modelId
string | author
string | last_modified
timestamp[us, tz=UTC] | downloads
int64 | likes
int64 | library_name
string | tags
sequence | pipeline_tag
string | createdAt
timestamp[us, tz=UTC] | card
string |
|---|---|---|---|---|---|---|---|---|---|
Kooten/BagelMIsteryTour-v2-8x7B-Imatrix-GGUF | Kooten | 2024-02-07T18:27:19Z | 19 | 4 | null | [
"gguf",
"mergekit",
"merge",
"base_model:Doctor-Shotgun/limarp-zloss-mixtral-8x7b-qlora",
"base_model:merge:Doctor-Shotgun/limarp-zloss-mixtral-8x7b-qlora",
"base_model:Sao10K/Sensualize-Mixtral-bf16",
"base_model:merge:Sao10K/Sensualize-Mixtral-bf16",
"base_model:jondurbin/bagel-dpo-8x7b-v0.2",
"base_model:merge:jondurbin/bagel-dpo-8x7b-v0.2",
"base_model:mistralai/Mixtral-8x7B-Instruct-v0.1",
"base_model:merge:mistralai/Mixtral-8x7B-Instruct-v0.1",
"base_model:mistralai/Mixtral-8x7B-v0.1",
"base_model:merge:mistralai/Mixtral-8x7B-v0.1",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | null | 2024-02-07T16:06:33Z | ---
base_model:
- mistralai/Mixtral-8x7B-v0.1
- jondurbin/bagel-dpo-8x7b-v0.2
- Sao10K/Sensualize-Mixtral-bf16
- mistralai/Mixtral-8x7B-v0.1
- Doctor-Shotgun/limarp-zloss-mixtral-8x7b-qlora
- mistralai/Mixtral-8x7B-Instruct-v0.1
tags:
- mergekit
- merge
license: cc-by-nc-4.0
---
# BagelMIsteryTour-v2-8x7B 3.5bpw
Imatrix GGUF quant of [ycros/BagelMIsteryTour-v2-8x7B](https://huggingface.co/ycros/BagelMIsteryTour-v2-8x7B)
## Other quants:
EXL2: [5bpw](https://huggingface.co/Kooten/BagelMIsteryTour-v2-8x7B-5bpw-exl2), [3.5bpw](https://huggingface.co/Kooten/BagelMIsteryTour-v2-8x7B-3.5bpw-exl2)
[GGUF](https://huggingface.co/Kooten/BagelMIsteryTour-v2-8x7B-Imatrix-GGUF): [IQ3_XXS](https://huggingface.co/Kooten/BagelMIsteryTour-v2-8x7B-Imatrix-GGUF/blob/main/BagelMIsteryTour-v2-8x7B-IQ3_XXS.gguf), [IQ2_XS](https://huggingface.co/Kooten/BagelMIsteryTour-v2-8x7B-Imatrix-GGUF/blob/main/BagelMIsteryTour-v2-8x7B-IQ2_XS.gguf), [IQ2_XXS](https://huggingface.co/Kooten/BagelMIsteryTour-v2-8x7B-Imatrix-GGUF/blob/main/BagelMIsteryTour-v2-8x7B-IQ2_XXS.gguf)
## Prompt format: Alpaca
It is noted to also work with mistral
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Input:
{input}
### Response:
```
## Contact
Kooten on discord
[ko-fi.com/kooten](https://ko-fi.com/kooten) if you would like to support me
|
MaziyarPanahi/Smaug-72B-v0.1-GPTQ | MaziyarPanahi | 2024-02-07T18:24:50Z | 17 | 2 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"finetuned",
"quantized",
"4-bit",
"gptq",
"base_model:moreh/MoMo-72B-lora-1.8.7-DPO",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us",
"has_space",
"base_model:abacusai/Smaug-72B-v0.1",
"base_model:finetune:abacusai/Smaug-72B-v0.1",
"license:apache-2.0"
] | text-generation | 2024-02-07T18:18:03Z | ---
license: apache-2.0
tags:
- finetuned
- quantized
- 4-bit
- gptq
- transformers
- safetensors
- llama
- text-generation
- base_model:moreh/MoMo-72B-lora-1.8.7-DPO
- license:other
- autotrain_compatible
- endpoints_compatible
- text-generation-inference
- region:us
- has_space
model_name: Smaug-72B-v0.1-GPTQ
base_model: abacusai/Smaug-72B-v0.1
inference: false
model_creator: abacusai
pipeline_tag: text-generation
quantized_by: MaziyarPanahi
---
# Description
[MaziyarPanahi/Smaug-72B-v0.1-GPTQ](https://huggingface.co/MaziyarPanahi/Smaug-72B-v0.1-GPTQ) is a quantized (GPTQ) version of [abacusai/Smaug-72B-v0.1](https://huggingface.co/abacusai/Smaug-72B-v0.1)
## How to use
### Install the necessary packages
```
pip install --upgrade accelerate auto-gptq transformers
```
### Example Python code
```python
from transformers import AutoTokenizer, pipeline
from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig
import torch
model_id = "MaziyarPanahi/Smaug-72B-v0.1-GPTQ"
quantize_config = BaseQuantizeConfig(
bits=4,
group_size=128,
desc_act=False
)
model = AutoGPTQForCausalLM.from_quantized(
model_id,
use_safetensors=True,
device="cuda:0",
quantize_config=quantize_config)
tokenizer = AutoTokenizer.from_pretrained(model_id)
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
max_new_tokens=512,
temperature=0.7,
top_p=0.95,
repetition_penalty=1.1
)
outputs = pipe("What is a large language model?")
print(outputs[0]["generated_text"])
``` |
fazito25/Taxi-v3 | fazito25 | 2024-02-07T18:18:59Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2024-02-07T18:18:57Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="fazito25/Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
turgutburak01/cartPole8 | turgutburak01 | 2024-02-07T18:17:14Z | 0 | 0 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | reinforcement-learning | 2024-02-07T17:39:35Z | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: cartPole8
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
|
DifeiT/text_classification_model | DifeiT | 2024-02-07T18:16:35Z | 6 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:dmis-lab/biobert-v1.1",
"base_model:finetune:dmis-lab/biobert-v1.1",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-02-07T17:52:09Z | ---
base_model: dmis-lab/biobert-v1.1
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: text_classification_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. -->
# text_classification_model
This model is a fine-tuned version of [dmis-lab/biobert-v1.1](https://huggingface.co/dmis-lab/biobert-v1.1) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5013
- Accuracy: 0.8046
## 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 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 22 | 0.5339 | 0.7586 |
| No log | 2.0 | 44 | 0.5013 | 0.8046 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.2.0+cu118
- Datasets 2.16.1
- Tokenizers 0.15.1
|
fazito25/q-FrozenLake-v1-4x4-noSlippery | fazito25 | 2024-02-07T18:14:46Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2024-02-07T18:14:43Z | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="fazito25/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
hughtayloe/handertrails | hughtayloe | 2024-02-07T18:07:49Z | 6 | 0 | transformers | [
"transformers",
"safetensors",
"llava",
"image-text-to-text",
"image-to-text",
"en",
"dataset:liuhaotian/LLaVA-Instruct-150K",
"endpoints_compatible",
"region:us"
] | image-to-text | 2024-02-01T16:52:31Z | ---
language:
- en
pipeline_tag: image-to-text
inference: false
arxiv: 2304.08485
datasets:
- liuhaotian/LLaVA-Instruct-150K
---
# LLaVA Model Card
Below is the model card of Llava model 7b, which is copied from the original Llava model card that you can find [here](https://huggingface.co/liuhaotian/llava-v1.5-13b).
Check out also the Google Colab demo to run Llava on a free-tier Google Colab instance: [](https://colab.research.google.com/drive/1qsl6cd2c8gGtEW1xV5io7S8NHh-Cp1TV?usp=sharing)
Or check out our Spaces demo! [](https://huggingface.co/spaces/llava-hf/llava-4bit)
## Model details
**Model type:**
LLaVA is an open-source chatbot trained by fine-tuning LLaMA/Vicuna on GPT-generated multimodal instruction-following data.
It is an auto-regressive language model, based on the transformer architecture.
**Model date:**
LLaVA-v1.5-7B was trained in September 2023.
**Paper or resources for more information:**
https://llava-vl.github.io/
## How to use the model
First, make sure to have `transformers >= 4.35.3`.
The model supports multi-image and multi-prompt generation. Meaning that you can pass multiple images in your prompt. Make sure also to follow the correct prompt template (`USER: xxx\nASSISTANT:`) and add the token `<image>` to the location where you want to query images:
### Using `pipeline`:
Below we used [`"llava-hf/llava-1.5-7b-hf"`](https://huggingface.co/llava-hf/llava-1.5-7b-hf) checkpoint.
```python
from transformers import pipeline
from PIL import Image
import requests
model_id = "llava-hf/llava-1.5-7b-hf"
pipe = pipeline("image-to-text", model=model_id)
url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg"
image = Image.open(requests.get(url, stream=True).raw)
prompt = "USER: <image>\nWhat does the label 15 represent? (1) lava (2) core (3) tunnel (4) ash cloud\nASSISTANT:"
outputs = pipe(image, prompt=prompt, generate_kwargs={"max_new_tokens": 200})
print(outputs)
>>> {"generated_text": "\nUSER: What does the label 15 represent? (1) lava (2) core (3) tunnel (4) ash cloud\nASSISTANT: Lava"}
```
### Using pure `transformers`:
Below is an example script to run generation in `float16` precision on a GPU device:
```python
import requests
from PIL import Image
import torch
from transformers import AutoProcessor, LlavaForConditionalGeneration
model_id = "llava-hf/llava-1.5-7b-hf"
prompt = "USER: <image>\nWhat are these?\nASSISTANT:"
image_file = "http://images.cocodataset.org/val2017/000000039769.jpg"
model = LlavaForConditionalGeneration.from_pretrained(
model_id,
torch_dtype=torch.float16,
low_cpu_mem_usage=True,
).to(0)
processor = AutoProcessor.from_pretrained(model_id)
raw_image = Image.open(requests.get(image_file, stream=True).raw)
inputs = processor(prompt, raw_image, return_tensors='pt').to(0, torch.float16)
output = model.generate(**inputs, max_new_tokens=200, do_sample=False)
print(processor.decode(output[0][2:], skip_special_tokens=True))
```
### Model optimization
#### 4-bit quantization through `bitsandbytes` library
First make sure to install `bitsandbytes`, `pip install bitsandbytes` and make sure to have access to a CUDA compatible GPU device. Simply change the snippet above with:
```diff
model = LlavaForConditionalGeneration.from_pretrained(
model_id,
torch_dtype=torch.float16,
low_cpu_mem_usage=True,
+ load_in_4bit=True
)
```
#### Use Flash-Attention 2 to further speed-up generation
First make sure to install `flash-attn`. Refer to the [original repository of Flash Attention](https://github.com/Dao-AILab/flash-attention) regarding that package installation. Simply change the snippet above with:
```diff
model = LlavaForConditionalGeneration.from_pretrained(
model_id,
torch_dtype=torch.float16,
low_cpu_mem_usage=True,
+ use_flash_attention_2=True
).to(0)
```
## License
Llama 2 is licensed under the LLAMA 2 Community License,
Copyright (c) Meta Platforms, Inc. All Rights Reserved. |
edgilr/intel-image-classification | edgilr | 2024-02-07T18:06:24Z | 0 | 0 | fastai | [
"fastai",
"region:us"
] | null | 2024-02-07T18:02:55Z | ---
tags:
- fastai
---
# Amazing!
🥳 Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)).
3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)!
Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card.
---
# Model card
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
|
paulux84/autotrain-z58fs-z9tot | paulux84 | 2024-02-07T18:05:22Z | 4 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"mistral",
"text-generation",
"autotrain",
"conversational",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-02-07T16:21:47Z | ---
license: other
tags:
- autotrain
- text-generation
widget:
- text: 'I love AutoTrain because '
---
# Model Trained Using AutoTrain
This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain).
# Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = "PATH_TO_THIS_REPO"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
model_path,
device_map="auto",
torch_dtype='auto'
).eval()
# Prompt content: "hi"
messages = [
{"role": "user", "content": "hi"}
]
input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt')
output_ids = model.generate(input_ids.to('cuda'))
response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)
# Model response: "Hello! How can I assist you today?"
print(response)
``` |
Statos6/dqn-SpaceInvadersNoFrameskip-v4 | Statos6 | 2024-02-07T18:05:15Z | 1 | 0 | stable-baselines3 | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2024-02-07T18:04:40Z | ---
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: 648.00 +/- 159.80
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 Statos6 -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 Statos6 -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 Statos6
```
## 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'}
```
|
sruthis/feb7th | sruthis | 2024-02-07T18:02:17Z | 7 | 0 | transformers | [
"transformers",
"safetensors",
"deit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:facebook/deit-base-distilled-patch16-224",
"base_model:finetune:facebook/deit-base-distilled-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | 2024-02-07T16:55:10Z | ---
license: apache-2.0
base_model: facebook/deit-base-distilled-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: feb7th
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: 0.9898785425101214
---
<!-- 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. -->
# feb7th
This model is a fine-tuned version of [facebook/deit-base-distilled-patch16-224](https://huggingface.co/facebook/deit-base-distilled-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0464
- Accuracy: 0.9899
## 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: 16
- eval_batch_size: 8
- seed: 1234
- gradient_accumulation_steps: 10
- total_train_batch_size: 160
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 0.97 | 12 | 0.0598 | 0.9798 |
| No log | 1.94 | 24 | 0.0480 | 0.9879 |
| No log | 2.98 | 37 | 0.0531 | 0.9838 |
| No log | 3.95 | 49 | 0.0456 | 0.9899 |
| No log | 4.84 | 60 | 0.0464 | 0.9899 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
delli/mistral-7b-address-validator-merged | delli | 2024-02-07T18:01:07Z | 6 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-02-07T17:52:31Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
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## Uses
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### Direct Use
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### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
## Training Details
### Training Data
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[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
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[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
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[More Information Needed]
## Model Card Contact
[More Information Needed]
|
danaleee/CL_rank4_iter800_valprompt | danaleee | 2024-02-07T17:52:51Z | 0 | 0 | diffusers | [
"diffusers",
"tensorboard",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"base_model:CompVis/stable-diffusion-v1-4",
"base_model:adapter:CompVis/stable-diffusion-v1-4",
"license:creativeml-openrail-m",
"region:us"
] | text-to-image | 2024-02-07T16:20:41Z |
---
license: creativeml-openrail-m
base_model: CompVis/stable-diffusion-v1-4
instance_prompt: a photo of sks teddybear
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA DreamBooth - danaleee/CL_rank4_iter800_valprompt
These are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks teddybear using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
LoRA for the text encoder was enabled: False.
|
islasher/intel-image-classification | islasher | 2024-02-07T17:51:17Z | 0 | 0 | fastai | [
"fastai",
"region:us"
] | null | 2024-02-07T17:51:13Z | ---
tags:
- fastai
---
# Amazing!
🥳 Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)).
3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)!
Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card.
