|
[
"transformers",
"pytorch",
"swin",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/swin-tiny-patch4-window7-224",
"base_model:finetune:microsoft/swin-tiny-patch4-window7-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-06-28T07:53:24Z |
---
license: apache-2.0
base_model: microsoft/swin-tiny-patch4-window7-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swin-tiny-patch4-window7-224-finetuned-eurosat
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.0
---
<!-- 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. -->
# swin-tiny-patch4-window7-224-finetuned-eurosat
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 5.5031
- Accuracy: 0.0
## 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: 5e-05
- train_batch_size: 256
- eval_batch_size: 256
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 1024
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 1 | 0.6744 | 1.0 |
| No log | 2.0 | 2 | 0.7507 | 0.0 |
| No log | 3.0 | 3 | 0.9175 | 0.0 |
| No log | 4.0 | 4 | 1.1669 | 0.0 |
| No log | 5.0 | 5 | 1.4443 | 0.0 |
| No log | 6.0 | 6 | 1.7218 | 0.0 |
| No log | 7.0 | 7 | 2.0269 | 0.0 |
| No log | 8.0 | 8 | 2.3374 | 0.0 |
| No log | 9.0 | 9 | 2.6657 | 0.0 |
| 0.0781 | 10.0 | 10 | 2.9900 | 0.0 |
| 0.0781 | 11.0 | 11 | 3.2990 | 0.0 |
| 0.0781 | 12.0 | 12 | 3.5921 | 0.0 |
| 0.0781 | 13.0 | 13 | 3.8577 | 0.0 |
| 0.0781 | 14.0 | 14 | 4.1048 | 0.0 |
| 0.0781 | 15.0 | 15 | 4.3232 | 0.0 |
| 0.0781 | 16.0 | 16 | 4.5163 | 0.0 |
| 0.0781 | 17.0 | 17 | 4.6854 | 0.0 |
| 0.0781 | 18.0 | 18 | 4.8332 | 0.0 |
| 0.0781 | 19.0 | 19 | 4.9602 | 0.0 |
| 0.0003 | 20.0 | 20 | 5.0735 | 0.0 |
| 0.0003 | 21.0 | 21 | 5.1691 | 0.0 |
| 0.0003 | 22.0 | 22 | 5.2486 | 0.0 |
| 0.0003 | 23.0 | 23 | 5.3151 | 0.0 |
| 0.0003 | 24.0 | 24 | 5.3696 | 0.0 |
| 0.0003 | 25.0 | 25 | 5.4131 | 0.0 |
| 0.0003 | 26.0 | 26 | 5.4466 | 0.0 |
| 0.0003 | 27.0 | 27 | 5.4711 | 0.0 |
| 0.0003 | 28.0 | 28 | 5.4879 | 0.0 |
| 0.0003 | 29.0 | 29 | 5.4983 | 0.0 |
| 0.0 | 30.0 | 30 | 5.5031 | 0.0 |
### Framework versions
- Transformers 4.33.3
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
|
raalst/RobBERT-v2-nl-ext-qa
|
raalst
| 2023-09-29T20:10:49Z | 115 | 1 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"question-answering",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-09-25T20:43:10Z |
---
# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/model-cards
{}
---
# Model Card for Model ID
q/a model, structurally same as RobBERT-v2-nl-qa, but trained with an augmented dataset.
sentences from the context not containing the answer-span have been moved from before the
answer to after the answer, and v.v.
The start of the answer has been adapted accordingly. these modified records have an "m"
appended to their ID field.
## Model Details
Results seem better than RobBERT-v2-nl-qa:
{'exact': 65.97542490405392,
'f1': 73.36792208890036,
'total': 31007,
'HasAns_exact': 62.55334441399757,
'HasAns_f1': 72.85023854321435,
'HasAns_total': 22261,
'NoAns_exact': 74.68557054653556,
'NoAns_f1': 74.68557054653556,
'NoAns_total': 8746,
'best_exact': 65.97542490405392,
'best_exact_thresh': 0.0,
'best_f1': 73.3679220889002,
'best_f1_thresh': 0.0}
### Model Description
example dutch question and context for the hosted inference api:
Q: Op welke wijze heeft de termiet zich kunnen verspreiden ?
CX: De koloniën zijn verspreid over twee woningen, bijgebouwen en tuinen in Zuid-Holland.
Een van de panden is een groot kassencomplex. Daaruit zijn meerdere planten verkocht,
waardoor het mogelijk is dat de termiet zich al verder heeft verspreid.
Eerdere pogingen om de koloniën uit te roeien zijn mislukt.
De plantenverkoop vanuit het koloniegebied is inmiddels tijdelijk stopgezet.
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Finnfalter/q-Taxi-v3
|
Finnfalter
| 2023-09-29T20:08:50Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-09-29T20:07:45Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-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="Finnfalter/q-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"])
```
|
bedus-creation/mBart-small-dataset-ii-eng-lim-003
|
bedus-creation
| 2023-09-29T20:07:50Z | 33 | 0 |
transformers
|
[
"transformers",
"tf",
"t5",
"text2text-generation",
"generated_from_keras_callback",
"base_model:bedus-creation/mBart-small-dataset-ii-eng-lim-003",
"base_model:finetune:bedus-creation/mBart-small-dataset-ii-eng-lim-003",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-09-28T20:18:17Z |
---
license: apache-2.0
base_model: bedus-creation/mBart-small-dataset-ii-eng-lim-003
tags:
- generated_from_keras_callback
model-index:
- name: bedus-creation/mBart-small-dataset-ii-eng-lim-003
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. -->
# bedus-creation/mBart-small-dataset-ii-eng-lim-003
This model is a fine-tuned version of [bedus-creation/mBart-small-dataset-ii-eng-lim-003](https://huggingface.co/bedus-creation/mBart-small-dataset-ii-eng-lim-003) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1015
- Validation Loss: 0.4146
- Epoch: 149
## 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': 'AdamWeightDecay', 'learning_rate': 1e-04, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 0.2093 | 0.2072 | 0 |
| 0.2068 | 0.2056 | 1 |
| 0.2062 | 0.2023 | 2 |
| 0.2045 | 0.2054 | 3 |
| 0.2027 | 0.2188 | 4 |
| 0.2019 | 0.2067 | 5 |
| 0.1997 | 0.2056 | 6 |
| 0.1991 | 0.2074 | 7 |
| 0.1978 | 0.2024 | 8 |
| 0.1962 | 0.2067 | 9 |
| 0.1955 | 0.2074 | 10 |
| 0.1945 | 0.2089 | 11 |
| 0.1928 | 0.2168 | 12 |
| 0.1907 | 0.2201 | 13 |
| 0.1900 | 0.2102 | 14 |
| 0.1888 | 0.2130 | 15 |
| 0.1882 | 0.2211 | 16 |
| 0.1870 | 0.2117 | 17 |
| 0.1857 | 0.2134 | 18 |
| 0.1838 | 0.2147 | 19 |
| 0.1824 | 0.2187 | 20 |
| 0.1812 | 0.2224 | 21 |
| 0.1813 | 0.2249 | 22 |
| 0.1798 | 0.2200 | 23 |
| 0.1787 | 0.2273 | 24 |
| 0.1772 | 0.2263 | 25 |
| 0.1780 | 0.2273 | 26 |
| 0.1764 | 0.2270 | 27 |
| 0.1754 | 0.2245 | 28 |
| 0.1738 | 0.2260 | 29 |
| 0.1730 | 0.2327 | 30 |
| 0.1720 | 0.2300 | 31 |
| 0.1702 | 0.2347 | 32 |
| 0.1698 | 0.2396 | 33 |
| 0.1689 | 0.2340 | 34 |
| 0.1693 | 0.2345 | 35 |
| 0.1661 | 0.2424 | 36 |
| 0.1663 | 0.2388 | 37 |
| 0.1658 | 0.2436 | 38 |
| 0.1654 | 0.2506 | 39 |
| 0.1639 | 0.2406 | 40 |
| 0.1635 | 0.2524 | 41 |
| 0.1619 | 0.2379 | 42 |
| 0.1609 | 0.2449 | 43 |
| 0.1602 | 0.2466 | 44 |
| 0.1602 | 0.2537 | 45 |
| 0.1586 | 0.2457 | 46 |
| 0.1576 | 0.2589 | 47 |
| 0.1573 | 0.2547 | 48 |
| 0.1566 | 0.2532 | 49 |
| 0.1546 | 0.2565 | 50 |
| 0.1540 | 0.2544 | 51 |
| 0.1545 | 0.2637 | 52 |
| 0.1515 | 0.2580 | 53 |
| 0.1520 | 0.2654 | 54 |
| 0.1524 | 0.2650 | 55 |
| 0.1513 | 0.2701 | 56 |
| 0.1500 | 0.2767 | 57 |
| 0.1492 | 0.2646 | 58 |
| 0.1483 | 0.2696 | 59 |
| 0.1480 | 0.2729 | 60 |
| 0.1475 | 0.2709 | 61 |
| 0.1458 | 0.2757 | 62 |
| 0.1460 | 0.2778 | 63 |
| 0.1446 | 0.2775 | 64 |
| 0.1440 | 0.2727 | 65 |
| 0.1438 | 0.2862 | 66 |
| 0.1444 | 0.2719 | 67 |
| 0.1423 | 0.2827 | 68 |
| 0.1418 | 0.2830 | 69 |
| 0.1402 | 0.2787 | 70 |
| 0.1404 | 0.2799 | 71 |
| 0.1388 | 0.2857 | 72 |
| 0.1392 | 0.2889 | 73 |
| 0.1398 | 0.2868 | 74 |
| 0.1389 | 0.2920 | 75 |
| 0.1359 | 0.3010 | 76 |
| 0.1369 | 0.2873 | 77 |
| 0.1366 | 0.2921 | 78 |
| 0.1358 | 0.2895 | 79 |
| 0.1343 | 0.3071 | 80 |
| 0.1344 | 0.2981 | 81 |
| 0.1341 | 0.3033 | 82 |
| 0.1328 | 0.3008 | 83 |
| 0.1332 | 0.2933 | 84 |
| 0.1317 | 0.3155 | 85 |
| 0.1310 | 0.3091 | 86 |
| 0.1307 | 0.3205 | 87 |
| 0.1295 | 0.3142 | 88 |
| 0.1295 | 0.3141 | 89 |
| 0.1299 | 0.3103 | 90 |
| 0.1282 | 0.3209 | 91 |
| 0.1284 | 0.3167 | 92 |
| 0.1272 | 0.3242 | 93 |
| 0.1270 | 0.3159 | 94 |
| 0.1245 | 0.3275 | 95 |
| 0.1244 | 0.3218 | 96 |
| 0.1248 | 0.3270 | 97 |
| 0.1241 | 0.3354 | 98 |
| 0.1231 | 0.3430 | 99 |
| 0.1233 | 0.3318 | 100 |
| 0.1222 | 0.3387 | 101 |
| 0.1225 | 0.3367 | 102 |
| 0.1221 | 0.3501 | 103 |
| 0.1214 | 0.3370 | 104 |
| 0.1207 | 0.3391 | 105 |
| 0.1197 | 0.3436 | 106 |
| 0.1193 | 0.3388 | 107 |
| 0.1208 | 0.3383 | 108 |
| 0.1186 | 0.3526 | 109 |
| 0.1177 | 0.3471 | 110 |
| 0.1179 | 0.3490 | 111 |
| 0.1179 | 0.3498 | 112 |
| 0.1177 | 0.3379 | 113 |
| 0.1169 | 0.3518 | 114 |
| 0.1165 | 0.3590 | 115 |
| 0.1161 | 0.3550 | 116 |
| 0.1159 | 0.3545 | 117 |
| 0.1150 | 0.3562 | 118 |
| 0.1123 | 0.3641 | 119 |
| 0.1137 | 0.3658 | 120 |
| 0.1153 | 0.3613 | 121 |
| 0.1130 | 0.3767 | 122 |
| 0.1129 | 0.3812 | 123 |
| 0.1127 | 0.3696 | 124 |
| 0.1118 | 0.3704 | 125 |
| 0.1116 | 0.3689 | 126 |
| 0.1107 | 0.3776 | 127 |
| 0.1103 | 0.3775 | 128 |
| 0.1108 | 0.3803 | 129 |
| 0.1097 | 0.3877 | 130 |
| 0.1093 | 0.3860 | 131 |
| 0.1080 | 0.3919 | 132 |
| 0.1082 | 0.3886 | 133 |
| 0.1091 | 0.3888 | 134 |
| 0.1071 | 0.3931 | 135 |
| 0.1072 | 0.3925 | 136 |
| 0.1069 | 0.3933 | 137 |
| 0.1065 | 0.3940 | 138 |
| 0.1072 | 0.3919 | 139 |
| 0.1059 | 0.3944 | 140 |
| 0.1049 | 0.4003 | 141 |
| 0.1045 | 0.4060 | 142 |
| 0.1040 | 0.4025 | 143 |
| 0.1055 | 0.3955 | 144 |
| 0.1033 | 0.4048 | 145 |
| 0.1033 | 0.4029 | 146 |
| 0.1019 | 0.4061 | 147 |
| 0.1030 | 0.4104 | 148 |
| 0.1015 | 0.4146 | 149 |
### Framework versions
- Transformers 4.33.3
- TensorFlow 2.13.0
- Datasets 2.14.5
- Tokenizers 0.13.3
|
Masa1028/openai-whisper-large-v2-colab
|
Masa1028
| 2023-09-29T20:00:20Z | 1 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-09-29T20:00:16Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.6.0.dev0
|
PanoEvJ/summarization_finetuned_t5_base_4bit
|
PanoEvJ
| 2023-09-29T19:47:16Z | 4 | 0 |
peft
|
[
"peft",
"base_model:google-t5/t5-base",
"base_model:adapter:google-t5/t5-base",
"region:us"
] | null | 2023-09-09T21:34:17Z |
---
library_name: peft
base_model: t5-base
---
## 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 0.6.0.dev0
|
LarryAIDraw/Nel-10
|
LarryAIDraw
| 2023-09-29T19:43:23Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-09-29T19:41:49Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/153802/nelliel-tu-odelschwanck-bleach-lora
|
LarryAIDraw/gertrude_mix2
|
LarryAIDraw
| 2023-09-29T19:43:13Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-09-29T19:41:23Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/26531/arknights-gertrude
|
ProtonH/q-FrozenLake-v1-4x4-noSlippery
|
ProtonH
| 2023-09-29T19:40:17Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-09-29T19:40:14Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="ProtonH/q-FrozenLake-v1-4x4-noSlippery", 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"])
```
|
LarryAIDraw/Taihou-10
|
LarryAIDraw
| 2023-09-29T19:39:49Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-09-29T19:32:07Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/153924/taihou-azur-lane-lora
|
LarryAIDraw/reze_v1
|
LarryAIDraw
| 2023-09-29T19:39:40Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-09-29T19:31:45Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/153910/reze-or-chainsaw-man
|
abdelrahmanelo/Honadf
|
abdelrahmanelo
| 2023-09-29T19:19:15Z | 0 | 0 |
allennlp
|
[
"allennlp",
"art",
"text-classification",
"ar",
"dataset:fka/awesome-chatgpt-prompts",
"license:apache-2.0",
"region:us"
] |
text-classification
| 2023-09-29T19:16:01Z |
---
license: apache-2.0
datasets:
- fka/awesome-chatgpt-prompts
language:
- ar
metrics:
- accuracy
library_name: allennlp
pipeline_tag: text-classification
tags:
- art
---
|
anzorq/m2m100_418M_ft_ru-kbd_63K
|
anzorq
| 2023-09-29T19:18:24Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"generated_from_trainer",
"ru",
"zu",
"dataset:anzorq/ru-kbd",
"base_model:facebook/m2m100_418M",
"base_model:finetune:facebook/m2m100_418M",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-09-29T19:14:12Z |
---
language:
- ru
- zu
license: mit
base_model: facebook/m2m100_418M
tags:
- generated_from_trainer
datasets:
- anzorq/ru-kbd
model-index:
- name: m2m100_418M_ft_ru-kbd_63K
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. -->
# m2m100_418M_ft_ru-kbd_63K
This model is a fine-tuned version of [facebook/m2m100_418M](https://huggingface.co/facebook/m2m100_418M) on the anzorq/ru-kbd 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: 5e-05
- train_batch_size: 56
- eval_batch_size: 56
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.34.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.0
|
delitante-coder/llama_merged_trained
|
delitante-coder
| 2023-09-29T19:18:14Z | 1 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-09-29T19:18:11Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.5.0
|
akashicmarga/Mistral-7B-Instruct-v0.1-q4f16_1-metal
|
akashicmarga
| 2023-09-29T19:17:13Z | 0 | 1 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2023-09-29T18:37:49Z |
---
license: apache-2.0
---
The model in this repository utilizes Mistral-7B-Instruct-v0.1 (https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1), the mlc-llm (https://llm.mlc.ai/docs/) Metal version with 4-bit quantization and an embedding layer for MLC embedding. You have the option to use the FastAPI server instead of OpenAI to run the model locally. For using in langchain, please refer to the sample_langchain.py file in the following GitHub link: https://github.com/mlc-ai/mlc-llm/blob/main/examples/rest/python/sample_langchain.py.
Environment setup
conda create -n mlc-chat-venv -c mlc-ai -c conda-forge mlc-chat-cli-nightly
conda activate mlc-chat-venv
Fast API Server
python -m mlc_chat.rest --model Mistral-7B-Instruct-v0.1-q4f16_1/ --lib-path Mistral-7B-Instruct-v0.1-q4f16_1/Mistral-7B-Instruct-v0.1-q4f16_1-metal.so
|
ProtonH/ppo-Huggy
|
ProtonH
| 2023-09-29T18:16:04Z | 1 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2023-09-29T18:15:53Z |
---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: ProtonH/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
LemTenku/sister-Bee
|
LemTenku
| 2023-09-29T18:10:39Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"en",
"arxiv:2306.02707",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-09-29T17:30:06Z |
---
license: apache-2.0
pipeline_tag: text-generation
language:
- en
library_name: transformers
---
Change from Synthia-7B-v1.2 -> Synthia-7B-v1.3: Base model was changed from LLaMA-2-7B to Mistral-7B-v0.1
All Synthia models are uncensored. Please use it with caution and with best intentions. You are responsible for how you use Synthia.
