modelId
stringlengths 5
139
| author
stringlengths 2
42
| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-03 00:36:49
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 535
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
2025-09-03 00:36:49
| card
stringlengths 11
1.01M
|
|---|---|---|---|---|---|---|---|---|---|
TheBloke/CAMEL-13B-Role-Playing-Data-fp16
|
TheBloke
| 2023-06-14T07:38:04Z | 11 | 3 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"arxiv:2303.17760",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-06-07T20:58:16Z |
---
inference: false
license: other
---
<!-- header start -->
<div style="width: 100%;">
<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><a href="https://discord.gg/Jq4vkcDakD">Chat & support: my new Discord server</a></p>
</div>
<div style="display: flex; flex-direction: column; align-items: flex-end;">
<p><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
</div>
</div>
<!-- header end -->
# Camel AI's CAMEL 13B Role Playing Data fp16
These files are pytorch format fp16 model files for [Camel AI's CAMEL 13B Role Playing Data](https://huggingface.co/camel-ai/CAMEL-13B-Role-Playing-Data).
It is the result of merging and/or converting the source repository to float16.
## Repositories available
* [4-bit GPTQ models for GPU inference](https://huggingface.co/TheBloke/CAMEL-13B-Role-Playing-Data-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference](https://huggingface.co/TheBloke/CAMEL-13B-Role-Playing-Data-GGML)
* [Unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/TheBloke/CAMEL-13B-Role-Playing-Data-fp16)
<!-- footer start -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/Jq4vkcDakD)
## Thanks, and how to contribute.
Thanks to the [chirper.ai](https://chirper.ai) team!
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**: Luke from CarbonQuill, Aemon Algiz, Dmitriy Samsonov.
**Patreon special mentions**: Oscar Rangel, Eugene Pentland, Talal Aujan, Cory Kujawski, Luke, Asp the Wyvern, Ai Maven, Pyrater, Alps Aficionado, senxiiz, Willem Michiel, Junyu Yang, trip7s trip, Sebastain Graf, Joseph William Delisle, Lone Striker, Jonathan Leane, Johann-Peter Hartmann, David Flickinger, Spiking Neurons AB, Kevin Schuppel, Mano Prime, Dmitriy Samsonov, Sean Connelly, Nathan LeClaire, Alain Rossmann, Fen Risland, Derek Yates, Luke Pendergrass, Nikolai Manek, Khalefa Al-Ahmad, Artur Olbinski, John Detwiler, Ajan Kanaga, Imad Khwaja, Trenton Dambrowitz, Kalila, vamX, webtim, Illia Dulskyi.
Thank you to all my generous patrons and donaters!
<!-- footer end -->
# Original model card: Camel AI's CAMEL 13B Role Playing Data
CAMEL-13B-Role-Playing-Data is a chat large language model obtained by finetuning LLaMA-13B model on a total of 229K conversations created through our role-playing framework proposed in [CAMEL](https://arxiv.org/abs/2303.17760). We evaluate our model offline using EleutherAI's language model evaluation harness used by Huggingface's Open LLM Benchmark. CAMEL-13B scores an average of **57.2**, outperfroming LLaMA-30B (56.9)!
| Model | size | ARC-C (25 shots, acc_norm) | HellaSwag (10 shots, acc_norm) | MMLU (5 shots, acc_norm) | TruthfulQA (0 shot, mc2) | Average | Delta |
|-------------|:----:|:---------------------------:|:-------------------------------:|:-------------------------:|:-------------------------:|:-------:|-------|
| LLaMA | 13B | 50.8 | 78.9 | 37.7 | 39.9 | 51.8 | - |
| Vicuna | 13B | 47.4 | 75.2 | 39.6 | 49.8 | 53.7 | 1.9 |
| CAMEL | 13B | 54.9 | 79.3 | 48.5 | 46.2 | **57.2** | 5.4 |
| LLaMA | 30B | 57.1 | 82.6 | 45.7 | 42.3 | 56.9 | 5.1 |
---
license: cc-by-nc-4.0
---
|
NeviduJ/distilbert-base-uncased-finetuned-emotion
|
NeviduJ
| 2023-06-14T07:21:08Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-06-13T06:39:27Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: split
split: validation
args: split
metrics:
- name: Accuracy
type: accuracy
value: 0.9265
- name: F1
type: f1
value: 0.9263759459699279
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2197
- Accuracy: 0.9265
- F1: 0.9264
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8565 | 1.0 | 250 | 0.3140 | 0.91 | 0.9083 |
| 0.2514 | 2.0 | 500 | 0.2197 | 0.9265 | 0.9264 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
rahuldshetty/starchat-beta-8bit
|
rahuldshetty
| 2023-06-14T07:10:45Z | 8 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt_bigcode",
"text-generation",
"generated_from_trainer",
"license:bigcode-openrail-m",
"autotrain_compatible",
"text-generation-inference",
"8-bit",
"region:us"
] |
text-generation
| 2023-06-13T15:43:57Z |
---
inference: false
tags:
- generated_from_trainer
model-index:
- name: starchat-beta
results: []
license: bigcode-openrail-m
---
# HuggingFaceH4's Starchat Beta 8-bit quantized
## Prompt template
```
<|system|> system message goes here <|end|>
<|user|> prompt goes here <|end|>
<|assistant|>
```
Example:
```
<|system|> Below is a conversation between a human user and a helpful AI coding assistant. <|end|>
<|user|> How do I sort a list in Python? <|end|>
<|assistant|>
```
# Original model card: HuggingFaceH4's Starchat Beta
<img src="https://huggingface.co/HuggingFaceH4/starchat-beta/resolve/main/model_logo.png" alt="StarChat Beta Logo" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
# Model Card for StarChat Beta
StarChat is a series of language models that are trained to act as helpful coding assistants. StarChat Beta is the second model in the series, and is a fine-tuned version of [StarCoderPlus](https://huggingface.co/bigcode/starcoderplus) that was trained on an ["uncensored"](https://erichartford.com/uncensored-models) variant of the [`openassistant-guanaco` dataset](https://huggingface.co/datasets/timdettmers/openassistant-guanaco). We found that removing the in-built alignment of the OpenAssistant dataset boosted performance on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) and made the model more helpful at coding tasks. However, this means that model is likely to generate problematic text when prompted to do so and should only be used for educational and research purposes.
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Model type:** A 16B parameter GPT-like model fine-tuned on an ["uncensored"](https://erichartford.com/uncensored-models) variant of the [`openassistant-guanaco` dataset](https://huggingface.co/datasets/timdettmers/openassistant-guanaco).
- **Language(s) (NLP):** Primarily English and 80+ programming languages.
- **License:** BigCode Open RAIL-M v1
- **Finetuned from model:** [bigcode/starcoderplus](https://huggingface.co/bigcode/starcoderplus)
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** https://github.com/bigcode-project/starcoder
- **Demo:** https://huggingface.co/spaces/HuggingFaceH4/starchat-playground
## Intended uses & limitations
The model was fine-tuned on a variant of the [`OpenAssistant/oasst1`](https://huggingface.co/datasets/OpenAssistant/oasst1) dataset, which contains a diverse range of dialogues in over 35 languages. As a result, the model can be used for chat and you can check out our [demo](https://huggingface.co/spaces/HuggingFaceH4/starchat-playground) to test its coding capabilities.
Here's how you can run the model using the `pipeline()` function from 🤗 Transformers:
```python
import torch
from transformers import pipeline
pipe = pipeline("text-generation", model="rahuldshetty/starchat-beta", device_map="auto")
prompt_template = "<|system|>\n<|end|>\n<|user|>\n{query}<|end|>\n<|assistant|>"
prompt = prompt_template.format(query="How do I sort a list in Python?")
# We use a special <|end|> token with ID 49155 to denote ends of a turn
outputs = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.2, top_k=50, top_p=0.95, eos_token_id=49155)
# You can sort a list in Python by using the sort() method. Here's an example:\n\n```\nnumbers = [3, 1, 4, 1, 5, 9, 2, 6, 5, 3, 5]\nnumbers.sort()\nprint(numbers)\n```\n\nThis will sort the list in place and print the sorted list.
```
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
StarChat Alpha has not been aligned to human preferences with techniques like RLHF or deployed with in-the-loop filtering of responses like ChatGPT, so the model can produce problematic outputs (especially when prompted to do so).
Models trained primarily on code data will also have a more skewed demographic bias commensurate with the demographics of the GitHub community, for more on this see the [StarCoder dataset](https://huggingface.co/datasets/bigcode/starcoderdata) which is derived from The Stack.
Since the base model was pretrained on a large corpus of code, it may produce code snippets that are syntactically valid but semantically incorrect.
For example, it may produce code that does not compile or that produces incorrect results.
It may also produce code that is vulnerable to security exploits.
We have observed the model also has a tendency to produce false URLs which should be carefully inspected before clicking.
StarChat Alpha was fine-tuned from the base model [StarCoder Base](https://huggingface.co/bigcode/starcoderbase), please refer to its model card's [Limitations Section](https://huggingface.co/bigcode/starcoderbase#limitations) for relevant information.
In particular, the model was evaluated on some categories of gender biases, propensity for toxicity, and risk of suggesting code completions with known security flaws; these evaluations are reported in its [technical report](https://drive.google.com/file/d/1cN-b9GnWtHzQRoE7M7gAEyivY0kl4BYs/view).
## Training and evaluation data
StarChat Beta is trained on an ["uncensored"](https://erichartford.com/uncensored-models) variant of the [`openassistant-guanaco` dataset](https://huggingface.co/datasets/timdettmers/openassistant-guanaco). We applied the same [recipe](https://huggingface.co/datasets/ehartford/WizardLM_alpaca_evol_instruct_70k_unfiltered/blob/main/wizardlm_clean.py) used to filter the ShareGPT datasets behind the [WizardLM](https://huggingface.co/datasets/ehartford/WizardLM_alpaca_evol_instruct_70k_unfiltered).
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 8
- total_train_batch_size: 256
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.5321 | 0.98 | 15 | 1.2856 |
| 1.2071 | 1.97 | 30 | 1.2620 |
| 1.0162 | 2.95 | 45 | 1.2853 |
| 0.8484 | 4.0 | 61 | 1.3274 |
| 0.6981 | 4.98 | 76 | 1.3994 |
| 0.5668 | 5.9 | 90 | 1.4720 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
## Citation
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
```
@article{Tunstall2023starchat-alpha,
author = {Tunstall, Lewis and Lambert, Nathan and Rajani, Nazneen and Beeching, Edward and Le Scao, Teven and von Werra, Leandro and Han, Sheon and Schmid, Philipp and Rush, Alexander},
title = {Creating a Coding Assistant with StarCoder},
journal = {Hugging Face Blog},
year = {2023},
note = {https://huggingface.co/blog/starchat},
}
```
|
madatnlp/nllb-moe-54b-8bit
|
madatnlp
| 2023-06-14T07:08:56Z | 5 | 1 |
transformers
|
[
"transformers",
"pytorch",
"nllb-moe",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"8-bit",
"region:us"
] |
text2text-generation
| 2023-06-14T06:02:02Z |
nothing changed.
the model just pushed after load model in 8bit
|
avishetty/smnthstyle
|
avishetty
| 2023-06-14T07:05:35Z | 38 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-06-14T07:01:20Z |
---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### smnthstyle Dreambooth model trained by avishetty 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:
|
Ankurkhurana03/ppo-LunarLander-v2
|
Ankurkhurana03
| 2023-06-14T06:51:31Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-14T06:51:08Z |
---
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: 243.90 +/- 14.82
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
...
```
|
Yhyu13/airoboros-13b-gpt4-1.1-gptq-4bit
|
Yhyu13
| 2023-06-14T06:49:45Z | 8 | 1 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-06-14T06:44:23Z |
---
license: apache-2.0
---
GPTQ 4-bit no actor version for compatibility that works in textgen-webui
Generated by using scripts from https://gitee.com/yhyu13/llama_-tools
Original weight : https://huggingface.co/jondurbin/airoboros-13b-gpt4
|
nolanaatama/rvcv2crpsvc40rdnjpmykswshr
|
nolanaatama
| 2023-06-14T06:39:57Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-06-14T06:13:52Z |
---
license: creativeml-openrail-m
---
|
Eitanli/resume_label_summary_model
|
Eitanli
| 2023-06-14T06:35:43Z | 108 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-06-13T11:51:06Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: resume_label_summary_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# resume_label_summary_model
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9802
- Rouge1: 0.3129
- Rouge2: 0.191
- Rougel: 0.3133
- Rougelsum: 0.3126
- Gen Len: 15.4611
## 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: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| No log | 1.0 | 49 | 2.6885 | 0.209 | 0.0961 | 0.21 | 0.2094 | 18.4456 |
| No log | 2.0 | 98 | 1.9802 | 0.3129 | 0.191 | 0.3133 | 0.3126 | 15.4611 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.1
- Datasets 2.10.1
- Tokenizers 0.13.2
|
pushkin05/ppo-LunarLander-v2
|
pushkin05
| 2023-06-14T06:25:21Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-14T06:23:45Z |
---
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: 248.68 +/- 22.97
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
...
```
|
takashiinui/distilbert-base-uncased-finetuned-emotion
|
takashiinui
| 2023-06-14T05:59:35Z | 112 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-06-14T05:55:07Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: split
split: validation
args: split
metrics:
- name: Accuracy
type: accuracy
value: 0.924
- name: F1
type: f1
value: 0.9240945316056002
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2184
- Accuracy: 0.924
- F1: 0.9241
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8083 | 1.0 | 250 | 0.3117 | 0.907 | 0.9033 |
| 0.2488 | 2.0 | 500 | 0.2184 | 0.924 | 0.9241 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Zhichao-W/minilm-finetuned-emotion
|
Zhichao-W
| 2023-06-14T05:54:53Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-06-14T05:51:20Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- f1
model-index:
- name: minilm-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: split
split: validation
args: split
metrics:
- name: F1
type: f1
value: 0.904319162911674
---
<!-- 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. -->
# minilm-finetuned-emotion
This model is a fine-tuned version of [microsoft/MiniLM-L12-H384-uncased](https://huggingface.co/microsoft/MiniLM-L12-H384-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4243
- F1: 0.9043
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.3683 | 1.0 | 250 | 1.0425 | 0.5804 |
| 0.8949 | 2.0 | 500 | 0.7223 | 0.7873 |
| 0.6448 | 3.0 | 750 | 0.5382 | 0.8796 |
| 0.4985 | 4.0 | 1000 | 0.4578 | 0.8987 |
| 0.4346 | 5.0 | 1250 | 0.4243 | 0.9043 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
dayvidwang/twitter_ner
|
dayvidwang
| 2023-06-14T05:37:53Z | 4,735 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-06-14T05:32:28Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: twitter_ner
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. -->
# twitter_ner
This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0142
- Precision: 0.9328
- Recall: 0.9504
- F1: 0.9415
- Accuracy: 0.9978
## 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: 8e-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: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 30 | 0.2020 | 0.2932 | 0.0479 | 0.0824 | 0.9516 |
| No log | 2.0 | 60 | 0.1365 | 0.4653 | 0.3328 | 0.3880 | 0.9651 |
| No log | 3.0 | 90 | 0.0783 | 0.6292 | 0.6125 | 0.6207 | 0.9803 |
| No log | 4.0 | 120 | 0.0465 | 0.7196 | 0.7793 | 0.7483 | 0.9877 |
| No log | 5.0 | 150 | 0.0285 | 0.8493 | 0.8725 | 0.8608 | 0.9936 |
| No log | 6.0 | 180 | 0.0197 | 0.9012 | 0.9204 | 0.9107 | 0.9963 |
| No log | 7.0 | 210 | 0.0157 | 0.9124 | 0.9350 | 0.9235 | 0.9969 |
| No log | 8.0 | 240 | 0.0142 | 0.9328 | 0.9504 | 0.9415 | 0.9978 |
### Framework versions
- Transformers 4.30.1
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Honkware/Manticore-13b-Landmark
|
Honkware
| 2023-06-14T05:37:48Z | 6 | 8 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"custom_code",
"arxiv:2305.16300",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-06-13T01:02:34Z |
---
license: other
---
# Manticore-13b-Landmark
## Key Features
- **[Landmark Attention](https://arxiv.org/pdf/2305.16300v1.pdf)**
- **[Large Context Size (~18k)](https://i.ibb.co/tLLGLNc/image.jpg)**
## Composition
Manticore-13b-Landmark is a blend of:
- [Manticore-13B](https://huggingface.co/openaccess-ai-collective/manticore-13b)
- [Manticore-13B-Landmark-QLoRA](https://huggingface.co/Honkware/Manticore-13b-Landmark-QLoRA)
## Using [Oobabooga](https://github.com/oobabooga/text-generation-webui)
- Trust Remote Code - **(Enabled)**
- Add the bos_token to the beginning of prompts - **(Disabled)**
- Truncate the prompt up to this length - **(Increased)**
## Landmark Training Code
See [GitHub](https://github.com/eugenepentland/landmark-attention-qlora) for the training code.
|
irfanamal/bert-base-uncased-classification-chain-1
|
irfanamal
| 2023-06-14T05:29:33Z | 111 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-06-13T15:47:43Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: bert-base-uncased-classification-chain-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. -->
# bert-base-uncased-classification-chain-1
This model is a fine-tuned version of [irfanamal/bert-base-uncased-finetuned-amazonreviews](https://huggingface.co/irfanamal/bert-base-uncased-finetuned-amazonreviews) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4652
- Accuracy: 0.8391
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3913 | 1.0 | 1250 | 0.4652 | 0.8391 |
| 0.2536 | 2.0 | 2500 | 0.4956 | 0.8367 |
| 0.1675 | 3.0 | 3750 | 0.5490 | 0.8382 |
| 0.1013 | 4.0 | 5000 | 0.6493 | 0.839 |
| 0.0639 | 5.0 | 6250 | 0.7283 | 0.8384 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
|
rafrito/Reinforce-cartpole
|
rafrito
| 2023-06-14T05:17:53Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-14T05:17:44Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-cartpole
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
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
|
mamun4105/poca-SoccerTwos
|
mamun4105
| 2023-06-14T05:07:37Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"SoccerTwos",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SoccerTwos",
"region:us"
] |
reinforcement-learning
| 2023-06-14T05:07:36Z |
---
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: mamun4105/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
alisawuffles/roberta-large-wanli
|
alisawuffles
| 2023-06-14T04:58:48Z | 1,889 | 8 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"roberta",
"text-classification",
"en",
"dataset:alisawuffles/WANLI",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-30T20:00:10Z |
---
language:
- en
tags:
- text-classification
widget:
- text: "I almost forgot to eat lunch.</s></s>I didn't forget to eat lunch."
- text: "I almost forgot to eat lunch.</s></s>I forgot to eat lunch."
- text: "I ate lunch.</s></s>I almost forgot to eat lunch."
datasets:
- alisawuffles/WANLI
---
This is an off-the-shelf roberta-large model finetuned on WANLI, the Worker-AI Collaborative NLI dataset ([Liu et al., 2022](https://aclanthology.org/2022.findings-emnlp.508/)). It outperforms the `roberta-large-mnli` model on eight out-of-domain test sets, including by 11% on HANS and 9% on Adversarial NLI.