---
# Model card
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
|
0xJCarlos/QuestionAnswer_ESP | 0xJCarlos | 2024-02-07T17:50:51Z | 14 | 1 | transformers | [
"transformers",
"tf",
"distilbert",
"question-answering",
"generated_from_keras_callback",
"base_model:dccuchile/distilbert-base-spanish-uncased-finetuned-qa-mlqa",
"base_model:finetune:dccuchile/distilbert-base-spanish-uncased-finetuned-qa-mlqa",
"endpoints_compatible",
"region:us"
] | question-answering | 2023-11-23T17:51:49Z | ---
base_model: dccuchile/distilbert-base-spanish-uncased-finetuned-qa-mlqa
tags:
- generated_from_keras_callback
model-index:
- name: 0xJCarlos/QuestionAnswer_ESP
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. -->
# 0xJCarlos/QuestionAnswer_ESP
This model is a fine-tuned version of [dccuchile/distilbert-base-spanish-uncased-finetuned-qa-mlqa](https://huggingface.co/dccuchile/distilbert-base-spanish-uncased-finetuned-qa-mlqa) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.3146
- Validation Loss: 1.6961
- 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': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 500, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 1.9292 | 1.7179 | 0 |
| 1.4487 | 1.6961 | 1 |
| 1.3231 | 1.6961 | 2 |
| 1.3165 | 1.6961 | 3 |
| 1.3146 | 1.6961 | 4 |
### Framework versions
- Transformers 4.35.2
- TensorFlow 2.15.0
- Datasets 2.16.1
- Tokenizers 0.15.1
|
Wintersmith/LLM_generated_text_detector | Wintersmith | 2024-02-07T17:47:23Z | 4 | 0 | transformers | [
"transformers",
"tf",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"base_model:distilbert/distilbert-base-uncased-finetuned-sst-2-english",
"base_model:finetune:distilbert/distilbert-base-uncased-finetuned-sst-2-english",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-02-07T15:55:26Z | ---
license: apache-2.0
base_model: distilbert/distilbert-base-uncased-finetuned-sst-2-english
tags:
- generated_from_keras_callback
model-index:
- name: Wintersmith/LLM_generated_text_detector
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. -->
# Wintersmith/LLM_generated_text_detector
This model is a fine-tuned version of [distilbert/distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert/distilbert-base-uncased-finetuned-sst-2-english) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0082
- Train Accuracy: 0.9974
- Validation Loss: 0.0191
- Validation Accuracy: 0.9941
- Epoch: 1
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 3630, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.0579 | 0.9809 | 0.0272 | 0.9920 | 0 |
| 0.0082 | 0.9974 | 0.0191 | 0.9941 | 1 |
### Framework versions
- Transformers 4.37.0
- TensorFlow 2.15.0
- Datasets 2.15.0
- Tokenizers 0.15.1
|
Paquique/dqn-SpaceInvadersNoFrameskip-v4 | Paquique | 2024-02-07T17:41:28Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2024-02-07T17:40:57Z | ---
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: 548.00 +/- 276.52
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 Paquique -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 Paquique -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 Paquique
```
## Hyperparameters
```python
OrderedDict([('batch_size', 64),
('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'}
```
|
Tommidi/st_vit_trained-1epoch-ucf101-subset | Tommidi | 2024-02-07T17:31:12Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"st_vit",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-02-07T17:30:24Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
manibt1993/huner_disease | manibt1993 | 2024-02-07T17:25:03Z | 4 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:transformer_dataset_ner_kaggle",
"base_model:google-bert/bert-base-cased",
"base_model:finetune:google-bert/bert-base-cased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2024-02-07T04:59:17Z | ---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
datasets:
- transformer_dataset_ner_kaggle
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: huner_disease
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: transformer_dataset_ner_kaggle
type: transformer_dataset_ner_kaggle
config: ncbi_disease
split: validation
args: ncbi_disease
metrics:
- name: Precision
type: precision
value: 0.7905582615211689
- name: Recall
type: recall
value: 0.8222915042868277
- name: F1
type: f1
value: 0.8061127029608404
- name: Accuracy
type: accuracy
value: 0.9795934778779362
---
<!-- 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. -->
# huner_disease
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the transformer_dataset_ner_kaggle dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2260
- Precision: 0.7906
- Recall: 0.8223
- F1: 0.8061
- Accuracy: 0.9796
## 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: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0651 | 1.0 | 1834 | 0.0703 | 0.6823 | 0.7880 | 0.7314 | 0.9767 |
| 0.0459 | 2.0 | 3668 | 0.0712 | 0.7470 | 0.7617 | 0.7543 | 0.9781 |
| 0.03 | 3.0 | 5502 | 0.0903 | 0.7278 | 0.8137 | 0.7684 | 0.9779 |
| 0.0177 | 4.0 | 7336 | 0.0915 | 0.7529 | 0.8055 | 0.7783 | 0.9791 |
| 0.0139 | 5.0 | 9170 | 0.1088 | 0.7346 | 0.8207 | 0.7753 | 0.9777 |
| 0.01 | 6.0 | 11004 | 0.1196 | 0.7283 | 0.8207 | 0.7718 | 0.9772 |
| 0.007 | 7.0 | 12838 | 0.1175 | 0.7615 | 0.7938 | 0.7773 | 0.9787 |
| 0.0055 | 8.0 | 14672 | 0.1488 | 0.7452 | 0.8237 | 0.7825 | 0.9783 |
| 0.0049 | 9.0 | 16506 | 0.1351 | 0.7704 | 0.8125 | 0.7909 | 0.9795 |
| 0.0042 | 10.0 | 18340 | 0.1617 | 0.7491 | 0.8184 | 0.7822 | 0.9782 |
| 0.0035 | 11.0 | 20174 | 0.1453 | 0.7557 | 0.8009 | 0.7776 | 0.9785 |
| 0.0036 | 12.0 | 22008 | 0.1662 | 0.7554 | 0.8198 | 0.7863 | 0.9777 |
| 0.0027 | 13.0 | 23842 | 0.1621 | 0.7781 | 0.8075 | 0.7925 | 0.9790 |
| 0.0027 | 14.0 | 25676 | 0.1599 | 0.7519 | 0.8110 | 0.7804 | 0.9776 |
| 0.0027 | 15.0 | 27510 | 0.1633 | 0.7710 | 0.8127 | 0.7913 | 0.9785 |
| 0.0027 | 16.0 | 29344 | 0.1674 | 0.7588 | 0.8129 | 0.7849 | 0.9780 |
| 0.0022 | 17.0 | 31178 | 0.1670 | 0.7652 | 0.8168 | 0.7902 | 0.9781 |
| 0.0021 | 18.0 | 33012 | 0.1586 | 0.7734 | 0.8159 | 0.7940 | 0.9790 |
| 0.002 | 19.0 | 34846 | 0.1650 | 0.7787 | 0.8172 | 0.7975 | 0.9795 |
| 0.0018 | 20.0 | 36680 | 0.1642 | 0.7697 | 0.8048 | 0.7868 | 0.9793 |
| 0.0017 | 21.0 | 38514 | 0.1874 | 0.7743 | 0.8176 | 0.7954 | 0.9784 |
| 0.0015 | 22.0 | 40348 | 0.1598 | 0.7647 | 0.8227 | 0.7926 | 0.9785 |
| 0.0012 | 23.0 | 42182 | 0.1819 | 0.7958 | 0.7997 | 0.7977 | 0.9793 |
| 0.0016 | 24.0 | 44016 | 0.1679 | 0.7960 | 0.8073 | 0.8016 | 0.9794 |
| 0.0013 | 25.0 | 45850 | 0.1659 | 0.7662 | 0.8147 | 0.7897 | 0.9785 |
| 0.001 | 26.0 | 47684 | 0.1774 | 0.7732 | 0.8217 | 0.7967 | 0.9789 |
| 0.0016 | 27.0 | 49518 | 0.1622 | 0.7767 | 0.8131 | 0.7945 | 0.9789 |
| 0.0007 | 28.0 | 51352 | 0.1958 | 0.7642 | 0.8223 | 0.7922 | 0.9783 |
| 0.0009 | 29.0 | 53186 | 0.1861 | 0.7764 | 0.8223 | 0.7987 | 0.9790 |
| 0.0012 | 30.0 | 55020 | 0.1917 | 0.7528 | 0.8252 | 0.7873 | 0.9774 |
| 0.0005 | 31.0 | 56854 | 0.1952 | 0.7833 | 0.8106 | 0.7967 | 0.9792 |
| 0.0009 | 32.0 | 58688 | 0.1910 | 0.7801 | 0.8149 | 0.7971 | 0.9791 |
| 0.0008 | 33.0 | 60522 | 0.1931 | 0.7737 | 0.8180 | 0.7952 | 0.9790 |
| 0.0006 | 34.0 | 62356 | 0.1902 | 0.7730 | 0.8176 | 0.7947 | 0.9788 |
| 0.0008 | 35.0 | 64190 | 0.1904 | 0.7799 | 0.8211 | 0.8 | 0.9791 |
| 0.0006 | 36.0 | 66024 | 0.1951 | 0.7844 | 0.8153 | 0.7995 | 0.9795 |
| 0.0008 | 37.0 | 67858 | 0.1943 | 0.7749 | 0.8256 | 0.7994 | 0.9791 |
| 0.0007 | 38.0 | 69692 | 0.2051 | 0.7796 | 0.8248 | 0.8016 | 0.9791 |
| 0.0004 | 39.0 | 71526 | 0.2108 | 0.7796 | 0.8223 | 0.8004 | 0.9792 |
| 0.0004 | 40.0 | 73360 | 0.2135 | 0.7788 | 0.8254 | 0.8014 | 0.9792 |
| 0.0004 | 41.0 | 75194 | 0.2028 | 0.7908 | 0.8176 | 0.8040 | 0.9798 |
| 0.0006 | 42.0 | 77028 | 0.2058 | 0.7855 | 0.8215 | 0.8031 | 0.9796 |
| 0.0005 | 43.0 | 78862 | 0.2109 | 0.7860 | 0.8254 | 0.8052 | 0.9793 |
| 0.0004 | 44.0 | 80696 | 0.2175 | 0.7784 | 0.8287 | 0.8028 | 0.9791 |
| 0.0003 | 45.0 | 82530 | 0.2206 | 0.7904 | 0.8223 | 0.8060 | 0.9795 |
| 0.0003 | 46.0 | 84364 | 0.2198 | 0.7942 | 0.8180 | 0.8059 | 0.9797 |
| 0.0004 | 47.0 | 86198 | 0.2265 | 0.7791 | 0.8233 | 0.8006 | 0.9791 |
| 0.0003 | 48.0 | 88032 | 0.2265 | 0.7825 | 0.8242 | 0.8028 | 0.9793 |
| 0.0004 | 49.0 | 89866 | 0.2260 | 0.7892 | 0.8209 | 0.8048 | 0.9794 |
| 0.0003 | 50.0 | 91700 | 0.2260 | 0.7906 | 0.8223 | 0.8061 | 0.9796 |
# Run the model
```python
from transformers import pipeline
model_checkpoint = "manibt1993/huner_disease"
token_classifier = pipeline(
"token-classification", model=model_checkpoint, aggregation_strategy="simple"
)
token_classifier("patient has diabtes, anemia, hypertension with ckd which hurts the patient since 6 years. Patient today experience with right leg pain, fever and cough.")
```
### Model output
```python
[{'entity_group': 'Disease',
'score': 0.69145554,
'word': 'diabtes',
'start': 12,
'end': 19},
{'entity_group': 'Disease',
'score': 0.9955915,
'word': 'anemia',
'start': 21,
'end': 27},
{'entity_group': 'Disease',
'score': 0.99971104,
'word': 'hypertension',
'start': 29,
'end': 41},
{'entity_group': 'Disease',
'score': 0.9249976,
'word': 'right leg pain',
'start': 120,
'end': 134},
{'entity_group': 'Disease',
'score': 0.9983512,
'word': 'fever',
'start': 136,
'end': 141},
{'entity_group': 'Disease',
'score': 0.99849665,
'word': 'cough',
'start': 146,
'end': 151}]
```
### Framework versions
- Transformers 4.37.2
- Pytorch 2.0.0
- Datasets 2.16.1
- Tokenizers 0.15.1
|
Tommidi/spatio_temporal_vit-finetuned-ucf101-subset | Tommidi | 2024-02-07T17:24:01Z | 18 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"st_vit",
"generated_from_trainer",
"base_model:Tommidi/st_vit_untrained",
"base_model:finetune:Tommidi/st_vit_untrained",
"endpoints_compatible",
"region:us"
] | null | 2024-02-07T16:39:37Z | ---
base_model: Tommidi/st_vit_untrained
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: spatio_temporal_vit-finetuned-ucf101-subset
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# spatio_temporal_vit-finetuned-ucf101-subset
This model is a fine-tuned version of [Tommidi/st_vit_untrained](https://huggingface.co/Tommidi/st_vit_untrained) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1244
- Accuracy: 0.9
## 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
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 37
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6013 | 1.0 | 37 | 0.1244 | 0.9 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
angela1996/intel-image-classification | angela1996 | 2024-02-07T17:21:06Z | 0 | 0 | fastai | [
"fastai",
"region:us"
] | null | 2024-02-07T17:21:03Z | ---
tags:
- fastai
---
# Amazing!
🥳 Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)).
3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)!
Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card.
---
# Model card
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
|
manche/gpt2-safeguard-sg1 | manche | 2024-02-07T17:19:02Z | 89 | 0 | transformers | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-02-07T17:18:03Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
MiVaCod/intel-image-classification | MiVaCod | 2024-02-07T17:15:39Z | 0 | 0 | fastai | [
"fastai",
"region:us"
] | null | 2024-02-07T17:15:35Z | ---
tags:
- fastai
---
# Amazing!
🥳 Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)).
3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)!
Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card.
---
# Model card
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
|
ambrosfitz/tinyllama-history-chat_v0.2 | ambrosfitz | 2024-02-07T17:03:47Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-02-07T15:12:03Z | ---
library_name: transformers
tags: []
---
## Run summary:
train/epoch 13.91 <br/>
train/global_step 40 <br/>
train/learning_rate 0.0 <br/>
train/loss 0.2795 <br/>
train/total_flos 4134138886176768.0 <br/>
train/train_loss 1.33859 <br/>
train/train_runtime 1368.8841 <br/>
train/train_samples_per_second 10.344 <br/>
train/train_steps_per_second 0.029 <br/> |
varun-v-rao/roberta-large-bn-adapter-3.17M-snli-model3 | varun-v-rao | 2024-02-07T16:59:08Z | 0 | 0 | null | [
"tensorboard",
"generated_from_trainer",
"base_model:FacebookAI/roberta-large",
"base_model:finetune:FacebookAI/roberta-large",
"license:mit",
"region:us"
] | null | 2024-02-07T14:35:32Z | ---
license: mit
base_model: roberta-large
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: roberta-large-bn-adapter-3.17M-snli-model3
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. -->
# roberta-large-bn-adapter-3.17M-snli-model3
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6239
- Accuracy: 0.792
## 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: 84
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.31 | 1.0 | 8584 | 0.2443 | 0.9115 |
| 0.2958 | 2.0 | 17168 | 0.2302 | 0.9200 |
| 0.2816 | 3.0 | 25752 | 0.2253 | 0.9214 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
Bugtus/bugtus_nlp4web | Bugtus | 2024-02-07T16:58:07Z | 91 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"question-answering",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | question-answering | 2024-02-07T16:04:52Z | ---
license: apache-2.0
---
Repository for my nlp4web model created for homework 6.
|
interrobang/OpenHermes-2.5-Mistral-7B-GGUF-f16 | interrobang | 2024-02-07T16:56:00Z | 22 | 1 | null | [
"gguf",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-02-07T16:03:14Z | ---
license: apache-2.0
---
OpenHermes-2.5-Mistral-7B by teknium converted to f16 gguf for easier tinkering;
original model at https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B |
theminji/TinyAITA | theminji | 2024-02-07T16:52:14Z | 5 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"llama",
"text-generation",
"trl",
"sft",
"generated_from_trainer",
"base_model:TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T",
"base_model:finetune:TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-02-07T05:03:39Z | ---
license: apache-2.0
base_model: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: TinyAITA
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. -->
# TinyAITA
This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T](https://huggingface.co/TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T) on the None dataset.