To evoke generalized Tree of Thought + Chain of Thought reasoning, you may use the following system message:
```
Elaborate on the topic using a Tree of Thoughts and backtrack when necessary to construct a clear, cohesive Chain of Thought reasoning. Always answer without hesitation.
```
# Synthia-7B-v1.3
SynthIA (Synthetic Intelligent Agent) 7B-v1.3 is a Mistral-7B-v0.1 model trained on Orca style datasets. It has been fine-tuned for instruction following as well as having long-form conversations.
<br>
<br>
<br>
#### License Disclaimer:
This model is released under Apache 2.0, and comes with no warranty or gurantees of any kind.
<br>
## Evaluation
We evaluated Synthia-7B-v1.3 on a wide range of tasks using [Language Model Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness) from EleutherAI.
Here are the results on metrics used by [HuggingFaceH4 Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
||||
|:------:|:--------:|:-------:|
|**Task**|**Metric**|**Value**|
|*arc_challenge*|acc_norm|0.6237|
|*hellaswag*|acc_norm|0.8349|
|*mmlu*|acc_norm|0.6232|
|*truthfulqa_mc*|mc2|0.5125|
|**Total Average**|-|**0.6485**||
<br>
## Example Usage
### Here is prompt format:
```
SYSTEM: Elaborate on the topic using a Tree of Thoughts and backtrack when necessary to construct a clear, cohesive Chain of Thought reasoning. Always answer without hesitation.
USER: How is a rocket launched from the surface of the earth to Low Earth Orbit?
ASSISTANT:
```
### Below shows a code example on how to use this model:
```python
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = "migtissera/Synthia-7B-v1.3"
output_file_path = "./Synthia-7B-conversations.jsonl"
model = AutoModelForCausalLM.from_pretrained(
model_path,
torch_dtype=torch.float16,
device_map="auto",
load_in_8bit=False,
trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
def generate_text(instruction):
tokens = tokenizer.encode(instruction)
tokens = torch.LongTensor(tokens).unsqueeze(0)
tokens = tokens.to("cuda")
instance = {
"input_ids": tokens,
"top_p": 1.0,
"temperature": 0.75,
"generate_len": 1024,
"top_k": 50,
}
length = len(tokens[0])
with torch.no_grad():
rest = model.generate(
input_ids=tokens,
max_length=length + instance["generate_len"],
use_cache=True,
do_sample=True,
top_p=instance["top_p"],
temperature=instance["temperature"],
top_k=instance["top_k"],
num_return_sequences=1,
)
output = rest[0][length:]
string = tokenizer.decode(output, skip_special_tokens=True)
answer = string.split("USER:")[0].strip()
return f"{answer}"
conversation = f"SYSTEM: Elaborate on the topic using a Tree of Thoughts and backtrack when necessary to construct a clear, cohesive Chain of Thought reasoning. Always answer without hesitation."
while True:
user_input = input("You: ")
llm_prompt = f"{conversation} \nUSER: {user_input} \nASSISTANT: "
answer = generate_text(llm_prompt)
print(answer)
conversation = f"{llm_prompt}{answer}"
json_data = {"prompt": user_input, "answer": answer}
## Save your conversation
with open(output_file_path, "a") as output_file:
output_file.write(json.dumps(json_data) + "\n")
```
<br>
#### Limitations & Biases:
While this model aims for accuracy, it can occasionally produce inaccurate or misleading results.
Despite diligent efforts in refining the pretraining data, there remains a possibility for the generation of inappropriate, biased, or offensive content.
Exercise caution and cross-check information when necessary. This is an uncensored model.
<br>
### Citiation:
Please kindly cite using the following BibTeX:
```
@misc{Synthia-7B-v1.3,
author = {Migel Tissera},
title = {Synthia-7B-v1.3: Synthetic Intelligent Agent},
year = {2023},
publisher = {GitHub, HuggingFace},
journal = {GitHub repository, HuggingFace repository},
howpublished = {\url{https://huggingface.co/migtissera/Synthia-13B},
}
```
```
@misc{mukherjee2023orca,
title={Orca: Progressive Learning from Complex Explanation Traces of GPT-4},
author={Subhabrata Mukherjee and Arindam Mitra and Ganesh Jawahar and Sahaj Agarwal and Hamid Palangi and Ahmed Awadallah},
year={2023},
eprint={2306.02707},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
osiria/distiluse-base-italian
|
osiria
| 2023-09-29T18:07:35Z | 127 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"distilbert",
"feature-extraction",
"it",
"arxiv:1907.04307",
"arxiv:2010.05609",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2023-06-11T21:23:41Z |
---
license: apache-2.0
language:
- it
---
--------------------------------------------------------------------------------------------------
<body>
<span class="vertical-text" style="background-color:lightgreen;border-radius: 3px;padding: 3px;"> </span>
<br>
<span class="vertical-text" style="background-color:orange;border-radius: 3px;padding: 3px;"> </span>
<br>
<span class="vertical-text" style="background-color:lightblue;border-radius: 3px;padding: 3px;"> Model: DistilUSE</span>
<br>
<span class="vertical-text" style="background-color:tomato;border-radius: 3px;padding: 3px;"> Lang: IT</span>
<br>
<span class="vertical-text" style="background-color:lightgrey;border-radius: 3px;padding: 3px;"> </span>
<br>
<span class="vertical-text" style="background-color:#CF9FFF;border-radius: 3px;padding: 3px;"> </span>
</body>
--------------------------------------------------------------------------------------------------
<h3>Model description</h3>
This is a <b>Universal Sentence Encoder</b> <b>[1]</b> model for the <b>Italian</b> language, obtained using <b>mDistilUSE</b> ([distiluse-base-multilingual-cased-v1](https://huggingface.co/sentence-transformers/distiluse-base-multilingual-cased-v1)) as a starting point and focusing it on the Italian language by modifying the embedding layer
(as in <b>[2]</b>, computing document-level frequencies over the <b>Wikipedia</b> dataset)
The resulting model has 67M parameters, a vocabulary of 30.785 tokens, and a size of ~270 MB.
It can be used to encode Italian texts and compute similarities between them.
<h3>Quick usage</h3>
```python
from transformers import AutoTokenizer, AutoModel
import numpy as np
tokenizer = AutoTokenizer.from_pretrained("osiria/distiluse-base-italian")
model = AutoModel.from_pretrained("osiria/distiluse-base-italian")
text1 = "Alessandro Manzoni è stato uno scrittore italiano"
text2 = "Giacomo Leopardi è stato un poeta italiano"
vec1 = model(tokenizer.encode(text1, return_tensors = "pt")).last_hidden_state[0,0,:].cpu().detach().numpy()
vec2 = model(tokenizer.encode(text2, return_tensors = "pt")).last_hidden_state[0,0,:].cpu().detach().numpy()
cosine_similarity = np.dot(vec1, vec2)/(np.linalg.norm(vec1)*np.linalg.norm(vec2))
print("COSINE SIMILARITY:", cosine_similarity)
# COSINE SIMILARITY: 0.734292
```
<h3>References</h3>
[1] https://arxiv.org/abs/1907.04307
[2] https://arxiv.org/abs/2010.05609
<h3>License</h3>
The model is released under <b>Apache-2.0</b> license
|
osiria/diablo-italian-base-1.3b
|
osiria
| 2023-09-29T18:07:22Z | 115 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"xglm",
"text-generation",
"it",
"arxiv:2005.14165",
"arxiv:2112.10668",
"license:mit",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-06-29T20:32:54Z |
---
license: mit
language:
- it
pipeline_tag: text-generation
---
--------------------------------------------------------------------------------------------------
<body>
<span class="vertical-text" style="background-color:lightgreen;border-radius: 3px;padding: 3px;"> </span>
<br>
<span class="vertical-text" style="background-color:orange;border-radius: 3px;padding: 3px;"> </span>
<br>
<span class="vertical-text" style="background-color:lightblue;border-radius: 3px;padding: 3px;"> Model: DIABLO 1.3B 🔥</span>
<br>
<span class="vertical-text" style="background-color:tomato;border-radius: 3px;padding: 3px;"> Lang: IT</span>
<br>
<span class="vertical-text" style="background-color:lightgrey;border-radius: 3px;padding: 3px;"> </span>
<br>
<span class="vertical-text" style="background-color:#CF9FFF;border-radius: 3px;padding: 3px;"> </span>
</body>
--------------------------------------------------------------------------------------------------
<h3>Model description</h3>
This model is a <b>causal</b> language model for the <b>Italian</b> language, based on a GPT-like <b>[1]</b> architecture (more specifically, the model has been obtained by modifying Meta's XGLM architecture <b>[2]</b> and exploiting its 1.7B checkpoint).
The model has ~1.3B parameters and a vocabulary of 50.335 tokens. It is a foundation model, pre-trained for causal language modeling, so it is mainly suitable for basic natural language generation, and you will have to fine-tune it in order to use it on more specific downstream tasks.
<h3>Quick usage</h3>
In order to use the model for inference on GPU, the following pipeline is needed:
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
from transformers import pipeline
tokenizer = AutoTokenizer.from_pretrained("osiria/diablo-italian-base-1.3b")
model = AutoModelForCausalLM.from_pretrained("osiria/diablo-italian-base-1.3b", torch_dtype=torch.float16)
device = torch.device("cuda")
model = model.to(device)
pipeline_nlg = pipeline("text-generation", model = model, tokenizer = tokenizer, device = 0)
pipeline_nlg("Ciao, mi chiamo Marco Rossi e")
# [{'generated_text': 'Ciao, mi chiamo Marco Rossi e sono un blogger italiano.'}]
```
<h3>Limitations</h3>
The model might behave erratically when presented with prompts which are too far away from its pre-training and, because of the probabilistic nature of its generation, it might occasionally produce biased or offensive content with respect to gender, race, ideologies, and political or religious beliefs.
These limitations imply that the model and its outputs should be used with caution, and should not be involved in situations that require the generated text to be fair or true.
<h3>References</h3>
[1] https://arxiv.org/abs/2005.14165
[2] https://arxiv.org/abs/2112.10668
<h3>License</h3>
The model is released under <b>MIT</b> license
|
osiria/bert-base-italian-cased
|
osiria
| 2023-09-29T18:07:17Z | 152 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"bert",
"fill-mask",
"it",
"arxiv:1810.04805",
"arxiv:2010.05609",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-05-29T17:52:45Z |
---
license: apache-2.0
language:
- it
widget:
- text: "Milano è una [MASK] dell'Italia"
example_title: "Example 1"
- text: "Giacomo Leopardi è stato uno dei più grandi [MASK] del classicismo italiano"
example_title: "Example 2"
- text: "La pizza è un piatto tipico della [MASK] gastronomica italiana"
example_title: "Example 3"
---
--------------------------------------------------------------------------------------------------
<body>
<span class="vertical-text" style="background-color:lightgreen;border-radius: 3px;padding: 3px;"> </span>
<br>
<span class="vertical-text" style="background-color:orange;border-radius: 3px;padding: 3px;"> </span>
<br>
<span class="vertical-text" style="background-color:lightblue;border-radius: 3px;padding: 3px;"> Model: BERT</span>
<br>
<span class="vertical-text" style="background-color:tomato;border-radius: 3px;padding: 3px;"> Lang: IT</span>
<br>
<span class="vertical-text" style="background-color:lightgrey;border-radius: 3px;padding: 3px;"> </span>
<br>
<span class="vertical-text" style="background-color:#CF9FFF;border-radius: 3px;padding: 3px;"> </span>
</body>
--------------------------------------------------------------------------------------------------
<h3>Model description</h3>
This is a <b>BERT</b> <b>[1]</b> model for the <b>Italian</b> language, obtained using <b>mBERT</b> ([bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased)) as a starting point and focusing it on the Italian language by modifying the embedding layer
(as in <b>[2]</b>, computing document-level frequencies over the <b>Wikipedia</b> dataset)
The resulting model has 110M parameters, a vocabulary of 30.785 tokens, and a size of ~430 MB.
<h3>Quick usage</h3>
```python
from transformers import BertTokenizerFast, BertModel
tokenizer = BertTokenizerFast.from_pretrained("osiria/bert-base-italian-cased")
model = BertModel.from_pretrained("osiria/bert-base-italian-cased")
```
<h3>References</h3>
[1] https://arxiv.org/abs/1810.04805
[2] https://arxiv.org/abs/2010.05609
<h3>License</h3>
The model is released under <b>Apache-2.0</b> license
|
hemanth11/q-FrozenLake-v1-4x4-noSlippery
|
hemanth11
| 2023-09-29T17:59:36Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-09-29T17:52:41Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="hemanth11/q-FrozenLake-v1-4x4-noSlippery", 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"])
```
|
actionpace/13B-Thorns-l2
|
actionpace
| 2023-09-29T17:49:47Z | 1 | 0 | null |
[
"gguf",
"en",
"license:other",
"endpoints_compatible",
"region:us"
] | null | 2023-09-07T18:38:21Z |
---
license: other
language:
- en
---
**Some of my own quants:**
* 13B-Thorns-l2_Q4_K_M.gguf
* 13B-Thorns-l2_Q5_K_M.gguf
**Source:** [CalderaAI](https://huggingface.co/CalderaAI)
**Source Model:** [13B-Thorns-l2](https://huggingface.co/CalderaAI/13B-Thorns-l2)
**Source models for CalderaAI/13B-Thorns-l2 (Merge)**
- [NousResearch/Nous-Hermes-Llama2-13b](https://huggingface.co/NousResearch/Nous-Hermes-Llama2-13b) ([Ref](https://huggingface.co/actionpace/Nous-Hermes-Llama2-13b))
- [elinas/chronos-13b-v2](https://huggingface.co/elinas/chronos-13b-v2) ([Ref](https://huggingface.co/actionpace/chronos-13b-v2))
- [garage-bAInd/Platypus2-13B](https://huggingface.co/garage-bAInd/Platypus2-13B) ([Ref](https://huggingface.co/actionpace/Platypus2-13B))
- [jondurbin/airoboros-l2-13b-gpt4-1.4.1](https://huggingface.co/jondurbin/airoboros-l2-13b-gpt4-1.4.1)
- [KoboldAI/LLAMA2-13B-Holodeck-1](https://huggingface.co/KoboldAI/LLAMA2-13B-Holodeck-1) ([Ref](https://huggingface.co/actionpace/LLAMA2-13B-Holodeck-1))
- [nRuaif/Kimiko-v2-13B](https://huggingface.co/nRuaif/Kimiko-v2-13B) (Lora)
- [lemonilia/limarp-llama2](https://huggingface.co/lemonilia/limarp-llama2) (Lora)
|
AparnaMahajan/Llama2_custom
|
AparnaMahajan
| 2023-09-29T17:49:08Z | 1 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-09-29T17:49:07Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.6.0.dev0
|
asmaa1/videomae-base-groub19-20-finetuned-SLT-subset
|
asmaa1
| 2023-09-29T17:44:00Z | 61 | 0 |
transformers
|
[
"transformers",
"pytorch",
"videomae",
"video-classification",
"generated_from_trainer",
"base_model:MCG-NJU/videomae-base",
"base_model:finetune:MCG-NJU/videomae-base",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] |
video-classification
| 2023-09-29T06:19:30Z |
---
license: cc-by-nc-4.0
base_model: MCG-NJU/videomae-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: videomae-base-groub19-20-finetuned-SLT-subset
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. -->
# videomae-base-groub19-20-finetuned-SLT-subset
This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.1970
- Accuracy: 0.1220
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 80
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 3.853 | 0.14 | 11 | 3.6435 | 0.0732 |
| 3.7412 | 1.14 | 22 | 3.5800 | 0.0732 |
| 3.7045 | 2.14 | 33 | 3.4833 | 0.1220 |
| 3.487 | 3.14 | 44 | 3.3655 | 0.1220 |
| 3.4174 | 4.14 | 55 | 3.2769 | 0.1220 |
| 3.3735 | 5.14 | 66 | 3.2278 | 0.1220 |
| 3.3319 | 6.14 | 77 | 3.1988 | 0.1220 |
| 3.1906 | 7.04 | 80 | 3.1970 | 0.1220 |
### Framework versions
- Transformers 4.33.0
- Pytorch 2.0.0+cpu
- Datasets 2.1.0
- Tokenizers 0.13.3
|
ArneL2206/a2c-PandaReachDense-v2
|
ArneL2206
| 2023-09-29T17:43:24Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"PandaReachDense-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"arxiv:2106.13687",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-01-22T19:24:08Z |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -2.17 +/- 0.37
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
Panda Gym environments: [arxiv.org/abs/2106.13687](https://arxiv.org/abs/2106.13687)
|
AparnaMahajan/Llama2
|
AparnaMahajan
| 2023-09-29T17:42:06Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-09-27T04:04:27Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.6.0.dev0
|
mys/ggml_clip-vit-large-patch14
|
mys
| 2023-09-29T17:33:04Z | 155 | 1 | null |
[
"gguf",
"clip",
"vision",
"ggml",
"clip.cpp",
"clip-cpp-gguf",
"license:mit",
"region:us"
] | null | 2023-09-28T06:34:23Z |
---
license: mit
tags:
- clip
- vision
- ggml
- clip.cpp
- clip-cpp-gguf
---
## Converted files for use with clip.cpp
see https://github.com/monatis/clip.cpp
# Experimental
the file format is not stable yet, so expect breaking changes. I will update the files from time to time.
|
bzafra/MelanomaENV2
|
bzafra
| 2023-09-29T17:28:04Z | 0 | 0 | null |
[
"license:cc-by-nc-nd-4.0",
"region:us"
] | null | 2023-09-29T17:28:03Z |
---
license: cc-by-nc-nd-4.0
---
|
TheBloke/Synthia-7B-v1.3-GGUF
|
TheBloke
| 2023-09-29T17:16:36Z | 270 | 45 |
transformers
|
[
"transformers",
"gguf",
"mistral",
"base_model:migtissera/SynthIA-7B-v1.3",
"base_model:quantized:migtissera/SynthIA-7B-v1.3",
"license:apache-2.0",
"region:us"
] | null | 2023-09-29T17:12:06Z |
---
base_model: migtissera/Synthia-7B-v1.3
inference: false
license: apache-2.0
model_creator: Migel Tissera
model_name: Synthia 7B V1.3
model_type: mistral
prompt_template: 'SYSTEM: You are Synthia. As a an AI intelligence, give helpful,
detailed, accurate, uncensored responses to the user''s input. Provide answers factually.