### How to use
```python
from transformers import RobertaTokenizer, RobertaForSequenceClassification
model = RobertaForSequenceClassification.from_pretrained('alisawuffles/roberta-large-wanli')
tokenizer = RobertaTokenizer.from_pretrained('alisawuffles/roberta-large-wanli')
x = tokenizer("I almost forgot to eat lunch.", "I didn't forget to eat lunch.", return_tensors='pt', max_length=128, truncation=True)
logits = model(**x).logits
probs = logits.softmax(dim=1).squeeze(0)
label_id = torch.argmax(probs).item()
prediction = model.config.id2label[label_id]
```
### Citation
```
@inproceedings{liu-etal-2022-wanli,
title = "{WANLI}: Worker and {AI} Collaboration for Natural Language Inference Dataset Creation",
author = "Liu, Alisa and
Swayamdipta, Swabha and
Smith, Noah A. and
Choi, Yejin",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-emnlp.508",
pages = "6826--6847",
abstract = "A recurring challenge of crowdsourcing NLP datasets at scale is that human writers often rely on repetitive patterns when crafting examples, leading to a lack of linguistic diversity. We introduce a novel approach for dataset creation based on worker and AI collaboration, which brings together the generative strength of language models and the evaluative strength of humans. Starting with an existing dataset, MultiNLI for natural language inference (NLI), our approach uses dataset cartography to automatically identify examples that demonstrate challenging reasoning patterns, and instructs GPT-3 to compose new examples with similar patterns. Machine generated examples are then automatically filtered, and finally revised and labeled by human crowdworkers. The resulting dataset, WANLI, consists of 107,885 NLI examples and presents unique empirical strengths over existing NLI datasets. Remarkably, training a model on WANLI improves performance on eight out-of-domain test sets we consider, including by 11{\%} on HANS and 9{\%} on Adversarial NLI, compared to training on the 4x larger MultiNLI. Moreover, it continues to be more effective than MultiNLI augmented with other NLI datasets. Our results demonstrate the promise of leveraging natural language generation techniques and re-imagining the role of humans in the dataset creation process.",
}
```
|
ZidanSink/UnaEvos
|
ZidanSink
| 2023-06-14T04:39:11Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-06-14T04:30:44Z |
---
license: creativeml-openrail-m
---
|
intanm/fewshot-qa-003-20230614-001
|
intanm
| 2023-06-14T04:37:56Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"deberta-v2",
"question-answering",
"generated_from_trainer",
"license:mit",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-06-14T04:13:39Z |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: fewshot-qa-003-20230614-001
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. -->
# fewshot-qa-003-20230614-001
This model is a fine-tuned version of [timpal0l/mdeberta-v3-base-squad2](https://huggingface.co/timpal0l/mdeberta-v3-base-squad2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.7024
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 208 | 2.2878 |
| No log | 2.0 | 416 | 2.2958 |
| 2.2174 | 3.0 | 624 | 2.4204 |
| 2.2174 | 4.0 | 832 | 2.6886 |
| 1.1542 | 5.0 | 1040 | 3.0044 |
| 1.1542 | 6.0 | 1248 | 3.2229 |
| 1.1542 | 7.0 | 1456 | 3.4833 |
| 0.6136 | 8.0 | 1664 | 3.5356 |
| 0.6136 | 9.0 | 1872 | 3.6634 |
| 0.3843 | 10.0 | 2080 | 3.7024 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Shularp/en2arCkptfromgendata_02
|
Shularp
| 2023-06-14T04:34:21Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"marian",
"text2text-generation",
"translation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2023-06-14T04:18:13Z |
---
tags:
- translation
- generated_from_trainer
metrics:
- bleu
model-index:
- name: en2arCkptfromgendata_02
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. -->
# en2arCkptfromgendata_02
This model is a fine-tuned version of [Shularp/en2arCkptfromgendata_01](https://huggingface.co/Shularp/en2arCkptfromgendata_01) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4830
- Bleu: 37.6862
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.5739 | 1.0 | 38 | 1.4830 | 37.6862 |
| 0.5953 | 2.0 | 76 | 1.4830 | 37.6862 |
| 0.5959 | 3.0 | 114 | 1.4830 | 37.6862 |
| 0.586 | 4.0 | 152 | 1.4830 | 37.6862 |
| 0.6381 | 5.0 | 190 | 1.4830 | 37.6862 |
| 0.6583 | 6.0 | 228 | 1.4830 | 37.6862 |
| 0.6627 | 7.0 | 266 | 1.4830 | 37.6862 |
| 0.7228 | 8.0 | 304 | 1.4830 | 37.6862 |
| 0.6493 | 9.0 | 342 | 1.4830 | 37.6862 |
| 0.6138 | 10.0 | 380 | 1.4830 | 37.6862 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Shubass/shubass
|
Shubass
| 2023-06-14T04:25:33Z | 29 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-06-14T04:20:58Z |
---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### shubass Dreambooth model trained by Shubass 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:
.jpg)
.jpg)
.jpg)
.jpg)
.jpg)
.jpg)
.jpg)
.jpg)
.jpg)
.jpg)
.jpg)
.jpg)
|
EnterNameBros/Senko-san-medium-a
|
EnterNameBros
| 2023-06-14T04:22:01Z | 128 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-06-14T04:12:08Z |
---
pipeline_tag: conversational
---
**Senko San : Version A**
-------------------------
Poketwo Helper: [A]
Pros:
- Known certain video games
- Can say hello to User
- Can respond to clear pre-trained intents
Cons:
- Bad memory
- Can't keep a causal Conversation
- Predictable responses [non-developed]
- No personality
|
Shularp/ar2enCkptfromgendata_03
|
Shularp
| 2023-06-14T04:05:02Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"marian",
"text2text-generation",
"translation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2023-06-14T03:49:44Z |
---
tags:
- translation
- generated_from_trainer
metrics:
- bleu
model-index:
- name: ar2enCkptfromgendata_03
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. -->
# ar2enCkptfromgendata_03
This model is a fine-tuned version of [Shularp/ar2enCkptfromgendata_02](https://huggingface.co/Shularp/ar2enCkptfromgendata_02) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8021
- Bleu: 54.7767
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.1882 | 1.0 | 38 | 0.8021 | 54.7767 |
| 0.2384 | 2.0 | 76 | 0.8021 | 54.7767 |
| 0.2024 | 3.0 | 114 | 0.8021 | 54.7767 |
| 0.1823 | 4.0 | 152 | 0.8021 | 54.7767 |
| 0.2082 | 5.0 | 190 | 0.8021 | 54.7767 |
| 0.2366 | 6.0 | 228 | 0.8021 | 54.7767 |
| 0.2485 | 7.0 | 266 | 0.8021 | 54.7767 |
| 0.199 | 8.0 | 304 | 0.8021 | 54.7767 |
| 0.2307 | 9.0 | 342 | 0.8021 | 54.7767 |
| 0.2629 | 10.0 | 380 | 0.8021 | 54.7767 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
intanm/fewshot-qa-001-20230614-001
|
intanm
| 2023-06-14T04:00:10Z | 116 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-06-14T03:42:21Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: fewshot-qa-001-20230614-001
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. -->
# fewshot-qa-001-20230614-001
This model is a fine-tuned version of [intanm/mbert-squadv2](https://huggingface.co/intanm/mbert-squadv2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 4.8876
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 208 | 2.8002 |
| No log | 2.0 | 416 | 2.8132 |
| 2.5875 | 3.0 | 624 | 3.1406 |
| 2.5875 | 4.0 | 832 | 3.4779 |
| 1.1736 | 5.0 | 1040 | 3.8496 |
| 1.1736 | 6.0 | 1248 | 4.1895 |
| 1.1736 | 7.0 | 1456 | 4.4204 |
| 0.531 | 8.0 | 1664 | 4.7848 |
| 0.531 | 9.0 | 1872 | 4.8564 |
| 0.2596 | 10.0 | 2080 | 4.8876 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Lzhou286/ppo-LunarLander
|
Lzhou286
| 2023-06-14T03:50:10Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-14T03:49:52Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: ppo-mlpPolicy
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 272.99 +/- 12.08
name: mean_reward
verified: false
---
# **ppo-mlpPolicy** Agent playing **LunarLander-v2**
This is a trained model of a **ppo-mlpPolicy** 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
...
```
|
hw2942/bert-base-chinese-finetuning-financial-news-sentiment
|
hw2942
| 2023-06-14T03:30:15Z | 109 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"finance",
"zh",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-06-14T02:20:02Z |
---
language:
- zh
tags:
- finance
widget:
- text: 沪指收报3233.67点，涨0.15%，成交额3772亿元
- text: 中国5月新增社融和新增人民币贷款均较去年同期下降，社融新增1.56万亿元，居民中长期贷款增加1684亿元，居民存款增加5364亿元，M2-M1剪刀差缩窄
- text: 人民币兑美元中间价报7.1498，下调286点
- text: 发改委等八部门：支持符合条件的产教融合型企业上市融资
---
|
tebhotol/imneko
|
tebhotol
| 2023-06-14T03:25:01Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-06-14T03:22:15Z |
---
license: creativeml-openrail-m
---
|
Lzhou286/ppo-Huggy
|
Lzhou286
| 2023-06-14T03:12:40Z | 12 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2023-06-14T03:12:35Z |
---
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: Lzhou286/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Draconis42/ppo-Huggy
|
Draconis42
| 2023-06-14T03:08:04Z | 1 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2023-06-14T03:07:54Z |
---
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: Draconis42/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
kinshuk-h/flan-t5-kelm-tekgen-kg-direct-w-context-small-finetuned
|
kinshuk-h
| 2023-06-14T02:48:30Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"legal",
"en",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-06-14T02:47:59Z |
---
license: mit
language:
- en
pipeline_tag: text2text-generation
tags:
- legal
---
# flan-t5-kelm-tekgen-kg-direct-w-context-small-finetuned
[flan-t5-small](https://huggingface.co/google/flan-t5-small) finetuned over the KELM-TEKGEN training corpus using direct concise query prompts with additional variable length context alongside the prompts.
|
imsniper/nextphoto_v10
|
imsniper
| 2023-06-14T02:26:43Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-06-08T16:22:13Z |
---
license: creativeml-openrail-m
---
|
killah-t-cell/boxes_cn
|
killah-t-cell
| 2023-06-14T02:16:41Z | 1 | 0 |
diffusers
|
[
"diffusers",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"controlnet",
"base_model:stabilityai/stable-diffusion-2-1-base",
"base_model:adapter:stabilityai/stable-diffusion-2-1-base",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2023-06-13T21:49:22Z |
---
license: creativeml-openrail-m
base_model: stabilityai/stable-diffusion-2-1-base
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- controlnet
inference: true
---
# controlnet-killah-t-cell/boxes_cn
These are controlnet weights trained on stabilityai/stable-diffusion-2-1-base with new type of conditioning.
You can find some example images below.
prompt: Two men wearing hats with trees in the background
prompt: Two Girls smiling, professional dslr photograph, dark background, studio lights, high quality
prompt: a clown, oil on canvas, bittersweet expression
|
Hobospider132/DialoGPT-Mahiru-Proto
|
Hobospider132
| 2023-06-14T02:12:06Z | 143 | 1 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"gpt2",
"text-generation",
"conversational",
"en",
"dataset:Hobospider132/Mahiru-Proto",
"license:gpl-3.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-02-23T01:26:53Z |
---
license: gpl-3.0
datasets:
- Hobospider132/Mahiru-Proto
language:
- en
library_name: transformers
tags:
- conversational
---
|
luccazen/falcon-7b-alpaca-pt
|
luccazen
| 2023-06-14T02:07:36Z | 0 | 1 | null |
[
"region:us"
] | null | 2023-06-14T02:01:17Z |
Usage
```
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
model = AutoModelForCausalLM.from_pretrained(
"tiiuae/falcon-7b",
load_in_8bit=True,
trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained(
"tiiuae/falcon-7b",
)
model = PeftModel.from_pretrained(model, "/content/drive/MyDrive/ML/falcon/lora-falcao")
```
Data
From https://huggingface.co/22h/cabrita-lora-v0-1
Example outputs
Stanford Alpaca:
Instruction: Invente uma desculpa criativa pra dizer que não preciso ir à festa.
Response: Eu preciso se concentrar em trabalho importante para mim, por isso eu não posso ir à festa.
Falcon PT:
Instrução: Invente uma desculpa criativa pra dizer que não preciso ir à festa.
Resposta: "Eu preciso ficar em casa para cuidar dos meus animais de estimação."
|
alsonlai/ppo-LunarLander-v2
|
alsonlai
| 2023-06-14T02:04:38Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-14T02:04:19Z |
---
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: 272.98 +/- 20.80
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
...
```
|
roy23roy/my_poem_model
|
roy23roy
| 2023-06-14T01:30:00Z | 116 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt_neo",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-06-13T23:31:15Z |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: my_poem_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_poem_model
This model is a fine-tuned version of [EleutherAI/gpt-neo-125M](https://huggingface.co/EleutherAI/gpt-neo-125M) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3339
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.6126 | 1.0 | 799 | 1.4344 |
| 1.3488 | 2.0 | 1598 | 1.3601 |
| 1.2822 | 3.0 | 2397 | 1.3339 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
ebinem/food_classifier
|
ebinem
| 2023-06-14T00:47:31Z | 63 | 0 |
transformers
|
[
"transformers",
"tf",
"vit",
"image-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-06-13T19:22:40Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: ebinem/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. -->
# ebinem/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: 0.5539
- Validation Loss: 0.5933
- Train Accuracy: 0.7
- 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': 0.003, 'decay_steps': 4000, '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 |
|:----------:|:---------------:|:--------------:|:-----:|
| 0.8443 | 0.6034 | 0.7 | 0 |
| 0.5664 | 0.6011 | 0.7 | 1 |
| 0.5533 | 0.6098 | 0.7 | 2 |
| 0.6406 | 0.6150 | 0.7 | 3 |
| 0.5539 | 0.5933 | 0.7 | 4 |
### Framework versions
- Transformers 4.30.1
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
yangliu/chatbotV1
|
yangliu
| 2023-06-14T00:34:25Z | 0 | 3 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2023-06-13T15:36:05Z |
---
license: apache-2.0
---
单论对话机器人
调用方式
from transformers import AutoModelForCausalLM, AutoTokenizer
checkpoint = "model"
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForCausalLM.from_pretrained(checkpoint).cuda()
text = 'Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{你想问的文本}\n\n### Response:'
inputs = tokenizer.encode(generate_input(instruction= ”你想问的文本“), return_tensors="pt")
inputs = inputs.to(model.device)
print(inputs)
outputs = model.generate(inputs,num_beams=3,
max_new_tokens=1024,
penalty_alpha=0.9,
repetition_penalty=1.5)
print(tokenizer.decode(outputs[0]))
|
Janxxx/Kokoroaoshima
|
Janxxx
| 2023-06-14T00:29:08Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-06-14T00:25:26Z |
---
license: creativeml-openrail-m
---
|
NbAiLabArchive/scream_sextusdecimus_virtual_tsfix_small
|
NbAiLabArchive
| 2023-06-14T00:23:11Z | 9 | 0 |
transformers
|
[
"transformers",
"jax",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"audio",
"asr",
"hf-asr-leaderboard",
"no",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-06-13T13:05:42Z |
---
language:
- 'no'
license: apache-2.0
tags:
- audio
- asr
- automatic-speech-recognition
- hf-asr-leaderboard
model-index:
- name: scream_sextusdecimus_virtual_tsfix_small
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. -->
# scream_sextusdecimus_virtual_tsfix_small
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the NbAiLab/ncc_speech dataset.
It achieves the following results on the evaluation set:
- step: 19999
- eval_loss: 0.2913
- train_loss: 0.6610
- eval_wer: 8.7151
- eval_cer: 3.8962
- eval_exact_wer: 8.7151
- eval_exact_cer: 3.8962
## 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
- lr_scheduler_type: linear
- per_device_train_batch_size: 32
- total_train_batch_size_per_node: 128
- total_train_batch_size: 1024
- total_optimization_steps: 20,000
- starting_optimization_step: None
- finishing_optimization_step: 20,000
- num_train_dataset_workers: 32
- num_hosts: 8
- total_num_training_examples: 20,480,000
- steps_per_epoch: 11920
- num_beams: 5
- dropout: True
- bpe_dropout_probability: 0.1
- activation_dropout_probability: 0.1
### Training results
| step | eval_loss | train_loss | eval_wer | eval_cer | eval_exact_wer | eval_exact_cer |
|:-----:|:---------:|:----------:|:--------:|:--------:|:--------------:|:--------------:|
| 0 | 1.2807 | 3.0725 | 196.6092 | 157.4275 | 196.6092 | 157.4275 |
| 1000 | 0.5902 | 1.0592 | 15.1695 | 4.8382 | 15.1695 | 4.8382 |
| 2000 | 0.4240 | 0.8640 | 11.3623 | 3.9308 | 11.3623 | 3.9308 |
| 3000 | 0.4213 | 0.7930 | 9.4587 | 3.3537 | 9.4587 | 3.3537 |
| 4000 | 0.4353 | 0.7986 | 9.3694 | 3.5263 | 9.3694 | 3.5263 |
| 5000 | 0.4697 | 0.7580 | 9.7858 | 4.1478 | 9.7858 | 4.1478 |
| 6000 | 0.4535 | 0.7003 | 10.0238 | 4.2119 | 10.0238 | 4.2119 |
| 7000 | 0.4608 | 0.7296 | 8.8638 | 3.4228 | 8.8638 | 3.4228 |
| 8000 | 0.3902 | 0.7053 | 8.9233 | 3.6003 | 8.9233 | 3.6003 |
| 9000 | 0.3575 | 0.7124 | 9.3992 | 3.9702 | 9.3992 | 3.9702 |
| 10000 | 0.3648 | 0.6858 | 8.8043 | 3.4326 | 8.8043 | 3.4326 |
| 11000 | 0.3033 | 0.6916 | 9.1315 | 3.7236 | 9.1315 | 3.7236 |
| 12000 | 0.3021 | 0.7028 | 8.9827 | 3.6052 | 8.9827 | 3.6052 |
| 13000 | 0.2959 | 0.6567 | 8.6556 | 3.4918 | 8.6556 | 3.4918 |
| 14000 | 0.3055 | 0.6828 | 8.9827 | 3.6496 | 8.9827 | 3.6496 |
| 15000 | 0.2930 | 0.6707 | 8.8043 | 3.7976 | 8.8043 | 3.7976 |
| 16000 | 0.2822 | 0.6523 | 8.5068 | 3.5806 | 8.5068 | 3.5806 |
| 17000 | 0.2809 | 0.6581 | 8.6853 | 3.7828 | 8.6853 | 3.7828 |
| 18000 | 0.2927 | 0.6455 | 9.1315 | 4.2513 | 9.1315 | 4.2513 |
| 19000 | 0.2922 | 0.6369 | 9.1017 | 4.1034 | 9.1017 | 4.1034 |
| 19999 | 0.2913 | 0.6610 | 8.7151 | 3.8962 | 8.7151 | 3.8962 |
### Framework versions
- Transformers 4.30.0.dev0
- Datasets 2.12.1.dev0
- Tokenizers 0.13.3
|
camenduru/thrust
|
camenduru
| 2023-06-14T00:22:37Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-06-13T23:34:05Z |
# Thrust: The C++ Parallel Algorithms Library
<table><tr>
<th><b><a href="https://github.com/nvidia/thrust/tree/main/examples">Examples</a></b></th>
<th><b><a href="https://godbolt.org/z/8E8W764E6">Godbolt</a></b></th>
<th><b><a href="https://nvidia.github.io/thrust">Documentation</a></b></th>
</tr></table>
Thrust is the C++ parallel algorithms library which inspired the introduction
of parallel algorithms to the C++ Standard Library.
Thrust's **high-level** interface greatly enhances programmer **productivity**
while enabling performance portability between GPUs and multicore CPUs.
It builds on top of established parallel programming frameworks (such as CUDA,
TBB, and OpenMP).
It also provides a number of general-purpose facilities similar to those found
in the C++ Standard Library.
The NVIDIA C++ Standard Library is an open source project; it is available on
[GitHub] and included in the NVIDIA HPC SDK and CUDA Toolkit.
If you have one of those SDKs installed, no additional installation or compiler
flags are needed to use libcu++.
## Examples
Thrust is best learned through examples.
The following example generates random numbers serially and then transfers them
to a parallel device where they are sorted.