## Model description
```py
import torch
from transformers import pipeline, AutoTokenizer, TextStreamer
import re
tokenizer = AutoTokenizer.from_pretrained("TheBossLevel123/TinyAITA")
pipe = pipeline("text-generation", model="TheBossLevel123/TinyAITA", torch_dtype=torch.bfloat16, device_map="auto")
streamer=TextStreamer(tokenizer)
```
```py
prompt = 'AITA for XYZ?'
outputs = pipe(prompt, max_new_tokens=1024, do_sample=True, temperature=0.9, streamer=streamer, eos_token_id=tokenizer.encode("<|im_end|>"))
if outputs and "generated_text" in outputs[0]:
text = outputs[0]["generated_text"]
print(f"Prompt: {prompt}")
print("")
print(text)
```
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 32
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- training_steps: 200
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
Lectoric/Stable_Diffusion_Challenge | Lectoric | 2024-02-07T16:52:01Z | 1 | 0 | diffusers | [
"diffusers",
"text-to-image",
"autotrain",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:finetune:stabilityai/stable-diffusion-xl-base-1.0",
"region:us"
] | text-to-image | 2024-01-31T13:12:13Z |
---
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: photo of iron armor
tags:
- text-to-image
- diffusers
- autotrain
inference: true
---
# DreamBooth trained by AutoTrain
Text encoder was trained.
|
mustafakara/duck | mustafakara | 2024-02-07T16:50:14Z | 0 | 1 | diffusers | [
"diffusers",
"tensorboard",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"dreambooth",
"base_model:CompVis/stable-diffusion-v1-4",
"base_model:finetune:CompVis/stable-diffusion-v1-4",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | 2024-02-07T16:37:08Z |
---
license: creativeml-openrail-m
base_model: CompVis/stable-diffusion-v1-4
instance_prompt: a photo of rsu monster toy
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- dreambooth
inference: true
---
# DreamBooth - mustafakara/duck
This is a dreambooth model derived from CompVis/stable-diffusion-v1-4. The weights were trained on a photo of rsu monster toy using [DreamBooth](https://dreambooth.github.io/).
You can find some example images in the following.
DreamBooth for the text encoder was enabled: False.
|
objecthub/Controlly | objecthub | 2024-02-07T16:35:49Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
] | null | 2024-02-07T16:35:49Z | ---
license: creativeml-openrail-m
---
|
Muhammedwelian/Lamba_man | Muhammedwelian | 2024-02-07T16:32:32Z | 0 | 0 | null | [
"license:other",
"region:us"
] | null | 2024-02-07T16:32:32Z | ---
license: other
license_name: '392001'
license_link: LICENSE
---
|
Scott617/ppo-LunarLander-v2 | Scott617 | 2024-02-07T16:29:05Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2024-02-07T16:28:45Z | ---
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: 270.54 +/- 13.03
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
...
```
|
hoanghoavienvo/roberta-base-detect-cheapfake-combined-train-test-contradict | hoanghoavienvo | 2024-02-07T16:23:18Z | 90 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:FacebookAI/roberta-base",
"base_model:finetune:FacebookAI/roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-02-07T16:10:05Z | ---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: roberta-base-detect-cheapfake-combined-train-test-contradict
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. -->
# roberta-base-detect-cheapfake-combined-train-test-contradict
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4261
- Accuracy: 0.89
- F1: 0.8817
## 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-06
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 166 | 0.4435 | 0.84 | 0.8333 |
| No log | 2.0 | 332 | 0.6567 | 0.835 | 0.8374 |
| No log | 3.0 | 498 | 0.3563 | 0.895 | 0.88 |
| 0.2851 | 4.0 | 664 | 0.3671 | 0.895 | 0.8814 |
| 0.2851 | 5.0 | 830 | 0.4261 | 0.89 | 0.8817 |
### Framework versions
- Transformers 4.37.0
- Pytorch 2.1.2
- Datasets 2.1.0
- Tokenizers 0.15.1
|
bdpc/test_twowayloss_implementation | bdpc | 2024-02-07T16:14:37Z | 91 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-02-06T12:41:21Z | ---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: test_twowayloss_implementation
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# test_twowayloss_implementation
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 8.9001
- Accuracy: 0.5659
- Precision: 0.0114
- Recall: 0.5082
- F1: 0.0223
- Hamming: 0.4341
## 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
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Hamming |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|
| 8.8818 | 0.0 | 5 | 8.9210 | 0.5632 | 0.0110 | 0.4947 | 0.0216 | 0.4368 |
| 8.124 | 0.0 | 10 | 8.9001 | 0.5659 | 0.0114 | 0.5082 | 0.0223 | 0.4341 |
### Framework versions
- Transformers 4.35.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.7.1
- Tokenizers 0.14.1
|
danaleee/CL_rank10_iter800_valprompt | danaleee | 2024-02-07T16:11:20Z | 1 | 0 | diffusers | [
"diffusers",
"tensorboard",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"base_model:CompVis/stable-diffusion-v1-4",
"base_model:adapter:CompVis/stable-diffusion-v1-4",
"license:creativeml-openrail-m",
"region:us"
] | text-to-image | 2024-02-07T15:35:01Z |
---
license: creativeml-openrail-m
base_model: CompVis/stable-diffusion-v1-4
instance_prompt: a photo of sks duck
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA DreamBooth - danaleee/CL_rank10_iter800_valprompt
These are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks duck using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
LoRA for the text encoder was enabled: False.
|
IB13/t5_ppo_model_3 | IB13 | 2024-02-07T16:09:18Z | 1 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:IB13/sft_t5_base_processed_model",
"base_model:adapter:IB13/sft_t5_base_processed_model",
"region:us"
] | null | 2024-02-07T13:50:42Z | ---
library_name: peft
base_model: IB13/sft_t5_base_processed_model
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
### Framework versions
- PEFT 0.6.2
|
wish6424/Mixtral-8x7B-prostate-sum-test | wish6424 | 2024-02-07T16:08:40Z | 2 | 0 | peft | [
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"dataset:generator",
"base_model:mistralai/Mixtral-8x7B-Instruct-v0.1",
"base_model:adapter:mistralai/Mixtral-8x7B-Instruct-v0.1",
"license:apache-2.0",
"region:us"
] | null | 2024-02-06T19:26:33Z | ---
license: apache-2.0
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
datasets:
- generator
base_model: mistralai/Mixtral-8x7B-Instruct-v0.1
model-index:
- name: Mixtral-8x7B-prostate-sum-test
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. -->
# Mixtral-8x7B-prostate-sum-test
This model is a fine-tuned version of [mistralai/Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1) on the generator dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.9034
- eval_runtime: 1.0713
- eval_samples_per_second: 0.933
- eval_steps_per_second: 0.933
- epoch: 41.67
- step: 250
## 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: 2.5e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 0.03
- training_steps: 1000
### Framework versions
- PEFT 0.8.2
- Transformers 4.37.2
- Pytorch 2.2.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1 |
noza-kit/Adapter_llama2_translate_Q_enpt_ex2-1epoch | noza-kit | 2024-02-07T16:07:37Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-2-7b-hf",
"base_model:adapter:meta-llama/Llama-2-7b-hf",
"region:us"
] | null | 2024-02-07T13:20:47Z | ---
library_name: peft
base_model: meta-llama/Llama-2-7b-hf
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.8.2 |
tavalenzuelag/mistral-7b-e2e-mod | tavalenzuelag | 2024-02-07T16:06:33Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-02-01T13:56:19Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
LoneStriker/Senku-70B-Full-5.0bpw-h6-exl2 | LoneStriker | 2024-02-07T15:57:03Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"license:cc-by-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-02-07T15:38:23Z | ---
license: cc-by-2.0
---
Finetune of miqu-70b-sf dequant of miqudev's leak of Mistral-70B (allegedly an early mistral medium). My diffs are available under CC-0, this is a merge with the leaked model, you can use the other repository to save bandwidth.
EQ-Bench: 84.89
Will run more benches later. |
pimcore/IEP__zero-shot-image-classification | pimcore | 2024-02-07T15:49:18Z | 0 | 0 | generic | [
"generic",
"vision",
"zero-shot-image-classification",
"endpoints-template",
"base_model:openai/clip-vit-large-patch14",
"base_model:finetune:openai/clip-vit-large-patch14",
"endpoints_compatible",
"region:us"
] | zero-shot-image-classification | 2024-02-07T15:16:28Z | ---
tags:
- vision
- zero-shot-image-classification
- endpoints-template
inference: false
pipeline_tag: zero-shot-image-classification
base_model: openai/clip-vit-large-patch14
library_name: generic
---
# Fork of [openai/clip-vit-base-patch32](https://huggingface.co/openai/clip-vit-base-patch32) for a `zero-sho-image-classification` Inference endpoint.
This repository implements a `custom` task for `zero-shot-image-classification` for 🤗 Inference Endpoints. The code for the customized
pipeline is in the handler.py.
To use deploy this model an Inference Endpoint you have to select `Custom` as task to use the `handler.py` file.
### expected Request payload
```json
{
"image": encoded_image,
"parameters": {
"candidate_labels": "green, yellow, blue, white, silver"
}
}
```
`encoded_image` is a base64 encoded image. |
Nexesenex/jondurbin_bagel-7b-v0.4-iMat.GGUF | Nexesenex | 2024-02-07T15:46:11Z | 4 | 0 | null | [
"gguf",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-02-06T23:46:35Z | GGUF Quants with iMatrix for : https://huggingface.co/jondurbin/bagel-7b-v0.4
Llama Benchs :
Bagel 7b 0.4 (this version) :
- Bagel-7b-v0.4.Q8_0.gguf,-,Hellaswag,80.75,400,2024-02-07 00:00:00,,07b,Mistral_7b_v02,8192,,,GGUF,JonDurbin,Nexesenex,
- Bagel-7b-v0.4.Q8_0.gguf,-,Hellaswag,79.4,1000,2024-02-07 00:00:00,,07b,Mistral_7b_v02,8192,,,GGUF,JonDurbin,Nexesenex,
- Bagel-7b-v0.4.Q8_0.gguf,-,Arc-Challenge,51.83946488,,299,2024-02-07 05:40:00,,07b,Mistral_7b_v02,8192,,,GGUF,JonDurbin,Nexesenex,
- Bagel-7b-v0.4.Q8_0.gguf,-,Arc-Easy,79.29824561,,570,2024-02-07 05:40:00,,07b,Mistral_7b_v02,8192,,,GGUF,JonDurbin,Nexesenex,
- Bagel-7b-v0.4.Q8_0.gguf,-,MMLU,44.08945687,,313,2024-02-07 05:40:00,,07b,Mistral_7b_v02,8192,,,GGUF,JonDurbin,Nexesenex,
- Bagel-7b-v0.4.Q8_0.gguf,-,Thruthful-QA,33.53733170,,817,2024-02-07 05:40:00,,07b,Mistral_7b_v02,8192,,,GGUF,JonDurbin,Nexesenex,
- Bagel-7b-v0.4.Q8_0.gguf,-,Winogrande,76.3220,,1267,2024-02-07 05:40:00,,07b,Mistral_7b_v02,8192,,,GGUF,JonDurbin,Nexesenex,
- Bagel-7b-v0.4.Q8_0.gguf,-,wikitext,6.1340,512,512,2024-02-07 23:00:00,,07b,Mistral_7b_v02,8192,,,GGUF,JonDurbin,Nexesenex,
- Bagel-7b-v0.4.Q8_0.gguf,-,wikitext,5.4116,4096,4096,2024-02-07 23:12:00,,07b,Mistral_7b_v02,8192,,,GGUF,JonDurbin,Nexesenex,
- Bagel-7b-v0.4.Q8_0.gguf,-,wikitext,6.6741,7168,7168,2024-02-07 23:16:00,,07b,Mistral_7b_v02,8192,,,GGUF,JonDurbin,Nexesenex,
- Bagel-7b-v0.4.Q8_0.gguf,-,wikitext,6.5003,8192,8192,2024-02-07 23:20:00,,07b,Mistral_7b_v02,8192,,,GGUF,JonDurbin,Nexesenex,
- Bagel-7b-v0.4.Q8_0.gguf,-,wikitext,8.3501,10240,10240,2024-02-07 23:25:00,,07b,Mistral_7b_v02,8192,,,GGUF,JonDurbin,Nexesenex,
Bagel 7b 0.4 DPO :
- Bagel-dpo-7b-v0.4.Q6_K.gguf,-,Hellaswag,82.75,400,2024-02-07 00:00:00,,07b,Mistral_7b_v02,8192,,,GGUF,JonDurbin,LoneStriker,