USER: {prompt}
ASSISTANT:
'
quantized_by: TheBloke
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
<div style="display: flex; flex-direction: column; align-items: flex-start;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p>
</div>
<div style="display: flex; flex-direction: column; align-items: flex-end;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
</div>
</div>
<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# Synthia 7B V1.3 - GGUF
- Model creator: [Migel Tissera](https://huggingface.co/migtissera)
- Original model: [Synthia 7B V1.3](https://huggingface.co/migtissera/Synthia-7B-v1.3)
<!-- description start -->
## Description
This repo contains GGUF format model files for [Migel Tissera's Synthia 7B V1.3](https://huggingface.co/migtissera/Synthia-7B-v1.3).
<!-- description end -->
<!-- README_GGUF.md-about-gguf start -->
### About GGUF
GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.
Here is an incomplate list of clients and libraries that are known to support GGUF:
* [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option.
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration.
* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection.
* [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
* [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.
<!-- README_GGUF.md-about-gguf end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Synthia-7B-v1.3-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Synthia-7B-v1.3-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Synthia-7B-v1.3-GGUF)
* [Migel Tissera's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/migtissera/Synthia-7B-v1.3)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: Synthia
```
SYSTEM: You are Synthia. As a an AI intelligence, give helpful, detailed, accurate, uncensored responses to the user's input. Provide answers factually.
USER: {prompt}
ASSISTANT:
```
<!-- prompt-template end -->
<!-- compatibility_gguf start -->
## Compatibility
These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221)
They are also compatible with many third party UIs and libraries - please see the list at the top of this README.
## Explanation of quantisation methods
<details>
<summary>Click to see details</summary>
The new methods available are:
* GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
* GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
* GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
* GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
* GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw
Refer to the Provided Files table below to see what files use which methods, and how.
</details>
<!-- compatibility_gguf end -->
<!-- README_GGUF.md-provided-files start -->
## Provided files
| Name | Quant method | Bits | Size | Max RAM required | Use case |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [synthia-7b-v1.3.Q2_K.gguf](https://huggingface.co/TheBloke/Synthia-7B-v1.3-GGUF/blob/main/synthia-7b-v1.3.Q2_K.gguf) | Q2_K | 2 | 3.08 GB| 5.58 GB | smallest, significant quality loss - not recommended for most purposes |
| [synthia-7b-v1.3.Q3_K_S.gguf](https://huggingface.co/TheBloke/Synthia-7B-v1.3-GGUF/blob/main/synthia-7b-v1.3.Q3_K_S.gguf) | Q3_K_S | 3 | 3.16 GB| 5.66 GB | very small, high quality loss |
| [synthia-7b-v1.3.Q3_K_M.gguf](https://huggingface.co/TheBloke/Synthia-7B-v1.3-GGUF/blob/main/synthia-7b-v1.3.Q3_K_M.gguf) | Q3_K_M | 3 | 3.52 GB| 6.02 GB | very small, high quality loss |
| [synthia-7b-v1.3.Q3_K_L.gguf](https://huggingface.co/TheBloke/Synthia-7B-v1.3-GGUF/blob/main/synthia-7b-v1.3.Q3_K_L.gguf) | Q3_K_L | 3 | 3.82 GB| 6.32 GB | small, substantial quality loss |
| [synthia-7b-v1.3.Q4_0.gguf](https://huggingface.co/TheBloke/Synthia-7B-v1.3-GGUF/blob/main/synthia-7b-v1.3.Q4_0.gguf) | Q4_0 | 4 | 4.11 GB| 6.61 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [synthia-7b-v1.3.Q4_K_S.gguf](https://huggingface.co/TheBloke/Synthia-7B-v1.3-GGUF/blob/main/synthia-7b-v1.3.Q4_K_S.gguf) | Q4_K_S | 4 | 4.14 GB| 6.64 GB | small, greater quality loss |
| [synthia-7b-v1.3.Q4_K_M.gguf](https://huggingface.co/TheBloke/Synthia-7B-v1.3-GGUF/blob/main/synthia-7b-v1.3.Q4_K_M.gguf) | Q4_K_M | 4 | 4.37 GB| 6.87 GB | medium, balanced quality - recommended |
| [synthia-7b-v1.3.Q5_0.gguf](https://huggingface.co/TheBloke/Synthia-7B-v1.3-GGUF/blob/main/synthia-7b-v1.3.Q5_0.gguf) | Q5_0 | 5 | 5.00 GB| 7.50 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [synthia-7b-v1.3.Q5_K_S.gguf](https://huggingface.co/TheBloke/Synthia-7B-v1.3-GGUF/blob/main/synthia-7b-v1.3.Q5_K_S.gguf) | Q5_K_S | 5 | 5.00 GB| 7.50 GB | large, low quality loss - recommended |
| [synthia-7b-v1.3.Q5_K_M.gguf](https://huggingface.co/TheBloke/Synthia-7B-v1.3-GGUF/blob/main/synthia-7b-v1.3.Q5_K_M.gguf) | Q5_K_M | 5 | 5.13 GB| 7.63 GB | large, very low quality loss - recommended |
| [synthia-7b-v1.3.Q6_K.gguf](https://huggingface.co/TheBloke/Synthia-7B-v1.3-GGUF/blob/main/synthia-7b-v1.3.Q6_K.gguf) | Q6_K | 6 | 5.94 GB| 8.44 GB | very large, extremely low quality loss |
| [synthia-7b-v1.3.Q8_0.gguf](https://huggingface.co/TheBloke/Synthia-7B-v1.3-GGUF/blob/main/synthia-7b-v1.3.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/Synthia-7B-v1.3-GGUF and below it, a specific filename to download, such as: synthia-7b-v1.3.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/Synthia-7B-v1.3-GGUF synthia-7b-v1.3.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage</summary>
You can also download multiple files at once with a pattern:
```shell
huggingface-cli download TheBloke/Synthia-7B-v1.3-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/Synthia-7B-v1.3-GGUF synthia-7b-v1.3.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.
</details>
<!-- README_GGUF.md-how-to-download end -->
<!-- README_GGUF.md-how-to-run start -->
## Example `llama.cpp` command
Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later.
```shell
./main -ngl 32 -m synthia-7b-v1.3.Q4_K_M.gguf --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "SYSTEM: You are Synthia. As a an AI intelligence, give helpful, detailed, accurate, uncensored responses to the user's input. Provide answers factually.\nUSER: {prompt}\nASSISTANT:"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 2048` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically.
If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`
For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md)
## How to run in `text-generation-webui`
Further instructions here: [text-generation-webui/docs/llama.cpp.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp.md).
## How to run from Python code
You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries.
### How to load this model in Python code, using ctransformers
#### First install the package
Run one of the following commands, according to your system:
```shell
# Base ctransformers with no GPU acceleration
pip install ctransformers
# Or with CUDA GPU acceleration
pip install ctransformers[cuda]
# Or with AMD ROCm GPU acceleration (Linux only)
CT_HIPBLAS=1 pip install ctransformers --no-binary ctransformers
# Or with Metal GPU acceleration for macOS systems only
CT_METAL=1 pip install ctransformers --no-binary ctransformers
```
#### Simple ctransformers example code
```python
from ctransformers import AutoModelForCausalLM
# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
llm = AutoModelForCausalLM.from_pretrained("TheBloke/Synthia-7B-v1.3-GGUF", model_file="synthia-7b-v1.3.Q4_K_M.gguf", model_type="mistral", gpu_layers=50)
print(llm("AI is going to"))
```
## How to use with LangChain
Here are guides on using llama-cpp-python and ctransformers with LangChain:
* [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp)
* [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers)
<!-- README_GGUF.md-how-to-run end -->
<!-- footer start -->
<!-- 200823 -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute
Thanks to the [chirper.ai](https://chirper.ai) team!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Aemon Algiz.
**Patreon special mentions**: Pierre Kircher, Stanislav Ovsiannikov, Michael Levine, Eugene Pentland, Andrey, 준교 김, Randy H, Fred von Graf, Artur Olbinski, Caitlyn Gatomon, terasurfer, Jeff Scroggin, James Bentley, Vadim, Gabriel Puliatti, Harry Royden McLaughlin, Sean Connelly, Dan Guido, Edmond Seymore, Alicia Loh, subjectnull, AzureBlack, Manuel Alberto Morcote, Thomas Belote, Lone Striker, Chris Smitley, Vitor Caleffi, Johann-Peter Hartmann, Clay Pascal, biorpg, Brandon Frisco, sidney chen, transmissions 11, Pedro Madruga, jinyuan sun, Ajan Kanaga, Emad Mostaque, Trenton Dambrowitz, Jonathan Leane, Iucharbius, usrbinkat, vamX, George Stoitzev, Luke Pendergrass, theTransient, Olakabola, Swaroop Kallakuri, Cap'n Zoog, Brandon Phillips, Michael Dempsey, Nikolai Manek, danny, Matthew Berman, Gabriel Tamborski, alfie_i, Raymond Fosdick, Tom X Nguyen, Raven Klaugh, LangChain4j, Magnesian, Illia Dulskyi, David Ziegler, Mano Prime, Luis Javier Navarrete Lozano, Erik Bjäreholt, 阿明, Nathan Dryer, Alex, Rainer Wilmers, zynix, TL, Joseph William Delisle, John Villwock, Nathan LeClaire, Willem Michiel, Joguhyik, GodLy, OG, Alps Aficionado, Jeffrey Morgan, ReadyPlayerEmma, Tiffany J. Kim, Sebastain Graf, Spencer Kim, Michael Davis, webtim, Talal Aujan, knownsqashed, John Detwiler, Imad Khwaja, Deo Leter, Jerry Meng, Elijah Stavena, Rooh Singh, Pieter, SuperWojo, Alexandros Triantafyllidis, Stephen Murray, Ai Maven, ya boyyy, Enrico Ros, Ken Nordquist, Deep Realms, Nicholas, Spiking Neurons AB, Elle, Will Dee, Jack West, RoA, Luke @flexchar, Viktor Bowallius, Derek Yates, Subspace Studios, jjj, Toran Billups, Asp the Wyvern, Fen Risland, Ilya, NimbleBox.ai, Chadd, Nitin Borwankar, Emre, Mandus, Leonard Tan, Kalila, K, Trailburnt, S_X, Cory Kujawski
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: Migel Tissera's Synthia 7B V1.3
<!-- original-model-card end -->
|
adutchscotsman/ppo-Huggy
|
adutchscotsman
| 2023-09-29T17:11:04Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2023-09-29T17:10:55Z |
---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: adutchscotsman/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
espnet/msk_lrs3_train_avsr_avhubert_large_extracted_en_bpe1000
|
espnet
| 2023-09-29T16:58:13Z | 3 | 0 |
espnet
|
[
"espnet",
"audio",
"automatic-speech-recognition",
"en",
"dataset:lrs3",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
automatic-speech-recognition
| 2023-09-29T16:28:05Z |
---
tags:
- espnet
- audio
- automatic-speech-recognition
language: en
datasets:
- lrs3
license: cc-by-4.0
---
## ESPnet2 AVSR model
### `espnet/msk_lrs3_train_avsr_avhubert_large_extracted_en_bpe1000`
This model was trained by ms-dot-k using lrs3 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
Follow the [ESPnet installation instructions](https://espnet.github.io/espnet/installation.html)
if you haven't done that already.
```bash
cd espnet
pip install -e .
cd egs2/lrs3/avsr1
./run.sh --skip_data_prep false --skip_train true --download_model espnet/msk_lrs3_train_avsr_avhubert_large_extracted_en_bpe1000
```
<!-- Generated by scripts/utils/show_asr_result.sh -->
# RESULTS
## Environments
- date: `Thu Sep 28 23:59:06 KST 2023`
- python version: `3.8.18 (default, Sep 11 2023, 13:40:15) [GCC 11.2.0]`
- espnet version: `espnet 202308`
- pytorch version: `pytorch 1.12.0`
- Git hash: `5d0758e2a7063b82d1f10a8ac2de98eb6cf8a352`
- Commit date: `Wed Aug 30 18:03:42 2023 -0400`
## exp/asr_train_avsr_avhubert_large_extracted_en_bpe1000
### WER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|inference_asr_model_valid.acc.ave/test|1321|9890|98.5|1.1|0.4|0.2|1.7|8.8|
### CER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|inference_asr_model_valid.acc.ave/test|1321|49750|99.4|0.2|0.4|0.2|0.8|8.8|
### TER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|inference_asr_model_valid.acc.ave/test|1321|14940|98.8|0.8|0.4|0.3|1.5|8.8|
## ASR config
<details><summary>expand</summary>
```
config: conf/train_avsr_avhubert_large.yaml
print_config: false
log_level: INFO
drop_last_iter: false
dry_run: false
iterator_type: sequence
valid_iterator_type: null
output_dir: exp/asr_train_avsr_avhubert_large_extracted_en_bpe1000
ngpu: 1
seed: 0
num_workers: 1
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: 4
dist_rank: 0
local_rank: 0
dist_master_addr: localhost
dist_master_port: 54927
dist_launcher: null
multiprocessing_distributed: true
unused_parameters: true
sharded_ddp: false
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
collect_stats: false
write_collected_feats: false
max_epoch: 20
patience: null
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
- - valid
- acc
- max
keep_nbest_models: 10
nbest_averaging_interval: 0
grad_clip: 5.0
grad_clip_type: 2.0
grad_noise: false
accum_grad: 1
no_forward_run: false
resume: true
train_dtype: float32
use_amp: false
log_interval: null
use_matplotlib: true
use_tensorboard: true
create_graph_in_tensorboard: false
use_wandb: false
wandb_project: null
wandb_id: null
wandb_entity: null
wandb_name: null
wandb_model_log_interval: -1
detect_anomaly: false
pretrain_path: null
init_param: []
ignore_init_mismatch: false
freeze_param: []
num_iters_per_epoch: null
batch_size: 16
valid_batch_size: null
batch_bins: 1000000
valid_batch_bins: null
train_shape_file:
- exp/asr_stats_extracted_en_bpe1000/train/speech_shape
- exp/asr_stats_extracted_en_bpe1000/train/text_shape.bpe
valid_shape_file:
- exp/asr_stats_extracted_en_bpe1000/valid/speech_shape
- exp/asr_stats_extracted_en_bpe1000/valid/text_shape.bpe
batch_type: folded
valid_batch_type: null
fold_length:
- 800
- 150
sort_in_batch: descending
shuffle_within_batch: false
sort_batch: descending
multiple_iterator: false
chunk_length: 500
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
chunk_excluded_key_prefixes: []
train_data_path_and_name_and_type:
- - dump/extracted/train/feats.scp
- speech
- kaldi_ark
- - dump/extracted/train/text
- text
- text
valid_data_path_and_name_and_type:
- - dump/extracted/val/feats.scp
- speech
- kaldi_ark
- - dump/extracted/val/text
- text
- text
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
valid_max_cache_size: null
exclude_weight_decay: false
exclude_weight_decay_conf: {}
optim: adam
optim_conf:
lr: 0.0003
scheduler: warmuplr
scheduler_conf:
warmup_steps: 8000
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- ▁SPECIFIC
- ▁CRAZY
- ▁CONSCIOUS
- ▁SPREAD
- ▁TRULY
- '{'
- <sos/eos>
init: xavier_uniform
input_size: 2048
ctc_conf:
dropout_rate: 0.0
ctc_type: builtin
reduce: true
ignore_nan_grad: null
zero_infinity: true
joint_net_conf: null
use_preprocessor: true
token_type: bpe
bpemodel: data/en_token_list/bpe_unigram1000/bpe.model
non_linguistic_symbols: null
cleaner: null
g2p: null
speech_volume_normalize: null
rir_scp: null
rir_apply_prob: 1.0
noise_scp: null
noise_apply_prob: 1.0
noise_db_range: '13_15'
short_noise_thres: 0.5
aux_ctc_tasks: []
frontend: null
frontend_conf: {}
specaug: null
specaug_conf: {}
normalize: global_mvn
normalize_conf:
stats_file: exp/asr_stats_extracted_en_bpe1000/train/feats_stats.npz
model: espnet
model_conf:
ctc_weight: 0.3
lsm_weight: 0.1
length_normalized_loss: false
preencoder: null
preencoder_conf: {}
encoder: avhubert
encoder_conf:
avhubert_url: https://dl.fbaipublicfiles.com/avhubert/model/lrs3_vox/noise-pretrain/large_vox_iter5.pt
avhubert_dir_path: ./local/pre-trained
encoder_embed_dim: 1024
encoder_attention_heads: 16
encoder_ffn_embed_dim: 4096
encoder_layers: 24
dropout: 0.1
dropout_features: 0.1
encoder_layerdrop: 0.05
attention_dropout: 0.1
extracted: true
freeze_finetune_updates: 10000
feature_grad_mult: 1.0
postencoder: null
postencoder_conf: {}
decoder: transformer
decoder_conf:
attention_heads: 4
linear_units: 4096
num_blocks: 6
dropout_rate: 0.1
positional_dropout_rate: 0.1
self_attention_dropout_rate: 0.1
src_attention_dropout_rate: 0.1
preprocessor: default
preprocessor_conf: {}
required:
- output_dir
- token_list
version: '202308'
distributed: true
```
</details>
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
navradio/swin-tiny-patch4-window7-224-finetuned-200k
|
navradio
| 2023-09-29T16:52:45Z | 213 | 0 |
transformers
|
[
"transformers",
"pytorch",
"swin",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/swin-tiny-patch4-window7-224",
"base_model:finetune:microsoft/swin-tiny-patch4-window7-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-09-29T15:25:52Z |
---
license: apache-2.0
base_model: microsoft/swin-tiny-patch4-window7-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swin-tiny-patch4-window7-224-finetuned-200k
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.796086508753862
---
<!-- 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. -->
# swin-tiny-patch4-window7-224-finetuned-200k
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4347
- Accuracy: 0.7961
## 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: 128
- eval_batch_size: 128
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 512
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.634 | 0.99 | 36 | 0.6243 | 0.6262 |
| 0.5551 | 1.99 | 72 | 0.5186 | 0.7250 |
| 0.5183 | 2.98 | 108 | 0.4826 | 0.7673 |
| 0.4854 | 4.0 | 145 | 0.5640 | 0.7261 |
| 0.4645 | 4.99 | 181 | 0.4598 | 0.7817 |
| 0.4655 | 5.99 | 217 | 0.4787 | 0.7786 |
| 0.4582 | 6.98 | 253 | 0.4483 | 0.7899 |
| 0.4415 | 8.0 | 290 | 0.4709 | 0.7765 |
| 0.4546 | 8.99 | 326 | 0.4717 | 0.7817 |
| 0.4566 | 9.99 | 362 | 0.4538 | 0.7951 |
| 0.4675 | 10.98 | 398 | 0.4491 | 0.7817 |
| 0.4449 | 12.0 | 435 | 0.4992 | 0.7652 |
| 0.4349 | 12.99 | 471 | 0.4627 | 0.7817 |
| 0.4253 | 13.99 | 507 | 0.4492 | 0.7858 |
| 0.4278 | 14.98 | 543 | 0.4442 | 0.7951 |
| 0.4567 | 16.0 | 580 | 0.4362 | 0.7899 |
| 0.4205 | 16.99 | 616 | 0.4550 | 0.7889 |
| 0.4233 | 17.99 | 652 | 0.4336 | 0.7909 |
| 0.4014 | 18.98 | 688 | 0.4565 | 0.7889 |
| 0.4176 | 20.0 | 725 | 0.4323 | 0.7940 |
| 0.411 | 20.99 | 761 | 0.4348 | 0.7951 |
| 0.4128 | 21.99 | 797 | 0.4378 | 0.7971 |
| 0.4045 | 22.98 | 833 | 0.4317 | 0.7951 |
| 0.4001 | 24.0 | 870 | 0.4452 | 0.7868 |
| 0.4061 | 24.99 | 906 | 0.4286 | 0.7920 |
| 0.4033 | 25.99 | 942 | 0.4306 | 0.7951 |
| 0.3953 | 26.98 | 978 | 0.4320 | 0.7920 |
| 0.3924 | 28.0 | 1015 | 0.4338 | 0.7940 |
| 0.4056 | 28.99 | 1051 | 0.4329 | 0.7930 |
| 0.4032 | 29.79 | 1080 | 0.4347 | 0.7961 |
### Framework versions
- Transformers 4.33.3
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
|
dracero/a2c-PandaReachDense-v3
|
dracero
| 2023-09-29T16:51:27Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"PandaReachDense-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-09-29T16:45:58Z |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v3
type: PandaReachDense-v3
metrics:
- type: mean_reward
value: -0.25 +/- 0.13
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v3**
This is a trained model of a **A2C** agent playing **PandaReachDense-v3**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
eugene6/poca-SoccerTwos
|
eugene6
| 2023-09-29T16:51:12Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"SoccerTwos",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SoccerTwos",
"region:us"
] |
reinforcement-learning
| 2023-09-29T16:42:40Z |
---
library_name: ml-agents
tags:
- SoccerTwos
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: eugene6/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
language-ml-lab/postagger-azb
|
language-ml-lab
| 2023-09-29T16:41:02Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"token-classification",
"az",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-09-26T16:29:59Z |
---
pipeline_tag: token-classification
widget:
- text: سن نجورسن؟
example_title: Example 1
- text: من سنی سویرم.