```cuda
#include <thrust/host_vector.h>
#include <thrust/device_vector.h>
#include <thrust/generate.h>
#include <thrust/sort.h>
#include <thrust/copy.h>
#include <thrust/random.h>
int main() {
// Generate 32M random numbers serially.
thrust::default_random_engine rng(1337);
thrust::uniform_int_distribution<int> dist;
thrust::host_vector<int> h_vec(32 << 20);
thrust::generate(h_vec.begin(), h_vec.end(), [&] { return dist(rng); });
// Transfer data to the device.
thrust::device_vector<int> d_vec = h_vec;
// Sort data on the device.
thrust::sort(d_vec.begin(), d_vec.end());
// Transfer data back to host.
thrust::copy(d_vec.begin(), d_vec.end(), h_vec.begin());
}
```
[See it on Godbolt](https://godbolt.org/z/GeWEd8Er9)
This example demonstrates computing the sum of some random numbers in parallel:
```cuda
#include <thrust/host_vector.h>
#include <thrust/device_vector.h>
#include <thrust/generate.h>
#include <thrust/reduce.h>
#include <thrust/functional.h>
#include <thrust/random.h>
int main() {
// Generate random data serially.
thrust::default_random_engine rng(1337);
thrust::uniform_real_distribution<double> dist(-50.0, 50.0);
thrust::host_vector<double> h_vec(32 << 20);
thrust::generate(h_vec.begin(), h_vec.end(), [&] { return dist(rng); });
// Transfer to device and compute the sum.
thrust::device_vector<double> d_vec = h_vec;
double x = thrust::reduce(d_vec.begin(), d_vec.end(), 0, thrust::plus<int>());
}
```
[See it on Godbolt](https://godbolt.org/z/cnsbWWME7)
This example show how to perform such a reduction asynchronously:
```cuda
#include <thrust/host_vector.h>
#include <thrust/device_vector.h>
#include <thrust/generate.h>
#include <thrust/async/copy.h>
#include <thrust/async/reduce.h>
#include <thrust/functional.h>
#include <thrust/random.h>
#include <numeric>
int main() {
// Generate 32M random numbers serially.
thrust::default_random_engine rng(123456);
thrust::uniform_real_distribution<double> dist(-50.0, 50.0);
thrust::host_vector<double> h_vec(32 << 20);
thrust::generate(h_vec.begin(), h_vec.end(), [&] { return dist(rng); });
// Asynchronously transfer to the device.
thrust::device_vector<double> d_vec(h_vec.size());
thrust::device_event e = thrust::async::copy(h_vec.begin(), h_vec.end(),
d_vec.begin());
// After the transfer completes, asynchronously compute the sum on the device.
thrust::device_future<double> f0 = thrust::async::reduce(thrust::device.after(e),
d_vec.begin(), d_vec.end(),
0.0, thrust::plus<double>());
// While the sum is being computed on the device, compute the sum serially on
// the host.
double f1 = std::accumulate(h_vec.begin(), h_vec.end(), 0.0, thrust::plus<double>());
}
```
[See it on Godbolt](https://godbolt.org/z/be54efaKj)
## Getting The Thrust Source Code
Thrust is a header-only library; there is no need to build or install the project
unless you want to run the Thrust unit tests.
The CUDA Toolkit provides a recent release of the Thrust source code in
`include/thrust`. This will be suitable for most users.
Users that wish to contribute to Thrust or try out newer features should
recursively clone the Thrust Github repository:
```
git clone --recursive https://github.com/NVIDIA/thrust.git
```
## Using Thrust From Your Project
For CMake-based projects, we provide a CMake package for use with
`find_package`. See the [CMake README](thrust/cmake/README.md) for more
information. Thrust can also be added via `add_subdirectory` or tools like
the [CMake Package Manager](https://github.com/cpm-cmake/CPM.cmake).
For non-CMake projects, compile with:
- The Thrust include path (`-I<thrust repo root>`)
- The libcu++ include path (`-I<thrust repo root>/dependencies/libcudacxx/`)
- The CUB include path, if using the CUDA device system (`-I<thrust repo root>/dependencies/cub/`)
- By default, the CPP host system and CUDA device system are used.
These can be changed using compiler definitions:
- `-DTHRUST_HOST_SYSTEM=THRUST_HOST_SYSTEM_XXX`,
where `XXX` is `CPP` (serial, default), `OMP` (OpenMP), or `TBB` (Intel TBB)
- `-DTHRUST_DEVICE_SYSTEM=THRUST_DEVICE_SYSTEM_XXX`, where `XXX` is
`CPP`, `OMP`, `TBB`, or `CUDA` (default).
## Developing Thrust
Thrust uses the [CMake build system] to build unit tests, examples, and header
tests.
To build Thrust as a developer, it is recommended that you use our
containerized development system:
```bash
# Clone Thrust and CUB repos recursively:
git clone --recursive https://github.com/NVIDIA/thrust.git
cd thrust
# Build and run tests and examples:
ci/local/build.bash
```
That does the equivalent of the following, but in a clean containerized
environment which has all dependencies installed:
```bash
# Clone Thrust and CUB repos recursively:
git clone --recursive https://github.com/NVIDIA/thrust.git
cd thrust
# Create build directory:
mkdir build
cd build
# Configure -- use one of the following:
cmake .. # Command line interface.
ccmake .. # ncurses GUI (Linux only).
cmake-gui # Graphical UI, set source/build directories in the app.
# Build:
cmake --build . -j ${NUM_JOBS} # Invokes make (or ninja, etc).
# Run tests and examples:
ctest
```
By default, a serial `CPP` host system, `CUDA` accelerated device system, and
C++14 standard are used.
This can be changed in CMake and via flags to `ci/local/build.bash`
More information on configuring your Thrust build and creating a pull request
can be found in the [contributing section].
## Licensing
Thrust is an open source project developed on [GitHub].
Thrust is distributed under the [Apache License v2.0 with LLVM Exceptions];
some parts are distributed under the [Apache License v2.0] and the
[Boost License v1.0].
## CI Status
<a href='https://gpuci.gpuopenanalytics.com/job/nvidia/job/thrust/job/branch/job/thrust-gpu-build/CXX_TYPE=gcc,CXX_VER=9,OS_TYPE=ubuntu,OS_VER=20.04,SDK_TYPE=cuda,SDK_VER=11.7.0-devel/'><img src='https://gpuci.gpuopenanalytics.com/job/nvidia/job/thrust/job/branch/job/thrust-gpu-build/CXX_TYPE=gcc,CXX_VER=9,OS_TYPE=ubuntu,OS_VER=20.04,SDK_TYPE=cuda,SDK_VER=11.7.0-devel/badge/icon?subject=NVCC%2011.7.0%20%2B%20GCC%209%20build%20and%20device%20tests'></a>
<a href='https://gpuci.gpuopenanalytics.com/job/nvidia/job/thrust/job/branch/job/thrust-cpu-build/CXX_TYPE=gcc,CXX_VER=11,OS_TYPE=ubuntu,OS_VER=20.04,SDK_TYPE=cuda,SDK_VER=11.7.0-devel/'><img src='https://gpuci.gpuopenanalytics.com/job/nvidia/job/thrust/job/branch/job/thrust-cpu-build/CXX_TYPE=gcc,CXX_VER=11,OS_TYPE=ubuntu,OS_VER=20.04,SDK_TYPE=cuda,SDK_VER=11.7.0-devel/badge/icon?subject=NVCC%2011.7.0%20%2B%20GCC%2011%20build%20and%20host%20tests'></a>
<a href='https://gpuci.gpuopenanalytics.com/job/nvidia/job/thrust/job/branch/job/thrust-cpu-build/CXX_TYPE=gcc,CXX_VER=10,OS_TYPE=ubuntu,OS_VER=20.04,SDK_TYPE=cuda,SDK_VER=11.7.0-devel/'><img src='https://gpuci.gpuopenanalytics.com/job/nvidia/job/thrust/job/branch/job/thrust-cpu-build/CXX_TYPE=gcc,CXX_VER=10,OS_TYPE=ubuntu,OS_VER=20.04,SDK_TYPE=cuda,SDK_VER=11.7.0-devel/badge/icon?subject=NVCC%2011.7.0%20%2B%20GCC%2010%20build%20and%20host%20tests'></a>
<a href='https://gpuci.gpuopenanalytics.com/job/nvidia/job/thrust/job/branch/job/thrust-cpu-build/CXX_TYPE=gcc,CXX_VER=9,OS_TYPE=ubuntu,OS_VER=20.04,SDK_TYPE=cuda,SDK_VER=11.7.0-devel/'><img src='https://gpuci.gpuopenanalytics.com/job/nvidia/job/thrust/job/branch/job/thrust-cpu-build/CXX_TYPE=gcc,CXX_VER=9,OS_TYPE=ubuntu,OS_VER=20.04,SDK_TYPE=cuda,SDK_VER=11.7.0-devel/badge/icon?subject=NVCC%2011.7.0%20%2B%20GCC%209%20build%20and%20host%20tests'></a>
<a href='https://gpuci.gpuopenanalytics.com/job/nvidia/job/thrust/job/branch/job/thrust-cpu-build/CXX_TYPE=gcc,CXX_VER=8,OS_TYPE=ubuntu,OS_VER=20.04,SDK_TYPE=cuda,SDK_VER=11.7.0-devel/'><img src='https://gpuci.gpuopenanalytics.com/job/nvidia/job/thrust/job/branch/job/thrust-cpu-build/CXX_TYPE=gcc,CXX_VER=8,OS_TYPE=ubuntu,OS_VER=20.04,SDK_TYPE=cuda,SDK_VER=11.7.0-devel/badge/icon?subject=NVCC%2011.7.0%20%2B%20GCC%208%20build%20and%20host%20tests'></a>
<a href='https://gpuci.gpuopenanalytics.com/job/nvidia/job/thrust/job/branch/job/thrust-cpu-build/CXX_TYPE=gcc,CXX_VER=7,OS_TYPE=ubuntu,OS_VER=20.04,SDK_TYPE=cuda,SDK_VER=11.7.0-devel/'><img src='https://gpuci.gpuopenanalytics.com/job/nvidia/job/thrust/job/branch/job/thrust-cpu-build/CXX_TYPE=gcc,CXX_VER=7,OS_TYPE=ubuntu,OS_VER=20.04,SDK_TYPE=cuda,SDK_VER=11.7.0-devel/badge/icon?subject=NVCC%2011.7.0%20%2B%20GCC%207%20build%20and%20host%20tests'></a>
<a href='https://gpuci.gpuopenanalytics.com/job/nvidia/job/thrust/job/branch/job/thrust-cpu-build/CXX_TYPE=gcc,CXX_VER=6,OS_TYPE=ubuntu,OS_VER=20.04,SDK_TYPE=cuda,SDK_VER=11.7.0-devel/'><img src='https://gpuci.gpuopenanalytics.com/job/nvidia/job/thrust/job/branch/job/thrust-cpu-build/CXX_TYPE=gcc,CXX_VER=6,OS_TYPE=ubuntu,OS_VER=20.04,SDK_TYPE=cuda,SDK_VER=11.7.0-devel/badge/icon?subject=NVCC%2011.7.0%20%2B%20GCC%206%20build%20and%20host%20tests'></a>
<a href='https://gpuci.gpuopenanalytics.com/job/nvidia/job/thrust/job/branch/job/thrust-cpu-build/CXX_TYPE=gcc,CXX_VER=5,OS_TYPE=ubuntu,OS_VER=20.04,SDK_TYPE=cuda,SDK_VER=11.7.0-devel/'><img src='https://gpuci.gpuopenanalytics.com/job/nvidia/job/thrust/job/branch/job/thrust-cpu-build/CXX_TYPE=gcc,CXX_VER=5,OS_TYPE=ubuntu,OS_VER=20.04,SDK_TYPE=cuda,SDK_VER=11.7.0-devel/badge/icon?subject=NVCC%2011.7.0%20%2B%20GCC%205%20build%20and%20host%20tests'></a>
<a href='https://gpuci.gpuopenanalytics.com/job/nvidia/job/thrust/job/branch/job/thrust-cpu-build/CXX_TYPE=clang,CXX_VER=12,OS_TYPE=ubuntu,OS_VER=20.04,SDK_TYPE=cuda,SDK_VER=11.7.0-devel/'><img src='https://gpuci.gpuopenanalytics.com/job/nvidia/job/thrust/job/branch/job/thrust-cpu-build/CXX_TYPE=clang,CXX_VER=12,OS_TYPE=ubuntu,OS_VER=20.04,SDK_TYPE=cuda,SDK_VER=11.7.0-devel/badge/icon?subject=NVCC%2011.7.0%20%2B%20Clang%2012%20build%20and%20host%20tests'></a>
<a href='https://gpuci.gpuopenanalytics.com/job/nvidia/job/thrust/job/branch/job/thrust-cpu-build/CXX_TYPE=clang,CXX_VER=11,OS_TYPE=ubuntu,OS_VER=20.04,SDK_TYPE=cuda,SDK_VER=11.7.0-devel/'><img src='https://gpuci.gpuopenanalytics.com/job/nvidia/job/thrust/job/branch/job/thrust-cpu-build/CXX_TYPE=clang,CXX_VER=11,OS_TYPE=ubuntu,OS_VER=20.04,SDK_TYPE=cuda,SDK_VER=11.7.0-devel/badge/icon?subject=NVCC%2011.7.0%20%2B%20Clang%2011%20build%20and%20host%20tests'></a>
<a href='https://gpuci.gpuopenanalytics.com/job/nvidia/job/thrust/job/branch/job/thrust-cpu-build/CXX_TYPE=clang,CXX_VER=10,OS_TYPE=ubuntu,OS_VER=20.04,SDK_TYPE=cuda,SDK_VER=11.7.0-devel/'><img src='https://gpuci.gpuopenanalytics.com/job/nvidia/job/thrust/job/branch/job/thrust-cpu-build/CXX_TYPE=clang,CXX_VER=10,OS_TYPE=ubuntu,OS_VER=20.04,SDK_TYPE=cuda,SDK_VER=11.7.0-devel/badge/icon?subject=NVCC%2011.7.0%20%2B%20Clang%2010%20build%20and%20host%20tests'></a>
<a href='https://gpuci.gpuopenanalytics.com/job/nvidia/job/thrust/job/branch/job/thrust-cpu-build/CXX_TYPE=clang,CXX_VER=9,OS_TYPE=ubuntu,OS_VER=20.04,SDK_TYPE=cuda,SDK_VER=11.7.0-devel/'><img src='https://gpuci.gpuopenanalytics.com/job/nvidia/job/thrust/job/branch/job/thrust-cpu-build/CXX_TYPE=clang,CXX_VER=9,OS_TYPE=ubuntu,OS_VER=20.04,SDK_TYPE=cuda,SDK_VER=11.7.0-devel/badge/icon?subject=NVCC%2011.7.0%20%2B%20Clang%209%20build%20and%20host%20tests'></a>
<a href='https://gpuci.gpuopenanalytics.com/job/nvidia/job/thrust/job/branch/job/thrust-cpu-build/CXX_TYPE=clang,CXX_VER=8,OS_TYPE=ubuntu,OS_VER=20.04,SDK_TYPE=cuda,SDK_VER=11.7.0-devel/'><img src='https://gpuci.gpuopenanalytics.com/job/nvidia/job/thrust/job/branch/job/thrust-cpu-build/CXX_TYPE=clang,CXX_VER=8,OS_TYPE=ubuntu,OS_VER=20.04,SDK_TYPE=cuda,SDK_VER=11.7.0-devel/badge/icon?subject=NVCC%2011.7.0%20%2B%20Clang%208%20build%20and%20host%20tests'></a>
<a href='https://gpuci.gpuopenanalytics.com/job/nvidia/job/thrust/job/branch/job/thrust-cpu-build/CXX_TYPE=clang,CXX_VER=7,OS_TYPE=ubuntu,OS_VER=20.04,SDK_TYPE=cuda,SDK_VER=11.7.0-devel/'><img src='https://gpuci.gpuopenanalytics.com/job/nvidia/job/thrust/job/branch/job/thrust-cpu-build/CXX_TYPE=clang,CXX_VER=7,OS_TYPE=ubuntu,OS_VER=20.04,SDK_TYPE=cuda,SDK_VER=11.7.0-devel/badge/icon?subject=NVCC%2011.7.0%20%2B%20Clang%207%20build%20and%20host%20tests'></a>
<a href='https://gpuci.gpuopenanalytics.com/job/nvidia/job/thrust/job/branch/job/thrust-cpu-build/CXX_TYPE=icc,CXX_VER=latest,OS_TYPE=ubuntu,OS_VER=20.04,SDK_TYPE=cuda,SDK_VER=11.7.0-devel/'><img src='https://gpuci.gpuopenanalytics.com/job/nvidia/job/thrust/job/branch/job/thrust-cpu-build/CXX_TYPE=icc,CXX_VER=latest,OS_TYPE=ubuntu,OS_VER=20.04,SDK_TYPE=cuda,SDK_VER=11.7.0-devel/badge/icon?subject=NVCC%2011.7.0%20%2B%20ICC%20build%20and%20host%20tests'></a>
[GitHub]: https://github.com/nvidia/thrust
[CMake section]: https://nvidia.github.io/thrust/setup/cmake_options.html
[contributing section]: https://nvidia.github.io/thrust/contributing.html
[CMake build system]: https://cmake.org
[Apache License v2.0 with LLVM Exceptions]: https://llvm.org/LICENSE.txt
[Apache License v2.0]: https://www.apache.org/licenses/LICENSE-2.0.txt
[Boost License v1.0]: https://www.boost.org/LICENSE_1_0.txt
|
andreggalvao/ppo-LunarLander-v2
|
andreggalvao
| 2023-06-14T00:17:16Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-14T00:17:00Z |
---
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: 240.69 +/- 66.82
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
...
```
|
mamun4105/ML-Agents-Pyramids
|
mamun4105
| 2023-06-14T00:11:17Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] |
reinforcement-learning
| 2023-06-14T00:11:09Z |
---
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: mamun4105/ML-Agents-Pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
LemonFace0309/Pyramids_Training
|
LemonFace0309
| 2023-06-14T00:02:21Z | 3 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] |
reinforcement-learning
| 2023-06-14T00:02:05Z |
---
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: LemonFace0309/Pyramids_Training
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
zachixon/why
|
zachixon
| 2023-06-14T00:01:23Z | 0 | 0 | null |
[
"license:deepfloyd-if-license",
"region:us"
] | null | 2023-06-14T00:01:23Z |
---
license: deepfloyd-if-license
---
|
alpindale/landmark-33b
|
alpindale
| 2023-06-14T00:00:33Z | 47 | 6 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"custom_code",
"en",
"dataset:togethercomputer/RedPajama-Data-1T-Sample",
"arxiv:2305.16300",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-06-13T04:03:42Z |
---
license: apache-2.0
datasets:
- togethercomputer/RedPajama-Data-1T-Sample
language:
- en
---
# Landmark Attention LLaMA 33B
This model has been trained using the PEFT LoRA technique with the [Landmark Attention](https://arxiv.org/abs/2305.16300) method over 200 steps. Model will likely be trained further and updated later on.
## Usage
Requires `trust_remote_code` to be set to `True`. In [oobabooga](https://github.com/oobabooga/text-generation-webui), you can simply add the `--trust_remote_code` flag.
You will also need to disable the `Add the bos_token to the beginning of prompts` option in the settings.
## PEFT Checkpoint
You can probably merge the checkpoint with any other LLaMA-based model (provided they're 33B, of course). This repo contains the merged weights, but you can grab the adapter [here](https://anonfiles.com/F3Pb20wbz7).
## Training Code
You can find the training code [here](https://github.com/eugenepentland/landmark-attention-qlora).
|
james-xie-rng/voip_classification
|
james-xie-rng
| 2023-06-13T23:35:12Z | 191 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"audio-classification",
"generated_from_trainer",
"dataset:audiofolder",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
audio-classification
| 2023-06-07T22:01:53Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- audiofolder
metrics:
- accuracy
model-index:
- name: voip_classification
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# voip_classification
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the audiofolder dataset.
It achieves the following results on the evaluation set:
- Loss: nan
- Accuracy: 0.0099
## 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: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- 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
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.6603 | 0.99 | 41 | nan | 0.0099 |
| 0.0 | 1.99 | 82 | nan | 0.0099 |
| 0.0 | 2.98 | 123 | nan | 0.0099 |
| 0.0 | 4.0 | 165 | nan | 0.0099 |
| 0.0 | 4.99 | 206 | nan | 0.0099 |
| 0.0 | 5.99 | 247 | nan | 0.0099 |
| 0.0 | 6.98 | 288 | nan | 0.0099 |
| 0.0 | 8.0 | 330 | nan | 0.0099 |
| 0.0 | 8.99 | 371 | nan | 0.0099 |
| 0.0 | 9.94 | 410 | nan | 0.0099 |
### Framework versions
- Transformers 4.30.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
nathan-cai/dqn-SpaceInvadersNoFrameskip-v4
|
nathan-cai
| 2023-06-13T23:34:34Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-13T23:33:53Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 677.50 +/- 248.75
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga nathan-cai -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga nathan-cai -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga nathan-cai
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
TheBloke/CAMEL-13B-Role-Playing-Data-GGML
|
TheBloke
| 2023-06-13T23:22:11Z | 0 | 11 | null |
[
"arxiv:2303.17760",
"license:other",
"region:us"
] | null | 2023-06-07T20:58:16Z |
---
inference: false
license: other
---
<!-- header start -->
<div style="width: 100%;">
<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><a href="https://discord.gg/Jq4vkcDakD">Chat & support: my new Discord server</a></p>
</div>
<div style="display: flex; flex-direction: column; align-items: flex-end;">
<p><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
</div>
</div>
<!-- header end -->
# Camel AI's CAMEL 13B Role Playing Data GGML
These files are GGML format model files for [Camel AI's CAMEL 13B Role Playing Data](https://huggingface.co/camel-ai/CAMEL-13B-Role-Playing-Data).