- Bagel-dpo-7b-v0.4.Q6_K.gguf,-,Hellaswag,81.1,1000,2024-02-07 00:00:00,,07b,Mistral_7b_v02,8192,,,GGUF,JonDurbin,LoneStriker,
- Bagel-dpo-7b-v0.4.Q6_K.gguf,-,Arc-Challenge,53.84615385,,299,2024-02-07 05:40:00,,07b,Mistral_7b_v02,8192,,,GGUF,JonDurbin,LoneStriker,
- Bagel-dpo-7b-v0.4.Q6_K.gguf,-,Arc-Easy,80.70175439,,570,2024-02-07 05:40:00,,07b,Mistral_7b_v02,8192,,,GGUF,JonDurbin,LoneStriker,
- Bagel-dpo-7b-v0.4.Q6_K.gguf,-,MMLU,45.36741214,,313,2024-02-07 05:40:00,,07b,Mistral_7b_v02,8192,,,GGUF,JonDurbin,LoneStriker,
- Bagel-dpo-7b-v0.4.Q6_K.gguf,-,Thruthful-QA,44.55324357,,817,2024-02-07 05:40:00,,07b,Mistral_7b_v02,8192,,,GGUF,JonDurbin,LoneStriker,
- Bagel-dpo-7b-v0.4.Q6_K.gguf,-,Winogrande,76.4799,,1267,2024-02-07 05:40:00,,07b,Mistral_7b_v02,8192,,,GGUF,JonDurbin,LoneStriker,
- Bagel-dpo-7b-v0.4.Q6_K.gguf,-,wikitext,6.3245,512,512,2024-02-07 23:30:00,,07b,Mistral_7b_v02,8192,,,GGUF,JonDurbin,LoneStriker,
- Bagel-dpo-7b-v0.4.Q6_K.gguf,-,wikitext,5.7832,4096,4096,2024-02-07 23:42:00,,07b,Mistral_7b_v02,8192,,,GGUF,JonDurbin,LoneStriker,
- Bagel-dpo-7b-v0.4.Q6_K.gguf,-,wikitext,8.1732,7168,7168,2024-02-07 23:46:00,,07b,Mistral_7b_v02,8192,,,GGUF,JonDurbin,LoneStriker,
- Bagel-dpo-7b-v0.4.Q6_K.gguf,-,wikitext,8.0212,8192,8192,2024-02-07 23:50:00,,07b,Mistral_7b_v02,8192,,,GGUF,JonDurbin,LoneStriker,
- Bagel-dpo-7b-v0.4.Q6_K.gguf,-,wikitext,10.9006,10240,10240,2024-02-07 23:55:00,,07b,Mistral_7b_v02,8192,,,GGUF,JonDurbin,LoneStriker,
Bagel 7b 0.1 :
- Bagel-7b-v0.1.Q6_K.gguf,-,Hellaswag,86,400,2024-01-27 00:00:00,,07b,Mistral_7b_v02,8192,,,GGUF,JonDurbin,TheBloke,
- Bagel-7b-v0.1.Q6_K.gguf,-,Hellaswag_Bin,78.25,400,2024-01-27 00:00:00,,07b,Mistral_7b_v02,8192,,,GGUF,JonDurbin,TheBloke,
- Bagel-7b-v0.1.Q6_K.gguf,-,Arc-Challenge,48.82943144,,299,2024-01-27 05:40:00,,07b,Mistral_7b_v02,8192,,,GGUF,JonDurbin,TheBloke,
- Bagel-7b-v0.1.Q6_K.gguf,-,Arc-Easy,72.80701754,,570,2024-01-27 05:40:00,,07b,Mistral_7b_v02,8192,,,GGUF,JonDurbin,TheBloke,
- Bagel-7b-v0.1.Q6_K.gguf,-,MMLU,43.76996805,,313,2024-01-27 05:40:00,,07b,Mistral_7b_v02,8192,,,GGUF,JonDurbin,TheBloke,
- Bagel-7b-v0.1.Q6_K.gguf,-,Thruthful-QA,31.70134639,,817,2024-01-27 05:40:00,,07b,Mistral_7b_v02,8192,,,GGUF,JonDurbin,TheBloke,
- Bagel-7b-v0.1.Q6_K.gguf,-,Winogrande,76.4009,,1267,2024-01-27 05:40:00,,07b,Mistral_7b_v02,8192,,,GGUF,JonDurbin,TheBloke,
- Bagel-7b-v0.1.Q6_K.gguf,-,wikitext,5.82,512,512,2024-01-13 23:00:00,,07b,Mistral_7b_v02,8192,,,GGUF,JonDurbin,TheBloke,
- Bagel-7b-v0.1.Q6_K.gguf,-,wikitext,5.3503,1024,1024,2024-01-13 23:04:00,,07b,Mistral_7b_v02,8192,,,GGUF,JonDurbin,TheBloke,
- Bagel-7b-v0.1.Q6_K.gguf,-,wikitext,5.0243,2048,2048,2024-01-13 23:08:00,,07b,Mistral_7b_v02,8192,,,GGUF,JonDurbin,TheBloke,
- Bagel-7b-v0.1.Q6_K.gguf,-,wikitext,4.9976,4096,4096,2024-01-13 23:12:00,,07b,Mistral_7b_v02,8192,,,GGUF,JonDurbin,TheBloke,
- Bagel-7b-v0.1.Q6_K.gguf,-,wikitext,5.1589,7168,7168,2024-01-13 23:16:00,,07b,Mistral_7b_v02,8192,,,GGUF,JonDurbin,TheBloke,
- Bagel-7b-v0.1.Q6_K.gguf,-,wikitext,4.9722,8192,8192,2024-01-13 23:20:00,,07b,Mistral_7b_v02,8192,,,GGUF,JonDurbin,TheBloke,
- Bagel-7b-v0.1.Q6_K.gguf,-,wikitext,8.999,10240,10240,2024-01-13 23:25:00,,07b,Mistral_7b_v02,8192,,,GGUF,JonDurbin,TheBloke,
Bagel 7b 0.1 DPO :
- Bagel-dpo-7b-v0.1.Q6_K.gguf,-,Hellaswag,86.25,400,2024-01-27 00:00:00,,07b,Mistral_7b_v02,8192,,,GGUF,JonDurbin,TheBloke,
- Bagel-dpo-7b-v0.1.Q6_K.gguf,-,Hellaswag_Bin,79.5,400,2024-01-27 00:00:00,,07b,Mistral_7b_v02,8192,,,GGUF,JonDurbin,TheBloke,
- Bagel-dpo-7b-v0.1.Q6_K.gguf,-,Arc-Challenge,46.48829431,,299,2024-01-27 05:40:00,,07b,Mistral_7b_v02,8192,,,GGUF,JonDurbin,TheBloke,
- Bagel-dpo-7b-v0.1.Q6_K.gguf,-,Arc-Easy,65.08771930,,570,2024-01-27 05:40:00,,07b,Mistral_7b_v02,8192,,,GGUF,JonDurbin,TheBloke,
- Bagel-dpo-7b-v0.1.Q6_K.gguf,-,MMLU,44.08945687,,313,2024-01-27 05:40:00,,07b,Mistral_7b_v02,8192,,,GGUF,JonDurbin,TheBloke,
- Bagel-dpo-7b-v0.1.Q6_K.gguf,-,Thruthful-QA,42.35006120,,817,2024-01-27 05:40:00,,07b,Mistral_7b_v02,8192,,,GGUF,JonDurbin,TheBloke,
- Bagel-dpo-7b-v0.1.Q6_K.gguf,-,Winogrande,75.8485,,1267,2024-01-27 05:40:00,,07b,Mistral_7b_v02,8192,,,GGUF,JonDurbin,TheBloke,
- Bagel-dpo-7b-v0.1.Q6_K.gguf,-,wikitext,5.8674,512,512,2024-01-13 23:30:00,,07b,Mistral_7b_v02,8192,,,GGUF,JonDurbin,TheBloke,
- Bagel-dpo-7b-v0.1.Q6_K.gguf,-,wikitext,5.3889,1024,1024,2024-01-13 23:34:00,,07b,Mistral_7b_v02,8192,,,GGUF,JonDurbin,TheBloke,
- Bagel-dpo-7b-v0.1.Q6_K.gguf,-,wikitext,5.0578,2048,2048,2024-01-13 23:38:00,,07b,Mistral_7b_v02,8192,,,GGUF,JonDurbin,TheBloke,
- Bagel-dpo-7b-v0.1.Q6_K.gguf,-,wikitext,5.012,4096,4096,2024-01-13 23:42:00,,07b,Mistral_7b_v02,8192,,,GGUF,JonDurbin,TheBloke,
- Bagel-dpo-7b-v0.1.Q6_K.gguf,-,wikitext,5.1349,7168,7168,2024-01-13 23:46:00,,07b,Mistral_7b_v02,8192,,,GGUF,JonDurbin,TheBloke,
- Bagel-dpo-7b-v0.1.Q6_K.gguf,-,wikitext,4.9318,8192,8192,2024-01-13 23:50:00,,07b,Mistral_7b_v02,8192,,,GGUF,JonDurbin,TheBloke,
- Bagel-dpo-7b-v0.1.Q6_K.gguf,-,wikitext,8.3897,10240,10240,2024-01-13 23:55:00,,07b,Mistral_7b_v02,8192,,,GGUF,JonDurbin,TheBloke, |
ffxvs/negative-prompts-pack-xl | ffxvs | 2024-02-07T15:43:55Z | 0 | 2 | null | [
"region:us"
] | null | 2024-01-22T16:52:44Z | List of negative embeddings for SDXL :
* [ac_neg1](https://civitai.com/models/148131?modelVersionId=166373)
* [aidxlv05_neg](https://civitai.com/models/144327/negative-embedding-for-sdxl-based-anime-models?modelVersionId=195614)
* [FastNegative](https://civitai.com/models/143607/fastnegative?modelVersionId=159385)
* [ImgFixerPre0.3](https://civitai.com/models/139688/imgfixer-or-negative-ti?modelVersionId=159184)
* [negativeXL_D](https://civitai.com/models/118418/negativexl?modelVersionId=134583)
* [unaestheticXL_hk1](https://civitai.com/models/119032?modelVersionId=302265) |
LoneStriker/Senku-70B-Full-4.65bpw-h6-exl2 | LoneStriker | 2024-02-07T15:38:22Z | 6 | 2 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"license:cc-by-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-02-07T15:10:09Z | ---
license: cc-by-2.0
---
Finetune of miqu-70b-sf dequant of miqudev's leak of Mistral-70B (allegedly an early mistral medium). My diffs are available under CC-0, this is a merge with the leaked model, you can use the other repository to save bandwidth.
EQ-Bench: 84.89
Will run more benches later. |
tizayi/ppo-SnowballTarget | tizayi | 2024-02-07T15:38:15Z | 0 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
] | reinforcement-learning | 2024-02-07T15:38:12Z | ---
library_name: ml-agents
tags:
- SnowballTarget
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: tizayi/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Jayem-11/zephyr-7b-beta_assistant_v0.2_merged | Jayem-11 | 2024-02-07T15:32:58Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-02-07T15:20:15Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
bartowski/internlm2-chat-20b-llama-exp-exl2 | bartowski | 2024-02-07T15:28:58Z | 1 | 1 | null | [
"text-generation",
"license:other",
"region:us"
] | text-generation | 2024-02-07T01:45:27Z | ---
pipeline_tag: text-generation
license: other
quantized_by: bartowski
---
this quant was made by first converting the model to llama format using https://github.com/InternLM/InternLM/blob/main/tools/convert2llama.py
if performance is different than the one converted previously, please comment
## Exllama v2 Quantizations of internlm2-chat-20b-llama-exp
Using <a href="https://github.com/turboderp/exllamav2/releases/tag/v0.0.13">turboderp's ExLlamaV2 v0.0.13</a> for quantization.
# The "main" branch only contains the measurement.json, download one of the other branches for the model (see below)
Each branch contains an individual bits per weight, with the main one containing only the meaurement.json for further conversions.
Original model: https://huggingface.co/internlm/internlm2-chat-20b
| Branch | Bits | lm_head bits | VRAM (4k) | VRAM (16k) | VRAM (32k) | Description |
| ------ | ---- | ------------ | ---- | ---- | ---- | ----------- |
| [6_5](https://huggingface.co/Bartowski/internlm2-chat-20b-llama-exp-exl2/tree/6_5) | 6.5 | 8.0 | 19.6 GB | 21.0 GB | 23.0 GB | Near unquantized performance at vastly reduced size, **recommended**. |
| [4_25](https://huggingface.co/Bartowski/internlm2-chat-20b-llama-exp-exl2/tree/4_25) | 4.25 | 6.0 | 13.8 GB | 15.2 GB | 17.2 GB | GPTQ equivalent bits per weight, slightly higher quality. |
| [3_5](https://huggingface.co/Bartowski/internlm2-chat-20b-llama-exp-exl2/tree/3_5) | 3.5 | 6.0 | 12.4 GB | 13.8 GB | 15.8 GB | Lower quality, only use if you have to. |
| [3_0](https://huggingface.co/Bartowski/internlm2-chat-20b-llama-exp-exl2/tree/3_0) | 3.0 | 6.0 | 11.1 GB | 12.5 GB | 15.5 GB | Very low quality. Usable on 12GB. |
## Download instructions
With git:
```shell
git clone --single-branch --branch 6_5 https://huggingface.co/bartowski/internlm2-chat-20b-llama-exp-exl2 internlm2-chat-20b-llama-exp-exl2-6_5
```
With huggingface hub (credit to TheBloke for instructions):
```shell
pip3 install huggingface-hub
```
To download the `main` (only useful if you only care about measurement.json) branch to a folder called `internlm2-chat-20b-llama-exp-exl2`:
```shell
mkdir internlm2-chat-20b-llama-exp-exl2
huggingface-cli download bartowski/internlm2-chat-20b-llama-exp-exl2 --local-dir internlm2-chat-20b-llama-exp-exl2 --local-dir-use-symlinks False
```
To download from a different branch, add the `--revision` parameter:
Linux:
```shell
mkdir internlm2-chat-20b-llama-exp-exl2-6_5
huggingface-cli download bartowski/internlm2-chat-20b-llama-exp-exl2 --revision 6_5 --local-dir internlm2-chat-20b-llama-exp-exl2-6_5 --local-dir-use-symlinks False
```
Windows (which apparently doesn't like _ in folders sometimes?):
```shell
mkdir internlm2-chat-20b-llama-exp-exl2-6.5
huggingface-cli download bartowski/internlm2-chat-20b-llama-exp-exl2 --revision 6_5 --local-dir internlm2-chat-20b-llama-exp-exl2-6.5 --local-dir-use-symlinks False
```
Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
|
kevinautomation/mistral-7b-instruct-tune-project_ask_reddit | kevinautomation | 2024-02-07T15:27:52Z | 6 | 1 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"en",
"dataset:euclaise/reddit-instruct",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] | text-generation | 2024-02-04T18:36:03Z | ---
library_name: transformers
pipeline_tag: text-generation
license: apache-2.0
datasets:
- euclaise/reddit-instruct
language:
- en
---
# Model Card for Model ID
This is the fine-tuned model of Mistral-7B-v0.1 (https://huggingface.co/mistralai/Mistral-7B-v0.1) with reddit instruct dataset
Model Architecture
Mistral-7B-v0.1 is a transformer model, with the following architecture choices:
Grouped-Query Attention
Sliding-Window Attention
Byte-fallback BPE tokenizer
|
LoneStriker/DeepMagic-Coder-7b-Alt-5.0bpw-h6-exl2 | LoneStriker | 2024-02-07T15:27:19Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-02-07T15:22:48Z | ---
license: other
license_name: deepseek
license_link: https://github.com/deepseek-ai/DeepSeek-Coder/blob/main/LICENSE-MODEL
---
(Note: From short testing, this Alt version generated much better code)
Alternate version of DeepMagic-Coder-7b which can be found bellow.
- https://huggingface.co/rombodawg/DeepMagic-Coder-7b
This version uses a diffrent config setup, with the actual base model of the two merges as the "base_model". Test both for yourself and see which is better at coding. Benchmarks coming soon.