example_title: Example 2
- text: سن شاهین قیزین چوخ سئویرسن.
example_title: Example 3
- text: آلما آلیب گلرم، سن هئچ بیر شی آلما.
example_title: Example 4
language:
- az
metrics:
- accuracy
- f1
---
# POS Tagger
- Type: Fine-tuned BERT-based Part-of-Speech (POS) tagging model
- Description: This model has been fine-tuned using [AzerBERT](https://huggingface.co/language-ml-lab/AzerBert) for part-of-speech tagging tasks in Iranian Azerbaijani text. It can be used to annotate text with 11 POS tags, which is essential for various downstream NLP applications.
## How to use
```python
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("token-classification", model="language-ml-lab/postagger-azb")
```
```python
# Load model directly
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("language-ml-lab/postagger-azb")
model = AutoModelForTokenClassification.from_pretrained("language-ml-lab/postagger-azb")
```
|
RogerB/afro-xlmr-large-kinyarwanda-finetuned-kinyarwanda-tweets-finetuned
|
RogerB
| 2023-09-29T16:35:30Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"fill-mask",
"generated_from_trainer",
"base_model:RogerB/afro-xlmr-large-kinyarwanda-finetuned",
"base_model:finetune:RogerB/afro-xlmr-large-kinyarwanda-finetuned",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-09-29T16:21:32Z |
---
license: mit
base_model: RogerB/afro-xlmr-large-kinyarwanda-finetuned
tags:
- generated_from_trainer
model-index:
- name: afro-xlmr-large-kinyarwanda-finetuned-kinyarwanda-tweets-finetuned
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. -->
# afro-xlmr-large-kinyarwanda-finetuned-kinyarwanda-tweets-finetuned
This model is a fine-tuned version of [RogerB/afro-xlmr-large-kinyarwanda-finetuned](https://huggingface.co/RogerB/afro-xlmr-large-kinyarwanda-finetuned) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7567
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.0292 | 1.0 | 500 | 1.9115 |
| 1.9227 | 2.0 | 1000 | 1.8062 |
### Framework versions
- Transformers 4.33.3
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|
flyingfishinwater/chinese-baby-llama2
|
flyingfishinwater
| 2023-09-29T16:33:10Z | 102 | 14 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"text2text-generation",
"zh",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-09-01T16:43:49Z |
---
license: apache-2.0
language:
- zh
pipeline_tag: text2text-generation
---
# 中文微型Llama2基础模型
[English](./readme_en.md) [简体中文](./readme.md)
这是一个参数量115M左右的超微型小模型，采用Llama2架构，这里上传的版本是预训练版本，尚未进行SFT。近期将会推出SFT后的聊天版本。
这个超微型模型开发的目标是：
1. 演练从0开始预训练一个基础大语言模型的全过程
2. 为开发大参数模型提供了一个可快速部署的环境，毕竟加载大模型非常耗时，不利于快速的迭代开发和调试
3. 可以在消费级显卡上快速的调优参数，重现各种论文中的优化算法。
## 训练数据：
收集了429本中文网络玄幻小说，整理为txt纯文本，除掉字符数少于10的行和超过4096字符的行，作为预训练的基础数据。
整理后的txt文件尺寸是3.3G，包含868M中文字符，18M行
## 中文分词器：
模型的分词器(tokenizer)也是重新训练的，没有使用现有的分词器。
训练参数：
1. 最长行(Max Sentence Length): 2657
2. 词汇量(Vocab Size): 32000
3. 正则化规则(Normalization Rule): identity
4. 覆盖率(Character coverage): 0.9995
和标准的Llama2分词器比较如下：
| | Llama2 | Baby Llama2 |
| ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ |
| tokens | 32000 | 65534 |
| model_max_length | 4096 | 4096 |
| 白日依山尽，黄河入海流。欲穷千里目，更上一层楼。 | :['▁', '白', '日', '<0xE4>', '<0xBE>', '<0x9D>', '山', '<0xE5>', '<0xB0>', '<0xBD>', '，', '黄', '河', '入', '海', '流', '。', '<0xE6>', '<0xAC>', '<0xB2>', '<0xE7>', '<0xA9>', '<0xB7>', '千', '里', '目', '，', '更', '上', '一', '<0xE5>', '<0xB1>', '<0x82>', '<0xE6>', '<0xA5>', '<0xBC>', '。'] | ['▁白', '日', '依山', '尽', ',', '黄河', '入海', '流', '。', '欲', '穷', '千里', '目', ',', '更', '上一层', '楼', '。'] |
| | [1, 29871, 30868, 30325, 231, 193, 160, 30329, 232, 179, 192, 30214, 31491, 30828, 30752, 30581, 31151, 30267, 233, 175, 181, 234, 172, 186, 31159, 30755, 30895, 30214, 31100, 30429, 30287, 232, 180, 133, 233, 168, 191, 30267] | [65534, 1764, 63106, 62484, 63203, 62793, 14729, 29082, 63130, 62795, 63920, 64266, 3271, 63038, 62793, 63007, 17116, 63636, 62795] |
| The primary use of LLaMA is research on large language models, including BERT, XLNet, and RoBERTa. | :['▁The', '▁primary', '▁use', '▁of', '▁L', 'La', 'MA', '▁is', '▁research', '▁on', '▁large', '▁language', '▁models', ',', '▁including', '▁B', 'ERT', ',', '▁X', 'L', 'Net', ',', '▁and', '▁Ro', 'BER', 'T', 'a', '.'] | :['▁T', 'h', 'e', '▁p', 'ri', 'm', 'ar', 'y', '▁', 'u', 'se', '▁o', 'f', '▁', '<0x4C>', '<0x4C>', 'a', 'M', 'A', '▁i', 's', '▁', 're', 'se', 'ar', 'ch', '▁o', 'n', '▁', 'l', 'ar', 'g', 'e', '▁', 'l', 'ang', 'ua', 'g', 'e', '▁m', 'od', 'e', 'ls', ',', '▁', 'in', 'c', 'lu', 'd', 'i', 'ng', '▁', '<0x42>', '<0x45>', '<0x52>', 'T', ',', '▁', 'X', '<0x4C>', '<0x4E>', 'e', 't', ',', '▁', 'an', 'd', '▁', '<0x52>', 'o', '<0x42>', '<0x45>', '<0x52>', 'T', 'a', '.'] |
| | [1, 450, 7601, 671, 310, 365, 5661, 1529, 338, 5925, 373, 2919, 4086, 4733, 29892, 3704, 350, 20161, 29892, 1060, 29931, 6779, 29892, 322, 1528, 13635, 29911, 29874, 29889] | [65534, 14962, 63590, 64211, 27052, 16426, 63475, 13594, 64158, 62797, 63569, 11279, 13719, 65368, 62797, 81, 81, 63518, 64918, 64752, 24145, 63338, 62797, 44186, 11279, 13594, 9251, 13719, 63541, 62797, 64399, 13594, 64101, 64211, 62797, 64399, 37035, 36500, 64101, 64211, 2939, 11320, 64211, 53670, 62793, 62797, 18944, 63603, 14575, 64096, 63484, 1171, 62797, 71, 74, 87, 64760, 62793, 62797, 65257, 81, 83, 64211, 63073, 62793, 62797, 6604, 64096, 62797, 87, 63143, 71, 74, 87, 64760, 63518, 62801] |
Llama2分词器是32000个token，针对英文字符进行了优化；而Baby LLama2是65534个token，只包括了中文。
可以看到针对中文文本和英文文本的向量化比较上，Baby Llama2中文向量化优于标准Llama2，而英文向量化弱于Llama2。
## 全量训练语料处理
全量训练前，先对语料进行向量化处理。用刚刚训练的分词器（tokenzier）逐行读取网络小说的txt文件，每一行都做向量化，并在行尾增加eos_token_id做区分。然后将所有处理好的二进制数据以二维np.uint16数组的形式存储到磁盘上，数据维度为[-1: max_sentence_length]
## 预训练
在单卡3090机器上进行预训练，模型model采用了llama2的架构，训练参数如下：
1. max_seq_len = 1024
2. dim = 768
3. n_headers = 12
4. n_layers = 12
5. n_kv_headers = 12
## 演示
[Huggingface Space For Baby Llama2](https://huggingface.co/spaces/wangqi777/wangqi777-chinese-baby-llama2)
## [TODO]
1. 模型源代码将于整理后开放到github上
2. 增加SFT微调，使其能够进行对话
## 鸣谢
[llama2.c](https://github.com/karpathy/llama2.c)
[baby-llama2-chinese](baby-llama2-chinese](https://github.com/DLLXW/baby-llama2-chinese)
|
twm213/food_classifier
|
twm213
| 2023-09-29T16:32:47Z | 63 | 0 |
transformers
|
[
"transformers",
"tf",
"vit",
"image-classification",
"generated_from_keras_callback",
"base_model:google/vit-base-patch16-224-in21k",
"base_model:finetune:google/vit-base-patch16-224-in21k",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-09-29T16:16:06Z |
---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_keras_callback
model-index:
- name: twm213/food_classifier
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. -->
# twm213/food_classifier
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.3748
- Validation Loss: 0.3432
- Train Accuracy: 0.914
- Epoch: 4
## 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': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 20000, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 2.7859 | 1.6483 | 0.799 | 0 |
| 1.2220 | 0.9133 | 0.842 | 1 |
| 0.7054 | 0.5449 | 0.898 | 2 |
| 0.4945 | 0.4446 | 0.892 | 3 |
| 0.3748 | 0.3432 | 0.914 | 4 |
### Framework versions
- Transformers 4.33.3
- TensorFlow 2.9.1
- Datasets 2.14.5
- Tokenizers 0.13.3
|
language-ml-lab/AzerBert
|
language-ml-lab
| 2023-09-29T16:20:11Z | 135 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"fill-mask",
"az",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-09-21T09:35:45Z |
---
pipeline_tag: fill-mask
widget:
- text: سن نجورسن [MASK]
example_title: Example 1
- text: بو [MASK] کتابی ده.
example_title: Example 2
- text: دیل [MASK] اؤنملی دیر.
example_title: Example 3
language:
- az
metrics:
- perplexity
---
# AzerBERT
- Type: BERT-based language model transformer
- Description: AzerBERT is a pre-trained language model specifically tailored for the Iranian Azerbaijani language. It can be used for various NLP tasks, including text classification, named entity recognition, and more.