GGML files are for CPU + GPU inference using [llama.cpp](https://github.com/ggerganov/llama.cpp) and libraries and UIs which support this format, such as:
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui)
* [KoboldCpp](https://github.com/LostRuins/koboldcpp)
* [ParisNeo/GPT4All-UI](https://github.com/ParisNeo/gpt4all-ui)
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python)
* [ctransformers](https://github.com/marella/ctransformers)
## Repositories available
* [4-bit GPTQ models for GPU inference](https://huggingface.co/TheBloke/CAMEL-13B-Role-Playing-Data-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference](https://huggingface.co/TheBloke/CAMEL-13B-Role-Playing-Data-GGML)
* [Unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/TheBloke/CAMEL-13B-Role-Playing-Data-fp16)
<!-- compatibility_ggml start -->
## Compatibility
### Original llama.cpp quant methods: `q4_0, q4_1, q5_0, q5_1, q8_0`
I have quantized these 'original' quantisation methods using an older version of llama.cpp so that they remain compatible with llama.cpp as of May 19th, commit `2d5db48`.
They should be compatible with all current UIs and libraries that use llama.cpp, such as those listed at the top of this README.
### New k-quant methods: `q2_K, q3_K_S, q3_K_M, q3_K_L, q4_K_S, q4_K_M, q5_K_S, q6_K`
These new quantisation methods are only compatible with llama.cpp as of June 6th, commit `2d43387`.
They will NOT be compatible with koboldcpp, text-generation-ui, and other UIs and libraries yet. Support is expected to come over the next few days.
## Explanation of the new k-quant methods
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
* GGML_TYPE_Q8_K - "type-0" 8-bit quantization. Only used for quantizing intermediate results. The difference to the existing Q8_0 is that the block size is 256. All 2-6 bit dot products are implemented for this quantization type.
Refer to the Provided Files table below to see what files use which methods, and how.
<!-- compatibility_ggml end -->
## Provided files
| Name | Quant method | Bits | Size | Max RAM required | Use case |
| ---- | ---- | ---- | ---- | ---- | ----- |
| camel-13b-roleplay.ggmlv3.q2_K.bin | q2_K | 2 | 5.51 GB | 8.01 GB | New k-quant method. Uses GGML_TYPE_Q4_K for the attention.vw and feed_forward.w2 tensors, GGML_TYPE_Q2_K for the other tensors. |
| camel-13b-roleplay.ggmlv3.q3_K_L.bin | q3_K_L | 3 | 6.93 GB | 9.43 GB | New k-quant method. Uses GGML_TYPE_Q5_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else GGML_TYPE_Q3_K |
| camel-13b-roleplay.ggmlv3.q3_K_M.bin | q3_K_M | 3 | 6.31 GB | 8.81 GB | New k-quant method. Uses GGML_TYPE_Q4_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else GGML_TYPE_Q3_K |
| camel-13b-roleplay.ggmlv3.q3_K_S.bin | q3_K_S | 3 | 5.66 GB | 8.16 GB | New k-quant method. Uses GGML_TYPE_Q3_K for all tensors |
| camel-13b-roleplay.ggmlv3.q4_0.bin | q4_0 | 4 | 7.32 GB | 9.82 GB | Original llama.cpp quant method, 4-bit. |
| camel-13b-roleplay.ggmlv3.q4_1.bin | q4_1 | 4 | 8.14 GB | 10.64 GB | Original llama.cpp quant method, 4-bit. Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models. |
| camel-13b-roleplay.ggmlv3.q4_K_M.bin | q4_K_M | 4 | 7.87 GB | 10.37 GB | New k-quant method. Uses GGML_TYPE_Q6_K for half of the attention.wv and feed_forward.w2 tensors, else GGML_TYPE_Q4_K |
| camel-13b-roleplay.ggmlv3.q4_K_S.bin | q4_K_S | 4 | 7.37 GB | 9.87 GB | New k-quant method. Uses GGML_TYPE_Q4_K for all tensors |
| camel-13b-roleplay.ggmlv3.q5_0.bin | q5_0 | 5 | 8.95 GB | 11.45 GB | Original llama.cpp quant method, 5-bit. Higher accuracy, higher resource usage and slower inference. |
| camel-13b-roleplay.ggmlv3.q5_1.bin | q5_1 | 5 | 9.76 GB | 12.26 GB | Original llama.cpp quant method, 5-bit. Even higher accuracy, resource usage and slower inference. |
| camel-13b-roleplay.ggmlv3.q5_K_M.bin | q5_K_M | 5 | 9.23 GB | 11.73 GB | New k-quant method. Uses GGML_TYPE_Q6_K for half of the attention.wv and feed_forward.w2 tensors, else GGML_TYPE_Q5_K |
| camel-13b-roleplay.ggmlv3.q5_K_S.bin | q5_K_S | 5 | 8.97 GB | 11.47 GB | New k-quant method. Uses GGML_TYPE_Q5_K for all tensors |
| camel-13b-roleplay.ggmlv3.q6_K.bin | q6_K | 6 | 10.68 GB | 13.18 GB | New k-quant method. Uses GGML_TYPE_Q8_K - 6-bit quantization - for all tensors |
| camel-13b-roleplay.ggmlv3.q8_0.bin | q8_0 | 8 | 13.83 GB | 16.33 GB | Original llama.cpp quant method, 8-bit. Almost indistinguishable from float16. High resource use and slow. Not recommended for most users. |
**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.
## How to run in `llama.cpp`
I use the following command line; adjust for your tastes and needs:
```
./main -t 10 -ngl 32 -m camel-13b-roleplay.ggmlv3.q5_0.bin --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "### Instruction: Write a story about llamas\n### Response:"
```
Change `-t 10` to the number of physical CPU cores you have. For example if your system has 8 cores/16 threads, use `-t 8`.
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`
## How to run in `text-generation-webui`
Further instructions here: [text-generation-webui/docs/llama.cpp-models.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp-models.md).
<!-- footer start -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/Jq4vkcDakD)
## Thanks, and how to contribute.
Thanks to the [chirper.ai](https://chirper.ai) team!
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**: Luke from CarbonQuill, Aemon Algiz, Dmitriy Samsonov.
**Patreon special mentions**: Oscar Rangel, Eugene Pentland, Talal Aujan, Cory Kujawski, Luke, Asp the Wyvern, Ai Maven, Pyrater, Alps Aficionado, senxiiz, Willem Michiel, Junyu Yang, trip7s trip, Sebastain Graf, Joseph William Delisle, Lone Striker, Jonathan Leane, Johann-Peter Hartmann, David Flickinger, Spiking Neurons AB, Kevin Schuppel, Mano Prime, Dmitriy Samsonov, Sean Connelly, Nathan LeClaire, Alain Rossmann, Fen Risland, Derek Yates, Luke Pendergrass, Nikolai Manek, Khalefa Al-Ahmad, Artur Olbinski, John Detwiler, Ajan Kanaga, Imad Khwaja, Trenton Dambrowitz, Kalila, vamX, webtim, Illia Dulskyi.
Thank you to all my generous patrons and donaters!
<!-- footer end -->
# Original model card: Camel AI's CAMEL 13B Role Playing Data
CAMEL-13B-Role-Playing-Data is a chat large language model obtained by finetuning LLaMA-13B model on a total of 229K conversations created through our role-playing framework proposed in [CAMEL](https://arxiv.org/abs/2303.17760). We evaluate our model offline using EleutherAI's language model evaluation harness used by Huggingface's Open LLM Benchmark. CAMEL-13B scores an average of **57.2**, outperfroming LLaMA-30B (56.9)!
| Model | size | ARC-C (25 shots, acc_norm) | HellaSwag (10 shots, acc_norm) | MMLU (5 shots, acc_norm) | TruthfulQA (0 shot, mc2) | Average | Delta |
|-------------|:----:|:---------------------------:|:-------------------------------:|:-------------------------:|:-------------------------:|:-------:|-------|
| LLaMA | 13B | 50.8 | 78.9 | 37.7 | 39.9 | 51.8 | - |
| Vicuna | 13B | 47.4 | 75.2 | 39.6 | 49.8 | 53.7 | 1.9 |
| CAMEL | 13B | 54.9 | 79.3 | 48.5 | 46.2 | **57.2** | 5.4 |
| LLaMA | 30B | 57.1 | 82.6 | 45.7 | 42.3 | 56.9 | 5.1 |
---
license: cc-by-nc-4.0
---
|
roy23roy/my_poem_gptneo-model
|
roy23roy
| 2023-06-13T23:00:01Z | 118 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt_neo",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-06-13T20:01:02Z |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: my_poem_gptneo-model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_poem_gptneo-model
This model is a fine-tuned version of [EleutherAI/gpt-neo-125M](https://huggingface.co/EleutherAI/gpt-neo-125M) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6780
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 156 | 1.7800 |
| No log | 2.0 | 312 | 1.6987 |
| No log | 3.0 | 468 | 1.6780 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
nasa-impact/bert-e-base-mlm
|
nasa-impact
| 2023-06-13T22:27:12Z | 127 | 7 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
This model is further trained on top of scibert-base using masked language modeling loss (MLM). The corpus is roughly abstracts from 270,000 earth science-based publications.
The tokenizer used is AutoTokenizer, which is trained on the same corpus.
Stay tuned for further downstream task tests and updates to the model.
in the works
- MLM + NSP task loss
- Add more data sources for training
- Test using downstream tasks
|
HarryP/ppo-LunarLander-v2
|
HarryP
| 2023-06-13T22:02:28Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-13T22:02:06Z |
---
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: 242.18 +/- 16.66
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
...
```
|
ouail15031/donut-base-sroie-v2
|
ouail15031
| 2023-06-13T21:32:10Z | 45 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vision-encoder-decoder",
"image-text-to-text",
"generated_from_trainer",
"dataset:imagefolder",
"license:mit",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2023-06-13T20:03:31Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- imagefolder
model-index:
- name: donut-base-sroie-v2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# donut-base-sroie-v2
This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) on the imagefolder dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
### Framework versions
- Transformers 4.31.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
xared1001/bloom-7b1_pytorch
|
xared1001
| 2023-06-13T21:02:39Z | 12 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bloom",
"text-generation",
"ak",
"ar",
"as",
"bm",
"bn",
"ca",
"code",
"en",
"es",
"eu",
"fon",
"fr",
"gu",
"hi",
"id",
"ig",
"ki",
"kn",
"lg",
"ln",
"ml",
"mr",
"ne",
"nso",
"ny",
"or",
"pa",
"pt",
"rn",
"rw",
"sn",
"st",
"sw",
"ta",
"te",
"tn",
"ts",
"tum",
"tw",
"ur",
"vi",
"wo",
"xh",
"yo",
"zh",
"zhs",
"zht",
"zu",
"arxiv:1909.08053",
"arxiv:2110.02861",
"arxiv:2108.12409",
"license:bigscience-bloom-rail-1.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-06-13T19:47:59Z |
---
license: bigscience-bloom-rail-1.0
language:
- ak
- ar
- as
- bm
- bn
- ca
- code
- en
- es
- eu
- fon
- fr
- gu
- hi
- id
- ig
- ki
- kn
- lg
- ln
- ml
- mr
- ne
- nso
- ny
- or
- pa
- pt
- rn
- rw
- sn
- st
- sw
- ta
- te
- tn
- ts
- tum
- tw
- ur
- vi
- wo
- xh
- yo
- zh
- zhs
- zht
- zu
pipeline_tag: text-generation
---
<h1 style='text-align: center '>BLOOM LM</h1>
<h2 style='text-align: center '><em>BigScience Large Open-science Open-access Multilingual Language Model</em> </h2>
<h3 style='text-align: center '>Model Card</h3>
<img src="https://s3.amazonaws.com/moonup/production/uploads/1657124309515-5f17f0a0925b9863e28ad517.png" alt="BigScience Logo" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
Version 1.0 / 26.May.2022
## Table of Contents
1. [Model Details](#model-details)
2. [Uses](#uses)
3. [Training Data](#training-data)
4. [Risks and Limitations](#risks-and-limitations)
5. [Evaluation](#evaluation)
6. [Recommendations](#recommendations)
7. [Glossary and Calculations](#glossary-and-calculations)
8. [More Information](#more-information)
9. [Model Card Authors](#model-card-authors)
## Model Details
### Basics
*This section provides information for anyone who wants to know about the model.*
<details>
<summary>Click to expand</summary> <br/>
**Developed by:** BigScience ([website](https://bigscience.huggingface.co))
* All collaborators are either volunteers or have an agreement with their employer. *(Further breakdown of participants forthcoming.)*
**Model Type:** Transformer-based Language Model
**Version:** 1.0.0
**Languages:** Multiple; see [training data](#training-data)
**License:** RAIL License v1.0 ([link](https://huggingface.co/spaces/bigscience/license))
**Release Date Estimate:** Monday, 11.July.2022
**Send Questions to:** bigscience-contact@googlegroups.com
**Cite as:** BigScience, _BigScience Language Open-science Open-access Multilingual (BLOOM) Language Model_. International, May 2021-May 2022
**Funded by:**
* The French government.
* Hugging Face ([website](https://huggingface.co)).
* Organizations of contributors. *(Further breakdown of organizations forthcoming.)*
</details>
### Technical Specifications
*This section provides information for people who work on model development.*
<details>
<summary>Click to expand</summary><br/>
Please see [the BLOOM training README](https://github.com/bigscience-workshop/bigscience/tree/master/train/tr11-176B-ml#readme) for full details on replicating training.
**Model Architecture:** Modified from Megatron-LM GPT2 (see [paper](https://arxiv.org/abs/1909.08053), [BLOOM Megatron code](https://github.com/bigscience-workshop/Megatron-DeepSpeed)):
* Decoder-only architecture
* Layer normalization applied to word embeddings layer (`StableEmbedding`; see [code](https://github.com/facebookresearch/bitsandbytes), [paper](https://arxiv.org/pdf/2110.02861.pdf))
* ALiBI positional encodings (see [paper](https://arxiv.org/pdf/2108.12409.pdf)), with GeLU activation functions
* 7,069,016,064 parameters:
* 1,027,604,480 embedding parameters
* 30 layers, 32 attention heads
* Hidden layers are 4096-dimensional
* Sequence length of 2048 tokens used (see [BLOOM tokenizer](https://huggingface.co/bigscience/tokenizer), [tokenizer description](#tokenization))
**Objective Function:** Cross Entropy with mean reduction (see [API documentation](https://pytorch.org/docs/stable/generated/torch.nn.CrossEntropyLoss.html#torch.nn.CrossEntropyLoss)).
**Compute infrastructure:** Jean Zay Public Supercomputer, provided by the French government (see [announcement](https://www.enseignementsup-recherche.gouv.fr/fr/signature-du-marche-d-acquisition-de-l-un-des-supercalculateurs-les-plus-puissants-d-europe-46733)).
* Hardware: 384 A100 80GB GPUs (48 nodes):
* Additional 32 A100 80GB GPUs (4 nodes) in reserve
* 8 GPUs per node Using NVLink 4 inter-gpu connects, 4 OmniPath links
* CPU: AMD
* CPU memory: 512GB per node
* GPU memory: 640GB per node
* Inter-node connect: Omni-Path Architecture (OPA)
* NCCL-communications network: a fully dedicated subnet
* Disc IO network: shared network with other types of nodes
* Software:
* Megatron-DeepSpeed ([Github link](https://github.com/bigscience-workshop/Megatron-DeepSpeed))
* DeepSpeed ([Github link](https://github.com/microsoft/DeepSpeed))
* PyTorch (pytorch-1.11 w/ CUDA-11.5; see [Github link](https://github.com/pytorch/pytorch))
* apex ([Github link](https://github.com/NVIDIA/apex))
#### **Training**
Training logs: [Tensorboard link](https://huggingface.co/tensorboard/bigscience/tr11c-2B5-logs)
- Number of epochs: 1 (*current target*)
- Dates:
- Started 11th March, 2022 11:42am PST
- Ended 5th July, 2022
- Estimated cost of training: Equivalent of $2-5M in cloud computing (including preliminary experiments)
- Server training location: Île-de-France, France
#### **Tokenization**
The BLOOM tokenizer ([link](https://huggingface.co/bigscience/tokenizer)) is a learned subword tokenizer trained using:
- A byte-level Byte Pair Encoding (BPE) algorithm
- A simple pre-tokenization rule, no normalization
- A vocabulary size of 250,680
It was trained on a subset of a preliminary version of the corpus using alpha-weighting per language.
</details>
### Environmental Impact
<details>
<summary>Click to expand</summary><br/>
The training supercomputer, Jean Zay ([website](http://www.idris.fr/eng/jean-zay/jean-zay-presentation-eng.html)), uses mostly nuclear energy. The heat generated by it is reused for heating campus housing.
**Estimated carbon emissions:** *(Forthcoming upon completion of training.)*
**Estimated electricity usage:** *(Forthcoming upon completion of training.)*
</details>
<p> </p>
## Uses
*This section addresses questions around how the model is intended to be used, discusses the foreseeable users of the model (including those affected by the model), and describes uses that are considered out of scope or misuse of the model.
It provides information for anyone considering using the model or who is affected by the model.*
<details>
<summary>Click to expand</summary><br/>
### Intended Use
This model is being created in order to enable public research on large language models (LLMs). LLMs are intended to be used for language generation or as a pretrained base model that can be further fine-tuned for specific tasks. Use cases below are not exhaustive.
#### **Direct Use**
- Text generation
- Exploring characteristics of language generated by a language model
- Examples: Cloze tests, counterfactuals, generations with reframings
#### **Downstream Use**
- Tasks that leverage language models include: Information Extraction, Question Answering, Summarization
### Misuse and Out-of-scope Use
*This section addresses what users ought not do with the model.*
See the [BLOOM License](https://huggingface.co/spaces/bigscience/license), Attachment A, for detailed usage restrictions. The below list is non-exhaustive, but lists some easily foreseeable problematic use cases.
#### **Out-of-scope Uses**
Using the model in [high-stakes](#high-stakes) settings is out of scope for this model. The model is not designed for [critical decisions](#critical-decisions) nor uses with any material consequences on an individual's livelihood or wellbeing. The model outputs content that appears factual but is not correct.
##### Out-of-scope Uses Include:
- Usage in biomedical domains, political and legal domains, or finance domains
- Usage for evaluating or scoring individuals, such as for employment, education, or credit
- Applying the model for critical automatic decisions, generating factual content, creating reliable summaries, or generating predictions that must be correct
#### **Misuse**
Intentionally using the model for harm, violating [human rights](#human-rights), or other kinds of malicious activities, is a misuse of this model. This includes:
- Spam generation
- Disinformation and influence operations
- Disparagement and defamation
- Harassment and abuse
- [Deception](#deception)
- Unconsented impersonation and imitation
- Unconsented surveillance
- Generating content without attribution to the model, as specified in the [RAIL License, Use Restrictions](https://huggingface.co/spaces/bigscience/license)
### Intended Users
#### **Direct Users**
- General Public
- Researchers
- Students
- Educators
- Engineers/developers
- Non-commercial entities
- Community advocates, including human and civil rights groups
#### Indirect Users
- Users of derivatives created by Direct Users, such as those using software with an [intended use](#intended-use)
- Users of [Derivatives of the Model, as described in the License](https://huggingface.co/spaces/bigscience/license)
#### Others Affected (Parties Prenantes)
- People and groups referred to by the LLM
- People and groups exposed to outputs of, or decisions based on, the LLM
- People and groups whose original work is included in the LLM
</details>
<p> </p>
## Training Data
*This section provides a high-level overview of the training data. It is relevant for anyone who wants to know the basics of what the model is learning.*
<details>
<summary>Click to expand</summary><br/>
Details for each dataset are provided in individual [Data Cards](https://huggingface.co/spaces/bigscience/BigScienceCorpus).
Training data includes:
- 45 natural languages
- 12 programming languages
- In 1.5TB of pre-processed text, converted into 350B unique tokens (see [the tokenizer section](#tokenization) for more.)
#### **Languages**
The pie chart shows the distribution of languages in training data.
The following table shows the further distribution of Niger-Congo and Indic languages in the training data.