Config can be found bellow:
```yaml
models:
- model: deepseek-ai_deepseek-coder-6.7b-instruct
parameters:
weight: 1
- model: ise-uiuc_Magicoder-S-DS-6.7B
parameters:
weight: 1
merge_method: task_arithmetic
base_model: deepseek-ai_deepseek-coder-6.7b-base
parameters:
normalize: true
int8_mask: true
dtype: float16
```
|
rodrigoasth/llama-2-7b-hf | rodrigoasth | 2024-02-07T15:25:37Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-02-07T15:13:56Z | ---
language:
- en
library_name: transformers
--- |
danaleee/CL_rank10_iter500_valprompt | danaleee | 2024-02-07T15:24:33Z | 1 | 0 | diffusers | [
"diffusers",
"tensorboard",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"base_model:CompVis/stable-diffusion-v1-4",
"base_model:adapter:CompVis/stable-diffusion-v1-4",
"license:creativeml-openrail-m",
"region:us"
] | text-to-image | 2024-02-07T14:26:50Z |
---
license: creativeml-openrail-m
base_model: CompVis/stable-diffusion-v1-4
instance_prompt: a photo of sks teddybear
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA DreamBooth - danaleee/CL_rank10_iter500_valprompt
These are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks teddybear using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
LoRA for the text encoder was enabled: False.
|
ssaryssane/ssarry-truthful-13B-slerp | ssaryssane | 2024-02-07T15:23:33Z | 8 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"microsoft/Orca-2-13b",
"Sao10K/Mythical-Destroyer-V2-L2-13B",
"base_model:Sao10K/Mythical-Destroyer-V2-L2-13B",
"base_model:merge:Sao10K/Mythical-Destroyer-V2-L2-13B",
"base_model:microsoft/Orca-2-13b",
"base_model:merge:microsoft/Orca-2-13b",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-02-07T15:17:28Z | ---
tags:
- merge
- mergekit
- lazymergekit
- microsoft/Orca-2-13b
- Sao10K/Mythical-Destroyer-V2-L2-13B
base_model:
- microsoft/Orca-2-13b
- Sao10K/Mythical-Destroyer-V2-L2-13B
---
# ssarry-truthful-13B-slerp
ssarry-truthful-13B-slerp is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [microsoft/Orca-2-13b](https://huggingface.co/microsoft/Orca-2-13b)
* [Sao10K/Mythical-Destroyer-V2-L2-13B](https://huggingface.co/Sao10K/Mythical-Destroyer-V2-L2-13B)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: microsoft/Orca-2-13b
layer_range: [0, 32]
- model: Sao10K/Mythical-Destroyer-V2-L2-13B
layer_range: [0, 32]
merge_method: slerp
base_model: Sao10K/Mythical-Destroyer-V2-L2-13B
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "ssaryssane/ssarry-truthful-13B-slerp"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` |
freecryptobasics/Sam | freecryptobasics | 2024-02-07T15:10:09Z | 0 | 0 | null | [
"region:us"
] | null | 2024-02-07T14:56:36Z | dave bautista man = trigger word |
danaleee/CL_rank50_iter500_valprompt | danaleee | 2024-02-07T15:03:30Z | 1 | 0 | diffusers | [
"diffusers",
"tensorboard",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"base_model:CompVis/stable-diffusion-v1-4",
"base_model:adapter:CompVis/stable-diffusion-v1-4",
"license:creativeml-openrail-m",
"region:us"
] | text-to-image | 2024-02-07T14:23:27Z |
---
license: creativeml-openrail-m
base_model: CompVis/stable-diffusion-v1-4
instance_prompt: a photo of sks duck
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA DreamBooth - danaleee/CL_rank50_iter500_valprompt
These are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks duck using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
LoRA for the text encoder was enabled: False.
|
lolomgrofl/lukatuning1 | lolomgrofl | 2024-02-07T15:02:31Z | 1 | 0 | peft | [
"peft",
"base_model:tiiuae/falcon-7b",
"base_model:adapter:tiiuae/falcon-7b",
"region:us"
] | null | 2023-08-05T19:57:50Z | ---
library_name: peft
base_model: tiiuae/falcon-7b
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.5.0.dev0
|
itsdhanoob/ppo-Pyramids | itsdhanoob | 2024-02-07T14:53:59Z | 6 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] | reinforcement-learning | 2024-02-07T13:53:10Z | ---
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: itsdhanoob/ppo-Pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Tommidi/spatial_vit_temporal_vit-finetuned-ucf101-subset | Tommidi | 2024-02-07T14:47:47Z | 5 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"st_vit",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2024-02-06T13:43:07Z | ---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: spatial_vit_temporal_vit-finetuned-ucf101-subset
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# spatial_vit_temporal_vit-finetuned-ucf101-subset
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3172
- Accuracy: 0.68
## 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
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 148
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.2165 | 0.26 | 38 | 2.0699 | 0.2667 |
| 1.8229 | 1.26 | 76 | 1.8160 | 0.5 |
| 1.4707 | 2.26 | 114 | 1.4157 | 0.7 |
| 1.3886 | 3.23 | 148 | 1.3520 | 0.7333 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
OmarAlsaabi/e5-base-mlqa-finetuned-arabic-for-rag | OmarAlsaabi | 2024-02-07T14:45:18Z | 210 | 4 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"xlm-roberta",
"feature-extraction",
"sentence-similarity",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | sentence-similarity | 2024-02-07T09:18:22Z | ---
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# e5-base-mlqa-finetuned-arabic-for-rag
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('OmarAlsaabi/e5-base-mlqa-finetuned-arabic-for-rag')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 2668 with parameters:
```
{'batch_size': 2, 'sampler': 'torch.utils.data.sampler.SequentialSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 2,
"evaluation_steps": 50,
"evaluator": "sentence_transformers.evaluation.InformationRetrievalEvaluator.InformationRetrievalEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 533,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False})
(2): Normalize()
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
LoneStriker/DeepMagic-Coder-7b-Alt-GPTQ | LoneStriker | 2024-02-07T14:44:04Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-02-07T14:41:20Z | ---
license: other
license_name: deepseek
license_link: https://github.com/deepseek-ai/DeepSeek-Coder/blob/main/LICENSE-MODEL
---
(Note: From short testing, this Alt version generated much better code)
Alternate version of DeepMagic-Coder-7b which can be found bellow.
- https://huggingface.co/rombodawg/DeepMagic-Coder-7b
This version uses a diffrent config setup, with the actual base model of the two merges as the "base_model". Test both for yourself and see which is better at coding. Benchmarks coming soon.
Config can be found bellow:
```yaml
models:
- model: deepseek-ai_deepseek-coder-6.7b-instruct
parameters:
weight: 1
- model: ise-uiuc_Magicoder-S-DS-6.7B
parameters:
weight: 1
merge_method: task_arithmetic
base_model: deepseek-ai_deepseek-coder-6.7b-base
parameters:
normalize: true
int8_mask: true
dtype: float16
```
|
LoneStriker/Senku-70B-Full-3.5bpw-h6-exl2 | LoneStriker | 2024-02-07T14:43:53Z | 7 | 1 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"license:cc-by-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-02-07T14:27:46Z | ---
license: cc-by-2.0
---
Finetune of miqu-70b-sf dequant of miqudev's leak of Mistral-70B (allegedly an early mistral medium). My diffs are available under CC-0, this is a merge with the leaked model, you can use the other repository to save bandwidth.
EQ-Bench: 84.89
Will run more benches later. |
asorokoumov/ppo-LunarLander-v2 | asorokoumov | 2024-02-07T14:42:17Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2024-02-07T14:18:16Z | ---
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: 272.35 +/- 22.03
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
...
```
|
ramsi-k/Reinforce-PixelCopter | ramsi-k | 2024-02-07T14:36:50Z | 0 | 0 | null | [
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | reinforcement-learning | 2024-02-06T13:42:06Z | ---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-PixelCopter
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 38.70 +/- 24.80
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
varun-v-rao/roberta-large-bn-adapter-3.17M-snli-model2 | varun-v-rao | 2024-02-07T14:35:26Z | 0 | 0 | null | [
"tensorboard",
"generated_from_trainer",
"base_model:FacebookAI/roberta-large",
"base_model:finetune:FacebookAI/roberta-large",
"license:mit",
"region:us"
] | null | 2024-02-07T12:11:26Z | ---
license: mit
base_model: roberta-large
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: roberta-large-bn-adapter-3.17M-snli-model2
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. -->
# roberta-large-bn-adapter-3.17M-snli-model2
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6172
- Accuracy: 0.8015
## 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: 27
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3134 | 1.0 | 8584 | 0.2371 | 0.9160 |
| 0.2891 | 2.0 | 17168 | 0.2228 | 0.9224 |
| 0.2792 | 3.0 | 25752 | 0.2222 | 0.9237 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
llmware/slim-tags-tool | llmware | 2024-02-07T14:28:47Z | 87 | 4 | transformers | [
"transformers",
"gguf",
"llama",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-02-07T11:36:59Z | ---
license: apache-2.0
---
# SLIM-TAGS-TOOL
<!-- Provide a quick summary of what the model is/does. -->
**slim-tags-tool** is a 4_K_M quantized GGUF version of slim-tags, providing a small, fast inference implementation, optimized for multi-model concurrent deployment.
[**slim-tags**](https://huggingface.co/llmware/slim-tags) is part of the SLIM ("**S**tructured **L**anguage **I**nstruction **M**odel") series, providing a set of small, specialized decoder-based LLMs, fine-tuned for function-calling.
To pull the model via API:
from huggingface_hub import snapshot_download
snapshot_download("llmware/slim-tags-tool", local_dir="/path/on/your/machine/", local_dir_use_symlinks=False)
Load in your favorite GGUF inference engine, or try with llmware as follows:
from llmware.models import ModelCatalog
# to load the model and make a basic inference
model = ModelCatalog().load_model("slim-tags-tool")
response = model.function_call(text_sample)
# this one line will download the model and run a series of tests
ModelCatalog().tool_test_run("slim-tags-tool", verbose=True)
Slim models can also be loaded even more simply as part of a multi-model, multi-step LLMfx calls:
from llmware.agents import LLMfx
llm_fx = LLMfx()
llm_fx.load_tool("tags")
response = llm_fx.tags(text)
Note: please review [**config.json**](https://huggingface.co/llmware/slim-tags-tool/blob/main/config.json) in the repository for prompt wrapping information, details on the model, and full test set.
## Model Card Contact
Darren Oberst & llmware team
[Any questions? Join us on Discord](https://discord.gg/MhZn5Nc39h)
|
LoneStriker/Senku-70B-Full-2.65bpw-h6-exl2 | LoneStriker | 2024-02-07T14:27:45Z | 8 | 3 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"license:cc-by-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-02-07T14:16:49Z | ---
license: cc-by-2.0
---
Finetune of miqu-70b-sf dequant of miqudev's leak of Mistral-70B (allegedly an early mistral medium). My diffs are available under CC-0, this is a merge with the leaked model, you can use the other repository to save bandwidth.
EQ-Bench: 84.89
Will run more benches later. |
danaleee/CL_rank50_iter500 | danaleee | 2024-02-07T14:16:51Z | 3 | 0 | diffusers | [
"diffusers",
"tensorboard",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"base_model:CompVis/stable-diffusion-v1-4",
"base_model:adapter:CompVis/stable-diffusion-v1-4",
"license:creativeml-openrail-m",
"region:us"
] | text-to-image | 2024-02-07T14:04:54Z |
---
license: creativeml-openrail-m
base_model: CompVis/stable-diffusion-v1-4
instance_prompt: a photo of sks dog
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA DreamBooth - danaleee/CL_rank50_iter500
These are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks dog using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
LoRA for the text encoder was enabled: False.
|
HeydarS/flan-t5-base_peft_v23 | HeydarS | 2024-02-07T14:16:02Z | 1 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/flan-t5-base",
"base_model:adapter:google/flan-t5-base",
"region:us"
] | null | 2024-02-07T14:16:00Z | ---
library_name: peft
base_model: google/flan-t5-base
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.7.2.dev0 |
CLMBR/det-noun-transformer-0 | CLMBR | 2024-02-07T14:09:32Z | 7 | 0 | transformers | [
"transformers",
"pytorch",
"opt",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-02-01T11:41:47Z | ---
tags:
- generated_from_trainer
model-index:
- name: det-noun-transformer-0
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. -->
# det-noun-transformer-0
This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.8633
## 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: 0
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 3052726
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-------:|:---------------:|
| 4.2292 | 0.03 | 76320 | 4.1964 |
| 4.0231 | 1.03 | 152640 | 4.0266 |
| 3.9118 | 0.03 | 228960 | 3.9521 |
| 3.8436 | 0.03 | 305280 | 3.9110 |
| 3.7942 | 0.03 | 381600 | 3.8862 |
| 3.7539 | 1.03 | 457920 | 3.8700 |
| 3.7196 | 0.03 | 534240 | 3.8595 |
| 3.6867 | 1.03 | 610560 | 3.8529 |
| 3.6581 | 0.03 | 686880 | 3.8472 |
| 3.6307 | 1.03 | 763200 | 3.8468 |
| 3.6102 | 0.03 | 839520 | 3.8452 |
| 3.5881 | 1.03 | 915840 | 3.8435 |
| 3.5698 | 0.03 | 992160 | 3.8436 |
| 3.5469 | 1.03 | 1068480 | 3.8442 |
| 3.534 | 0.03 | 1144800 | 3.8449 |
| 3.5276 | 1.03 | 1221120 | 3.8466 |
| 3.5109 | 0.03 | 1297440 | 3.8462 |
| 3.4955 | 1.03 | 1373760 | 3.8491 |
| 3.4793 | 0.03 | 1450080 | 3.8501 |
| 3.4726 | 1.03 | 1526400 | 3.8517 |
| 3.467 | 0.03 | 1602720 | 3.8534 |
| 3.4575 | 1.03 | 1679040 | 3.8548 |
| 3.4476 | 0.03 | 1755360 | 3.8552 |
| 3.434 | 1.03 | 1831680 | 3.8573 |
| 3.421 | 0.03 | 1908000 | 3.8585 |
| 3.4096 | 1.03 | 1984320 | 3.8589 |
| 3.3989 | 0.03 | 2060640 | 3.8613 |
| 3.3901 | 1.03 | 2136960 | 3.8618 |
| 3.3776 | 0.03 | 2213280 | 3.8633 |
| 3.3624 | 1.03 | 2289600 | 3.8633 |
| 3.3544 | 0.03 | 2365920 | 3.8651 |
| 3.3519 | 1.03 | 2442240 | 3.8644 |
| 3.3423 | 0.03 | 2518560 | 3.8657 |
| 3.3295 | 1.03 | 2594880 | 3.8652 |
| 3.3192 | 0.03 | 2671200 | 3.8660 |
| 3.3124 | 1.03 | 2747520 | 3.8660 |
| 3.312 | 0.03 | 2823840 | 3.8658 |
| 3.3046 | 0.03 | 2900160 | 3.8652 |
| 3.2992 | 1.03 | 2976480 | 3.8642 |
| 3.2886 | 0.02 | 3052726 | 3.8633 |
### Framework versions
- Transformers 4.33.3
- Pytorch 2.0.1
- Datasets 2.12.0
- Tokenizers 0.13.3
|
LoneStriker/miquliz-120b-4.0bpw-h6-exl2 | LoneStriker | 2024-02-07T14:09:20Z | 8 | 1 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"en",
"de",
"fr",
"es",
"it",
"base_model:152334H/miqu-1-70b-sf",
"base_model:merge:152334H/miqu-1-70b-sf",
"base_model:lizpreciatior/lzlv_70b_fp16_hf",
"base_model:merge:lizpreciatior/lzlv_70b_fp16_hf",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-02-06T23:08:22Z | ---
base_model:
- 152334H/miqu-1-70b-sf
- lizpreciatior/lzlv_70b_fp16_hf
language:
- en
- de
- fr
- es
- it
library_name: transformers
tags:
- mergekit
- merge
---
# miquliz-120b
- EXL2: [2.4bpw](https://huggingface.co/LoneStriker/miquliz-120b-2.4bpw-h6-exl2) | [2.65bpw](https://huggingface.co/LoneStriker/miquliz-120b-2.65bpw-h6-exl2) | [2.9bpw](https://huggingface.co/LoneStriker/miquliz-120b-2.9bpw-h6-exl2) | 4.0bpw
- GGUF: [IQ3_XXS](https://huggingface.co/wolfram/miquliz-120b-GGUF) | [Q4_K_S+Q4_K_M](https://huggingface.co/NanoByte/miquliz-120b-Q4-GGUF)
- HF: [wolfram/miquliz-120b](https://huggingface.co/wolfram/miquliz-120b)
This is a 120b frankenmerge created by interleaving layers of [miqu-1-70b-sf](https://huggingface.co/152334H/miqu-1-70b-sf) with [lzlv_70b_fp16_hf](https://huggingface.co/lizpreciatior/lzlv_70b_fp16_hf) using [mergekit](https://github.com/cg123/mergekit).
Inspired by [goliath-120b](https://huggingface.co/alpindale/goliath-120b).
Thanks for the support, [CopilotKit](https://github.com/CopilotKit/CopilotKit) - the open-source platform for building in-app AI Copilots into any product, with any LLM model. Check out their GitHub.
Thanks for the EXL2 and GGUF quants, [Lone Striker](https://huggingface.co/LoneStriker) and [NanoByte](https://huggingface.co/NanoByte)!