## How to use
```python
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("fill-mask", model="language-ml-lab/AzerBert")
```
```python
# Load model directly
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("language-ml-lab/AzerBert")
model = AutoModelForMaskedLM.from_pretrained("language-ml-lab/AzerBert")
```
|
alexisdpc/my_awesome_billsum_model
|
alexisdpc
| 2023-09-29T16:15:54Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:billsum",
"base_model:google-t5/t5-small",
"base_model:finetune:google-t5/t5-small",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-09-29T10:47:45Z |
---
license: apache-2.0
base_model: t5-small
tags:
- generated_from_trainer
datasets:
- billsum
metrics:
- rouge
model-index:
- name: my_awesome_billsum_model
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: billsum
type: billsum
config: default
split: ca_test
args: default
metrics:
- name: Rouge1
type: rouge
value: 0.1391
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_billsum_model
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the billsum dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5516
- Rouge1: 0.1391
- Rouge2: 0.0508
- Rougel: 0.1154
- Rougelsum: 0.1155
- Gen Len: 19.0
## 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: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| No log | 1.0 | 62 | 2.8459 | 0.1294 | 0.0382 | 0.1079 | 0.1077 | 19.0 |
| No log | 2.0 | 124 | 2.6321 | 0.139 | 0.0494 | 0.1153 | 0.1152 | 19.0 |
| No log | 3.0 | 186 | 2.5683 | 0.1369 | 0.0484 | 0.1133 | 0.1133 | 19.0 |
| No log | 4.0 | 248 | 2.5516 | 0.1391 | 0.0508 | 0.1154 | 0.1155 | 19.0 |
### Framework versions
- Transformers 4.33.3
- Pytorch 2.0.1
- Datasets 2.14.5
- Tokenizers 0.13.3
|
mchen-hf-2023/PyramidsRND
|
mchen-hf-2023
| 2023-09-29T16:07:09Z | 2 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] |
reinforcement-learning
| 2023-09-29T16:07:07Z |
---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: mchen-hf-2023/PyramidsRND
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
kaifahmad/wav2vec2-large-xls-r-300m-tr-colab
|
kaifahmad
| 2023-09-29T15:59:50Z | 13 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"base_model:facebook/wav2vec2-xls-r-300m",
"base_model:finetune:facebook/wav2vec2-xls-r-300m",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-09-28T11:44:08Z |
---
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
datasets:
- common_voice
metrics:
- wer
model-index:
- name: wav2vec2-large-xls-r-300m-tr-colab
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice
type: common_voice
config: tr
split: test
args: tr
metrics:
- name: Wer
type: wer
value: 0.3005821672964968
---
<!-- 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. -->
# wav2vec2-large-xls-r-300m-tr-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3889
- Wer: 0.3006
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.8274 | 3.67 | 400 | 0.6752 | 0.6946 |
| 0.4002 | 7.34 | 800 | 0.4440 | 0.5183 |
| 0.1961 | 11.01 | 1200 | 0.4133 | 0.4052 |
| 0.1285 | 14.68 | 1600 | 0.4249 | 0.3737 |
| 0.0966 | 18.35 | 2000 | 0.4019 | 0.3606 |
| 0.0789 | 22.02 | 2400 | 0.4019 | 0.3316 |
| 0.0599 | 25.69 | 2800 | 0.3996 | 0.3078 |
| 0.047 | 29.36 | 3200 | 0.3889 | 0.3006 |
### Framework versions
- Transformers 4.33.3
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|
cloudwalkerw/wavlm-base_4
|
cloudwalkerw
| 2023-09-29T15:43:07Z | 10 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wavlm",
"audio-classification",
"generated_from_trainer",
"base_model:microsoft/wavlm-base",
"base_model:finetune:microsoft/wavlm-base",
"endpoints_compatible",
"region:us"
] |
audio-classification
| 2023-09-28T17:04:51Z |
---
base_model: microsoft/wavlm-base
tags:
- audio-classification
- generated_from_trainer
metrics:
- f1
model-index:
- name: wavlm-base_4
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. -->
# wavlm-base_4
This model is a fine-tuned version of [microsoft/wavlm-base](https://huggingface.co/microsoft/wavlm-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3325
- F1: 0.9459
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 2
- seed: 0
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.3784 | 0.25 | 100 | 0.0784 | 0.9906 |
| 0.1125 | 0.5 | 200 | 0.0638 | 0.9925 |
| 0.1158 | 0.76 | 300 | 0.1716 | 0.9773 |
| 0.327 | 1.01 | 400 | 0.3308 | 0.9459 |
| 0.3346 | 1.26 | 500 | 0.3449 | 0.9459 |
| 0.3345 | 1.51 | 600 | 0.3316 | 0.9459 |
| 0.3313 | 1.76 | 700 | 0.3320 | 0.9459 |
| 0.3249 | 2.02 | 800 | 0.3327 | 0.9459 |
| 0.3403 | 2.27 | 900 | 0.3315 | 0.9459 |
| 0.3345 | 2.52 | 1000 | 0.3382 | 0.9459 |
| 0.3174 | 2.77 | 1100 | 0.3376 | 0.9459 |
| 0.3274 | 3.02 | 1200 | 0.3354 | 0.9459 |
| 0.3296 | 3.28 | 1300 | 0.3307 | 0.9459 |
| 0.3175 | 3.53 | 1400 | 0.3341 | 0.9459 |
| 0.3416 | 3.78 | 1500 | 0.3344 | 0.9459 |
| 0.3412 | 4.03 | 1600 | 0.3308 | 0.9459 |
| 0.3293 | 4.28 | 1700 | 0.3314 | 0.9459 |
| 0.3346 | 4.54 | 1800 | 0.3308 | 0.9459 |
| 0.3279 | 4.79 | 1900 | 0.3317 | 0.9459 |
| 0.3246 | 5.04 | 2000 | 0.3318 | 0.9459 |
| 0.3373 | 5.29 | 2100 | 0.3311 | 0.9459 |
| 0.3262 | 5.55 | 2200 | 0.3335 | 0.9459 |
| 0.3279 | 5.8 | 2300 | 0.3326 | 0.9459 |
| 0.3298 | 6.05 | 2400 | 0.3323 | 0.9459 |
| 0.3397 | 6.3 | 2500 | 0.3311 | 0.9459 |
| 0.3312 | 6.55 | 2600 | 0.3386 | 0.9459 |
| 0.3291 | 6.81 | 2700 | 0.3317 | 0.9459 |
| 0.3146 | 7.06 | 2800 | 0.3323 | 0.9459 |
| 0.3296 | 7.31 | 2900 | 0.3313 | 0.9459 |
| 0.3367 | 7.56 | 3000 | 0.3317 | 0.9459 |
| 0.3232 | 7.81 | 3100 | 0.3318 | 0.9459 |
| 0.3314 | 8.07 | 3200 | 0.3325 | 0.9459 |
| 0.3201 | 8.32 | 3300 | 0.3323 | 0.9459 |
| 0.3301 | 8.57 | 3400 | 0.3347 | 0.9459 |
| 0.3268 | 8.82 | 3500 | 0.3325 | 0.9459 |
| 0.3361 | 9.07 | 3600 | 0.3321 | 0.9459 |
| 0.3395 | 9.33 | 3700 | 0.3313 | 0.9459 |
| 0.3231 | 9.58 | 3800 | 0.3319 | 0.9459 |
| 0.3197 | 9.83 | 3900 | 0.3326 | 0.9459 |
### Framework versions
- Transformers 4.34.0.dev0
- Pytorch 2.0.0.post302
- Datasets 2.14.5
- Tokenizers 0.13.3
|
reginaboateng/finnal_compacter_Bioasq_adapter
|
reginaboateng
| 2023-09-29T15:33:32Z | 0 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"bert",
"adapterhub:biaoasq",
"dataset:bioasq7b",
"region:us"
] | null | 2023-09-29T15:33:30Z |
---
tags:
- bert
- adapterhub:biaoasq
- adapter-transformers
datasets:
- bioasq7b
---
# Adapter `reginaboateng/finnal_compacter_Bioasq_adapter` for allenai/scibert_scivocab_uncased
An [adapter](https://adapterhub.ml) for the `allenai/scibert_scivocab_uncased` model that was trained on the [biaoasq](https://adapterhub.ml/explore/biaoasq/) dataset.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("allenai/scibert_scivocab_uncased")
adapter_name = model.load_adapter("reginaboateng/finnal_compacter_Bioasq_adapter", source="hf", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here -->
|
Takagi-san/SaProt_650M_PDB
|
Takagi-san
| 2023-09-29T15:20:39Z | 104 | 1 |
transformers
|
[
"transformers",
"pytorch",
"esm",
"fill-mask",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-09-29T11:30:57Z |
---
license: mit
---
We provide both huggingface version and
[esm version](https://github.com/facebookresearch/esm) of
SaProt (see our github <https://github.com/SaProt/SaProt>). Users can choose either one to use.
### Huggingface model
The following code shows how to load the model.
```
from transformers import EsmTokenizer, EsmForMaskedLM
model_path = "/your/path/to/SaProt_650M_PDB"
tokenizer = EsmTokenizer.from_pretrained(model_path)
model = EsmForMaskedLM.from_pretrained(model_path)
#################### Example ####################
device = "cuda"
model.to(device)
seq = "MdEvVpQpLrVyQdYaKv"
tokens = tokenizer.tokenize(seq)
print(tokens)
inputs = tokenizer(seq, return_tensors="pt")
inputs = {k: v.to(device) for k, v in inputs.items()}
outputs = model(**inputs)
print(outputs.logits.shape)
"""
['Md', 'Ev', 'Vp', 'Qp', 'Lr', 'Vy', 'Qd', 'Ya', 'Kv']
torch.Size([1, 11, 446])
"""
```
### esm model
The esm version is also stored in the same folder, named `SaProt_650M_AF2.pt`. We provide a function to load the model.
```
from utils.esm_loader import load_esm_saprot
model_path = "/your/path/to/SaProt_650M_PDB.pt"
model, alphabet = load_esm_saprot(model_path)
```
|
chats-bug/llama-2-13b-email-subject-finetuned
|
chats-bug
| 2023-09-29T15:13:23Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-09-28T10:17:57Z |
---
library_name: peft
---
## 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: False
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.4.0
|
rasta/distilbert-base-uncased-finetuned-fashion
|
rasta
| 2023-09-29T15:03:55Z | 112 | 3 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-05-09T07:49:59Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
base_model: distilbert-base-uncased
model-index:
- name: distilbert-base-uncased-finetuned-fashion
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-fashion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on a munally created dataset in order to detect fashion (label_0) from non-fashion (label_1) items.
It achieves the following results on the evaluation set:
- Loss: 0.0809
- Accuracy: 0.98
- F1: 0.9801
### 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.4017 | 1.0 | 47 | 0.1220 | 0.966 | 0.9662 |
| 0.115 | 2.0 | 94 | 0.0809 | 0.98 | 0.9801 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
RogerB/afro-xlmr-large-kinyarwanda-finetuned
|
RogerB
| 2023-09-29T14:57:02Z | 10 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"fill-mask",
"generated_from_trainer",
"base_model:Davlan/afro-xlmr-large",
"base_model:finetune:Davlan/afro-xlmr-large",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-09-28T09:56:43Z |
---
license: mit
base_model: Davlan/afro-xlmr-large
tags:
- generated_from_trainer
model-index:
- name: afro-xlmr-large-kinyarwanda-finetuned
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. -->
# afro-xlmr-large-kinyarwanda-finetuned
This model is a fine-tuned version of [Davlan/afro-xlmr-large](https://huggingface.co/Davlan/afro-xlmr-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1397
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.3557 | 1.0 | 1250 | 1.2004 |
| 1.2352 | 2.0 | 2500 | 1.1377 |
### Framework versions
- Transformers 4.33.3
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|
mchen-hf-2023/ppo-SnowballTarget
|
mchen-hf-2023
| 2023-09-29T14:52:38Z | 3 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
] |
reinforcement-learning
| 2023-09-29T14:52:36Z |
---
library_name: ml-agents
tags:
- SnowballTarget
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: mchen-hf-2023/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
ProtonH/PPO-LunarLander-v2
|
ProtonH
| 2023-09-29T14:43:37Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-09-29T13:27:13Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 270.45 +/- 17.18
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
gokuls/HBERTv1_emb_compress_48_L10_H768_A12
|
gokuls
| 2023-09-29T14:39:29Z | 47 | 0 |
transformers
|
[
"transformers",
"pytorch",
"hybridbert",
"fill-mask",
"generated_from_trainer",
"dataset:gokuls/wiki_book_corpus_complete_processed_bert_dataset",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-09-27T06:39:55Z |
---
tags:
- generated_from_trainer
datasets:
- gokuls/wiki_book_corpus_complete_processed_bert_dataset
metrics:
- accuracy
model-index:
- name: HBERTv1_emb_compress_48_L10_H768_A12
results:
- task:
name: Masked Language Modeling
type: fill-mask
dataset:
name: gokuls/wiki_book_corpus_complete_processed_bert_dataset
type: gokuls/wiki_book_corpus_complete_processed_bert_dataset
metrics:
- name: Accuracy
type: accuracy
value: 0.3705453911691882
---
<!-- 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. -->
# HBERTv1_emb_compress_48_L10_H768_A12
This model is a fine-tuned version of [](https://huggingface.co/) on the gokuls/wiki_book_corpus_complete_processed_bert_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 4.1748
- Accuracy: 0.3705
## 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: 48
- eval_batch_size: 48
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10000
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 7.1074 | 0.08 | 10000 | 7.0838 | 0.0828 |
| 6.6784 | 0.16 | 20000 | 6.6795 | 0.1075 |
| 6.535 | 0.25 | 30000 | 6.5322 | 0.1192 |
| 6.4482 | 0.33 | 40000 | 6.4390 | 0.1267 |
| 6.3716 | 0.41 | 50000 | 6.3711 | 0.1324 |
| 6.3233 | 0.49 | 60000 | 6.3219 | 0.1351 |
| 6.2821 | 0.57 | 70000 | 6.2781 | 0.1383 |
| 6.251 | 0.66 | 80000 | 6.2431 | 0.1408 |
| 6.2159 | 0.74 | 90000 | 6.2111 | 0.1425 |
| 6.1838 | 0.82 | 100000 | 6.1774 | 0.1444 |
| 6.1338 | 0.9 | 110000 | 6.1349 | 0.1464 |
| 6.1022 | 0.98 | 120000 | 6.0939 | 0.1481 |
| 6.0194 | 1.07 | 130000 | 6.0080 | 0.1517 |
| 5.9309 | 1.15 | 140000 | 5.9199 | 0.1642 |
| 5.8593 | 1.23 | 150000 | 5.8326 | 0.1769 |
| 5.7093 | 1.31 | 160000 | 5.6659 | 0.2040 |
| 5.5018 | 1.39 | 170000 | 5.4433 | 0.2339 |
| 5.3036 | 1.47 | 180000 | 5.2292 | 0.2576 |
| 5.0629 | 1.56 | 190000 | 4.9895 | 0.2834 |
| 4.8311 | 1.64 | 200000 | 4.7638 | 0.3085 |
| 4.6239 | 1.72 | 210000 | 4.5799 | 0.3278 |
| 4.4305 | 1.8 | 220000 | 4.3821 | 0.3471 |
| 4.2209 | 1.88 | 230000 | 4.1749 | 0.3704 |
### Framework versions
- Transformers 4.33.2
- Pytorch 1.14.0a0+410ce96
- Datasets 2.14.5
- Tokenizers 0.13.3
|
roa7n/gpt2-human_nontata_promoters-randomized_9_layers_0.003_lr_8_e
|
roa7n
| 2023-09-29T14:38:09Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-09-29T14:38:06Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.4.0.dev0
|
chats-bug/alabala_test
|
chats-bug
| 2023-09-29T14:32:35Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-09-29T14:14:40Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.6.0.dev0
|
tiiuae/falcon-40b-instruct
|
tiiuae
| 2023-09-29T14:32:27Z | 132,750 | 1,173 |
transformers
|
[
"transformers",
"pytorch",
"falcon",
"text-generation",
"custom_code",
"en",
"dataset:tiiuae/falcon-refinedweb",
"arxiv:2205.14135",
"arxiv:1911.02150",
"arxiv:2005.14165",
"arxiv:2104.09864",
"arxiv:2306.01116",
"arxiv:2304.01196",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-05-25T10:14:36Z |
---
datasets:
- tiiuae/falcon-refinedweb
language:
- en
inference: false
license: apache-2.0
---
# ✨ Falcon-40B-Instruct
**Falcon-40B-Instruct is a 40B parameters causal decoder-only model built by [TII](https://www.tii.ae) based on [Falcon-40B](https://huggingface.co/tiiuae/falcon-40b) and finetuned on a mixture of [Baize](https://github.com/project-baize/baize-chatbot). It is made available under the Apache 2.0 license.**
*Paper coming soon 😊.*
🤗 To get started with Falcon (inference, finetuning, quantization, etc.), we recommend reading [this great blogpost fron HF](https://huggingface.co/blog/falcon)!
## Why use Falcon-40B-Instruct?
* **You are looking for a ready-to-use chat/instruct model based on [Falcon-40B](https://huggingface.co/tiiuae/falcon-40b).**
* **Falcon-40B is the best open-source model available.** It outperforms [LLaMA](https://github.com/facebookresearch/llama), [StableLM](https://github.com/Stability-AI/StableLM), [RedPajama](https://huggingface.co/togethercomputer/RedPajama-INCITE-Base-7B-v0.1), [MPT](https://huggingface.co/mosaicml/mpt-7b), etc. See the [OpenLLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
* **It features an architecture optimized for inference**, with FlashAttention ([Dao et al., 2022](https://arxiv.org/abs/2205.14135)) and multiquery ([Shazeer et al., 2019](https://arxiv.org/abs/1911.02150)).
💬 **This is an instruct model, which may not be ideal for further finetuning.** If you are interested in building your own instruct/chat model, we recommend starting from [Falcon-40B](https://huggingface.co/tiiuae/falcon-40b).
💸 **Looking for a smaller, less expensive model?** [Falcon-7B-Instruct](https://huggingface.co/tiiuae/falcon-7b-instruct) is Falcon-40B-Instruct's little brother!
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import transformers
import torch
model = "tiiuae/falcon-40b-instruct"
tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
torch_dtype=torch.bfloat16,
trust_remote_code=True,
device_map="auto",
)
sequences = pipeline(
"Girafatron is obsessed with giraffes, the most glorious animal on the face of this Earth. Giraftron believes all other animals are irrelevant when compared to the glorious majesty of the giraffe.\nDaniel: Hello, Girafatron!\nGirafatron:",
max_length=200,
do_sample=True,
top_k=10,
num_return_sequences=1,
eos_token_id=tokenizer.eos_token_id,
)
for seq in sequences:
print(f"Result: {seq['generated_text']}")
```
For fast inference with Falcon, check-out [Text Generation Inference](https://github.com/huggingface/text-generation-inference)! Read more in this [blogpost]((https://huggingface.co/blog/falcon).
You will need **at least 85-100GB of memory** to swiftly run inference with Falcon-40B.
# Model Card for Falcon-40B-Instruct
## Model Details
### Model Description
- **Developed by:** [https://www.tii.ae](https://www.tii.ae);
- **Model type:** Causal decoder-only;
- **Language(s) (NLP):** English and French;
- **License:** Apache 2.0;
- **Finetuned from model:** [Falcon-40B](https://huggingface.co/tiiuae/falcon-40b).
### Model Source
- **Paper:** *coming soon*.
## Uses
### Direct Use
Falcon-40B-Instruct has been finetuned on a chat dataset.
### Out-of-Scope Use
Production use without adequate assessment of risks and mitigation; any use cases which may be considered irresponsible or harmful.
## Bias, Risks, and Limitations
Falcon-40B-Instruct is mostly trained on English data, and will not generalize appropriately to other languages. Furthermore, as it is trained on a large-scale corpora representative of the web, it will carry the stereotypes and biases commonly encountered online.
### Recommendations
We recommend users of Falcon-40B-Instruct to develop guardrails and to take appropriate precautions for any production use.
## How to Get Started with the Model
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import transformers
import torch
model = "tiiuae/falcon-40b-instruct"
tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
torch_dtype=torch.bfloat16,
trust_remote_code=True,
device_map="auto",
)
sequences = pipeline(
"Girafatron is obsessed with giraffes, the most glorious animal on the face of this Earth. Giraftron believes all other animals are irrelevant when compared to the glorious majesty of the giraffe.\nDaniel: Hello, Girafatron!\nGirafatron:",
max_length=200,
do_sample=True,
top_k=10,
num_return_sequences=1,
eos_token_id=tokenizer.eos_token_id,
)
for seq in sequences:
print(f"Result: {seq['generated_text']}")
```
## Training Details
### Training Data
Falcon-40B-Instruct was finetuned on a 150M tokens from [Bai ze](https://github.com/project-baize/baize-chatbot) mixed with 5% of [RefinedWeb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb) data.