<details>
<summary>Click to expand</summary><br/>
| Niger Congo | Percentage | | Indic | Percentage |
|----------------|------------ |------ |-----------|------------|
| Chi Tumbuka | 0.00002 | | Assamese | 0.01 |
| Kikuyu | 0.00004 | | Odia | 0.04 |
| Bambara | 0.00004 | | Gujarati | 0.04 |
| Akan | 0.00007 | | Marathi | 0.05 |
| Xitsonga | 0.00007 | | Punjabi | 0.05 |
| Sesotho | 0.00007 | | Kannada | 0.06 |
| Chi Chewa | 0.0001 | | Nepali | 0.07 |
| Setswana | 0.0002 | | Telugu | 0.09 |
| Northern Sotho | 0.0002 | | Malayalam | 0.10 |
| Fon | 0.0002 | | Urdu | 0.10 |
| Kirundi | 0.0003 | | Tamil | 0.20 |
| Wolof | 0.0004 | | Bengali | 0.50 |
| Kuganda | 0.0004 | | Hindi | 0.70 |
| Chi Shona | 0.001 |
| Isi Zulu | 0.001 |
| Igbo | 0.001 |
| Xhosa | 0.001 |
| Kinyarwanda | 0.003 |
| Yoruba | 0.006 |
| Swahili | 0.02 |
</details>
The following table shows the distribution of programming languages.
<details>
<summary>Click to expand</summary><br/>
| Extension | Language | Number of files |
|----------------|------------|-----------------|
| java | Java | 5,407,724 |
| php | PHP | 4,942,186 |
| cpp | C++ | 2,503,930 |
| py | Python | 2,435,072 |
| js | JavaScript | 1,905,518 |
| cs | C# | 1,577,347 |
| rb | Ruby | 6,78,413 |
| cc | C++ | 443,054 |
| hpp | C++ | 391,048 |
| lua | Lua | 352,317 |
| go | GO | 227,763 |
| ts | TypeScript | 195,254 |
| C | C | 134,537 |
| scala | Scala | 92,052 |
| hh | C++ | 67,161 |
| H | C++ | 55,899 |
| tsx | TypeScript | 33,107 |
| rs | Rust | 29,693 |
| phpt | PHP | 9,702 |
| c++ | C++ | 1,342 |
| h++ | C++ | 791 |
| php3 | PHP | 540 |
| phps | PHP | 270 |
| php5 | PHP | 166 |
| php4 | PHP | 29 |
</details>
</details>
<p> </p>
## Risks and Limitations
*This section identifies foreseeable harms and misunderstandings.*
<details>
<summary>Click to expand</summary><br/>
Model may:
- Overrepresent some viewpoints and underrepresent others
- Contain stereotypes
- Contain [personal information](#personal-data-and-information)
- Generate:
- Hateful, abusive, or violent language
- Discriminatory or prejudicial language
- Content that may not be appropriate for all settings, including sexual content
- Make errors, including producing incorrect information as if it were factual
- Generate irrelevant or repetitive outputs
</details>
<p> </p>
## Evaluation
*This section describes the evaluation protocols and provides the results.*
<details>
<summary>Click to expand</summary><br/>
### Metrics
*This section describes the different ways performance is calculated and why.*
Includes:
| Metric | Why chosen |
|--------------------|--------------------------------------------------------------------|
| [Perplexity](#perplexity) | Standard metric for quantifying model improvements during training |
| Cross Entropy [Loss](#loss) | Standard objective for language models. |
And multiple different metrics for specific tasks. _(More evaluation metrics forthcoming upon completion of evaluation protocol.)_
### Factors
*This section lists some different aspects of BLOOM models. Its focus is on those aspects that are likely to give rise to high variance in model behavior.*
- Language, such as English or Yoruba
- Domain, such as newswire or stories
- Demographic characteristics, such as gender or nationality
### Results
*Results are based on the [Factors](#factors) and [Metrics](#metrics).*
**Train-time Evaluation:**
As of 25.May.2022, 15:00 PST:
- Training Loss: 2.3
- Validation Loss: 2.9
- Perplexity: 16
</details>
<p> </p>
## Recommendations
*This section provides information on warnings and potential mitigations.*
<details>
<summary>Click to expand</summary><br/>
- Indirect users should be made aware when the content they're working with is created by the LLM.
- Users should be aware of [Risks and Limitations](#risks-and-limitations), and include an appropriate age disclaimer or blocking interface as necessary.
- Models pretrained with the LLM should include an updated Model Card.
- Users of the model should provide mechanisms for those affected to provide feedback, such as an email address for comments.
</details>
<p> </p>
## Glossary and Calculations
*This section defines common terms and how metrics are calculated.*
<details>
<summary>Click to expand</summary><br/>
- <a name="loss">**Loss:**</a> A calculation of the difference between what the model has learned and what the data shows ("groundtruth"). The lower the loss, the better. The training process aims to minimize the loss.
- <a name="perplexity">**Perplexity:**</a> This is based on what the model estimates the probability of new data is. The lower the perplexity, the better. If the model is 100% correct at predicting the next token it will see, then the perplexity is 1. Mathematically this is calculated using entropy.
- <a name="high-stakes">**High-stakes settings:**</a> Such as those identified as "high-risk AI systems" and "unacceptable risk AI systems" in the European Union's proposed [Artificial Intelligence (AI) Act](https://artificialintelligenceact.eu/annexes/).
- <a name="critical-decisions">**Critical decisions:**</a> Such as those defined in [the United States' proposed Algorithmic Accountability Act](https://www.congress.gov/117/bills/s3572/BILLS-117s3572is.pdf).
- <a name="human-rights">**Human rights:**</a> Includes those rights defined in the [Universal Declaration of Human Rights](https://www.un.org/sites/un2.un.org/files/2021/03/udhr.pdf).
- <a name="personal-data-and-information">**Personal Data and Personal Information:**</a> Personal data and information is defined in multiple data protection regulations, such as "[personal data](https://gdpr-info.eu/issues/personal-data/)" in the [European Union's General Data Protection Regulation](https://gdpr-info.eu); and "personal information" in the Republic of South Africa's [Protection of Personal Information Act](https://www.gov.za/sites/default/files/gcis_document/201409/3706726-11act4of2013popi.pdf), The People's Republic of China's [Personal information protection law](http://en.npc.gov.cn.cdurl.cn/2021-12/29/c_694559.htm).
- <a name="sensitive-characteristics">**Sensitive characteristics:**</a> This includes specifically protected categories in human rights (see [UHDR, Article 2](https://www.un.org/sites/un2.un.org/files/2021/03/udhr.pdf)) and personal information regulation (see GDPR, [Article 9; Protection of Personal Information Act, Chapter 1](https://www.gov.za/sites/default/files/gcis_document/201409/3706726-11act4of2013popi.pdf))
- <a name="deception">**Deception:**</a> Doing something to intentionally mislead individuals to believe something that is false, such as by creating deadbots or chatbots on social media posing as real people, or generating text documents without making consumers aware that the text is machine generated.
</details>
<p> </p>
## More Information
<details>
<summary>Click to expand</summary><br/>
### Dataset Creation
Blog post detailing the design choices during the dataset creation: https://bigscience.huggingface.co/blog/building-a-tb-scale-multilingual-dataset-for-language-modeling
### Technical Specifications
Blog post summarizing how the architecture, size, shape, and pre-training duration where selected: https://bigscience.huggingface.co/blog/what-language-model-to-train-if-you-have-two-million-gpu-hours
More details on the architecture/optimizer: https://github.com/bigscience-workshop/bigscience/tree/master/train/tr11-176B-ml
Blog post on the hardware/engineering side: https://bigscience.huggingface.co/blog/which-hardware-to-train-a-176b-parameters-model
Details on the distributed setup used for the training: https://github.com/bigscience-workshop/bigscience/tree/master/train/tr11-176B-ml
Tensorboard updated during the training: https://huggingface.co/bigscience/tr11-176B-ml-logs/tensorboard#scalars&tagFilter=loss
Insights on how to approach training, negative results: https://github.com/bigscience-workshop/bigscience/blob/master/train/lessons-learned.md
Details on the obstacles overcome during the preparation on the engineering side (instabilities, optimization of training throughput, so many technical tricks and questions): https://github.com/bigscience-workshop/bigscience/blob/master/train/tr11-176B-ml/chronicles.md
### Initial Results
Initial prompting experiments using interim checkpoints: https://huggingface.co/spaces/bigscience/bloom-book
</details>
<p> </p>
## Model Card Authors
*Ordered roughly chronologically and by amount of time spent.*
Margaret Mitchell, Giada Pistilli, Yacine Jernite, Ezinwanne Ozoani, Marissa Gerchick, Nazneen Rajani, Sasha Luccioni, Irene Solaiman, Maraim Masoud, Somaieh Nikpoor, Carlos Muñoz Ferrandis, Stas Bekman, Christopher Akiki, Danish Contractor, David Lansky, Angelina McMillan-Major, Tristan Thrush, Suzana Ilić, Gérard Dupont, Shayne Longpre, Manan Dey, Stella Biderman, Douwe Kiela, Emi Baylor, Teven Le Scao, Aaron Gokaslan, Julien Launay, Niklas Muennighoff
|
Tsahhi/distilbert-base-uncased-finetuned-imdb
|
Tsahhi
| 2023-06-13T20:21:28Z | 118 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-06-13T20:21:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased-finetuned-imdb
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-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 4.8569
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 5.6796 | 1.0 | 2 | 5.4910 |
| 5.0924 | 2.0 | 4 | 5.5083 |
| 4.695 | 3.0 | 6 | 5.0343 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
GCopoulos/bert-base-uncased-finetuned-answer_pol
|
GCopoulos
| 2023-06-13T20:10:51Z | 62 | 0 |
transformers
|
[
"transformers",
"tf",
"tensorboard",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-06-08T21:05:07Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: GCopoulos/bert-base-uncased-finetuned-answer_pol
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. -->
# GCopoulos/bert-base-uncased-finetuned-answer_pol
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0261
- Validation Loss: 0.4267
- Train F1: 0.8825
- Epoch: 2
## 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': 'WarmUp', 'config': {'initial_learning_rate': 7e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 7e-05, 'decay_steps': 653, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 10, 'power': 1.0, '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 F1 | Epoch |
|:----------:|:---------------:|:--------:|:-----:|
| 0.3981 | 0.4720 | 0.8450 | 0 |
| 0.0820 | 0.3742 | 0.8866 | 1 |
| 0.0261 | 0.4267 | 0.8825 | 2 |
### Framework versions
- Transformers 4.30.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
TheBloke/tulu-7B-GGML
|
TheBloke
| 2023-06-13T20:03:50Z | 0 | 6 | null |
[
"en",
"dataset:databricks/databricks-dolly-15k",
"dataset:OpenAssistant/oasst1",
"dataset:sahil2801/CodeAlpaca-20k",
"arxiv:2306.04751",
"arxiv:2302.13971",
"arxiv:2301.13688",
"arxiv:2304.07327",
"arxiv:2304.03277",
"license:other",
"region:us"
] | null | 2023-06-10T23:49:12Z |
---
inference: false
license: other
datasets:
- databricks/databricks-dolly-15k
- OpenAssistant/oasst1
- sahil2801/CodeAlpaca-20k
language:
- en
---
<!-- header start -->
<div style="width: 100%;">
<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><a href="https://discord.gg/Jq4vkcDakD">Chat & support: my new Discord server</a></p>
</div>
<div style="display: flex; flex-direction: column; align-items: flex-end;">
<p><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
</div>
</div>
<!-- header end -->
# Allen AI's Tulu 7B GGML
These files are GGML format model files for [Allen AI's Tulu 7B](https://huggingface.co/allenai/tulu-7b).
GGML files are for CPU + GPU inference using [llama.cpp](https://github.com/ggerganov/llama.cpp) and libraries and UIs which support this format, such as:
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui)
* [KoboldCpp](https://github.com/LostRuins/koboldcpp)
* [ParisNeo/GPT4All-UI](https://github.com/ParisNeo/gpt4all-ui)
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python)
* [ctransformers](https://github.com/marella/ctransformers)
## Repositories available
* [4-bit GPTQ models for GPU inference](https://huggingface.co/TheBloke/tulu-7B-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference](https://huggingface.co/TheBloke/tulu-7B-GGML)
* [Unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/TheBloke/tulu-7B-fp16)
## Prompt template
The following template should be used:
```
<|user|>
prompt goes here
<|assistant|>
```
**Note**: There should be a newline after `<|assistant|>`. This appears to be very important for getting this model to respond correctly.
In other words, the prompt is:
```
<|user|>\nprompt goes here\n<|assistant|>\n
```
<!-- compatibility_ggml start -->
## Compatibility
### Original llama.cpp quant methods: `q4_0, q4_1, q5_0, q5_1, q8_0`
I have quantized these 'original' quantisation methods using an older version of llama.cpp so that they remain compatible with llama.cpp as of May 19th, commit `2d5db48`.
They should be compatible with all current UIs and libraries that use llama.cpp, such as those listed at the top of this README.
### New k-quant methods: `q2_K, q3_K_S, q3_K_M, q3_K_L, q4_K_S, q4_K_M, q5_K_S, q6_K`
These new quantisation methods are only compatible with llama.cpp as of June 6th, commit `2d43387`.
They will NOT be compatible with koboldcpp, text-generation-ui, and other UIs and libraries yet. Support is expected to come over the next few days.
## Explanation of the new k-quant methods
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
* GGML_TYPE_Q8_K - "type-0" 8-bit quantization. Only used for quantizing intermediate results. The difference to the existing Q8_0 is that the block size is 256. All 2-6 bit dot products are implemented for this quantization type.
Refer to the Provided Files table below to see what files use which methods, and how.
<!-- compatibility_ggml end -->
## Provided files
| Name | Quant method | Bits | Size | Max RAM required | Use case |
| ---- | ---- | ---- | ---- | ---- | ----- |
| tulu-7b.ggmlv3.q2_K.bin | q2_K | 2 | 2.80 GB | 5.30 GB | New k-quant method. Uses GGML_TYPE_Q4_K for the attention.vw and feed_forward.w2 tensors, GGML_TYPE_Q2_K for the other tensors. |
| tulu-7b.ggmlv3.q3_K_L.bin | q3_K_L | 3 | 3.55 GB | 6.05 GB | New k-quant method. Uses GGML_TYPE_Q5_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else GGML_TYPE_Q3_K |
| tulu-7b.ggmlv3.q3_K_M.bin | q3_K_M | 3 | 3.23 GB | 5.73 GB | New k-quant method. Uses GGML_TYPE_Q4_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else GGML_TYPE_Q3_K |
| tulu-7b.ggmlv3.q3_K_S.bin | q3_K_S | 3 | 2.90 GB | 5.40 GB | New k-quant method. Uses GGML_TYPE_Q3_K for all tensors |
| tulu-7b.ggmlv3.q4_0.bin | q4_0 | 4 | 3.79 GB | 6.29 GB | Original llama.cpp quant method, 4-bit. |
| tulu-7b.ggmlv3.q4_1.bin | q4_1 | 4 | 4.21 GB | 6.71 GB | Original llama.cpp quant method, 4-bit. Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models. |
| tulu-7b.ggmlv3.q4_K_M.bin | q4_K_M | 4 | 4.05 GB | 6.55 GB | New k-quant method. Uses GGML_TYPE_Q6_K for half of the attention.wv and feed_forward.w2 tensors, else GGML_TYPE_Q4_K |
| tulu-7b.ggmlv3.q4_K_S.bin | q4_K_S | 4 | 3.79 GB | 6.29 GB | New k-quant method. Uses GGML_TYPE_Q4_K for all tensors |
| tulu-7b.ggmlv3.q5_0.bin | q5_0 | 5 | 4.63 GB | 7.13 GB | Original llama.cpp quant method, 5-bit. Higher accuracy, higher resource usage and slower inference. |
| tulu-7b.ggmlv3.q5_1.bin | q5_1 | 5 | 5.06 GB | 7.56 GB | Original llama.cpp quant method, 5-bit. Even higher accuracy, resource usage and slower inference. |
| tulu-7b.ggmlv3.q5_K_M.bin | q5_K_M | 5 | 4.77 GB | 7.27 GB | New k-quant method. Uses GGML_TYPE_Q6_K for half of the attention.wv and feed_forward.w2 tensors, else GGML_TYPE_Q5_K |
| tulu-7b.ggmlv3.q5_K_S.bin | q5_K_S | 5 | 4.63 GB | 7.13 GB | New k-quant method. Uses GGML_TYPE_Q5_K for all tensors |
| tulu-7b.ggmlv3.q6_K.bin | q6_K | 6 | 5.53 GB | 8.03 GB | New k-quant method. Uses GGML_TYPE_Q8_K - 6-bit quantization - for all tensors |
| tulu-7b.ggmlv3.q8_0.bin | q8_0 | 8 | 7.16 GB | 9.66 GB | Original llama.cpp quant method, 8-bit. Almost indistinguishable from float16. High resource use and slow. Not recommended for most users. |
**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.
## How to run in `llama.cpp`
I use the following command line; adjust for your tastes and needs:
```
./main -t 10 -ngl 32 -m tulu-7b.ggmlv3.q5_0.bin --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<|user|>\nprompt goes here\n<|assistant|>\n"
```
Change `-t 10` to the number of physical CPU cores you have. For example if your system has 8 cores/16 threads, use `-t 8`.
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`
## How to run in `text-generation-webui`
Further instructions here: [text-generation-webui/docs/llama.cpp-models.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp-models.md).
<!-- footer start -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/Jq4vkcDakD)
## Thanks, and how to contribute.
Thanks to the [chirper.ai](https://chirper.ai) team!
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**: Luke from CarbonQuill, Aemon Algiz, Dmitriy Samsonov.
**Patreon special mentions**: Oscar Rangel, Eugene Pentland, Talal Aujan, Cory Kujawski, Luke, Asp the Wyvern, Ai Maven, Pyrater, Alps Aficionado, senxiiz, Willem Michiel, Junyu Yang, trip7s trip, Sebastain Graf, Joseph William Delisle, Lone Striker, Jonathan Leane, Johann-Peter Hartmann, David Flickinger, Spiking Neurons AB, Kevin Schuppel, Mano Prime, Dmitriy Samsonov, Sean Connelly, Nathan LeClaire, Alain Rossmann, Fen Risland, Derek Yates, Luke Pendergrass, Nikolai Manek, Khalefa Al-Ahmad, Artur Olbinski, John Detwiler, Ajan Kanaga, Imad Khwaja, Trenton Dambrowitz, Kalila, vamX, webtim, Illia Dulskyi.
Thank you to all my generous patrons and donaters!
<!-- footer end -->
# Original model card: Allen AI's Tulu 7B
# Tulu 7B
This model is a 7B LLaMa model finetuned on a mixture of instruction datasets (FLAN V2, CoT, Dolly, Open Assistant 1, GPT4-Alpaca, Code-Alpaca, and ShareGPT).
*Please note this is a model diff - see below for usage instructions*.
This was trained as part of the paper [How Far Can Camels Go? Exploring the State of Instruction Tuning on Open Resources](https://arxiv.org/abs/2306.04751).
The codebase used to train and evaluate this model can be found at [https://github.com/allenai/open-instruct](https://github.com/allenai/open-instruct).
This model is licensed under the AI model license given in LICENSE.txt along with the original Llama license (llama_license.txt).
## Usage
We assume you have access to a LLaMa model in HF format already. You can find details on getting access and converting the model here:
[https://huggingface.co/docs/transformers/main/model_doc/llama](https://huggingface.co/docs/transformers/main/model_doc/llama)
Clone [https://github.com/allenai/open-instruct](https://github.com/allenai/open-instruct) and install the required dependencies, or just copy `scripts/weight_diff.py`
and install the minimal requirements listed in `weight-diff-requirements.txt`. Then download or clone this model diff to the same machine.
Then, run:
```bash
python scripts/weight_diff.py recover --path_raw ${hf_llama_path} --path_tuned ${output_path} --path_diff ${diff_location}
```
And you will have a recovered model! Note this takes up a decent amount of RAM, especially for the larger models.
## Input Format
The model is trained to use the following format (note the newlines):
```
<|user|>
Your message here!
<|assistant|>
```
For best results, format all inputs in this manner.