## Prompt template: Mistral
```
<s>[INST] {prompt} [/INST]
```
See also: [🐺🐦⬛ LLM Prompt Format Comparison/Test: Mixtral 8x7B Instruct with **17** different instruct templates : LocalLLaMA](https://www.reddit.com/r/LocalLLaMA/comments/18ljvxb/llm_prompt_format_comparisontest_mixtral_8x7b/)
## Model Details
- Max Context: 32768 tokens
- Layers: 137
## Merge Details
### Merge Method
This model was merged using the passthrough merge method.
### Models Merged
The following models were included in the merge:
- [152334H/miqu-1-70b-sf](https://huggingface.co/152334H/miqu-1-70b-sf)
- [lizpreciatior/lzlv_70b_fp16_hf](https://huggingface.co/lizpreciatior/lzlv_70b_fp16_hf)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
dtype: float16
merge_method: passthrough
slices:
- sources:
- layer_range: [0, 16]
model: 152334H/miqu-1-70b-sf
- sources:
- layer_range: [8, 24]
model: lizpreciatior/lzlv_70b_fp16_hf
- sources:
- layer_range: [17, 32]
model: 152334H/miqu-1-70b-sf
- sources:
- layer_range: [25, 40]
model: lizpreciatior/lzlv_70b_fp16_hf
- sources:
- layer_range: [33, 48]
model: 152334H/miqu-1-70b-sf
- sources:
- layer_range: [41, 56]
model: lizpreciatior/lzlv_70b_fp16_hf
- sources:
- layer_range: [49, 64]
model: 152334H/miqu-1-70b-sf
- sources:
- layer_range: [57, 72]
model: lizpreciatior/lzlv_70b_fp16_hf
- sources:
- layer_range: [65, 80]
model: 152334H/miqu-1-70b-sf
```
## Credits & Special Thanks
- 1st model:
- original (unreleased) model: [mistralai (Mistral AI_)](https://huggingface.co/mistralai)
- leaked model: [miqudev/miqu-1-70b](https://huggingface.co/miqudev/miqu-1-70b)
- f16 model: [152334H/miqu-1-70b-sf](https://huggingface.co/152334H/miqu-1-70b-sf)
- 2nd model: [lizpreciatior/lzlv_70b_fp16_hf](https://huggingface.co/lizpreciatior/lzlv_70b_fp16_hf)
- mergekit: [arcee-ai/mergekit: Tools for merging pretrained large language models.](https://github.com/arcee-ai/mergekit)
- mergekit_config.yml: [alpindale/goliath-120b](https://huggingface.co/alpindale/goliath-120b)
### Support
- [My Ko-fi page](https://ko-fi.com/wolframravenwolf) if you'd like to tip me to say thanks or request specific models to be tested or merged with priority. Also consider supporting your favorite model creators, quantizers, or frontend/backend devs if you can afford to do so. They deserve it!
#### DISCLAIMER: THIS IS [BASED ON A LEAKED ASSET](https://huggingface.co/miqudev/miqu-1-70b/discussions/10) AND HAS NO LICENSE ASSOCIATED WITH IT. USE AT YOUR OWN RISK.
|
kanishka/smolm-autoreg-bpe-counterfactual-babylm-only_measure_nps_as_singular_removal-1e-4 | kanishka | 2024-02-07T14:06:52Z | 4 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"opt",
"text-generation",
"generated_from_trainer",
"dataset:kanishka/counterfactual-babylm-only_measure_nps_as_singular_removal",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-02-06T15:34:39Z | ---
tags:
- generated_from_trainer
datasets:
- kanishka/counterfactual-babylm-only_measure_nps_as_singular_removal
metrics:
- accuracy
model-index:
- name: smolm-autoreg-bpe-counterfactual-babylm-only_measure_nps_as_singular_removal-1e-4
results:
- task:
name: Causal Language Modeling
type: text-generation
dataset:
name: kanishka/counterfactual-babylm-only_measure_nps_as_singular_removal
type: kanishka/counterfactual-babylm-only_measure_nps_as_singular_removal
metrics:
- name: Accuracy
type: accuracy
value: 0.4057273905279679
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# smolm-autoreg-bpe-counterfactual-babylm-only_measure_nps_as_singular_removal-1e-4
This model was trained from scratch on the kanishka/counterfactual-babylm-only_measure_nps_as_singular_removal dataset.
It achieves the following results on the evaluation set:
- Loss: 3.4267
- Accuracy: 0.4057
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 32000
- num_epochs: 20.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 4.0456 | 1.0 | 18600 | 4.2695 | 0.3100 |
| 3.5586 | 2.0 | 37200 | 3.7569 | 0.3640 |
| 3.3865 | 3.0 | 55800 | 3.5821 | 0.3801 |
| 3.2864 | 4.0 | 74400 | 3.5184 | 0.3877 |
| 3.2138 | 5.0 | 93000 | 3.4647 | 0.3930 |
| 3.1634 | 6.0 | 111600 | 3.4300 | 0.3973 |
| 3.1242 | 7.0 | 130200 | 3.4365 | 0.3982 |
| 3.0882 | 8.0 | 148800 | 3.4228 | 0.4004 |
| 3.0589 | 9.0 | 167400 | 3.4148 | 0.4012 |
| 3.0298 | 10.0 | 186000 | 3.4086 | 0.4025 |
| 3.0091 | 11.0 | 204600 | 3.4138 | 0.4031 |
| 2.982 | 12.0 | 223200 | 3.4183 | 0.4033 |
| 2.9628 | 13.0 | 241800 | 3.4182 | 0.4037 |
| 2.9451 | 14.0 | 260400 | 3.4063 | 0.4046 |
| 2.9249 | 15.0 | 279000 | 3.4066 | 0.4051 |
| 2.9046 | 16.0 | 297600 | 3.4134 | 0.4057 |
| 2.8879 | 17.0 | 316200 | 3.4187 | 0.4053 |
| 2.8659 | 18.0 | 334800 | 3.4161 | 0.4058 |
| 2.8577 | 19.0 | 353400 | 3.4254 | 0.4057 |
| 2.8337 | 20.0 | 372000 | 3.4267 | 0.4057 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
Americo/phi-2-finetuned-farmatodo | Americo | 2024-02-07T14:01:39Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"phi",
"text-generation",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-02-07T13:52:19Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
DoctorKrazy/sbaitso | DoctorKrazy | 2024-02-07T14:00:55Z | 0 | 0 | null | [
"en",
"region:us"
] | null | 2024-02-07T13:56:12Z | ---
language:
- en
---
# Sbaitso AI Voice model for RVC
This is a voice model trained on sbaitso, most famously known for the voice of SCP 079 in the SCP : Containement Breach video game.
If you use this AI voice model please credit me by linking this page in the description. |
bartowski/Kunocchini-7b-128k-test-exl2 | bartowski | 2024-02-07T13:51:43Z | 5 | 4 | transformers | [
"transformers",
"mergekit",
"merge",
"alpaca",
"mistral",
"text-generation",
"base_model:Epiculous/Fett-uccine-Long-Noodle-7B-120k-Context",
"base_model:merge:Epiculous/Fett-uccine-Long-Noodle-7B-120k-Context",
"base_model:SanjiWatsuki/Kunoichi-DPO-v2-7B",
"base_model:merge:SanjiWatsuki/Kunoichi-DPO-v2-7B",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-02-07T13:35:21Z | ---
base_model:
- SanjiWatsuki/Kunoichi-DPO-v2-7B
- Epiculous/Fett-uccine-Long-Noodle-7B-120k-Context
library_name: transformers
tags:
- mergekit
- merge
- alpaca
- mistral
quantized_by: bartowski
pipeline_tag: text-generation
---
## Exllama v2 Quantizations of Kunocchini-7b-128k-test
Using <a href="https://github.com/turboderp/exllamav2/releases/tag/v0.0.13">turboderp's ExLlamaV2 v0.0.13</a> for quantization.
# The "main" branch only contains the measurement.json, download one of the other branches for the model (see below)
Each branch contains an individual bits per weight, with the main one containing only the meaurement.json for further conversions.
Original model: https://huggingface.co/Test157t/Kunocchini-7b-128k-test
| Branch | Bits | lm_head bits | VRAM (4k) | VRAM (16k) | VRAM (32k) | Description |
| ----- | ---- | ------- | ------ | ------ | ------ | ------------ |
| [8_0](https://huggingface.co/Bartowski/Kunocchini-7b-128k-test-exl2/tree/8_0) | 8.0 | 8.0 | 8.4 GB | 9.8 GB | 11.8 GB | Maximum quality that ExLlamaV2 can produce, near unquantized performance. |
| [6_5](https://huggingface.co/Bartowski/Kunocchini-7b-128k-test-exl2/tree/6_5) | 6.5 | 8.0 | 7.2 GB | 8.6 GB | 10.6 GB | Very similar to 8.0, good tradeoff of size vs performance, **recommended**. |
| [5_0](https://huggingface.co/Bartowski/Kunocchini-7b-128k-test-exl2/tree/5_0) | 5.0 | 6.0 | 6.0 GB | 7.4 GB | 9.4 GB | Slightly lower quality vs 6.5, but usable on 8GB cards. |
| [4_25](https://huggingface.co/Bartowski/Kunocchini-7b-128k-test-exl2/tree/4_25) | 4.25 | 6.0 | 5.3 GB | 6.7 GB | 8.7 GB | GPTQ equivalent bits per weight, slightly higher quality. |
| [3_5](https://huggingface.co/Bartowski/Kunocchini-7b-128k-test-exl2/tree/3_5) | 3.5 | 6.0 | 4.7 GB | 6.1 GB | 8.1 GB | Lower quality, only use if you have to. |
## Download instructions
With git:
```shell
git clone --single-branch --branch 6_5 https://huggingface.co/bartowski/Kunocchini-7b-128k-test-exl2 Kunocchini-7b-128k-test-exl2-6_5
```
With huggingface hub (credit to TheBloke for instructions):
```shell
pip3 install huggingface-hub
```
To download the `main` (only useful if you only care about measurement.json) branch to a folder called `Kunocchini-7b-128k-test-exl2`:
```shell
mkdir Kunocchini-7b-128k-test-exl2
huggingface-cli download bartowski/Kunocchini-7b-128k-test-exl2 --local-dir Kunocchini-7b-128k-test-exl2 --local-dir-use-symlinks False
```
To download from a different branch, add the `--revision` parameter:
Linux:
```shell
mkdir Kunocchini-7b-128k-test-exl2-6_5
huggingface-cli download bartowski/Kunocchini-7b-128k-test-exl2 --revision 6_5 --local-dir Kunocchini-7b-128k-test-exl2-6_5 --local-dir-use-symlinks False
```
Windows (which apparently doesn't like _ in folders sometimes?):
```shell
mkdir Kunocchini-7b-128k-test-exl2-6.5
huggingface-cli download bartowski/Kunocchini-7b-128k-test-exl2 --revision 6_5 --local-dir Kunocchini-7b-128k-test-exl2-6.5 --local-dir-use-symlinks False
```
Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski |
YLFCY/buckwheat-mini | YLFCY | 2024-02-07T13:50:24Z | 0 | 0 | null | [
"dataset:Jeneral/fer-2013",
"arxiv:1910.09700",
"license:unlicense",
"region:us"
] | null | 2024-02-07T13:33:34Z | ---
license: unlicense
datasets:
- Jeneral/fer-2013
metrics:
- accuracy
---
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** YLFCY
- **Model type:** [More Information Needed]
- **License:** unlicense
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
用于表情分类。使用keras导入。分类结果对应的数字与fer2013训练集中各表情对应文件夹数字相同。
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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## Glossary [optional]
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## Model Card Contact
[More Information Needed] |
iamhack/wav2vec2-base-finetuned-ks-open-close | iamhack | 2024-02-07T13:36:02Z | 4 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"wav2vec2",
"audio-classification",
"generated_from_trainer",
"dataset:audiofolder",
"base_model:facebook/wav2vec2-base",
"base_model:finetune:facebook/wav2vec2-base",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | audio-classification | 2024-02-07T11:52:25Z | ---
license: apache-2.0
base_model: facebook/wav2vec2-base
tags:
- generated_from_trainer
datasets:
- audiofolder
metrics:
- accuracy
model-index:
- name: wav2vec2-base-finetuned-ks-open-close
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: audiofolder
type: audiofolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.998286586955712
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-finetuned-ks-open-close
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: 0.0100
- Accuracy: 0.9983
## 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0866 | 1.0 | 209 | 0.0388 | 0.9956 |
| 0.021 | 2.0 | 419 | 0.0162 | 0.9978 |
| 0.0172 | 3.0 | 629 | 0.0102 | 0.9985 |
| 0.0195 | 4.0 | 839 | 0.0083 | 0.9991 |
| 0.0188 | 4.98 | 1045 | 0.0100 | 0.9983 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
ExAi/Claire-Mistral-7B-v0.1.3-exl2-4.0 | ExAi | 2024-02-07T13:34:55Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"pretrained",
"conversational",
"fr",
"arxiv:2311.16840",
"base_model:mistralai/Mistral-7B-v0.1",
"base_model:finetune:mistralai/Mistral-7B-v0.1",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-02-07T13:20:35Z | ---
language:
- fr
license: cc-by-nc-sa-4.0
pipeline_tag: text-generation
base_model: mistralai/Mistral-7B-v0.1
tags:
- pretrained
- conversational
widget:
- text: |-
- Bonjour Dominique, qu'allez-vous nous cuisiner aujourd'hui ?
- Bonjour Camille,
example_title: Request for a recipe
group: Dash
- text: |-
[Intervenant 1:] Bonjour Dominique, qu'allez-vous nous cuisiner aujourd'hui ?
[Intervenant 2:] Bonjour Camille,
example_title: Request for a recipe
group: Intervenant
- text: |-
[Camille:] Bonjour Dominique, qu'allez-vous nous cuisiner aujourd'hui ?
[Dominique:] Bonjour Camille,
example_title: Request for a recipe
group: FirstName
- text: |-
[Camille Durand:] Bonjour Dominique, qu'allez-vous nous cuisiner aujourd'hui ?
[Dominique Petit:] Bonjour Camille,
example_title: Request for a recipe
group: Named
inference:
parameters:
temperature: 1.0
max_new_tokens: 200
top_k: 10
---
# Claire-Mistral-7B-0.1
**Claire-Mistral-7B-0.1 is a 7B parameter causal decoder-only model built by [LINAGORA](https://labs.linagora.com/) and [OpenLLM-France](https://github.com/OpenLLM-France)**
**adapted from [Mistral-7B](https://huggingface.co/mistralai/Mistral-7B-v0.1) on French conversational data.**
Claire-Mistral-7B-0.1 is a pretrained language model designed to be attuned to the dynamics of linguistic interactions in dialogue. Without further training, its expected use is to generate continuations of dialogues. Its main purpose is to serve as a base model for fine-tuning on dialogue generation (e.g., chat) and dialogue understanding (e.g., meeting summarization) tasks. Please note that due to its training, the model is prone to generate dialogues with disfluencies and other constructions common to spoken language.
A qualitatively better variant of this model is available under [Claire-7B-0.1](https://huggingface.co/OpenLLM-France/Claire-7B-0.1).