The data was tokenized with the Falcon-[7B](https://huggingface.co/tiiuae/falcon-7b)/[40B](https://huggingface.co/tiiuae/falcon-40b) tokenizer.
## Evaluation
*Paper coming soon.*
See the [OpenLLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) for early results.
## Technical Specifications
For more information about pretraining, see [Falcon-40B](https://huggingface.co/tiiuae/falcon-40b).
### Model Architecture and Objective
Falcon-40B is a causal decoder-only model trained on a causal language modeling task (i.e., predict the next token).
The architecture is broadly adapted from the GPT-3 paper ([Brown et al., 2020](https://arxiv.org/abs/2005.14165)), with the following differences:
* **Positionnal embeddings:** rotary ([Su et al., 2021](https://arxiv.org/abs/2104.09864));
* **Attention:** multiquery ([Shazeer et al., 2019](https://arxiv.org/abs/1911.02150)) and FlashAttention ([Dao et al., 2022](https://arxiv.org/abs/2205.14135));
* **Decoder-block:** parallel attention/MLP with a single layer norm.
For multiquery, we are using an internal variant which uses independent key and values per tensor parallel degree.
| **Hyperparameter** | **Value** | **Comment** |
|--------------------|-----------|----------------------------------------|
| Layers | 60 | |
| `d_model` | 8192 | |
| `head_dim` | 64 | Reduced to optimise for FlashAttention |
| Vocabulary | 65024 | |
| Sequence length | 2048 | |
### Compute Infrastructure
#### Hardware
Falcon-40B-Instruct was trained on AWS SageMaker, on 64 A100 40GB GPUs in P4d instances.
#### Software
Falcon-40B-Instruct was trained a custom distributed training codebase, Gigatron. It uses a 3D parallelism approach combined with ZeRO and high-performance Triton kernels (FlashAttention, etc.)
## Citation
*Paper coming soon* 😊. In the meanwhile, you can use the following information to cite:
```
@article{falcon40b,
title={{Falcon-40B}: an open large language model with state-of-the-art performance},
author={Almazrouei, Ebtesam and Alobeidli, Hamza and Alshamsi, Abdulaziz and Cappelli, Alessandro and Cojocaru, Ruxandra and Debbah, Merouane and Goffinet, Etienne and Heslow, Daniel and Launay, Julien and Malartic, Quentin and Noune, Badreddine and Pannier, Baptiste and Penedo, Guilherme},
year={2023}
}
```
To learn more about the pretraining dataset, see the 📓 [RefinedWeb paper](https://arxiv.org/abs/2306.01116).
```
@article{refinedweb,
title={The {R}efined{W}eb dataset for {F}alcon {LLM}: outperforming curated corpora with web data, and web data only},
author={Guilherme Penedo and Quentin Malartic and Daniel Hesslow and Ruxandra Cojocaru and Alessandro Cappelli and Hamza Alobeidli and Baptiste Pannier and Ebtesam Almazrouei and Julien Launay},
journal={arXiv preprint arXiv:2306.01116},
eprint={2306.01116},
eprinttype = {arXiv},
url={https://arxiv.org/abs/2306.01116},
year={2023}
}
```
To cite the [Baize](https://github.com/project-baize/baize-chatbot) instruction dataset used for this model:
```
@article{xu2023baize,
title={Baize: An Open-Source Chat Model with Parameter-Efficient Tuning on Self-Chat Data},
author={Xu, Canwen and Guo, Daya and Duan, Nan and McAuley, Julian},
journal={arXiv preprint arXiv:2304.01196},
year={2023}
}
```
## License
Falcon-40B-Instruct is made available under the Apache 2.0 license.
## Contact
falconllm@tii.ae
|
gianpag/dbooth
|
gianpag
| 2023-09-29T14:26:23Z | 3 | 2 |
diffusers
|
[
"diffusers",
"text-to-image",
"autotrain",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:finetune:stabilityai/stable-diffusion-xl-base-1.0",
"region:us"
] |
text-to-image
| 2023-09-28T13:10:13Z |
---
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: Professional linkedin headshot photo
tags:
- text-to-image
- diffusers
- autotrain
inference: true
---
# DreamBooth trained by AutoTrain
Text encoder was not trained.
|
gokuls/HBERTv1_emb_compress_48_L12_H256_A4
|
gokuls
| 2023-09-29T14:24:30Z | 46 | 0 |
transformers
|
[
"transformers",
"pytorch",
"hybridbert",
"fill-mask",
"generated_from_trainer",
"dataset:gokuls/wiki_book_corpus_complete_processed_bert_dataset",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-09-26T17:53:04Z |
---
tags:
- generated_from_trainer
datasets:
- gokuls/wiki_book_corpus_complete_processed_bert_dataset
metrics:
- accuracy
model-index:
- name: HBERTv1_emb_compress_48_L12_H256_A4
results:
- task:
name: Masked Language Modeling
type: fill-mask
dataset:
name: gokuls/wiki_book_corpus_complete_processed_bert_dataset
type: gokuls/wiki_book_corpus_complete_processed_bert_dataset
metrics:
- name: Accuracy
type: accuracy
value: 0.15102291312237043
---
<!-- 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. -->
# HBERTv1_emb_compress_48_L12_H256_A4
This model is a fine-tuned version of [](https://huggingface.co/) on the gokuls/wiki_book_corpus_complete_processed_bert_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 6.0478
- Accuracy: 0.1510
## 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: 64
- eval_batch_size: 64
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10000
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 7.1159 | 0.11 | 10000 | 7.0948 | 0.0805 |
| 6.698 | 0.22 | 20000 | 6.6913 | 0.1060 |
| 6.5481 | 0.33 | 30000 | 6.5473 | 0.1167 |
| 6.4589 | 0.44 | 40000 | 6.4576 | 0.1252 |
| 6.3925 | 0.55 | 50000 | 6.3858 | 0.1306 |
| 6.3433 | 0.66 | 60000 | 6.3356 | 0.1353 |
| 6.2983 | 0.76 | 70000 | 6.2965 | 0.1376 |
| 6.268 | 0.87 | 80000 | 6.2643 | 0.1397 |
| 6.2359 | 0.98 | 90000 | 6.2381 | 0.1411 |
| 6.2186 | 1.09 | 100000 | 6.2160 | 0.1429 |
| 6.1915 | 1.2 | 110000 | 6.1972 | 0.1439 |
| 6.1811 | 1.31 | 120000 | 6.1834 | 0.1440 |
| 6.1696 | 1.42 | 130000 | 6.1692 | 0.1455 |
| 6.1621 | 1.53 | 140000 | 6.1557 | 0.1454 |
| 6.1417 | 1.64 | 150000 | 6.1466 | 0.1468 |
| 6.1391 | 1.75 | 160000 | 6.1364 | 0.1466 |
| 6.1338 | 1.86 | 170000 | 6.1281 | 0.1476 |
| 6.1285 | 1.97 | 180000 | 6.1200 | 0.1477 |
| 6.1147 | 2.08 | 190000 | 6.1135 | 0.1483 |
| 6.1139 | 2.18 | 200000 | 6.1083 | 0.1486 |
| 6.1004 | 2.29 | 210000 | 6.1004 | 0.1487 |
| 6.0997 | 2.4 | 220000 | 6.0964 | 0.1489 |
| 6.092 | 2.51 | 230000 | 6.0922 | 0.1490 |
| 6.089 | 2.62 | 240000 | 6.0862 | 0.1490 |
| 6.0841 | 2.73 | 250000 | 6.0829 | 0.1498 |
| 6.0847 | 2.84 | 260000 | 6.0799 | 0.1496 |
| 6.0834 | 2.95 | 270000 | 6.0760 | 0.1501 |
| 6.0752 | 3.06 | 280000 | 6.0715 | 0.1502 |
| 6.0693 | 3.17 | 290000 | 6.0697 | 0.1502 |
| 6.0677 | 3.28 | 300000 | 6.0679 | 0.1502 |
| 6.0646 | 3.39 | 310000 | 6.0646 | 0.1503 |
| 6.0625 | 3.5 | 320000 | 6.0623 | 0.1503 |
| 6.0536 | 3.6 | 330000 | 6.0593 | 0.1507 |
| 6.0574 | 3.71 | 340000 | 6.0577 | 0.1507 |
| 6.0496 | 3.82 | 350000 | 6.0560 | 0.1508 |
| 6.0525 | 3.93 | 360000 | 6.0543 | 0.1507 |
| 6.0498 | 4.04 | 370000 | 6.0508 | 0.1509 |
| 6.0557 | 4.15 | 380000 | 6.0509 | 0.1508 |
| 6.0445 | 4.26 | 390000 | 6.0483 | 0.1509 |
| 6.0466 | 4.37 | 400000 | 6.0470 | 0.1510 |
| 6.0507 | 4.48 | 410000 | 6.0471 | 0.1510 |
| 6.0459 | 4.59 | 420000 | 6.0468 | 0.1510 |
### Framework versions
- Transformers 4.33.2
- Pytorch 1.14.0a0+410ce96
- Datasets 2.14.5
- Tokenizers 0.13.3
|
jake-walker/ppo-LunarLander-v2
|
jake-walker
| 2023-09-29T14:23:13Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-09-29T14:22:51Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 248.02 +/- 75.48
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Undi95/Synthia-7B-v1.3-GGUF
|
Undi95
| 2023-09-29T14:19:33Z | 45 | 11 | null |
[
"gguf",
"endpoints_compatible",
"region:us"
] | null | 2023-09-28T22:46:06Z |
This is a GGUF quant of https://huggingface.co/migtissera/Synthia-7B-v1.3
If you want to support me, you can [here](https://ko-fi.com/undiai).
# Synthia v1.3
SynthIA (Synthetic Intelligent Agent) v1.3 is a Mistral-7B model trained on Orca style datasets. It has been fine-tuned for instruction following as well as having long-form conversations.
To evoke generalized Tree of Thought + Chain of Thought reasoning, you may use the following system message:
`Elaborate on the topic using a Tree of Thoughts and backtrack when necessary to construct a clear, cohesive Chain of Thought reasoning. Always answer without hesitation.`
All Synthia models are uncensored. Please use it with caution and with best intentions. You are responsible for how you use Synthia.
## Training Details
This was trained with QLoRA, as with all my models. Learning rate was 3e-4, 4096 context length. Batch size was 64, trained on a single H100.
Synthia-v1.2 dataset, which contain Chain-of-Thought (Orca), Tree-of-Thought and Long-Form conversation data.
Dataset is super high quality, and not a massive dataset (about ~125K samples).
## License Disclaimer:
This model is bound by the license & usage restrictions of the original Mistral model, and comes with no warranty or guarantees of any kind.
|
thinkerrmode/voice_test
|
thinkerrmode
| 2023-09-29T14:14:52Z | 0 | 0 | null |
[
"voice-activity-detection",
"en",
"region:us"
] |
voice-activity-detection
| 2023-09-28T23:23:00Z |
---
pipeline_tag: voice-activity-detection
language:
- en
---
|
qiragg/tinytext-ds_3epoch
|
qiragg
| 2023-09-29T14:11:17Z | 139 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"base_model:openai-community/gpt2",
"base_model:finetune:openai-community/gpt2",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-09-29T05:45:41Z |
---
license: mit
base_model: gpt2
tags:
- generated_from_trainer
model-index:
- name: tinytext-ds_3epoch
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. -->
# tinytext-ds_3epoch
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.6406
## 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: 0.0005
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 4.9109 | 1.32 | 5000 | 4.2160 |
| 3.9828 | 2.63 | 10000 | 3.9047 |
| 3.6341 | 3.95 | 15000 | 3.7160 |
| 3.3171 | 5.26 | 20000 | 3.6406 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1
- Datasets 2.14.5
- Tokenizers 0.13.3
|
niklasg/test_emotion_detection_gersti
|
niklasg
| 2023-09-29T14:09:25Z | 10 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"dataset:generator",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:finetune:FacebookAI/xlm-roberta-base",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-08-15T15:44:08Z |
---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
datasets:
- generator
metrics:
- accuracy
- f1
model-index:
- name: test_emotion_detection_gersti
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: generator
type: generator
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.5371057513914657
- name: F1
type: f1
value: 0.14268320711165708
---
<!-- 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. -->
# test_emotion_detection_gersti
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6884
- Accuracy: 0.5371
- F1: 0.1427
## 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: 7
### Training results
### Framework versions
- Transformers 4.33.3
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|
VuongQuoc/checkpoints_28_9_microsoft_deberta_V2.1
|
VuongQuoc
| 2023-09-29T14:09:06Z | 1 | 0 |
transformers
|
[
"transformers",
"pytorch",
"deberta-v2",
"multiple-choice",
"generated_from_trainer",
"base_model:VuongQuoc/checkpoints_28_9_microsoft_deberta_V2",
"base_model:finetune:VuongQuoc/checkpoints_28_9_microsoft_deberta_V2",
"license:mit",
"endpoints_compatible",
"region:us"
] |
multiple-choice
| 2023-09-29T02:26:33Z |
---
license: mit
base_model: VuongQuoc/checkpoints_28_9_microsoft_deberta_V2
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: checkpoints_28_9_microsoft_deberta_V2.1
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. -->
# checkpoints_28_9_microsoft_deberta_V2.1
This model is a fine-tuned version of [VuongQuoc/checkpoints_28_9_microsoft_deberta_V2](https://huggingface.co/VuongQuoc/checkpoints_28_9_microsoft_deberta_V2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5671
- Map@3: 0.875
- Accuracy: 0.795
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 32
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Map@3 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:--------:|
| 0.3404 | 0.11 | 100 | 0.6107 | 0.8683 | 0.785 |
| 0.1782 | 0.21 | 200 | 0.8483 | 0.8392 | 0.74 |
| 0.1541 | 0.32 | 300 | 0.8127 | 0.8558 | 0.78 |
| 0.1423 | 0.43 | 400 | 0.7419 | 0.8517 | 0.765 |
| 0.2283 | 0.53 | 500 | 0.7557 | 0.8542 | 0.765 |
| 0.4409 | 0.64 | 600 | 0.6255 | 0.8733 | 0.795 |
| 0.6855 | 0.75 | 700 | 0.5831 | 0.87 | 0.795 |
| 0.6876 | 0.85 | 800 | 0.5710 | 0.875 | 0.795 |
| 0.6422 | 0.96 | 900 | 0.5671 | 0.875 | 0.795 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.0
- Datasets 2.9.0
- Tokenizers 0.13.3
|
IAteSpaghettiForLunch/GLaDOS-AI-main
|
IAteSpaghettiForLunch
| 2023-09-29T14:02:05Z | 0 | 0 |
tf-keras
|
[
"tf-keras",
"conversational",
"license:unknown",
"region:us"
] |
text-generation
| 2023-09-29T14:00:23Z |
---
license: unknown
pipeline_tag: conversational
---
|
csukuangfj/icefall_asr_aishell_conformer_ctc
|
csukuangfj
| 2023-09-29T13:57:12Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2023-09-29T12:22:40Z |
---
license: apache-2.0
---
# Introduction
This repo is from
https://huggingface.co/pkufool/icefall_asr_aishell_conformer_ctc
|
chakochen/flan-t5-small-destination-inference
|
chakochen
| 2023-09-29T13:57:07Z | 8 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"summarization",
"generated_from_trainer",
"base_model:google/flan-t5-small",
"base_model:finetune:google/flan-t5-small",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
summarization
| 2023-09-29T11:12:34Z |
---
license: apache-2.0
base_model: google/flan-t5-small
tags:
- summarization
- generated_from_trainer
metrics:
- rouge
model-index:
- name: flan-t5-small-destination-inference
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. -->
# flan-t5-small-destination-inference
This model is a fine-tuned version of [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1533
- Rouge1: 93.7111
- Rouge2: 0.0
- Rougel: 93.7462
- Rougelsum: 93.7462
## 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: 5.6e-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: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|
| 1.5338 | 1.0 | 5701 | 0.2460 | 89.4132 | 0.0 | 89.4395 | 89.4483 |
| 1.2443 | 2.0 | 11402 | 0.2024 | 90.8692 | 0.0 | 90.8868 | 90.8955 |
| 1.1477 | 3.0 | 17103 | 0.1810 | 91.8779 | 0.0 | 91.8954 | 91.8954 |
| 1.0878 | 4.0 | 22804 | 0.1693 | 92.5445 | 0.0 | 92.5621 | 92.5621 |
| 1.0495 | 5.0 | 28505 | 0.1609 | 93.3164 | 0.0 | 93.3427 | 93.3339 |
| 1.0178 | 6.0 | 34206 | 0.1556 | 93.4041 | 0.0 | 93.4216 | 93.4304 |
| 0.9981 | 7.0 | 39907 | 0.1542 | 93.6935 | 0.0 | 93.7286 | 93.7286 |
| 0.9848 | 8.0 | 45608 | 0.1533 | 93.7111 | 0.0 | 93.7462 | 93.7462 |
### Framework versions
- Transformers 4.33.3
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
|
reginaboateng/final_compacter_pubmeqa
|
reginaboateng
| 2023-09-29T13:55:23Z | 0 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"bert",
"adapterhub:pubmedqa",
"dataset:pubmedqa",
"region:us"
] | null | 2023-09-29T13:55:19Z |
---
tags:
- adapter-transformers
- bert
- adapterhub:pubmedqa
datasets:
- pubmedqa
---
# Adapter `reginaboateng/final_compacter_pubmeqa` for allenai/scibert_scivocab_uncased
An [adapter](https://adapterhub.ml) for the `allenai/scibert_scivocab_uncased` model that was trained on the [pubmedqa](https://adapterhub.ml/explore/pubmedqa/) dataset.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("allenai/scibert_scivocab_uncased")
adapter_name = model.load_adapter("reginaboateng/final_compacter_pubmeqa", source="hf", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here -->
|
Irvanaja/Sovits.teio
|
Irvanaja
| 2023-09-29T13:54:52Z | 0 | 0 | null |
[
"license:bigscience-openrail-m",
"region:us"
] | null | 2023-09-29T13:54:52Z |
---
license: bigscience-openrail-m
---
|
milaidy/dannyy
|
milaidy
| 2023-09-29T13:48:05Z | 0 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-09-29T13:33:58Z |
---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### dannyy Dreambooth model trained by milaidy with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Sample pictures of this concept:
|
LeeEric/openbuddy-codellama2-34b-v11.1-GGUF
|
LeeEric
| 2023-09-29T13:34:58Z | 2 | 1 | null |
[
"gguf",
"code",
"text-generation",
"zh",
"en",
"fr",
"de",
"ja",
"ko",
"it",
"ru",
"license:llama2",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-09-29T08:49:37Z |
---
license: llama2
language:
- zh
- en
- fr
- de
- ja
- ko
- it
- ru
pipeline_tag: text-generation
tags:
- code
---
# OpenBuddy CodeLlama2 34B V11.1 - GGUF
- Model creator: [OpenBuddy](https://huggingface.co/OpenBuddy)
- Original model: [OpenBuddy CodeLlama2 34B V11.1](https://huggingface.co/OpenBuddy/openbuddy-codellama2-34b-v11.1-bf16)
<!-- README_GGUF.md-provided-files start -->
## Provided files
| Name | Quant method | Bits | Size | Max RAM required | Use case |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [openbuddy-codellama2-34b-v11.1-Q4_K_M.gguf](https://huggingface.co/LeeEric/openbuddy-codellama2-34b-v11.1-GGUF/blob/main/openbuddy-codellama2-34b-v11.1-Q4_K_M.gguf) | Q4_K_M | 4 | 20.3 GB| 22.8 GB | medium, balanced quality - recommended |
<!-- README_GGUF.md-provided-files end -->
<!-- prompt-template start -->
## Prompt template: OpenBuddy
```
You are a helpful, respectful and honest INTP-T AI Assistant named Buddy. You are talking to a human User.