## Performance
Here is the performance of this model across benchmarks explored in our paper [How Far Can Camels Go? Exploring the State of Instruction Tuning on Open Resources](https://arxiv.org/abs/2306.04751):
| MMLU 0-shot | MMLU 5-shot | GSM Direct | GSM CoT | BBH Direct | BBH CoT | TydiQA Gold-Passage | TydiQA Closed-book | Codex-Eval Pass@1 | Codex-Eval Pass@10 | AlpacaFarm vs Davinci-003 | Average |
|:-----------:|:-----------:|:----------:|:-------:|:----------:|:-------:|:-------------------:|:------------------:|:-----------------:|:------------------:|:-------------------------:|---------|
| 44.5 | 47.0 | 6.0 | 27.0 | 38.1 | 39.2 | 45.7 | 7.7 | 17.5 | 27.8 | 48.3 | 33.1 |
If you use this model, please cite our work, the llama paper, and the original datasets:
```
@misc{wang2023far,
title={How Far Can Camels Go? Exploring the State of Instruction Tuning on Open Resources},
author={Yizhong Wang and Hamish Ivison and Pradeep Dasigi and Jack Hessel and Tushar Khot and Khyathi Raghavi Chandu and David Wadden and Kelsey MacMillan and Noah A. Smith and Iz Beltagy and Hannaneh Hajishirzi},
year={2023},
eprint={2306.04751},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
```
@misc{touvron2023llama,
title={LLaMA: Open and Efficient Foundation Language Models},
author={Hugo Touvron and Thibaut Lavril and Gautier Izacard and Xavier Martinet and Marie-Anne Lachaux and Timothée Lacroix and Baptiste Rozière and Naman Goyal and Eric Hambro and Faisal Azhar and Aurelien Rodriguez and Armand Joulin and Edouard Grave and Guillaume Lample},
year={2023},
eprint={2302.13971},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
```
@misc{dolly,
author = {Databricks},
title = {Free Dolly: Introducing the World's First Truly Open Instruction-Tuned LLM},
year = {2023},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {Blog post},
url = {https://www.databricks.com/blog/2023/04/12/dolly-first-open-commercially-viable-instruction-tuned-llm}
}
```
```
@article{longpre2023flan,
title={The Flan Collection: Designing Data and Methods for Effective Instruction Tuning},
author={Longpre, Shayne and Hou, Le and Vu, Tu and Webson, Albert and Chung, Hyung Won and Tay, Yi and Zhou, Denny and Le, Quoc V and Zoph, Barret and Wei, Jason and others},
journal={arXiv preprint arXiv:2301.13688},
year={2023}
}
```
```
@misc{köpf2023openassistant,
title={OpenAssistant Conversations -- Democratizing Large Language Model Alignment},
author={Andreas Köpf and Yannic Kilcher and Dimitri von Rütte and Sotiris Anagnostidis and Zhi-Rui Tam and Keith Stevens and Abdullah Barhoum and Nguyen Minh Duc and Oliver Stanley and Richárd Nagyfi and Shahul ES and Sameer Suri and David Glushkov and Arnav Dantuluri and Andrew Maguire and Christoph Schuhmann and Huu Nguyen and Alexander Mattick},
year={2023},
eprint={2304.07327},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
```
@article{peng2023instruction,
title={Instruction Tuning with GPT-4},
author={Peng, Baolin and Li, Chunyuan and He, Pengcheng and Galley, Michel and Gao, Jianfeng},
journal={arXiv preprint arXiv:2304.03277},
year={2023}
}
```
```
@misc{codealpaca,
author = {Sahil Chaudhary},
title = {Code Alpaca: An Instruction-following LLaMA model for code generation},
year = {2023},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/sahil280114/codealpaca}},
}
```
|
TheBloke/tulu-13B-fp16
|
TheBloke
| 2023-06-13T20:02:38Z | 1,578 | 2 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"en",
"dataset:databricks/databricks-dolly-15k",
"dataset:OpenAssistant/oasst1",
"dataset:sahil2801/CodeAlpaca-20k",
"arxiv:2306.04751",
"arxiv:2302.13971",
"arxiv:2301.13688",
"arxiv:2304.07327",
"arxiv:2304.03277",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-06-10T22:53:01Z |
---
license: other
inference: true
datasets:
- databricks/databricks-dolly-15k
- OpenAssistant/oasst1
- sahil2801/CodeAlpaca-20k
language:
- en
---
<!-- header start -->
<div style="width: 100%;">
<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><a href="https://discord.gg/Jq4vkcDakD">Chat & support: my new Discord server</a></p>
</div>
<div style="display: flex; flex-direction: column; align-items: flex-end;">
<p><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
</div>
</div>
<!-- header end -->
# Allen AI's Tulu 13B fp16
These files are pytorch format fp16 model files for [Allen AI's Tulu 13B](https://huggingface.co/allenai/tulu-13b).
It is the result of merging and/or converting the source repository to float16.
## Repositories available
* [4-bit GPTQ models for GPU inference](https://huggingface.co/TheBloke/tulu-13B-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference](https://huggingface.co/TheBloke/tulu-13B-GGML)
* [Unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/TheBloke/tulu-13B-fp16)
## Prompt template
The following template should be used:
```
<|user|>
prompt goes here
<|assistant|>
```
**Note**: There should be a newline after `<|assistant|>`. This appears to be very important for getting this model to respond correctly.
In other words, the prompt is:
```
<|user|>\nprompt goes here\n<|assistant|>\n
```
<!-- footer start -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/Jq4vkcDakD)
## Thanks, and how to contribute.
Thanks to the [chirper.ai](https://chirper.ai) team!
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**: Luke from CarbonQuill, Aemon Algiz, Dmitriy Samsonov.
**Patreon special mentions**: Oscar Rangel, Eugene Pentland, Talal Aujan, Cory Kujawski, Luke, Asp the Wyvern, Ai Maven, Pyrater, Alps Aficionado, senxiiz, Willem Michiel, Junyu Yang, trip7s trip, Sebastain Graf, Joseph William Delisle, Lone Striker, Jonathan Leane, Johann-Peter Hartmann, David Flickinger, Spiking Neurons AB, Kevin Schuppel, Mano Prime, Dmitriy Samsonov, Sean Connelly, Nathan LeClaire, Alain Rossmann, Fen Risland, Derek Yates, Luke Pendergrass, Nikolai Manek, Khalefa Al-Ahmad, Artur Olbinski, John Detwiler, Ajan Kanaga, Imad Khwaja, Trenton Dambrowitz, Kalila, vamX, webtim, Illia Dulskyi.
Thank you to all my generous patrons and donaters!
<!-- footer end -->
# Original model card: Allen AI's Tulu 13B
# Tulu 13B
This model is a 13B LLaMa model finetuned on a mixture of instruction datasets (FLAN V2, CoT, Dolly, Open Assistant 1, GPT4-Alpaca, Code-Alpaca, and ShareGPT).
*Please note this is a model diff - see below for usage instructions*.
This was trained as part of the paper [How Far Can Camels Go? Exploring the State of Instruction Tuning on Open Resources](https://arxiv.org/abs/2306.04751).
The codebase used to train and evaluate this model can be found at [https://github.com/allenai/open-instruct](https://github.com/allenai/open-instruct).
This model is licensed under the AI model license given in LICENSE.txt along with the original Llama license (llama_license.txt).
## Usage
We assume you have access to a LLaMa model in HF format already. You can find details on getting access and converting the model here:
[https://huggingface.co/docs/transformers/main/model_doc/llama](https://huggingface.co/docs/transformers/main/model_doc/llama)
Clone [https://github.com/allenai/open-instruct](https://github.com/allenai/open-instruct) and install the required dependencies, or just copy `scripts/weight_diff.py`
and install the minimal requirements listed in `weight-diff-requirements.txt`. Then download or clone this model diff to the same machine.
Then, run:
```bash
python scripts/weight_diff.py recover --path_raw ${hf_llama_path} --path_tuned ${output_path} --path_diff ${diff_location}
```
And you will have a recovered model! Note this takes up a decent amount of RAM, especially for the larger models.
## Input Format
The model is trained to use the following format (note the newlines):
```
<|user|>
Your message here!
<|assistant|>
```
For best results, format all inputs in this manner.
## Performance
Here is the performance of this model across benchmarks explored in our paper [How Far Can Camels Go? Exploring the State of Instruction Tuning on Open Resources](https://arxiv.org/abs/2306.04751):
| MMLU 0-shot | MMLU 5-shot | GSM Direct | GSM CoT | BBH Direct | BBH CoT | TydiQA Gold-Passage | TydiQA Closed-book | Codex-Eval Pass@1 | Codex-Eval Pass@10 | AlpacaFarm vs Davinci-003 | Average |
|:-----------:|:-----------:|:----------:|:-------:|:----------:|:-------:|:-------------------:|:------------------:|:-----------------:|:------------------:|:-------------------------:|---------|
| 49.2 | 51.8 | 5.0 | 36.5 | 41.3 | 42.8 | 46.1 | 9.2 | 21.3 | 35.0 | 53.9 |37.2 |
If you use this model, please cite our work, the llama paper, and the original datasets:
```
@misc{wang2023far,
title={How Far Can Camels Go? Exploring the State of Instruction Tuning on Open Resources},
author={Yizhong Wang and Hamish Ivison and Pradeep Dasigi and Jack Hessel and Tushar Khot and Khyathi Raghavi Chandu and David Wadden and Kelsey MacMillan and Noah A. Smith and Iz Beltagy and Hannaneh Hajishirzi},
year={2023},
eprint={2306.04751},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
```
@misc{touvron2023llama,
title={LLaMA: Open and Efficient Foundation Language Models},
author={Hugo Touvron and Thibaut Lavril and Gautier Izacard and Xavier Martinet and Marie-Anne Lachaux and Timothée Lacroix and Baptiste Rozière and Naman Goyal and Eric Hambro and Faisal Azhar and Aurelien Rodriguez and Armand Joulin and Edouard Grave and Guillaume Lample},
year={2023},
eprint={2302.13971},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
```
@misc{dolly,
author = {Databricks},
title = {Free Dolly: Introducing the World's First Truly Open Instruction-Tuned LLM},
year = {2023},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {Blog post},
url = {https://www.databricks.com/blog/2023/04/12/dolly-first-open-commercially-viable-instruction-tuned-llm}
}
```
```
@article{longpre2023flan,
title={The Flan Collection: Designing Data and Methods for Effective Instruction Tuning},
author={Longpre, Shayne and Hou, Le and Vu, Tu and Webson, Albert and Chung, Hyung Won and Tay, Yi and Zhou, Denny and Le, Quoc V and Zoph, Barret and Wei, Jason and others},
journal={arXiv preprint arXiv:2301.13688},
year={2023}
}
```
```
@misc{köpf2023openassistant,
title={OpenAssistant Conversations -- Democratizing Large Language Model Alignment},
author={Andreas Köpf and Yannic Kilcher and Dimitri von Rütte and Sotiris Anagnostidis and Zhi-Rui Tam and Keith Stevens and Abdullah Barhoum and Nguyen Minh Duc and Oliver Stanley and Richárd Nagyfi and Shahul ES and Sameer Suri and David Glushkov and Arnav Dantuluri and Andrew Maguire and Christoph Schuhmann and Huu Nguyen and Alexander Mattick},
year={2023},
eprint={2304.07327},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
```
@article{peng2023instruction,
title={Instruction Tuning with GPT-4},
author={Peng, Baolin and Li, Chunyuan and He, Pengcheng and Galley, Michel and Gao, Jianfeng},
journal={arXiv preprint arXiv:2304.03277},
year={2023}
}
```
```
@misc{codealpaca,
author = {Sahil Chaudhary},
title = {Code Alpaca: An Instruction-following LLaMA model for code generation},
year = {2023},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/sahil280114/codealpaca}},
}
```
|
MrPiotrek/wiking
|
MrPiotrek
| 2023-06-13T19:54:03Z | 0 | 0 | null |
[
"license:bigscience-bloom-rail-1.0",
"region:us"
] | null | 2023-06-13T19:54:03Z |
---
license: bigscience-bloom-rail-1.0
---
|
iamnambiar/ppo-Huggy
|
iamnambiar
| 2023-06-13T19:51:37Z | 8 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2023-06-13T19:51:33Z |
---
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: iamnambiar/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
facebook/convnext-base-224-22k
|
facebook
| 2023-06-13T19:41:22Z | 2,398 | 5 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"convnext",
"image-classification",
"vision",
"dataset:imagenet-21k",
"arxiv:2201.03545",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- vision
- image-classification
datasets:
- imagenet-21k
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
example_title: Palace
---
# ConvNeXT (base-sized model)
ConvNeXT model trained on ImageNet-22k at resolution 224x224. It was introduced in the paper [A ConvNet for the 2020s](https://arxiv.org/abs/2201.03545) by Liu et al. and first released in [this repository](https://github.com/facebookresearch/ConvNeXt).
Disclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
ConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them. The authors started from a ResNet and "modernized" its design by taking the Swin Transformer as inspiration.
## Intended uses & limitations
You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=convnext) to look for
fine-tuned versions on a task that interests you.
### How to use
Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
```python
from transformers import ConvNextImageProcessor, ConvNextForImageClassification
import torch
from datasets import load_dataset
dataset = load_dataset("huggingface/cats-image")
image = dataset["test"]["image"][0]
processor = ConvNextImageProcessor.from_pretrained("facebook/convnext-base-224-22k")
model = ConvNextForImageClassification.from_pretrained("facebook/convnext-base-224-22k")
inputs = processor(image, return_tensors="pt")
with torch.no_grad():
logits = model(**inputs).logits
# model predicts one of the 22k ImageNet classes
predicted_label = logits.argmax(-1).item()
print(model.config.id2label[predicted_label]),
```
For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/convnext).
### BibTeX entry and citation info
```bibtex
@article{DBLP:journals/corr/abs-2201-03545,
author = {Zhuang Liu and
Hanzi Mao and
Chao{-}Yuan Wu and
Christoph Feichtenhofer and
Trevor Darrell and
Saining Xie},
title = {A ConvNet for the 2020s},
journal = {CoRR},
volume = {abs/2201.03545},
year = {2022},
url = {https://arxiv.org/abs/2201.03545},
eprinttype = {arXiv},
eprint = {2201.03545},
timestamp = {Thu, 20 Jan 2022 14:21:35 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-2201-03545.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
|
facebook/convnext-xlarge-384-22k-1k
|
facebook
| 2023-06-13T19:40:50Z | 304 | 4 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"convnext",
"image-classification",
"vision",
"dataset:imagenet-21k",
"dataset:imagenet-1k",
"arxiv:2201.03545",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- vision
- image-classification
datasets:
- imagenet-21k
- imagenet-1k
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
example_title: Palace
---
# ConvNeXT (xlarge-sized model)
ConvNeXT model pre-trained on ImageNet-22k and fine-tuned on ImageNet-1k at resolution 384x384. It was introduced in the paper [A ConvNet for the 2020s](https://arxiv.org/abs/2201.03545) by Liu et al. and first released in [this repository](https://github.com/facebookresearch/ConvNeXt).
Disclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
ConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them. The authors started from a ResNet and "modernized" its design by taking the Swin Transformer as inspiration.
## Intended uses & limitations
You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=convnext) to look for
fine-tuned versions on a task that interests you.
### How to use
Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
```python
from transformers import ConvNextImageProcessor, ConvNextForImageClassification
import torch
from datasets import load_dataset
dataset = load_dataset("huggingface/cats-image")
image = dataset["test"]["image"][0]
processor = ConvNextImageProcessor.from_pretrained("facebook/convnext-xlarge-384-22k-1k")
model = ConvNextForImageClassification.from_pretrained("facebook/convnext-xlarge-384-22k-1k")
inputs = processor(image, return_tensors="pt")
with torch.no_grad():
logits = model(**inputs).logits
# model predicts one of the 1000 ImageNet classes
predicted_label = logits.argmax(-1).item()
print(model.config.id2label[predicted_label]),
```
For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/convnext).
### BibTeX entry and citation info
```bibtex
@article{DBLP:journals/corr/abs-2201-03545,
author = {Zhuang Liu and
Hanzi Mao and
Chao{-}Yuan Wu and
Christoph Feichtenhofer and
Trevor Darrell and
Saining Xie},
title = {A ConvNet for the 2020s},
journal = {CoRR},
volume = {abs/2201.03545},
year = {2022},
url = {https://arxiv.org/abs/2201.03545},
eprinttype = {arXiv},
eprint = {2201.03545},
timestamp = {Thu, 20 Jan 2022 14:21:35 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-2201-03545.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
|
facebook/convnext-base-224
|
facebook
| 2023-06-13T19:40:09Z | 9,032 | 9 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"convnext",
"image-classification",
"vision",
"dataset:imagenet-1k",
"arxiv:2201.03545",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- vision
- image-classification
datasets:
- imagenet-1k
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
example_title: Palace
---
# ConvNeXT (base-sized model)
ConvNeXT model trained on ImageNet-1k at resolution 224x224. It was introduced in the paper [A ConvNet for the 2020s](https://arxiv.org/abs/2201.03545) by Liu et al. and first released in [this repository](https://github.com/facebookresearch/ConvNeXt).
Disclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
ConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them. The authors started from a ResNet and "modernized" its design by taking the Swin Transformer as inspiration.
## Intended uses & limitations
You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=convnext) to look for
fine-tuned versions on a task that interests you.
### How to use
Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
```python
from transformers import ConvNextImageProcessor, ConvNextForImageClassification
import torch
from datasets import load_dataset
dataset = load_dataset("huggingface/cats-image")
image = dataset["test"]["image"][0]
processor = ConvNextImageProcessor.from_pretrained("facebook/convnext-base-224")
model = ConvNextForImageClassification.from_pretrained("facebook/convnext-base-224")
inputs = processor(image, return_tensors="pt")
with torch.no_grad():
logits = model(**inputs).logits
# model predicts one of the 1000 ImageNet classes
predicted_label = logits.argmax(-1).item()
print(model.config.id2label[predicted_label]),
```
For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/convnext).
### BibTeX entry and citation info
```bibtex
@article{DBLP:journals/corr/abs-2201-03545,
author = {Zhuang Liu and
Hanzi Mao and
Chao{-}Yuan Wu and
Christoph Feichtenhofer and
Trevor Darrell and
Saining Xie},
title = {A ConvNet for the 2020s},
journal = {CoRR},
volume = {abs/2201.03545},
year = {2022},
url = {https://arxiv.org/abs/2201.03545},
eprinttype = {arXiv},
eprint = {2201.03545},
timestamp = {Thu, 20 Jan 2022 14:21:35 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-2201-03545.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
|
facebook/convnext-large-224
|
facebook
| 2023-06-13T19:39:50Z | 37,514 | 26 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"convnext",
"image-classification",
"vision",
"dataset:imagenet-1k",
"arxiv:2201.03545",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- vision
- image-classification
datasets:
- imagenet-1k
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
example_title: Palace
---
# ConvNeXT (large-sized model)
ConvNeXT model trained on ImageNet-1k at resolution 224x224. It was introduced in the paper [A ConvNet for the 2020s](https://arxiv.org/abs/2201.03545) by Liu et al. and first released in [this repository](https://github.com/facebookresearch/ConvNeXt).
Disclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
ConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them. The authors started from a ResNet and "modernized" its design by taking the Swin Transformer as inspiration.
## Intended uses & limitations
You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=convnext) to look for
fine-tuned versions on a task that interests you.
### How to use
Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
```python
from transformers import ConvNextImageProcessor, ConvNextForImageClassification
import torch
from datasets import load_dataset
dataset = load_dataset("huggingface/cats-image")
image = dataset["test"]["image"][0]
processor = ConvNextImageProcessor.from_pretrained("facebook/convnext-large-224")
model = ConvNextForImageClassification.from_pretrained("facebook/convnext-large-224")
inputs = processor(image, return_tensors="pt")
with torch.no_grad():
logits = model(**inputs).logits
# model predicts one of the 1000 ImageNet classes
predicted_label = logits.argmax(-1).item()
print(model.config.id2label[predicted_label]),
```
For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/convnext).
### BibTeX entry and citation info
```bibtex
@article{DBLP:journals/corr/abs-2201-03545,
author = {Zhuang Liu and
Hanzi Mao and
Chao{-}Yuan Wu and
Christoph Feichtenhofer and
Trevor Darrell and
Saining Xie},
title = {A ConvNet for the 2020s},
journal = {CoRR},
volume = {abs/2201.03545},
year = {2022},
url = {https://arxiv.org/abs/2201.03545},
eprinttype = {arXiv},
eprint = {2201.03545},
timestamp = {Thu, 20 Jan 2022 14:21:35 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-2201-03545.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
|
peteozegov/ppo-CartPole-v1
|
peteozegov
| 2023-06-13T19:39:27Z | 0 | 0 | null |
[
"tensorboard",
"CartPole-v1",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-course",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-13T19:39:21Z |
---
tags:
- CartPole-v1
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-course
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 183.40 +/- 115.68
name: mean_reward
verified: false
---
# PPO Agent Playing CartPole-v1
This is a trained model of a PPO agent playing CartPole-v1.