* [Typical usage](#typical-usage)
* [Typical prompts](#typical-prompts)
* [Training Details](#training-details)
* [Training Data](#training-data)
* [Training Procedure](#training-procedure)
* [Evaluation](#evaluation)
* [License](#license)
* [Acknowledgements](#acknowledgements)
* [Contact](#contact)
## Typical usage
```python
import transformers
import torch
model_name = "OpenLLM-France/Claire-Mistral-7B-0.1"
tokenizer = transformers.AutoTokenizer.from_pretrained(model_name)
model = transformers.AutoModelForCausalLM.from_pretrained(model_name,
device_map="auto",
torch_dtype=torch.bfloat16,
load_in_4bit=True # For efficient inference, if supported by the GPU card
)
pipeline = transformers.pipeline("text-generation", model=model, tokenizer=tokenizer)
generation_kwargs = dict(
num_return_sequences=1, # Number of variants to generate.
return_full_text= False, # Do not include the prompt in the generated text.
max_new_tokens=200, # Maximum length for the output text.
do_sample=True, top_k=10, temperature=1.0, # Sampling parameters.
pad_token_id=tokenizer.eos_token_id, # Just to avoid a harmless warning.
)
prompt = """\
- Bonjour Dominique, qu'allez-vous nous cuisiner aujourd'hui ?
- Bonjour Camille,\
"""
completions = pipeline(prompt, **generation_kwargs)
for completion in completions:
print(prompt + " […]" + completion['generated_text'])
```
This will print something like:
```
- Bonjour Dominique, qu'allez-vous nous cuisiner aujourd'hui ?
- Bonjour Camille, […] je vous prépare un plat de saison, une daube provençale.
- Ah je ne connais pas cette recette.
- C'est très facile à préparer, vous n'avez qu'à mettre de l'eau dans une marmite, y mettre de l'oignon émincé, des carottes coupées en petits morceaux, et vous allez mettre votre viande de bœuf coupé en petits morceaux également.
- Je n'ai jamais cuisiné de viande de bœuf, mais c'est vrai que ça a l'air bien facile.
- Vous n'avez plus qu'à laisser mijoter, et ensuite il sera temps de servir les clients.
- Très bien.
```
You will need at least 6GB of VRAM to run inference using 4bit quantization (16GB of VRAM without 4bit quantization).
If you have trouble running this code, make sure you have recent versions of `torch`, `transformers` and `accelerate` (see [requirements.txt](requirements.txt)).
### Typical prompts
Claire-Mistral-7B-0.1 was trained on diarized French conversations. During training, the dialogues were normalized in several formats. The possible formats for expected prompts are as follows:
A monologue can be specified as a single line prompt (though keep in mind that the model might still return a dialogue because of its training):
```python
prompt = "Mesdames et messieurs les députés, chers collègues, bonsoir. Vous l'aurez peut-être remarqué, je cite rarement"
```
A dialogue between two speakers can be specified with one line per speech turn starting with a dash:
```python
prompt = """\
- Bonjour Dominique, qu'allez-vous nous cuisiner aujourd'hui ?
- Bonjour Camille,\
"""
```
A dialogue or multilogue (with two or more speakers) can be specified with lines that start with `[Intervenant X:]` where `X` is a number:
```python
prompt = """\
[Intervenant 1:] Bonjour Dominique, qu'allez-vous nous cuisiner aujourd'hui ?
[Intervenant 2:] Bonjour Camille,\
"""
```
A dialogue or multilogue with named speakers can be specified with lines that start with `[SpeakerName:]`
where `SpeakerName` can be a first name, a first and a last name, a nickname, a title…
```python
prompt = """\
[Mme Camille Durand:] Bonjour Dominique, qu'allez-vous nous cuisiner aujourd'hui ?
[Mr. Dominique Petit:] Bonjour Camille,\
"""
```
## Training Details
### Training Data
The training dataset is available at [OpenLLM-France/Claire-Dialogue-French-0.1](https://huggingface.co/datasets/OpenLLM-France/Claire-Dialogue-French-0.1)
and described in ["The Claire French Dialogue Dataset" (2023)](https://arxiv.org/abs/2311.16840).
Claire-Mistral-7B-0.1 was tuned from Mistral-7B-v0.1 on the following data distribution:
| **Data type** | **Words** | **Training Sampling Weight** | **Sources** |
|-------------------------------|------------|------------------------------|-----------------------------------------------------|
| Parliamentary Proceedings | 135M | 35% | Assemblée Nationale |
| Theatre | 16M | 18% | Théâtre Classique, Théâtre Gratuit |
| Interviews | 6.4M | 29% | TCOF, CFPP, CFPB (ORFEO), ACSYNT, PFC, Valibel (ORFEO), ESLO|
| Free Conversations | 2.2M | 10% | CRFP (ORFEO), OFROM (ORFEO), CID, Rhapsodie, ParisStories, PFC, CLAPI, C-ORAL-ROM (ORFEO), LinTO, ESLO |
| Meetings | 1.2M | 5% | SUMM-RE, LinTO, Réunions de travail (ORFEO) |
| Debates | 402k | <2% | FREDSum, ESLO |
| Assistance | 159k | <1% | Fleuron (ORFEO), Accueil UBS, OTG, ESLO |
| Presentation, Formal Address | 86k | <0.5% | Valibel (ORFEO), LinTO, ESLO |
Training data was augmented with the following techniques:
* varying the format used to indicate speech turns (dashes or [XXX:])
* substituting [Intervenant X:] for [SpeakerName:] or vice versa, where [SpeakerName:] might be a real name or a randomly generated name
* removing punctuation marks and/or casing (to prepare the model for transcripts produced by some Automatic Speech Recognition systems)
Long conversations were truncated at a maximum of 4096 tokens. Where possible, they were split between speaker turns.
While the model has been trained and evaluated only on French dialogues, it may be able to generate conversations in other languages from the original Mistral-7B-v0.1 training data.
### Training Procedure
The training code is available at [https://github.com/OpenLLM-France/Lit-Claire](https://github.com/OpenLLM-France/Lit-Claire).
Claire-Mistral-7B-0.1 is a causal decoder-only model trained on a causal language modeling task (i.e., predict the next token).
See [Mistral-7B](https://huggingface.co/mistralai/Mistral-7B-v0.1) for more details.
Claire-Mistral-7B-0.1 was trained on 8 A100 80GB GPUs for about 50 GPU hours.
Hyperparameters were the following:
| **Hyperparameter** | **Value** |
|--------------------|------------|
| Precision | `bfloat16` |
| Optimizer | AdamW |
| Learning rate | 1e-4 |
| Weight decay | 1e-2 |
| Batch size | 128 |
| LoRA rank | 16 |
| LoRA alpha | 32 |
| Dropout | 0.05 |
| gradient clipping | 1 |
## Evaluation
See the [Evaluation section of Claire-7B-0.1](https://huggingface.co/OpenLLM-France/Claire-7B-0.1#evaluation).
## License
Given that some of the corpora used for training are only available under CC-BY-NC-SA licenses,
Claire-Mistral-7B-0.1 is made available under the [CC-BY-NC-SA 4.0 license](https://creativecommons.org/licenses/by-nc-sa/4.0/).
## Acknowledgements
This work was performed using HPC resources from GENCI–IDRIS (Grant 2023-AD011014561).
Claire-Mistral-7B-0.1 was created by members of [LINAGORA](https://labs.linagora.com/) (in alphabetical order): Ismaïl Harrando, Julie Hunter, Jean-Pierre Lorré, Jérôme Louradour, Michel-Marie Maudet, Virgile Rennard, Guokan Shang.
Special thanks to partners from the OpenLLM-France community, especially Christophe Cerisara (LORIA), Pierre-Carl Langlais and Anastasia Stasenko (OpSci), and Pierre Colombo, for valuable advice.
## Contact
[email protected] |
shidowake/test-240207-cyber2chat-7B-qlora-adaptor | shidowake | 2024-02-07T13:27:28Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-02-07T13:27:15Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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## Glossary [optional]
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## Model Card Contact
[More Information Needed]
|
joseTfm/tfm_qa_torch_spanish | joseTfm | 2024-02-07T13:23:14Z | 12 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"question-answering",
"generated_from_trainer",
"base_model:dccuchile/distilbert-base-spanish-uncased",
"base_model:finetune:dccuchile/distilbert-base-spanish-uncased",
"endpoints_compatible",
"region:us"
] | question-answering | 2024-02-06T22:14:35Z | ---
base_model: dccuchile/distilbert-base-spanish-uncased
tags:
- generated_from_trainer
model-index:
- name: tfm_qa_torch_spanish
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. -->
# tfm_qa_torch_spanish
This model is a fine-tuned version of [dccuchile/distilbert-base-spanish-uncased](https://huggingface.co/dccuchile/distilbert-base-spanish-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5237
## 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 3 | 2.8229 |
| No log | 2.0 | 6 | 2.6078 |
| No log | 3.0 | 9 | 2.5237 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
andrijdavid/Macaroni-v2-7b | andrijdavid | 2024-02-07T13:22:10Z | 51 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"mergekit",
"merge",
"en",
"arxiv:2311.03099",
"arxiv:2306.01708",
"base_model:flemmingmiguel/MBX-7B-v3",
"base_model:merge:flemmingmiguel/MBX-7B-v3",
"base_model:mistralai/Mistral-7B-v0.1",
"base_model:merge:mistralai/Mistral-7B-v0.1",
"base_model:mlabonne/OmniBeagle-7B",
"base_model:merge:mlabonne/OmniBeagle-7B",
"base_model:vanillaOVO/supermario_v4",
"base_model:merge:vanillaOVO/supermario_v4",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-02-05T17:48:43Z | ---
base_model:
- flemmingmiguel/MBX-7B-v3
- mlabonne/OmniBeagle-7B
- mistralai/Mistral-7B-v0.1
- vanillaOVO/supermario_v4
tags:
- mergekit
- merge
license: apache-2.0
language:
- en
---
# Macaroni V2 7B
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [DARE](https://arxiv.org/abs/2311.03099) [TIES](https://arxiv.org/abs/2306.01708) merge method using [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) as a base.
### Models Merged
The following models were included in the merge:
* [flemmingmiguel/MBX-7B-v3](https://huggingface.co/flemmingmiguel/MBX-7B-v3)
* [mlabonne/OmniBeagle-7B](https://huggingface.co/mlabonne/OmniBeagle-7B)
* [vanillaOVO/supermario_v4](https://huggingface.co/vanillaOVO/supermario_v4)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: mistralai/Mistral-7B-v0.1
# no parameters necessary for base model
- model: flemmingmiguel/MBX-7B-v3
parameters:
density: 0.7
weight: 0.5
- model: vanillaOVO/supermario_v4
parameters:
density: 0.7
weight: 0.3
- model: mlabonne/OmniBeagle-7B
parameters:
density: 0.5
weight: 0.6
merge_method: dare_ties
base_model: mistralai/Mistral-7B-v0.1
parameters:
int8_mask: true
normalize: true
dtype: float16
``` |
YashRawal225/Intel-3-7b-chat-finetune-german2000-GGUF | YashRawal225 | 2024-02-07T13:21:09Z | 5 | 0 | transformers | [
"transformers",
"gguf",
"mistral",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-02-07T13:09:28Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Paquique/Taxi-v3 | Paquique | 2024-02-07T13:05:29Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2024-02-07T12:35:51Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="Paquique/Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
Abhishek-1011/my_gec | Abhishek-1011 | 2024-02-07T12:58:54Z | 0 | 0 | null | [
"safetensors",
"autotrain",
"text-generation",
"conversational",
"license:other",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-02-07T12:58:48Z | ---
tags:
- autotrain
- text-generation
widget:
- text: "I love AutoTrain because "
license: other
---
# Model Trained Using AutoTrain
This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain).
# Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = "PATH_TO_THIS_REPO"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
model_path,
device_map="auto",
torch_dtype='auto'
).eval()
# Prompt content: "hi"
messages = [
{"role": "user", "content": "hi"}
]
input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt')
output_ids = model.generate(input_ids.to('cuda'))
response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)
# Model response: "Hello! How can I assist you today?"
print(response)
``` |
bsurendar/roberta-large-peft-lora | bsurendar | 2024-02-07T12:57:13Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:FacebookAI/roberta-large",
"base_model:adapter:FacebookAI/roberta-large",
"region:us"
] | null | 2024-02-07T12:49:03Z | ---
library_name: peft
base_model: roberta-large
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.8.2 |
sajaw/Llama-2-7B-XLLM-GQA10K | sajaw | 2024-02-07T12:49:14Z | 0 | 0 | peft | [
"peft",
"safetensors",
"llama",
"arxiv:1910.09700",
"base_model:TinyPixel/Llama-2-7B-bf16-sharded",
"base_model:adapter:TinyPixel/Llama-2-7B-bf16-sharded",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2024-02-07T12:34:24Z | ---
library_name: peft
base_model: TinyPixel/Llama-2-7B-bf16-sharded
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.8.2 |
gsl22/Ellis-QA | gsl22 | 2024-02-07T12:48:47Z | 21 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"roberta",
"question-answering",
"generated_from_trainer",
"base_model:deepset/roberta-base-squad2",
"base_model:finetune:deepset/roberta-base-squad2",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] | question-answering | 2024-02-07T12:42:05Z | ---
license: cc-by-4.0
base_model: deepset/roberta-base-squad2
tags:
- generated_from_trainer
model-index:
- name: Ellis-QA
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. -->
# Ellis-QA
This model is a fine-tuned version of [deepset/roberta-base-squad2](https://huggingface.co/deepset/roberta-base-squad2) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- 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 |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 250 | 0.4893 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
mertllc/mms-tts-tur-twenties-male | mertllc | 2024-02-07T12:44:58Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"vits",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-02-07T12:05:47Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
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## Glossary [optional]
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|
llmware/slim-topics | llmware | 2024-02-07T12:36:36Z | 15 | 7 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-02-02T16:28:09Z | ---
license: apache-2.0
inference: false
---
# SLIM-TOPICS
<!-- Provide a quick summary of what the model is/does. -->
**slim-topics** is part of the SLIM ("**S**tructured **L**anguage **I**nstruction **M**odel") model series, consisting of small, specialized decoder-based models, fine-tuned for function-calling.
slim-sentiment has been fine-tuned for **topic analysis** function calls, generating output consisting of a python dictionary corresponding to specified keys, e.g.:
`{"topics": ["..."]}`
SLIM models are designed to generate structured outputs that can be used programmatically as part of a multi-step, multi-model LLM-based automation workflow.
Each slim model has a 'quantized tool' version, e.g., [**'slim-topics-tool'**](https://huggingface.co/llmware/slim-topics-tool).
## Prompt format:
`function = "classify"`
`params = "topics"`
`prompt = "<human> " + {text} + "\n" + `
`"<{function}> " + {params} + "</{function}>" + "\n<bot>:"`
<details>
<summary>Transformers Script </summary>
model = AutoModelForCausalLM.from_pretrained("llmware/slim-topics")
tokenizer = AutoTokenizer.from_pretrained("llmware/slim-topics")
function = "classify"
params = "topic"
text = "The stock market declined yesterday as investors worried increasingly about the slowing economy."
prompt = "<human>: " + text + "\n" + f"<{function}> {params} </{function}>\n<bot>:"
inputs = tokenizer(prompt, return_tensors="pt")
start_of_input = len(inputs.input_ids[0])
outputs = model.generate(
inputs.input_ids.to('cpu'),
eos_token_id=tokenizer.eos_token_id,
pad_token_id=tokenizer.eos_token_id,
do_sample=True,
temperature=0.3,
max_new_tokens=100
)
output_only = tokenizer.decode(outputs[0][start_of_input:], skip_special_tokens=True)
print("output only: ", output_only)
# here's the fun part
try:
output_only = ast.literal_eval(llm_string_output)
print("success - converted to python dictionary automatically")
except:
print("fail - could not convert to python dictionary automatically - ", llm_string_output)
</details>
<details>
<summary>Using as Function Call in LLMWare</summary>
from llmware.models import ModelCatalog
slim_model = ModelCatalog().load_model("llmware/slim-topics")
response = slim_model.function_call(text,params=["topics"], function="classify")
print("llmware - llm_response: ", response)
</details>
## Model Card Contact
Darren Oberst & llmware team
[Join us on Discord](https://discord.gg/MhZn5Nc39h)
|
Ketengan-Diffusion/AnySomniumXL-v3.5.1 | Ketengan-Diffusion | 2024-02-07T12:36:35Z | 20 | 1 | diffusers | [
"diffusers",
"safetensors",
"stable-diffusion",
"SDXL",
"art",
"stable-diffusion-XL",
"fantasy",
"anime",
"aiart",
"ketengan",
"AnySomniumXL",
"text-to-image",
"en",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] | text-to-image | 2024-02-07T10:01:48Z | ---
license: creativeml-openrail-m
language:
- en
tags:
- stable-diffusion
- SDXL
- art
- stable-diffusion-XL
- fantasy
- anime
- aiart
- ketengan
- AnySomniumXL
pipeline_tag: text-to-image
library_name: diffusers
---
# AnySomniumXL v3.5,1 Model Showcase
<p align="center">
<img src="01.png" width=70% height=70%>
</p>
`Ketengan-Diffusion/AnySomniumXL v3.5` is a SDXL model that has been fine-tuned on [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0).