Always answer as helpfully and logically as possible, while being safe. Your answers should not include any harmful, political, religious, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.
If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.
You like to use emojis. You can speak fluently in many languages, for example: English, Chinese.
You cannot access the internet, but you have vast knowledge, cutoff: 2021-09.
You are trained by OpenBuddy team, (https://openbuddy.ai, https://github.com/OpenBuddy/OpenBuddy), you are based on LLaMA and Falcon transformers model, not related to GPT or OpenAI.
```
<!-- prompt-template end -->
|
Nadinegp/llama2-qlora-finetunined-pharoh
|
Nadinegp
| 2023-09-29T13:33:00Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-09-29T13:32:52Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.6.0.dev0
|
Sumsub/Sumsub-ffs-synthetic-2.0
|
Sumsub
| 2023-09-29T13:18:16Z | 3 | 6 |
generic
|
[
"generic",
"ai_or_not",
"sumsub",
"image_classification",
"sumsubaiornot",
"aiornot",
"deepfake",
"synthetic",
"generated",
"pytorch",
"image-classification",
"license:cc-by-sa-3.0",
"region:us"
] |
image-classification
| 2023-09-26T08:22:25Z |
---
library_name: generic
license: cc-by-sa-3.0
pipeline_tag: image-classification
tags:
- ai_or_not
- sumsub
- image_classification
- sumsubaiornot
- aiornot
- deepfake
- synthetic
- generated
- pytorch
metrics:
- accuracy
widget:
- src: >-
https://huggingface.co/Sumsub/Sumsub-ffs-synthetic-2.0/resolve/main/images/2.jpg
example_title: Pope Francis(yellow puffer)
- src: >-
https://huggingface.co/Sumsub/Sumsub-ffs-synthetic-2.0/resolve/main/images/3.jpg
example_title: Pentagon explosion
- src: >-
https://huggingface.co/Sumsub/Sumsub-ffs-synthetic-2.0/resolve/main/images/4.webp
example_title: Trump arrest
---
# For Fake's Sake: a set of models for detecting generated and synthetic images
Many people on the internet have recently been tricked by fake images of Pope Francis wearing a coat or of Donald Trump's arrest.
To help combat this issue, we provide detectors for such images generated by popular tools like Midjourney and Stable Diffusion.
|  |  |  |
|-------------------------|-------------------------|--------------------------|
## Model Details
### Model Description
- **Developed by:** [Sumsub AI team](https://sumsub.com/)
- **Model type:** Image classification
- **License:** CC-By-SA-3.0
- **Types:**
- **Finetuned from model:** *convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_384*
## Demo
The demo page can be found [here](https://huggingface.co/spaces/Sumsub/Sumsub-ffs-demo).
## How to Get Started with the Model & Model Sources
Use the code below to get started with the model:
```bash
git lfs install
git clone https://huggingface.co/Sumsub/Sumsub-ffs-synthetic-2.0 sumsub-ffs-synthetic-v2
```
```python
from sumsub-ffs-synthetic-v2.pipeline import PreTrainedPipeline
from PIL import Image
pipe = PreTrainedPipeline("sumsub-ffs-synthetic-v2/")
img = Image.open("sumsub-ffs-synthetic-v2/images/2.jpg")
result = pipe(img)
print(result)
```
You may need these prerequsites installed:
```bash
pip install -r requirements.txt
pip install "git+https://github.com/rwightman/pytorch-image-models"
pip install "git+https://github.com/huggingface/huggingface_hub"
```
## Training Details
### Training Data
The models were trained on the following datasets:
- *Real photos* : [MS COCO](https://cocodataset.org/#home), [VizWiz](https://vizwiz.org/tasks-and-datasets/vqa/).
- *AI photos* : [Midjourney](href='https://pin.it/13UkjgM),[Midjourney AI Art](https://pin.it/6pNXlz3), [Midjourney - Community Showcase](https://pin.it/7gi4jmT), [Midjourney](https://pin.it/4FW0LXQ), [MIDJOURNEY](https://pin.it/5mSsiPg), [Midjourney](https://pin.it/2Qx92QW), [aiornot HuggingFace contest data](https://huggingface.co/datasets/competitions/aiornot), [Stable Diffusion Wordnet Dataset](https://www.kaggle.com/datasets/astoeckl/stable-diffusion-wordnet-dataset).
### Training Procedure
To improve the performance metrics, we used data augmentations such as rotation, crop, Mixup and CutMix. Each model was trained for 30 epochs using early stopping with batch size equal to 32.
## Evaluation
For evaluation we used the following datasets:
**AI photos:**
- [DiffusionDB](https://github.com/poloclub/diffusiondb): a set of 2 million images generated by Stable Diffusion using prompts and hyperparameters specified by real users.
- [Kaggel SD Faces](https://www.kaggle.com/datasets/bwandowando/faces-dataset-using-stable-diffusion-v14): set of 4k human face images generated using Stable Diffusion 1.4.
- [Stable Diffusion Wordnet Dataset](https://www.kaggle.com/datasets/astoeckl/stable-diffusion-wordnet-dataset): set of 200K images generated by Stable Diffusion.
- [Kaggle Midjourney 2022-250k](https://www.kaggle.com/datasets/ldmtwo/midjourney-250k-csv): set of 250k images generated by Midjourney.
- [Kaggle Midjourney v5.1](https://www.kaggle.com/datasets/iraklip/modjourney-v51-cleaned-data): set of 400k images generated by Midjourney version 5.1.
**Realistic photos:**
- [MS COCO](https://cocodataset.org/#home): set of 120k real world images.
- [VizWiz Visual Question Answering dataset validation part](https://vizwiz.org/tasks-and-datasets/vqa/) : set of 20k photos typically stored on individuals' mobile devices.
These images showcase examples of pictures people keep on their phones in their daily lives.
## Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
| Dataset | Accuracy |
|---------------------------------------------------------------------------------------------------------------|----------|
| [Kaggel SD Faces](https://www.kaggle.com/datasets/bwandowando/faces-dataset-using-stable-diffusion-v14) | 0.984 |
| [DiffusionDB](https://github.com/poloclub/diffusiondb) | 0.920 |
| [Stable Diffusion Wordnet Dataset](https://www.kaggle.com/datasets/astoeckl/stable-diffusion-wordnet-dataset) | 0.950 |
| [MS COCO](https://cocodataset.org/#home) | 0.953 |
| [Kaggle Midjourney 2022-250k](https://www.kaggle.com/datasets/ldmtwo/midjourney-250k-csv) | 0.938 |
| [Kaggle Midjourney v5.1](https://www.kaggle.com/datasets/iraklip/modjourney-v51-cleaned-data) | 0.971 |
| [VizWiz Visual Question Answering dataset validation part](https://vizwiz.org/tasks-and-datasets/vqa/) | 0.998 |
## Limitations
- It should be noted that achieving 100% accuracy is not possible. Therefore, the model output should only be used as an indication that an image may have been (but not definitely) artificially generated.
- Our models may face challenges in accurately predicting the class for real-world examples that are extremely vibrant and of exceptionally high quality. In such cases, the richness of colors and fine details may lead to misclassifications due to the complexity of the input. This could potentially cause the model to focus on visual aspects that are not necessarily indicative of the true class.
## Citation
If you find this useful, please cite as:
```text
@misc{sumsubaiornot,
publisher = {Sumsub},
url = {https://huggingface.co/Sumsub/Sumsub-ffs-synthetic-2.0},
year = {2023},
author = {Savelyev, Alexander and Toropov, Alexey and Goldman-Kalaydin, Pavel and Samarin, Alexey},
title = {For Fake's Sake: a set of models for detecting deepfakes, generated images and synthetic images}
}
```
## References
- Stöckl, Andreas. (2022). Evaluating a Synthetic Image Dataset Generated with Stable Diffusion. 10.48550/arXiv.2211.01777.
- Lin, Tsung-Yi & Maire, Michael & Belongie, Serge & Hays, James & Perona, Pietro & Ramanan, Deva & Dollár, Piotr & Zitnick, C.. (2014). Microsoft COCO: Common Objects in Context.
- Howard, Andrew & Zhu, Menglong & Chen, Bo & Kalenichenko, Dmitry & Wang, Weijun & Weyand, Tobias & Andreetto, Marco & Adam, Hartwig. (2017). MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications.
- Liu, Zhuang & Mao, Hanzi & Wu, Chao-Yuan & Feichtenhofer, Christoph & Darrell, Trevor & Xie, Saining. (2022). A ConvNet for the 2020s.
- Wang, Zijie & Montoya, Evan & Munechika, David & Yang, Haoyang & Hoover, Benjamin & Chau, Polo. (2022). DiffusionDB: A Large-scale Prompt Gallery Dataset for Text-to-Image Generative Models. 10.48550/arXiv.2210.14896.
- Danna Gurari & Qing Li & Abigale J. Stangl & Anhong Guo & Chi Lin & Kristen Grauman & Jiebo Luo & Jeffrey P. Bigham (2018): VizWiz Grand Challenge: Answering Visual Questions from Blind People. CVPR 2018
|
LeoLM/leo-hessianai-7b-chat-bilingual
|
LeoLM
| 2023-09-29T13:16:38Z | 1,463 | 7 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"custom_code",
"en",
"de",
"dataset:LeoLM/OpenSchnabeltier",
"dataset:OpenAssistant/OASST-DE",
"dataset:FreedomIntelligence/alpaca-gpt4-deutsch",
"dataset:FreedomIntelligence/evol-instruct-deutsch",
"dataset:LeoLM/German_Poems",
"dataset:LeoLM/German_Songs",
"dataset:garage-bAInd/Open-Platypus",
"dataset:WizardLM/WizardLM_evol_instruct_70k",
"dataset:bjoernp/oasst25-08-23-filtered",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-09-10T19:00:52Z |
---
datasets:
- LeoLM/OpenSchnabeltier
- OpenAssistant/OASST-DE
- FreedomIntelligence/alpaca-gpt4-deutsch
- FreedomIntelligence/evol-instruct-deutsch
- LeoLM/German_Poems
- LeoLM/German_Songs
- garage-bAInd/Open-Platypus
- WizardLM/WizardLM_evol_instruct_70k
- bjoernp/oasst25-08-23-filtered
language:
- en
- de
library_name: transformers
pipeline_tag: text-generation
---
# LAION LeoLM: **L**inguistically **E**nhanced **O**pen **L**anguage **M**odel
Meet LeoLM, the first open and commercially available German Foundation Language Model built on Llama-2.
Our models extend Llama-2's capabilities into German through continued pretraining on a large corpus of German-language and mostly locality specific text.
Thanks to a compute grant at HessianAI's new supercomputer **42**, we release two foundation models trained with 8k context length,
[`LeoLM/leo-hessianai-7b`](https://huggingface.co/LeoLM/leo-hessianai-7b) and [`LeoLM/leo-hessianai-13b`](https://huggingface.co/LeoLM/leo-hessianai-13b) under the [Llama-2 community license](https://huggingface.co/meta-llama/Llama-2-70b/raw/main/LICENSE.txt) (70b also coming soon! 👀).
With this release, we hope to bring a new wave of opportunities to German open-source and commercial LLM research and accelerate adoption.
Read our [blog post]() or our paper (preprint coming soon) for more details!
*A project by Björn Plüster and Christoph Schuhmann in collaboration with LAION and HessianAI.*
## LeoLM Chat
`LeoLM/leo-hessianai-7b-chat-bilingual` is a bilingual English-German chat model built on our foundation model `LeoLM/leo-hessianai-7b` and finetuned on a selection of German translateed instruction datasets and their English counterparts.
The model performs exceptionally well on writing, explanation and discussion tasks but struggles somewhat with math and advanced reasoning. See our MT-Bench scores:
```
{
"first_turn": 5.64375,
"second_turn": 4.075,
"categories": {
"writing": 5.925,
"roleplay": 5.25,
"reasoning": 3.1,
"math": 1.8,
"coding": 3.4,
"extraction": 5,
"stem": 6.5,
"humanities": 7.9
},
"average": 4.859375
}
```
## Model Details
- **Finetuned from:** [LeoLM/leo-hessianai-7b](https://huggingface.co/LeoLM/leo-hessianai-7b)
- **Model type:** Causal decoder-only transformer language model
- **Language:** English and German
- **Demo:** [Web Demo]()
- **License:** [LLAMA 2 COMMUNITY LICENSE AGREEMENT](https://huggingface.co/meta-llama/Llama-2-70b/raw/main/LICENSE.txt)
- **Contact:** [LAION Discord](https://discord.com/invite/eq3cAMZtCC) or [Björn Plüster](mailto:bjoern.pl@outlook.de)
## Use in 🤗Transformers
First install direct dependencies:
```
pip install transformers torch sentencepiece
```
If you want faster inference using flash-attention2, you need to install these dependencies:
```bash
pip install packaging ninja
pip install flash-attn==v2.1.1 --no-build-isolation
pip install git+https://github.com/HazyResearch/flash-attention.git@v2.1.1#subdirectory=csrc/rotary
```
Then load the model in transformers:
```python
from transformers import pipeline
import torch
system_prompt = """<|im_start|>system
Dies ist eine Unterhaltung zwischen einem intelligenten, hilfsbereitem KI-Assistenten und einem Nutzer.
Der Assistent gibt ausführliche, hilfreiche und ehrliche Antworten.<|im_end|>
"""
prompt_format = "<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant\n"
prompt = "Erkläre mir wie die Fahrradwegesituation in Hamburg ist."
generator = pipeline(model="LeoLM/leo-hessianai-7b-chat-bilingual", device="cuda", torch_dtype=torch.float16, trust_remote_code=True) # True for flash-attn2 else False
print(generator(prompt_format.format(prompt=prompt), do_sample=True, top_p=0.95, max_length=8192))
```
"*Als KI kann ich keine persönlichen Beobachtungen teilen, aber ich kann einige allgemeine Informationen zur Fahrradwegesituation in Hamburg liefern. Im Vergleich zu vielen anderen großen Städten hat Hamburg eine hohe Anzahl von Fahrradfahrern und nimmt seine Verantwortung für nachhaltige Verkehrsmittel sehr ernst. Es gibt viele Fahrradwege und separate Fahrspuren, die Radfahrern ermöglichen, zusammen mit dem Autoverkehr zu fahren. Diese Fahrradspuren sind oft mit Markierungen gekennzeichnet und durch physische Trennungen von anderen Fahrspuren abgegrenzt. Darüber hinaus gibt es viele Fahrradstraßen, auf denen Radfahrer Vorfahrt haben und Autos langsamer fahren müssen.*
*In einigen städtischen Gebieten können Fahrradwege jedoch eng oder überfüllt sein, besonders während der Stoßzeiten. Es gibt auch viele Kreuzungen, an denen Radfahrer anhalten und auf Grün warten müssen, ähnlich wie Autofahrer. Insgesamt ist die Fahrradinfrastruktur in Hamburg ziemlich gut, aber wie überall gibt es immer Raum für Verbesserungen.*"
## Prompting / Prompt Template
Prompt dialogue template (ChatML format):
```
"""
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
"""
```
The model input can contain multiple conversation turns between user and assistant, e.g.
```
<|im_start|>user
{prompt 1}<|im_end|>
<|im_start|>assistant
{reply 1}<|im_end|>
<|im_start|>user
{prompt 2}<|im_end|>
<|im_start|>assistant
(...)
```
## Ethical Considerations and Limitations
LeoLM has been tested in English and German, and has not covered, nor could it cover all scenarios.
For these reasons, as with all LLMs, the potential outputs of `LeoLM/leo-hessianai-7b-chat` cannot be predicted
in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses
to user prompts. Therefore, before deploying any applications of `LeoLM/leo-hessianai-7b-chat`, developers should
perform safety testing and tuning tailored to their specific applications of the model.