# Hyperparameters
```python
{'f': '/root/.local/share/jupyter/runtime/kernel-bf3127e1-7483-4925-b541-30f385e37474.json'
'exp_name': '__file__'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': False
'env_id': 'CartPole-v1'
'total_timesteps': 50000
'learning_rate': 0.00025
'num_envs': 4
'num_steps': 128
'anneal_lr': True
'gae': True
'gamma': 0.99
'gae_lambda': 0.95
'num_minibatches': 4
'update_epochs': 4
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'repo_id': 'peteozegov/ppo-CartPole-v1'
'batch_size': 512
'minibatch_size': 128}
```
|
VineetTambe/ppo-Huggy
|
VineetTambe
| 2023-06-13T19:23:30Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2023-06-13T18:42:21Z |
---
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: VineetTambe/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Gayathri142214002/Pegasus_paraphraser
|
Gayathri142214002
| 2023-06-13T19:07:41Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-06-13T06:54:46Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: pegasus_paraphraser
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. -->
# pegasus_paraphraser
This model is a fine-tuned version of [tuner007/pegasus_paraphrase](https://huggingface.co/tuner007/pegasus_paraphrase) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3852
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 7
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.8224 | 0.19 | 10 | 1.2976 |
| 1.3213 | 0.37 | 20 | 1.2302 |
| 1.2333 | 0.56 | 30 | 1.2068 |
| 1.1097 | 0.74 | 40 | 1.1991 |
| 1.3741 | 0.93 | 50 | 1.2260 |
| 1.0593 | 1.12 | 60 | 1.2005 |
| 1.1234 | 1.3 | 70 | 1.2103 |
| 1.0116 | 1.49 | 80 | 1.2098 |
| 0.8591 | 1.67 | 90 | 1.1709 |
| 0.9176 | 1.86 | 100 | 1.1830 |
| 0.7524 | 2.05 | 110 | 1.2122 |
| 0.7762 | 2.23 | 120 | 1.2398 |
| 0.677 | 2.42 | 130 | 1.2440 |
| 0.8364 | 2.6 | 140 | 1.2356 |
| 0.7489 | 2.79 | 150 | 1.2542 |
| 0.7113 | 2.98 | 160 | 1.2678 |
| 0.5462 | 3.16 | 170 | 1.3100 |
| 0.6775 | 3.35 | 180 | 1.3193 |
| 0.6417 | 3.53 | 190 | 1.3157 |
| 0.547 | 3.72 | 200 | 1.3172 |
| 0.5357 | 3.91 | 210 | 1.3311 |
| 0.6796 | 4.09 | 220 | 1.3236 |
| 0.4884 | 4.28 | 230 | 1.3288 |
| 0.483 | 4.47 | 240 | 1.3423 |
| 0.667 | 4.65 | 250 | 1.3702 |
| 0.5785 | 4.84 | 260 | 1.3817 |
| 0.6123 | 5.02 | 270 | 1.3728 |
| 0.4735 | 5.21 | 280 | 1.3731 |
| 0.5278 | 5.4 | 290 | 1.3783 |
| 0.5393 | 5.58 | 300 | 1.3904 |
| 0.4631 | 5.77 | 310 | 1.3884 |
| 0.4538 | 5.95 | 320 | 1.3800 |
| 0.5137 | 6.14 | 330 | 1.3766 |
| 0.5514 | 6.33 | 340 | 1.3815 |
| 0.4629 | 6.51 | 350 | 1.3849 |
| 0.5013 | 6.7 | 360 | 1.3855 |
| 0.4566 | 6.88 | 370 | 1.3852 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
tzmtwtr/albert-embedding-ja
|
tzmtwtr
| 2023-06-13T19:07:13Z | 23 | 1 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"albert",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2023-06-13T18:12:45Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
日本語のSentence Embedding用モデル
以下のモデルから転移学習を実施。
https://huggingface.co/ken11/albert-base-japanese-v1-with-japanese-tokenizer
学習データには以下を使用。
https://huggingface.co/datasets/tzmtwtr/tw-posts-ja
# モチベーション
ベクトル検索のために小規模言語モデルが必要になった。
AWS Lambdaで動かせるようにしたい。
|
uuiduu/HELA
|
uuiduu
| 2023-06-13T18:54:45Z | 0 | 0 | null |
[
"license:cc-by-nc-sa-4.0",
"region:us"
] | null | 2023-06-13T18:54:45Z |
---
license: cc-by-nc-sa-4.0
---
|
rami8k/Reinforce-CartPole-v1
|
rami8k
| 2023-06-13T18:51:47Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-09T00:08:50Z |
---
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: 500.00 +/- 0.00
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
|
Yuyu12347/Tohkalora
|
Yuyu12347
| 2023-06-13T18:49:41Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-06-13T18:45:17Z |
---
license: creativeml-openrail-m
---
|
TurboSosiska/neco-arc
|
TurboSosiska
| 2023-06-13T18:47:16Z | 0 | 0 | null |
[
"art",
"dataset:TurboSosiska/neco-arc-dataset",
"region:us"
] | null | 2023-06-13T18:22:02Z |
---
datasets:
- TurboSosiska/neco-arc-dataset
tags:
- art
---
# Neco Arc LoRA
LoRA trained on pictures from Danbooru by tag **neko-arc**
This thing definitely needs to be trained more
[download](https://huggingface.co/TurboSosiska/neco-arc/resolve/main/output/neco-arc.safetensors)
## Training
Steps: ~1800
Model used: Waifu diffusion v1.3
## Examples
masterpiece, best quality, standing, solo, <lora:neco-arc:1.2>, looking at viewer, blonde_hair, cat ears, red eyes,
|
Todmy/ppo-Huggy
|
Todmy
| 2023-06-13T18:44:43Z | 12 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2023-06-13T18:38:48Z |
---
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: Todmy/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
bucuralexandra/SwinT
|
bucuralexandra
| 2023-06-13T18:29:49Z | 218 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"swin",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-06-11T00:52:13Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: SwinT
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: 0.9156268568033273
---
<!-- 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. -->
# SwinT
This model is a fine-tuned version of [bucuralexandra/SwinT](https://huggingface.co/bucuralexandra/SwinT) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2254
- Accuracy: 0.9156
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3285 | 1.0 | 118 | 0.2617 | 0.9026 |
| 0.2934 | 2.0 | 237 | 0.2401 | 0.9061 |
| 0.2963 | 3.0 | 355 | 0.2417 | 0.9079 |
| 0.305 | 4.0 | 474 | 0.2318 | 0.9127 |
| 0.2607 | 5.0 | 592 | 0.2703 | 0.9085 |
| 0.268 | 5.97 | 708 | 0.2254 | 0.9156 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
VineetTambe/ppo-LunarLander-v2
|
VineetTambe
| 2023-06-13T18:27:24Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-13T18:27:07Z |
---
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: 264.44 +/- 14.89
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
...
```
|
ugiugi/inisw08-RoBERT-mlm-lion_8bit
|
ugiugi
| 2023-06-13T18:24:37Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-06-13T15:33:11Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: inisw08-RoBERT-mlm-lion_8bit
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. -->
# inisw08-RoBERT-mlm-lion_8bit
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 7.5985
- Accuracy: 0.0339
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.31.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
hopkins/strict-small-3h
|
hopkins
| 2023-06-13T18:09:32Z | 133 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"dataset:generator",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-06-12T15:55:59Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- generator
model-index:
- name: strict-small-3h
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. -->
# strict-small-3h
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 512
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.30.1
- Pytorch 2.0.1
- Datasets 2.12.0
- Tokenizers 0.13.3
|
pminhyung12/gpt2-base-v0
|
pminhyung12
| 2023-06-13T18:05:19Z | 62 | 0 |
transformers
|
[
"transformers",
"tf",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"dataset:corpus_v1",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-06-11T15:01:33Z |
---
license: mit
tags:
- generated_from_keras_callback
datasets:
- corpus_v1
model-index:
- name: pminhyung12/gpt2-base-v0
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. -->
# pminhyung12/gpt2-base-v0
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the corpus_v1 dataset.
It achieves the following results on the evaluation set:
- Train Loss: 5.1058
- Validation Loss: 6.2552
- Epoch: 7
## 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': 'AdamW', 'weight_decay': 0.004, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 1e-04, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: mixed_bfloat16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 0.9128 | 0.9515 | 0 |
| 0.6172 | 0.8965 | 1 |
| 0.5713 | 0.8763 | 2 |
| 1.2088 | 1.8300 | 3 |
| 3.6887 | 5.8700 | 4 |
| 2.8920 | 3.6422 | 5 |
| 3.8045 | 8.9555 | 6 |
| 5.1058 | 6.2552 | 7 |
### Framework versions
- Transformers 4.26.1
- TensorFlow 2.12.0
- Datasets 2.9.0
- Tokenizers 0.13.2
|
ramyakeerthyt/t5-small-finetuned
|
ramyakeerthyt
| 2023-06-13T17:46:24Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-06-13T16:32:34Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: t5-small-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. -->
# t5-small-finetuned
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0835
## 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: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.6962 | 1.0 | 7883 | 0.0835 |
### Framework versions
- Transformers 4.30.1
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
fastrolling/uvr
|
fastrolling
| 2023-06-13T17:35:10Z | 0 | 5 | null |
[
"onnx",
"region:us"
] | null | 2023-06-13T13:35:33Z |
## Ultimate Vocal Remover Pretrained Models
```
uvr/
├── Demucs_Models
│ ├── 14fc6a69-a89dd0ee.th
│ ├── 464b36d7-e5a9386e.th
│ ├── 5d2d6c55-db83574e.th
│ ├── 7fd6ef75-a905dd85.th
│ ├── 83fc094f-4a16d450.th
│ ├── a1d90b5c-ae9d2452.th
│ ├── cfa93e08-61801ae1.th
│ ├── e51eebcc-c1b80bdd.th
│ ├── ebf34a2db.th
│ ├── ebf34a2d.th
│ ├── mdx_extra_q.yaml
│ ├── mdx_extra.yaml
│ ├── UVR_Demucs_Model_1.yaml
│ ├── UVR_Demucs_Model_2.yaml
│ └── UVR_Demucs_Model_Bag.yaml
├── Main_Models
│ ├── 1_HP-UVR.pth
│ ├── 2_HP-UVR.pth
│ ├── 3_HP-Vocal-UVR.pth
│ ├── 4_HP-Vocal-UVR.pth
│ └── 5_HP-Karaoke-UVR.pth
└── MDX_Net_Models
├── UVR_MDXNET_1_9703.onnx
├── UVR_MDXNET_2_9682.onnx
├── UVR_MDXNET_3_9662.onnx
└── UVR_MDXNET_KARA.onnx
```
|
Benned/AnnaYamada
|
Benned
| 2023-06-13T17:16:58Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-06-13T17:15:09Z |
---
license: creativeml-openrail-m
---
|
squarelike/polyglot1.3B-ko-chatbot-classification-LoRA
|
squarelike
| 2023-06-13T16:39:06Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2023-06-07T16:55:20Z |
---
license: apache-2.0
---
Trained polyglot 1.3B with the QLORA method using the [Chatbot_data_for_Korean](https://github.com/songys/Chatbot_data) dataset.
The hyper-parameters used for training are as follows.
- batch-size: 16
- max_steps: 3000
- Learning rate: 3e-4
- Lora r: 8
- Lora target modules: query_key_value
Prompt Template:
```
### 질문: {문장}
### 응답: {문장}
### 유형: {일반 또는 연애}
```
|
squarelike/polyglot1.3B-ko-nsmc-classification-LoRA
|
squarelike
| 2023-06-13T16:35:20Z | 0 | 1 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2023-06-06T16:14:52Z |
---
license: apache-2.0
---
Trained polyglot 1.3B with the QLORA method using the [nsmc](https://github.com/e9t/nsmc) dataset.
The hyper-parameters used for training are as follows.
- batch-size: 16
- max_steps: 10000
- Learning rate: 3e-4
- Lora r: 8
- Lora target modules: query_key_value
Prompt Template:
```
### 문장: {문장}
### 감정: {긍정 또는 부정}
```
|
mamun4105/reinforce-cartpole-v1
|
mamun4105
| 2023-06-13T16:10:02Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-13T16:03:42Z |
---
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: 500.00 +/- 0.00
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
|
vhahvhah/my_Portugalian_model
|
vhahvhah
| 2023-06-13T16:08:00Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:opus_books",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-06-07T17:13:02Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- opus_books
metrics:
- bleu
model-index:
- name: my_Portugalian_model
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: opus_books
type: opus_books
config: en-pt
split: train
args: en-pt
metrics:
- name: Bleu
type: bleu
value: 0.8381
---
<!-- 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_Portugalian_model
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the opus_books dataset.
It achieves the following results on the evaluation set:
- Loss: 3.5905
- Bleu: 0.8381
- Gen Len: 17.1601
## 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: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|
| No log | 1.0 | 71 | 3.6818 | 0.8636 | 17.0356 |
| No log | 2.0 | 142 | 3.5905 | 0.8381 | 17.1601 |
### Framework versions
- Transformers 4.30.1
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
factored/distilbert-fr-explorer-classification
|
factored
| 2023-06-13T15:58:01Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-04-19T23:14:16Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilbert-fr-explorer-classification
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-fr-explorer-classification
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu117
- Datasets 2.11.0
- Tokenizers 0.13.2
|
ibm-research/otter_ubc_distmult
|
ibm-research
| 2023-06-13T15:51:59Z | 0 | 2 | null |
[
"dataset:ibm/otter_uniprot_bindingdb_chembl",
"license:mit",
"region:us"
] | null | 2023-06-12T09:45:26Z |
---
license: mit
inference: false
datasets:
- ibm/otter_uniprot_bindingdb_chembl
---
# Otter UBC DistMult Model Card
## Model details
Otter models are based on Graph Neural Networks (GNN) that propagates initial embeddings through a set of layers that upgrade input embedding according to the node neighbours.
The architecture of GNN consists of two main blocks: encoder and decoder.
- For encoder we first define a projection layer which consists of a set of linear transformations for each node modality and projects nodes into common dimensionality, then we apply several multi-relational graph convolutional layers (R-GCN) which distinguish between different types of edges between source and target nodes by having a set of trainable parameters for each edge type.
- For decoder we consider link prediction task, which consists of a scoring function that maps each triple of source and target nodes and the corresponding edge and maps that to a scalar number defined over interval [0; 1].
**Model type:**
For link prediction, we consider three choices of scoring functions: DistMult, TransE and a Binary Classifier that are commonly used in the literature. The outcomes of scoring of each triple are then compared against actual labels using negative log likelihood loss function.
- Flow control: One crucial aspect of pretraining the GNN involves addressing the disparity between the data accessible during pretraining and the data accessible during subsequent tasks. Specifically, during pretraining, there are numerous attributes associated with proteins or drugs, whereas during downstream fine-tuning, only amino acid sequences and SMILES are available. Consequently, during pretraining, we explore two scenarios: one which controls the information propagated to the Drug/Protein entities and one without such control. In our experiments, we present results for both cases to provide an insight on the impact of restricting information flow during pretraining on the subsequent tasks.
- Noisy Links: An additional significant consideration is the presence of noisy links within the up-stream data and how they affect the downstream tasks. To investigate the potential impact on these tasks, we manually handpick a subset of links from each database that are relevant to drug discovery (see details in the Appendix). We then compare the outcomes when training the GNN using only these restricted links versus using all possible links present in the graphs.
- Regression: Certain pretraining datasets, like Uniprot, contain numerical data properties. Hence, we incorporate an extra regression objective aimed at minimizing the root mean square error (MSE) of the predicted numerical data properties. In the learning process, we combine the regression objective and the link prediction objective to create a single objective function.
| Scoring Type | Noisy Links | Flow Control | Regression |
|--------------|:-----------:|--------------|------------|
| DistMult | No | Yes | No |
**Model training data:**
The model was trained over *UBC*. *UBC* is a dataset comprising entities (Proteins/Drugs) from Uniprot (U), BindingDB (B) and. ChemBL (C). It contains 6,207,654 triples.
**Model results:**
<style type="text/css">
.tg {border-collapse:collapse;border-spacing:0;}
.tg td{border-color:black;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;
overflow:hidden;padding:10px 5px;word-break:normal;}
.tg th{border-color:black;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;
font-weight:normal;overflow:hidden;padding:10px 5px;word-break:normal;}
.tg .tg-c3ow{border-color:inherit;text-align:center;vertical-align:top}
.tg .tg-0pky{border-color:inherit;text-align:center;vertical-align:centr;text-emphasis:bold}
</style>
<table class="tg">
<thead>
<tr>
<th class="tg-0pky">Dataset</th>
<th class="tg-c3ow">DTI DG</th>
<th class="tg-c3ow" colspan="3">DAVIS</th>
<th class="tg-c3ow" colspan="3">KIBA</th>
</tr>
</thead>
<tbody>
<tr>
<td class="tg-0pky">Splits</td>
<td class="tg-c3ow">Temporal</td>
<td class="tg-c3ow">Random</td>
<td class="tg-c3ow">Target</td>
<td class="tg-c3ow">Drug</td>
<td class="tg-c3ow">Random</td>
<td class="tg-c3ow">Target</td>
<td class="tg-c3ow">Drug</td>
</tr>
<tr>
<td class="tg-0pky">Results</td>
<td class="tg-c3ow">0.578</td>
<td class="tg-c3ow">0.808</td>
<td class="tg-c3ow">0.572</td>
<td class="tg-c3ow">0.152</td>
<td class="tg-c3ow">0.859</td>
<td class="tg-c3ow">0.627</td>
<td class="tg-c3ow">0.593</td>
</tr>
</tbody>
</table>
**Paper or resources for more information:**
- [GitHub Repo](https://github.com/IBM/otter-knowledge)
**License:**
MIT
**Where to send questions or comments about the model:**
- [GitHub Repo](https://github.com/IBM/otter-knowledge)
## How to use
Clone the repo:
```sh
git clone https://github.com/IBM/otter-knowledge.git
cd otter-knowledge
```
- Run the inference for Proteins:
*Replace test_data with the path to a CSV file containing the protein sequences, name_of_the_column with the name of the column of the protein sequence in the CSV and output_path with the filename of the JSON file to be created with the embeddings.*
```python
python inference.py --input_path test_data --sequence_column name_of_the_column --model_path ibm/otter_ubc_distmult --output_path output_path
```
- Run the inference for Drugs:
*Replace test_data with the path to a CSV file containing the Drug SMILES, name_of_the_column with the name of the column of the SMILES in the CSV and output_path with the filename of the JSON file to be created with the embeddings.*.*
```python
python inference.py --input_path test_data --sequence_column name_of_the_column input_type Drug --relation_name smiles --model_path ibm/otter_ubc_distmult --output_path output_path
```
|
ibm-research/otter_ubc_transe
|
ibm-research
| 2023-06-13T15:51:45Z | 0 | 4 | null |
[
"dataset:ibm/otter_uniprot_bindingdb_chembl",
"license:mit",
"region:us"
] | null | 2023-06-12T09:41:02Z |
---
license: mit
inference: false
datasets:
- ibm/otter_uniprot_bindingdb_chembl
---
# Otter UBC TransE Model Card
## Model details
Otter models are based on Graph Neural Networks (GNN) that propagates initial embeddings through a set of layers that upgrade input embedding according to the node neighbours.
The architecture of GNN consists of two main blocks: encoder and decoder.
- For encoder we first define a projection layer which consists of a set of linear transformations for each node modality and projects nodes into common dimensionality, then we apply several multi-relational graph convolutional layers (R-GCN) which distinguish between different types of edges between source and target nodes by having a set of trainable parameters for each edge type.
- For decoder we consider link prediction task, which consists of a scoring function that maps each triple of source and target nodes and the corresponding edge and maps that to a scalar number defined over interval [0; 1].
**Model type:**
For link prediction, we consider three choices of scoring functions: DistMult, TransE and a Binary Classifier that are commonly used in the literature. The outcomes of scoring of each triple are then compared against actual labels using negative log likelihood loss function.
- Flow control: One crucial aspect of pretraining the GNN involves addressing the disparity between the data accessible during pretraining and the data accessible during subsequent tasks. Specifically, during pretraining, there are numerous attributes associated with proteins or drugs, whereas during downstream fine-tuning, only amino acid sequences and SMILES are available. Consequently, during pretraining, we explore two scenarios: one which controls the information propagated to the Drug/Protein entities and one without such control. In our experiments, we present results for both cases to provide an insight on the impact of restricting information flow during pretraining on the subsequent tasks.