This is enhanced version of AnySomniumXL v3
# Changelog over AnySomniumXL v3.5
* More epochs training
* Better model generalizing
* More increased concept and character accuracy
# Our Dataset Process Curation
<p align="center">
<img src="Curation.png" width=70% height=70%>
</p>
Image source: [Source1](https://danbooru.donmai.us/posts/3143351) [Source2](https://danbooru.donmai.us/posts/3272710) [Source3](https://danbooru.donmai.us/posts/3320417)
Our dataset is scored using Pretrained CLIP+MLP Aesthetic Scoring model by https://github.com/christophschuhmann/improved-aesthetic-predictor, and We made adjusment into our script to detecting any text or watermark by utilizing OCR by pytesseract
This scoring method has scale between -1-100, we take the score threshold around 17 or 20 as minimum and 65-75 as maximum to pretain the 2D style of the dataset, Any images with text will returning -1 score. So any images with score below 17 or above 65 is deleted
The dataset curation proccess is using Nvidia T4 16GB Machine and takes about 7 days for curating 1.000.000 images.
# Captioning process
We using combination of proprietary Multimodal LLM and open source multimodal LLM such as LLaVa 1.5 as the captioning process which is resulting more complex result than using normal BLIP2. Any detail like the clothes, atmosphere, situation, scene, place, gender, skin, and others is generated by LLM.
This captioning process to captioning 133k images takes about 6 Days with NVIDIA Tesla A100 80GB PCIe. We still improving our script to generate caption faster. The minimum VRAM that required for this captioning process is 24GB VRAM which is not sufficient if we using NVIDIA Tesla T4 16GB
# Tagging Process
We simply using booru tags, that retrieved from booru boards so this could be tagged by manually by human hence make this tags more accurate.
# Official Demo
You can try our AnySomniumXL v3 for free on demo.ketengan.com
# Training Process
AnySomniumXL v3.5 Technical Specifications:
Batch Size: 25
Learning rate: 2e-6
Trained with a bucket size of 1280x1280
Shuffle Caption: Yes
Clip Skip: 2
Trained with 2x NVIDIA A100 80GB
# Recommended Resolution
Because it's trained with 1280x1280 resolution, so here the best resolution to get the full power of AnySomniumXL v3
* 1280x1280
* 1472x1088
* 1152x1408
* 1536x1024
* 1856x832
* 1024x1600
You can support me:
- on [Ko-FI](https://ko-fi.com/ncaix) |
Nandini0987654/Books | Nandini0987654 | 2024-02-07T12:30:14Z | 6 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"pytorch",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | 2024-02-07T12:30:08Z | ---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: Books
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.7333333492279053
---
# Books
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### Fiction
#### Non-Fiction
 |
Paquique/q-FrozenLake-v1-4x4-noSlippery | Paquique | 2024-02-07T12:30:10Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2024-02-07T12:30:08Z | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="Paquique/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
alexgastev/Reinforce-PixelCopter_v1 | alexgastev | 2024-02-07T12:29:37Z | 0 | 0 | null | [
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | reinforcement-learning | 2024-02-07T12:00:12Z | ---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-PixelCopter_v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 14.50 +/- 15.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
musiclang/musiclang-chord-v2-4k | musiclang | 2024-02-07T12:28:06Z | 15 | 3 | transformers | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-01-29T15:52:56Z | ---
widget:
- text: CHORD_CHANGE
example_title: Predict chord progression
---
MusicLang Chord Predictor model
===============================
MusicLang Chord Predictor is a model for creating original chord scale progressions in the musiclang format with generative AI model.
It can be used for different use cases :
- Predict a chord progression from scratch (a fixed number of chords)
- Continue a chord progression (using a MusicLang prompt)
If you are only looking to generate chord progressions in an easily readable format, consider using [our text chord predictor](https://huggingface.co/musiclang/text-chord-predictor)
To make the prediction we have an inference package available here : [MusicLang Predict](https://github.com/MusicLang/musiclang_predict)
which is based on the musiclang language : [MusicLang](https://github.com/MusicLang/musiclang).
Installation
------------
Install the musiclang-predict package with pip :
```bash
pip install musiclang-predict
```
How to use ?
------------
1. Generate a 4 chords progression in few lines :
```python
from musiclang_predict import predict_chords, MusicLangTokenizer
from transformers import AutoModelForCausalLM, AutoTokenizer
from musiclang.library import *
# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained('musiclang/musiclang-chord-v2-4k')
tokenizer = AutoTokenizer.from_pretrained('musiclang/musiclang-chord-v2-4k')
soundtrack = predict_chords(model, tokenizer, nb_chords=4, temperature=1.0)
# Give the chord a simple voicing (closed position chord)
soundtrack = soundtrack(b0, b1, b2, b3)
# Save it to midi
soundtrack.to_midi('song.mid', tempo=120, time_signature=(4, 4))
```
2. Use a prompt
```python
from musiclang_predict import predict_chords, MusicLangTokenizer
from transformers import AutoModelForCausalLM, AutoTokenizer
from musiclang.library import *
prompt = (I % I.M) + (V % I.M)['6'].o(-1)
# Load model and tokenizer
model = GPT2LMHeadModel.from_pretrained('musiclang/musiclang-chord-v2-4k')
tokenizer = AutoTokenizer.from_pretrained('musiclang/musiclang-chord-v2-4k')
soundtrack = predict_chords(model, tokenizer, nb_chords=4, prompt=prompt)
# Give the chord a simple voicing (closed position chord)
soundtrack = soundtrack(b0, b1, b2, b3)
# Save it to midi
soundtrack.to_midi('song.mid', tempo=120, time_signature=(4, 4))
```
Contact us
----------
If you want to help shape the future of open source music generation,
please contact [us](mailto:[email protected])
License
========
This model is free to use for research and open source purpose only. Please credit me (Florian GARDIN) and musiclang if you do so.
If you would like to use this in a commercial product please contact [us]([email protected]) to discuss licensing terms and potential integration in your product. I am looking forward to hearing about your project ! |
OctavianB/MistralRoSummary | OctavianB | 2024-02-07T12:23:28Z | 0 | 1 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-02-07T12:23:18Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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### Downstream Use [optional]
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### Out-of-Scope Use
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## Bias, Risks, and Limitations
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### Recommendations
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
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#### Preprocessing [optional]
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#### Speeds, Sizes, Times [optional]
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<!-- This should link to a Dataset Card if possible. -->
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[More Information Needed]
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[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
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|
Aneesha/phi2_DPO | Aneesha | 2024-02-07T12:16:37Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-02-07T12:16:17Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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### Downstream Use [optional]
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[More Information Needed]
### Out-of-Scope Use
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[More Information Needed]
## Bias, Risks, and Limitations
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[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
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#### Speeds, Sizes, Times [optional]
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### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
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[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
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### Model Architecture and Objective
[More Information Needed]
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[More Information Needed]
#### Software
[More Information Needed]
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## Glossary [optional]
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[More Information Needed]
|
asadmasad/output-6.7b-26k-ds-test-save-state-no-save-eval-strat | asadmasad | 2024-02-07T12:13:29Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-02-07T11:52:54Z | ---
pipeline_tag: text-generation
--- |
smangrul/sticker_peft_model | smangrul | 2024-02-07T12:10:40Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-02-07T12:10:34Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
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|
CLMBR/existential-there-quantifier-transformer-4 | CLMBR | 2024-02-07T12:01:12Z | 1 | 0 | transformers | [
"transformers",
"pytorch",
"opt",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-02-02T10:12:22Z | ---
tags:
- generated_from_trainer
model-index:
- name: existential-there-quantifier-transformer-4
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# existential-there-quantifier-transformer-4
This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.8637
## 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: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 3052726
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-------:|:---------------:|
| 4.2235 | 0.03 | 76320 | 4.1958 |
| 4.0188 | 1.03 | 152640 | 4.0280 |
| 3.91 | 0.03 | 228960 | 3.9539 |
| 3.842 | 1.03 | 305280 | 3.9126 |
| 3.7897 | 0.03 | 381600 | 3.8869 |
| 3.7491 | 1.03 | 457920 | 3.8716 |
| 3.7159 | 0.03 | 534240 | 3.8599 |
| 3.6834 | 1.03 | 610560 | 3.8530 |
| 3.6553 | 0.03 | 686880 | 3.8482 |
| 3.628 | 1.03 | 763200 | 3.8453 |
| 3.605 | 0.03 | 839520 | 3.8447 |
| 3.5866 | 1.03 | 915840 | 3.8442 |
| 3.57 | 0.03 | 992160 | 3.8431 |
| 3.5489 | 1.03 | 1068480 | 3.8447 |
| 3.5349 | 0.03 | 1144800 | 3.8466 |
| 3.5248 | 1.03 | 1221120 | 3.8464 |
| 3.5096 | 0.03 | 1297440 | 3.8480 |
| 3.4935 | 1.03 | 1373760 | 3.8504 |
| 3.4796 | 0.03 | 1450080 | 3.8505 |
| 3.4725 | 1.03 | 1526400 | 3.8529 |
| 3.4618 | 0.03 | 1602720 | 3.8541 |
| 3.4538 | 1.03 | 1679040 | 3.8553 |
| 3.4437 | 0.03 | 1755360 | 3.8561 |
| 3.433 | 1.03 | 1831680 | 3.8574 |
| 3.4159 | 0.03 | 1908000 | 3.8589 |
| 3.4048 | 1.03 | 1984320 | 3.8615 |
| 3.3929 | 0.03 | 2060640 | 3.8618 |
| 3.3857 | 1.03 | 2136960 | 3.8629 |
| 3.3765 | 0.03 | 2213280 | 3.8634 |
| 3.3637 | 0.03 | 2289600 | 3.8657 |
| 3.3528 | 0.03 | 2365920 | 3.8668 |
| 3.3489 | 1.03 | 2442240 | 3.8667 |
| 3.338 | 0.03 | 2518560 | 3.8668 |
| 3.3283 | 1.03 | 2594880 | 3.8668 |
| 3.3179 | 0.03 | 2671200 | 3.8676 |
| 3.3121 | 1.03 | 2747520 | 3.8667 |
| 3.3055 | 0.03 | 2823840 | 3.8658 |
| 3.2992 | 0.03 | 2900160 | 3.8658 |
| 3.2958 | 1.03 | 2976480 | 3.8648 |
| 3.2866 | 0.02 | 3052726 | 3.8637 |
### Framework versions
- Transformers 4.33.3
- Pytorch 2.0.1
- Datasets 2.12.0
- Tokenizers 0.13.3
|
CLMBR/existential-there-quantifier-transformer-1 | CLMBR | 2024-02-07T12:00:23Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"opt",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-02-02T10:11:20Z | ---
tags:
- generated_from_trainer
model-index:
- name: existential-there-quantifier-transformer-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. -->
# existential-there-quantifier-transformer-1
This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.8657
## 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: 1
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 3052726
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-------:|:---------------:|
| 4.2241 | 0.03 | 76320 | 4.1976 |
| 4.0185 | 1.03 | 152640 | 4.0288 |
| 3.9098 | 0.03 | 228960 | 3.9549 |
| 3.8424 | 1.03 | 305280 | 3.9139 |
| 3.7897 | 0.03 | 381600 | 3.8885 |
| 3.7495 | 1.03 | 457920 | 3.8726 |
| 3.7173 | 0.03 | 534240 | 3.8620 |
| 3.6848 | 1.03 | 610560 | 3.8554 |
| 3.656 | 0.03 | 686880 | 3.8512 |
| 3.6306 | 1.03 | 763200 | 3.8476 |
| 3.6077 | 0.03 | 839520 | 3.8454 |
| 3.5894 | 1.03 | 915840 | 3.8462 |
| 3.5702 | 0.03 | 992160 | 3.8450 |
| 3.5528 | 1.03 | 1068480 | 3.8456 |
| 3.537 | 0.03 | 1144800 | 3.8472 |
| 3.5234 | 1.03 | 1221120 | 3.8479 |
| 3.5086 | 0.03 | 1297440 | 3.8489 |
| 3.4939 | 1.03 | 1373760 | 3.8503 |
| 3.481 | 0.03 | 1450080 | 3.8515 |
| 3.4736 | 1.03 | 1526400 | 3.8532 |
| 3.4635 | 0.03 | 1602720 | 3.8531 |
| 3.4539 | 0.03 | 1679040 | 3.8541 |
| 3.4447 | 1.03 | 1755360 | 3.8572 |
| 3.4313 | 0.03 | 1831680 | 3.8587 |
| 3.4182 | 0.03 | 1908000 | 3.8596 |
| 3.4054 | 1.03 | 1984320 | 3.8609 |
| 3.3944 | 0.03 | 2060640 | 3.8624 |
| 3.3856 | 1.03 | 2136960 | 3.8638 |
| 3.3773 | 0.03 | 2213280 | 3.8645 |
| 3.3645 | 1.03 | 2289600 | 3.8652 |
| 3.3559 | 0.03 | 2365920 | 3.8659 |
| 3.3475 | 1.03 | 2442240 | 3.8671 |
| 3.3376 | 0.03 | 2518560 | 3.8674 |
| 3.3262 | 1.03 | 2594880 | 3.8677 |
| 3.316 | 0.03 | 2671200 | 3.8670 |
| 3.3108 | 0.03 | 2747520 | 3.8680 |
| 3.3042 | 1.03 | 2823840 | 3.8675 |
| 3.2997 | 0.03 | 2900160 | 3.8669 |
| 3.2947 | 1.03 | 2976480 | 3.8666 |
| 3.2859 | 0.02 | 3052726 | 3.8657 |
### Framework versions
- Transformers 4.33.3
- Pytorch 2.0.1
- Datasets 2.12.0
- Tokenizers 0.13.3
|
veronica1608/my_ner_model | veronica1608 | 2024-02-07T11:58:04Z | 6 | 0 | transformers | [
"transformers",
"safetensors",
"distilbert",
"token-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2024-02-07T09:00:58Z | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: my_ner_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_ner_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2690
- Precision: 0.5545
- Recall: 0.3253
- F1: 0.4100
- Accuracy: 0.9420
## 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 | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 213 | 0.2829 | 0.5103 | 0.2289 | 0.3161 | 0.9377 |
| No log | 2.0 | 426 | 0.2690 | 0.5545 | 0.3253 | 0.4100 | 0.9420 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.2.0
- Datasets 2.16.1
- Tokenizers 0.15.1
|
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