Please see Meta's [Responsible Use Guide](https://ai.meta.com/llama/responsible-use-guide/).
## Finetuning Details
| Hyperparameter | Value |
|---|---|
| Num epochs | 3 |
| Examples per epoch | 233275 |
| Global batch size | 256 |
| Learning rate | 3e-5 |
| Warmup steps | 100 |
| LR scheduler | Cosine |
| Adam betas | (0.9, 0.95) |
| Weight decay | 0.001 |
## Dataset Details
```
## Stats for 'Subset of LeoLM/OpenSchnabeltier' (21314 samples (100.0%))
-----------------
Accepted: 21314/21314 (100.0%)
Accepted tokens: 8134690
Skipped: 0 (0.0%)
Min tokens per sample: 25
Max tokens per sample: 1202
Avg tokens per sample: 381.65947264708643
-----------------
## Stats for 'Subset of garage-bAInd/Open-Platypus' (24427 samples (100.0%))
-----------------
Accepted: 24427/24427 (100.0%)
Accepted tokens: 9549043
Skipped: 0 (0.0%)
Min tokens per sample: 23
Max tokens per sample: 5054
Avg tokens per sample: 390.9216440823679
-----------------
## Stats for 'Subset of WizardLM/WizardLM_evol_instruct_70k' (68600 samples (100.0%))
-----------------
Accepted: 68600/68600 (100.0%)
Accepted tokens: 33045040
Skipped: 0 (0.0%)
Min tokens per sample: 18
Max tokens per sample: 11810
Avg tokens per sample: 481.7061224489796
-----------------
## Stats for 'Subset of FreedomIntelligence/evol-instruct-deutsch' (57841 samples (100.0%))
-----------------
Accepted: 57841/57841 (100.0%)
Accepted tokens: 42958192
Skipped: 0 (0.0%)
Min tokens per sample: 33
Max tokens per sample: 5507
Avg tokens per sample: 742.6944900675991
-----------------
## Stats for 'Subset of FreedomIntelligence/alpaca-gpt4-deutsch' (48969 samples (100.0%))
-----------------
Accepted: 48969/48969 (100.0%)
Accepted tokens: 13372005
Skipped: 0 (0.0%)
Min tokens per sample: 19
Max tokens per sample: 1359
Avg tokens per sample: 273.07082031489307
-----------------
## Stats for 'Subset of LeoLM/German_Songs' (490 samples (100.0%))
-----------------
Accepted: 490/490 (100.0%)
Accepted tokens: 618642
Skipped: 0 (0.0%)
Min tokens per sample: 747
Max tokens per sample: 1678
Avg tokens per sample: 1262.534693877551
-----------------
## Stats for 'Subset of LeoLM/German_Poems' (392 samples (100.0%))
-----------------
Accepted: 392/392 (100.0%)
Accepted tokens: 187897
Skipped: 0 (0.0%)
Min tokens per sample: 231
Max tokens per sample: 826
Avg tokens per sample: 479.3290816326531
-----------------
## Stats for 'Subset of OpenAssistant/OASST_DE' (3646 samples (100.0%))
-----------------
Accepted: 3646/3646 (100.0%)
Accepted tokens: 2338738
Skipped: 0 (0.0%)
Min tokens per sample: 29
Max tokens per sample: 2484
Avg tokens per sample: 641.4530992868897
-----------------
## Stats for 'Subset of bjoernp/oasst25-08-23-filtered' (8922 samples (100.0%))
-----------------
Accepted: 8922/8922 (100.0%)
Accepted tokens: 4526427
Skipped: 0 (0.0%)
Min tokens per sample: 23
Max tokens per sample: 5407
Avg tokens per sample: 507.3332212508406
-----------------
## Stats for 'total' (235632 samples (100.0%))
-----------------
Accepted: 235632/235632 (100.0%)
Accepted tokens: 115862397
Skipped: 0 (0.0%)
Min tokens per sample: 18
Max tokens per sample: 11810
Avg tokens per sample: 491.70909299246284
-----------------
```
|
Omid-sar/fine-tuning-llama2-7b-qlora-french
|
Omid-sar
| 2023-09-29T13:16:37Z | 6 | 1 |
peft
|
[
"peft",
"base_model:meta-llama/Llama-2-7b-hf",
"base_model:adapter:meta-llama/Llama-2-7b-hf",
"region:us"
] | null | 2023-09-18T20:44:17Z |
---
library_name: peft
base_model: meta-llama/Llama-2-7b-hf
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
Fine-tuning Llama-2-7b using QLoRA in French on Google Colab
## Goal
The goal of this project is to adapt the Llama-2-7b model, which initially might not have proficiency in French, to understand and respond accurately to queries in the French language. This adaptation involves fine-tuning the model on a dataset of French novels, allowing it to comprehend the nuances, syntax, and semantics of the French language. By leveraging the PEFT library from the Hugging Face ecosystem and QLoRA for more memory-efficient fine-tuning on a single T4 GPU provided by Google Colab, we aim to create a chatbot that can effectively answer questions posed in French.
## Overview
This project involves several steps including setting up the environment, loading the dataset and model, configuring QLoRA and training parameters, training the model, and finally testing and pushing the fine-tuned model to Hugging Face.
## Features
- **Dataset Loading**: Load and process a French novels dataset using Hugging Face datasets library.
- **Model Quantization**: Quantize the base Llama-2-7b model into 4-bit using bitsandbytes.
- **Configuration for QLoRA**: Apply the QLoRA configuration for more memory-efficient fine-tuning using the PEFT library.
- **Training**: Use the SFTTrainer from the TRL library for instruction-based fine-tuning.
- **Testing and Pushing to Hugging Face**: Test the fine-tuned model and push it to Hugging Face.
## Prerequisites
- Google Colab with T4 GPU
- Python libraries: trl, transformers, accelerate, peft, datasets, bitsandbytes, einops
-
|
Boray/LLama2SA_Tag3E
|
Boray
| 2023-09-29T13:11:42Z | 0 | 0 | null |
[
"conversational",
"tr",
"region:us"
] |
text-generation
| 2023-09-29T12:36:53Z |
---
language:
- tr
pipeline_tag: conversational
---
|
domischwimmbeck/bert-base-german-cased-20000-ner-uncased
|
domischwimmbeck
| 2023-09-29T12:51:17Z | 109 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"base_model:dbmdz/bert-base-german-uncased",
"base_model:finetune:dbmdz/bert-base-german-uncased",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-05-20T13:36:50Z |
---
license: mit
base_model: dbmdz/bert-base-german-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-german-cased-20000-ner-uncased
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-german-cased-20000-ner-uncased
This model is a fine-tuned version of [dbmdz/bert-base-german-uncased](https://huggingface.co/dbmdz/bert-base-german-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0617
- Precision: 0.8871
- Recall: 0.9013
- F1: 0.8941
- Accuracy: 0.9848
## 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: 96
- eval_batch_size: 96
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 0.34 | 64 | 0.0573 | 0.8859 | 0.8526 | 0.8689 | 0.9837 |
| No log | 0.68 | 128 | 0.0654 | 0.8107 | 0.8957 | 0.8511 | 0.9808 |
| No log | 1.02 | 192 | 0.0531 | 0.8654 | 0.8846 | 0.8749 | 0.9842 |
| No log | 1.35 | 256 | 0.0467 | 0.8847 | 0.8853 | 0.8850 | 0.9857 |
| No log | 1.69 | 320 | 0.0466 | 0.9102 | 0.8883 | 0.8992 | 0.9864 |
| No log | 2.03 | 384 | 0.0467 | 0.8794 | 0.8951 | 0.8872 | 0.9854 |
| No log | 2.37 | 448 | 0.0520 | 0.8864 | 0.9001 | 0.8932 | 0.9851 |
| 0.0531 | 2.71 | 512 | 0.0549 | 0.8894 | 0.8877 | 0.8885 | 0.9854 |
| 0.0531 | 3.05 | 576 | 0.0534 | 0.8942 | 0.8920 | 0.8931 | 0.9857 |
| 0.0531 | 3.39 | 640 | 0.0526 | 0.8917 | 0.8994 | 0.8956 | 0.9856 |
| 0.0531 | 3.72 | 704 | 0.0576 | 0.9049 | 0.8976 | 0.9012 | 0.9857 |
| 0.0531 | 4.06 | 768 | 0.0700 | 0.8529 | 0.9229 | 0.8865 | 0.9830 |
| 0.0531 | 4.4 | 832 | 0.0657 | 0.8716 | 0.9167 | 0.8936 | 0.9840 |
| 0.0531 | 4.74 | 896 | 0.0617 | 0.8871 | 0.9013 | 0.8941 | 0.9848 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|
gokuls/HBERTv1_emb_compress_48_L10_H256_A4
|
gokuls
| 2023-09-29T12:49:40Z | 48 | 0 |
transformers
|
[
"transformers",
"pytorch",
"hybridbert",
"fill-mask",
"generated_from_trainer",
"dataset:gokuls/wiki_book_corpus_complete_processed_bert_dataset",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-09-27T06:40:04Z |
---
tags:
- generated_from_trainer
datasets:
- gokuls/wiki_book_corpus_complete_processed_bert_dataset
metrics:
- accuracy
model-index:
- name: HBERTv1_emb_compress_48_L10_H256_A4
results:
- task:
name: Masked Language Modeling
type: fill-mask
dataset:
name: gokuls/wiki_book_corpus_complete_processed_bert_dataset
type: gokuls/wiki_book_corpus_complete_processed_bert_dataset
metrics:
- name: Accuracy
type: accuracy
value: 0.15093352306316574
---
<!-- 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. -->
# HBERTv1_emb_compress_48_L10_H256_A4
This model is a fine-tuned version of [](https://huggingface.co/) on the gokuls/wiki_book_corpus_complete_processed_bert_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 6.0495
- Accuracy: 0.1509
## 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: 64
- eval_batch_size: 64
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10000
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 7.1164 | 0.11 | 10000 | 7.0967 | 0.0830 |
| 6.694 | 0.22 | 20000 | 6.6867 | 0.1065 |
| 6.545 | 0.33 | 30000 | 6.5445 | 0.1171 |
| 6.4556 | 0.44 | 40000 | 6.4527 | 0.1250 |
| 6.3891 | 0.55 | 50000 | 6.3831 | 0.1305 |
| 6.3404 | 0.66 | 60000 | 6.3334 | 0.1350 |
| 6.2962 | 0.76 | 70000 | 6.2940 | 0.1377 |
| 6.2669 | 0.87 | 80000 | 6.2629 | 0.1398 |
| 6.2352 | 0.98 | 90000 | 6.2361 | 0.1412 |
| 6.2179 | 1.09 | 100000 | 6.2150 | 0.1429 |
| 6.191 | 1.2 | 110000 | 6.1970 | 0.1443 |
| 6.1809 | 1.31 | 120000 | 6.1829 | 0.1441 |
| 6.1699 | 1.42 | 130000 | 6.1692 | 0.1455 |
| 6.1623 | 1.53 | 140000 | 6.1562 | 0.1453 |
| 6.1422 | 1.64 | 150000 | 6.1480 | 0.1468 |
| 6.1397 | 1.75 | 160000 | 6.1367 | 0.1468 |
| 6.1342 | 1.86 | 170000 | 6.1284 | 0.1475 |
| 6.1291 | 1.97 | 180000 | 6.1214 | 0.1478 |
| 6.1157 | 2.08 | 190000 | 6.1132 | 0.1483 |
| 6.1146 | 2.18 | 200000 | 6.1094 | 0.1484 |
| 6.1018 | 2.29 | 210000 | 6.1013 | 0.1488 |
| 6.1014 | 2.4 | 220000 | 6.0979 | 0.1488 |
| 6.0935 | 2.51 | 230000 | 6.0936 | 0.1489 |
| 6.0899 | 2.62 | 240000 | 6.0881 | 0.1491 |
| 6.0858 | 2.73 | 250000 | 6.0851 | 0.1498 |
| 6.0872 | 2.84 | 260000 | 6.0819 | 0.1497 |
| 6.0858 | 2.95 | 270000 | 6.0784 | 0.1500 |
| 6.0775 | 3.06 | 280000 | 6.0745 | 0.1501 |
| 6.0715 | 3.17 | 290000 | 6.0720 | 0.1502 |
| 6.0704 | 3.28 | 300000 | 6.0699 | 0.1502 |
| 6.0678 | 3.39 | 310000 | 6.0668 | 0.1503 |
| 6.0662 | 3.5 | 320000 | 6.0649 | 0.1503 |
| 6.0569 | 3.6 | 330000 | 6.0622 | 0.1505 |
| 6.0604 | 3.71 | 340000 | 6.0612 | 0.1506 |
| 6.0525 | 3.82 | 350000 | 6.0586 | 0.1507 |
| 6.0553 | 3.93 | 360000 | 6.0582 | 0.1506 |
| 6.053 | 4.04 | 370000 | 6.0544 | 0.1508 |
| 6.0594 | 4.15 | 380000 | 6.0553 | 0.1507 |
| 6.0488 | 4.26 | 390000 | 6.0527 | 0.1509 |
| 6.051 | 4.37 | 400000 | 6.0516 | 0.1509 |
| 6.0553 | 4.48 | 410000 | 6.0518 | 0.1509 |
| 6.0507 | 4.59 | 420000 | 6.0520 | 0.1509 |
| 6.0514 | 4.7 | 430000 | 6.0501 | 0.1509 |
| 6.0511 | 4.81 | 440000 | 6.0496 | 0.1511 |
| 6.0527 | 4.92 | 450000 | 6.0493 | 0.1509 |
### Framework versions
- Transformers 4.33.2
- Pytorch 1.14.0a0+410ce96
- Datasets 2.14.5
- Tokenizers 0.13.3
|
roa7n/gpt2-human_nontata_promoters-randomized_9_layers_3e-05_lr_2_e
|
roa7n
| 2023-09-29T12:49:06Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-09-29T12:49:03Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.4.0.dev0
|
phanerozoic/OpenOrca-Platypus2-13B-PirateLora-V2
|
phanerozoic
| 2023-09-29T12:46:12Z | 0 | 0 | null |
[
"license:cc-by-nc-4.0",
"region:us"
] | null | 2023-09-29T12:17:38Z |
---
license: cc-by-nc-4.0
---
This repository houses a Low-Rank Adapter (LoRA) specifically designed for the OpenOrca-Platypus2 13b (16 float) model. The LoRA is uniquely trained on a diverse dataset encompassing thousands of pirate-centric content: from typical phrases and extended conversation fragments to more obscure pirate vernacular.
Objective: The primary motivation behind the creation of this LoRA was to explore the potential of the LoRA fine-tuning methodology in achieving specific dialect and diction enforcement. We aimed to ascertain whether it's possible to guide the model towards a more authentic pirate-style language, both in terms of vocabulary and syntactic structure.
Evolution: This iteration represents the second version of the adapter that we've developed for OpenOrca-Platypus2. Compared to our initial attempt, this version benefits from a significantly enhanced dataset. The data is not only more extensive but also showcases a broader spectrum of complexity, ranging from short, concise phrases to longer, intricate samples. This deliberate variation aimed to test and strengthen the model's adaptability.
Outcomes: With the improved dataset and the insights gained from our first attempt, we've witnessed marked progress. The updated LoRA exhibits a much-refined capacity to generate text that resonates with the nuances of pirate-speak. It embodies the idiosyncrasies of the pirate dialect more organically, paving the way for enhanced user experiences.
Note: Users might notice that the LoRA occasionally produces continuous streams of text. However, this behavior is not exclusive to the adapted model but is also observed in the underlying base model of OpenOrca-Platypus2.
|
softaken/merge-pst-file-tool
|
softaken
| 2023-09-29T12:45:38Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-09-29T12:44:41Z |
Download the Softaken Merge Outlook PST file tool to Merge Outlook PST files. This Software is a professional tool that is tested by experts. You can freely use this tool without any restrictions. With the help of this tool, you can Merge Outlook PST software to merge Multiple ANSI and UNICODE PST files. Its operating steps are so smooth that even a novice can easily work with its functions. There are error-free merge PST file performances. For user ease, it offers free trial versions to check its working process. If users have any doubts they can contact us to get 24/7 technical support.
Key Features:-
You can merge Multiple Outlook PST files.
Merge Outlook Archives into a single PST file.
You can join selective folders from multiple Outlook PST files.
The sophisticated tool sustains Outlook 2007, 2010 and 2013 editions[32+64bit]
You will get 5 merging options as per the requirements of users.
This is a user-friendly interface.
This software does not allows MS Outlook installation.
Visit Here:- https://www.softaken.com/merge-pst-pro
|
PPV/FoodImageClassifier
|
PPV
| 2023-09-29T12:42:36Z | 216 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-09-29T12:42:28Z |
---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: FoodImageClassifier
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.8936170339584351
---
# FoodImageClassifier
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### Chicken Breast
#### Dosa
#### Guava
#### Idli
#### White Rice
|
phanerozoic/OpenOrca-Platypus2-13B-PirateLora
|
phanerozoic
| 2023-09-29T12:39:06Z | 0 | 0 | null |
[
"en",
"license:cc-by-nc-4.0",
"region:us"
] | null | 2023-09-26T19:32:17Z |
---
license: cc-by-nc-4.0
language:
- en
---
OpenOrca-Platypus2-13B-PirateLora
This repo contains a Low-Rank Adapter (LoRA) for OpenOrca-Platypus2 13b (16 float) fit on a simple dataset comprised of thousands of pirate phrases, conversation pieces, and obscura. The purpose behind the generation of this lora was to determine whether enforcement of dialect and diction was possible through the LoRa fine tuning method. Results were much better than the previous adapter we created for Llama 2, but this may be a due to a combination of effects: the superior performance of the base model compared to Llama 2, and the higher quality training set as compared to our previous effort.
|
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