- Noisy Links: An additional significant consideration is the presence of noisy links within the up-stream data and how they affect the downstream tasks. To investigate the potential impact on these tasks, we manually handpick a subset of links from each database that are relevant to drug discovery (see details in the Appendix). We then compare the outcomes when training the GNN using only these restricted links versus using all possible links present in the graphs.
- Regression: Certain pretraining datasets, like Uniprot, contain numerical data properties. Hence, we incorporate an extra regression objective aimed at minimizing the root mean square error (MSE) of the predicted numerical data properties. In the learning process, we combine the regression objective and the link prediction objective to create a single objective function.
| Scoring Type | Noisy Links | Flow Control | Regression |
|--------------|:-----------:|--------------|------------|
| TransE | No | Yes | No |
**Model training data:**
The model was trained over *UBC*. *UBC* is a dataset comprising entities (Proteins/Drugs) from Uniprot (U), BindingDB (B) and. ChemBL (C). It contains 6,207,654 triples.
**Model results:**
<style type="text/css">
.tg {border-collapse:collapse;border-spacing:0;}
.tg td{border-color:black;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;
overflow:hidden;padding:10px 5px;word-break:normal;}
.tg th{border-color:black;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;
font-weight:normal;overflow:hidden;padding:10px 5px;word-break:normal;}
.tg .tg-c3ow{border-color:inherit;text-align:center;vertical-align:top}
.tg .tg-0pky{border-color:inherit;text-align:center;vertical-align:centr;text-emphasis:bold}
</style>
<table class="tg">
<thead>
<tr>
<th class="tg-0pky">Dataset</th>
<th class="tg-c3ow">DTI DG</th>
<th class="tg-c3ow" colspan="3">DAVIS</th>
<th class="tg-c3ow" colspan="3">KIBA</th>
</tr>
</thead>
<tbody>
<tr>
<td class="tg-0pky">Splits</td>
<td class="tg-c3ow">Temporal</td>
<td class="tg-c3ow">Random</td>
<td class="tg-c3ow">Target</td>
<td class="tg-c3ow">Drug</td>
<td class="tg-c3ow">Random</td>
<td class="tg-c3ow">Target</td>
<td class="tg-c3ow">Drug</td>
</tr>
<tr>
<td class="tg-0pky">Results</td>
<td class="tg-c3ow">0.577</td>
<td class="tg-c3ow">0.807</td>
<td class="tg-c3ow">0.571</td>
<td class="tg-c3ow">0.130</td>
<td class="tg-c3ow">0.858</td>
<td class="tg-c3ow">0.644</td>
<td class="tg-c3ow">0.583</td>
</tr>
</tbody>
</table>
**Paper or resources for more information:**
- [GitHub Repo](https://github.com/IBM/otter-knowledge)
**License:**
MIT
**Where to send questions or comments about the model:**
- [GitHub Repo](https://github.com/IBM/otter-knowledge)
## How to use
Clone the repo:
```sh
git clone https://github.com/IBM/otter-knowledge.git
cd otter-knowledge
```
- Run the inference for Proteins:
*Replace test_data with the path to a CSV file containing the protein sequences, name_of_the_column with the name of the column of the protein sequence in the CSV and output_path with the filename of the JSON file to be created with the embeddings.*
```python
python inference.py --input_path test_data --sequence_column name_of_the_column --model_path ibm/otter_ubc_transe --output_path output_path
```
- Run the inference for Drugs:
*Replace test_data with the path to a CSV file containing the Drug SMILES, name_of_the_column with the name of the column of the SMILES in the CSV and output_path with the filename of the JSON file to be created with the embeddings.*.*
```python
python inference.py --input_path test_data --sequence_column name_of_the_column input_type Drug --relation_name smiles --model_path ibm/otter_ubc_transe --output_path output_path
```
|
ibm-research/otter_ubc_classifier
|
ibm-research
| 2023-06-13T15:51:27Z | 0 | 2 | null |
[
"dataset:ibm/otter_uniprot_bindingdb_chembl",
"license:mit",
"region:us"
] | null | 2023-06-09T10:24:49Z |
---
license: mit
inference: false
datasets:
- ibm/otter_uniprot_bindingdb_chembl
---
# Otter UBC Classifier Model Card
## Model details
Otter models are based on Graph Neural Networks (GNN) that propagates initial embeddings through a set of layers that upgrade input embedding according to the node neighbours.
The architecture of GNN consists of two main blocks: encoder and decoder.
- For encoder we first define a projection layer which consists of a set of linear transformations for each node modality and projects nodes into common dimensionality, then we apply several multi-relational graph convolutional layers (R-GCN) which distinguish between different types of edges between source and target nodes by having a set of trainable parameters for each edge type.
- For decoder we consider link prediction task, which consists of a scoring function that maps each triple of source and target nodes and the corresponding edge and maps that to a scalar number defined over interval [0; 1].
**Model type:**
For link prediction, we consider three choices of scoring functions: DistMult, TransE and a Binary Classifier that are commonly used in the literature. The outcomes of scoring of each triple are then compared against actual labels using negative log likelihood loss function.
- Flow control: One crucial aspect of pretraining the GNN involves addressing the disparity between the data accessible during pretraining and the data accessible during subsequent tasks. Specifically, during pretraining, there are numerous attributes associated with proteins or drugs, whereas during downstream fine-tuning, only amino acid sequences and SMILES are available. Consequently, during pretraining, we explore two scenarios: one which controls the information propagated to the Drug/Protein entities and one without such control. In our experiments, we present results for both cases to provide an insight on the impact of restricting information flow during pretraining on the subsequent tasks.
- Noisy Links: An additional significant consideration is the presence of noisy links within the up-stream data and how they affect the downstream tasks. To investigate the potential impact on these tasks, we manually handpick a subset of links from each database that are relevant to drug discovery (see details in the Appendix). We then compare the outcomes when training the GNN using only these restricted links versus using all possible links present in the graphs.
- Regression: Certain pretraining datasets, like Uniprot, contain numerical data properties. Hence, we incorporate an extra regression objective aimed at minimizing the root mean square error (MSE) of the predicted numerical data properties. In the learning process, we combine the regression objective and the link prediction objective to create a single objective function.
| Scoring Type | Noisy Links | Flow Control | Regression |
|--------------|:-----------:|--------------|------------|
| Classifier Head | No | Yes | No |
**Model training data:**
The model was trained over *UBC*. *UBC* is a dataset comprising entities (Proteins/Drugs) from Uniprot (U), BindingDB (B) and. ChemBL (C). It contains 6,207,654 triples.
**Model results:**
<style type="text/css">
.tg {border-collapse:collapse;border-spacing:0;}
.tg td{border-color:black;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;
overflow:hidden;padding:10px 5px;word-break:normal;}
.tg th{border-color:black;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;
font-weight:normal;overflow:hidden;padding:10px 5px;word-break:normal;}
.tg .tg-c3ow{border-color:inherit;text-align:center;vertical-align:top}
.tg .tg-0pky{border-color:inherit;text-align:center;vertical-align:centr;text-emphasis:bold}
</style>
<table class="tg">
<thead>
<tr>
<th class="tg-0pky">Dataset</th>
<th class="tg-c3ow">DTI DG</th>
<th class="tg-c3ow" colspan="3">DAVIS</th>
<th class="tg-c3ow" colspan="3">KIBA</th>
</tr>
</thead>
<tbody>
<tr>
<td class="tg-0pky">Splits</td>
<td class="tg-c3ow">Temporal</td>
<td class="tg-c3ow">Random</td>
<td class="tg-c3ow">Target</td>
<td class="tg-c3ow">Drug</td>
<td class="tg-c3ow">Random</td>
<td class="tg-c3ow">Target</td>
<td class="tg-c3ow">Drug</td>
</tr>
<tr>
<td class="tg-0pky">Results</td>
<td class="tg-c3ow">0.580</td>
<td class="tg-c3ow">0.810</td>
<td class="tg-c3ow">0.573</td>
<td class="tg-c3ow">0.104</td>
<td class="tg-c3ow">0.861</td>
<td class="tg-c3ow">0.631</td>
<td class="tg-c3ow">0.616</td>
</tr>
</tbody>
</table>
**Paper or resources for more information:**
- [GitHub Repo](https://github.com/IBM/otter-knowledge)
**License:**
MIT
**Where to send questions or comments about the model:**
- [GitHub Repo](https://github.com/IBM/otter-knowledge)
## How to use
Clone the repo:
```sh
git clone https://github.com/IBM/otter-knowledge.git
cd otter-knowledge
```
- Run the inference for Proteins:
*Replace test_data with the path to a CSV file containing the protein sequences, name_of_the_column with the name of the column of the protein sequence in the CSV and output_path with the filename of the JSON file to be created with the embeddings.*
```python
python inference.py --input_path test_data --sequence_column name_of_the_column --model_path ibm/otter_ubc_classifier --output_path output_path
```
- Run the inference for Drugs:
*Replace test_data with the path to a CSV file containing the Drug SMILES, name_of_the_column with the name of the column of the SMILES in the CSV and output_path with the filename of the JSON file to be created with the embeddings.*.*
```python
python inference.py --input_path test_data --sequence_column name_of_the_column input_type Drug --relation_name smiles --model_path ibm/otter_ubc_classifier --output_path output_path
```
|
therealcyberlord/stanford-car-vit-patch16
|
therealcyberlord
| 2023-06-13T15:34:35Z | 1,357 | 5 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"vit",
"image-classification",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-08-08T03:25:33Z |
---
license: apache-2.0
---
# ViT Fine-tuned on Stanford Car Dataset
Base model: https://huggingface.co/google/vit-base-patch16-224
This achieves around 86% on the testing set, you can use it as a baseline for further tuning.
# Dataset Description
The Stanford car dataset contains 16,185 images of 196 classes of cars. Classes are typically at the level of Make, Model, Year, e.g. 2012 Tesla Model S or 2012 BMW M3 coupe. The data is split into 8144 training images, 6,041 testing images, and 2000 validation images in this case.
** Please note: this dataset does not contain newer car models **
# Using the Model in the Transformer Library
```
from transformers import AutoFeatureExtractor, AutoModelForImageClassification
extractor = AutoFeatureExtractor.from_pretrained("therealcyberlord/stanford-car-vit-patch16")
model = AutoModelForImageClassification.from_pretrained("therealcyberlord/stanford-car-vit-patch16")
```
# Citations
3D Object Representations for Fine-Grained Categorization
Jonathan Krause, Michael Stark, Jia Deng, Li Fei-Fei
4th IEEE Workshop on 3D Representation and Recognition, at ICCV 2013 (3dRR-13). Sydney, Australia. Dec. 8, 2013.
|
victorivus/poca-SoccerTwos
|
victorivus
| 2023-06-13T15:08:13Z | 25 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"SoccerTwos",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SoccerTwos",
"region:us"
] |
reinforcement-learning
| 2023-06-13T15:07:49Z |
---
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: victorivus/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
aravind-selvam/x_rotatedv4
|
aravind-selvam
| 2023-06-13T15:00:31Z | 45 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vision-encoder-decoder",
"image-text-to-text",
"generated_from_trainer",
"dataset:imagefolder",
"license:mit",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2023-06-13T13:44:14Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- imagefolder
model-index:
- name: x_rotatedv4
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. -->
# x_rotatedv4
This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) on the imagefolder dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.27.4
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
ouail15031/donut-base-sroie
|
ouail15031
| 2023-06-13T14:50:59Z | 45 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vision-encoder-decoder",
"image-text-to-text",
"generated_from_trainer",
"dataset:imagefolder",
"license:mit",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2023-06-13T10:41:12Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- imagefolder
model-index:
- name: donut-base-sroie
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# donut-base-sroie
This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) on the imagefolder dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.31.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
SaturnMk2/train
|
SaturnMk2
| 2023-06-13T14:42:14Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-06-13T14:40:41Z |
---
license: creativeml-openrail-m
---
|
ZidanSink/Rachelicia
|
ZidanSink
| 2023-06-13T14:37:07Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-06-13T14:26:52Z |
---
license: creativeml-openrail-m
---
|
dico97/distilgpt2-finetuned-wikitext2-datos-propios
|
dico97
| 2023-06-13T14:29:04Z | 61 | 0 |
transformers
|
[
"transformers",
"tf",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-06-13T13:56:01Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: dico97/distilgpt2-finetuned-wikitext2-datos-propios
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. -->
# dico97/distilgpt2-finetuned-wikitext2-datos-propios
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 2.1457
- Validation Loss: 2.4673
- Epoch: 14
## 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': 2e-05, '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 |
|:----------:|:---------------:|:-----:|
| 3.2488 | 2.9547 | 0 |
| 2.9889 | 2.8030 | 1 |
| 2.8373 | 2.7189 | 2 |
| 2.7251 | 2.6685 | 3 |
| 2.6403 | 2.6278 | 4 |
| 2.5661 | 2.6034 | 5 |
| 2.5023 | 2.5710 | 6 |
| 2.4410 | 2.5560 | 7 |
| 2.3893 | 2.5280 | 8 |
| 2.3409 | 2.5150 | 9 |
| 2.2976 | 2.5084 | 10 |
| 2.2565 | 2.4861 | 11 |
| 2.2148 | 2.4663 | 12 |
| 2.1813 | 2.4622 | 13 |
| 2.1457 | 2.4673 | 14 |
### Framework versions
- Transformers 4.28.0
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
pintileipetru/autotrain-language_model-66295136456
|
pintileipetru
| 2023-06-13T14:25:51Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"marian",
"text2text-generation",
"autotrain",
"translation",
"unk",
"dataset:pintileipetru/autotrain-data-language_model",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2023-06-13T14:22:54Z |
---
tags:
- autotrain
- translation
language:
- unk
- unk
datasets:
- pintileipetru/autotrain-data-language_model
co2_eq_emissions:
emissions: 0.32396303700981144
---
# Model Trained Using AutoTrain
- Problem type: Translation
- Model ID: 66295136456
- CO2 Emissions (in grams): 0.3240
## Validation Metrics
- Loss: 0.410
- SacreBLEU: 74.380
- Gen len: 13.460
|
almanach/manta-lm-small
|
almanach
| 2023-06-13T14:01:00Z | 134 | 4 |
transformers
|
[
"transformers",
"pytorch",
"manta",
"text2text-generation",
"custom_code",
"en",
"license:mit",
"autotrain_compatible",
"region:us"
] |
text2text-generation
| 2023-06-08T16:22:38Z |
---
license: mit
language:
- en
pipeline_tag: text2text-generation
---
# MANTa-LM (small)
Pretrained MANTa-LM architecture as introduced in the paper [MANTa: Efficient Gradient-Based Tokenization for Robust End-to-End Language Modeling](https://aclanthology.org/2022.findings-emnlp.207.pdf).
<center><img src="https://github.com/NathanGodey/nathangodey.github.io/raw/main/img/posts/full_difftok_schema.png" width="600"></center>
## Model Details
### Model Description
The MANTa tokenizer aims at mimicking the combination of a subword tokenizer and an embedding matrix in a classical language model in a differentiable way.
This trainable tokenizer is thus added as the first layer of an encoder-decoder model and trained using the language modeling objective.
Our results show that MANTa-LM only slightly degrades the performance of a T5 equivalent on the GLUE benchmark while being **much more robust** to artificial and user-generated noise.
### Model Sources
- **Paper:** [MANTa: Efficient Gradient-Based Tokenization for Robust End-to-End Language Modeling](https://aclanthology.org/2022.findings-emnlp.207.pdf) (EMNLP 2022 Findings)
## Uses
### Direct Use
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("almanach/manta-lm-small", trust_remote_code=True)
manta_model = AutoModelForSeq2SeqLM.from_pretrained("almanach/manta-lm-small", trust_remote_code=True)
tokens = tokenizer("The name of the capital of France is <extra_id_0> and it is a very big city.", return_tensors="pt")
output = manta_model.generate(**tokens, decoder_start_token_id=0, repetition_penalty=1.5, do_sample=True)
print(tokenizer.batch_decode(output))
```
### Recommendations
We recommend using a smaller learning rate for the tokenizer module during fine-tuning (byte embeddings, frontier predictor, pooler).
## Training Details
### Training Data
This model was trained on the C4 dataset.
### Training Procedure
The training objective is the same as ByT5, but most hyperparameters are taken from T5.
## Citation
**BibTeX:**
```
@inproceedings{godey-etal-2022-manta,
title = "{MANT}a: Efficient Gradient-Based Tokenization for End-to-End Robust Language Modeling",
author = "Godey, Nathan and
Castagn{\'e}, Roman and
de la Clergerie, {\'E}ric and
Sagot, Beno{\^\i}t",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-emnlp.207",
pages = "2859--2870",
}
```
## Model Card Authors
[Nathan Godey](https://nathangodey.github.io/)
[Roman Castagné](https://romancast.github.io/)
|
codeparrot/starcoder-conala
|
codeparrot
| 2023-06-13T13:58:22Z | 12 | 3 |
transformers
|
[
"transformers",
"pytorch",
"gpt_bigcode",
"text-generation",
"text2text-generation",
"dataset:codeparrot/conala-mined-curated",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-06-09T16:22:54Z |
---
datasets:
- codeparrot/conala-mined-curated
pipeline_tag: text2text-generation
---
# Model Card for Starcoder-conala
<!-- Provide a quick summary of what the model is/does. -->
This model is an instruction-tuned version of ⭐️ StarCoder. The instruction dataset involved is [Conala-mined-curated](https://huggingface.co/datasets/codeparrot/conala-mined-curated)
which was built by boostrapping by predicting the column *rewritten_intent* of the mined subset of the [CoNaLa corpus](https://huggingface.co/datasets/neulab/conala).
## Usage
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
The model was fine-tuned with the following template
```
Question: <instruction>
Answer: <output>
```
If you have your model and tokenizer loaded, you can use the following code to make the model generate the right output to a given instruction
```python
instruction = "Write a function to compute the GCD between two integers a and b"
prompt = f"Question:{instruction}\n\nAnswer:"
input_ids = tokenizer(prompt, return_tensors="pt")["input_ids"]
completion = model.generate(input_ids, max_length=200)
print(tokenizer.batch_decode(completion[:,input_ids.shape[1]:])[0])
```
## More information
For additional information, check
- [Conala-mined-curated](https://huggingface.co/datasets/codeparrot/conala-mined-curated)
- [Starcoder](https://huggingface.co/bigcode/starcoder)
|
Subsets and Splits
Filtered Qwen2.5 Distill Models
Identifies specific configurations of models by filtering cards that contain 'distill', 'qwen2.5', '7b' while excluding certain base models and incorrect model ID patterns, uncovering unique model variants.
Filtered Model Cards Count
Finds the count of entries with specific card details that include 'distill', 'qwen2.5', '7b' but exclude certain base models, revealing valuable insights about the dataset's content distribution.
Filtered Distill Qwen 7B Models
Filters for specific card entries containing 'distill', 'qwen', and '7b', excluding certain strings and patterns, to identify relevant model configurations.
Filtered Qwen-7b Model Cards
The query performs a detailed filtering based on specific keywords and excludes certain entries, which could be useful for identifying a specific subset of cards but does not provide deeper insights or trends.
Filtered Qwen 7B Model Cards
The query filters for specific terms related to "distilled" or "distill", "qwen", and "7b" in the 'card' column but excludes certain base models, providing a limited set of entries for further inspection.
Qwen 7B Distilled Models
The query provides a basic filtering of records to find specific card names that include keywords related to distilled Qwen 7b models, excluding a particular base model, which gives limited insight but helps in focusing on relevant entries.
Qwen 7B Distilled Model Cards
The query filters data based on specific keywords in the modelId and card fields, providing limited insight primarily useful for locating specific entries rather than revealing broad patterns or trends.
Qwen 7B Distilled Models
Finds all entries containing the terms 'distilled', 'qwen', and '7b' in a case-insensitive manner, providing a filtered set of records but without deeper analysis.
Distilled Qwen 7B Models
The query filters for specific model IDs containing 'distilled', 'qwen', and '7b', providing a basic retrieval of relevant entries but without deeper analysis or insight.
Filtered Model Cards with Distill Qwen2.
Filters and retrieves records containing specific keywords in the card description while excluding certain phrases, providing a basic count of relevant entries.
Filtered Model Cards with Distill Qwen 7
The query filters specific variations of card descriptions containing 'distill', 'qwen', and '7b' while excluding a particular base model, providing limited but specific data retrieval.
Distill Qwen 7B Model Cards
The query filters and retrieves rows where the 'card' column contains specific keywords ('distill', 'qwen', and '7b'), providing a basic filter result that can help in identifying specific entries.